From fb09f79454986b53ec7b1846b5a025fef6e0ff19 Mon Sep 17 00:00:00 2001
From: Brooke Anderson <brooke.anderson@colostate.edu>
Date: Wed, 26 Aug 2020 09:11:11 -0600
Subject: [PATCH] Update in-course exercise chapter 1

---
 RProgrammingForResearch.log                   |  10 +--
 docs/RProgrammingForResearch.pdf              | Bin 18522930 -> 18404704 bytes
 .../figure-html/gg-1.png                      | Bin 42169 -> 43768 bytes
 .../figure-html/q-1.png                       | Bin 45324 -> 45361 bytes
 .../figure-html/unnamed-chunk-250-1.png       | Bin 48292 -> 38650 bytes
 .../figure-html/unnamed-chunk-324-1.png       | Bin 63980 -> 62227 bytes
 .../figure-html/unnamed-chunk-526-1.png       | Bin 286479 -> 285814 bytes
 .../figure-html/unnamed-chunk-527-1.png       | Bin 108490 -> 107977 bytes
 .../figure-html/unnamed-chunk-535-1.png       | Bin 130020 -> 130058 bytes
 .../figure-html/w-1.png                       | Bin 51846 -> 51866 bytes
 .../figure-html/z-1.png                       | Bin 36160 -> 35292 bytes
 docs/entering-and-cleaning-data-2.html        |  66 +++++++--------
 docs/exploring-data-2.html                    |   2 +-
 docs/exploring-data-3.html                    |  60 ++++++-------
 docs/reporting-data-results-3.html            |  80 +++++++++++-------
 docs/reproducible-research-1.html             |   6 +-
 docs/reproducible-research-3.html             |   2 +-
 docs/search_index.json                        |  12 +--
 18 files changed, 127 insertions(+), 111 deletions(-)

diff --git a/RProgrammingForResearch.log b/RProgrammingForResearch.log
index 76162ea6..188c3bd5 100644
--- a/RProgrammingForResearch.log
+++ b/RProgrammingForResearch.log
@@ -1,4 +1,4 @@
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@@ -4762,8 +4762,8 @@ File: RProgrammingForResearch_files/figure-latex/z-1.pdf Graphic file (type pdf
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-Overfull \hbox (342.6602pt too wide) in paragraph at lines 19151--19166
+Overfull \hbox (351.05376pt too wide) in paragraph at lines 19151--19166
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diff --git a/docs/RProgrammingForResearch.pdf b/docs/RProgrammingForResearch.pdf
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diff --git a/docs/entering-and-cleaning-data-2.html b/docs/entering-and-cleaning-data-2.html
index 5fafbb38..6cf151ec 100644
--- a/docs/entering-and-cleaning-data-2.html
+++ b/docs/entering-and-cleaning-data-2.html
@@ -686,19 +686,19 @@ <h2><span class="header-section-number">6.3</span> Longer data</h2>
 <p>Here is some simulated wide data:</p>
 <div class="sourceCode" id="cb531"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb531-1"><a href="entering-and-cleaning-data-2.html#cb531-1"></a>wide_stocks[<span class="dv">1</span><span class="op">:</span><span class="dv">3</span>, ]</span></code></pre></div>
 <pre><code>## # A tibble: 3 x 4
-##   time            X      Y      Z
-##   &lt;date&gt;      &lt;dbl&gt;  &lt;dbl&gt;  &lt;dbl&gt;
-## 1 2009-01-01  0.902 -0.454 -7.09 
-## 2 2009-01-02  0.181 -1.87  -0.346
-## 3 2009-01-03 -0.636 -3.00  -0.202</code></pre>
+##   time            X      Y     Z
+##   &lt;date&gt;      &lt;dbl&gt;  &lt;dbl&gt; &lt;dbl&gt;
+## 1 2009-01-01 -0.141  2.00   6.45
+## 2 2009-01-02 -0.857 -0.286  2.43
+## 3 2009-01-03 -0.434  2.19  -6.25</code></pre>
 <p>In the <code>wide_stocks</code> dataset, there are separate columns for three different stocks (<code>X</code>, <code>Y</code>, and <code>Z</code>). Each cell gives the value for a certain stock on a certain day. This data isn’t “tidy”, because the identify of the stock (<code>X</code>, <code>Y</code>, or <code>Z</code>) is a variable, and you’ll probably want to include it as a variable in modeling.</p>
 <div class="sourceCode" id="cb533"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb533-1"><a href="entering-and-cleaning-data-2.html#cb533-1"></a>wide_stocks[<span class="dv">1</span><span class="op">:</span><span class="dv">3</span>, ]</span></code></pre></div>
 <pre><code>## # A tibble: 3 x 4
-##   time            X      Y      Z
-##   &lt;date&gt;      &lt;dbl&gt;  &lt;dbl&gt;  &lt;dbl&gt;
-## 1 2009-01-01  0.902 -0.454 -7.09 
-## 2 2009-01-02  0.181 -1.87  -0.346
-## 3 2009-01-03 -0.636 -3.00  -0.202</code></pre>
+##   time            X      Y     Z
+##   &lt;date&gt;      &lt;dbl&gt;  &lt;dbl&gt; &lt;dbl&gt;
+## 1 2009-01-01 -0.141  2.00   6.45
+## 2 2009-01-02 -0.857 -0.286  2.43
+## 3 2009-01-03 -0.434  2.19  -6.25</code></pre>
 <p>If you want to convert the dataframe to have all stock values in a single column, you can use <code>pivot_longer</code> to convert wide data to long data:</p>
 <div class="sourceCode" id="cb535"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb535-1"><a href="entering-and-cleaning-data-2.html#cb535-1"></a>long_stocks &lt;-<span class="st"> </span><span class="kw">pivot_longer</span>(<span class="dt">data =</span> wide_stocks,</span>
 <span id="cb535-2"><a href="entering-and-cleaning-data-2.html#cb535-2"></a>                            <span class="dt">cols =</span> <span class="op">-</span>time,</span>
@@ -708,21 +708,21 @@ <h2><span class="header-section-number">6.3</span> Longer data</h2>
 <pre><code>## # A tibble: 5 x 3
 ##   time       stock  price
 ##   &lt;date&gt;     &lt;chr&gt;  &lt;dbl&gt;
-## 1 2009-01-01 X      0.902
-## 2 2009-01-01 Y     -0.454
-## 3 2009-01-01 Z     -7.09 
-## 4 2009-01-02 X      0.181
-## 5 2009-01-02 Y     -1.87</code></pre>
+## 1 2009-01-01 X     -0.141
+## 2 2009-01-01 Y      2.00 
+## 3 2009-01-01 Z      6.45 
+## 4 2009-01-02 X     -0.857
+## 5 2009-01-02 Y     -0.286</code></pre>
 <p>In this “longer” dataframe, there is now one column that gives the identify of the stock (<code>stock</code>) and another column that gives the price of that stock that day (<code>price</code>):</p>
 <div class="sourceCode" id="cb537"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb537-1"><a href="entering-and-cleaning-data-2.html#cb537-1"></a>long_stocks[<span class="dv">1</span><span class="op">:</span><span class="dv">5</span>, ]</span></code></pre></div>
 <pre><code>## # A tibble: 5 x 3
 ##   time       stock  price
 ##   &lt;date&gt;     &lt;chr&gt;  &lt;dbl&gt;
-## 1 2009-01-01 X      0.902
-## 2 2009-01-01 Y     -0.454
-## 3 2009-01-01 Z     -7.09 
-## 4 2009-01-02 X      0.181
-## 5 2009-01-02 Y     -1.87</code></pre>
+## 1 2009-01-01 X     -0.141
+## 2 2009-01-01 Y      2.00 
+## 3 2009-01-01 Z      6.45 
+## 4 2009-01-02 X     -0.857
+## 5 2009-01-02 Y     -0.286</code></pre>
 <p>The format for a <code>pivots_longer</code> call is:</p>
 <div class="sourceCode" id="cb539"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb539-1"><a href="entering-and-cleaning-data-2.html#cb539-1"></a><span class="co">## Generic code</span></span>
 <span id="cb539-2"><a href="entering-and-cleaning-data-2.html#cb539-2"></a>new_df &lt;-<span class="st"> </span><span class="kw">pivot_longer</span>(old_df,</span>
@@ -747,13 +747,13 @@ <h2><span class="header-section-number">6.3</span> Longer data</h2>
 <span id="cb541-3"><a href="entering-and-cleaning-data-2.html#cb541-3"></a>                      <span class="dt">values_from =</span> price)</span>
 <span id="cb541-4"><a href="entering-and-cleaning-data-2.html#cb541-4"></a>stocks[<span class="dv">1</span><span class="op">:</span><span class="dv">5</span>, ]</span></code></pre></div>
 <pre><code>## # A tibble: 5 x 4
-##   time            X      Y      Z
-##   &lt;date&gt;      &lt;dbl&gt;  &lt;dbl&gt;  &lt;dbl&gt;
-## 1 2009-01-01  0.902 -0.454 -7.09 
-## 2 2009-01-02  0.181 -1.87  -0.346
-## 3 2009-01-03 -0.636 -3.00  -0.202
-## 4 2009-01-04  1.28   0.902 -3.60 
-## 5 2009-01-05 -3.02  -0.914  5.74</code></pre>
+##   time             X      Y     Z
+##   &lt;date&gt;       &lt;dbl&gt;  &lt;dbl&gt; &lt;dbl&gt;
+## 1 2009-01-01 -0.141   2.00   6.45
+## 2 2009-01-02 -0.857  -0.286  2.43
+## 3 2009-01-03 -0.434   2.19  -6.25
+## 4 2009-01-04 -1.78    1.29  -2.42
+## 5 2009-01-05 -0.0842  3.67  -2.05</code></pre>
 <p>Notice that this reverses the action of <code>pivot_longer</code>.</p>
 <p>The “wider” your data the less likely it is to be tidy, so won’t use <code>pivot_wider</code> frequently when you are preparing data for analysis. However, <code>pivot_wider</code> can be very helpful in creating tables for final reports and presentations.</p>
 <p>For example, if you wanted to create a table with means and standard deviations for each of the three stocks, you could use <code>pivot_wider</code> to rearrange the final summary to create an attractive table.</p>
@@ -765,9 +765,9 @@ <h2><span class="header-section-number">6.3</span> Longer data</h2>
 <pre><code>## # A tibble: 3 x 4
 ##   stock     N   mean    sd
 ##   &lt;chr&gt; &lt;int&gt;  &lt;dbl&gt; &lt;dbl&gt;
-## 1 X        10  0.182  1.30
-## 2 Y        10 -0.289  2.52
-## 3 Z        10 -2.30   3.59</code></pre>
+## 1 X        10 -0.333 0.746
+## 2 Y        10  1.12  2.69 
+## 3 Z        10 -1.11  3.63</code></pre>
 <div class="sourceCode" id="cb547"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb547-1"><a href="entering-and-cleaning-data-2.html#cb547-1"></a>stock_summary <span class="op">%&gt;%</span></span>
 <span id="cb547-2"><a href="entering-and-cleaning-data-2.html#cb547-2"></a><span class="st">  </span><span class="kw">mutate</span>(<span class="st">&quot;Mean (Std.dev.)&quot;</span> =<span class="st"> </span><span class="kw">paste0</span>(<span class="kw">round</span>(mean, <span class="dv">2</span>), <span class="st">&quot; (&quot;</span>,</span>
 <span id="cb547-3"><a href="entering-and-cleaning-data-2.html#cb547-3"></a>                                    <span class="kw">round</span>(sd, <span class="dv">2</span>), <span class="st">&quot;)&quot;</span>)) <span class="op">%&gt;%</span></span>
@@ -794,9 +794,9 @@ <h2><span class="header-section-number">6.3</span> Longer data</h2>
 </tr>
 <tr class="even">
 <td align="left">Mean (Std.dev.)</td>
-<td align="left">0.18 (1.3)</td>
-<td align="left">-0.29 (2.52)</td>
-<td align="left">-2.3 (3.59)</td>
+<td align="left">-0.33 (0.75)</td>
+<td align="left">1.12 (2.69)</td>
+<td align="left">-1.11 (3.63)</td>
 </tr>
 </tbody>
 </table>
diff --git a/docs/exploring-data-2.html b/docs/exploring-data-2.html
index ab22af9a..b59497e8 100644
--- a/docs/exploring-data-2.html
+++ b/docs/exploring-data-2.html
@@ -750,7 +750,7 @@ <h2><span class="header-section-number">7.3</span> Lists</h2>
 <pre><code>## [1] &quot;coefficients&quot;  &quot;residuals&quot;     &quot;fitted.values&quot;</code></pre>
 <div class="sourceCode" id="cb710"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb710-1"><a href="exploring-data-2.html#cb710-1"></a>my_mod[[<span class="st">&quot;coefficients&quot;</span>]]</span></code></pre></div>
 <pre><code>## (Intercept)     c(1:10) 
-##   0.2759347  -0.1316436</code></pre>
+##   -1.030100    0.163329</code></pre>
 <p>A list can even contain other lists. We can use the <code>str</code> function to see the structure of a list:</p>
 <div class="sourceCode" id="cb712"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb712-1"><a href="exploring-data-2.html#cb712-1"></a>a_list &lt;-<span class="st"> </span><span class="kw">list</span>(<span class="kw">list</span>(<span class="st">&quot;a&quot;</span>, <span class="st">&quot;b&quot;</span>), <span class="kw">list</span>(<span class="dv">1</span>, <span class="dv">2</span>))</span>
 <span id="cb712-2"><a href="exploring-data-2.html#cb712-2"></a></span>
diff --git a/docs/exploring-data-3.html b/docs/exploring-data-3.html
index 35d50edc..61d1ded8 100644
--- a/docs/exploring-data-3.html
+++ b/docs/exploring-data-3.html
@@ -577,17 +577,17 @@ <h3><span class="header-section-number">11.1.1</span> The lady tasting tea</h3>
 <div class="sourceCode" id="cb1111"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1111-1"><a href="exploring-data-3.html#cb1111-1"></a>n_cups &lt;-<span class="st"> </span><span class="dv">8</span></span>
 <span id="cb1111-2"><a href="exploring-data-3.html#cb1111-2"></a>cups &lt;-<span class="st"> </span><span class="kw">sample</span>(<span class="kw">rep</span>(<span class="kw">c</span>(<span class="st">&quot;milk&quot;</span>, <span class="st">&quot;tea&quot;</span>), <span class="dt">each =</span> n_cups <span class="op">/</span><span class="st"> </span><span class="dv">2</span>))</span>
 <span id="cb1111-3"><a href="exploring-data-3.html#cb1111-3"></a>cups</span></code></pre></div>
-<pre><code>## [1] &quot;tea&quot;  &quot;tea&quot;  &quot;milk&quot; &quot;milk&quot; &quot;tea&quot;  &quot;tea&quot;  &quot;milk&quot; &quot;milk&quot;</code></pre>
+<pre><code>## [1] &quot;tea&quot;  &quot;milk&quot; &quot;milk&quot; &quot;tea&quot;  &quot;milk&quot; &quot;milk&quot; &quot;tea&quot;  &quot;tea&quot;</code></pre>
 <div class="sourceCode" id="cb1113"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1113-1"><a href="exploring-data-3.html#cb1113-1"></a>guesses &lt;-<span class="st"> </span><span class="kw">sample</span>(<span class="kw">rep</span>(<span class="kw">c</span>(<span class="st">&quot;milk&quot;</span>, <span class="st">&quot;tea&quot;</span>), <span class="dt">each =</span> n_cups <span class="op">/</span><span class="st"> </span><span class="dv">2</span>))</span>
 <span id="cb1113-2"><a href="exploring-data-3.html#cb1113-2"></a>guesses</span></code></pre></div>
-<pre><code>## [1] &quot;tea&quot;  &quot;tea&quot;  &quot;milk&quot; &quot;milk&quot; &quot;tea&quot;  &quot;milk&quot; &quot;milk&quot; &quot;tea&quot;</code></pre>
+<pre><code>## [1] &quot;tea&quot;  &quot;milk&quot; &quot;milk&quot; &quot;tea&quot;  &quot;milk&quot; &quot;milk&quot; &quot;tea&quot;  &quot;tea&quot;</code></pre>
 <p>For this simulation, determine how many cups she got right (i.e., guess equals the true value):</p>
 <div class="sourceCode" id="cb1115"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1115-1"><a href="exploring-data-3.html#cb1115-1"></a>cup_results &lt;-<span class="st"> </span>cups <span class="op">==</span><span class="st"> </span>guesses</span>
 <span id="cb1115-2"><a href="exploring-data-3.html#cb1115-2"></a>cup_results</span></code></pre></div>
-<pre><code>## [1]  TRUE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE FALSE</code></pre>
+<pre><code>## [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE</code></pre>
 <div class="sourceCode" id="cb1117"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1117-1"><a href="exploring-data-3.html#cb1117-1"></a>n_right &lt;-<span class="st"> </span><span class="kw">sum</span>(cup_results)</span>
 <span id="cb1117-2"><a href="exploring-data-3.html#cb1117-2"></a>n_right</span></code></pre></div>
-<pre><code>## [1] 6</code></pre>
+<pre><code>## [1] 8</code></pre>
 <p>Write a function that will run one simulation. It takes the argument <code>n_cups</code>— in real life, they used eight cups (<code>n_cups = 8</code>). Note that this function just wraps the code we just walked through.</p>
 <div class="sourceCode" id="cb1119"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1119-1"><a href="exploring-data-3.html#cb1119-1"></a>sim_null_tea &lt;-<span class="st"> </span><span class="cf">function</span>(n_cups){</span>
 <span id="cb1119-2"><a href="exploring-data-3.html#cb1119-2"></a>  cups &lt;-<span class="st"> </span><span class="kw">sample</span>(<span class="kw">rep</span>(<span class="kw">c</span>(<span class="st">&quot;milk&quot;</span>, <span class="st">&quot;tea&quot;</span>), <span class="dt">each =</span> n_cups <span class="op">/</span><span class="st"> </span><span class="dv">2</span>))</span>
@@ -597,23 +597,23 @@ <h3><span class="header-section-number">11.1.1</span> The lady tasting tea</h3>
 <span id="cb1119-6"><a href="exploring-data-3.html#cb1119-6"></a>  <span class="kw">return</span>(n_right)</span>
 <span id="cb1119-7"><a href="exploring-data-3.html#cb1119-7"></a>}</span>
 <span id="cb1119-8"><a href="exploring-data-3.html#cb1119-8"></a><span class="kw">sim_null_tea</span>(<span class="dt">n_cups =</span> <span class="dv">8</span>)</span></code></pre></div>
-<pre><code>## [1] 4</code></pre>
+<pre><code>## [1] 6</code></pre>
 <p>Now, we need to run a lot of simulations, to see what happens on average if she guesses. You can use the <code>replicate</code> function to do that.</p>
 <div class="sourceCode" id="cb1121"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1121-1"><a href="exploring-data-3.html#cb1121-1"></a><span class="co">## Generic code</span></span>
 <span id="cb1121-2"><a href="exploring-data-3.html#cb1121-2"></a><span class="kw">replicate</span>(<span class="dt">n =</span> [number of replications to run],</span>
 <span id="cb1121-3"><a href="exploring-data-3.html#cb1121-3"></a>          <span class="dt">eval =</span> [code to replicate each time])</span></code></pre></div>
 <div class="sourceCode" id="cb1122"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1122-1"><a href="exploring-data-3.html#cb1122-1"></a>tea_sims &lt;-<span class="st"> </span><span class="kw">replicate</span>(<span class="dv">5</span>, <span class="kw">sim_null_tea</span>(<span class="dt">n_cups =</span> <span class="dv">8</span>))</span>
 <span id="cb1122-2"><a href="exploring-data-3.html#cb1122-2"></a>tea_sims</span></code></pre></div>
-<pre><code>## [1] 8 4 4 4 6</code></pre>
+<pre><code>## [1] 4 2 4 6 4</code></pre>
 <p>This call gives a vector with the number of cups she got right for each simulation. You can replicate the simulation many times to get a better idea of what to expect if she just guesses, including what percent of the time she gets all cups right.</p>
 <div class="sourceCode" id="cb1124"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1124-1"><a href="exploring-data-3.html#cb1124-1"></a>tea_sims &lt;-<span class="st"> </span><span class="kw">replicate</span>(<span class="dv">1000</span>, <span class="kw">sim_null_tea</span>(<span class="dt">n_cups =</span> <span class="dv">8</span>))</span>
 <span id="cb1124-2"><a href="exploring-data-3.html#cb1124-2"></a><span class="kw">mean</span>(tea_sims)</span></code></pre></div>
-<pre><code>## [1] 3.902</code></pre>
+<pre><code>## [1] 4.062</code></pre>
 <div class="sourceCode" id="cb1126"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1126-1"><a href="exploring-data-3.html#cb1126-1"></a><span class="kw">quantile</span>(tea_sims, <span class="dt">probs =</span> <span class="kw">c</span>(<span class="fl">0.025</span>, <span class="fl">0.975</span>))</span></code></pre></div>
 <pre><code>##  2.5% 97.5% 
 ##     2     6</code></pre>
 <div class="sourceCode" id="cb1128"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1128-1"><a href="exploring-data-3.html#cb1128-1"></a><span class="kw">mean</span>(tea_sims <span class="op">==</span><span class="st"> </span><span class="dv">8</span>)</span></code></pre></div>
-<pre><code>## [1] 0.012</code></pre>
+<pre><code>## [1] 0.016</code></pre>
 <p>Now we’d like to know, for different numbers of cups of tea, what is the probability that the lady will get all cups right?</p>
 <p>For this, we can apply the replication code across different values of <code>n_cups</code>:</p>
 <div class="sourceCode" id="cb1130"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1130-1"><a href="exploring-data-3.html#cb1130-1"></a>n_cups &lt;-<span class="st"> </span><span class="kw">seq</span>(<span class="dt">from =</span> <span class="dv">2</span>, <span class="dt">to =</span> <span class="dv">14</span>, <span class="dt">by =</span> <span class="dv">2</span>)</span>
@@ -623,7 +623,7 @@ <h3><span class="header-section-number">11.1.1</span> The lady tasting tea</h3>
 <span id="cb1130-5"><a href="exploring-data-3.html#cb1130-5"></a>  <span class="kw">return</span>(out)</span>
 <span id="cb1130-6"><a href="exploring-data-3.html#cb1130-6"></a>})</span>
 <span id="cb1130-7"><a href="exploring-data-3.html#cb1130-7"></a>perc_all_right</span></code></pre></div>
-<pre><code>## [1] 0.488 0.166 0.051 0.019 0.003 0.000 0.000</code></pre>
+<pre><code>## [1] 0.477 0.182 0.046 0.020 0.003 0.001 0.001</code></pre>
 <div class="sourceCode" id="cb1132"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1132-1"><a href="exploring-data-3.html#cb1132-1"></a>tea_sims &lt;-<span class="st"> </span><span class="kw">data_frame</span>(n_cups, perc_all_right)</span>
 <span id="cb1132-2"><a href="exploring-data-3.html#cb1132-2"></a><span class="kw">ggplot</span>(tea_sims, <span class="kw">aes</span>(<span class="dt">x =</span> n_cups, <span class="dt">y =</span> perc_all_right)) <span class="op">+</span><span class="st"> </span></span>
 <span id="cb1132-3"><a href="exploring-data-3.html#cb1132-3"></a><span class="st">  </span><span class="kw">geom_point</span>() <span class="op">+</span><span class="st"> </span><span class="kw">xlab</span>(<span class="st">&quot;# of cups tested&quot;</span>) <span class="op">+</span><span class="st"> </span></span>
@@ -725,13 +725,13 @@ <h3><span class="header-section-number">11.1.2</span> Playing darts</h3>
 <span id="cb1142-2"><a href="exploring-data-3.html#cb1142-2"></a>throw.x &lt;-<span class="st"> </span><span class="kw">runif</span>(n.throws, <span class="dt">min =</span> <span class="dv">-1</span>, <span class="dt">max =</span> <span class="dv">1</span>)</span>
 <span id="cb1142-3"><a href="exploring-data-3.html#cb1142-3"></a>throw.y &lt;-<span class="st"> </span><span class="kw">runif</span>(n.throws, <span class="dt">min =</span> <span class="dv">-1</span>, <span class="dt">max =</span> <span class="dv">1</span>)</span>
 <span id="cb1142-4"><a href="exploring-data-3.html#cb1142-4"></a><span class="kw">head</span>(<span class="kw">cbind</span>(throw.x, throw.y))</span></code></pre></div>
-<pre><code>##         throw.x    throw.y
-## [1,]  0.5524726 -0.8217305
-## [2,] -0.1481548 -0.2206223
-## [3,]  0.9452591  0.6049874
-## [4,] -0.5267736 -0.7348108
-## [5,] -0.7088718  0.5798019
-## [6,]  0.9778563 -0.8479374</code></pre>
+<pre><code>##         throw.x      throw.y
+## [1,]  0.7683825 -0.913790450
+## [2,] -0.4551363  0.534574261
+## [3,] -0.8513250 -0.723720532
+## [4,] -0.6361512  0.516585769
+## [5,]  0.5396098  0.981598570
+## [6,]  0.6168388  0.006258808</code></pre>
 <div class="sourceCode" id="cb1144"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1144-1"><a href="exploring-data-3.html#cb1144-1"></a><span class="kw">plot</span>(<span class="kw">c</span>(<span class="op">-</span><span class="dv">1</span>, <span class="dv">1</span>), <span class="kw">c</span>(<span class="op">-</span><span class="dv">1</span>,<span class="dv">1</span>), <span class="dt">type =</span> <span class="st">&quot;n&quot;</span>, <span class="dt">asp=</span><span class="dv">1</span>,</span>
 <span id="cb1144-2"><a href="exploring-data-3.html#cb1144-2"></a>     <span class="dt">xlab =</span> <span class="st">&quot;&quot;</span>, <span class="dt">ylab =</span> <span class="st">&quot;&quot;</span>, <span class="dt">axes =</span> <span class="ot">FALSE</span>)</span>
 <span id="cb1144-3"><a href="exploring-data-3.html#cb1144-3"></a><span class="kw">rect</span>( <span class="dv">-1</span>, <span class="dv">-1</span>, <span class="dv">1</span>, <span class="dv">1</span>) </span>
@@ -751,21 +751,21 @@ <h3><span class="header-section-number">11.1.2</span> Playing darts</h3>
 <p>Use these rules to “score” each random throw:</p>
 <div class="sourceCode" id="cb1145"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1145-1"><a href="exploring-data-3.html#cb1145-1"></a>throw.dist &lt;-<span class="st"> </span><span class="kw">sqrt</span>(throw.x<span class="op">^</span><span class="dv">2</span> <span class="op">+</span><span class="st"> </span>throw.y<span class="op">^</span><span class="dv">2</span>)</span>
 <span id="cb1145-2"><a href="exploring-data-3.html#cb1145-2"></a><span class="kw">head</span>(throw.dist)</span></code></pre></div>
-<pre><code>## [1] 0.9901853 0.2657518 1.1222854 0.9041225 0.9157890 1.2942955</code></pre>
+<pre><code>## [1] 1.1939115 0.7020817 1.1173744 0.8194810 1.1201404 0.6168706</code></pre>
 <div class="sourceCode" id="cb1147"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1147-1"><a href="exploring-data-3.html#cb1147-1"></a>throw.score &lt;-<span class="st"> </span><span class="kw">cut</span>(throw.dist,</span>
 <span id="cb1147-2"><a href="exploring-data-3.html#cb1147-2"></a>                   <span class="dt">breaks =</span> <span class="kw">c</span>(<span class="dv">0</span>, <span class="fl">.25</span>, <span class="fl">.5</span>, <span class="fl">.75</span>, <span class="fl">1.5</span>),</span>
 <span id="cb1147-3"><a href="exploring-data-3.html#cb1147-3"></a>                   <span class="dt">labels =</span> <span class="kw">c</span>(<span class="st">&quot;20&quot;</span>, <span class="st">&quot;15&quot;</span>, <span class="st">&quot;10&quot;</span>, <span class="st">&quot;0&quot;</span>),</span>
 <span id="cb1147-4"><a href="exploring-data-3.html#cb1147-4"></a>                   <span class="dt">right =</span> <span class="ot">FALSE</span>)</span>
 <span id="cb1147-5"><a href="exploring-data-3.html#cb1147-5"></a><span class="kw">head</span>(throw.score)</span></code></pre></div>
-<pre><code>## [1] 0  15 0  0  0  0 
+<pre><code>## [1] 0  10 0  0  0  10
 ## Levels: 20 15 10 0</code></pre>
 <p>Now that we’ve scored each throw, let’s tally up the total:</p>
 <div class="sourceCode" id="cb1149"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1149-1"><a href="exploring-data-3.html#cb1149-1"></a><span class="kw">table</span>(throw.score)</span></code></pre></div>
 <pre><code>## throw.score
 ## 20 15 10  0 
-##  0  3  3 14</code></pre>
+##  0  1  5 14</code></pre>
 <div class="sourceCode" id="cb1151"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1151-1"><a href="exploring-data-3.html#cb1151-1"></a><span class="kw">mean</span>(<span class="kw">as.numeric</span>(<span class="kw">as.character</span>(throw.score)))</span></code></pre></div>
-<pre><code>## [1] 3.75</code></pre>
+<pre><code>## [1] 3.25</code></pre>
 <p>So, this just showed <em>one</em> example of what might happen under the null. If we had a lot of examples like this (someone with no skill throwing 20 darts), what would we expect the mean scores to be?</p>
 <p><em>Questions to consider:</em></p>
 <ul>
@@ -830,15 +830,15 @@ <h3><span class="header-section-number">11.1.2</span> Playing darts</h3>
 <span id="cb1155-11"><a href="exploring-data-3.html#cb1155-11"></a>                   <span class="dt">labels =</span> <span class="kw">c</span>(<span class="st">&quot;20&quot;</span>, <span class="st">&quot;15&quot;</span>, <span class="st">&quot;10&quot;</span>, <span class="st">&quot;0&quot;</span>),</span>
 <span id="cb1155-12"><a href="exploring-data-3.html#cb1155-12"></a>                   <span class="dt">right =</span> <span class="ot">FALSE</span>)</span></code></pre></div>
 <div class="sourceCode" id="cb1156"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1156-1"><a href="exploring-data-3.html#cb1156-1"></a>dist.throws[<span class="dv">1</span><span class="op">:</span><span class="dv">3</span>,<span class="dv">1</span><span class="op">:</span><span class="dv">5</span>]</span></code></pre></div>
-<pre><code>##           [,1]      [,2]      [,3]      [,4]      [,5]
-## [1,] 0.8316972 0.8431826 0.8722920 0.5525720 1.3335355
-## [2,] 0.6574708 1.3203518 0.8397787 0.4103512 0.4317604
-## [3,] 0.6139829 0.2888675 0.4662491 1.0717015 0.8835623</code></pre>
+<pre><code>##           [,1]      [,2]       [,3]      [,4]      [,5]
+## [1,] 0.5478433 0.8309107 1.03368286 0.5909183 0.3177004
+## [2,] 0.5192013 0.5977724 0.97286645 0.3394507 0.3402281
+## [3,] 0.6441868 0.3762599 0.03928263 0.3861539 0.2078762</code></pre>
 <div class="sourceCode" id="cb1158"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1158-1"><a href="exploring-data-3.html#cb1158-1"></a>score.throws[<span class="dv">1</span><span class="op">:</span><span class="dv">3</span>,<span class="dv">1</span><span class="op">:</span><span class="dv">5</span>]</span></code></pre></div>
 <pre><code>##      [,1] [,2] [,3] [,4] [,5]
-## [1,] &quot;0&quot;  &quot;0&quot;  &quot;0&quot;  &quot;10&quot; &quot;0&quot; 
-## [2,] &quot;10&quot; &quot;0&quot;  &quot;0&quot;  &quot;15&quot; &quot;15&quot;
-## [3,] &quot;10&quot; &quot;15&quot; &quot;15&quot; &quot;0&quot;  &quot;0&quot;</code></pre>
+## [1,] &quot;10&quot; &quot;0&quot;  &quot;0&quot;  &quot;10&quot; &quot;15&quot;
+## [2,] &quot;10&quot; &quot;10&quot; &quot;0&quot;  &quot;15&quot; &quot;15&quot;
+## [3,] &quot;10&quot; &quot;15&quot; &quot;20&quot; &quot;15&quot; &quot;20&quot;</code></pre>
 <div class="sourceCode" id="cb1160"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1160-1"><a href="exploring-data-3.html#cb1160-1"></a>mean.scores &lt;-<span class="st"> </span><span class="kw">apply</span>(score.throws, <span class="dt">MARGIN =</span> <span class="dv">1</span>,</span>
 <span id="cb1160-2"><a href="exploring-data-3.html#cb1160-2"></a>                     <span class="cf">function</span>(x){</span>
 <span id="cb1160-3"><a href="exploring-data-3.html#cb1160-3"></a>                             out &lt;-<span class="st"> </span><span class="kw">mean</span>(<span class="kw">as.numeric</span>(</span>
@@ -846,13 +846,13 @@ <h3><span class="header-section-number">11.1.2</span> Playing darts</h3>
 <span id="cb1160-5"><a href="exploring-data-3.html#cb1160-5"></a>                             <span class="kw">return</span>(out)</span>
 <span id="cb1160-6"><a href="exploring-data-3.html#cb1160-6"></a>                     })</span>
 <span id="cb1160-7"><a href="exploring-data-3.html#cb1160-7"></a><span class="kw">head</span>(mean.scores)</span></code></pre></div>
-<pre><code>## [1] 1.25 6.75 3.75 5.25 7.25 6.25</code></pre>
+<pre><code>## [1] 5.00 6.25 7.25 5.50 6.25 4.00</code></pre>
 <p><img src="RProgrammingForResearch_files/figure-html/gg-1.png" width="0.8\textwidth" style="display: block; margin: auto;" /></p>
 <p>Let’s check the simulated mean and variance against the theoretical values:</p>
 <div class="sourceCode" id="cb1162"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1162-1"><a href="exploring-data-3.html#cb1162-1"></a><span class="kw">mean</span>(mean.scores) <span class="co">## Theoretical: 5.635</span></span></code></pre></div>
-<pre><code>## [1] 5.648225</code></pre>
+<pre><code>## [1] 5.632425</code></pre>
 <div class="sourceCode" id="cb1164"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1164-1"><a href="exploring-data-3.html#cb1164-1"></a><span class="kw">var</span>(mean.scores) <span class="co">## Theoretical: 2.27</span></span></code></pre></div>
-<pre><code>## [1] 2.301028</code></pre>
+<pre><code>## [1] 2.260558</code></pre>
 </div>
 <div id="simulations-in-research" class="section level3">
 <h3><span class="header-section-number">11.1.3</span> Simulations in research</h3>
diff --git a/docs/reporting-data-results-3.html b/docs/reporting-data-results-3.html
index 762d586e..9513223b 100644
--- a/docs/reporting-data-results-3.html
+++ b/docs/reporting-data-results-3.html
@@ -793,6 +793,11 @@ <h3><span class="header-section-number">12.1.8</span> Creating output in <code>s
 <div class="sourceCode" id="cb1187"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1187-1"><a href="reporting-data-results-3.html#cb1187-1"></a>knitr<span class="op">::</span><span class="kw">kable</span>(tweet_sum[<span class="dv">1</span><span class="op">:</span><span class="dv">5</span>, ],</span>
 <span id="cb1187-2"><a href="reporting-data-results-3.html#cb1187-2"></a>             <span class="dt">col.names =</span> <span class="kw">c</span>(<span class="st">&quot;Tag&quot;</span>, <span class="st">&quot;# of Tweets&quot;</span>, <span class="st">&quot;Example Tweet&quot;</span>))</span></code></pre></div>
 <table>
+<colgroup>
+<col width="8%" />
+<col width="7%" />
+<col width="84%" />
+</colgroup>
 <thead>
 <tr class="header">
 <th align="left">Tag</th>
@@ -804,19 +809,30 @@ <h3><span class="header-section-number">12.1.8</span> Creating output in <code>s
 <tr class="odd">
 <td align="left">#Paris</td>
 <td align="right">7502</td>
-<td align="left">NA</td>
+<td align="left">RT Vince66240 Tirs a la Kalash au petit Cambodge dans le 10 eme a Paris plusieurs morts Secours et police sur place tir https t co B</td>
 </tr>
 <tr class="even">
 <td align="left">#PrayForParis</td>
 <td align="right">7250</td>
-<td align="left">RT french sultan paris attaque</td>
+<td align="left">NA</td>
+</tr>
+<tr class="odd">
+<td align="left">#13novembre</td>
+<td align="right">1255</td>
+<td align="left">RT taimaz 3 personnes a terre devant le Bataclan soign ees par les pompiers AFP 13novembre Paris</td>
+</tr>
+<tr class="even">
+<td align="left">#PorteOuverte</td>
+<td align="right">1147</td>
+<td align="left">RT hiyadisoufiane1 porteouverte Eiffel st Charles 37 rue Saint Charles si Jamais vs avez besoin</td>
+</tr>
+<tr class="odd">
+<td align="left">#fusillade</td>
+<td align="right">765</td>
+<td align="left">RT jacques lefort Coups de feu et haute tension a l angle rues de Charonne amp Keller Paris11 Gendarmes en position fusillade https t</td>
 </tr>
 </tbody>
 </table>
-<p>Paris10 france PrayForParis https t co TOKfFUfxT4 |
-|#13novembre | 1255|RT taimaz Enorme dispositif policier boulevard Voltaire AFP paris 13novembre https t co acgmB89EY5 |
-|#PorteOuverte | 1147|RT hiyadisoufiane1 porteouverte Eiffel st Charles 37 rue Saint Charles si Jamais vs avez besoin |
-|#fusillade | 765|RT Vince66240 Rue Bichat on mesure la violence de l attaque sur lepetitcambodge et LeCarillon ParisAttacks fusillade https t co 9E |</p>
 <p>For the Tweets that are geolocated, it’s possible to map the tweet locations using the following code:</p>
 <div class="sourceCode" id="cb1188"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1188-1"><a href="reporting-data-results-3.html#cb1188-1"></a>paris_map &lt;-<span class="st"> </span><span class="kw">get_map</span>(<span class="st">&quot;paris&quot;</span>, <span class="dt">zoom =</span> <span class="dv">12</span>, <span class="dt">color =</span> <span class="st">&quot;bw&quot;</span>)</span>
 <span id="cb1188-2"><a href="reporting-data-results-3.html#cb1188-2"></a></span>
@@ -998,15 +1014,15 @@ <h3><span class="header-section-number">12.2.2</span> <code>plotly</code> packag
 <span id="cb1193-2"><a href="reporting-data-results-3.html#cb1193-2"></a><span class="kw">data</span>(worldcup)</span>
 <span id="cb1193-3"><a href="reporting-data-results-3.html#cb1193-3"></a><span class="kw">library</span>(plotly)</span>
 <span id="cb1193-4"><a href="reporting-data-results-3.html#cb1193-4"></a><span class="kw">plot_ly</span>(worldcup, <span class="dt">type =</span> <span class="st">&quot;scatter&quot;</span>, <span class="dt">x =</span> <span class="op">~</span><span class="st"> </span>Time, <span class="dt">y =</span> <span class="op">~</span><span class="st"> </span>Shots)</span></code></pre></div>
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 <p>Just like with <code>ggplot2</code>, the mappings you need depend on the type of plot you are creating.</p>
 <p>For example, scatterplots (<code>type = "scatter"</code>) need <code>x</code> and <code>y</code> defined, while a surface plot (<code>type = "surface"</code>) can be created with a single vector of elevation (we’ll see an example in a few slides).</p>
 <p>The help file for <code>plot_ly</code> includes a link with more documentation on the types of plots that can be made and the required mappings for each.</p>
 <div class="sourceCode" id="cb1194"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1194-1"><a href="reporting-data-results-3.html#cb1194-1"></a><span class="kw">plot_ly</span>(worldcup, <span class="dt">type =</span> <span class="st">&quot;scatter&quot;</span>, <span class="dt">x =</span> <span class="op">~</span><span class="st"> </span>Time, <span class="dt">y =</span> <span class="op">~</span><span class="st"> </span>Shots,</span>
 <span id="cb1194-2"><a href="reporting-data-results-3.html#cb1194-2"></a>        <span class="dt">color =</span> <span class="op">~</span><span class="st"> </span>Position)</span></code></pre></div>
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 <p>The <code>plotly</code> package is designed so you can pipe data into <code>plot_ly</code> and add elements by piping into <code>add_*</code> functions (this idea is similar to adding elements to a <code>ggplot</code> object with <code>+</code>).</p>
 <div class="sourceCode" id="cb1195"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1195-1"><a href="reporting-data-results-3.html#cb1195-1"></a>worldcup <span class="op">%&gt;%</span></span>
 <span id="cb1195-2"><a href="reporting-data-results-3.html#cb1195-2"></a><span class="st">  </span><span class="kw">plot_ly</span>(<span class="dt">x =</span> <span class="op">~</span><span class="st"> </span>Time, <span class="dt">y =</span> <span class="op">~</span><span class="st"> </span>Shots, <span class="dt">color =</span> <span class="op">~</span><span class="st"> </span>Position) <span class="op">%&gt;%</span></span>
@@ -1025,15 +1041,15 @@ <h3><span class="header-section-number">12.2.2</span> <code>plotly</code> packag
 <div class="sourceCode" id="cb1196"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1196-1"><a href="reporting-data-results-3.html#cb1196-1"></a><span class="kw">plot_ly</span>(<span class="dt">x =</span> <span class="kw">time</span>(USAccDeaths), <span class="dt">y =</span> USAccDeaths) <span class="op">%&gt;%</span><span class="st"> </span></span>
 <span id="cb1196-2"><a href="reporting-data-results-3.html#cb1196-2"></a><span class="st">  </span><span class="kw">add_lines</span>() <span class="op">%&gt;%</span></span>
 <span id="cb1196-3"><a href="reporting-data-results-3.html#cb1196-3"></a><span class="st">  </span><span class="kw">rangeslider</span>()</span></code></pre></div>
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 <p>For a 3-D scatterplot, add a mapping to the <code>z</code> variable:</p>
 <div class="sourceCode" id="cb1197"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1197-1"><a href="reporting-data-results-3.html#cb1197-1"></a>worldcup <span class="op">%&gt;%</span></span>
 <span id="cb1197-2"><a href="reporting-data-results-3.html#cb1197-2"></a><span class="st">  </span><span class="kw">plot_ly</span>(<span class="dt">x =</span> <span class="op">~</span><span class="st"> </span>Time, <span class="dt">y =</span> <span class="op">~</span><span class="st"> </span>Shots, <span class="dt">z =</span> <span class="op">~</span><span class="st"> </span>Passes,</span>
 <span id="cb1197-3"><a href="reporting-data-results-3.html#cb1197-3"></a>          <span class="dt">color =</span> <span class="op">~</span><span class="st"> </span>Position, <span class="dt">size =</span> <span class="kw">I</span>(<span class="dv">3</span>)) <span class="op">%&gt;%</span></span>
 <span id="cb1197-4"><a href="reporting-data-results-3.html#cb1197-4"></a><span class="st">  </span><span class="kw">add_markers</span>()</span></code></pre></div>
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 <p>The <code>volcano</code> data comes with R and is in a matrix format. Each value gives the elevation for a particular pair of x- and y-coordinates.</p>
 <div class="sourceCode" id="cb1198"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1198-1"><a href="reporting-data-results-3.html#cb1198-1"></a><span class="kw">dim</span>(volcano)</span></code></pre></div>
 <pre><code>## [1] 87 61</code></pre>
@@ -1044,8 +1060,8 @@ <h3><span class="header-section-number">12.2.2</span> <code>plotly</code> packag
 ## [3,]  102  102  103  103
 ## [4,]  103  103  104  104</code></pre>
 <div class="sourceCode" id="cb1202"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1202-1"><a href="reporting-data-results-3.html#cb1202-1"></a><span class="kw">plot_ly</span>(<span class="dt">z =</span> <span class="op">~</span><span class="st"> </span>volcano, <span class="dt">type =</span> <span class="st">&quot;surface&quot;</span>)</span></code></pre></div>
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 <p>Mapping with <code>plotly</code> can build on some data that comes with base R or other packages you’ve likely added (or can add easily, as with the <code>map_data</code> function from <code>ggplot2</code>). For example, we can map state capitals and cities with &gt; 40,000 people using data in the <code>us.cities</code> dataframe in the <code>maps</code> package:</p>
 <div class="sourceCode" id="cb1203"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1203-1"><a href="reporting-data-results-3.html#cb1203-1"></a><span class="kw">head</span>(maps<span class="op">::</span>us.cities, <span class="dv">3</span>)</span></code></pre></div>
 <pre><code>##         name country.etc    pop   lat    long capital
@@ -1063,8 +1079,8 @@ <h3><span class="header-section-number">12.2.2</span> <code>plotly</code> packag
 <span id="cb1205-8"><a href="reporting-data-results-3.html#cb1205-8"></a>     <span class="dt">data =</span> <span class="kw">filter</span>(maps<span class="op">::</span>us.cities, <span class="dv">-125</span> <span class="op">&lt;</span><span class="st"> </span>long <span class="op">&amp;</span><span class="st"> </span>long <span class="op">&lt;</span><span class="st"> </span><span class="dv">-60</span> <span class="op">&amp;</span></span>
 <span id="cb1205-9"><a href="reporting-data-results-3.html#cb1205-9"></a><span class="st">                     </span><span class="dv">25</span> <span class="op">&lt;</span><span class="st"> </span>lat <span class="op">&amp;</span><span class="st"> </span>lat <span class="op">&lt;</span><span class="st"> </span><span class="dv">52</span>)) <span class="op">%&gt;%</span></span>
 <span id="cb1205-10"><a href="reporting-data-results-3.html#cb1205-10"></a><span class="st">   </span><span class="kw">layout</span>(<span class="dt">showlegend =</span> <span class="ot">FALSE</span>)</span></code></pre></div>
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/> 133313","Plymouth MN <br /> 73505","Pocatello ID <br /> 50496","Pomona CA <br /> 157501","Pompano Beach FL <br /> 90446","Pontiac MI <br /> 67952","Port Arthur TX <br /> 56728","Port Charlotte FL <br /> 49840","Port Orange FL <br /> 55891","Port Saint Lucie FL <br /> 126377","Portage MI <br /> 46666","Porterville CA <br /> 45703","Portland ME <br /> 62882","Portland OR <br /> 542751","Portsmouth VA <br /> 98664","Potomac MD <br /> 45119","Poway CA <br /> 49943","Prescott AZ <br /> 41432","Providence RI <br /> 178295","Provo UT <br /> 105832","Pueblo CO <br /> 104659","Quincy IL <br /> 39549","Quincy MA <br /> 89347","Racine WI <br /> 78703","Raleigh NC <br /> 350822","Rancho Cordova CA <br /> 59972","Rancho Cucamonga CA <br /> 177721","Rancho Palos Verdes CA <br /> 42966","Rancho Santa Margarita CA <br /> 52447","Rapid City SD <br /> 62302","Reading PA <br /> 79476","Redan GA <br /> 40141","Redding CA <br /> 93865","Redford MI <br /> 49542","Redlands CA <br /> 71605","Redmond WA <br /> 47662","Redondo Beach CA <br /> 68669","Redwood City CA <br /> 71340","Reno NV <br /> 206626","Renton WA <br /> 57369","Reston VA <br /> 61977","Revere MA <br /> 46259","Rialto CA <br /> 103423","Richardson TX <br /> 107481","Richland WA <br /> 46156","Richmond CA <br /> 103818","Richmond VA <br /> 189498","Rio Rancho NM <br /> 66833","Riverside CA <br /> 306351","Roanoke VA <br /> 90410","Rochester Hills MI <br /> 68879","Rochester MN <br /> 98227","Rochester NY <br /> 209587","Rock Hill SC <br /> 61899","Rockford IL <br /> 153148","Rocklin CA <br /> 59546","Rockville MD <br /> 63516","Rocky Mount NC <br /> 56008","Rogers AR <br /> 46984","Rohnert Park CA <br /> 41154","Romeoville IL <br /> 46960","Rosemead CA <br /> 56542","Roseville CA <br /> 120444","Roseville MI <br /> 47190","Roswell GA <br /> 76825","Roswell NM <br /> 43788","Round Rock TX <br /> 95330","Rowland Heights CA <br /> 53112","Rowlett TX <br /> 57174","Royal Oak MI <br /> 57043","Sacramento CA <br /> 480392","Saginaw MI <br /> 56685","Saint Charles MO <br /> 62284","Saint Clair Shores MI <br /> 60089","Saint Cloud MN <br /> 59074","Saint George UT <br /> 63952","Saint Joseph MO <br /> 71282","Saint Louis MO <br /> 315546","Saint Louis Park MN <br /> 44032","Saint Paul MN <br /> 272469","Saint Peters MO <br /> 54258","Saint Petersburg FL <br /> 245804","Salem NH <br /> 42859","Salem OR <br /> 148942","Salina KS <br /> 45715","Salinas CA <br /> 150050","Salt Lake City UT <br /> 177318","San Angelo TX <br /> 88143","San Antonio TX <br /> 1278171","San Bernardino CA <br /> 205743","San Bruno CA <br /> 39523","San Buenaventura CA <br /> 106518","San Clemente CA <br /> 64987","San Diego CA <br /> 1299352","San Francisco CA <br /> 723724","San Gabriel CA <br /> 41861","San Jose CA <br /> 897883","San Leandro CA <br /> 80015","San Luis Obispo CA <br /> 44098","San Marcos CA <br /> 73020","San Marcos TX <br /> 49051","San Mateo CA <br /> 89495","San Rafael CA <br /> 55133","San Ramon CA <br 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/> 133313","Plymouth MN <br /> 73505","Pocatello ID <br /> 50496","Pomona CA <br /> 157501","Pompano Beach FL <br /> 90446","Pontiac MI <br /> 67952","Port Arthur TX <br /> 56728","Port Charlotte FL <br /> 49840","Port Orange FL <br /> 55891","Port Saint Lucie FL <br /> 126377","Portage MI <br /> 46666","Porterville CA <br /> 45703","Portland ME <br /> 62882","Portland OR <br /> 542751","Portsmouth VA <br /> 98664","Potomac MD <br /> 45119","Poway CA <br /> 49943","Prescott AZ <br /> 41432","Providence RI <br /> 178295","Provo UT <br /> 105832","Pueblo CO <br /> 104659","Quincy IL <br /> 39549","Quincy MA <br /> 89347","Racine WI <br /> 78703","Raleigh NC <br /> 350822","Rancho Cordova CA <br /> 59972","Rancho Cucamonga CA <br /> 177721","Rancho Palos Verdes CA <br /> 42966","Rancho Santa Margarita CA <br /> 52447","Rapid City SD <br /> 62302","Reading PA <br /> 79476","Redan GA <br /> 40141","Redding CA <br /> 93865","Redford MI <br /> 49542","Redlands CA <br /> 71605","Redmond WA <br /> 47662","Redondo Beach CA <br /> 68669","Redwood City CA <br /> 71340","Reno NV <br /> 206626","Renton WA <br /> 57369","Reston VA <br /> 61977","Revere MA <br /> 46259","Rialto CA <br /> 103423","Richardson TX <br /> 107481","Richland WA <br /> 46156","Richmond CA <br /> 103818","Richmond VA <br /> 189498","Rio Rancho NM <br /> 66833","Riverside CA <br /> 306351","Roanoke VA <br /> 90410","Rochester Hills MI <br /> 68879","Rochester MN <br /> 98227","Rochester NY <br /> 209587","Rock Hill SC <br /> 61899","Rockford IL <br /> 153148","Rocklin CA <br /> 59546","Rockville MD <br /> 63516","Rocky Mount NC <br /> 56008","Rogers AR <br /> 46984","Rohnert Park CA <br /> 41154","Romeoville IL <br /> 46960","Rosemead CA <br /> 56542","Roseville CA <br /> 120444","Roseville MI <br /> 47190","Roswell GA <br /> 76825","Roswell NM <br /> 43788","Round Rock TX <br /> 95330","Rowland Heights CA <br /> 53112","Rowlett TX <br /> 57174","Royal Oak MI <br /> 57043","Sacramento CA <br /> 480392","Saginaw MI <br /> 56685","Saint Charles MO <br /> 62284","Saint Clair Shores MI <br /> 60089","Saint Cloud MN <br /> 59074","Saint George UT <br /> 63952","Saint Joseph MO <br /> 71282","Saint Louis MO <br /> 315546","Saint Louis Park MN <br /> 44032","Saint Paul MN <br /> 272469","Saint Peters MO <br /> 54258","Saint Petersburg FL <br /> 245804","Salem NH <br /> 42859","Salem OR <br /> 148942","Salina KS <br /> 45715","Salinas CA <br /> 150050","Salt Lake City UT <br /> 177318","San Angelo TX <br /> 88143","San Antonio TX <br /> 1278171","San Bernardino CA <br /> 205743","San Bruno CA <br /> 39523","San Buenaventura CA <br /> 106518","San Clemente CA <br /> 64987","San Diego CA <br /> 1299352","San Francisco CA <br /> 723724","San Gabriel CA <br /> 41861","San Jose CA <br /> 897883","San Leandro CA <br /> 80015","San Luis Obispo CA <br /> 44098","San Marcos CA <br /> 73020","San Marcos TX <br /> 49051","San Mateo CA <br /> 89495","San Rafael CA <br /> 55133","San Ramon CA <br 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58084","Whittier CA <br /> 86182","Wichita Falls TX <br /> 100616","Wichita KS <br /> 356051","Wilkes-Barre PA <br /> 40242","Wilmington DE <br /> 71065","Wilmington NC <br /> 93097","Wilson NC <br /> 47191","Winston-Salem NC <br /> 194826","Woodbury MN <br /> 52052","Woodland CA <br /> 52266","Woonsocket RI <br /> 46297","Worcester MA <br /> 177973","Wyoming MI <br /> 70474","Yakima WA <br /> 89815","Yonkers NY <br /> 198045","Yorba Linda CA <br /> 65932","Youngstown OH <br /> 76590","Yuba City CA <br /> 54086","Yucaipa CA <br /> 52333","Yuma AZ <br /> 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 <p>You can also make choropleths interactive. Remember that we earlier created a choropleth of US state populations with the following code:</p>
 <div class="sourceCode" id="cb1206"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1206-1"><a href="reporting-data-results-3.html#cb1206-1"></a><span class="kw">library</span>(choroplethr)</span>
 <span id="cb1206-2"><a href="reporting-data-results-3.html#cb1206-2"></a><span class="kw">data</span>(df_pop_state)</span>
@@ -1082,8 +1098,8 @@ <h3><span class="header-section-number">12.2.2</span> <code>plotly</code> packag
 <span id="cb1207-9"><a href="reporting-data-results-3.html#cb1207-9"></a><span class="st">  </span><span class="kw">add_markers</span>(<span class="dt">x =</span> state.center[[<span class="st">&quot;x&quot;</span>]], <span class="dt">y =</span> state.center[[<span class="st">&quot;y&quot;</span>]], </span>
 <span id="cb1207-10"><a href="reporting-data-results-3.html#cb1207-10"></a>    <span class="dt">size =</span> <span class="kw">I</span>(<span class="dv">2</span>), <span class="dt">symbol =</span> <span class="kw">I</span>(<span class="dv">8</span>), <span class="dt">color =</span> <span class="kw">I</span>(<span class="st">&quot;white&quot;</span>), <span class="dt">hoverinfo =</span> <span class="st">&quot;none&quot;</span>) <span class="op">%&gt;%</span></span>
 <span id="cb1207-11"><a href="reporting-data-results-3.html#cb1207-11"></a><span class="st">  </span><span class="kw">layout</span>(<span class="dt">geo =</span> us_map)</span></code></pre></div>
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 <p>The other way to create a <code>plotly</code> graph is to first create a <code>ggplot</code> object and then transform it into an interactive graphic using the <code>ggplotly</code> function.</p>
 <p>The following code can be used to plot Time versus Shots for the World Cup date in a regular, non-interactive plot:</p>
 <div class="sourceCode" id="cb1208"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1208-1"><a href="reporting-data-results-3.html#cb1208-1"></a>shots_vs_time &lt;-<span class="st"> </span>worldcup <span class="op">%&gt;%</span></span>
@@ -1096,8 +1112,8 @@ <h3><span class="header-section-number">12.2.2</span> <code>plotly</code> packag
 <p><img src="RProgrammingForResearch_files/figure-html/unnamed-chunk-653-1.png" width="672" /></p>
 <p>To make the plot interactive, just pass the <code>ggplot</code> object to <code>ggplotly</code>:</p>
 <div class="sourceCode" id="cb1209"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1209-1"><a href="reporting-data-results-3.html#cb1209-1"></a><span class="kw">ggplotly</span>(shots_vs_time)</span></code></pre></div>
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 <p>With R, not only can you pull things from another website using an API, you can also upload or submit things.</p>
 <p>There is a function in the <code>plotly</code> library, <code>plotly_POST</code>, that lets you post a plot you create in R to <a href="https://plot.ly" class="uri">https://plot.ly</a>.</p>
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@@ -1117,8 +1133,8 @@ <h3><span class="header-section-number">12.2.3</span> <code>rbokeh</code> packag
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 <p>This package can also be used to create interactive maps. For example, the following dataset has data on Oregon climate stations, including locations:</p>
 <div class="sourceCode" id="cb1213"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1213-1"><a href="reporting-data-results-3.html#cb1213-1"></a>orstationc &lt;-<span class="st"> </span><span class="kw">read.csv</span>(<span class="st">&quot;data/orstationc.csv&quot;</span>)</span>
 <span id="cb1213-2"><a href="reporting-data-results-3.html#cb1213-2"></a><span class="kw">head</span>(orstationc, <span class="dv">3</span>)</span></code></pre></div>
@@ -1134,8 +1150,8 @@ <h3><span class="header-section-number">12.2.3</span> <code>rbokeh</code> packag
 <div class="sourceCode" id="cb1215"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1215-1"><a href="reporting-data-results-3.html#cb1215-1"></a><span class="kw">gmap</span>(<span class="dt">lat =</span> <span class="fl">44.1</span>, <span class="dt">lng =</span> <span class="fl">-120.767</span>, <span class="dt">zoom =</span> <span class="dv">5</span>, <span class="dt">width =</span> <span class="dv">500</span>, <span class="dt">height =</span> <span class="dv">428</span>) <span class="op">%&gt;%</span></span>
 <span id="cb1215-2"><a href="reporting-data-results-3.html#cb1215-2"></a><span class="st">  </span><span class="kw">ly_points</span>(lon, lat, <span class="dt">data =</span> orstationc, <span class="dt">alpha =</span> <span class="fl">0.8</span>, <span class="dt">col =</span> <span class="st">&quot;red&quot;</span>,</span>
 <span id="cb1215-3"><a href="reporting-data-results-3.html#cb1215-3"></a>    <span class="dt">hover =</span> <span class="kw">c</span>(station, Name, elev, tann))</span></code></pre></div>
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 <p>You can get very creative with this package. The following code comes directly from the help documentation for the package and shows how to use this to create an interactive version of the periodic table:</p>
 <div class="sourceCode" id="cb1216"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1216-1"><a href="reporting-data-results-3.html#cb1216-1"></a><span class="co"># prepare data</span></span>
 <span id="cb1216-2"><a href="reporting-data-results-3.html#cb1216-2"></a>elements &lt;-<span class="st"> </span><span class="kw">subset</span>(elements, <span class="op">!</span><span class="kw">is.na</span>(group))</span>
@@ -1186,8 +1202,8 @@ <h3><span class="header-section-number">12.2.3</span> <code>rbokeh</code> packag
 <span id="cb1216-47"><a href="reporting-data-results-3.html#cb1216-47"></a>  <span class="dt">font_size =</span> <span class="st">&quot;4pt&quot;</span>, <span class="dt">align =</span> <span class="st">&quot;left&quot;</span>, <span class="dt">baseline =</span> <span class="st">&quot;middle&quot;</span>)</span>
 <span id="cb1216-48"><a href="reporting-data-results-3.html#cb1216-48"></a></span>
 <span id="cb1216-49"><a href="reporting-data-results-3.html#cb1216-49"></a>p</span></code></pre></div>
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 <h3><span class="header-section-number">12.2.4</span> <code>dygraphs</code> package</h3>
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 <div id="dt-package" class="section level3">
 <h3><span class="header-section-number">12.2.5</span> <code>DT</code> package</h3>
@@ -1210,8 +1226,8 @@ <h3><span class="header-section-number">12.2.5</span> <code>DT</code> package</h
 <p>There is a website with more information on this package at <a href="http://rstudio.github.io/DT/" class="uri">http://rstudio.github.io/DT/</a>.</p>
 <div class="sourceCode" id="cb1218"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1218-1"><a href="reporting-data-results-3.html#cb1218-1"></a><span class="kw">library</span>(DT)</span>
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 </div>
 <div id="creating-your-own-widget" class="section level3">
 <h3><span class="header-section-number">12.2.6</span> Creating your own widget</h3>
diff --git a/docs/reproducible-research-1.html b/docs/reproducible-research-1.html
index c442fb6c..7c0afa51 100644
--- a/docs/reproducible-research-1.html
+++ b/docs/reproducible-research-1.html
@@ -1069,15 +1069,15 @@ <h3><span class="header-section-number">5.5.4</span> Tables in R Markdown</h3>
 <tbody>
 <tr class="odd">
 <td align="right">a</td>
-<td align="center">-0.36</td>
+<td align="center">1.28</td>
 </tr>
 <tr class="even">
 <td align="right">b</td>
-<td align="center">-1.79</td>
+<td align="center">0.49</td>
 </tr>
 <tr class="odd">
 <td align="right">c</td>
-<td align="center">0.49</td>
+<td align="center">0.96</td>
 </tr>
 </tbody>
 </table>
diff --git a/docs/reproducible-research-3.html b/docs/reproducible-research-3.html
index 94f50ce8..fa285a5e 100644
--- a/docs/reproducible-research-3.html
+++ b/docs/reproducible-research-3.html
@@ -694,7 +694,7 @@ <h3><span class="header-section-number">13.2.1</span> Contents of weathermetrics
 ##     T.fahrenheit &lt;- round(T.fahrenheit, digits = round)
 ##     return(T.fahrenheit)
 ## }
-## &lt;bytecode: 0x7fea9ce1cef0&gt;
+## &lt;bytecode: 0x7fd2106b3ae8&gt;
 ## &lt;environment: namespace:weathermetrics&gt;</code></pre>
 <p>Here is part of the help file for this function:</p>
 <p><img src="figures/convert_temperature_helpfile.png" width="364" style="display: block; margin: auto;" /></p>
diff --git a/docs/search_index.json b/docs/search_index.json
index ea559428..bb6496d3 100644
--- a/docs/search_index.json
+++ b/docs/search_index.json
@@ -5,15 +5,15 @@
 ["entering-and-cleaning-data-1.html", "Chapter 2 Entering and cleaning data #1 2.1 Objectives 2.2 Overview 2.3 Reading data into R 2.4 Directories and pathnames 2.5 Data cleaning 2.6 Dates and filtering 2.7 Piping 2.8 In-course Exercise", " Chapter 2 Entering and cleaning data #1 Download a pdf of the lecture slides covering this topic. 2.1 Objectives After this chapter, you should (know / understand / be able to ): Understand what a flat file is and how it differs from data stored in a binary file format Be able to distinguish between delimited and fixed width formats for flat files Be able to identify the delimiter in a delimited file Be able to describe a working directory Be able to read in different types of flat files Be able to read in a few types of binary files (SAS, Excel) Understand the difference between relative and absolute file pathnames Describe the basics of your computer’s directory structure Reference files in different locations in your directory structure using relative and absolute pathnames Use the basic dplyr functions rename, select, mutate, slice, filter, and arrange to work with data in a dataframe object Convert a column to a date format using lubridate functions Extract information from a date object (e.g., month, year, day of week) using lubridate functions Define a logical operator and know the R syntax for common logical operators Use logical operators in conjunction with dplyr’s filter function to create subsets of a dataframe based on logical conditions Use piping to apply multiple dplyr functions in sequence to a dataframe 2.2 Overview There are four basic steps you will often repeat as you prepare to analyze data in R: Identify where the data is (If it’s on your computer, which directory? If it’s online, what’s the url?) Read data into R (e.g., read_delim, read_csv from the readr package) using the file path you figured out in step 1 Check to make sure the data came in correctly (dim, head, tail, str) Clean the data up In this chapter, I’ll go basics for each of these steps, as well as dive a bit deeper into some related topics you should learn now to make your life easier as you get started using R for research. 2.3 Reading data into R The following video covers the slides in this section on “Reading data into R” from 2018: Data comes in files of all shapes and sizes. R has the capability to read data in from many of these, even proprietary files for other software (e.g., Excel and SAS files). As a small sample, here are some of the types of data files that R can read and work with: Flat files (much more about these in just a minute) Files from other statistical packages (SAS, Excel, Stata, SPSS) Tables on webpages (e.g., the table on ebola outbreaks near the end of this Wikipedia page) Data in a database (e.g., MySQL, Oracle) Data in JSON and XML formats Really crazy data formats used in other disciplines (e.g., netCDF files from climate research, MRI data stored in Analyze, NIfTI, and DICOM formats) Geographic shapefiles Data through APIs Often, it is possible to read in and clean up even incredibly messy data, by using functions like scan and readLines to read the data in a line at a time, and then using regular expressions (which I’ll cover in the “Intermediate” section of the course) to clean up each line as it comes in. In over a decade of coding in R, I think the only time I’ve come across a data file I couldn’t get into R was for proprietary precision agriculture data collected at harvest by a combine. 2.3.1 Reading local flat files Much of the data that you will want to read in will be in flat files. Basically, these are files that you can open using a text editor; the most common type you’ll work with are probably comma-separated files (often with a .csv or .txt file extension). Most flat files come in two general categories: Fixed width files; and Delimited files: “.csv”: Comma-separated values “.tab”, “.tsv”: Tab-separated values Other possible delimiters: colon, semicolon, pipe (“|”) Fixed width files are files where a column always has the same width, for all the rows in the column. These tend to look very neat and easy-to-read when you open them in a text editor. For example, the first few rows of a fixed-width file might look like this: Course Number Day Time Intro to Epi 501 M/W/F 9:00-9:50 Advanced Epi 521 T/Th 1:00-2:15 Fixed width files used to be very popular, and they make it easier to look at data when you open the file in a text editor. However, now it’s pretty rare to just use a text editor to open a file and check out the data, and these files can be a bit of a pain to read into R and other programs because you sometimes have to specify exactly how wide each of the columns is. You may come across a fixed width file every now and then, though, particularly when working with older data sets, so it’s useful to be able to recognize one and to know how to read it in. Delimited files use some delimiter (for example, a column or a tab) to separate each column value within a row. The first few rows of a delimited file might look like this: Course, Number, Day, Time &quot;Intro to Epi&quot;, 501, &quot;M/W/F&quot;, &quot;9:00-9:50&quot; &quot;Advanced Epi&quot;, 521, &quot;T/Th&quot;, &quot;1:00-2:15&quot; Delimited files are very easy to read into R. You just need to be able to figure out what character is used as a delimiter (commas in the example above) and specify that to R in the function call to read in the data. These flat files can have a number of different file extensions. The most generic is .txt, but they will also have ones more specific to their format, like .csv for a comma-delimited file or .fwf for a fixed with file. R can read in data from both fixed with and delimited flat files. The only catch is that you need to tell R a bit more about the format of the flat file, including whether it is fixed width or delimited. If the file is fixed width, you will usually have to tell R the width of each column. If the file is delimited, you’ll need to tell R which delimiter is being used. If the file is delimited, you can use the read_delim family of functions from the readr package to read it in. This family of functions includes several specialized functions. All members of the read_delim family are doing the same basic thing. The only difference is what defaults each function has for the delimiter (delim). Members of the read_delim family include: Function Delimiter read_csv comma read_csv2 semi-colon read_table2 whitespace read_tsv tab You can use read_delim to read in any delimited file, regardless of the delimiter. However, you will need to specify the delimiter using the delim parameters. If you remember the more specialized function call (e.g., read_csv for a comma delimited file), therefore, you can save yourself some typing. For example, to read in the Ebola data, which is comma-delimited, you could either use read_table with a delim argument specified or use read_csv, in which case you don’t have to specify delim: library(package = &quot;readr&quot;) # The following two calls do the same thing ebola &lt;- read_delim(file = &quot;data/country_timeseries.csv&quot;, delim = &quot;,&quot;) ebola &lt;- read_csv(file = &quot;data/country_timeseries.csv&quot;) ## Parsed with column specification: ## cols( ## Date = col_character(), ## Day = col_double(), ## Cases_Guinea = col_double(), ## Cases_Liberia = col_double(), ## Cases_SierraLeone = col_double(), ## Cases_Nigeria = col_double(), ## Cases_Senegal = col_double(), ## Cases_UnitedStates = col_double(), ## Cases_Spain = col_double(), ## Cases_Mali = col_double(), ## Deaths_Guinea = col_double(), ## Deaths_Liberia = col_double(), ## Deaths_SierraLeone = col_double(), ## Deaths_Nigeria = col_double(), ## Deaths_Senegal = col_double(), ## Deaths_UnitedStates = col_double(), ## Deaths_Spain = col_double(), ## Deaths_Mali = col_double() ## ) The message that R prints after this call (“Parsed with column specification:..”) lets you know what classes were used for each column (this function tries to guess the appropriate class and typically gets it right). You can suppress the message using the cols_types = cols() argument. If readr doesn’t correctly guess some of the columns classes you can use the type_convert() function to take another go at guessing them after you’ve tweaked the formats of the rogue columns. This family of functions has a few other helpful options you can specify. For example, if you want to skip the first few lines of a file before you start reading in the data, you can use skip to set the number of lines to skip. If you only want to read in a few lines of the data, you can use the n_max option. For example, if you have a really long file, and you want to save time by only reading in the first ten lines as you figure out what other options to use in read_delim for that file, you could include the option n_max = 10 in the read_delim call. Here is a table of some of the most useful options common to the read_delim family of functions: Option Description skip How many lines of the start of the file should you skip? col_names What would you like to use as the column names? col_types What would you like to use as the column types? n_max How many rows do you want to read in? na How are missing values coded? Remember that you can always find out more about a function by looking at its help file. For example, check out ?read_delim and ?read_fwf. You can also use the help files to determine the default values of arguments for each function. So far, we’ve only looked at functions from the readr package for reading in data files. There is a similar family of functions available in base R, the read.table family of functions. The readr family of functions is very similar to the base R read.table functions, but have some more sensible defaults. Compared to the read.table family of functions, the readr functions: Work better with large datasets: faster, includes progress bar Have more sensible defaults (e.g., characters default to characters, not factors) I recommend that you always use the readr functions rather than their base R alternatives, given these advantages. However, you are likely to come across code that someone else has written that uses one of these base R functions, so it’s helpful to know what they are. Functions in the read.table family include: read.csv read.delim read.table read.fwf The readr package is a member of the tidyverse of packages. The tidyverse describes an evolving collection of R packages with a common philosophy, and they are unquestionably changing the way people code in R. Many were developed in part or full by Hadley Wickham and others at RStudio. Many of these packages are less than ten years old, but have been rapidly adapted by the R community. As a result, newer examples of R code will often look very different from the code in older R scripts, including examples in books that are more than a few years old. In this course, I’ll focus on “tidyverse” functions when possible, but I do put in details about base R equivalent functions or processes at some points—this will help you interpret older code. You can download all the tidyverse packages using install.packages(“tidyverse”), library(“tidyverse”) makes all the tidyverse functions available for use. 2.3.2 Reading in other file types Later in the course, we’ll talk about how to open a variety of other file types in R. However, you might find it immediately useful to be able to read in files from other statistical programs. There are two “tidyverse” packages—readxl and haven—that help with this. They allow you to read in files from the following formats: File type Function Package Excel read_excel readxl SAS read_sas haven SPSS read_spss haven Stata read_stata haven 2.4 Directories and pathnames The following video covers this section on “Directories and pathnames” from 2018: 2.4.1 Directory structure So far, we’ve only looked at reading in files that are located in your current working directory. For example, if you’re working in an R Project, by default the project will open with that directory as the working directory, so you can read files that are saved in that project’s main directory using only the file name as a reference. However, you’ll often want to read in files that are located somewhere else on your computer, or even files that are saved on another computer (for example, data files that are posted online). Doing this is very similar to reading in a file that is in your current working directory; the only difference is that you need to give R some directions so it can find the file. The most common case will be reading in files in a subdirectory of your current working directory. For example, you may have created a “data” subdirectory in one of your R Projects directories to keep all the project’s data files in the same place while keeping the structure of the main directory fairly clean. In this case, you’ll need to direct R into that subdirectory when you want to read one of those files. To understand how to give R these directions, you need to have some understanding of the directory structure of your computer. It seems a bit of a pain and a bit complex to have to think about computer directory structure in the “basics” part of this class, but this structure is not terribly complex once you get the idea of it. There are a couple of very good reasons why it’s worth learning now. First, many of the most frustrating errors you get when you start using R trace back to understanding directories and filepaths. For example, when you try to read a file into R using only the filename, and that file is not in your current working directory, you will get an error like: Error in file(file, &quot;rt&quot;) : cannot open the connection In addition: Warning message: In file(file, &quot;rt&quot;) : cannot open file &#39;Ex.csv&#39;: No such file or directory This error is especially frustrating when you’re new to R because it happens at the very beginning of your analysis—you can’t even get your data in. Also, if you don’t understand a bit about working directories and how R looks for the file you’re asking it to find, you’d have no idea where to start to fix this error. Second, once you understand how to use pathnames, especially relative pathnames, to tell R how to find a file that is in a directory other than your working directory, you will be able to organize all of your files for a project in a much cleaner way. For example, you can create a directory for your project, then create one subdirectory to store all of your R scripts, and another to store all of your data, and so on. This can help you keep very complex projects more structured and easier to navigate. We’ll talk about these ideas more in the course sections on Reproducible Research, but it’s good to start learning how directory structures and filepaths work early. Your computer organizes files through a collection of directories. Chances are, you are fairly used to working with these in your daily life already (although you may call them “folders” rather than “directories”). For example, you’ve probably created new directories to store data files and Word documents for a specific project. Figure 2.1 gives an example file directory structure for a hypothetical computer. Directories are shown in blue, and files in green. Figure 2.1: An example of file directory structure. You can notice a few interesting things from Figure 2.1. First, you might notice the structure includes a few of the directories that you use a lot on your own computer, like Desktop, Documents, and Downloads. Next, the directory at the very top is the computer’s root directory, /. For a PC, the root directory might something like C:; for Unix and Macs, it’s usually /. Finally, if you look closely, you’ll notice that it’s possible to have different files in different locations of the directory structure with the same file name. For example, in the figure, there are files names heat_mort.csv in both the CourseText directory and in the example_data directory. These are two different files with different contents, but they can have the same name as long as they’re in different directories. This fact—that you can have files with the same name in different places—should help you appreciate how useful it is that R requires you to give very clear directions to describe exactly which file you want R to read in, if you aren’t reading in something in your current working directory. You will have a home directory somewhere near the top of your structure, although it’s likely not your root directory. In the hypothetic computer in Figure 2.1, the home directory is /Users/brookeanderson. I’ll describe just a bit later how you can figure out what your own home directory is on your own computer. 2.4.2 Working directory When you run R, it’s always running from within some working directory, which will be one of the directories somewhere in your computer’s directory structure. At any time, you can figure out which directory R is working in by running the command getwd() (short for “get working directory”). For example, my R session is currently running in the following directory: getwd() ## [1] &quot;/Users/georgianaanderson/Documents/my_books/RProgrammingForResearch&quot; This means that, for my current R session, R is working in the RProgrammingForResearch subdirectory of my brookeanderson directory (which is my home directory). There are a few general rules for which working directory R will start in when you open an R session. These are not absolute rules, but they’re generally true. If you have R closed, and you open it by double-clicking on an R script, then R will generally open with, as its working directory, the directory in which that script is stored. This is often a very convenient convention, because often any of the data you’ll be reading in for that script is somewhere near where the script file is saved in the directory structure. If you open R by double-clicking on the R icon in “Applications” (or something similar on a PC), R will start in its default working directory. You can find out what this is, or change it, in RStudio’s “Preferences”. Finally, if you open an R Project, R will start in that project’s working directory (the directory in which the .Rproj file for the project is stored). 2.4.3 File and directory pathnames Once you get a picture of how your directories and files are organized, you can use pathnames, either absolute or relative, to read in files from different directories than your current working directory. Pathnames are the directions for getting to a directory or file stored on your computer. When you want to reference a directory or file, you can use one of two types of pathnames: Relative pathname: How to get to the file or directory from your current working directory Absolute pathname: How to get to the file or directory from anywhere on the computer Absolute pathnames are a bit more straightforward conceptually, because they don’t depend on your current working directory. However, they’re also a lot longer to write, and they’re much less convenient if you’ll be sharing some of your code with other people who might run it on their own computers. I’ll explain this second point a bit more later in this section. Absolute pathnames give the full directions to a directory or file, starting all the way at the root directory. For example, the heat_mort.csv file in the CourseText directory has the absolute pathname: &quot;/Users/brookeanderson/Desktop/RCourseFall2015/CourseText/heat_mort.csv&quot; You can use this absolute pathname to read this file in using any of the readr functions to read in data. This absolute pathname will always work, regardless of your current working directory, because it gives directions from the root—it will always be clear to R exactly what file you’re talking about. Here’s the code to use to read that file in using the read.csv function with the file’s absolute pathname: heat_mort &lt;- read_csv(file = &quot;/Users/brookeanderson/Desktop/RCourseFall2015/CourseText/heat_mort.csv&quot;) The relative pathname, on the other hand, gives R the directions for how to get to a directory or file from the current working directory. If the file or directory you’re looking for is pretty close to your current working directory in your directory structure, then a relative pathname can be a much shorter way to tell R how to get to the file than an absolute pathname. However, the relative pathname depends on your current working directory—the relative pathname that works perfectly when you’re working in one directory will not work at all once you move into a different working directory. As an example of a relative pathname, say you’re working in the directory RCourseFall2015 within the file structure shown in Figure 2.1, and you want to read in the heat_mort.csv file in the CourseText directory. To get from RCourseFall2015 to that file, you’d need to look in the subdirectory CourseText, where you could find heat_mort.csv. Therefore, the relative pathname from your working directory would be: &quot;CourseText/heat_mort.csv&quot; You can use this relative pathname to tell R where to find and read in the file: heat_mort &lt;- read_csv(&quot;CourseText/heat_mort.csv&quot;) While this pathname is much shorter than the absolute pathname, it is important to remember that if you are working in a different working directory, this relative pathname would no longer work. There are a few abbreviations that can be really useful for pathnames: Shorthand Meaning ~ Home directory . Current working directory .. One directory up from current working directory ../.. Two directories up from current working directory These can help you keep pathnames shorter and also help you move “up-and-over” to get to a file or directory that’s not on the direct path below your current working directory. For example, my home directory is /Users/brookeanderson. You can use the list.files() function to list all the files in a directory. If I wanted to list all the files in my Downloads directory, which is a direct sub-directory of my home directory, I could use: list.files(&quot;~/Downloads&quot;) As a second example, say I was working in the working directory CourseText, but I wanted to read in the heat_mort.csv file that’s in the example_data directory, rather than the one in the CourseText directory. I can use the .. abbreviation to tell R to look up one directory from the current working directory, and then down within a subdirectory of that. The relative pathname in this case is: &quot;../Week2_Aug31/example_data/heat_mort.csv&quot; This tells R to look one directory up from the working directory (..) (this is also known as the parent directory of the current directory), which in this case is to RCourseFall2015, and then down within that directory to Week2_Aug31, then to example_data, and then to look there for the file heat_mort.csv. The relative pathname to read this file while R is working in the CourseTest directory would be: heat_mort &lt;- read_csv(&quot;../Week2_Aug31/example_data/heat_mort.csv&quot;) Relative pathnames “break” as soon as you tried them from a different working directory—this fact might make it seem like you would never want to use relative pathnames, and would always want to use absolute ones instead, even if they’re longer. If that were the only consideration (length of the pathname), then perhaps that would be true. However, as you do more and more in R, there will likely be many occasions when you want to use relative pathnames instead. They are particularly useful if you ever want to share a whole directory, with all subdirectories, with a collaborator. In that case, if you’ve used relative pathnames, all the code should work fine for the person you share with, even though they’re running it on their own computer. Conversely, if you’d used absolute pathnames, none of them would work on another computer, because the “top” of the directory structure (i.e., for me, /Users/brookeanderson/Desktop) will almost definitely be different for your collaborator’s computer than it is for yours. If you’re getting errors reading in files, and you think it’s related to the relative pathname you’re using, it’s often helpful to use list.files() to make sure the file you’re trying to load is in the directory that the relative pathname you’re using is directing R to. 2.4.4 Diversion: paste This is a good opportunity to explain how to use some functions that can be very helpful when you’re using relative or absolute pathnames: paste() and paste0(). As a bit of important background information, it’s important that you understand that you can save a pathname (absolute or relative) as an R object, and then use that R object in calls to later functions like list.files() and read_csv(). For example, to use the absolute pathname to read the heat_mort.csv file in the CourseText directory, you could run: my_file &lt;- &quot;/Users/brookeanderson/Desktop/RCourseFall2015/CourseText/heat_mort.csv&quot; heat_mort &lt;- read_csv(file = my_file) You’ll notice from this code that the pathname to get to a directory or file can sometimes become ungainly and long. To keep your code cleaner, you can address this by using the paste or paste0 functions. These functions come in handy in a lot of other applications, too, but this is a good place to introduce them. The paste() function is very straightforward. It takes, as inputs, a series of different character strings you want to join together, and it pastes them together in a single character string. (As a note, this means that your result vector will only be one element long, for basic uses of paste(), while the inputs will be several different character stings.) You separate all the different things you want to paste together using with commas in the function call. For example: paste(&quot;Sunday&quot;, &quot;Monday&quot;, &quot;Tuesday&quot;) ## [1] &quot;Sunday Monday Tuesday&quot; length(x = c(&quot;Sunday&quot;, &quot;Monday&quot;, &quot;Tuesday&quot;)) ## [1] 3 length(x = paste(&quot;Sunday&quot;, &quot;Monday&quot;, &quot;Tuesday&quot;)) ## [1] 1 The paste() function has an option called sep =. This tells R what you want to use to separate the values you’re pasting together in the output. The default is for R to use a space, as shown in the example above. To change the separator, you can change this option, and you can put in just about anything you want. For example, if you wanted to paste all the values together without spaces, you could use sep = \"\": paste(&quot;Sunday&quot;, &quot;Monday&quot;, &quot;Tuesday&quot;, sep = &quot;&quot;) ## [1] &quot;SundayMondayTuesday&quot; As a shortcut, instead of using the sep = \"\" option, you could achieve the same thing using the paste0 function. This function is almost exactly like paste, but it defaults to \"\" (i.e., no space) as the separator between values by default: paste0(&quot;Sunday&quot;, &quot;Monday&quot;, &quot;Tuesday&quot;) ## [1] &quot;SundayMondayTuesday&quot; With pathnames, you will usually not want spaces. Therefore, you could think about using paste0() to write an object with the pathname you want to ultimately use in commands like list.files() and setwd(). This will allow you to keep your code cleaner, since you can now divide long pathnames over multiple lines: my_file &lt;- paste0(&quot;/Users/brookeanderson/Desktop/&quot;, &quot;RCourseFall2015/CourseText/heat_mort.csv&quot;) heat_mort &lt;- read_csv(file = my_file) You will end up using paste() and paste0() for many other applications, but this is a good example of how you can start using these functions to start to get a feel for them. 2.4.5 Reading online flat files So far, I’ve only shown you how to read in data from files that are saved to your computer. R can also read in data directly from the web. If a flat file is posted online, you can read it into R in almost exactly the same way that you would read in a local file. The only difference is that you will use the file’s url instead of a local file path for the file argument. With the read_* family of functions, you can do this both for flat files from a non-secure webpage (i.e., one that starts with http) and for files from a secure webpage (i.e., one that starts with https), including GitHub and Dropbox. For example, to read in data from this GitHub repository of Ebola data, you can run: url &lt;- paste0(&quot;https://raw.githubusercontent.com/cmrivers/&quot;, &quot;ebola/master/country_timeseries.csv&quot;) ebola &lt;- read_csv(file = url) slice(.data = (select(.data = ebola, 1:3)), 1:3) ## # A tibble: 3 x 3 ## Date Day Cases_Guinea ## &lt;chr&gt; &lt;dbl&gt; &lt;dbl&gt; ## 1 1/5/2015 289 2776 ## 2 1/4/2015 288 2775 ## 3 1/3/2015 287 2769 2.5 Data cleaning The following video covers the section on “Data Cleaning” from 2018: Once you have loaded data into R, you’ll likely need to clean it up a little before you’re ready to analyze it. Here, I’ll go over the first steps of how to do that with functions from dplyr, another package in the tidyverse. Here are some of the most common data-cleaning tasks, along with the corresponding dplyr function for each: Task dplyr function Renaming columns rename Filtering to certain rows filter Selecting certain columns select Adding or changing columns mutate In this section, I’ll describe how to do each of these four tasks; in later sections of the course, we’ll go much deeper into how to clean messier data. For the examples in this section, I’ll use example data listing guests to the Daily Show. To follow along with these examples, you’ll want to load that data, as well as load the dplyr package (install it using install.packages if you have not already): library(&quot;dplyr&quot;) daily_show &lt;- read_csv(file = &quot;data/daily_show_guests.csv&quot;, skip = 4) I’ve used this data in previous examples, but as a reminder, here’s what it looks like: head(x = daily_show) ## # A tibble: 6 x 5 ## YEAR GoogleKnowlege_Occupation Show Group Raw_Guest_List ## &lt;dbl&gt; &lt;chr&gt; &lt;chr&gt; &lt;chr&gt; &lt;chr&gt; ## 1 1999 actor 1/11/99 Acting Michael J. Fox ## 2 1999 Comedian 1/12/99 Comedy Sandra Bernhard ## 3 1999 television actress 1/13/99 Acting Tracey Ullman ## 4 1999 film actress 1/14/99 Acting Gillian Anderson ## 5 1999 actor 1/18/99 Acting David Alan Grier ## 6 1999 actor 1/19/99 Acting William Baldwin 2.5.1 Renaming columns A first step is often re-naming the columns of the dataframe. It can be hard to work with a column name that: is long includes spaces or other special characters includes upper case You can check out the column names for a dataframe using the colnames function, with the dataframe object as the argument. Several of the column names in daily_show have some of these issues: colnames(x = daily_show) ## [1] &quot;YEAR&quot; &quot;GoogleKnowlege_Occupation&quot; ## [3] &quot;Show&quot; &quot;Group&quot; ## [5] &quot;Raw_Guest_List&quot; To rename these columns, use rename. The basic syntax is: ## Generic code rename(.data = dataframe, new_column_name_1 = old_column_name_1, new_column_name_2 = old_column_name_2) The first argument is the dataframe for which you’d like to rename columns. Then you list each pair of new versus old column names (in that order) for each of the columns you want to rename. To rename columns in the daily_show data using rename, for example, you would run: daily_show &lt;- rename(.data = daily_show, year = YEAR, job = GoogleKnowlege_Occupation, date = Show, category = Group, guest_name = Raw_Guest_List) head(x = daily_show, 3) ## # A tibble: 3 x 5 ## year job date category guest_name ## &lt;dbl&gt; &lt;chr&gt; &lt;chr&gt; &lt;chr&gt; &lt;chr&gt; ## 1 1999 actor 1/11/99 Acting Michael J. Fox ## 2 1999 Comedian 1/12/99 Comedy Sandra Bernhard ## 3 1999 television actress 1/13/99 Acting Tracey Ullman Many of the functions in tidyverse packages, including those in dplyr, provide exceptions to the general rule about when to use quotation marks versus when to leave them off. Unfortunately, this may make it a bit hard to learn when to use quotation marks versus when not to. One way to think about this, which is a bit of an oversimplification but can help as you’re learning, is to assume that anytime you’re using a dplyr function, every column in the dataframe you’re working with has been loaded to your R session as its own object. 2.5.2 Selecting columns Next, you may want to select only some columns of the dataframe. You can use the select function from dplyr to subset the dataframe to certain columns. The basic structure of this command is: ## Generic code select(.data = dataframe, column_name_1, column_name_2, ...) In this call, you first specify the dataframe to use and then list all of the column names to include in the output dataframe, with commas between each column name. For example, to select all columns in daily_show except year (since that information is already included in date), run: select(.data = daily_show, job, date, category, guest_name) ## # A tibble: 2,693 x 4 ## job date category guest_name ## &lt;chr&gt; &lt;chr&gt; &lt;chr&gt; &lt;chr&gt; ## 1 actor 1/11/99 Acting Michael J. Fox ## 2 Comedian 1/12/99 Comedy Sandra Bernhard ## 3 television actress 1/13/99 Acting Tracey Ullman ## 4 film actress 1/14/99 Acting Gillian Anderson ## 5 actor 1/18/99 Acting David Alan Grier ## 6 actor 1/19/99 Acting William Baldwin ## 7 Singer-lyricist 1/20/99 Musician Michael Stipe ## 8 model 1/21/99 Media Carmen Electra ## 9 actor 1/25/99 Acting Matthew Lillard ## 10 stand-up comedian 1/26/99 Comedy David Cross ## # … with 2,683 more rows Don’t forget that, if you want to change column names in the saved object, you must reassign the object to be the output of rename. If you run one of these cleaning functions without reassigning the object, R will print out the result, but the object itself won’t change. You can take advantage of this, as I’ve done in this example, to look at the result of applying a function to a dataframe without changing the original dataframe. This can be helpful as you’re figuring out how to write your code. The select function also provides some time-saving tools. For example, in the last example, we wanted all the columns except one. Instead of writing out all the columns we want, we can use - with the columns we don’t want to save time: daily_show &lt;- select(.data = daily_show, -year) head(x = daily_show, n = 3) ## # A tibble: 3 x 4 ## job date category guest_name ## &lt;chr&gt; &lt;chr&gt; &lt;chr&gt; &lt;chr&gt; ## 1 actor 1/11/99 Acting Michael J. Fox ## 2 Comedian 1/12/99 Comedy Sandra Bernhard ## 3 television actress 1/13/99 Acting Tracey Ullman 2.5.3 Add or change columns You can change a column or add a new column using the mutate function from the dplyr package. That function has the syntax: # Generic code mutate(.data = dataframe, changed_column = function(changed_column), new_column = function(other arguments)) For example, the job column in daily_show sometimes uses upper case and sometimes does not (this call uses the unique function to list only unique values in this column): head(x = unique(x = daily_show$job), n = 10) ## [1] &quot;actor&quot; &quot;Comedian&quot; &quot;television actress&quot; ## [4] &quot;film actress&quot; &quot;Singer-lyricist&quot; &quot;model&quot; ## [7] &quot;stand-up comedian&quot; &quot;actress&quot; &quot;comedian&quot; ## [10] &quot;Singer-songwriter&quot; To make all the observations in the job column lowercase, use the str_to_lower function from the stringr package within a mutate function: library(package = &quot;stringr&quot;) mutate(.data = daily_show, job = str_to_lower(string = job)) ## # A tibble: 2,693 x 4 ## job date category guest_name ## &lt;chr&gt; &lt;chr&gt; &lt;chr&gt; &lt;chr&gt; ## 1 actor 1/11/99 Acting Michael J. Fox ## 2 comedian 1/12/99 Comedy Sandra Bernhard ## 3 television actress 1/13/99 Acting Tracey Ullman ## 4 film actress 1/14/99 Acting Gillian Anderson ## 5 actor 1/18/99 Acting David Alan Grier ## 6 actor 1/19/99 Acting William Baldwin ## 7 singer-lyricist 1/20/99 Musician Michael Stipe ## 8 model 1/21/99 Media Carmen Electra ## 9 actor 1/25/99 Acting Matthew Lillard ## 10 stand-up comedian 1/26/99 Comedy David Cross ## # … with 2,683 more rows 2.5.4 Base R equivalents to dplyr functions Just so you know, all of these dplyr functions have alternatives, either functions or processes, in base R: dplyr Base R equivalent rename Reassign colnames select Square bracket indexing filter subset mutate Use $ to change / create columns You will see these alternatives used in older code examples. 2.6 Dates and filtering The following video covers the lecture material on working with dates in R and on filtering a dataframe to a subset of rows using logical operators from 2018: 2.6.1 Dates in R As part of the data cleaning process, you may want to change the class of some of the columns in the dataframe. For example, you may want to change a column from a character to a date. Here are some of the most common vector classes in R: Class Example character “Chemistry”, “Physics”, “Mathematics” numeric 10, 20, 30, 40 factor Male [underlying number: 1], Female [2] Date “2010-01-01” [underlying number: 14,610] logical TRUE, FALSE To find out the class of a vector (including a column in a dataframe – remember each column can be thought of as a vector), you can use class(): class(daily_show$date) ## [1] &quot;character&quot; It is especially common to need to convert dates during the data cleaning process, since date columns will usually be read into R as characters or factors—you can do some interesting things with vectors that are in a Date class that you cannot do with a vector in a character class. To convert a vector to the Date class, if you’d like to only use base R, you can use the as.Date function. I’ll walk through how to use as.Date, since it’s often used in older R code. However, I recommend in your own code that you instead use the lubridate package, which I’ll talk about later in this section. To convert a vector to the Date class, you can use functions in the lubridate package. This package has a series of functions based on the order that date elements are given in the incoming character with date information. For example, in “12/31/99”, the date elements are given in the order of month (m), day (d), year (y), so this character string could be converted to the date class with the function mdy. As another example, the ymd function from lubridate can be used to parse a column into a Date class, regardless of the original format of the date, as long as the date elements are in the order: year, month, day. For example: library(&quot;lubridate&quot;) ymd(&quot;2008-10-13&quot;) ## [1] &quot;2008-10-13&quot; ymd(&quot;&#39;08 Oct 13&quot;) ## [1] &quot;2008-10-13&quot; ymd(&quot;&#39;08 Oct 13&quot;) ## [1] &quot;2008-10-13&quot; To convert the date column in the daily_show data into a Date class, then, you can run: library(package = &quot;lubridate&quot;) class(x = daily_show$date) # Check the class of the &#39;date&#39; column before mutating it ## [1] &quot;character&quot; daily_show &lt;- mutate(.data = daily_show, date = mdy(date)) head(x = daily_show, n = 3) ## # A tibble: 3 x 4 ## job date category guest_name ## &lt;chr&gt; &lt;date&gt; &lt;chr&gt; &lt;chr&gt; ## 1 actor 1999-01-11 Acting Michael J. Fox ## 2 Comedian 1999-01-12 Comedy Sandra Bernhard ## 3 television actress 1999-01-13 Acting Tracey Ullman class(x = daily_show$date) # Check the class of the &#39;date&#39; column after mutating it ## [1] &quot;Date&quot; Once you have an object in the Date class, you can do things like plot by date, calculate the range of dates, and calculate the total number of days the dataset covers: range(daily_show$date) diff(x = range(daily_show$date)) We could have used these to transform the date in daily_show, using the following pipe chain: daily_show &lt;- read_csv(file = &quot;data/daily_show_guests.csv&quot;, skip = 4) %&gt;% rename(job = GoogleKnowlege_Occupation, date = Show, category = Group, guest_name = Raw_Guest_List) %&gt;% select(-YEAR) %&gt;% mutate(date = mdy(date)) %&gt;% filter(category == &quot;Science&quot;) head(x = daily_show, n = 2) ## # A tibble: 2 x 4 ## job date category guest_name ## &lt;chr&gt; &lt;date&gt; &lt;chr&gt; &lt;chr&gt; ## 1 neurosurgeon 2003-04-28 Science Dr Sanjay Gupta ## 2 scientist 2004-01-13 Science Catherine Weitz The lubridate package also includes functions to pull out certain elements of a date, including: wday mday yday month quarter year For example, we could use wday to create a new column with the weekday of each show: mutate(.data = daily_show, show_day = wday(x = date, label = TRUE)) %&gt;% select(date, show_day, guest_name) %&gt;% slice(1:5) ## # A tibble: 5 x 3 ## date show_day guest_name ## &lt;date&gt; &lt;ord&gt; &lt;chr&gt; ## 1 2003-04-28 Mon Dr Sanjay Gupta ## 2 2004-01-13 Tue Catherine Weitz ## 3 2004-06-15 Tue Hassan Ibrahim ## 4 2005-09-06 Tue Dr. Marc Siegel ## 5 2006-02-13 Mon Astronaut Mike Mullane R functions tend to use the timezone of YOUR computer’s operating system by default, or UTC, or GMT. You need to be careful when working with dates and times to either specify the time zone or convince yourself the default behavior works for your application. 2.6.2 Filtering to certain rows Next, you might want to filter the dataset down so that it only includes certain rows. For example, you might want to get a dataset with only the guests from 2015, or only guests who are scientists. You can use the filter function from dplyr to filter a dataframe down to a subset of rows. The syntax is: ## Generic code filter(.data = dataframe, logical expression) The logical expression in this call gives the condition that a row must meet to be included in the output data frame. For example, if you want to create a data frame that only includes guests who were scientists, you can run: scientists &lt;- filter(.data = daily_show, category == &quot;Science&quot;) head(x = scientists) ## # A tibble: 6 x 4 ## job date category guest_name ## &lt;chr&gt; &lt;date&gt; &lt;chr&gt; &lt;chr&gt; ## 1 neurosurgeon 2003-04-28 Science Dr Sanjay Gupta ## 2 scientist 2004-01-13 Science Catherine Weitz ## 3 physician 2004-06-15 Science Hassan Ibrahim ## 4 doctor 2005-09-06 Science Dr. Marc Siegel ## 5 astronaut 2006-02-13 Science Astronaut Mike Mullane ## 6 Astrophysicist 2007-01-30 Science Neil deGrasse Tyson To build a logical expression to use in filter, you’ll need to know some of R’s logical operators. Some of the most commonly used ones are: Operator Meaning Example == equals category == \"Acting\" != does not equal category != \"Comedy %in% is in category %in% c(\"Academic\", \"Science\") is.na() is missing is.na(job) !is.na() is not missing !is.na(job) &amp; and year == 2015 &amp; category == \"Academic\" | or year == 2015 | category == \"Academic\" We’ll use these logical operators and expressions a lot more as the course continues, so they’re worth learning by heart. Two common errors with logical operators are: (1) Using = instead of == to check if two values are equal; and (2) Using == NA instead of is.na to check for missing observations. 2.7 Piping The following video covers the lecture material on piping from 2018: So far, I’ve shown how to use these dplyr functions one at a time to clean up the data, reassigning the dataframe object at each step. However, there’s a trick called “piping” that will let you clean up your code a bit when you’re writing a script to clean data. If you look at the format of these dplyr functions, you’ll notice that they all take a dataframe as their first argument: # Generic code rename(.data = dataframe, new_column_name_1 = old_column_name_1, new_column_name_2 = old_column_name_2) select(.data = dataframe, column_name_1, column_name_2) filter(.data = dataframe, logical expression) mutate(.data = dataframe, changed_column = function(changed_column), new_column = function(other arguments)) Without piping, you have to reassign the dataframe object at each step of this cleaning if you want the changes saved in the object: daily_show &lt;-read_csv(file = &quot;data/daily_show_guests.csv&quot;, skip = 4) daily_show &lt;- rename(.data = daily_show, job = GoogleKnowlege_Occupation, date = Show, category = Group, guest_name = Raw_Guest_List) daily_show &lt;- select(.data = daily_show, -YEAR) daily_show &lt;- mutate(.data = daily_show, job = str_to_lower(job)) daily_show &lt;- filter(.data = daily_show, category == &quot;Science&quot;) Piping lets you clean this code up a bit. It can be used with any function that inputs a dataframe as its first argument. It pipes the dataframe created right before the pipe (%&gt;%) into the function right after the pipe. With piping, therefore, the same data cleaning looks like: daily_show &lt;-read_csv(file = &quot;data/daily_show_guests.csv&quot;, skip = 4) %&gt;% rename(job = GoogleKnowlege_Occupation, date = Show, category = Group, guest_name = Raw_Guest_List) %&gt;% select(-YEAR) %&gt;% mutate(job = str_to_lower(job)) %&gt;% filter(category == &quot;Science&quot;) Notice that, when piping, the first argument (the name of the dataframe) is excluded from all function calls that follow a pipe. This is because piping sends the dataframe from the last step into each of these functions as the dataframe argument. 2.8 In-course Exercise 2.8.1 Downloading and checking out the example data Download the whole directory for this week from Github (https://github.com/geanders/week_2_data). To do that, go the the GitHub page with data for this week’s exercise and, in the top right, choose “Clone or Download” and then choose “Download ZIP”. This will download a compressed file with the full directory of data, probably to your computer’s “Downloads” folder. Then move the directory into your course Project directory and “unzip” it (try double-clicking the file, or right click on the file and see if there’s a “decompress” or “unzip” option). All the files will be in a subdirectory—move them into the main Project directory (don’t do this in R, just use whatever technique you usually use on your computer to move files between directories). Look through the structure of the “data” directory. What files are in the directory? Which files are flat files? Which are delimited (one category of flat files), and what are their delimiters? Create a new R script to put all the code you use for this exercise. Create a subdirectory in your course directory called “R” and save this script there using a .R extension (e.g., “week_2.R”). 2.8.2 Reading in different types of files Now you’ll try reading in data from a variety of types of file formats. Try the following tasks: What type of flat file do you think the “ld_genetics.txt” file is? See if you can read it in and save it as the R object ld_genetics. Use the summary function to check out basic statistics on the data. Check out the file “measles_data/02-09-2015.txt”. What type of flat file do you think it is? Since it’s in a subdirectory, you’ll need to tell R how to get to it from the project directory, using something called a relative pathname (we’ll talk about this a lot more in the next section of the lecture). Read this file into R as an object named ca_measles, using the relative pathname (“measles_data/02-09-2015.txt”) in place of the file name in the read_tsv function call. Use the col_names option to name the columns “city” and “count”. What would the default column names be if you didn’t use this option (try this out by running read_csv without the col_names option)? Read in the Excel file “icd-10.xls” and assign it to the object name idc10. Use the readxl package to do that (examples are at the bottom of the linked page). Read in the SAS file icu.sas7bdat. To do this, use the haven package. Read the file into the R object icu. Example R code: # Load the `readr` package library(package = &quot;readr&quot;) # Use `read_tsv` to read this file. ld_genetics &lt;- read_tsv(file = &quot;ld_genetics.txt&quot;) ## Parsed with column specification: ## cols( ## pos = col_double(), ## nA = col_double(), ## nC = col_double(), ## nG = col_double(), ## nT = col_double(), ## GCsk = col_double(), ## TAsk = col_double(), ## cGCsk = col_double(), ## cTAsk = col_double() ## ) summary(object = ld_genetics) ## pos nA nC nG ## Min. : 500 Min. :185 Min. :120.0 Min. : 85.0 ## 1st Qu.: 876000 1st Qu.:288 1st Qu.:173.0 1st Qu.:172.0 ## Median :1751500 Median :308 Median :190.0 Median :189.0 ## Mean :1751500 Mean :309 Mean :191.9 Mean :191.8 ## 3rd Qu.:2627000 3rd Qu.:329 3rd Qu.:209.0 3rd Qu.:208.0 ## Max. :3502500 Max. :463 Max. :321.0 Max. :326.0 ## nT GCsk TAsk cGCsk ## Min. :188.0 Min. :-189.0000 Min. :-254.000 Min. : -453 ## 1st Qu.:286.0 1st Qu.: -30.0000 1st Qu.: -36.000 1st Qu.:10796 ## Median :306.0 Median : 0.0000 Median : -2.000 Median :23543 ## Mean :307.2 Mean : -0.1293 Mean : -1.736 Mean :22889 ## 3rd Qu.:328.0 3rd Qu.: 29.0000 3rd Qu.: 32.500 3rd Qu.:34940 ## Max. :444.0 Max. : 134.0000 Max. : 205.000 Max. :46085 ## cTAsk ## Min. :-6247 ## 1st Qu.: 1817 ## Median : 7656 ## Mean : 7855 ## 3rd Qu.:15036 ## Max. :19049 # Use `read_tsv` to read this file. Because the first line # of the file is *not* the column names, you need to specify what the column # names should be with the `col_names` parameter. ca_measles &lt;- read_tsv(file = &quot;measles_data/02-09-2015.txt&quot;, col_names = c(&quot;city&quot;, &quot;count&quot;)) ## Parsed with column specification: ## cols( ## city = col_character(), ## count = col_double() ## ) head(x = ca_measles) ## # A tibble: 6 x 2 ## city count ## &lt;chr&gt; &lt;dbl&gt; ## 1 ALAMEDA 6 ## 2 LOS ANGELES 20 ## 3 City of Long Beach 2 ## 4 City of Pasadena 4 ## 5 MARIN 2 ## 6 ORANGE 34 # You&#39;ll need the `readxl` package to read in the Excel file. Load that. library(package = &quot;readxl&quot;) # Use the `read_excel` function to read in the file. icd10 &lt;- read_excel(path = &quot;icd-10.xls&quot;) head(x = icd10) ## # A tibble: 6 x 2 ## Code `ICD Title` ## &lt;chr&gt; &lt;chr&gt; ## 1 A00-B99 I. Certain infectious and parasitic diseases ## 2 A00-A09 Intestinal infectious diseases ## 3 A00 Cholera ## 4 A00.0 Cholera due to Vibrio cholerae 01, biovar cholerae ## 5 A00.1 Cholera due to Vibrio cholerae 01, biovar eltor ## 6 A00.9 Cholera, unspecified # You&#39;ll need the `haven` function to read in the SAS file. Load that. library(package = &quot;haven&quot;) # Use the `read_sas` function to read in this file. icu &lt;- read_sas(data_file = &quot;icu.sas7bdat&quot;) library(package = &quot;dplyr&quot;) slice(.data = select(.data = icu, 1:5), 1:5) ## # A tibble: 5 x 5 ## ID STA AGE GENDER RACE ## &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; ## 1 4 1 87 1 1 ## 2 8 0 27 1 1 ## 3 12 0 59 0 1 ## 4 14 0 77 0 1 ## 5 27 1 76 1 1 2.8.3 Directory structure Once you have the data, I’d like you to try using getwd() to figure out your current working directory and list.files() to figure out which files you have in the directories near that current working directory. Start by creating a new subdirectory called “data” in your R Project directory for this class (if you don’t already have that subdirectory). Move the data you downloaded in the start of this In-Course Exercise into that “data” subdirectory. (For this, use whatever tools you would normally use on your computer to move files from one directory to another—you don’t have to do this part in R.) Keep the “measles” data in its own subdirectory (so, the “data” subdirectory of your project will have its own “measles” subdirectory, which will have those files). Check that you are, in fact, in the working directory you think you’re in. Run: getwd() Does it agree with the R project name in the top right hand corner of your R Studio window? Now, use the list.files function to print out which files or subdirectories you have in your current working directory: list.files() Try the following tasks: Read in the ebola data in country_timeseries.csv from your current working directory. This will require you to use a relative filename to direct R to how to find that file in the “data” subdirectory of your current working directory. Assign the data you read in to an R object named ebola. How many rows and columns does it have? What are the names of the columns? While staying in the same working directory, used list.files() to print the names of the available files in the “data” subdirectory using the path argument. How about in the “R” subdirectory (if you have one)? Try to list the files in your “data” subdirectory using: A relative pathname An absolute pathname Now use a relative pathname along with list.files() to list all the files in the “measles_data” subdirectory. Then try to read in the Ebola data using the appropriate readr function and a relative pathname. Which method (absolute or relative pathnames) always used the same code, regardless of your current working directory? Which method used different code, depending on the starting working directory? Read in the Ebola data in country_timeseries.csv from your current working directory. This will require you to use a relative filename to direct R to how to find that file in the “data” subdirectory of your current working directory. ebola &lt;- read_csv(file = &quot;data/country_timeseries.csv&quot;) How many rows and columns does it have? What are the names of the columns? dim(x = ebola) # Get the dimensions of the data (`nrow` and `ncol` would also work) colnames(x = ebola) # Get the column names (you can also just print the object: `ebola`) While staying in the same working directory, used list.files() to print the names of the available files in the “data” subdirectory by using the path argument. How about in the “R” subdirectory (if you have one)? list.files(path = &quot;data&quot;) list.files(path = &quot;R&quot;) Try to list the files in your “data” subdirectory using: + A relative pathname + An absolute pathname list.files(path = &quot;data&quot;) # This is using a relative pathname list.files(path = &quot;/Users/brookeanderson/Documents/r_course_2018/data&quot;) # Absolute pathname # (Yours will be different and will depend on how your computer file # structure is set up.) Now use a relative pathname along with list.files() to list all the files in the “measles_data” subdirectory. list.files(path = &quot;data/measles_data&quot;) Then try to read in the Ebola data using the appropriate readr function and a relative pathname ebola &lt;- read_csv(file = &quot;data/country_timeseries.csv&quot;) If you have extra time: Find out some more about this Ebola dataset by checking out Caitlin Rivers’ Ebola data GitHub repository. Who is Caitlin Rivers? How did she put this dataset together? Search for R code related to Ebola research on GitHub. Go to the GitHub home page and use the search bar to search for “ebola”. On the results page, scroll down and use the “Language” sidebar on the left to choose repositories with R code. Did you find any interesting projects? When you list.files() for the “data” subdirectory, almost everything listed has a file extension, like .csv, .xls, .sas7bdat. One thing does not. Which one? Why does this listing not have a file extension? 2.8.4 Cleaning up data #1 Try out the following tasks: Copy the following code into an R script. Figure out what each line does, and add comments to each line of code describing what the code is doing. Use the helpfiles for functions as needed to figure out functions we haven’t covered yet. # Copy this code to an R script and add comments describing what each line is doing # Install any packages that the code loads but that you don&#39;t have. library(package = &quot;haven&quot;) library(package = &quot;forcats&quot;) library(package = &quot;stringr&quot;) icu &lt;- read_sas(data_file = &quot;data/icu.sas7bdat&quot;) icu &lt;- select(.data = icu, ID, AGE, GENDER) icu &lt;- rename(.data = icu, id = ID, age = AGE, gender = GENDER) icu &lt;- mutate(.data = icu, gender = as_factor(x = gender), gender = fct_recode(.f = gender, Male = &quot;0&quot;, Female = &quot;1&quot;), id = str_c(id)) icu Following previous parts of the in-course exercise, you have an R object called ebola (if you need to, use some code from earlier in this in-course exercise to read in the data and create that object). Create an object called ebola_liberia that only has the columns with the date and the number of cases and deaths in Liberia. How many columns does this new dataframe have? How many observations? Change the column names to date, cases, and deaths. Add a column called ratio that has the ratio of deaths to cases for each observation (i.e., death counts divided by case counts). Example R code: # Load the dplyr package library(package = &quot;dplyr&quot;) ## Create a subset with just the Liberia columns and Date ebola_liberia &lt;- select(.data = ebola, Date, Cases_Liberia, Deaths_Liberia) head(x = ebola_liberia) ## # A tibble: 6 x 3 ## Date Cases_Liberia Deaths_Liberia ## &lt;chr&gt; &lt;dbl&gt; &lt;dbl&gt; ## 1 1/5/2015 NA NA ## 2 1/4/2015 NA NA ## 3 1/3/2015 8166 3496 ## 4 1/2/2015 8157 3496 ## 5 12/31/2014 8115 3471 ## 6 12/28/2014 8018 3423 ## How many colums and rows does the whole dataset have (could also use `dim`)? ncol(x = ebola_liberia) ## [1] 3 nrow(x = ebola_liberia) ## [1] 122 ## Rename the columns ebola_liberia &lt;- rename(.data = ebola_liberia, date = Date, cases = Cases_Liberia, deaths = Deaths_Liberia) head(ebola_liberia) ## # A tibble: 6 x 3 ## date cases deaths ## &lt;chr&gt; &lt;dbl&gt; &lt;dbl&gt; ## 1 1/5/2015 NA NA ## 2 1/4/2015 NA NA ## 3 1/3/2015 8166 3496 ## 4 1/2/2015 8157 3496 ## 5 12/31/2014 8115 3471 ## 6 12/28/2014 8018 3423 ## Add a `ratio` column ebola_liberia &lt;- mutate(.data = ebola_liberia, ratio = deaths / cases) head(x = ebola_liberia) ## # A tibble: 6 x 4 ## date cases deaths ratio ## &lt;chr&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; ## 1 1/5/2015 NA NA NA ## 2 1/4/2015 NA NA NA ## 3 1/3/2015 8166 3496 0.428 ## 4 1/2/2015 8157 3496 0.429 ## 5 12/31/2014 8115 3471 0.428 ## 6 12/28/2014 8018 3423 0.427 2.8.5 Cleaning up data #2 Filter out all rows from the ebola_liberia dataframe that are missing death counts for Liberia. How many rows are in the dataframe now? Create a new object called first_five that has only the five observations with the highest death counts in Liberia. What date in this dataset had the most deaths? Example R code: ## Filter out the rows that are missing death counts for Liberia ebola_liberia &lt;- filter(.data = ebola_liberia, !is.na(deaths)) head(x = ebola_liberia) ## # A tibble: 6 x 4 ## date cases deaths ratio ## &lt;chr&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; ## 1 1/3/2015 8166 3496 0.428 ## 2 1/2/2015 8157 3496 0.429 ## 3 12/31/2014 8115 3471 0.428 ## 4 12/28/2014 8018 3423 0.427 ## 5 12/24/2014 7977 3413 0.428 ## 6 12/20/2014 7862 3384 0.430 nrow(x = ebola_liberia) ## [1] 81 ## Create an object with just the top five observations in terms of death counts first_five &lt;- arrange(.data = ebola_liberia, desc(deaths)) # First, rearrange the rows by deaths first_five &lt;- slice(.data = first_five, 1:5) # Limit the dataframe to the first five rows first_five # Two days tied for the highest deaths counts (Jan. 2 and 3, 2015). ## # A tibble: 5 x 4 ## date cases deaths ratio ## &lt;chr&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; ## 1 1/3/2015 8166 3496 0.428 ## 2 1/2/2015 8157 3496 0.429 ## 3 12/31/2014 8115 3471 0.428 ## 4 12/28/2014 8018 3423 0.427 ## 5 12/24/2014 7977 3413 0.428 2.8.6 Piping Try the following tasks: Copy the following “piped” code into an R script. Figure out what each line does, and add comments to each line of code describing what the code is doing. # Copy this code to an R script and add comments describing what each line is doing library(package = &quot;haven&quot;) icu &lt;- read_sas(data_file = &quot;data/icu.sas7bdat&quot;) %&gt;% select(ID, AGE, GENDER) %&gt;% rename(id = ID, age = AGE, gender = GENDER) %&gt;% mutate(gender = as_factor(x = gender), gender = fct_recode(.f = gender, Male = &quot;0&quot;, Female = &quot;1&quot;), id = str_c(id)) %&gt;% arrange(age) %&gt;% slice(1:10) icu In previous sections of the in-course exercise, you have created code to read in and clean the Ebola dataset to create ebola_liberia. This included the following cleaning steps: (1) selecting certain columns, (2) renaming those columns, (3) adding a ratio column, and (4) removing observations for which the count of deaths in Liberia is missing. Re-write this code to create and clean ebola_liberia as “piped” code. Start from reading in the raw data. Example R code: ebola_liberia &lt;- read_csv(file = &quot;data/country_timeseries.csv&quot;) %&gt;% select(Date, Cases_Liberia, Deaths_Liberia) %&gt;% rename(date = Date, cases = Cases_Liberia, deaths = Deaths_Liberia) %&gt;% mutate(ratio = deaths / cases) %&gt;% filter(!is.na(x = cases)) head(x = ebola_liberia) ## # A tibble: 6 x 4 ## date cases deaths ratio ## &lt;chr&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; ## 1 1/3/2015 8166 3496 0.428 ## 2 1/2/2015 8157 3496 0.429 ## 3 12/31/2014 8115 3471 0.428 ## 4 12/28/2014 8018 3423 0.427 ## 5 12/24/2014 7977 3413 0.428 ## 6 12/20/2014 7862 3384 0.430 "],
 ["exploring-data-1.html", "Chapter 3 Exploring data #1 3.1 Objectives 3.2 Data from a package 3.3 Logical vectors 3.4 Simple statistics functions 3.5 Plots to explore data 3.6 In-course exercise", " Chapter 3 Exploring data #1 Download a pdf of the lecture slides covering this topic. 3.1 Objectives After this chapter, you should (know / understand / be able to ): Be able to load and use datasets from R packages Be able to describe and use logical vectors Understand how logical vectors check logical statements against other R vector(s) and store TRUE / FALSE values as 0 / 1 at a deeper level Be able to use the dplyr function mutate to create a logical vector as a new column in a dataframe and the dplyr function filter with that new column to filter a dataframe to a subset of rows Be able to use the bang operator (!) to reverse a logical vector Know what the “tidyverse” is and name some of its packages Be able to use some simple statistical functions (e.g., min, max, mean, median, cor, summary), including how to handle missing values when using these Be able to use the dplyr function summarize to summarize data, with and without grouping using group_by, including with special functions n, n_distinct, first, and last Understand the three basic elements of ggplot plots: data, aesthetics, and geoms Be able to create a ggplot object, set its data using data = ... and its aesthetics using mapping = aes(...), and add on layers (including geoms) with + Be able to create some basic plots (e.g., scatterplots, boxplots, histograms) using ggplot2 functions Understand the difference between setting an aesthetic by mapping it to a column of the dataframe versus setting it to a constant value Understand the difference between “statistical” geoms (e.g., histograms, boxplots) and geoms that add one geom element per dataframe observation (row) 3.2 Data from a package So far we’ve covered two ways to get data into R: From flat files (either on your computer or online) From binary file formats like SAS and Excel. Many R packages come with their own data, which is very easy to load and use. For example, the faraway package, which complements Julian Faraway’s book Linear Models with R, has a dataset called worldcup that I’ll use for some examples and that you’ll use for part of this week’s in-course exercise. To load this dataset, first load the package with the data (faraway) and then use the data() function with the dataset name (“worldcup”) as the argument to the data function: library(faraway) data(&quot;worldcup&quot;) Unlike most data objects you’ll work with, datasets that are part of an R package will often have their own help files. You can access this help file for a dataset using the ? operator with the dataset’s name: ?worldcup This helpful will usually include information about the size of the dataset, as well as definitions for each of the columns. To get a list of all of the datasets that are available in the packages you currently have loaded, run data() without an option inside the parentheses: data() If you run the library function without any arguments—library()—it works in a similar way. R will open a list of all the R packages that you have installed on your computer and can open with a library call. For this chapter, we’ll be working with a modified version of the nepali dataset from the faraway package. This gives data from a study of the health of a group of Nepalese children. Each observation is a single measurement for a child; there can be multiple observations per child. We’ll use a modified version of this dataframe that limits it to the columns with the child’s id, sex, weight, height, and age, and limited to each child’s first measurement. To create this modified dataset, run the following code: library(dplyr) library(faraway) data(nepali) nepali &lt;- nepali %&gt;% # Limit to certain columns select(id, sex, wt, ht, age) %&gt;% # Convert id and sex to factors mutate(id = factor(id), sex = factor(sex, levels = c(1, 2), labels = c(&quot;Male&quot;, &quot;Female&quot;))) %&gt;% # Limit to first obs. per child distinct(id, .keep_all = TRUE) The first few rows of the data should now look like: nepali %&gt;% slice(1:4) ## id sex wt ht age ## 1 120011 Male 12.8 91.2 41 ## 2 120012 Female 14.9 103.9 57 ## 3 120021 Female 7.7 70.1 8 ## 4 120022 Female 12.1 86.4 35 3.3 Logical vectors Last week, you learned a lot about logical statements and how to use them with the filter function from the dplyr package. You can also use logical vectors, created with these logical statements, for a lot of other things. For example, you can use them directly in the square bracket indexing ([..., ...]) to pull out just the rows of a dataframe that meet a certain condition. For using logical statements in either context, it is helpful to understand a bit more about logical vectors. When you run a logical statement on a vector, you create a logical vector the same length as the original vector: length(nepali$sex) ## [1] 200 length(nepali$sex == &quot;Male&quot;) ## [1] 200 The logical vector (nepali$sex == \"Male\" in this example) will have the value TRUE at any position where the original vector (nepali$sex in this example) met the logical condition you tested, and FALSE anywhere else: head(nepali$sex) ## [1] Male Female Female Female Male Male ## Levels: Male Female head(nepali$sex == &quot;Male&quot;) ## [1] TRUE FALSE FALSE FALSE TRUE TRUE You can “flip” this logical vector (i.e., change every TRUE to FALSE and vice-versa) using the bang operator, !: is_male &lt;- nepali$sex == &quot;Male&quot; # Save this logical vector as the object named `is_male` head(is_male) ## [1] TRUE FALSE FALSE FALSE TRUE TRUE head(!is_male) ## [1] FALSE TRUE TRUE TRUE FALSE FALSE The bang operator turns out to be very useful. You will often find cases where it’s difficult to write a logical vector to get what you want, but fairly easy to write the inverse (find everything you don’t want). One example is filtering down to non-missing values—the is.na function will return TRUE for any value that is NA, so you can use !is.na() to identify any non-missing values. You can do a few cool things with a logical vector. For example, you can use it inside a filter function to pull out just the rows of a dataframe where is_male is TRUE: nepali %&gt;% filter(is_male) %&gt;% head() ## id sex wt ht age ## 1 120011 Male 12.8 91.2 41 ## 2 120023 Male 14.2 99.4 49 ## 3 120031 Male 13.9 96.4 46 ## 4 120051 Male 8.3 69.5 8 ## 5 120053 Male 15.8 96.0 54 ## 6 120062 Male 12.1 89.9 57 Or, with !, just the rows where is_male is FALSE: nepali %&gt;% filter(!is_male) %&gt;% head() ## id sex wt ht age ## 1 120012 Female 14.9 103.9 57 ## 2 120021 Female 7.7 70.1 8 ## 3 120022 Female 12.1 86.4 35 ## 4 120052 Female 11.8 83.6 32 ## 5 120061 Female 8.7 78.5 26 ## 6 120082 Female 11.2 79.8 36 You can also use sum() and table() with a logical vector to find out how many of the values in the vector are TRUE AND FALSE. You can use sum because R saves logical vectors at a basic level as 0 for FALSE and 1 for TRUE. Therefore, if you add up all the values in a logical vector, you’re adding up the number of observations with the value TRUE. In the example, you can use these functions to find out how many males and females are in the dataset: sum(is_male) ## [1] 107 sum(!is_male) ## [1] 93 table(is_male) ## is_male ## FALSE TRUE ## 93 107 Note that you could also achieve the same thing with dplyr functions. For example, you could use mutate with a logical statement to create an is_male column in the nepali dataframe, then group by the new is_male column and count the number of observations in each group using count: library(dplyr) nepali %&gt;% mutate(is_male = sex == &quot;Male&quot;) %&gt;% group_by(is_male) %&gt;% count() ## # A tibble: 2 x 2 ## # Groups: is_male [2] ## is_male n ## &lt;lgl&gt; &lt;int&gt; ## 1 FALSE 93 ## 2 TRUE 107 We will cover using summarize, including with data that has been grouped with group_by, later in this chapter. 3.4 Simple statistics functions 3.4.1 Summary statistics To explore your data, you’ll need to be able to calculate some simple statistics for vectors, including calculating the mean and range of continuous variables and counting the number of values in each category of a factor or logical vector. Here are some simple statistics functions you will likely use often: Function Description range() Range (minimum and maximum) of vector min(), max() Minimum or maximum of vector mean(), median() Mean or median of vector sd() Standard deviation of vector table() Number of observations per level for a factor vector cor() Determine correlation(s) between two or more vectors summary() Summary statistics, depends on class All of these functions take, as the main argument, the vector or vectors for which you want the statistic. If there are missing values in the vector, you’ll typically need to add an argument to say what to do with the missing values. The parameter name for this varies by function, but for many of these functions it’s na.rm = TRUE or use=\"complete.obs\". mean(nepali$wt, na.rm = TRUE) ## [1] 10.18432 range(nepali$ht, na.rm = TRUE) ## [1] 52.4 104.1 sd(nepali$ht, na.rm = TRUE) ## [1] 12.64529 table(nepali$sex) ## ## Male Female ## 107 93 Most of these functions take a single vector as the input. The cor function, however, calculates the correlation between vectors and so takes two or more vectors. If you give it multiple values, it will give the correlation matrix for all the vectors. cor(nepali$wt, nepali$ht, use = &quot;complete.obs&quot;) ## [1] 0.9571535 cor((nepali %&gt;% select(wt, ht, age)), use = &quot;complete.obs&quot;) ## wt ht age ## wt 1.0000000 0.9571535 0.8931195 ## ht 0.9571535 1.0000000 0.9287129 ## age 0.8931195 0.9287129 1.0000000 R supports object-oriented programming. Your first taste of this shows up with the summary function. For the summary function, R does not run the same code every time. Instead, R first checks what type of object was input to summary, and then it runs a function (method) specific to that type of object. For example, if you input a continuous vector, like the ht column in nepali, to summary, the function will return the mean, median, range, and 25th and 75th percentile values: summary(nepali$wt) ## Min. 1st Qu. Median Mean 3rd Qu. Max. NA&#39;s ## 3.80 7.90 10.10 10.18 12.40 16.70 15 However, if you submit a factor vector, like the sex column in nepali, the summary function will return a count of how many elements of the vector are in each factor level (as a note, you could do the same thing with the table function): summary(nepali$sex) ## Male Female ## 107 93 The summary function can also input other data structures, including dataframes, lists, and special object types, like regression model objects. In each case, it performs different actions specific to the object type. Later in this section, we’ll cover regression models, and see what the summary function returns when it is used with regression model objects. 3.4.2 summarize function You will often want to use these functions in conjunction with the summarize function in dplyr. For example, to create a new dataframe with the mean weight of children in the nepali dataset, you can use mean inside a summarize function: library(dplyr) nepali %&gt;% summarize(mean_wt = mean(wt, na.rm = TRUE)) ## mean_wt ## 1 10.18432 There are also some special functions that are particularly useful with summarize and other dplyr functions. For example, the n function will calculate the number of observations and the first function will return the first value of a column: nepali %&gt;% summarize(n_children =n(), first_id = first(id)) ## n_children first_id ## 1 200 120011 See the “summary function” section of the the RStudio Data Wrangling cheatsheet for more examples of these special functions. Often, you will be more interested in summaries within certain groupings of your data, rather than overall summaries. For example, you may be interested in mean height and weight by sex, rather than across all children, for the nepali data. It is very easy to calculate these grouped summaries using dplyr—you just need to group data using the group_by function (also a dplyr function) before you run the summarize function: nepali %&gt;% group_by(sex) %&gt;% summarize(mean_wt = mean(wt, na.rm = TRUE), n_children =n(), first_id = first(id)) ## `summarise()` ungrouping output (override with `.groups` argument) ## # A tibble: 2 x 4 ## sex mean_wt n_children first_id ## &lt;fct&gt; &lt;dbl&gt; &lt;int&gt; &lt;fct&gt; ## 1 Male 10.5 107 120011 ## 2 Female 9.82 93 120012 Don’t forget that you need to save the output to a new object if you want to use it later. The above code, which creates a dataframe with summaries for Nepali children by sex, will only be printed out to your console if run as-is. If you’d like to save this output as an object to use later (for example, for a plot or table), you need to assign it to an R object. 3.5 Plots to explore data Exploratory data analysis is a key step in data analysis and plotting your data in different ways is an important part of this process. In this section, I will focus on the basics of ggplot2 plotting, to get you started creating some plots to explore your data. This section will focus on making useful, rather than attractive graphs, since at this stage we are focusing on exploring data for yourself rather than presenting results to others. Next week, I will explain more about how you can customize ggplot objects, to help you make plots to communicate with others. All of the plots we’ll make today will use the ggplot2 package (another member of the tidyverse!). If you don’t already have that installed, you’ll need to install it. You then need to load the package in your current session of R: # install.packages(&quot;ggplot2&quot;) ## Uncomment and run if you don&#39;t have `ggplot2` installed library(ggplot2) The process of creating a plot using ggplot2 follows conventions that are a bit different than most of the code you’ve seen so far in R (although it is somewhat similar to the idea of piping I introduced in the last chapter). The basic steps behind creating a plot with ggplot2 are: Create an object of the ggplot class, typically specifying the data and some or all of the aesthetics; Add on geoms and other elements to create and customize the plot, using +. You can add on one or many geoms and other elements to create plots that range from very simple to very customized. This week, we’ll focus on simple geoms and added elements, and then explore more detailed customization next week. If R gets to the end of a line and there is not some indication that the call is not over (e.g., %&gt;% for piping or + for ggplot2 plots), R interprets that as a message to run the call without reading in further code. A common error when writing ggplot2 code is to put the + to add a geom or element at the beginning of a line rather than the end of a previous line– in this case, R will try to execute the call too soon. To avoid errors, be sure to end lines with +, don’t start lines with it. 3.5.1 Initializing a ggplot object The first step in creating a plot using ggplot2 is to create a ggplot object. This object will not, by itself, create a plot with anything in it. Instead, it typically specifies the data frame you want to use and which aesthetics will be mapped to certain columns of that data frame (aesthetics are explained more in the next subsection). Use the following conventions to initialize a ggplot object: ## Generic code object &lt;- ggplot(dataframe, aes(x = column_1, y = column_2)) The data frame is the first parameter in a ggplot call and, if you like, you can use the parameter definition with that call (e.g., data = dataframe). Aesthetics are defined within an aes function call that typically is used within the ggplot call. While the ggplot call is the place where you will most often see an aes call, aes can also be used within the calls to add specific geoms. This can be particularly useful if you want to map aesthetics differently for different geoms in your plot. We’ll see some examples of this use of aes more in later sections, when we talk about customizing plots. The data parameter can also be used in geom calls, to use a different data frame from the one defined when creating the original ggplot object, although this tends to be less common. 3.5.2 Plot aesthetics Aesthetics are properties of the plot that can show certain elements of the data. For example, in Figure 3.1, color shows (is mapped to) gender, x-position shows height, and y-position shows weight in a sample data set of measurements of children in Nepal. Figure 3.1: Example of how different properties of a plot can show different elements to the data. Here, color indicates gender, position along the x-axis shows height, and position along the y-axis shows weight. This example is a subset of data from the nepali dataset in the faraway package. Any of these aesthetics could also be given a constant value, instead of being mapped to an element of the data. For example, all the points could be red, instead of showing gender. Which aesthetics are required for a plot depend on which geoms (more on those in a second) you’re adding to the plot. You can find out the aesthetics you can use for a geom in the “Aesthetics” section of the geom’s help file (e.g., ?geom_point). Required aesthetics are in bold in this section of the help file and optional ones are not. Common plot aesthetics you might want to specify include: Code Description x Position on x-axis y Position on y-axis shape Shape color Color of border of elements fill Color of inside of elements size Size alpha Transparency (1: opaque; 0: transparent) linetype Type of line (e.g., solid, dashed) 3.5.3 Adding geoms Next, you’ll want to add one or more geoms to create the plot. You can add these with + after the ggplot statement to initialize the ggplot object. Some of the most common geoms are: Plot type ggplot2 function Histogram (1 numeric variable) geom_histogram Scatterplot (2 numeric variables) geom_point Boxplot (1 numeric variable, possibly 1 factor variable) geom_boxplot Line graph (2 numeric variables) geom_line 3.5.4 Constant aesthetics Instead of mapping an aesthetic to an element of your data, you can use a constant value for it. For example, you may want to make all the points green, rather than having color map to gender: In this case, you’ll define that aesthetic when you add the geom, outside of an aes statement. In R, you can specify the shape of points with a number. Figure 3.2 shows the shapes that correspond to the numbers 1 to 25 in the shape aesthetic. This figure also provides an example of the difference between color (black for all these example points) and fill (red for these examples). You can see that some point shapes include a fill (21 for example), while some are either empty (1) or solid (19). ## Warning: `data_frame()` is deprecated as of tibble 1.1.0. ## Please use `tibble()` instead. ## This warning is displayed once every 8 hours. ## Call `lifecycle::last_warnings()` to see where this warning was generated. Figure 3.2: Examples of the shapes corresponding to different numeric choices for the shape aesthetic. For all examples, color is set to black and fill to red. If you want to set color to be a constant value, you can do that in R using character strings for different colors. Figure 3.3 gives an example of some of the different blues available in R. To find links to listings of different R colors, google “R colors” and search by “Images”. Figure 3.3: Example of available shades of blue in R. 3.5.5 Useful plot additions There are also a number of elements that you can add onto a ggplot object using +. A few that are used very frequently are: Element Description ggtitle Plot title xlab, ylab x- and y-axis labels xlim, ylim Limits of x- and y-axis 3.5.6 Example dataset For the example plots, I’ll use a dataset in the faraway package called nepali. This gives data from a study of the health of a group of Nepalese children. library(faraway) data(nepali) I’ll be using functions from dplyr and ggplot2, so those need to be loaded: library(dplyr) library(ggplot2) Each observation is a single measurement for a child; there can be multiple observations per child. I used the following code to select only the columns for child id, sex, weight, height, and age. I also used distinct to limit the dataset to only include one measurement for each chile, the child’s first measurement in the dataset. nepali &lt;- nepali %&gt;% select(id, sex, wt, ht, age) %&gt;% mutate(id = factor(id), sex = factor(sex, levels = c(1, 2), labels = c(&quot;Male&quot;, &quot;Female&quot;))) %&gt;% distinct(id, .keep_all = TRUE) After this cleaning, the data looks like this: head(nepali) ## id sex wt ht age ## 1 120011 Male 12.8 91.2 41 ## 2 120012 Female 14.9 103.9 57 ## 3 120021 Female 7.7 70.1 8 ## 4 120022 Female 12.1 86.4 35 ## 5 120023 Male 14.2 99.4 49 ## 6 120031 Male 13.9 96.4 46 3.5.7 Histograms Histograms show the distribution of a single variable. Therefore, geom_histogram() requires only one main aesthetic, x, the (numeric) vector for which you want to create a histogram. For example, to create a histogram of children’s heights for the Nepali dataset (Figure 3.4), run: ggplot(nepali, aes(x = ht)) + geom_histogram() Figure 3.4: Basic example of plotting a histogram with ggplot2. This histogram shows the distribution of heights for the first recorded measurements of each child in the nepali dataset. If you run the code with no arguments for binwidth or bins in geom_histogram, you will get a message saying “stat_bin() using bins = 30. Pick better value with binwidth.”. This message is just saying that a default number of bins was used to create the histogram. You can use arguments to change the number of bins used, but often this default is fine. You may also get a message that observations with missing values were removed. You can add some elements to the histogram now to customize it a bit. For example (Figure @ref()), you can add a figure title (ggtitle) and clearer labels for the x-axis (xlab). You can also change the range of values shown by the x-axis (xlim). ggplot(nepali, aes(x = ht)) + geom_histogram(fill = &quot;lightblue&quot;, color = &quot;black&quot;) + ggtitle(&quot;Height of children&quot;) + xlab(&quot;Height (cm)&quot;) + xlim(c(0, 120)) Figure 3.5: Example of adding ggplot elements to customize a histogram. The geom geom_histogram also has special argument for setting the number of width of the bins used in the histogram. Figure ?? shows an example of how you can use the bins argument to change the number of bins that are used to make the histogram of height for the nepali dataset. ggplot(nepali, aes(x = ht)) + geom_histogram(fill = &quot;lightblue&quot;, color = &quot;black&quot;, bins = 40) Figure 3.6: Example of using the bins argument to change the number of bins used in a histogram. Similarly, the binwidth argument can be used to set the width of bins. Figure 3.7 shows an example of using this function to create a histogram of the Nepali children’s heights with binwidths of 10 centimeters (note that this argument is set in the same units as the x variable). ggplot(nepali, aes(x = ht)) + geom_histogram(fill = &quot;lightblue&quot;, color = &quot;black&quot;, binwidth = 10) Figure 3.7: Example of using the binwidth argument to set the width of each bin used in a histogram. 3.5.8 Scatterplots A scatterplot shows how one variable changes as another changes. You can use the geom_point geom to create a scatterplot. For example, to create a scatterplot of height versus age for the Nepali data (Figure 3.8), you can run the following code: ggplot(nepali, aes(x = ht, y = wt)) + geom_point() Figure 3.8: Example of creating a scatterplot. This scatterplot shows the relationship between children’s heights and weights within the nepali dataset. Again, you can use some of the options and additions to change the plot appearance. For example, to add a title, change the x- and y-axis labels, and change the color and size of the points on the scatterplot (Figure 3.9), you can run: ggplot(nepali, aes(x = ht, y = wt)) + geom_point(color = &quot;blue&quot;, size = 0.5) + ggtitle(&quot;Weight versus Height&quot;) + xlab(&quot;Height (cm)&quot;) + ylab(&quot;Weight (kg)&quot;) Figure 3.9: Example of adding ggplot elements to customize a scatterplot. You can also try mapping another variable in the dataset to the color aesthetic. For example, to use color to show the sex of each child in the scatterplot (Figure 3.10), you can run: ggplot(nepali, aes(x = ht, y = wt, color = sex)) + geom_point(size = 0.5) + ggtitle(&quot;Weight versus Height&quot;) + xlab(&quot;Height (cm)&quot;) + ylab(&quot;Weight (kg)&quot;) Figure 3.10: Example of mapping color to an element of the data in a scatterplot. 3.5.9 Boxplots Boxplots can be used to show the distribution of a continuous variable. To create a boxplot, you can use the geom_boxplot geom. To plot a boxplot for a single, continuous variable, you can map that variable to y in the aes call, and map x to the constant 1. For example, to create a boxplot of the heights of children in the Nepali dataset (Figure 3.11), you can run: ggplot(nepali, aes(x = 1, y = ht)) + geom_boxplot() + xlab(&quot;&quot;)+ ylab(&quot;Height (cm)&quot;) Figure 3.11: Example of creating a boxplot. The example shows the distribution of height data for children in the nepali dataset. You can also create separate boxplots, one for each level of a factor (Figure 3.12). In this case, you’ll need to include two aesthetics (x and y) when you initialize the ggplot object The y variable is the variable for which the distribution will be shown, and the x variable should be a discrete (categorical or TRUE/FALSE) variable, and will be used to group the variable. This x variable should also be specified as the grouping variable, using group within the aesthetic call. ggplot(nepali, aes(x = sex, y = ht, group = sex)) + geom_boxplot() + xlab(&quot;Sex&quot;)+ ylab(&quot;Height (cm)&quot;) Figure 3.12: Example of creating separate boxplots, divided by a categorical grouping variable in the data. 3.6 In-course exercise 3.6.1 Loading data from an R package The data we’ll be using today is from a dataset called worldcup in the package faraway. Load that data so you can use it on your computer (note: you will need to load and install the faraway package to do this). Use the help file for the data to find out more about the dataset. Use some basic functions, like head, tail, colnames, str, and summary to check out the data a bit. See if you can figure out: What variables are included in this dataset? (Check the column names.) What class is each column currently? In particular, which are numbers and which are factors? 3.6.1.1 Example R code: Load the faraway package using library() and then load the data using data(): ## Uncomment the next line if you need to install the package # install.packages(&quot;faraway&quot;) library(faraway) data(&quot;worldcup&quot;) Check out the help file for the worldcup dataset to find out more about the data. (Note: Only datasets that are parts of packages will have help files.) ?worldcup Check out the data a bit: str(worldcup) ## &#39;data.frame&#39;: 595 obs. of 7 variables: ## $ Team : Factor w/ 32 levels &quot;Algeria&quot;,&quot;Argentina&quot;,..: 1 16 9 9 5 32 11 11 18 20 ... ## $ Position: Factor w/ 4 levels &quot;Defender&quot;,&quot;Forward&quot;,..: 4 4 1 4 2 2 1 2 4 1 ... ## $ Time : int 16 351 180 270 46 72 138 33 21 103 ... ## $ Shots : int 0 0 0 1 2 0 0 0 5 0 ... ## $ Passes : int 6 101 91 111 16 15 51 9 22 38 ... ## $ Tackles : int 0 14 6 5 0 0 2 0 0 1 ... ## $ Saves : int 0 0 0 0 0 0 0 0 0 0 ... head(worldcup) ## Team Position Time Shots Passes Tackles Saves ## Abdoun Algeria Midfielder 16 0 6 0 0 ## Abe Japan Midfielder 351 0 101 14 0 ## Abidal France Defender 180 0 91 6 0 ## Abou Diaby France Midfielder 270 1 111 5 0 ## Aboubakar Cameroon Forward 46 2 16 0 0 ## Abreu Uruguay Forward 72 0 15 0 0 tail(worldcup) ## Team Position Time Shots Passes Tackles Saves ## van Bommel Netherlands Midfielder 540 2 307 31 0 ## van Bronckhorst Netherlands Defender 540 1 271 10 0 ## van Persie Netherlands Forward 479 14 108 1 0 ## von Bergen Switzerland Defender 234 0 79 3 0 ## Alvaro Pereira Uruguay Midfielder 409 6 140 17 0 ## Ozil Germany Midfielder 497 7 266 3 0 colnames(worldcup) ## [1] &quot;Team&quot; &quot;Position&quot; &quot;Time&quot; &quot;Shots&quot; &quot;Passes&quot; &quot;Tackles&quot; &quot;Saves&quot; summary(worldcup) ## Team Position Time Shots ## Slovakia : 21 Defender :188 Min. : 1.0 Min. : 0.000 ## Uruguay : 21 Forward :143 1st Qu.: 88.0 1st Qu.: 0.000 ## Argentina: 20 Goalkeeper: 36 Median :191.0 Median : 1.000 ## Cameroon : 20 Midfielder:228 Mean :208.9 Mean : 2.304 ## Chile : 20 3rd Qu.:270.0 3rd Qu.: 3.000 ## Paraguay : 20 Max. :570.0 Max. :27.000 ## (Other) :473 ## Passes Tackles Saves ## Min. : 0.00 Min. : 0.000 Min. : 0.0000 ## 1st Qu.: 29.00 1st Qu.: 1.000 1st Qu.: 0.0000 ## Median : 61.00 Median : 3.000 Median : 0.0000 ## Mean : 84.52 Mean : 4.192 Mean : 0.6672 ## 3rd Qu.:115.50 3rd Qu.: 6.000 3rd Qu.: 0.0000 ## Max. :563.00 Max. :34.000 Max. :20.0000 ## 3.6.2 Exploring the data using simple statistics and summarize Next, try checking out the data using some basic commands for simple statistics, like mean(), range(), max(), and min(), as well as the summarize and group_by functions from the dplyr package. Try to answer the following questions: What is the mean number of saves that players made? What is the mean number of saves just among the goalkeepers? Did players from any position other than goalkeeper make a save? How many players were there in each position? How many forwards were there on each team? Which team had the most shots in total among all its forwards? Which team(s) had the defender with the most tackles? If you have extra time, continuing using the “Data Wrangling” cheatsheet to come up with some other ideas for how you can explore this data, and write up and test code to do that. 3.6.2.1 Example R code: To calculate the mean number of saves among all the players, use the mean function, either by itself or within a summarize call: mean(worldcup$Saves) ## [1] 0.6672269 worldcup %&gt;% summarize(mean_saves = mean(Saves)) ## mean_saves ## 1 0.6672269 There are a few ways to figure out the mean number of saves just among the goalkeepers. One way is to filter the dataset to only goalies and then use summarize to calculate the mean number of saves in this filtered subset of the data: worldcup %&gt;% filter(Position == &quot;Goalkeeper&quot;) %&gt;% summarize(mean_saves = mean(Saves)) ## mean_saves ## 1 11.02778 The next question is if players from any position other than goalkeeper made a save. One way to figure this out is to group the data by position and then summarize the maximum number of saves. Based on this, it looks like there were not saves from players in any position except goalie: worldcup %&gt;% group_by(Position) %&gt;% summarize(max_saves = max(Saves)) ## `summarise()` ungrouping output (override with `.groups` argument) ## # A tibble: 4 x 2 ## Position max_saves ## &lt;fct&gt; &lt;int&gt; ## 1 Defender 0 ## 2 Forward 0 ## 3 Goalkeeper 20 ## 4 Midfielder 0 To figure out how many players were there in each position, you can can group the data by position and then use the count function from dplyr to count the number of observations in each group: worldcup %&gt;% group_by(Position) %&gt;% count() ## # A tibble: 4 x 2 ## # Groups: Position [4] ## Position n ## &lt;fct&gt; &lt;int&gt; ## 1 Defender 188 ## 2 Forward 143 ## 3 Goalkeeper 36 ## 4 Midfielder 228 For the next set of questions, you can filter the data to only Forwards, then group by team to use summarize to count up the number of Forwards on each team. You can also use the same summarize call to figure out the total number of shots by all Forwards on each team. To figure out which team had the most shots in total among all its forwards, you can use the arrange function to re-order the data from the team with the most total shots to the least. It turns out that Uruguay had the most shots by forwards on its team, with a total of 46 shots. worldcup %&gt;% filter(Position == &quot;Forward&quot;) %&gt;% group_by(Team) %&gt;% summarize(n_forwards = n(), total_forward_shots = sum(Shots)) %&gt;% arrange(desc(total_forward_shots)) ## `summarise()` ungrouping output (override with `.groups` argument) ## # A tibble: 32 x 3 ## Team n_forwards total_forward_shots ## &lt;fct&gt; &lt;int&gt; &lt;int&gt; ## 1 Uruguay 5 46 ## 2 Argentina 6 45 ## 3 Germany 6 41 ## 4 Netherlands 5 34 ## 5 Spain 3 33 ## 6 Ghana 5 32 ## 7 Portugal 4 28 ## 8 Paraguay 5 25 ## 9 Brazil 4 23 ## 10 USA 5 21 ## # … with 22 more rows To figure out which team(s) had the defender with the most tackles, you can filter to only defenders and then use the top_n function to identify the players with the top number of tackles. It turns out these players were on the England, Germany, and Chile teams. worldcup %&gt;% filter(Position == &quot;Defender&quot;) %&gt;% top_n(n = 1, wt = Tackles) ## Team Position Time Shots Passes Tackles Saves ## Glen Johnson England Defender 357 3 173 17 0 ## Lahm Germany Defender 540 0 360 17 0 ## Vidal Chile Defender 306 6 178 17 0 3.6.3 Exploring the data using logical statements Next, try checking out the data using logical statements and some of the dplyr code we covered last week (filter and arrange, for example), to help you answer the following questions: What is the range of time that players spent in the game? Which player or players played the most time in this World Cup? How many players are goalies in this dataset? Create a new R object named brazil_players that is limited to the players in this dataset that are (1) on the Brazil team and (2) not goalies. If you have additional time, look over the “Data Manipulation” cheatsheet available in RStudio’s Help section. Make a list of questions you would like to figure out from this example data, and start to plan out how you might be able to answer those questions using functions from dplyr. Write the related code and see if it works. 3.6.3.1 Example R code: To figure out the range of time, you could use arrange twice, once with desc and once without, to figure out the maximum and minimum values # Minimum time arrange(worldcup, Time) %&gt;% select(Time) %&gt;% slice(1) ## Time ## Barron 1 # Maximum time arrange(worldcup, desc(Time)) %&gt;% select(Time) %&gt;% slice(1) ## Time ## Arevalo Rios 570 Later, we will learn about the n() function, which you can use within piped code to represent the total number of rows in the dataframe. If you’d like to get the full range of the Time column in one pipeline of code, you can use n() as a reference within slice, to pull both the first and last rows of the dataframe: arrange(worldcup, Time) %&gt;% select(Time) %&gt;% slice(c(1, n())) ## Time ## Barron 1 ## Muslera 570 Finally, you could also use min() and max() functions to get the minimum and maximum values of the Time column in the worldcup dataframe (remember that you can use the dataframe$column_name notation to pull a column from a dataframe). Similarly, you there is a function called range() you could use to find out the range of time these players played in the World Cup. range(worldcup$Time) ## [1] 1 570 To figure out which player or players played for the most time, there are a few approaches you can take. Here I’m showing two: (1) using filter from the dplyr package to filter down to rows where where the Time for that row equals the maximum play time that you determined from an earlier task (570 minutes); and (2) using the top_n function from dplyr to pick out the rows with the maximum value (n = 1) of the Time column (see the help file for top_n if you are unfamiliar with this function; we have not covered it in class yet). worldcup %&gt;% filter(Time == 570) ## Team Position Time Shots Passes Tackles Saves ## Arevalo Rios Uruguay Midfielder 570 5 195 21 0 ## Maxi Pereira Uruguay Midfielder 570 5 182 15 0 ## Muslera Uruguay Goalkeeper 570 0 75 0 16 worldcup %&gt;% top_n(n = 1, wt = Time) ## Team Position Time Shots Passes Tackles Saves ## Arevalo Rios Uruguay Midfielder 570 5 195 21 0 ## Maxi Pereira Uruguay Midfielder 570 5 182 15 0 ## Muslera Uruguay Goalkeeper 570 0 75 0 16 Note: You may have noticed that you lost the players names when you did this using the dplyr pipechain. That’s because dplyr functions convert the data to a dataframe format that does not include rownames. If you want to keep players’ names, you can use a function from the tibble package called rownames_to_column to move those names from the rownames of the data into a column in the dataframe. Use the var parameter of this function to specify what you want the new column to be named. For example: library(tibble) worldcup %&gt;% rownames_to_column(var = &quot;Name&quot;) %&gt;% filter(Time == 570) ## Name Team Position Time Shots Passes Tackles Saves ## 1 Arevalo Rios Uruguay Midfielder 570 5 195 21 0 ## 2 Maxi Pereira Uruguay Midfielder 570 5 182 15 0 ## 3 Muslera Uruguay Goalkeeper 570 0 75 0 16 There are a few ways to figure out how many players are goalies in this dataset. One way is to use sum() on a logical vector of whether the player’s position is “Goalkeeper”: is_goalie &lt;- worldcup$Position == &quot;Goalkeeper&quot; sum(is_goalie) ## [1] 36 Another way is to use filter from dplyr, along with a logical statement, to filter the data to only players with the position of “Goalkeeper”, and then pipe that filtered subset into the nrow function to count the number of rows in the filtered dataframe: worldcup %&gt;% filter(Position == &quot;Goalkeeper&quot;) %&gt;% nrow() ## [1] 36 Next, create a new R object named brazil_players that is limited to the players in this dataset that are (1) on the Brazil team and (2) not goalies. You can use a logical statement to filter to rows that meet both these conditions by joing two logical statements in the filter function with an &amp;: brazil_players &lt;- worldcup %&gt;% filter(Team == &quot;Brazil&quot; &amp; Position != &quot;Goalkeeper&quot;) head(brazil_players) ## Team Position Time Shots Passes Tackles Saves ## Baptista Brazil Midfielder 82 0 42 1 0 ## Daniel Alves Brazil Defender 310 11 215 6 0 ## Elano Brazil Midfielder 140 5 57 6 0 ## Fabiano Brazil Forward 418 9 89 4 0 ## Gilberto Brazil Defender 33 0 6 4 0 ## Gilberto Silva Brazil Midfielder 450 3 299 11 0 3.6.4 Exploring the data using basic plots #1 Use some basic plots to check out this data. Try the following: Create a scatterplot of the worldcup data, where each point is a player, the x-axis shows the amount of time the player played in the World Cup, and the y-axis shows the number of passes the player had. Try writing the code both with and without “piping in” the data you want to plot into the ggplot function. Create the same scatterplot, but have each point in the scatterplot show that player’s position using some aesthetic besides the x or y position (e.g., color, point shape). Add “rug plots” to the margins. Create a scatterplot of number of shots (x-axis) versus number of tackles (y-axis) for just players on one of the four teams that made the semi-finals (Spain, Netherlands, Germany, Uruguay). Use color to show player’s position and shape to show player’s team. (Hint: you will want to use some dplyr code to clean the data before plotting to do this.) Create a scatterplot of player time versus passes. Use color to show whether the player was on one of the top 4 teams or not. (Hint: Again, you’ll want to use some dplyr code before plotting to do this.) For an extra challenge, also try adding each player’s name on top of each point. (Hint: check out the rownames_to_column function from the tibble package to help with this.) Did you notice any interesting features of the data when you did any of the graphs in this section? 3.6.4.1 Example R code: Create a scatterplot of Time versus Passes. # Without piping ggplot(worldcup) + geom_point(mapping = aes(x = Time, y = Passes)) # With piping worldcup %&gt;% ggplot() + geom_point(mapping = aes(x = Time, y = Passes)) Create the same scatterplot, but have each point in the scatterplot show that player’s position. ggplot(worldcup, mapping = aes(x = Time, y = Passes, color = Position)) + geom_point() + geom_rug() Create a scatterplot of number of shots (x-axis) versus number of tackles (y-axis) for just players on one of the four teams that made the semi-finals (Spain, Netherlands, Germany, Uruguay). Use color to show player’s position and shape to show player’s team. For an extra challenge, also try adding each player’s name on top of each point. worldcup %&gt;% rownames_to_column(var = &quot;Name&quot;) %&gt;% filter(Team %in% c(&quot;Spain&quot;, &quot;Netherlands&quot;, &quot;Germany&quot;, &quot;Uruguay&quot;)) %&gt;% ggplot() + geom_point(aes(x = Shots, y = Tackles, color = Position, shape = Team)) + geom_text(mapping = aes(x = Shots, y = Tackles, color = Position, label = Name), size = 2.5) Create a scatterplot of player time versus passes. Use color to show whether the player was on one of the top 4 teams or not. worldcup %&gt;% mutate(top_4 = Team %in% c(&quot;Spain&quot;, &quot;Netherlands&quot;, &quot;Germany&quot;, &quot;Uruguay&quot;)) %&gt;% ggplot() + geom_point(aes(x = Time, y = Passes, color = top_4)) 3.6.5 Exploring the data using basic plots #2 Go back to the code you used in the previous section to create a scatterplot of the worldcup data, where each point is a player, the x-axis shows the amount of time the player played in the World Cup, and the y-axis shows the number of passes the player had. Try the following modifications: Make all the points blue. Google “R colors” to find a list of color names in R. Pick your favorite and make all the points in the scatterplot that color. Change the size of the points to make them smaller (hint: check out the size aesthetic). Make it so the color of the points shows the player’s position and all the points are slightly transparent. Change the title of the x-axis to “Time (minutes)” and the y-axis to “Number of passes”. Add the title “World Cup statistics” and the subtitle “2010 World Cup”. 3.6.5.1 Example R code: Make all the points blue. ggplot(worldcup) + geom_point(mapping = aes(x = Time, y = Passes), color = &quot;blue&quot;) Google “R colors” to find a list of color names in R. Pick your favorite and make all the points in the scatterplot that color. # Make the points &quot;darkseagreen4&quot; ggplot(worldcup) + geom_point(mapping = aes(x = Time, y = Passes), color = &quot;darkseagreen4&quot;) Change the size of the points to make them smaller (hint: check out the size aesthetic). ggplot(worldcup) + geom_point(mapping = aes(x = Time, y = Passes), size = 0.8) Make it so the color of the points shows the player’s position and all the points are slightly transparent. ggplot(worldcup) + geom_point(mapping = aes(x = Time, y = Passes), alpha = 0.3) Change the title of the x-axis to “Time (minutes)” and the y-axis to “Number of passes”. ggplot(worldcup) + geom_point(mapping = aes(x = Time, y = Passes)) + labs(x = &quot;Time (minutes)&quot;, y = &quot;Number of passes&quot;) Add the title “World Cup statistics” and the subtitle “2010 World Cup”. ggplot(worldcup) + geom_point(mapping = aes(x = Time, y = Passes)) + ggtitle(&quot;World Cup statistics&quot;, subtitle = &quot;2010 World Cup&quot;) 3.6.6 Exploring the data using basic plots #3 Try out creating some plots using the “statistical” geoms to check out this data. Try the following: Plot histograms of all the numeric variables (Time, Shot, Passes, Tackles, Saves) in the dataset. Try customizing the number of bins used for one of the histograms plotted in the previous step. Try using constant values for some of the aesthetics (e.g., customize the color and the fill) of the histogram created in the previous step. Create a boxplot of Shots by position. Create a top_teams subset with just the four teams that made the semi-finals in the 2010 World Cup (Spain, the Netherlands, Germany, and Uruguay). Plot boxplots of Shots and Saves by team for just these teams. Create a histogram using data only from the four top teams for the amount of time each player played. Use the color aesthetic of the histogram to show team. 3.6.6.1 Example R code Use histograms to explore the distribution of different variables. If you want to change the number of bins in the histogram, try playing around with the bins and binwidth arguments. You can use the bins argument to say how many bins you want (e.g., bins = 50). You can use the binwidth argument to say how wide you want the bins to be (e.g., binwidth = 10 if you wanted bins to be 10 units wide, in the units of the variable mapped to the x aesthetic. Try using fill and color to change the appearance of the plot. Google “R colors” and search the images to find links to listings of different R colors. ggplot(worldcup, aes(x = Time)) + geom_histogram() ggplot(worldcup, aes(x = Time)) + geom_histogram(bins = 50) ggplot(worldcup, aes(x = Time)) + geom_histogram(binwidth = 100) ggplot(worldcup, aes(x = Time)) + geom_histogram(binwidth = 50, color = &quot;white&quot;, fill = &quot;cyan4&quot;) To create a boxplot of Shots by Position, you can use geom_boxplot: ggplot(worldcup, aes(x = Position, y = Shots)) + geom_boxplot() The top four teams in this World Cup were Spain, the Netherlands, Germany, and Uruguay. Create a subset with just the data for these four teams: top_teams &lt;- worldcup %&gt;% filter(Team %in% c(&quot;Spain&quot;, &quot;Netherlands&quot;, &quot;Germany&quot;, &quot;Uruguay&quot;)) Now, you can plot the boxplots, mapping Team to the x aesthetic and Shots to the y aesthetic: ggplot(top_teams, aes(x = Team, y = Shots)) + geom_boxplot() + ggtitle(&quot;Shots per player in World Cup 2010&quot;) Create a histogram using data only from the four top teams for the amount of time each player played. Use the color aesthetic of the histogram to show team. ggplot(data = top_teams) + geom_histogram(aes(x = Time, fill = Team)) ## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`. Note that you can also explore other values for geom_histogram arguments. For example, you could change the binwidths to be 90 minutes (since games are 90 minutes). ggplot(data = top_teams) + geom_histogram(aes(x = Time, fill = Team), binwidth = 90) "],
 ["reporting-data-results-1.html", "Chapter 4 Reporting data results #1 4.1 Guidelines for good plots 4.2 High data density 4.3 Meaningful labels 4.4 References 4.5 Highlighting 4.6 Order 4.7 Small multiples 4.8 Advanced customization 4.9 To find out more 4.10 In-course exercise", " Chapter 4 Reporting data results #1 Download a pdf of the lecture slides covering this topic. 4.1 Guidelines for good plots There are a number of very thoughtful books and articles about creating graphics that effectively communicate information. Some of the authors I highly recommend (and from whose work I’ve pulled the guidelines for good graphics we’ll talk about this week) are: Edward Tufte Howard Wainer Stephen Few Nathan Yau You should plan, in particular, to read The Visual Display of Quantitative Information by Edward Tufte before you graduate. This week, we’ll focus on six guidelines for good graphics, based on the writings of these and other specialists in data display. The guidelines are: Aim for high data density. Use clear, meaningful labels. Provide useful references. Highlight interesting aspects of the data. Make order meaningful. When possible, use small multiples. For the examples, I’ll use dplyr for data cleaning and, for plotting, the packages ggplot2, gridExtra, and ggthemes. library(tidyverse) ## Loads `dplyr` and `ggplot2` library(gridExtra) library(ggthemes) You can load the data for today’s examples with the following code: library(faraway) data(nepali) data(worldcup) library(dlnm) data(chicagoNMMAPS) chic &lt;- chicagoNMMAPS chic_july &lt;- chic %&gt;% filter(month == 7 &amp; year == 1995) 4.2 High data density Guideline 1: Aim for high data density. You should try to increase, as much as possible, the data to ink ratio in your graphs. This is the ratio of “ink” providing information to all ink used in the figure. One way to think about this is that the only graphs you make that use up a lot of your printer’s ink should be packed with information. The two graphs in Figure 4.1 show the same information, but use very different amounts of ink. Each shows the number of players in each of four positions in the worldcup dataset. Notice how, in the plot on the right, a single dot for each category shows the same information that a whole filled bar is showing on the left. Further, the plot on the right has removed the gridded background, removing even more “ink”. ## `summarise()` ungrouping output (override with `.groups` argument) Figure 4.1: Example of plots with lower (left) and higher (right) data-to-ink ratios. Each plot shows the number of players in each position in the worldcup dataset from the faraway package. Figure 4.2 gives another example of two plots that show the same information but with very different data densities. This figure uses the chicagoNMMAPS data from the dlnm package, which includes daily mortality, weather, and air pollution data for Chicago, IL. Both plots show daily mortality counts during July 1995, when a very severe heat wave hit Chicago. Notice how many of the elements in the plot on the left, including the shading under the mortality time series and the colored background and grid lines, are unnecessary for interpreting the message from the data. Figure 4.2: Example of plots with lower (left) and higher (right) data-to-ink ratios. Each plot shows daily mortality in Chicago, IL, in July 1995 using the chicagoNMMAPS data from the dlnm package. By increasing the data-to-ink ratio in a plot, you can help viewers see the message of the data more quickly. A cluttered plot is harder to interpret. Further, you leave room to add some of the other elements I’ll talk about, including highlighting interesting data and adding useful references. Notice how the plots on the left in Figures 4.1 and 4.2 are already cluttered and leave little room for adding extra elements, while the plots on the right of those figures have much more room for additions. One quick way to increase data density in ggplot2 is to change the theme for the plot. The theme specifies a number of the “background” elements to a plot, including elements like the plot grid, background color, and the font used for labeling. Some themes come with ggplot2, including: theme_bw theme_minimal theme_void You can find more themes in packages that extend ggplot2. The ggthemes package, in particular, has some excellent additional themes. Figures 4.3 shows some examples of the effects of using different themes. All show the same information– a plot of daily deaths in Chicago in July 1995. The top left graph shows the graph with the default theme. The other plots show the effects of adding different themes, including the black-and-white theme that comes with ggplot2 (top right) and various themes from the ggthemes package. You can even use themes to add some questionable choices for different elements, like the Excel theme (bottom left). Figure 4.3: Daily mortality in Chicago, IL, in July 1995. This figure gives an example of the plot using different themes. 4.3 Meaningful labels Guideline 2: Use clear, meaningful labels. Graphs often default to use abbreviations for axis labels and other labeling. For example, the default is for ggplot2 plots to use column names for the x- and y-axes of a scatterplot. While this is convenient for exploratory plots, it’s often not adequate for plots for presentations and papers. You’ll want to use short and easy-to-type column names in your dataframe to make coding easier, but you should use longer and more meaningful labeling in plots and tables that others need to interpret. Furthermore, text labels can sometimes be aligned in a way that makes them hard to read. For example, when plotting a categorical variable along the x-axis, it can be difficult to fit labels for each category that are long enough to be meaningful. Figure 4.4 gives an example of the same information shown with labels that are harder to interpret (left) versus with clear, meaningful labels (right). Notice how the graph on the left is using abbreviations for the categorical variable (“DF” for “Defense”), abbreviations for axis labels (“Pos” for “Position” and “Pls” for “Number of players”), and has the player position labels in a vertical alignment. On the right graph, I have made the graph easier to quickly read and interpret by spelling out all labels and switching the x- and y-axes, so that there’s room to fully spell out each position while still keeping the alignment horizontal, so the reader doesn’t have to turn the page (or their head) to read the values. Figure 4.4: The number of players in each position in the worldcup data from the faraway package. Both graphs show the same information, but the left graph has murkier labels, while the right graph has labels that are easier to read and interpret. There are a few strategies you can use to make labels clearer when plotting with ggplot2: Add xlab and ylab elements to the plot, rather than relying on the column names in the original data. You can also relabel x- and y-axes with scale elements (e.g., scale_x_continuous), and the scale functions give you more power to also make other changes to the x- and y-axes (e.g., changing break points for the axis ticks). However, if you only need to change axis labels, xlab and ylab are often quicker. Include units of measurement in axis titles when relevant. If units are dollars or percent, check out the scales package, which allows you to add labels directly to axis elements by including arguments like labels = percent in scale elements. See the helpfile for scale_x_continuous for some examples. If the x-variable requires longer labels, as is often the case with categorical data (for example, player positions Figure 4.4), consider flipping the coordinates, rather than abbreviating or rotating the labels. You can use coord_flip to do this. 4.4 References Guideline 3: Provide useful references. Data is easier to interpret when you add references. For example, if you show what it typical, it helps viewers interpret how unusual outliers are. Figure 4.5 shows daily mortality during July 1995 in Chicago, IL. The graph on the right has added shading showing the range of daily death counts in July in Chicago for neighboring years (1990–1994 and 1996–2000). This added reference helps clarify for viewers how unusual the number of deaths during the July 1995 heat wave was. Figure 4.5: Daily mortality during July 1995 in Chicago, IL. In the graph on the right, I have added a shaded region showing the range of daily mortality counts for neighboring years, to show how unusual this event was. Another useful way to add references is to add a linear or smooth fit to the data, to help clarify trends in the data. Figure 4.6 shows the relationship between passes and shots for Forwards in the worldcup dataset. The plot on the right has added a smooth function of the relationship between these two variables. Figure 4.6: Relationship between passes and shots taken among Forwards in the worldcup dataset from the faraway package. The plot on the right has a smooth function added to help show the relationship between these two variables. For scatterplots created with ggplot2, you can use the function geom_smooth to add a smooth or linear reference line. Here is the code that produces Figure 4.6: ggplot(filter(worldcup, Position == &quot;Forward&quot;), geom_point(size = 1.5) + theme_few() + geom_smooth() The most useful geom_smooth parameters to know are: method: The default is to add a loess curve if the data includes less than 1000 points and a generalized additive model for 1000 points or more. However, you can change to show the fitted line from a linear model using method = \"lm\" or from a generalized linear model using method = \"glm\". span: How wiggly or smooth the smooth line should be (smaller value: more wiggly; larger value: more smooth) se: TRUE or FALSE, indicating whether to include shading for 95% confidence intervals. level: Confidence level for confidence interval (e.g., 0.90 for 90% confidence intervals) Lines and polygons can also be useful for adding references, as in Figure 4.5. Useful geoms for such shapes include: geom_hline, geom_vline: Add a horizontal or vertical line geom_abline: Add a line with an intercept and slope geom_polygon: Add a filled polygon geom_path: Add an unfilled polygon You want these references to support the main data shown in the plot, but not overwhelm it. When adding these references: Add reference elements first, so they will be plotted under the data, instead of on top of it. Use alpha to add transparency to these elements. Use colors that are unobtrusive (e.g., grays). For lines, consider using non-solid line types (e.g., linetype = 3). 4.5 Highlighting Guideline 4: Highlight interesting aspects. Consider adding elements to highlight noteworthy elements of the data. For example, in the graph on the right of Figure 4.7, the days of the heat wave (based on temperature measurements) have been highlighted over the mortality time series by using a thick red line. Figure 4.7: Mortality in Chicago, July 1995. In the plot on the right, a thick red line has been added to show the dates of a heat wave. In the below graphs, the names of the players with the most shots and passes have been added to highlight these unusual points. One helpful way to annotate is with text, using geom_text(). For this, you’ll first need to create a dataframe with the hottest day in the data: hottest_day &lt;- chic_july %&gt;% filter(temp == max(temp)) hottest_day[ , 1:6] ## date time year month doy dow ## 1 1995-07-13 3116 1995 7 194 Thursday chic_plot + geom_text(data = hottest_day, label = &quot;Max&quot;, size = 3) With geom_text, you’ll often want to use position adjustment (the position parameter) to move the text so it won’t be right on top of the data points: chic_plot + geom_text(data = hottest_day, label = &quot;Max&quot;, size = 3, hjust = 0, vjust = -1) You can also use lines to highlight. For this, it is often useful to create a new dataframe with data for the reference. To add a line for the Chicago heat wave, I’ve added a dataframe called hw with the relevant date range. I’m setting the y-value to be high enough (425) to ensure the line will be placed above the mortality data. hw &lt;- data.frame(date = c(as.Date(&quot;1995-07-12&quot;), as.Date(&quot;1995-07-16&quot;)), death = c(425, 425)) b &lt;- chic_plot + geom_line(data = hw, aes(x = date, y = death), size = 2) b 4.6 Order Guideline 5: Make order meaningful. You can make the ranking of data clearer from a graph by using order to show rank. Often, factor or categorical variables are ordered by something that is not interesting, like alphabetical order. ## `summarise()` ungrouping output (override with `.groups` argument) You can re-order factor variables in a graph by resetting the factor using the factor function and changing the order that levels are included in the levels parameter. 4.7 Small multiples Guideline 6: When possible, use small multiples. Small multiples are graphs that use many small plots showing the same thing for different facets of the data. For example, instead of using color in a single plot to show data for males and females, you could use two small plots, one each for males and females. Typically, in small multiples, all plots use the same x- and y-axes. This makes it easier to compare across plots, and it also allows you to save room by limiting axis annotation. You can use the facet functions to create small multiples. This separates the graph into several small graphs, one for each level of a factor. The facet functions are: facet_grid() facet_wrap() For example, to create small multiples by sex for the Nepali dataset, when plotting height versus weight, you can call: ggplot(nepali, aes(ht, wt)) + geom_point() + facet_grid(. ~ sex) The facet_grid function can facet by one or two variables. One will be shown by rows, and one by columns: ## Generic code facet_grid([factor for rows] ~ [factor for columns]) The facet_wrap() function can only facet by one variable, but it can “wrap” the small graphs for that variable, so the don’t all have to be in one row or column: ## Generic code facet_wrap(~ [factor for faceting], ncol = [number of columns]) Often, when you do faceting, you’ll want to re-name your factors levels or re-order them. For this, you’ll need to use the factor() function on the original vector. For example, to rename the sex factor levels from “1” and “2” to “Male” and “Female”, you can run: nepali &lt;- nepali %&gt;% mutate(sex = factor(sex, levels = c(1, 2), labels = c(&quot;Male&quot;, &quot;Female&quot;))) Notice that the labels for the two graphs have now changed: ggplot(nepali, aes(ht, wt)) + geom_point() + facet_grid(. ~ sex) To re-order the factor, and show the plot for “Female” first, you can use factor to change the order of the levels: nepali &lt;- nepali %&gt;% mutate(sex = factor(sex, levels = c(&quot;Female&quot;, &quot;Male&quot;))) Now notice that the order of the plots has changed: ggplot(nepali, aes(ht, wt)) + geom_point() + facet_grid(. ~ sex) 4.8 Advanced customization 4.8.1 Scales There are a number of different functions for adjusting scales. These follow the following convention: ## Generic code scale_[aesthetic]_[vector type] For example, to adjust the x-axis scale for a continuous variable, you’d use scale_x_continuous. You can use a scale function for an axis to change things like the axis label (which you could also change with xlab or ylab) as well as position and labeling of breaks. For example, here is the default for plotting time versus passes for the worldcup dataset, with the number of shots taken shown by size and position shown by color: ggplot(worldcup, aes(x = Time, y = Passes, color = Position, size = Shots)) + geom_point(alpha = 0.5) ggplot(worldcup, aes(x = Time, y = Passes, color = Position, size = Shots)) + geom_point(alpha = 0.5) + scale_x_continuous(name = &quot;Time played (minutes)&quot;, breaks = 90 * c(2, 4, 6), minor_breaks = 90 * c(1, 3, 5)) Parameters you might find useful in scale functions include: Parameter Description name Label or legend name breaks Vector of break points minor_breaks Vector of minor break points labels Labels to use for each break limits Limits to the range of the axis For dates, you can use scale functions like scale_x_date and scale_x_datetime. For example, here’s a plot of deaths in Chicago in July 1995 using default values for the x-axis: ggplot(chic_july, aes(x = date, y = death)) + geom_line() And here’s an example of changing the formating and name of the x-axis: ggplot(chic_july, aes(x = date, y = death)) + geom_line() + scale_x_date(name = &quot;Date in July 1995&quot;, date_labels = &quot;%m-%d&quot;) You can also use the scale functions to transform an axis. For example, to show the Chicago plot with “deaths” on a log scale, you can run: ggplot(chic_july, aes(x = date, y = death)) + geom_line() + scale_y_log10() For colors and fills, the conventions for the names of the scale functions can vary. For example, to adjust the color scale when you’re mapping a discrete variable (i.e., categorical, like gender or animal breed) to color, you’d use scale_color_hue. To adjust the color scale for a continuous variable, like age, you’ll use scale_color_gradient. For any color scales, consider starting with brewer first (e.g., scale_color_brewer, scale_color_distiller). Scale functions from brewer allow you to set colors using different palettes. You can explore these palettes at http://colorbrewer2.org/. The Brewer palettes fall into three categories: sequential, divergent, and qualitative. You should use sequential or divergent for continuous data and qualitative for categorical data. Use display.brewer.pal to show the palette for a given number of colors. library(RColorBrewer) display.brewer.pal(name = &quot;Set1&quot;, n = 8) display.brewer.pal(name = &quot;PRGn&quot;, n = 8) display.brewer.pal(name = &quot;PuBuGn&quot;, n = 8) Use the palette argument within a scales function to customize the palette: a &lt;- ggplot(data.frame(x = 1:5, y = rnorm(5), group = letters[1:5]), aes(x = x, y = y, color = group)) + geom_point() b &lt;- a + scale_color_brewer(palette = &quot;Set1&quot;) c &lt;- a + scale_color_brewer(palette = &quot;Pastel2&quot;) + theme_dark() grid.arrange(a, b, c, ncol = 3) ggplot(worldcup, aes(x = Time, y = Passes, color = Position, size = Shots)) + geom_point(alpha = 0.5) + scale_color_brewer(palette = &quot;Dark2&quot;, name = &quot;Player position&quot;) You can also set colors manually: ggplot(worldcup, aes(x = Time, y = Passes, color = Position, size = Shots)) + geom_point(alpha = 0.5) + scale_color_manual(values = c(&quot;blue&quot;, &quot;red&quot;, &quot;darkgreen&quot;, &quot;darkgray&quot;)) 4.9 To find out more Some excellent further references for plotting are: R Graphics Cookbook (book and website) Google images For more technical details about plotting in R: ggplot2: Elegant Graphics for Data Analysis, Hadley Wickham R Graphics, Paul Murrell 4.10 In-course exercise 4.10.1 Designing a plot For today’s exercise, you’ll be building a plot using the worldcup data from the faraway package. First, load in that data. The name of each player is in the rownames of this data. Use the tibble::rownames_to_column() function to move those rownames into a new column named Player. Also install and load the ggplot2 and ggthemes packages. Next, say you want to look at the relationship between the number of minutes that a player played in the 2010 World Cup (Time) and the number of shots the player took on goal (Shots). On a sheet of paper, and talking with your partner, decide how the two of you would design a plot to explore and present this relationship. How would you incorporate some of the principles of creating good graphs? 4.10.1.1 Example R code For this section, the only code needed is code to load the required packages, load the data, and move the rownames to a column named Player. library(faraway) data(worldcup) head(worldcup, 2) ## Team Position Time Shots Passes Tackles Saves ## Abdoun Algeria Midfielder 16 0 6 0 0 ## Abe Japan Midfielder 351 0 101 14 0 This dataset has the players’ names as rownames, rather than in a column. Once we start using dplyr functions, we’ll lose these rownames. Therefore, start by converting the rownames to a column called Player: library(dplyr) worldcup &lt;- worldcup %&gt;% tibble::rownames_to_column(var = &quot;Player&quot;) head(worldcup, 2) ## Player Team Position Time Shots Passes Tackles Saves ## 1 Abdoun Algeria Midfielder 16 0 6 0 0 ## 2 Abe Japan Midfielder 351 0 101 14 0 Install and load the ggplot2 package: # install.packages(&quot;ggplot2&quot;) library(ggplot2) # install.packages(&quot;ggthemes&quot;) library(ggthemes) 4.10.2 Implementing plot guidelines #1 In this section, we’ll work on creating a plot like this: Do the following tasks: Create a simple scatterplot of Time versus Shots for the World Cup data. It should look like this: Next, before any more coding, talk with your group members about how this graph is different from the simple one you created with ggplot in the last section. Also discuss what you can figure out from this new graph that was less clear from a simpler scatterplot of Time versus Shots for this data. Often, in graphs with a lot of points, it’s hard to see some of the points, because they overlap other points. Three strategies to address this are: (a) make the points smaller; and (b) make the points somewhat transparent. Try doing these first two with the scatterplot you’re creating. At this point, the plot should look something like this: Create a new column in the worldcup data called top_four that specifies whether or not the Team for that observation was one of the top four teams in the tournament (Netherlands, Uruguay, Spain, and Germany). Make the colors of the points correspond to whether the team was a top-four team. At this point, the plot should look something like this: Increase data density: Try changing the theme, to come up with a graph with a bit less non-data ink. From the ggthemes package, try some of the following themes: theme_few(), theme_tufte(), theme_stata(), theme_fivethirtyeight(), theme_economist_white(), and theme_wsj(). Pick a theme that helps increase the graph’s data density. At this point, the plot should look something like this: Use meaningful labels: Use the labs() function to make a clearer title for the x-axis. (You may have already written this code in the last section of this exercise.) In addition to setting the x-axis title with the labs function, you can also set the title for the color scale (use color = within the labs function). You may want to make a line break in the color title– you can use the linebreak character (\\n) inside the character string with the title to do that. At this point, the plot should look something like this: Provide useful references: The standard time for a soccer game is 90 minutes. In the World Cup, all teams play at least three games, and then the top teams continue and play more games. Add a reference line at 270 minutes (i.e., the amount of standard time played for the three games that all teams play). At this point, the plot should look something like this: 4.10.2.1 Example R code As a reminder, here’s the code to do a simple scatterplot ot Shots by Time for the worldcup data: ggplot(data = worldcup) + geom_point(mapping = aes(x = Time, y = Shots)) Next, try to make it clearer to see the points by making them smaller and somewhat transparent. This can be done with the size and alpha aesthetics for geom_points. For the size aesthetic, a value smaller than about 2 = smaller than default, larger than about 2 = larger than default. For the alpha aesthetic, closer to 0 = more tranparent, closer to 1 = more opaque. As a reminder, in this case you are changing all of the points in the same way, so you will be setting those aesthetics to constant values. That means that you should specify the values outside of an aes call. This code could make these changes: ggplot(data = worldcup) + geom_point(mapping = aes(x = Time, y = Shots), size = 1, alpha = 0.5) To create a new column called top_four, first create vector that lists those top four teams, then create a logical vector in the dataframe for whether the team for that observation is in one of the top four teams: worldcup &lt;- worldcup %&gt;% mutate(top_4 = Team %in% c(&quot;Spain&quot;, &quot;Germany&quot;, &quot;Uruguay&quot;, &quot;Netherlands&quot;)) head(worldcup) ## Team Position Time Shots Passes Tackles Saves Player top_4 ## 1 Algeria Midfielder 16 0 6 0 0 Abdoun FALSE ## 2 Japan Midfielder 351 0 101 14 0 Abe FALSE ## 3 France Defender 180 0 91 6 0 Abidal FALSE ## 4 France Midfielder 270 1 111 5 0 Abou Diaby FALSE ## 5 Cameroon Forward 46 2 16 0 0 Aboubakar FALSE ## 6 Uruguay Forward 72 0 15 0 0 Abreu TRUE summary(worldcup$top_4) ## Mode FALSE TRUE ## logical 517 78 To color points by this variable, use color = in the aes() part of the ggplot() call: ggplot(data = worldcup) + geom_point(mapping = aes(x = Time, y = Shots, color = top_4), size = 1, alpha = 0.5) To increase the data density, try out different themes for the plot. First, I’ll save everything we’ve done so far as the object shot_plot, then I’ll try adding different themes: shot_plot &lt;- ggplot(data = worldcup) + geom_point(mapping = aes(x = Time, y = Shots, color = top_4), size = 1, alpha = 0.5) shot_plot + theme_few() shot_plot + theme_tufte() shot_plot + theme_wsj() shot_plot + theme_fivethirtyeight() shot_plot + theme_stata() shot_plot + theme_economist_white() The data density is increased with the theme_few() theme, so I’ll use that: ggplot(data = worldcup) + geom_point(mapping = aes(x = Time, y = Shots, color = top_4), size = 1, alpha = 0.5) + theme_few() To change the titles for some of the scales (the x-axis and color scale), you can use the labs() function. Note that you can use \\n to add a line break inside one of these titles (I’ve done that for the title for the color scale): ggplot(data = worldcup) + geom_point(mapping = aes(x = Time, y = Shots, color = top_4), size = 1, alpha = 0.5) + theme_few() + labs(x = &quot;Time played in World Cup (minutes)&quot;, color = &quot;Team&#39;s final\\nranking&quot;) As an extra note, if you want to create nicer labels for the legend for color, convert the top_four column into the factor class, with the labels you want to use in the figure legend: worldcup &lt;- worldcup %&gt;% mutate(top_4 = factor(top_4, levels = c(TRUE, FALSE), labels = c(&quot;Top 4&quot;, &quot;Other&quot;))) summary(worldcup$top_4) ## Top 4 Other ## 78 517 ggplot(data = worldcup) + geom_point(mapping = aes(x = Time, y = Shots, color = top_4), size = 1, alpha = 0.5) + theme_few() + labs(x = &quot;Time played in World Cup (minutes)&quot;, color = &quot;Team&#39;s final\\nranking&quot;) To add a reference line at 270 minutes of time, use the geom_vline() function. You’ll want to make it a light color (like light gray) and dashed or dotted (linetype of 2 or 3), so it won’t be too prominent on the graph: ggplot(data = worldcup) + geom_vline(xintercept = 270, color = &quot;lightgray&quot;, linetype = 2) + geom_point(mapping = aes(x = Time, y = Shots, color = top_4), size = 1, alpha = 0.5) + theme_few() + labs(x = &quot;Time played in World Cup (minutes)&quot;, color = &quot;Team&#39;s final\\nranking&quot;) 4.10.3 Implementing plot guidelines #2 Highlighting interesting data: Who had the most shots in the 2010 World Cup? Was he on a top-four team? Use geom_text() to label his point on the graph with his name (try out some different values of hjust and vjust in this function call to get the label in a place you like). At this point, the plot should look something like this: For labeling the player with the top number of shots, instead of only using the player’s name, use the following format: “[Player’s name], [Player’s team]”. (Hint: You may want to use mutate to add a new column, where you used paste0 to paste together the player’s name, \", \", and the team name.) At this point, the plot should look something like this: Create small multiples. The relationship between time played and shots taken is probably different by the players’ positions. Use faceting to create different graphs for each position. At this point, the plot should look something like this: Make order meaningful: What order are the faceted graphs currently in? Offensive players have more chances to take shots than defensive players, so that might be a useful ordering for the facets. Re-order the Position factor column to go from nearest your own goal to nearest the opponents goal, and then re-plot the graph from the previous step. 4.10.3.1 Example R code To add a text label with just the player with the most shots, you’ll want to create a new dataframe with just the top player. You can use the top_n function to do that (the wt option is specifying that we want the top player in terms of values in the Shots column): top_player &lt;- worldcup %&gt;% top_n(n = 1, wt = Shots) Now you can use geom_text() to label this player’s point on the graph with his name. You may need to mess around with some of the options in geom_text(), like size, hjust, and vjust (hjust and vjust say where, in relation to the point location, to put the label), to get something you’re happy with. If you want to put both the player’s name and his team, you can add a mutate() function when you create the new dataframe with just the top player, and then use this for the label: To create small multiples, use the facet_wrap() command (you’ll probably want to use ncol to specify to use four columns): To re-order the Position column of the dataframe, add a mutate statement before you pipe into the plotting code. Use the levels option of the factor() function– whatever order you put the factors in for this argument will be the new order in which R saves the levels of this factor. Note from this code example that you can use the levels function to find out the levels and their order for a factor-class vector. worldcup &lt;- worldcup %&gt;% mutate(Position = factor(Position, levels = c(&quot;Goalkeeper&quot;, &quot;Defender&quot;, &quot;Midfielder&quot;, &quot;Forward&quot;))) levels(worldcup$Position) ## [1] &quot;Goalkeeper&quot; &quot;Defender&quot; &quot;Midfielder&quot; &quot;Forward&quot; 4.10.4 Data visualization cheatsheet RStudio comes with some excellent cheatsheets, which provide quick references to functions and code you might find useful for different tasks. For this part of the group exercise, you’ll explore their cheatsheet for data visualization, both to learn some new ggplot2 code and to become familiar with how to use this cheatsheet as you do your own analysis. Open the data visualization cheatsheet. You can do this from RStudio by going to “Help” -&gt; “Cheatsheets” -&gt; “Data Visualization with ggplot2”. Notice that different sections give examples with some datasets that come with either base R or ggplot2. For example, under the “Graphical Primitives” section, there is code defining the object a as a ggplot object using the “seals” dataset: a &lt;- ggplot(seals, aes(x = long, y = lat)). Go through the cheatsheet and list all of the example datasets that are used in this cheatsheet. Open their helpfiles to learn more about the data. Create the example datasets a through l and s through t using the code given on the cheatsheet. Pick at least one example to try out from each of the following sections: “Graphical Primitives”, “One Variable”, at least three subsections of “Two Variables”, “Three Variables”, “Scales”, “Faceting”, and “Position Adjustments”. As you try these, try to figure out any aesthetics that you aren’t familiar with (e.g., ymin, ymax). Also, use helpfiles for the geoms to look up parameters you aren’t familiar with (e.g., stat for geom_area). If you can’t figure out how to interpret a plot, check the helpfile for the associated geom. Note: For the n geom used in “scales”, it should be defined as n &lt;- d + geom_bar(aes(fill = fl)). 4.10.4.1 Example R code The code for opening the helpfiles for the example datasets is: ?seals ?economics ?mpg ?diamonds ?USArrests Note that, for USArrests, only some of the columns are pulled out (e.g., murder = USArrests$murder) to use in the data example dataframe. Further, the “Visualizing error” examples use a dataframe created specifically for these examples, called df. Some of the base R and ggplot2 example datasets have become fairly well-known. Some that you’ll see very often in examples are the iris, mpg, and diamonds datasets. All of the code to create the datasets a through l and s through t is given somewhere on the cheatsheet. Here it is in full: a &lt;- ggplot(seals, aes(x = long, y = lat)) b &lt;- ggplot(economics, aes(date, unemploy)) c &lt;- ggplot(mpg, aes(hwy)) d &lt;- ggplot(mpg, aes(fl)) e &lt;- ggplot(mpg, aes(cty, hwy)) f &lt;- ggplot(mpg, aes(class, hwy)) g &lt;- ggplot(diamonds, aes(cut, color)) h &lt;- ggplot(diamonds, aes(carat, price)) i &lt;- ggplot(economics, aes(date, unemploy)) df &lt;- data.frame(grp = c(&quot;A&quot;, &quot;B&quot;), fit = 4.5, se = 1:2) j &lt;- ggplot(df, aes(grp, fit, ymin = fit - se, ymax = fit + se)) data &lt;- data.frame(murder = USArrests$Murder, state = tolower(rownames(USArrests))) map &lt;- map_data(&quot;state&quot;) k &lt;- ggplot(data, aes(fill = murder)) seals$z &lt;- with(seals, sqrt(delta_long^2 + delta_lat^2)) l &lt;- ggplot(seals, aes(long, lat)) s &lt;- ggplot(mpg, aes(fl, fill = drv)) t &lt;- ggplot(mpg, aes(cty, hwy)) + geom_point() Notice that, in some places, the aesthetics are defined using the full aesthetic name-value pair (e.g., aes(x = long, y = lat)), while in other places the code relies on position for defining which column of a dataframe maps to which aesthetic (e.g., aes(cty, hwy) or aes(fl)). Either is fine, although relying on position can result in errors if you are not very familiar with the order in which parameters are defined for a function. This code will vary based on the examples you try, but here is some code for one set of examples: b + geom_ribbon(aes(ymin = unemploy - 900, ymax = unemploy + 900)) c + geom_dotplot() f + geom_violin(scale = &quot;area&quot;) h + geom_hex() j + geom_pointrange() k + geom_map(aes(map_id = state), map = map) + expand_limits(x = map$long, y = map$lat) l + geom_contour(aes(z = z)) n &lt;- d + geom_bar(aes(fill = fl)) n + scale_fill_brewer(palette = &quot;Blues&quot;) o &lt;- c + geom_dotplot(aes(fill = ..x..)) o + scale_fill_gradient(low = &quot;red&quot;, high = &quot;yellow&quot;) t + facet_grid(year ~ fl) s + geom_bar(position = &quot;fill&quot;) "],
-["reproducible-research-1.html", "Chapter 5 Reproducible research #1 5.1 What is reproducible research? 5.2 Markdown 5.3 Literate programming in R 5.4 Style guidelines 5.5 More with knitr 5.6 In-course exercise", " Chapter 5 Reproducible research #1 Download a pdf of the lecture slides covering this topic. 5.1 What is reproducible research? A data analysis is reproducible if all the information (data, files, etc.) required is available for someone else to re-do your entire analysis. This includes: Data available All code for cleaning raw data All code and software (specific versions, packages) for analysis Some advantages of making your research reproducible are: You can (easily) figure out what you did six months from now. You can (easily) make adjustments to code or data, even early in the process, and re-run all analysis. When you’re ready to publish, you can (easily) do a last double-check of your full analysis, from cleaning the raw data through generating figures and tables for the paper. You can pass along or share a project with others. You can give useful code examples to people who want to extend your research. Here is a famous research example of the dangers of writing code that is hard to double-check or confirm: The Economist The New York Times Simply Statistics Some of the steps required to making research reproducible are: All your raw data should be saved in the project directory. You should have clear documentation on the source of all this data. Scripts should be included with all the code used to clean this data into the data set(s) used for final analyses and to create any figures and tables. You should include details on the versions of any software used in analysis (for R, this includes the version of R as well as versions of all packages used). If possible, there should be no “by hand” steps used in the analysis; instead, all steps should be done using code saved in scripts. For example, you should use a script to clean data, rather than cleaning it by hand in Excel. If any “non-scriptable” steps are unavoidable, you should very clearly document those steps. There are several software tools that can help you improve the reproducibility of your research: knitr: Create files that include both your code and text. These can be rendered to create final reports and papers. They keep code within the final file for the report. knitr complements: Create fancier tables and figures within RMarkdown documents. Packages include tikzDevice, animate, xtables, and pander. packrat: Save versions of each package used for the analysis, then load those package versions when code is run again in the future. In this section, I will focus on using knitr and RMarkdown files. 5.2 Markdown R Markdown files are mostly written using Markdown. To write R Markdown files, you need to understand what markup languages like Markdown are and how they work. In Word and other word processing programs you have used, you can add formatting using buttons and keyboard shortcuts (e.g., “Ctrl-B” for bold). The file saves the words you type. It also saves the formatting, but you see the final output, rather than the formatting markup, when you edit the file (WYSIWYG – what you see is what you get). In markup languages, on the other hand, you markup the document directly to show what formatting the final version should have (e.g., you type **bold** in the file to end up with a document with bold). Examples of markup languages include: HTML (HyperText Markup Language) LaTex Markdown (a “lightweight” markup language) For example, Figure 5.1 some marked-up HTML code from CSU’s website, while Figure 5.2 shows how that file looks when it’s rendered by a web browser. Figure 5.1: Example of the source of an HTML file. Figure 5.2: Example of a rendered HTML file. To write a file in Markdown, you’ll need to learn the conventions for creating formatting. This table shows what you would need to write in a flat file for some common formatting choices: Code Rendering Explanation **text** text boldface *text* text italicized [text](www.google.com) text hyperlink # text first-level header ## text second-level header Some other simple things you can do in Markdown include: Lists (ordered or bulleted) Equations Tables Figures from file Block quotes Superscripts For more Markdown conventions, see RStudio’s R Markdown Reference Guide (link also available through “Help” in RStudio). 5.3 Literate programming in R Literate programming, an idea developed by Donald Knuth, mixes code that can be executed with regular text. The files you create can then be rendered, to run any embedded code. The final output will have results from your code and the regular text. The knitr package can be used for literate programming in R. In essence, knitr allows you to write an R Markdown file that can be rendered into a pdf, Word, or HTML document. Here are the basics of opening and rendering an R Markdown file in RStudio: To open a new R Markdown file, go to “File” -&gt; “New File” -&gt; “RMarkdown…” -&gt; for now, chose a “Document” in “HTML” format. This will open a new R Markdown file in RStudio. The file extension for R Markdown files is “.Rmd”. The new file comes with some example code and text. You can run the file as-is to try out the example. You will ultimately delete this example code and text and replace it with your own. Once you “knit” the R Markdown file, R will render an HTML file with the output. This is automatically saved in the same directory where you saved your .Rmd file. Write everything besides R code using Markdown syntax. To include R code in an RMarkdown document, you need to separate off the code chunk using the following syntax: ```{r} my_vec &lt;- 1:10 ``` This syntax tells R how to find the start and end of pieces of R code when the file is rendered. R will walk through, find each piece of R code, run it and create output (printed output or figures, for example), and then pass the file along to another program to complete rendering (e.g., Tex for pdf files). You can specify a name for each chunk, if you’d like, by including it after “r” when you begin your chunk. For example, to give the name load_nepali to a code chunk that loads the nepali dataset, specify that name in the start of the code chunk: ```{r load_nepali} library(faraway) data(nepali) ``` Here are a couple of tips for naming code chunks: Chunk names must be unique across a document. Any chunks you don’t name are given numbers by knitr. You do not have to name each chunk. However, there are some advantages: It will be easier to find any errors. You can use the chunk labels in referencing for figure labels. You can reference chunks later by name. You can add options when you start a chunk. Many of these options can be set as TRUE / FALSE and include: Option Action echo Print out the R code? eval Run the R code? messages Print out messages? warnings Print out warnings? include If FALSE, run code, but don’t print code or results Other chunk options take values other than TRUE / FALSE. Some you might want to include are: Option Action results How to print results (e.g., hide runs the code, but doesn’t print the results) fig.width Width to print your figure, in inches (e.g., fig.width = 4) fig.height Height to print your figure Add these options in the opening brackets and separate multiple ones with commas: ```{r messages = FALSE, echo = FALSE} nepali[1, 1:3] ``` I will cover other chunk options later, once you’ve gotten the chance to try writting R Markdown files. You can set “global” options at the beginning of the document. This will create new defaults for all of the chunks in the document. For example, if you want echo, warning, and message to be FALSE by default in all code chunks, you can run: ```{r global_options} knitr::opts_chunk$set(echo = FALSE, message = FALSE, warning = FALSE) ``` If you set both global and local chunk options that you set specifically for a chunk will take precedence over global options. For example, running a document with: ```{r global_options} knitr::opts_chunk$set(echo = FALSE, message = FALSE, warning = FALSE) ``` ```{r check_nepali, echo = TRUE} head(nepali, 1) ``` would print the code for the check_nepali chunk, because the option specified for that specific chunk (echo = TRUE) would override the global option (echo = FALSE). You can also include R output directly in your text (“inline”) using backticks: “There are `r nrow(nepali)` observations in the nepali data set. The average age is `r mean(nepali$age, na.rm = TRUE)` months.” Once the file is rendered, this gives: “There are 1000 observations in the nepali data set. The average age is 37.662 months.” Here are two tips that will help you diagnose some problems rendering R Markdown files: Be sure to save your R Markdown file before you run it. All the code in the file will run “from scratch”– as if you just opened a new R session. The code will run using, as a working directory, the directory where you saved the R Markdown file. You’ll want to try out pieces of your code as you write an R Markdown document. There are a few ways you can do that: You can run code in chunks just like you can run code from a script (Ctrl-Return or the “Run” button). You can run all the code in a chunk (or all the code in all chunks) using the different options under the “Run” button in RStudio. All the “Run” options have keyboard shortcuts, so you can use those. You can render R Markdown documents to other formats: Word Pdf (requires that you’ve installed “Tex” on your computer.) Slides (ioslides) Click the button to the right of “Knit” to see different options for rendering on your computer. You can freely post your RMarkdown documents at RPubs. If you want to post to RPubs, you need to create an account. Once you do, you can click the “Publish” button on the window that pops up with your rendered file. RPubs can also be a great place to look for interesting example code, although it sometimes can be pretty overwhelmed with MOOC homework. If you’d like to find out more, here are two good how-to books on reproducible research in R (the CSU library has both in hard copy): Reproducible Research with R and RStudio, Christopher Gandrud Dynamic Documents with R and knitr, Yihui Xie 5.4 Style guidelines R style guidelines provide rules for how to format code in an R script. Some people develop their own style as they learn to code. However, it is easy to get in the habit of following style guidelines, and they offer some important advantages: Clean code is easier to read and interpret later. It’s easier to catch and fix mistakes when code is clear. Others can more easily follow and adapt your code if it’s clean. Some style guidelines will help prevent possible problems (e.g., avoiding . in function names). For this course, we will use R style guidelines from two sources: Google’s R style guidelines Hadley Wickham’s R style guidelines These two sets of style guidelines are very similar. Hear are a few guidelines we’ve already covered in class: Use &lt;-, not =, for assignment. Guidelines for naming objects: All lowercase letters or numbers Use underscore (_) to separate words, not camelCase or a dot (.) (this differs for Google and Wickham style guides) Have some consistent names to use for “throw-away” objects (e.g., df, ex, a, b) Make names meaningful Descriptive names for R scripts (“random_group_assignment.R”) Nouns for objects (todays_groups for an object with group assignments) Verbs for functions (make_groups for the function to assign groups) 5.4.1 Line length Google: Keep lines to 80 characters or less To set your script pane to be limited to 80 characters, go to “RStudio” -&gt; “Preferences” -&gt; “Code” -&gt; “Display”, and set “Margin Column” to 80. # Do my_df &lt;- data.frame(n = 1:3, letter = c(&quot;a&quot;, &quot;b&quot;, &quot;c&quot;), cap_letter = c(&quot;A&quot;, &quot;B&quot;, &quot;C&quot;)) # Don&#39;t my_df &lt;- data.frame(n = 1:3, letter = c(&quot;a&quot;, &quot;b&quot;, &quot;c&quot;), cap_letter = c(&quot;A&quot;, &quot;B&quot;, &quot;C&quot;)) This guideline helps ensure that your code is formatted in a way that you can see all of the code without scrolling horizontally (left and right). 5.4.2 Spacing Binary operators (e.g., &lt;-, +, -) should have a space on either side A comma should have a space after it, but not before. Colons should not have a space on either side. Put spaces before and after = when assigning parameter arguments # Do shots_per_min &lt;- worldcup$Shots / worldcup$Time #Don&#39;t shots_per_min&lt;-worldcup$Shots/worldcup$Time #Do ave_time &lt;- mean(worldcup[1:10 , &quot;Time&quot;]) #Don&#39;t ave_time&lt;-mean(worldcup[1 : 10 ,&quot;Time&quot;]) 5.4.3 Semicolons Although you can use a semicolon to put two lines of code on the same line, you should avoid it. # Do a &lt;- 1:10 b &lt;- 3 # Don&#39;t a &lt;- 1:10; b &lt;- 3 5.4.4 Commenting For a comment on its own line, use #. Follow with a space, then the comment. You can put a short comment at the end of a line of R code. In this case, put two spaces after the end of the code, one #, and one more space before the comment. If it helps make it easier to read your code, separate sections using a comment character followed by many hyphens (e.g., #------------). Anything after the comment character is “muted”. # Read in health data --------------------------- # Clean exposure data --------------------------- 5.4.5 Indentation Google: Within function calls, line up new lines with first letter after opening parenthesis for parameters to function calls: Example: # Relabel sex variable nepali$sex &lt;- factor(nepali$sex, levels = c(1, 2), labels = c(&quot;Male&quot;, &quot;Female&quot;)) 5.4.6 Code grouping Group related pieces of code together. Separate blocks of code by empty spaces. # Load data library(faraway) data(nepali) # Relabel sex variable nepali$sex &lt;- factor(nepali$sex, levels = c(1, 2), labels = c(&quot;Male&quot;, &quot;Female&quot;)) Note that this grouping often happens naturally when using tidyverse functions, since they encourage piping (%&gt;% and +). 5.4.7 Broader guidelines Omit needless code. Don’t repeat yourself. We’ll learn more about satisfying these guidelines when we talk about writing your own functions in the next part of the class. 5.5 More with knitr 5.5.1 Equations in knitr You can write equations in RMarkdown documents by setting them apart with dollar signs ($). For an equation on a line by itself (display equation), you two $s before and after the equation, on separate lines, then use LaTex syntax for writing the equations. To help with this, you may want to use this LaTex math cheat sheet.. You may also find an online LaTex equation editor like Codecogs.com helpful. Note: Equations denoted this way will always compile for pdf documents, but won’t always come through on Markdown files (for example, GitHub won’t compile math equations). For example, writing this in your R Markdown file: $$ E(Y_{t}) \\sim \\beta_{0} + \\beta_{1}X_{1} $$ will result in this rendered equation: \\[ E(Y_{t}) \\sim \\beta_{0} + \\beta_{1}X_{1} \\] To put math within a sentence (inline equation), just use one $ on either side of the math. For example, writing this in a R Markdown file: &quot;We are trying to model $E(Y_{t})$.&quot; The rendered document will show up as: “We are trying to model \\(E(Y_{t})\\).” 5.5.2 Figures from file You can include not only figures that you create with R, but also figures that you have saved on your computer. The best way to do that is with the include_graphics function in knitr: library(knitr) include_graphics(&quot;figures/CSU_ram.png&quot;) This example would include a figure with the filename “MyFigure.png” that is saved in the “figures” sub-directory of the parent directory of the directory where your .Rmd is saved. Don’t forget that you will need to give an absolute pathway or the relative pathway from the directory where the .Rmd file is saved. 5.5.3 Saving graphics files You can save figures that you create in R. Typically, you won’t need to save figures for an R Markdown file, since you can include figure code directly. However, you will sometimes want to save a figure from a script. You have two options: Use the “Export” choice in RStudio Write code to export the figure in your R script To make your research more reproducible, use the second choice. To use code export a figure you created in R, take three steps: Open a graphics device (e.g., pdf(\"MyFile.pdf\")). Write the code to print your plot. Close the graphics device using dev.off(). For example, the following code would save a scatterplot of time versus passes as a pdf named “MyFigure” in the “figures” subdirectory of the current working directory: pdf(&quot;figures/MyFigure.pdf&quot;, width = 8, height = 6) ggplot(worldcup, aes(x = Time, y = Passes)) + geom_point(aes(color = Position)) + theme_bw() dev.off() If you create multiple plots before you close the device, they’ll all save to different pages of the same pdf file. You can open a number of different graphics devices. Here are some of the functions you can use to open graphics devices: pdf png bmp jpeg tiff svg You will use a device-specific function to open a graphics device (e.g., pdf). However, you will always close these devices with dev.off. Most of the functions to open graphics devices include parameters like height and width. These can be used to specify the size of the output figure. The units for these depend on the device (e.g., inches for pdf, pixels by default for png). Use the helpfile for the function to determine these details. 5.5.4 Tables in R Markdown If you want to create a nice, formatted table from an R dataframe, you can do that using kable from the knitr package. my_df &lt;- data.frame(letters = c(&quot;a&quot;, &quot;b&quot;, &quot;c&quot;), numbers = 1:3) kable(my_df) letters numbers a 1 b 2 c 3 There are a few options for the kable function: arg expl colnames Column names (default: column name in the dataframe) align A vector giving the alignment for each column (‘l’, ‘c’, ‘r’) caption Table caption digits Number of digits to round to. If you want to round columns different amounts, use a vector with one element for each column. my.df &lt;- data.frame(letters = c(&quot;a&quot;, &quot;b&quot;, &quot;c&quot;), numbers = rnorm(3)) kable(my.df, digits = 2, align = c(&quot;r&quot;, &quot;c&quot;), caption = &quot;My new table&quot;, col.names = c(&quot;First 3 letters&quot;, &quot;First 3 numbers&quot;)) Table 5.1: My new table First 3 letters First 3 numbers a -0.36 b -1.79 c 0.49 From Yihui: “Want more features? No, that is all I have. You should turn to other packages for help. I’m not going to reinvent their wheels.” If you want to do fancier tables, you may want to explore the xtable and pander packages. As a note, these might both be more effective when compiling to pdf, rather than html. 5.6 In-course exercise For all of today’s tasks, you’ll use the code from last week’s in-course exercise to do the exercises. This week we are not focusing on writing new code, but rather on how to take R code and put it in an R Markdown file, so we can create reports from files that include the original code. 5.6.1 Creating a Markdown document First, you’ll create a Markdown document, without any R code in it yet. In RStudio, go to “File” -&gt; “New File” -&gt; “R Markdown”. From the window that brings up, choose “Document” on the left-hand column and “HTML” as the output format. A new file will open in the script pane of your RStudio session. Save this file (you may pick the name and directory). The file extension should be “.Rmd”. First, before you try to write your own Markdown, try rendering the example that the script includes by default. (This code is always included, as a template, when you first open a new RMarkdown file using the RStudio “New file” interface we used in this example.) Try rendering this default R Markdown example by clicking the “Knit” button at the top of the script file. For some of you, you may not yet have everything you need on your computer to be able to get this to work. If so, let me know. RStudio usually includes all the necessary tools when you install it, but there may be some exceptions. If you could get the document to knit, do the following tasks: Look through the HTML document that was created. Compare it to the R Markdown script that created it, and see if you can understand, at least broadly, what’s going on. Look in the directory where you saved the R Markdown file. You should now also see a new, .html file in that folder. Try opening it with a web browser like Safari. Go back to the R Markdown file. Delete everything after the initial header information (everything after the 6th line). In the header information, make sure the title, author, and date are things you’re happy with. If not, change them. Using Markdown syntax, write up a description of the data (worldcup) we used last week to create the fancier figure. Try to include the following elements: Bold and italic text Hyperlinks A list, either ordered or bulleted Headers 5.6.2 Adding in R code Now incorporate the R code from previous weeks’ exercises into your document. Once you get the document to render with some basic pieces of code in it, try the following: Try some different chunk options. For example, try setting echo = FALSE in some of your code chunks. Similarly, try using the options results = \"hide\" and include = FALSE. You should have at least one code chunk that generates figures. Try experimenting with the fig.width and fig.height options for the chunk to change the size of the figure. Try using the global commands. See if you can switch the echo default value for this document from TRUE (the usual default) to FALSE. 5.6.3 Working with R Markdown documents Finally, try the following tasks to get some experience working with R Markdown files in RStudio: Go to one of your code chunks. Locate the small gray arrow just to the left of the line where you initiate the code chunk. Click on it and see what happens. Then click on it again. Put your cursor inside one of your code chunks. Try using the “Run” button (or Ctrl-Return) to run code in that chunk at your R console. Did it work? Pick a code chunk in your document. Put your cursor somewhere in the code in that chunk. Click on the “Run” button and choose “Run All Chunks Above”. What did that do? If it did not work, what do you think might be going on? (Hint: Check getwd() and think about which directory you’ve used to save your R Markdown file.) Pick another chunk of code. Put the cursor somewhere in the code for that chunk. Click on the “Run” button and choose “Run Current Chunk”. Then try “Run Next Chunk”. Try to figure out all the options the “Run” button gives you and when each might be useful. Click on the small gray arrow to the right of the “Knit HTML” button. If the option is offered, select “Knit Word” and try it. What does this do? 5.6.4 R style guidelines Go through all the R code in your R Markdown file. Are there are places where your code is not following style conventions for R? Clean up your code to correct any of these issues. "],
-["entering-and-cleaning-data-2.html", "Chapter 6 Entering and cleaning data #2 6.1 Joining datasets 6.2 Tidy data 6.3 Longer data 6.4 Working with factors 6.5 String operations and regular expressions 6.6 Tidy select 6.7 In-course exercise", " Chapter 6 Entering and cleaning data #2 Download a pdf of the lecture slides covering this topic. 6.1 Joining datasets So far, you have only worked with a single data source at a time. When you work on your own projects, however, you typically will need to merge together two or more datasets to create the a data frame to answer your research question. For example, for air pollution epidemiology, you will often have to join several datasets: Health outcome data (e.g., number of deaths per day) Air pollution concentrations Weather measurements (since weather can be a confounder) Demographic data The dplyr package has a family of different functions to join two dataframes together, the *_join family of functions. All combine two dataframes, which I’ll call x and y here. The functions include: inner_join(x, y): Keep only rows where there are observations in both x and y. left_join(x, y): Keep all rows from x, whether they have a match in y or not. right_join(x, y): Keep all rows from y, whether they have a match in x or not. full_join(x, y): Keep all rows from both x and y, whether they have a match in the other dataset or not. In the examples, I’ll use two datasets, x and y. Both datasets include the column course. The other column in x is grade, while the other column in y is day. Observations exist for courses x and y in both datasets, but for w and z in only one dataset. x &lt;- data.frame(course = c(&quot;x&quot;, &quot;y&quot;, &quot;z&quot;), grade = c(90, 82, 78)) y &lt;- data.frame(course = c(&quot;w&quot;, &quot;x&quot;, &quot;y&quot;), day = c(&quot;Tues&quot;, &quot;Mon / Fri&quot;, &quot;Tue&quot;)) Here is what these two example datasets look like: x ## course grade ## 1 x 90 ## 2 y 82 ## 3 z 78 y ## course day ## 1 w Tues ## 2 x Mon / Fri ## 3 y Tue With inner_join, you’ll only get the observations that show up in both datasets. That means you’ll lose data on z (only in the first dataset) and w (only in the second dataset). inner_join(x, y) ## Joining, by = &quot;course&quot; ## course grade day ## 1 x 90 Mon / Fri ## 2 y 82 Tue With left_join, you’ll keep everything in x (the “left” dataset), but not keep things in y that don’t match something in x. That means that, here, you’ll lose w: left_join(x, y) ## Joining, by = &quot;course&quot; ## course grade day ## 1 x 90 Mon / Fri ## 2 y 82 Tue ## 3 z 78 &lt;NA&gt; right_join is the opposite: right_join(x, y) ## Joining, by = &quot;course&quot; ## course grade day ## 1 x 90 Mon / Fri ## 2 y 82 Tue ## 3 w NA Tues full_join keeps everything from both datasets: full_join(x, y) ## Joining, by = &quot;course&quot; ## course grade day ## 1 x 90 Mon / Fri ## 2 y 82 Tue ## 3 z 78 &lt;NA&gt; ## 4 w NA Tues 6.2 Tidy data All of the material in this section comes directly from Hadley Wickham’s paper on tidy data. You will need to read this paper to prepare for the quiz on this section. Getting your data into a “tidy” format makes it easier to model and plot. By taking the time to tidy your data at the start of an analysis, you will save yourself time, and make it easier to plan out later steps. Characteristics of tidy data are: Each variable forms a column. Each observation forms a row. Each type of observational unit forms a table. Here are five common problems that Hadley Wickham has identified that keep data from being tidy: Column headers are values, not variable names. Multiple variables are stored in one column. Variables are stored in both rows and columns. Multiple types of observational units are stored in the same table. A single observational unit is stored in multiple tables. Here are examples (again, from Hadley Wickham’s paper on tidy data, which is required reading for this week of the course) of each of these problems. Column headers are values, not variable names. Solution: Multiple variables are stored in one column. Solution: Variables are stored in both rows and columns. Solution: Multiple types of observational units are stored in the same table. Solution: A single observational unit is stored in multiple tables. Example: exposure and outcome data stored in different files: File 1: Daily mortality counts File 2: Daily air pollution measurements 6.3 Longer data There are two functions from the tidyr package (another member of the tidyverse) that you can use to change between wider and longr data: pivot_longer and pivot_wider. Here is a description of these two functions: pivot_longer: Takes several columns and pivots them down into two columns. One of the new columns contains the former column names and the other contains the former cell values. pivot_wider: Takes two columns and pivots them up into multiple columns. Column names for the new columns will come from one column and the cell values from the other. The following examples are show the effects of making a dataset longer or wider. Here is some simulated wide data: wide_stocks[1:3, ] ## # A tibble: 3 x 4 ## time X Y Z ## &lt;date&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; ## 1 2009-01-01 0.902 -0.454 -7.09 ## 2 2009-01-02 0.181 -1.87 -0.346 ## 3 2009-01-03 -0.636 -3.00 -0.202 In the wide_stocks dataset, there are separate columns for three different stocks (X, Y, and Z). Each cell gives the value for a certain stock on a certain day. This data isn’t “tidy”, because the identify of the stock (X, Y, or Z) is a variable, and you’ll probably want to include it as a variable in modeling. wide_stocks[1:3, ] ## # A tibble: 3 x 4 ## time X Y Z ## &lt;date&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; ## 1 2009-01-01 0.902 -0.454 -7.09 ## 2 2009-01-02 0.181 -1.87 -0.346 ## 3 2009-01-03 -0.636 -3.00 -0.202 If you want to convert the dataframe to have all stock values in a single column, you can use pivot_longer to convert wide data to long data: long_stocks &lt;- pivot_longer(data = wide_stocks, cols = -time, names_to = &quot;stock&quot;, values_to = &quot;price&quot;) long_stocks[1:5, ] ## # A tibble: 5 x 3 ## time stock price ## &lt;date&gt; &lt;chr&gt; &lt;dbl&gt; ## 1 2009-01-01 X 0.902 ## 2 2009-01-01 Y -0.454 ## 3 2009-01-01 Z -7.09 ## 4 2009-01-02 X 0.181 ## 5 2009-01-02 Y -1.87 In this “longer” dataframe, there is now one column that gives the identify of the stock (stock) and another column that gives the price of that stock that day (price): long_stocks[1:5, ] ## # A tibble: 5 x 3 ## time stock price ## &lt;date&gt; &lt;chr&gt; &lt;dbl&gt; ## 1 2009-01-01 X 0.902 ## 2 2009-01-01 Y -0.454 ## 3 2009-01-01 Z -7.09 ## 4 2009-01-02 X 0.181 ## 5 2009-01-02 Y -1.87 The format for a pivots_longer call is: ## Generic code new_df &lt;- pivot_longer(old_df, cols = [name(s) of the columns you want to make longer], names_to = [name of new column to store the old column names], values_to = [name of new column to store the old values]) Three important notes: Everything is pivoted into one of two columns – one column with the old column names, and one column with the old cell values With the names_to and values_to arguments, you are just providing column names for the two columns that everything’s pivoted into. If there is a column you don’t want to include in the pivot (date in the example), use - to exclude it in the cols argument. Notice how easy it is, now that the data is longer, to use stock for aesthetics of faceting in a ggplot2 call: ggplot(long_stocks, aes(x = time, y = price)) + geom_line() + facet_grid(. ~ stock) + theme_bw() If you have data in a “longer” format and would like to make it “wider”, you can use pivot_wider to do that: stocks &lt;- pivot_wider(long_stocks, names_from = &quot;stock&quot;, values_from = price) stocks[1:5, ] ## # A tibble: 5 x 4 ## time X Y Z ## &lt;date&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; ## 1 2009-01-01 0.902 -0.454 -7.09 ## 2 2009-01-02 0.181 -1.87 -0.346 ## 3 2009-01-03 -0.636 -3.00 -0.202 ## 4 2009-01-04 1.28 0.902 -3.60 ## 5 2009-01-05 -3.02 -0.914 5.74 Notice that this reverses the action of pivot_longer. The “wider” your data the less likely it is to be tidy, so won’t use pivot_wider frequently when you are preparing data for analysis. However, pivot_wider can be very helpful in creating tables for final reports and presentations. For example, if you wanted to create a table with means and standard deviations for each of the three stocks, you could use pivot_wider to rearrange the final summary to create an attractive table. stock_summary &lt;- long_stocks %&gt;% group_by(stock) %&gt;% summarize(N = n(), mean = mean(price), sd = sd(price)) ## `summarise()` ungrouping output (override with `.groups` argument) stock_summary ## # A tibble: 3 x 4 ## stock N mean sd ## &lt;chr&gt; &lt;int&gt; &lt;dbl&gt; &lt;dbl&gt; ## 1 X 10 0.182 1.30 ## 2 Y 10 -0.289 2.52 ## 3 Z 10 -2.30 3.59 stock_summary %&gt;% mutate(&quot;Mean (Std.dev.)&quot; = paste0(round(mean, 2), &quot; (&quot;, round(sd, 2), &quot;)&quot;)) %&gt;% dplyr::select(- mean, - sd) %&gt;% mutate(N = as.character(N)) %&gt;% # might be able to deal with this in pivot_longer call pivot_longer(cols = -stock, names_to = &quot;Statistic&quot;, values_to = &quot;Value&quot;) %&gt;% pivot_wider(names_from = &quot;stock&quot;, values_from = &quot;Value&quot;) %&gt;% knitr::kable() Statistic X Y Z N 10 10 10 Mean (Std.dev.) 0.18 (1.3) -0.29 (2.52) -2.3 (3.59) 6.4 Working with factors Hadley Wickham has developed a package called forcats that helps you work with categorical variables (factors). I’ll show some examples of its functions using the worldcup dataset: library(forcats) library(faraway) data(worldcup) The fct_recode function can be used to change the labels of a function (along the lines of using factor with levels and labels to reset factor labels). One big advantage is that fct_recode lets you change labels for some, but not all, levels. For example, here are the team names: library(stringr) worldcup %&gt;% filter(str_detect(Team, &quot;^US&quot;)) %&gt;% slice(1:3) %&gt;% select(Team, Position, Time) ## Team Position Time ## Beasley USA Midfielder 10 ## Bocanegra USA Defender 390 ## Bornstein USA Defender 200 If you just want to change “USA” to “United States,” you can run: worldcup &lt;- worldcup %&gt;% mutate(Team = fct_recode(Team, `United States` = &quot;USA&quot;)) worldcup %&gt;% filter(str_detect(Team, &quot;^Un&quot;)) %&gt;% slice(1:3) %&gt;% select(Team, Position, Time) ## Team Position Time ## 1 United States Midfielder 10 ## 2 United States Defender 390 ## 3 United States Defender 200 You can use the fct_lump function to lump uncommon factors into an “Other” category. For example, to lump the two least common positions together, you can run (n specifies how many categories to keep outside of “Other”): worldcup %&gt;% dplyr::mutate(Position = forcats::fct_lump(Position, n = 2)) %&gt;% dplyr::count(Position) ## Position n ## 1 Defender 188 ## 2 Midfielder 228 ## 3 Other 179 You can use the fct_infreq function to reorder the levels of a factor from most common to least common: levels(worldcup$Position) ## [1] &quot;Defender&quot; &quot;Forward&quot; &quot;Goalkeeper&quot; &quot;Midfielder&quot; worldcup &lt;- worldcup %&gt;% mutate(Position = fct_infreq(Position)) levels(worldcup$Position) ## [1] &quot;Midfielder&quot; &quot;Defender&quot; &quot;Forward&quot; &quot;Goalkeeper&quot; If you want to reorder one factor by another variable (ascending order), you can use fct_reorder (e.g., homework 3). For example, to relevel Position by the average shots on goals for each position, you can run: levels(worldcup$Position) ## [1] &quot;Midfielder&quot; &quot;Defender&quot; &quot;Forward&quot; &quot;Goalkeeper&quot; worldcup &lt;- worldcup %&gt;% group_by(Position) %&gt;% mutate(ave_shots = mean(Shots)) %&gt;% ungroup() %&gt;% mutate(Position = fct_reorder(Position, ave_shots)) levels(worldcup$Position) ## [1] &quot;Goalkeeper&quot; &quot;Defender&quot; &quot;Midfielder&quot; &quot;Forward&quot; 6.5 String operations and regular expressions For these examples, we’ll use some data on passengers of the Titanic. You can load this data using: # install.packages(&quot;titanic&quot;) library(titanic) data(&quot;titanic_train&quot;) We will be using the stringr package: library(stringr) This data includes a column called “Name” with passenger names. This column is somewhat messy and includes several elements that we might want to separate (last name, first name, title). Here are the first few values of “Name”: titanic_train %&gt;% select(Name) %&gt;% slice(1:3) ## Name ## 1 Braund, Mr. Owen Harris ## 2 Cumings, Mrs. John Bradley (Florence Briggs Thayer) ## 3 Heikkinen, Miss. Laina We’ve already done some things to manipulate strings. For example, if we wanted to separate “Name” into last name and first name (including title), we could actually do that with the separate function: titanic_train %&gt;% select(Name) %&gt;% slice(1:3) %&gt;% separate(Name, c(&quot;last_name&quot;, &quot;first_name&quot;), sep = &quot;, &quot;) ## last_name first_name ## 1 Braund Mr. Owen Harris ## 2 Cumings Mrs. John Bradley (Florence Briggs Thayer) ## 3 Heikkinen Miss. Laina Notice that separate is looking for a regular pattern (“,”) and then doing something based on the location of that pattern in each string (splitting the string). There are a variety of functions in R that can perform manipulations based on finding regular patterns in character strings. The str_detect function will look through each element of a character vector for a designated pattern. If the pattern is there, it will return TRUE, and otherwise FALSE. The convention is: ## Generic code str_detect(string = [vector you want to check], pattern = [pattern you want to check for]) For example, to create a logical vector specifying which of the Titanic passenger names include “Mrs.”, you can call: mrs &lt;- str_detect(titanic_train$Name, &quot;Mrs.&quot;) head(mrs) ## [1] FALSE TRUE FALSE TRUE FALSE FALSE The result is a logical vector, so str_detect can be used in filter to subset data to only rows where the passenger’s name includes “Mrs.”: titanic_train %&gt;% filter(str_detect(Name, &quot;Mrs.&quot;)) %&gt;% select(Name) %&gt;% slice(1:3) ## Name ## 1 Cumings, Mrs. John Bradley (Florence Briggs Thayer) ## 2 Futrelle, Mrs. Jacques Heath (Lily May Peel) ## 3 Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg) There is an older, base R function called grepl that does something very similar (although note that the order of the arguments is reversed). titanic_train %&gt;% filter(grepl(&quot;Mrs.&quot;, Name)) %&gt;% select(Name) %&gt;% slice(1:3) ## Name ## 1 Cumings, Mrs. John Bradley (Florence Briggs Thayer) ## 2 Futrelle, Mrs. Jacques Heath (Lily May Peel) ## 3 Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg) The str_extract function can be used to extract a string (if it exists) from each value in a character vector. It follows similar conventions to str_detect: ## Generic code str_extract(string = [vector you want to check], pattern = [pattern you want to check for]) For example, you might want to extract “Mrs.” if it exists in a passenger’s name: titanic_train %&gt;% mutate(mrs = str_extract(Name, &quot;Mrs.&quot;)) %&gt;% select(Name, mrs) %&gt;% slice(1:3) ## Name mrs ## 1 Braund, Mr. Owen Harris &lt;NA&gt; ## 2 Cumings, Mrs. John Bradley (Florence Briggs Thayer) Mrs. ## 3 Heikkinen, Miss. Laina &lt;NA&gt; Notice that now we’re creating a new column (mrs) that either has “Mrs.” (if there’s a match) or is missing (NA) if there’s not a match. For this first example, we were looking for an exact string (“Mrs”). However, you can use patterns that match a particular pattern, but not an exact string. For example, we could expand the regular expression to find “Mr.” or “Mrs.”: titanic_train %&gt;% mutate(title = str_extract(Name, &quot;Mr\\\\.|Mrs\\\\.&quot;)) %&gt;% select(Name, title) %&gt;% slice(1:3) ## Name title ## 1 Braund, Mr. Owen Harris Mr. ## 2 Cumings, Mrs. John Bradley (Florence Briggs Thayer) Mrs. ## 3 Heikkinen, Miss. Laina &lt;NA&gt; Note that this pattern uses a special operator (|) to find one pattern or another. Double backslashes (\\\\) escape the special character “.”. As a note, in regular expressions, all of the following characters are special characters that need to be escaped with backslashes if you want to use them literally: . * + ^ ? $ \\ | ( ) [ ] { } Notice that “Mr.” and “Mrs.” both start with “Mr”, end with “.”, and may or may not have an “s” in between. titanic_train %&gt;% mutate(title = str_extract(Name, &quot;Mr(s)*\\\\.&quot;)) %&gt;% select(Name, title) %&gt;% slice(1:3) ## Name title ## 1 Braund, Mr. Owen Harris Mr. ## 2 Cumings, Mrs. John Bradley (Florence Briggs Thayer) Mrs. ## 3 Heikkinen, Miss. Laina &lt;NA&gt; This pattern uses (s)* to match zero or more “s”s at this spot in the pattern. In the previous code, we found “Mr.” and “Mrs.”, but missed “Miss.”. We could tweak the pattern again to try to capture that, as well. For all three, we have the pattern that it starts with “M”, has some lowercase letters, and then ends with “.”. titanic_train %&gt;% mutate(title = str_extract(Name, &quot;M[a-z]+\\\\.&quot;)) %&gt;% select(Name, title) %&gt;% slice(1:3) ## Name title ## 1 Braund, Mr. Owen Harris Mr. ## 2 Cumings, Mrs. John Bradley (Florence Briggs Thayer) Mrs. ## 3 Heikkinen, Miss. Laina Miss. The last pattern used [a-z]+ to match one or more lowercase letters. The [a-z]is a character class. You can also match digits ([0-9]), uppercase letters ([A-Z]), just some letters ([aeiou]), etc. You can negate a character class by starting it with ^. For example, [^0-9] will match anything that isn’t a digit. Sometimes, you want to match a pattern, but then only subset a part of it. For example, each passenger seems to have a title (“Mr.”, “Mrs.”, etc.) that comes after “,” and before “.”. We can use this pattern to find the title, but then we get some extra stuff with the match: titanic_train %&gt;% mutate(title = str_extract(Name, &quot;,\\\\s[A-Za-z]*\\\\.\\\\s&quot;)) %&gt;% select(title) %&gt;% slice(1:3) ## title ## 1 , Mr. ## 2 , Mrs. ## 3 , Miss. As a note, in this pattern, \\\\s is used to match a space. We are getting things like “, Mr. ”, when we really want “Mr”. We can use the str_match function to do this. We group what we want to extract from the pattern in parentheses, and then the function returns a matrix. The first column is the full pattern match, and each following column gives just what matches within the groups. head(str_match(titanic_train$Name, pattern = &quot;,\\\\s([A-Za-z]*)\\\\.\\\\s&quot;)) ## [,1] [,2] ## [1,] &quot;, Mr. &quot; &quot;Mr&quot; ## [2,] &quot;, Mrs. &quot; &quot;Mrs&quot; ## [3,] &quot;, Miss. &quot; &quot;Miss&quot; ## [4,] &quot;, Mrs. &quot; &quot;Mrs&quot; ## [5,] &quot;, Mr. &quot; &quot;Mr&quot; ## [6,] &quot;, Mr. &quot; &quot;Mr&quot; To get just the title, then, we can run: titanic_train %&gt;% mutate(title = str_match(Name, &quot;,\\\\s([A-Za-z]*)\\\\.\\\\s&quot;)[ , 2]) %&gt;% select(Name, title) %&gt;% slice(1:3) ## Name title ## 1 Braund, Mr. Owen Harris Mr ## 2 Cumings, Mrs. John Bradley (Florence Briggs Thayer) Mrs ## 3 Heikkinen, Miss. Laina Miss The [ , 2] pulls out just the second column from the matrix returned by str_match. Here are some of the most common titles: titanic_train %&gt;% mutate(title = str_match(Name, &quot;,\\\\s([A-Za-z]*)\\\\.\\\\s&quot;)[ , 2]) %&gt;% group_by(title) %&gt;% summarize(n = n()) %&gt;% arrange(desc(n)) %&gt;% slice(1:5) ## `summarise()` ungrouping output (override with `.groups` argument) ## # A tibble: 5 x 2 ## title n ## &lt;chr&gt; &lt;int&gt; ## 1 Mr 517 ## 2 Miss 182 ## 3 Mrs 125 ## 4 Master 40 ## 5 Dr 7 Here are a few other examples of regular expressions in action with this dataset. Get just names that start with (“^”) the letter “A”: titanic_train %&gt;% filter(str_detect(Name, &quot;^A&quot;)) %&gt;% select(Name) %&gt;% slice(1:3) ## Name ## 1 Allen, Mr. William Henry ## 2 Andersson, Mr. Anders Johan ## 3 Asplund, Mrs. Carl Oscar (Selma Augusta Emilia Johansson) Get names with “II” or “III” ({2,} says to match at least two times): titanic_train %&gt;% filter(str_detect(Name, &quot;I{2,}&quot;)) %&gt;% select(Name) %&gt;% slice(1:3) ## Name ## 1 Carter, Master. William Thornton II ## 2 Roebling, Mr. Washington Augustus II Get names with “Andersen” or “Anderson” (alternatives in square brackets): titanic_train %&gt;% filter(str_detect(Name, &quot;Anders[eo]n&quot;)) %&gt;% select(Name) ## Name ## 1 Andersen-Jensen, Miss. Carla Christine Nielsine ## 2 Anderson, Mr. Harry ## 3 Walker, Mr. William Anderson ## 4 Olsvigen, Mr. Thor Anderson ## 5 Soholt, Mr. Peter Andreas Lauritz Andersen Get names that start with (“^” outside of brackets) the letters “A” and “B”: titanic_train %&gt;% filter(str_detect(Name, &quot;^[AB]&quot;)) %&gt;% select(Name) %&gt;% slice(1:3) ## Name ## 1 Braund, Mr. Owen Harris ## 2 Allen, Mr. William Henry ## 3 Bonnell, Miss. Elizabeth Get names that end with (“$”) the letter “b” (either lowercase or uppercase): titanic_train %&gt;% filter(str_detect(Name, &quot;[bB]$&quot;)) %&gt;% select(Name) ## Name ## 1 Emir, Mr. Farred Chehab ## 2 Goldschmidt, Mr. George B ## 3 Cook, Mr. Jacob ## 4 Pasic, Mr. Jakob Some useful regular expression operators include: Operator Meaning . Any character * Match 0 or more times (greedy) *? Match 0 or more times (non-greedy) + Match 1 or more times (greedy) +? Match 1 or more times (non-greedy) ^ Starts with (in brackets, negates) $ Ends with […] Character classes For more on these patterns, see: Help file for the stringi-search-regex function in the stringi package (which should install when you install stringr) Chapter 14 of R For Data Science http://gskinner.com/RegExr: Interactive tool for helping you build regular expression pattern strings 6.6 Tidy select There are tidyverse functions to make selecting variables more straightforwards. You can call these functions as arguments of the select function to streamline variable selection. Examples include: starts_with(), ends_with(), and contains(). Here we use starts_with(\"t\") to select all variables that begin with t. titanic_train %&gt;% select(starts_with(&quot;t&quot;)) %&gt;% slice(1:3) ## Ticket ## 1 A/5 21171 ## 2 PC 17599 ## 3 STON/O2. 3101282 The are also tidyverse functions that allow us to easily operate on a selection of variables. These functions are called scoped varients. You can identify these functions by these _all, _at, and _if suffixes. Here we use select_if to select all the numeric variables in a dataframe and covert their names to lower case (a handy function to tidy the variable names). titanic_train %&gt;% select_if(is.numeric, tolower) %&gt;% slice(1:3) ## passengerid survived pclass age sibsp parch fare ## 1 1 0 3 22 1 0 7.2500 ## 2 2 1 1 38 1 0 71.2833 ## 3 3 1 3 26 0 0 7.9250 The select_if function takes the following form. ## Generic code new_df &lt;- select_if(old_df, .predicate [selects the variable to keep], .funs = [the function to apply to the selected columns]) Here we use select_at to select all the variables that contain ss in their name and then covert their names to lower case (a handy function to tidy the variable names). titanic_train %&gt;% select_at(vars(contains(&quot;ss&quot;)), tolower) %&gt;% slice(1:3) ## passengerid pclass ## 1 1 3 ## 2 2 1 ## 3 3 3 6.7 In-course exercise For today’s exercise, we’ll be using the following three datasets (click on the file name to access the correct file for today’s class for each dataset): File name Description country_timeseries.csv Ebola cases by country for the 2014 outbreak mexico_exposure.csv and mexico_deaths.csv Daily death counts and environmental measurements for Mexico City, Mexico, for 2008 measles_data/ Number of cases of measles in CA since end of Dec. 2014 Note that you likely have already downloaded all the files in the measles_data folder, since we used them in an earlier in-course exercise. If so, there is no need to re-download those files. Here are the sources for this data: country_timeseries.csv : Caitlin Rivers’ Ebola repository (Caitlin originally collected this data from the WHO and WHO Situation reports) mexico_exposure.csv and mexico_deaths.csv : one of Hadley Wickham’s GitHub repos (Hadley got the data originally from the Secretaria de Salud of Mexico’s website, although it appears the link is now broken. I separated the data into two dataframes so students could practice merging.) measles_data/: one of scarpino’s GitHub repos (Data originally from pdfs from the California Department of Public Health) If you want to use these data further, you should go back and pull them from their original sources. They are here only for use in R code examples for this course. Here are some of the packages you will need for this exercise: library(dplyr) library(gridExtra) library(ggthemes) 6.7.1 Designing tidy data Check out the country_timeseries.csv file on Ebola for this week’s example data. Talk with your partner and decide what changes you would need to make to this dataset to turn it into a “tidy” dataset, in particular which of the five common “untidy” problems the data currently has and why. Do the same for the data on daily mortality and daily weather in Mexico. Do the same for the set of files with measles data. 6.7.2 Easier data wrangling Use read_csv to read the Mexico data (exposure and mortality) directly from GitHub into your R session. Call the dataframes mex_deaths and mex_exp. Are there any values of the day column in mex_deaths that is not present in the day column of mex_exp? How about vice-versa? (Hint: There are a few ways you could check this. One is to try filtering down to just rows in one dataframe where the day values are not present in the day values from the other dataframe. The %in% logical vector may be useful.) Merge the two datasets together to create the dataframe mexico. Exclude all columns except the outcome (deaths), day, and mean temperature. Convert the day to a Date class. If you did not already, try combining all the steps in the previous task into one “chained” pipeline of code using the pipe operator, %&gt;%. Use this new dataframe to plot deaths by date in Mexico using ggplot2. The final plot should look like this: 6.7.2.1 Example R code Use read_csv to read the mexico data (exposure and mortality) directly from GitHub into your R session. Call the dataframes mex_deaths and mex_exp: deaths_url &lt;- paste0(&quot;https://github.com/geanders/RProgrammingForResearch/&quot;, &quot;raw/master/data/mexico_deaths.csv&quot;) mex_deaths &lt;- read_csv(deaths_url) head(mex_deaths) ## # A tibble: 6 x 2 ## day deaths ## &lt;chr&gt; &lt;dbl&gt; ## 1 1/1/08 296 ## 2 1/2/08 274 ## 3 1/3/08 339 ## 4 1/4/08 300 ## 5 1/5/08 327 ## 6 1/6/08 332 exposure_url &lt;- paste0(&quot;https://github.com/geanders/RProgrammingForResearch/&quot;, &quot;raw/master/data/mexico_exposure.csv&quot;) mex_exp &lt;- read_csv(exposure_url) head(mex_exp) ## # A tibble: 6 x 14 ## day temp_min temp_max temp_mean humidity wind NO NO2 NOX O3 ## &lt;chr&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; ## 1 1/1/… 7.8 17.8 11.8 53.5 2.66 0.00925 0.0187 0.0278 NA ## 2 1/2/… 2.6 9.8 6.64 61.7 3.35 0.00542 0.0187 0.0241 0.0201 ## 3 1/3/… 1.1 15.6 7.04 59.9 1.89 0.0160 0.0381 0.0540 0.0184 ## 4 1/4/… 3.1 20.6 10.9 57.5 1.20 0.0408 0.0584 0.0993 0.0215 ## 5 1/5/… 6 21.3 13.4 45.7 0.988 0.0469 0.0602 0.107 0.0239 ## 6 1/6/… 7.2 22.1 14.3 40.8 0.854 0.0286 0.051 0.0795 0.0249 ## # … with 4 more variables: CO &lt;dbl&gt;, SO2 &lt;dbl&gt;, PM10 &lt;dbl&gt;, PM25 &lt;dbl&gt; Check if there are any values of the day column in mex_deaths that are not present in the day column of mex_exp and vice-versa. mex_deaths %&gt;% filter(!(day %in% mex_exp$day)) ## # A tibble: 0 x 2 ## # … with 2 variables: day &lt;chr&gt;, deaths &lt;dbl&gt; mex_exp %&gt;% filter(!(day %in% mex_deaths$day)) ## # A tibble: 0 x 14 ## # … with 14 variables: day &lt;chr&gt;, temp_min &lt;dbl&gt;, temp_max &lt;dbl&gt;, ## # temp_mean &lt;dbl&gt;, humidity &lt;dbl&gt;, wind &lt;dbl&gt;, NO &lt;dbl&gt;, NO2 &lt;dbl&gt;, ## # NOX &lt;dbl&gt;, O3 &lt;dbl&gt;, CO &lt;dbl&gt;, SO2 &lt;dbl&gt;, PM10 &lt;dbl&gt;, PM25 &lt;dbl&gt; One important note is that, when you’re doing this check, you do not want to overwrite your original dataframe, so be sure that you do not reassign this output to mex_deaths or mex_exp. An even quicker way to do check this is to create a logical vector that checks this and use sum to add up the values in the logical vector. If the sum is zero, that tells you that the logical check is never true, so there are no cases where there is a day value in one dataframe that is not also in the other dataframe. sum(!(mex_deaths$day %in% mex_exp$day)) ## [1] 0 sum(!(mex_exp$day %in% mex_deaths$day)) ## [1] 0 Merge the two datasets together to create the dataframe mexico. Exclude all columns except the outcome (deaths), date, and mean temperature. mexico &lt;- full_join(mex_deaths, mex_exp, by = &quot;day&quot;) mexico &lt;- select(mexico, day, deaths, temp_mean) Convert the date to a date class. library(lubridate) ## For parsing dates mexico &lt;- mutate(mexico, day = mdy(day)) Try combining all the steps in the previous task into one “chained” command: mexico &lt;- full_join(mex_deaths, mex_exp, by = &quot;day&quot;) %&gt;% select(day, deaths, temp_mean) %&gt;% mutate(day = mdy(day)) head(mexico) ## # A tibble: 6 x 3 ## day deaths temp_mean ## &lt;date&gt; &lt;dbl&gt; &lt;dbl&gt; ## 1 2008-01-01 296 11.8 ## 2 2008-01-02 274 6.64 ## 3 2008-01-03 339 7.04 ## 4 2008-01-04 300 10.9 ## 5 2008-01-05 327 13.4 ## 6 2008-01-06 332 14.3 Note that, in this case, all the values of day in mex_deaths have one and only one matching value in mex_exp, and vice-versa. Because of this, we would have gotten the same mexico dataframe if we’d used inner_join, left_join or right_join instead of full_join. The differences between these *_join functions come into play when you have some values of your matching column that aren’t in both of the dataframes you’re joining. Use this new dataframe to plot deaths by date using ggplot: ggplot(data = mexico) + geom_point(mapping = aes(x = day, y = deaths), size = 1.5, alpha = 0.5) + labs(x = &quot;Date in 2008&quot;, y = &quot;# of deaths&quot;) + ggtitle(&quot;Deaths by date&quot;) + theme_few() 6.7.3 More extensive data wrangling Read the Ebola data directly from GitHub into your R session. Call the dataframe ebola. Use dplyr functions to create a tidy dataset. First, change it from “wide” data to “long” data. Name the new column with the key variable and the new column with the values count. The first few lines of the “long” version of the dataset should look like this: ## # A tibble: 6 x 4 ## Date Day variable count ## &lt;chr&gt; &lt;dbl&gt; &lt;chr&gt; &lt;dbl&gt; ## 1 1/5/2015 289 Cases_Guinea 2776 ## 2 1/5/2015 289 Cases_Liberia NA ## 3 1/5/2015 289 Cases_SierraLeone 10030 ## 4 1/5/2015 289 Cases_Nigeria NA ## 5 1/5/2015 289 Cases_Senegal NA ## 6 1/5/2015 289 Cases_UnitedStates NA Convert the Date column to a Date class. Use the separate function to separate the variable column into two columns, type (“Cases” or “Deaths”) and country (“Guinea”, “Liberia”, etc.). At this point, the data should look like this: ## # A tibble: 6 x 5 ## Date Day type country count ## &lt;date&gt; &lt;dbl&gt; &lt;chr&gt; &lt;chr&gt; &lt;dbl&gt; ## 1 2015-01-05 289 Cases Guinea 2776 ## 2 2015-01-05 289 Cases Liberia NA ## 3 2015-01-05 289 Cases SierraLeone 10030 ## 4 2015-01-05 289 Cases Nigeria NA ## 5 2015-01-05 289 Cases Senegal NA ## 6 2015-01-05 289 Cases UnitedStates NA Use the pivot_wider function to convert the data so you have separate columns for the two variables of numbers of Cases and Deaths. At this point, the dataframe should look like this: ## # A tibble: 6 x 5 ## Date Day country Cases Deaths ## &lt;date&gt; &lt;dbl&gt; &lt;chr&gt; &lt;dbl&gt; &lt;dbl&gt; ## 1 2015-01-05 289 Guinea 2776 1786 ## 2 2015-01-05 289 Liberia NA NA ## 3 2015-01-05 289 SierraLeone 10030 2977 ## 4 2015-01-05 289 Nigeria NA NA ## 5 2015-01-05 289 Senegal NA NA ## 6 2015-01-05 289 UnitedStates NA NA Remove any observations where counts of both cases and deaths are missing for that country on that date. Now that your data is tidy, create one plot showing Ebola cases by date, faceted by country, and one showing Ebola deaths by date, also faceted by country. Try using the option scales = \"free_y\" in the facet_wrap function and see how that changes these graphs. Discuss with your group the advantages and disadvantages of using this option when creating these small multiple plots. The plots should look something like this (if you’re using the scales = \"free_y\" option): Based on these plots, what would your next questions be about this data before you used it for an analysis? Can you put all of the steps of this cleaning process into just a few “chained” code pipelines using %&gt;%? If you have extra time (super-challenge!): There is a function called fct_reorder in the forcats package that can be used to reorder the levels of a factor in a dataframe based on another column in the same dataframe. This function can be very useful for using a meaningful order when plotting. We’ll cover the forcats package in a later class, but today check out the help file for fct_reorder and see if you can figure out how to use it to reorder the small multiple plots in order of the maximum number of cases or deaths (for the two plots respectively) in each country. You’ll be able to do this by changing the code in facet_wrap from ~ country to ~ fct_reorder(country, ...), but with the ... replaced with certain arguments. If you’re getting stuck, try running the examples in the fct_reorder helpfile to get a feel for how this function can be used when plotting. The plots will look something like this: 6.7.3.1 Example R code Read the data in using read_csv. ebola_url &lt;- paste0(&quot;https://github.com/geanders/RProgrammingForResearch/&quot;, &quot;raw/master/data/country_timeseries.csv&quot;) ebola &lt;- read_csv(ebola_url) head(ebola) ## # A tibble: 6 x 18 ## Date Day Cases_Guinea Cases_Liberia Cases_SierraLeo… Cases_Nigeria ## &lt;chr&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; ## 1 1/5/… 289 2776 NA 10030 NA ## 2 1/4/… 288 2775 NA 9780 NA ## 3 1/3/… 287 2769 8166 9722 NA ## 4 1/2/… 286 NA 8157 NA NA ## 5 12/3… 284 2730 8115 9633 NA ## 6 12/2… 281 2706 8018 9446 NA ## # … with 12 more variables: Cases_Senegal &lt;dbl&gt;, Cases_UnitedStates &lt;dbl&gt;, ## # Cases_Spain &lt;dbl&gt;, Cases_Mali &lt;dbl&gt;, Deaths_Guinea &lt;dbl&gt;, ## # Deaths_Liberia &lt;dbl&gt;, Deaths_SierraLeone &lt;dbl&gt;, Deaths_Nigeria &lt;dbl&gt;, ## # Deaths_Senegal &lt;dbl&gt;, Deaths_UnitedStates &lt;dbl&gt;, Deaths_Spain &lt;dbl&gt;, ## # Deaths_Mali &lt;dbl&gt; Change the data to long data using the pivoter_longer function from tidyr: ebola &lt;- ebola %&gt;% pivot_longer(cols = c(-Date, -Day), names_to = &quot;variable&quot;, values_to = &quot;count&quot;) head(ebola) ## # A tibble: 6 x 4 ## Date Day variable count ## &lt;chr&gt; &lt;dbl&gt; &lt;chr&gt; &lt;dbl&gt; ## 1 1/5/2015 289 Cases_Guinea 2776 ## 2 1/5/2015 289 Cases_Liberia NA ## 3 1/5/2015 289 Cases_SierraLeone 10030 ## 4 1/5/2015 289 Cases_Nigeria NA ## 5 1/5/2015 289 Cases_Senegal NA ## 6 1/5/2015 289 Cases_UnitedStates NA Convert Date to a date class: ebola &lt;- ebola %&gt;% mutate(Date = mdy(Date)) head(ebola) ## # A tibble: 6 x 4 ## Date Day variable count ## &lt;date&gt; &lt;dbl&gt; &lt;chr&gt; &lt;dbl&gt; ## 1 2015-01-05 289 Cases_Guinea 2776 ## 2 2015-01-05 289 Cases_Liberia NA ## 3 2015-01-05 289 Cases_SierraLeone 10030 ## 4 2015-01-05 289 Cases_Nigeria NA ## 5 2015-01-05 289 Cases_Senegal NA ## 6 2015-01-05 289 Cases_UnitedStates NA Split variable into type and country: ebola &lt;- ebola %&gt;% separate(variable, c(&quot;type&quot;, &quot;country&quot;), sep = &quot;_&quot;) head(ebola) ## # A tibble: 6 x 5 ## Date Day type country count ## &lt;date&gt; &lt;dbl&gt; &lt;chr&gt; &lt;chr&gt; &lt;dbl&gt; ## 1 2015-01-05 289 Cases Guinea 2776 ## 2 2015-01-05 289 Cases Liberia NA ## 3 2015-01-05 289 Cases SierraLeone 10030 ## 4 2015-01-05 289 Cases Nigeria NA ## 5 2015-01-05 289 Cases Senegal NA ## 6 2015-01-05 289 Cases UnitedStates NA Convert the data so you have separate columns for the two variables of numbers of Cases and Deaths: ebola &lt;- pivot_wider(ebola, names_from = type, values_from = count) head(ebola) ## # A tibble: 6 x 5 ## Date Day country Cases Deaths ## &lt;date&gt; &lt;dbl&gt; &lt;chr&gt; &lt;dbl&gt; &lt;dbl&gt; ## 1 2015-01-05 289 Guinea 2776 1786 ## 2 2015-01-05 289 Liberia NA NA ## 3 2015-01-05 289 SierraLeone 10030 2977 ## 4 2015-01-05 289 Nigeria NA NA ## 5 2015-01-05 289 Senegal NA NA ## 6 2015-01-05 289 UnitedStates NA NA Remove any observations where counts of cases or deaths are missing for that country: ebola &lt;- filter(ebola, !is.na(Cases) &amp; !is.na(Deaths)) head(ebola) ## # A tibble: 6 x 5 ## Date Day country Cases Deaths ## &lt;date&gt; &lt;dbl&gt; &lt;chr&gt; &lt;dbl&gt; &lt;dbl&gt; ## 1 2015-01-05 289 Guinea 2776 1786 ## 2 2015-01-05 289 SierraLeone 10030 2977 ## 3 2015-01-04 288 Guinea 2775 1781 ## 4 2015-01-04 288 SierraLeone 9780 2943 ## 5 2015-01-03 287 Guinea 2769 1767 ## 6 2015-01-03 287 Liberia 8166 3496 Now that your data is tidy, create one plot showing ebola cases by date, faceted by country, and one showing ebola deaths by date, also faceted by country: ggplot(ebola, aes(x = Date, y = Cases)) + geom_line() + facet_wrap(~ country, ncol = 4) + theme_classic() ggplot(ebola, aes(x = Date, y = Deaths)) + geom_line() + facet_wrap(~ country, ncol = 4) + theme_classic() Try using the option scales = \"free_y\" in the facet_wrap() function (in the gridExtra package) and see how that changes these graphs: ggplot(ebola, aes(x = Date, y = Cases)) + geom_line() + facet_wrap(~ country, ncol = 4, scales = &quot;free_y&quot;) + theme_classic() ggplot(ebola, aes(x = Date, y = Deaths)) + geom_line() + facet_wrap(~ country, ncol = 4, scales = &quot;free_y&quot;) + theme_classic() Put all of the steps of this cleaning process into just a few “chaining” calls. ebola &lt;- read_csv(ebola_url) %&gt;% pivot_longer(cols = c(-Date, -Day), names_to = &quot;variable&quot;, values_to = &quot;count&quot;) %&gt;% mutate(Date = mdy(Date)) %&gt;% separate(variable, c(&quot;type&quot;, &quot;country&quot;), sep = &quot;_&quot;) %&gt;% pivot_wider(names_from = type, values_from = count) %&gt;% filter(!is.na(Cases) &amp; !is.na(Deaths)) ggplot(ebola, aes(x = Date, y = Cases)) + geom_line() + facet_wrap(~ country, ncol = 4) + theme_classic() ggplot(ebola, aes(x = Date, y = Deaths)) + geom_line() + facet_wrap(~ country, ncol = 4) + theme_classic() Use the fct_reorder function inside the facet_wrap function call to reorder the small-multiple graphs. library(forcats) ggplot(ebola, aes(x = Date, y = Cases)) + geom_line() + facet_wrap(~ fct_reorder(country, Cases, .fun = max, .desc = TRUE), ncol = 4) + theme_classic() ggplot(ebola, aes(x = Date, y = Deaths)) + geom_line() + facet_wrap(~ fct_reorder(country, Deaths, .fun = max, .desc = TRUE), ncol = 4) + theme_classic() 6.7.4 Tidying VADeaths data R comes with a dataset called VADeaths that gives death rates per 1,000 people in Virginia in 1940 by age, sex, and rural / urban. Use data(\"VADeaths\") to load this data. Make sure you understand what each column and row is showing – use the helpfile (?VADeaths) if you need. Go through the three characteristics of tidy data and the five common problems in untidy data that we talked about in class. Sketch out (you’re welcome to use the whiteboards) what a tidy version of this data would look like. Open a new R script file. Write R code to transform this dataset into a tidy dataset. Try using a pipe chain, with %&gt;% and tidyverse functions, to clean the data. Use the tidy data to create the following graph: There is no example R code for this – try to figure out the code yourselves. We will go over a solution in class. You may find the RStudio Data Wrangling cheatsheet helpful for remembering which tidyverse functions do what. 6.7.5 Baby names In the Fall 2018 session, we seem to have an unusually high percent of the class with names that start with an “A” or “K”. In this part of the exercise, we’ll see if we can figure out whether the proportion of “A” and “K” names is unusual. There is a package on CRAN called babynames with data on baby names by year in the United States, based on data from the U.S.’s Social Security Administration. We will use this data to compare the proportion of “A” and “K” names in our class with the proportion in these baby names. We’ll also do a few other things to explore this data. First, check out patterns in your own name. Is your name included in this dataset? Has your name been used for males and females? How have the patterns in the proportion of babies with your name, for both males and females, changed over time (use a plot to look at this)? In the year you were born, what were the 5 most popular baby names for males and females? Try to come up with some attractive ways (figures and tables) to show this. 6.7.5.1 Example R code Install and load the babynames package and its “babynames” dataframe: # install.packages(&quot;babynames&quot;) library(babynames) data(&quot;babynames&quot;) Remember that you can use ?babynames to find out more about this dataframe. Check out patterns in your own name. Is your name included in this dataset? You can use filter to create a subset of this data where you’ve filtered down to just the rows with your name. To see if your name ever shows up, you can use count on this dataframe—if your name never shows up, then you will have 0 rows in the new dataframe. As long as there’s at least one row, your name shows up somewhere. (If your name is not in here, try your middle or last name, or the name of a fictional character you like, for the rest of these exercises.) my_name &lt;- babynames %&gt;% filter(name == &quot;Brooke&quot;) my_name %&gt;% count() ## # A tibble: 1 x 1 ## n ## &lt;int&gt; ## 1 174 Has your name been used for males and females? To figure this out, you can group the dataset with rows with your name by the sex column and then use count to count the number of rows in the dataset for males and females. If your name has only been used for one gender, then only one row will result from running this code (for an example, try my first name, “Georgiana”). my_name %&gt;% group_by(sex) %&gt;% count() ## # A tibble: 2 x 2 ## # Groups: sex [2] ## sex n ## &lt;chr&gt; &lt;int&gt; ## 1 F 82 ## 2 M 92 How have the patterns in the proportion of babies with your name, for both males and females, changed over time (use a plot to look at this)? To check this out, I recommend you create a plot of the proportion of babies with your name (prop) versus year (year). You can use color to show these patterns for males and females separately. I’ve done some extra things here to (1) relabel the sex factor, so the label shows up with clearer names and (2) change the labels for the x-, y-, and color scales. library(forcats) my_name %&gt;% mutate(sex = fct_recode(sex, Male = &quot;M&quot;, Female = &quot;F&quot;)) %&gt;% ggplot(aes(x = year, y = prop, color = sex)) + geom_line() + labs(x = &quot;Year&quot;, y = &quot;Proportion of babies\\nof each sex named &#39;Brooke&#39;&quot;, color = &quot;&quot;) In the year you were born, what were the 5 most popular baby names for males and females? top_my_year &lt;- babynames %&gt;% filter(year == 1981) %&gt;% group_by(sex) %&gt;% arrange(desc(prop)) %&gt;% slice(1:5) top_my_year ## # A tibble: 10 x 5 ## # Groups: sex [2] ## year sex name n prop ## &lt;dbl&gt; &lt;chr&gt; &lt;chr&gt; &lt;int&gt; &lt;dbl&gt; ## 1 1981 F Jennifer 57049 0.0319 ## 2 1981 F Jessica 42532 0.0238 ## 3 1981 F Amanda 34374 0.0192 ## 4 1981 F Sarah 28173 0.0158 ## 5 1981 F Melissa 28000 0.0157 ## 6 1981 M Michael 68765 0.0369 ## 7 1981 M Christopher 50233 0.0270 ## 8 1981 M Matthew 43330 0.0233 ## 9 1981 M Jason 41932 0.0225 ## 10 1981 M David 40659 0.0218 If you’d like to show this in a prettier way, you could show this as a table: library(knitr) top_my_year %&gt;% mutate(rank = 1:n()) %&gt;% # Since the data is grouped by sex, this will rank # separately for females and males ungroup() %&gt;% # You have to ungroup before you can run `mutate` on `sex` mutate(sex = fct_recode(sex, Male = &quot;M&quot;, Female = &quot;F&quot;), percent = round(100 * prop, 1), percent = paste(percent, &quot;%&quot;, sep = &quot;&quot;)) %&gt;% select(sex, rank, name, percent) %&gt;% kable() sex rank name percent Female 1 Jennifer 3.2% Female 2 Jessica 2.4% Female 3 Amanda 1.9% Female 4 Sarah 1.6% Female 5 Melissa 1.6% Male 1 Michael 3.7% Male 2 Christopher 2.7% Male 3 Matthew 2.3% Male 4 Jason 2.3% Male 5 David 2.2% You could also show it as a figure: library(scales) top_my_year %&gt;% ungroup() %&gt;% mutate(name = fct_reorder(name, prop, .desc = TRUE), sex = fct_recode(sex, Male = &quot;M&quot;, Female = &quot;F&quot;)) %&gt;% ggplot(aes(x = name)) + geom_bar(aes(weight = prop)) + coord_flip() + labs(x = &quot;&quot;, y = &quot;Percent of babies with name in 1981&quot;) + theme(legend.position = &quot;top&quot;) + scale_y_continuous(labels = percent) + facet_wrap(~ sex, scales = &quot;free_y&quot;) + theme_classic() "],
-["exploring-data-2.html", "Chapter 7 Exploring data #2 7.1 Simple statistical tests in R 7.2 Matrices 7.3 Lists 7.4 Apply a test multiple times 7.5 Regression models 7.6 Handling model objects 7.7 Functions 7.8 In-course exercise", " Chapter 7 Exploring data #2 Download a pdf of the lecture slides covering this topic. 7.1 Simple statistical tests in R Let’s pull the fatal accident data just for the county that includes Las Vegas, NV. Each US county has a unique identifier (FIPS code), composed of a two-digit state FIPS and a three-digit county FIPS code. The state FIPS for Nevada is 32; the county FIPS for Clark County is 003. Therefore, we can filter down to Clark County data in the FARS data we collected with the following code: library(readr) library(dplyr) clark_co_accidents &lt;- read_csv(&quot;data/accident.csv&quot;) %&gt;% filter(STATE == 32 &amp; COUNTY == 3) We can also check the number of accidents: clark_co_accidents %&gt;% count() ## # A tibble: 1 x 1 ## n ## &lt;int&gt; ## 1 201 We want to test if the probability, on a Friday or Saturday, of a fatal accident occurring is higher than on other days of the week. Let’s clean the data up a bit as a start: library(tidyr) library(lubridate) clark_co_accidents &lt;- clark_co_accidents %&gt;% select(DAY, MONTH, YEAR) %&gt;% unite(date, DAY, MONTH, YEAR, sep = &quot;-&quot;) %&gt;% mutate(date = dmy(date)) Here’s what the data looks like now: clark_co_accidents %&gt;% slice(1:5) ## # A tibble: 5 x 1 ## date ## &lt;date&gt; ## 1 2016-01-10 ## 2 2016-02-21 ## 3 2016-01-06 ## 4 2016-01-13 ## 5 2016-02-18 Next, let’s get the count of accidents by date: clark_co_accidents &lt;- clark_co_accidents %&gt;% group_by(date) %&gt;% count() %&gt;% ungroup() clark_co_accidents %&gt;% slice(1:3) ## # A tibble: 3 x 2 ## date n ## &lt;date&gt; &lt;int&gt; ## 1 2016-01-03 1 ## 2 2016-01-06 1 ## 3 2016-01-09 3 We’re missing the dates without a fatal crash, so let’s add those. First, create a dataframe with all dates in 2016: all_dates &lt;- tibble(date = seq(ymd(&quot;2016-01-01&quot;), ymd(&quot;2016-12-31&quot;), by = 1)) all_dates %&gt;% slice(1:5) ## # A tibble: 5 x 1 ## date ## &lt;date&gt; ## 1 2016-01-01 ## 2 2016-01-02 ## 3 2016-01-03 ## 4 2016-01-04 ## 5 2016-01-05 Then merge this with the original dataset on Las Vegas fatal crashes and make any day missing from the fatal crashes dataset have a “0” for number of fatal accidents (n): clark_co_accidents &lt;- clark_co_accidents %&gt;% right_join(all_dates, by = &quot;date&quot;) %&gt;% # If `n` is missing, set to 0. Otherwise keep value. mutate(n = ifelse(is.na(n), 0, n)) clark_co_accidents %&gt;% slice(1:3) ## # A tibble: 3 x 2 ## date n ## &lt;date&gt; &lt;dbl&gt; ## 1 2016-01-03 1 ## 2 2016-01-06 1 ## 3 2016-01-09 3 Next, let’s add some information about day of week and weekend: clark_co_accidents &lt;- clark_co_accidents %&gt;% mutate(weekday = wday(date, label = TRUE), weekend = weekday %in% c(&quot;Fri&quot;, &quot;Sat&quot;)) clark_co_accidents %&gt;% slice(1:3) ## # A tibble: 3 x 4 ## date n weekday weekend ## &lt;date&gt; &lt;dbl&gt; &lt;ord&gt; &lt;lgl&gt; ## 1 2016-01-03 1 Sun FALSE ## 2 2016-01-06 1 Wed FALSE ## 3 2016-01-09 3 Sat TRUE Now let’s calculate the probability that a day has at least one fatal crash, separately for weekends and weekdays: clark_co_accidents &lt;- clark_co_accidents %&gt;% mutate(any_crash = n &gt; 0) crash_prob &lt;- clark_co_accidents %&gt;% group_by(weekend) %&gt;% summarize(n_days = n(), crash_days = sum(any_crash)) %&gt;% mutate(prob_crash_day = crash_days / n_days) ## `summarise()` ungrouping output (override with `.groups` argument) crash_prob ## # A tibble: 2 x 4 ## weekend n_days crash_days prob_crash_day ## &lt;lgl&gt; &lt;int&gt; &lt;int&gt; &lt;dbl&gt; ## 1 FALSE 260 107 0.412 ## 2 TRUE 106 43 0.406 In R, you can use prop.test to test if two proportions are equal. Inputs include the total number of trials in each group (n =) and the number of “successes”\" (x =): prop.test(x = crash_prob$crash_days, n = crash_prob$n_days) ## ## 2-sample test for equality of proportions with continuity correction ## ## data: crash_prob$crash_days out of crash_prob$n_days ## X-squared = 1.5978e-30, df = 1, p-value = 1 ## alternative hypothesis: two.sided ## 95 percent confidence interval: ## -0.1109757 0.1227318 ## sample estimates: ## prop 1 prop 2 ## 0.4115385 0.4056604 I won’t be teaching in this course how to find the correct statistical test. That’s something you’ll hopefully learn in a statistics course. There are also a variety of books that can help you with this, including some that you can access free online through CSU’s library. One servicable introduction is “Statistical Analysis with R for Dummies”. You can create an object from the output of any statistical test in R. Typically, this will be (at least at some level) in an object class called a “list”: vegas_test &lt;- prop.test(x = crash_prob$crash_days, n = crash_prob$n_days) is.list(vegas_test) ## [1] TRUE So far, we’ve mostly worked with two object types in R, dataframes and vectors. In the next subsection we’ll look more at two object classes we haven’t looked at much, matrices and lists. Both have important roles once you start applying more advanced methods to analyze your data. 7.2 Matrices A matrix is like a data frame, but all the values in all columns must be of the same class (e.g., numeric, character). R uses matrices a lot for its underlying math (e.g., for the linear algebra operations required for fitting regression models). R can do matrix operations quite quickly. You can create a matrix with the matrix function. Input a vector with the values to fill the matrix and ncol to set the number of columns: foo &lt;- matrix(1:10, ncol = 5) foo ## [,1] [,2] [,3] [,4] [,5] ## [1,] 1 3 5 7 9 ## [2,] 2 4 6 8 10 By default, the matrix will fill up by column. You can fill it by row with the byrow function: foo &lt;- matrix(1:10, ncol = 5, byrow = TRUE) foo ## [,1] [,2] [,3] [,4] [,5] ## [1,] 1 2 3 4 5 ## [2,] 6 7 8 9 10 In certain situations, you might want to work with a matrix instead of a data frame (for example, in cases where you were concerned about speed – a matrix is more memory efficient than the corresponding data frame). If you want to convert a data frame to a matrix, you can use the as.matrix function: foo &lt;- tibble(col_1 = 1:2, col_2 = 3:4, col_3 = 5:6, col_4 = 7:8, col_5 = 9:10) (foo &lt;- as.matrix(foo)) ## col_1 col_2 col_3 col_4 col_5 ## [1,] 1 3 5 7 9 ## [2,] 2 4 6 8 10 You can index matrices with square brackets, just like data frames: foo[1, 1:2] ## col_1 col_2 ## 1 3 You cannot, however, use dplyr functions with matrices: foo %&gt;% filter(col_1 == 1) All elements in a matrix must have the same class. The matrix will default to make all values the most general class of any of the values, in any column. For example, if we replaced one numeric value with the character “a”, everything would turn into a character: foo[1, 1] &lt;- &quot;a&quot; foo ## col_1 col_2 col_3 col_4 col_5 ## [1,] &quot;a&quot; &quot;3&quot; &quot;5&quot; &quot;7&quot; &quot;9&quot; ## [2,] &quot;2&quot; &quot;4&quot; &quot;6&quot; &quot;8&quot; &quot;10&quot; 7.3 Lists A list has different elements, just like a data frame has different columns. However, the different elements of a list can have different lengths (unlike the columns of a data frame). The different elements can also have different classes. bar &lt;- list(some_letters = letters[1:3], some_numbers = 1:5, some_logical_values = c(TRUE, FALSE)) bar ## $some_letters ## [1] &quot;a&quot; &quot;b&quot; &quot;c&quot; ## ## $some_numbers ## [1] 1 2 3 4 5 ## ## $some_logical_values ## [1] TRUE FALSE To index an element from a list, use double square brackets. You can use bracket indexing either with numbers (which element in the list?) or with names: bar[[1]] ## [1] &quot;a&quot; &quot;b&quot; &quot;c&quot; bar[[&quot;some_numbers&quot;]] ## [1] 1 2 3 4 5 You can also index lists with the $ operator: bar$some_logical_values ## [1] TRUE FALSE To access a specific value within a list element we can index the element e.g.: bar[[1]][[2]] ## [1] &quot;b&quot; Lists can be used to contain data with an unusual structure and / or lots of different components. For example, the information from fitting a regression is often stored as a list: my_mod &lt;- glm(rnorm(10) ~ c(1:10)) is.list(my_mod) ## [1] TRUE The names function returns the name of each element in the list: head(names(my_mod), 3) ## [1] &quot;coefficients&quot; &quot;residuals&quot; &quot;fitted.values&quot; my_mod[[&quot;coefficients&quot;]] ## (Intercept) c(1:10) ## 0.2759347 -0.1316436 A list can even contain other lists. We can use the str function to see the structure of a list: a_list &lt;- list(list(&quot;a&quot;, &quot;b&quot;), list(1, 2)) str(a_list) ## List of 2 ## $ :List of 2 ## ..$ : chr &quot;a&quot; ## ..$ : chr &quot;b&quot; ## $ :List of 2 ## ..$ : num 1 ## ..$ : num 2 Sometimes you’ll see unnecessary lists-of-lists, perhaps when importing data into R created. Or a list with multiple elements that you would like to combine. You can remove a level of hierarchy from a list using the flatten function from the purrr package: library(purrr) a_list ## [[1]] ## [[1]][[1]] ## [1] &quot;a&quot; ## ## [[1]][[2]] ## [1] &quot;b&quot; ## ## ## [[2]] ## [[2]][[1]] ## [1] 1 ## ## [[2]][[2]] ## [1] 2 flatten(a_list) ## [[1]] ## [1] &quot;a&quot; ## ## [[2]] ## [1] &quot;b&quot; ## ## [[3]] ## [1] 1 ## ## [[4]] ## [1] 2 Let’s look at the list object from the statistical test we ran for Las Vegas: str(vegas_test) ## List of 9 ## $ statistic : Named num 1.6e-30 ## ..- attr(*, &quot;names&quot;)= chr &quot;X-squared&quot; ## $ parameter : Named num 1 ## ..- attr(*, &quot;names&quot;)= chr &quot;df&quot; ## $ p.value : num 1 ## $ estimate : Named num [1:2] 0.412 0.406 ## ..- attr(*, &quot;names&quot;)= chr [1:2] &quot;prop 1&quot; &quot;prop 2&quot; ## $ null.value : NULL ## $ conf.int : num [1:2] -0.111 0.123 ## ..- attr(*, &quot;conf.level&quot;)= num 0.95 ## $ alternative: chr &quot;two.sided&quot; ## $ method : chr &quot;2-sample test for equality of proportions with continuity correction&quot; ## $ data.name : chr &quot;crash_prob$crash_days out of crash_prob$n_days&quot; ## - attr(*, &quot;class&quot;)= chr &quot;htest&quot; Using str to print out the list’s structure doesn’t produce the easiest to digest output. We can use the jsonedit function from the listviewer package to create a widget in the viewer pane to more esily explore our list. library(listviewer) jsonedit(vegas_test) We can pull out an element using the $ notation: vegas_test$p.value ## [1] 1 Or using the [[ notation: vegas_test[[4]] ## prop 1 prop 2 ## 0.4115385 0.4056604 You may have noticed, though, that this output is not a tidy dataframe. Ack! That means we can’t use all the tidyverse tricks we’ve learned so far in the course! Fortunately, David Robinson noticed this problem and came up with a package called broom that can “tidy up” a lot of these kinds of objects. The broom package has three main functions: glance: Return a one-row, tidy dataframe from a model or other R object tidy: Return a tidy dataframe from a model or other R object augment: “Augment” the dataframe you input to the statistical function Here is the output for tidy for the vegas_test object (augment won’t work for this type of object, and glance gives the same thing as tidy): library(broom) tidy(vegas_test) ## # A tibble: 1 x 9 ## estimate1 estimate2 statistic p.value parameter conf.low conf.high method ## &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;chr&gt; ## 1 0.412 0.406 1.60e-30 1.00 1 -0.111 0.123 2-sam… ## # … with 1 more variable: alternative &lt;chr&gt; 7.4 Apply a test multiple times Often, we don’t want to just apply a statistical test to our entire data set. Let’s look at an example from the microbiome package. library(microbiome) data(peerj32) print(names(peerj32)) ## [1] &quot;lipids&quot; &quot;microbes&quot; &quot;meta&quot; &quot;phyloseq&quot; Like we saw before, the list-like phyloseq objects require a little tidying before we can use them easiliy. library(dplyr) peerj32_tibble &lt;- (psmelt(peerj32$phyloseq)) %&gt;% dplyr::select(-Sample) %&gt;% rename_all(tolower) %&gt;% as_tibble() ## # A tibble: 3 x 10 ## otu abundance time sex subject sample group phylum family genus ## &lt;chr&gt; &lt;dbl&gt; &lt;int&gt; &lt;fct&gt; &lt;fct&gt; &lt;chr&gt; &lt;fct&gt; &lt;chr&gt; &lt;chr&gt; &lt;chr&gt; ## 1 Bacteroi… 9734 1 male S19 sampl… LGG Bacter… Bacter… Bactero… ## 2 Bacteroi… 5883 1 female S14 sampl… Plac… Bacter… Bacter… Bactero… ## 3 Bacteroi… 5134 2 female S8 sampl… LGG Bacter… Bacter… Bactero… We can make a plot from the resulting tibble to help us explore the data. library(ggplot2) ggplot(peerj32_tibble, aes(x = subject, y = abundance, color = sex)) + geom_point() + theme_bw() + facet_wrap(~phylum, ncol = 4, scales = &quot;free&quot;) + theme(axis.text.x = element_blank(), legend.position = &quot;top&quot;) + xlab(&quot;Subject ID&quot;) We can group the data by phylum and group and then use the nest function from the tidyr package to create a new dataframe containing the nested data: library(tidyr) nested_data &lt;- peerj32_tibble %&gt;% group_by(phylum, group) %&gt;% nest() slice(nested_data, 1:3) ## # A tibble: 16 x 3 ## # Groups: group, phylum [16] ## group phylum data ## &lt;fct&gt; &lt;chr&gt; &lt;list&gt; ## 1 LGG Actinobacteria &lt;tibble [128 × 8]&gt; ## 2 LGG Bacteroidetes &lt;tibble [256 × 8]&gt; ## 3 LGG Cyanobacteria &lt;tibble [16 × 8]&gt; ## 4 LGG Firmicutes &lt;tibble [1,216 × 8]&gt; ## 5 LGG Fusobacteria &lt;tibble [16 × 8]&gt; ## 6 LGG Proteobacteria &lt;tibble [416 × 8]&gt; ## 7 LGG Spirochaetes &lt;tibble [16 × 8]&gt; ## 8 LGG Verrucomicrobia &lt;tibble [16 × 8]&gt; ## 9 Placebo Actinobacteria &lt;tibble [224 × 8]&gt; ## 10 Placebo Bacteroidetes &lt;tibble [448 × 8]&gt; ## 11 Placebo Cyanobacteria &lt;tibble [28 × 8]&gt; ## 12 Placebo Firmicutes &lt;tibble [2,128 × 8]&gt; ## 13 Placebo Fusobacteria &lt;tibble [28 × 8]&gt; ## 14 Placebo Proteobacteria &lt;tibble [728 × 8]&gt; ## 15 Placebo Spirochaetes &lt;tibble [28 × 8]&gt; ## 16 Placebo Verrucomicrobia &lt;tibble [28 × 8]&gt; We can then use the map function from the purrr package to apply functions to each nested dataframe. Let’s start by counting the rows in each nested dataframe and filtering out dataframes with less than 25 rows. library(purrr) filtered_data &lt;- nested_data %&gt;% mutate(n_rows = purrr::map(data, ~ nrow(.x))) %&gt;% filter(n_rows &gt; 25) Now let’s perform a t-test on each out the nested dataframes. Remember, each nested dataframe is one unique combination of phylum and group. t_tests &lt;- filtered_data %&gt;% mutate(t_test = purrr::map(data, ~t.test(abundance ~ sex, data = .x))) t_tests ## # A tibble: 12 x 5 ## # Groups: group, phylum [12] ## group phylum data n_rows t_test ## &lt;fct&gt; &lt;chr&gt; &lt;list&gt; &lt;list&gt; &lt;list&gt; ## 1 LGG Bacteroidetes &lt;tibble [256 × 8]&gt; &lt;int [1]&gt; &lt;htest&gt; ## 2 Placebo Bacteroidetes &lt;tibble [448 × 8]&gt; &lt;int [1]&gt; &lt;htest&gt; ## 3 Placebo Firmicutes &lt;tibble [2,128 × 8]&gt; &lt;int [1]&gt; &lt;htest&gt; ## 4 LGG Firmicutes &lt;tibble [1,216 × 8]&gt; &lt;int [1]&gt; &lt;htest&gt; ## 5 Placebo Verrucomicrobia &lt;tibble [28 × 8]&gt; &lt;int [1]&gt; &lt;htest&gt; ## 6 LGG Actinobacteria &lt;tibble [128 × 8]&gt; &lt;int [1]&gt; &lt;htest&gt; ## 7 Placebo Actinobacteria &lt;tibble [224 × 8]&gt; &lt;int [1]&gt; &lt;htest&gt; ## 8 Placebo Proteobacteria &lt;tibble [728 × 8]&gt; &lt;int [1]&gt; &lt;htest&gt; ## 9 LGG Proteobacteria &lt;tibble [416 × 8]&gt; &lt;int [1]&gt; &lt;htest&gt; ## 10 Placebo Cyanobacteria &lt;tibble [28 × 8]&gt; &lt;int [1]&gt; &lt;htest&gt; ## 11 Placebo Fusobacteria &lt;tibble [28 × 8]&gt; &lt;int [1]&gt; &lt;htest&gt; ## 12 Placebo Spirochaetes &lt;tibble [28 × 8]&gt; &lt;int [1]&gt; &lt;htest&gt; The resulting dataframe contains a new column, which contains the model objects. Just as we mapped the t.test function, we can map the tidying functions from the broom package to extract the information we want from the nested model objects. summary &lt;- t_tests %&gt;% mutate(summary = purrr::map(t_test, broom::glance)) summary ## # A tibble: 12 x 6 ## # Groups: group, phylum [12] ## group phylum data n_rows t_test summary ## &lt;fct&gt; &lt;chr&gt; &lt;list&gt; &lt;list&gt; &lt;list&gt; &lt;list&gt; ## 1 LGG Bacteroidetes &lt;tibble [256 × 8]&gt; &lt;int [1]&gt; &lt;htes… &lt;tibble [1 × 10… ## 2 Placebo Bacteroidetes &lt;tibble [448 × 8]&gt; &lt;int [1]&gt; &lt;htes… &lt;tibble [1 × 10… ## 3 Placebo Firmicutes &lt;tibble [2,128 × 8… &lt;int [1]&gt; &lt;htes… &lt;tibble [1 × 10… ## 4 LGG Firmicutes &lt;tibble [1,216 × 8… &lt;int [1]&gt; &lt;htes… &lt;tibble [1 × 10… ## 5 Placebo Verrucomicrobia &lt;tibble [28 × 8]&gt; &lt;int [1]&gt; &lt;htes… &lt;tibble [1 × 10… ## 6 LGG Actinobacteria &lt;tibble [128 × 8]&gt; &lt;int [1]&gt; &lt;htes… &lt;tibble [1 × 10… ## 7 Placebo Actinobacteria &lt;tibble [224 × 8]&gt; &lt;int [1]&gt; &lt;htes… &lt;tibble [1 × 10… ## 8 Placebo Proteobacteria &lt;tibble [728 × 8]&gt; &lt;int [1]&gt; &lt;htes… &lt;tibble [1 × 10… ## 9 LGG Proteobacteria &lt;tibble [416 × 8]&gt; &lt;int [1]&gt; &lt;htes… &lt;tibble [1 × 10… ## 10 Placebo Cyanobacteria &lt;tibble [28 × 8]&gt; &lt;int [1]&gt; &lt;htes… &lt;tibble [1 × 10… ## 11 Placebo Fusobacteria &lt;tibble [28 × 8]&gt; &lt;int [1]&gt; &lt;htes… &lt;tibble [1 × 10… ## 12 Placebo Spirochaetes &lt;tibble [28 × 8]&gt; &lt;int [1]&gt; &lt;htes… &lt;tibble [1 × 10… The final step is to return a tidy dataframe, we can do this using the unnest function from the tidyr package. Note: it is also wise to ungroup after you are done operating on your grouped variables - you may run into problems if you forget your dataframe is grouped later on! unnested &lt;- summary %&gt;% unnest(summary) %&gt;% ungroup() %&gt;% dplyr::select(group, phylum, p.value) unnested ## # A tibble: 12 x 3 ## group phylum p.value ## &lt;fct&gt; &lt;chr&gt; &lt;dbl&gt; ## 1 LGG Bacteroidetes 0.930 ## 2 Placebo Bacteroidetes 0.828 ## 3 Placebo Firmicutes 0.344 ## 4 LGG Firmicutes 0.131 ## 5 Placebo Verrucomicrobia 0.106 ## 6 LGG Actinobacteria 0.600 ## 7 Placebo Actinobacteria 0.997 ## 8 Placebo Proteobacteria 0.856 ## 9 LGG Proteobacteria 0.0111 ## 10 Placebo Cyanobacteria 0.193 ## 11 Placebo Fusobacteria 0.265 ## 12 Placebo Spirochaetes 0.728 We can then plot the results. Let’s do this for the “LGG” (non-placebo) group, using our principles for good graphics. p_data &lt;- unnested %&gt;% filter(group == &quot;LGG&quot;) %&gt;% mutate(phylum = fct_reorder(phylum, p.value)) ggplot(p_data, aes(y = p.value, x = phylum)) + facet_wrap(~group) + geom_bar(stat=&quot;identity&quot;, fill = &quot;grey90&quot;, color = &quot;grey20&quot;) + coord_flip() + theme_bw() + ggtitle(&quot;Difference in abundance by gender&quot;) + xlab(&quot;&quot;) + ylab(&quot;p-value from t-test&quot;) 7.5 Regression models 7.5.1 Formula structure Regression models can be used to estimate how the expected value of a dependent variable changes as independent variables change. In R, regression formulas take this structure: ## Generic code [response variable] ~ [indep. var. 1] + [indep. var. 2] + ... Notice that a tilde, ~, is used to separate the independent and dependent variables and that a plus sign, +, is used to join independent variables. This format mimics the statistical notation: \\[ Y_i \\sim X_1 + X_2 + X_3 \\] You will use this type of structure in R fo a lot of different function calls, including those for linear models (fit with the lm function) and generalized linear models (fit with the glm function). There are some conventions that can be used in R formulas. Common ones include: Convention Meaning I() evaluate the formula inside I() before fitting (e.g., I(x1 + x2)) : fit the interaction between x1 and x2 variables * fit the main effects and interaction for both variables (e.g., x1*x2 equals x1 + x2 + x1:x2) . include as independent variables all variables other than the response (e.g., y ~ .) 1 intercept (e.g., y ~ 1 for an intercept-only model) - do not include a variable in the data frame as an independent variables (e.g., y ~ . - x1); usually used in conjunction with . or 1 7.5.2 Linear models To fit a linear model, you can use the function lm(). This function is part of the stats package, which comes installed with base R. In this function, you can use the data option to specify the data frame from which to get the vectors. ## Warning: Problem with `mutate()` input `sex`. ## ℹ Unknown levels in `f`: 1, 2 ## ℹ Input `sex` is `fct_recode(factor(sex), Male = &quot;1&quot;, Female = &quot;2&quot;)`. ## Warning: Unknown levels in `f`: 1, 2 mod_a &lt;- lm(wt ~ ht, data = nepali) This previous call fits the model: \\[ Y_{i} = \\beta_{0} + \\beta_{1}X_{1,i} + \\epsilon_{i} \\] where: \\(Y_{i}\\) : weight of child \\(i\\) \\(X_{1,i}\\) : height of child \\(i\\) If you run the lm function without saving it as an object, R will fit the regression and print out the function call and the estimated model coefficients: lm(wt ~ ht, data = nepali) ## ## Call: ## lm(formula = wt ~ ht, data = nepali) ## ## Coefficients: ## (Intercept) ht ## -8.7581 0.2342 However, to be able to use the model later for things like predictions and model assessments, you should save the output of the function as an R object: mod_a &lt;- lm(wt ~ ht, data = nepali) This object has a special class, lm: class(mod_a) ## [1] &quot;lm&quot; This class is a special type of list object. If you use is.list to check, you can confirm that this object is a list: is.list(mod_a) ## [1] TRUE There are a number of functions that you can apply to an lm object. These include: Function Description summary Get a variety of information on the model, including coefficients and p-values for the coefficients coefficients Pull out just the coefficients for a model fitted Get the fitted values from the model (for the data used to fit the model) plot Create plots to help assess model assumptions residuals Get the model residuals For example, you can get the coefficients from the model by running: coefficients(mod_a) ## (Intercept) ht ## -8.7581466 0.2341969 The estimated coefficient for the intercept is always given under the name “(Intercept)”. Estimated coefficients for independent variables are given based on their column names in the original data (“ht” here, for \\(\\beta_1\\), or the estimated increase in expected weight for a one unit increase in height). You can use the output from a coefficients call to plot a regression line based on the model fit on top of points showing the original data (Figure 7.1). mod_coef &lt;- coefficients(mod_a) ggplot(nepali, aes(x = ht, y = wt)) + geom_point(size = 0.2) + xlab(&quot;Height (cm)&quot;) + ylab(&quot;Weight (kg)&quot;) + geom_abline(aes(intercept = mod_coef[1], slope = mod_coef[2]), col = &quot;blue&quot;) Figure 7.1: Example of using the output from a coefficients call to add a regression line to a scatterplot. You can also add a linear regression line to a scatterplot by adding the geom geom_smooth using the argument method = “lm”. You can use the function residuals on an lm object to pull out the residuals from the model fit: head(residuals(mod_a)) ## 1 2 3 4 6 7 ## 0.1993922 -0.4329393 -0.4373953 -0.1355300 -0.6749080 -1.0838199 The result of a residuals call is a vector with one element for each of the non-missing observations (rows) in the data frame you used to fit the model. Each value gives the different between the model fitted value and the observed value for each of these observations, in the same order the observations show up in the data frame. The residuals are in the same order as the observations in the original data frame. You can also use the shorter function coef as an alternative to coefficients and the shorter function resid as an alternative to residuals. As noted in the subsection on simple statistics functions, the summary function returns different output depending on the type of object that is input to the function. If you input a regression model object to summary, the function gives you a lot of information about the model. For example, here is the output returned by running summary for the linear regression model object we just created: summary(mod_a) ## ## Call: ## lm(formula = wt ~ ht, data = nepali) ## ## Residuals: ## Min 1Q Median 3Q Max ## -3.0077 -0.5479 -0.0293 0.4972 3.3514 ## ## Coefficients: ## Estimate Std. Error t value Pr(&gt;|t|) ## (Intercept) -8.758147 0.211529 -41.40 &lt;2e-16 *** ## ht 0.234197 0.002459 95.23 &lt;2e-16 *** ## --- ## Signif. codes: 0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1 ## ## Residual standard error: 0.8733 on 875 degrees of freedom ## (123 observations deleted due to missingness) ## Multiple R-squared: 0.912, Adjusted R-squared: 0.9119 ## F-statistic: 9068 on 1 and 875 DF, p-value: &lt; 2.2e-16 This output includes a lot of useful elements, including (1) basic summary statistics for the residuals (to meet model assumptions, the median should be around zero and the absolute values fairly similar for the first and third quantiles), (2) coefficient estimates, standard errors, and p-values, and (3) some model summary statistics, including residual standard error, degrees of freedom, number of missing observations, and F-statistic. The object returned by the summary() function when it is applied to an lm object is a list, which you can confirm using the is.list function: is.list(summary(mod_a)) ## [1] TRUE With any list, you can use the names function to get the names of all of the different elements of the object: names(summary(mod_a)) ## [1] &quot;call&quot; &quot;terms&quot; &quot;residuals&quot; &quot;coefficients&quot; ## [5] &quot;aliased&quot; &quot;sigma&quot; &quot;df&quot; &quot;r.squared&quot; ## [9] &quot;adj.r.squared&quot; &quot;fstatistic&quot; &quot;cov.unscaled&quot; &quot;na.action&quot; You can use the $ operator to pull out any element of the list. For example, to pull out the table with information on the estimated model coefficients, you can run: summary(mod_a)$coefficients ## Estimate Std. Error t value Pr(&gt;|t|) ## (Intercept) -8.7581466 0.211529182 -41.40396 2.411051e-208 ## ht 0.2341969 0.002459347 95.22726 0.000000e+00 The plot function, like the summary function, will give different output depending on the class of the object that you input. For an lm object, you can use the plot function to get a number of useful diagnostic plots that will help you check regression assumptions (Figure 7.2): plot(mod_a) Figure 7.2: Example output from running the plot function with an lm object as the input. You can also use binary variables or factors as independent variables in regression models. For example, in the nepali dataset, sex is a factor variable with the levels “Male” and “Female”. You can fit a linear model of weight regressed on sex for this data with the call: mod_b &lt;- lm(wt ~ sex, data = nepali) This call fits the model: \\[ Y_{i} = \\beta_{0} + \\beta_{1}X_{1,i} + \\epsilon_{i} \\] where \\(X_{1,i}\\) : sex of child \\(i\\), where 0 = male and 1 = female. Here are the estimated coefficients from fitting this model: summary(mod_b)$coefficients ## Estimate Std. Error t value Pr(&gt;|t|) ## (Intercept) 11.4226374 0.1375427 83.047920 0.00000000 ## sexFemale -0.4866184 0.1982810 -2.454185 0.01431429 You’ll notice that, in addition to an estimated intercept ((Intercept)), the other estimated coefficient is sexFemale rather than just sex, although the column name in the data frame input to lm for this variable is sex. This is because, when a factor or binary variable is input as an independent variable in a linear regression model, R will fit an estimated coefficient for all levels of factors except the first factor level. By default, this first factor level is used as the baseline level, and so its estimated mean is given by the estimated intercept, while the other model coefficients give the estimated difference from this baseline. For example, the model fit above tells us that the estimated mean weight of males is 11.4, while the estimated mean weight of females is 11.4 + -0.5 = 10.9. 7.6 Handling model objects The broom package contains tools for converting statistical objects into nice tidy data frames. The tools in the broom package make it easy to process statistical results in R using the tools of the tidyverse. 7.6.1 broom::tidy The tidy() function returns a data frame with information on the fitted model terms. For example, when applied to one of our linear models we get: library(broom) kable(tidy(mod_a), digits = 3) term estimate std.error statistic p.value (Intercept) -8.758 0.212 -41.404 0 ht 0.234 0.002 95.227 0 class(tidy(mod_a)) ## [1] &quot;tbl_df&quot; &quot;tbl&quot; &quot;data.frame&quot; You can pass arguments to the tidy() function. For example, include confidence intervals: kable(tidy(mod_a, conf.int = TRUE), digits = 3) term estimate std.error statistic p.value conf.low conf.high (Intercept) -8.758 0.212 -41.404 0 -9.173 -8.343 ht 0.234 0.002 95.227 0 0.229 0.239 7.6.2 broom::augment The augment() function adds information about a fitted model to the dataset used to fit the model. For example, when applied to one of our linear models we get information on the fitted values and residuals included in the output: kable(head(broom::augment(mod_a), 3), digits = 3) .rownames wt ht .fitted .resid .std.resid .hat .sigma .cooksd 1 12.8 91.2 12.601 0.199 0.228 0.001 0.874 0 2 12.8 93.9 13.233 -0.433 -0.496 0.002 0.874 0 3 13.1 95.2 13.537 -0.437 -0.501 0.002 0.874 0 7.6.3 broom::glance The glance() functions returns a one row summary of a fitted model object: For example: kable(glance(mod_a, conf.int = TRUE), digits = 3) r.squared adj.r.squared sigma statistic p.value df logLik AIC BIC deviance df.residual nobs 0.912 0.912 0.873 9068.23 0 1 -1124.615 2255.23 2269.56 667.351 875 877 7.6.4 References– statistics in R One great (and free online for CSU students through our library) book to find out more about using R for basic statistics is: Introductory Statistics with R If you want all the details about fitting linear models and GLMs in R, Julian Faraway’s books are fantastic. He has one on linear models and one on extensions including logistic and Poisson models: Linear Models with R (also free online through the CSU library) Extending the Linear Model with R 7.7 Functions As you move to larger projects, you will find yourself using the same code a lot. Examples include: Reading in data from a specific type of equipment (air pollution monitor, accelerometer) Running a specific type of analysis Creating a specific type of plot or map If you find yourself cutting and pasting a lot, convert the code to a function. Advantages of writing functions include: Coding is more efficient Easier to change your code (if you’ve cut and paste code and you want to change something, you have to change it everywhere - this is an easy way to accidentally create bugs in your code) Easier to share code with others You can name a function anything you want (although try to avoid names of preexisting-existing functions). You then define any inputs (arguments; separate multiple arguments with commas) and put the code to run in braces: ## Note: this code will not run [function name] &lt;- function([any arguments]){ [code to run] } Here is an example of a very basic function. This function takes a number as input and adds 1 to that number. add_one &lt;- function(number){ out &lt;- number + 1 return(out) } add_one(number = 3) ## [1] 4 add_one(number = -1) ## [1] 0 Functions can input any type of R object (for example, vectors, data frames, even other functions and ggplot objects) Similarly, functions can output any type of R object When defining a function, you can set default values for some of the parameters You can explicitly specify the value to return from the function For example, the following function inputs a data frame (datafr) and a one-element vector (child_id) and returns only rows in the data frame where it’s id column matches child_id. It includes a default value for datafr, but not for child_id. subset_nepali &lt;- function(datafr = nepali, child_id){ datafr &lt;- datafr %&gt;% filter(id == child_id) return(datafr) } If an argument is not given for a parameter with a default, the function will run using the default value for that parameter. For example: subset_nepali(child_id = &quot;120011&quot;) ## id sex wt ht mage lit died alive age ## 1 120011 Male 12.8 91.2 35 0 2 5 41 ## 2 120011 Male 12.8 93.9 35 0 2 5 45 ## 3 120011 Male 13.1 95.2 35 0 2 5 49 ## 4 120011 Male 13.8 96.9 35 0 2 5 53 ## 5 120011 Male NA NA 35 0 2 5 57 If an argument is not given for a parameter without a default, the function call will result in an error. For example: subset_nepali(datafr = nepali) ## Error: Problem with `filter()` input `..1`. ## x argument &quot;child_id&quot; is missing, with no default ## ℹ Input `..1` is `id == child_id`. By default, the function will return the last defined object, although the choice of using return can affect printing behavior when you run the function. For example, I could have written the subset_nepali function like this: subset_nepali &lt;- function(datafr = nepali, child_id){ datafr &lt;- datafr %&gt;% filter(id == child_id) } In this case, the output will not automatically print out when you call the function without assigning it to an R object: subset_nepali(child_id = &quot;120011&quot;) However, the output can be assigned to an R object in the same way as when the function was defined without return: first_childs_data &lt;- subset_nepali(child_id = &quot;120011&quot;) first_childs_data ## id sex wt ht mage lit died alive age ## 1 120011 Male 12.8 91.2 35 0 2 5 41 ## 2 120011 Male 12.8 93.9 35 0 2 5 45 ## 3 120011 Male 13.1 95.2 35 0 2 5 49 ## 4 120011 Male 13.8 96.9 35 0 2 5 53 ## 5 120011 Male NA NA 35 0 2 5 57 R is very “good” at running functions! It will look for (scope) the variables in your function in various places (environments). So your functions may run even when you don’t expect them to, potentially, with unexpected results! The return function can also be used to return an object other than the last defined object (although this doesn’t tend to be something you need to do very often). If you did not use return in the following code, it will output “Test output”: subset_nepali &lt;- function(datafr = nepali, child_id){ datafr &lt;- datafr %&gt;% filter(id == child_id) a &lt;- &quot;Test output&quot; } (subset_nepali(child_id = &quot;120011&quot;)) ## [1] &quot;Test output&quot; Conversely, you can use return to output datafr, even though it’s not the last object defined: subset_nepali &lt;- function(datafr = nepali, child_id){ datafr &lt;- datafr %&gt;% filter(id == child_id) a &lt;- &quot;Test output&quot; return(datafr) } subset_nepali(child_id = &quot;120011&quot;) ## id sex wt ht mage lit died alive age ## 1 120011 Male 12.8 91.2 35 0 2 5 41 ## 2 120011 Male 12.8 93.9 35 0 2 5 45 ## 3 120011 Male 13.1 95.2 35 0 2 5 49 ## 4 120011 Male 13.8 96.9 35 0 2 5 53 ## 5 120011 Male NA NA 35 0 2 5 57 7.7.1 if / else statements There are other control structures you can use in your R code. Two that you will commonly use within R functions are if and ifelse statements. An if statement tells R that, if a certain condition is true, do run some code. For example, if you wanted to print out only odd numbers between 1 and 5, one way to do that is with an if statement: (Note: the %% operator in R returns the remainder of the first value (i) divided by the second value (2).) for(i in 1:5){ if(i %% 2 == 1){ print(i) } } ## [1] 1 ## [1] 3 ## [1] 5 The if statement runs some code if a condition is true, but does nothing if it is false. If you’d like different code to run depending on whether the condition is true or false, you can us an if / else or an if / else if / else statement. for(i in 1:5){ if(i %% 2 == 1){ print(i) } else { print(paste(i, &quot;is even&quot;)) } } ## [1] 1 ## [1] &quot;2 is even&quot; ## [1] 3 ## [1] &quot;4 is even&quot; ## [1] 5 What would this code do? for(i in 1:100){ if(i %% 3 == 0 &amp; i %% 5 == 0){ print(&quot;FizzBuzz&quot;) } else if(i %% 3 == 0){ print(&quot;Fizz&quot;) } else if(i %% 5 == 0){ print(&quot;Buzz&quot;) } else { print(i) } } If / else statements are extremely useful in functions. In R, the if statement evaluates everything in the parentheses and, if that evaluates to TRUE, runs everything in the braces. This means that you can trigger code in an if statement with a single-value logical vector: weekend &lt;- TRUE if(weekend){ print(&quot;It&#39;s the weekend!&quot;) } ## [1] &quot;It&#39;s the weekend!&quot; This functionality can be useful with parameters you choose to include when writing your own functions (e.g., print = TRUE). 7.7.2 Some other control structures The control structure you are most likely to use in data analysis with R is the “if / else” statement. However, there are a few other control structures you may occasionally find useful: next break while You can use the next structure to skip to the next round of a loop when a certain condition is met. For example, we could have used this code to print out odd numbers between 1 and 5: i &lt;- 0 while(i &lt; 5){ i &lt;- 1 + i if(i %% 2 == 0){ next } print(i) } ## [1] 1 ## [1] 3 ## [1] 5 You can use break to break out of a loop if a certain condition is met. For example, the final code will break out of the loop once i is over 2, so it will only print the numbers 1 through 3: i &lt;- 0 while(i &lt;= 5){ if(i &gt; 2){ break } i &lt;- 1 + i print(i) } ## [1] 1 ## [1] 2 ## [1] 3 7.8 In-course exercise 7.8.1 Exploring taxonomic profiling data We’ll be using a package on Bioconductor called microbiome. You’ll need to install that package from Bioconductor. This uses code that’s different from the default you use to download a package from CRAN. Go to the Bioconductor page for the microbiome package and figure out how to install this package based on instructions on that page. The microbiome that includes tools for exploring and analysing microbiome profiling data. This package has a website with tutorial information here. We want to explore a dataset on genus-level microbiota profiling (atlas1006). Navigate to the tutorial webpage to figure out how you can get this example raw data loaded in your R session. Use the class and str functions to start exploring this data. Is it in a dataframe (tibble)? Is it in a tidy format? How is the data structured? On the microbiome page, find the documentation describing the atlas1006 data. Look through this documentation to figure out what information is included in the data. Also, check the helpfile for this dataset and look up the original article describing the data (you can find the article information in the help resources). The atlas1006 data is stored in a special object class called a “phyloseq” object (you should have seen this when you used class with the object). You can pull certain parts of this data using special functions called “accessors”. One is get_variable. Try running get_variable with the atlas1006 data. What do you think this data is showing? Which different nationalities are represented by the study subjects, based on the dataframe you extracted in the last step? How many samples have each nationality? Which different BMI groups are included? Does it look like the study was balanced among these groups? Based on the data you extracted, does it look like diversity varies much between males and females? Across BMI groups? Discuss what steps you would need to take to create the following plot. To start, don’t write any code, just develop a plan. Talk about what the dataset should look like right before you create the plot and what functions you could use to get the data from its current format to that format. Try to write the code to create this plot. This will include some code for cleaning the data and some code for plotting. I will add one example answer after class, but I’d like you to try to figure it out yourselves first. 7.8.1.1 Example R code Install the microbiome package from Bioconductor: if (!requireNamespace(&quot;BiocManager&quot;, quietly = TRUE)) install.packages(&quot;BiocManager&quot;) BiocManager::install(&quot;microbiome&quot;) Load the atlas1006 example data in the microbiome package and explore it with str and class: library(microbiome) data(atlas1006) class(atlas1006) ## [1] &quot;phyloseq&quot; ## attr(,&quot;package&quot;) ## [1] &quot;phyloseq&quot; str(atlas1006) ## Formal class &#39;phyloseq&#39; [package &quot;phyloseq&quot;] with 5 slots ## ..@ otu_table:Formal class &#39;otu_table&#39; [package &quot;phyloseq&quot;] with 2 slots ## .. .. ..@ .Data : num [1:130, 1:1151] 0 0 0 21 1 72 0 0 176 10 ... ## .. .. .. ..- attr(*, &quot;dimnames&quot;)=List of 2 ## .. .. .. .. ..$ : chr [1:130] &quot;Actinomycetaceae&quot; &quot;Aerococcus&quot; &quot;Aeromonas&quot; &quot;Akkermansia&quot; ... ## .. .. .. .. ..$ : chr [1:1151] &quot;Sample-1&quot; &quot;Sample-2&quot; &quot;Sample-3&quot; &quot;Sample-4&quot; ... ## .. .. ..@ taxa_are_rows: logi TRUE ## ..@ tax_table:Formal class &#39;taxonomyTable&#39; [package &quot;phyloseq&quot;] with 1 slot ## .. .. ..@ .Data: chr [1:130, 1:3] &quot;Actinobacteria&quot; &quot;Firmicutes&quot; &quot;Proteobacteria&quot; &quot;Verrucomicrobia&quot; ... ## .. .. .. ..- attr(*, &quot;dimnames&quot;)=List of 2 ## .. .. .. .. ..$ : chr [1:130] &quot;Actinomycetaceae&quot; &quot;Aerococcus&quot; &quot;Aeromonas&quot; &quot;Akkermansia&quot; ... ## .. .. .. .. ..$ : chr [1:3] &quot;Phylum&quot; &quot;Family&quot; &quot;Genus&quot; ## ..@ sam_data :&#39;data.frame&#39;: 1151 obs. of 10 variables: ## Formal class &#39;sample_data&#39; [package &quot;phyloseq&quot;] with 4 slots ## .. .. ..@ .Data :List of 10 ## .. .. .. ..$ : int [1:1151] 28 24 52 22 25 42 25 27 21 25 ... ## .. .. .. ..$ : Factor w/ 2 levels &quot;female&quot;,&quot;male&quot;: 2 1 2 1 1 2 1 1 1 1 ... ## .. .. .. ..$ : Factor w/ 6 levels &quot;CentralEurope&quot;,..: 6 6 6 6 6 6 6 6 6 6 ... ## .. .. .. ..$ : Factor w/ 3 levels &quot;o&quot;,&quot;p&quot;,&quot;r&quot;: NA NA NA NA NA NA NA NA NA NA ... ## .. .. .. ..$ : Factor w/ 40 levels &quot;1&quot;,&quot;2&quot;,&quot;3&quot;,&quot;4&quot;,..: 1 1 1 1 1 1 1 1 1 1 ... ## .. .. .. ..$ : num [1:1151] 5.76 6.06 5.5 5.87 5.89 5.53 5.49 5.38 5.34 5.64 ... ## .. .. .. ..$ : Factor w/ 6 levels &quot;underweight&quot;,..: 5 4 2 1 2 2 1 2 2 2 ... ## .. .. .. ..$ : Factor w/ 1006 levels &quot;1&quot;,&quot;2&quot;,&quot;3&quot;,&quot;4&quot;,..: 1 2 3 4 5 6 7 8 9 10 ... ## .. .. .. ..$ : num [1:1151] 0 0 0 0 0 0 0 0 0 0 ... ## .. .. .. ..$ : chr [1:1151] &quot;Sample-1&quot; &quot;Sample-2&quot; &quot;Sample-3&quot; &quot;Sample-4&quot; ... ## .. .. ..@ names : chr [1:10] &quot;age&quot; &quot;sex&quot; &quot;nationality&quot; &quot;DNA_extraction_method&quot; ... ## .. .. ..@ row.names: chr [1:1151] &quot;Sample-1&quot; &quot;Sample-2&quot; &quot;Sample-3&quot; &quot;Sample-4&quot; ... ## .. .. ..@ .S3Class : chr &quot;data.frame&quot; ## ..@ phy_tree : NULL ## ..@ refseq : NULL Pull out the data frame that contains information on each study subject in the atlas1006 data by using the get_variable accessor function: get_variable(atlas1006) %&gt;% head() ## age sex nationality DNA_extraction_method project diversity ## Sample-1 28 male US &lt;NA&gt; 1 5.76 ## Sample-2 24 female US &lt;NA&gt; 1 6.06 ## Sample-3 52 male US &lt;NA&gt; 1 5.50 ## Sample-4 22 female US &lt;NA&gt; 1 5.87 ## Sample-5 25 female US &lt;NA&gt; 1 5.89 ## Sample-6 42 male US &lt;NA&gt; 1 5.53 ## bmi_group subject time sample ## Sample-1 severeobese 1 0 Sample-1 ## Sample-2 obese 2 0 Sample-2 ## Sample-3 lean 3 0 Sample-3 ## Sample-4 underweight 4 0 Sample-4 ## Sample-5 lean 5 0 Sample-5 ## Sample-6 lean 6 0 Sample-6 Note: Since the first argument to get_variable is the phyloseq object (here, the atlas1006 data object), you can pipe into the function, like this: atlas1006 %&gt;% get_variable() %&gt;% head() Find out the different nationalities included in the data: atlas1006 %&gt;% get_variable() %&gt;% as_tibble() %&gt;% # Change output from a data.frame to a tibble group_by(nationality) %&gt;% count() ## # A tibble: 7 x 2 ## # Groups: nationality [7] ## nationality n ## &lt;fct&gt; &lt;int&gt; ## 1 CentralEurope 650 ## 2 EasternEurope 15 ## 3 Scandinavia 271 ## 4 SouthEurope 89 ## 5 UKIE 50 ## 6 US 44 ## 7 &lt;NA&gt; 32 It looks like most subjects were from Central Europe, with the next-largest group from Scandinavia. Note: If you wanted to rearrange this summary to give the nationalities in order of the number of subjects in each, you could add on a forcats function: atlas1006 %&gt;% get_variable() %&gt;% as_tibble() %&gt;% # Change output from a data.frame to a tibble mutate(nationality = fct_infreq(nationality)) %&gt;% group_by(nationality) %&gt;% count() ## # A tibble: 7 x 2 ## # Groups: nationality [7] ## nationality n ## &lt;fct&gt; &lt;int&gt; ## 1 CentralEurope 650 ## 2 Scandinavia 271 ## 3 SouthEurope 89 ## 4 UKIE 50 ## 5 US 44 ## 6 EasternEurope 15 ## 7 &lt;NA&gt; 32 Find out the different BMI groups included in the data and if the study seemed to be balanced across those groups: atlas1006 %&gt;% get_variable() %&gt;% as_tibble() %&gt;% # Change output from a data.frame to a tibble group_by(bmi_group) %&gt;% count() ## # A tibble: 7 x 2 ## # Groups: bmi_group [7] ## bmi_group n ## &lt;fct&gt; &lt;int&gt; ## 1 underweight 21 ## 2 lean 484 ## 3 overweight 197 ## 4 obese 222 ## 5 severeobese 99 ## 6 morbidobese 22 ## 7 &lt;NA&gt; 106 There were six different BMI groups. Over 100 study subjects had this information missing. The samples were not evenly distributed across these BMI groups. Instead, the most common (lean) had almost 500 subjects, while the smaller BMI-group samples were around 20 people. See if it looks like diversity varies much between males and females: atlas1006 %&gt;% get_variable() %&gt;% as_tibble() %&gt;% group_by(sex) %&gt;% summarize(mean_diversity = mean(diversity)) ## `summarise()` ungrouping output (override with `.groups` argument) ## # A tibble: 3 x 2 ## sex mean_diversity ## &lt;fct&gt; &lt;dbl&gt; ## 1 female 5.84 ## 2 male 5.85 ## 3 &lt;NA&gt; 5.76 See if it looks like diversity varies much across BMI groups: atlas1006 %&gt;% get_variable() %&gt;% as_tibble() %&gt;% group_by(bmi_group) %&gt;% summarize(mean_diversity = mean(diversity)) ## `summarise()` ungrouping output (override with `.groups` argument) ## # A tibble: 7 x 2 ## bmi_group mean_diversity ## &lt;fct&gt; &lt;dbl&gt; ## 1 underweight 5.84 ## 2 lean 5.89 ## 3 overweight 5.83 ## 4 obese 5.82 ## 5 severeobese 5.74 ## 6 morbidobese 5.67 ## 7 &lt;NA&gt; 5.81 Here is the code for the plot: library(tidyverse) library(ggthemes) library(microbiome) data(atlas1006) atlas1006 %&gt;% get_variable() %&gt;% ggplot(aes(x = bmi_group, y = diversity, color = sex)) + geom_boxplot(alpha = 0.2) + facet_wrap(~ nationality) 7.8.2 More with baby names Let’s look at baby names that we started looking at last class, based on the letter they start with. For the full dataframe, what proportion of baby names start with each letter? See if you can create a figure to help show this. Create the same plot using the names of people from our class. What proportion of names start with “C” or “S” across the full dataset? 7.8.2.1 Example R code For the full dataframe, what proportion of baby names start with “S”? To start, create a column with the first letter of each name. You can use functions in the stringr package to do this. The easiest might be to use the position of the first letter to pull that information. library(babynames) library(stringr) top_letters &lt;- babynames %&gt;% mutate(first_letter = str_sub(name, 1, 1)) top_letters %&gt;% select(name, first_letter) %&gt;% slice(1:5) ## # A tibble: 5 x 2 ## name first_letter ## &lt;chr&gt; &lt;chr&gt; ## 1 Mary M ## 2 Anna A ## 3 Emma E ## 4 Elizabeth E ## 5 Minnie M Now we can group by letter and figure out these proportions. First, while the data is grouped, count the number of names with each letter. Then, ungroup and use mutate to divide this by the total number of names: top_letters &lt;- top_letters %&gt;% group_by(first_letter) %&gt;% summarize(n = sum(n)) %&gt;% ungroup() %&gt;% mutate(prop = n / sum(n)) %&gt;% arrange(desc(prop)) ## `summarise()` ungrouping output (override with `.groups` argument) top_letters ## # A tibble: 26 x 3 ## first_letter n prop ## &lt;chr&gt; &lt;int&gt; &lt;dbl&gt; ## 1 J 44612175 0.128 ## 2 M 32864210 0.0944 ## 3 A 28855232 0.0829 ## 4 C 25533863 0.0733 ## 5 D 24240271 0.0696 ## 6 R 23702794 0.0681 ## 7 S 21373830 0.0614 ## 8 L 18942067 0.0544 ## 9 E 17033760 0.0489 ## 10 K 17006684 0.0489 ## # … with 16 more rows Here’s one way to visualize this: library(scales) top_letters %&gt;% mutate(first_letter = fct_reorder(first_letter, prop)) %&gt;% ggplot(aes(x = first_letter)) + geom_bar(aes(weight = prop)) + coord_flip() + scale_y_continuous(labels = percent) + labs(x = &quot;&quot;, y = &quot;Percent of names that start with ...&quot;) Create the same plot using the names of people in our class. First, create a vector with the names of people in our class: student_list &lt;- data_frame(name = c(&quot;Burton&quot;, &quot;Caroline&quot;, &quot;Chaoyu&quot;, &quot;Collin&quot;, &quot;Daniel&quot;, &quot;Eric&quot;, &quot;Erin&quot;, &quot;Heather&quot;, &quot;Jacob&quot;, &quot;Jessica&quot;, &quot;Khum&quot;, &quot;Kyle&quot;, &quot;Matthew&quot;, &quot;Molly&quot;, &quot;Nikki&quot;, &quot;Rachel&quot;, &quot;Sere&quot;, &quot;Shayna&quot;, &quot;Sherry&quot;, &quot;Sunny&quot;)) student_list &lt;- student_list %&gt;% mutate(first_letter = str_sub(name, 1, 1)) student_list ## # A tibble: 20 x 2 ## name first_letter ## &lt;chr&gt; &lt;chr&gt; ## 1 Burton B ## 2 Caroline C ## 3 Chaoyu C ## 4 Collin C ## 5 Daniel D ## 6 Eric E ## 7 Erin E ## 8 Heather H ## 9 Jacob J ## 10 Jessica J ## 11 Khum K ## 12 Kyle K ## 13 Matthew M ## 14 Molly M ## 15 Nikki N ## 16 Rachel R ## 17 Sere S ## 18 Shayna S ## 19 Sherry S ## 20 Sunny S library(scales) student_list %&gt;% group_by(first_letter) %&gt;% count() %&gt;% ungroup() %&gt;% mutate(prop = n / sum(n)) %&gt;% mutate(first_letter = fct_reorder(first_letter, prop)) %&gt;% ggplot(aes(x = first_letter)) + geom_bar(aes(weight = prop)) + coord_flip() + scale_y_continuous(labels = percent) + labs(x = &quot;&quot;, y = &quot;Percent of students with\\na name that starts with ...&quot;) What proportion of names start with “C” or “S” across the full dataset? You can create a dataframe that (1) pulls out the first letter of each name (just like we did for the last part of the question) and (2) tests whether that first letter is an “A” or a “K” (using a logical statement): c_or_s &lt;- babynames %&gt;% mutate(first_letter = str_sub(name, 1, 1), c_or_s = first_letter %in% c(&quot;C&quot;, &quot;S&quot;)) c_or_s %&gt;% select(name, first_letter, c_or_s) %&gt;% slice(1:5) ## # A tibble: 5 x 3 ## name first_letter c_or_s ## &lt;chr&gt; &lt;chr&gt; &lt;lgl&gt; ## 1 Mary M FALSE ## 2 Anna A FALSE ## 3 Emma E FALSE ## 4 Elizabeth E FALSE ## 5 Minnie M FALSE Next, group by this logical column (c_or_s) and figure out the number of baby names for each group. Then, to get the proportion of the total, ungroup and mutate to divide by the total number across the data: c_or_s %&gt;% group_by(c_or_s) %&gt;% count() %&gt;% ungroup() %&gt;% mutate(prop = n / sum(n)) ## # A tibble: 2 x 3 ## c_or_s n prop ## &lt;lgl&gt; &lt;int&gt; &lt;dbl&gt; ## 1 FALSE 1655755 0.860 ## 2 TRUE 268910 0.140 7.8.3 Running a simple statistical test In the last part of the in-course exercise, we found out that about 14% of babies born in the United States between 1980 and 1995 had names that started with an “C” or “S” (268,910 babies out of 1,924,665). What is the proportion of people with names that start with an “C” or “S” in our class? Use a simple statistical test to test the hypothesis that the class comes from a binomial distribution with the same distribution as babies born in the US over the time tracked by this data, in terms of chance of having a name that starts with “C” or “S”. (Hint: You will be comparing two proportions. Try googling for a statistical test in R that does that.) See if you can figure out a way to make a single “tidy” pipeline for the whole analysis (and output the result as a tidy dataframe). Does the tidy function from broom give different information from this test than the output we got for the Shapiro-Wilk test? You may get the warning “Chi-squared approximation may be incorrect”. See if you can figure out this warning if you get it with the test you used. 7.8.3.1 Example R code Here is a vector with names in our class: library(stringr) student_list &lt;- tibble(name = c(&quot;Burton&quot;, &quot;Caroline&quot;, &quot;Chaoyu&quot;, &quot;Collin&quot;, &quot;Daniel&quot;, &quot;Eric&quot;, &quot;Erin&quot;, &quot;Heather&quot;, &quot;Jacob&quot;, &quot;Jessica&quot;, &quot;Khum&quot;, &quot;Kyle&quot;, &quot;Matthew&quot;, &quot;Molly&quot;, &quot;Nikki&quot;, &quot;Rachel&quot;, &quot;Sere&quot;, &quot;Shayna&quot;, &quot;Sherry&quot;, &quot;Sunny&quot;)) student_list &lt;- student_list %&gt;% mutate(first_letter = str_sub(name, 1, 1)) student_list %&gt;% slice(1:3) ## # A tibble: 3 x 2 ## name first_letter ## &lt;chr&gt; &lt;chr&gt; ## 1 Burton B ## 2 Caroline C ## 3 Chaoyu C Let’s get the total number of students, and then the total number with a name that starts with “C” or “S”: tot_students &lt;- student_list %&gt;% count() tot_students ## # A tibble: 1 x 1 ## n ## &lt;int&gt; ## 1 20 c_or_s_students &lt;- student_list %&gt;% mutate(c_or_s = first_letter %in% c(&quot;C&quot;, &quot;S&quot;)) %&gt;% group_by(c_or_s) %&gt;% count() c_or_s_students ## # A tibble: 2 x 2 ## # Groups: c_or_s [2] ## c_or_s n ## &lt;lgl&gt; &lt;int&gt; ## 1 FALSE 13 ## 2 TRUE 7 The proportion of students with names starting with “A” or “K” are 7 / 20 = 0.35. You could run a statistical test comparing these two proportions (check the help file for the function to figure out where to include each piece): prop.test(x = c(268910, 7), n = c(1924665, 20)) ## Warning in prop.test(x = c(268910, 7), n = c(1924665, 20)): Chi-squared ## approximation may be incorrect ## ## 2-sample test for equality of proportions with continuity correction ## ## data: c(268910, 7) out of c(1924665, 20) ## X-squared = 5.7121, df = 1, p-value = 0.01685 ## alternative hypothesis: two.sided ## 95 percent confidence interval: ## -0.44432032 0.02375596 ## sample estimates: ## prop 1 prop 2 ## 0.1397178 0.3500000 There are a few different ways you could run this test. For example, you could also test whether the proportion in our class is consistent with the null hypothesis that you were drawn from a binomial distribution with a proportion of 0.14 (in-line with the national values): prop.test(x = 7, n = 20, p = 0.14) ## Warning in prop.test(x = 7, n = 20, p = 0.14): Chi-squared approximation may be ## incorrect ## ## 1-sample proportions test with continuity correction ## ## data: 7 out of 20, null probability 0.14 ## X-squared = 5.6852, df = 1, p-value = 0.01711 ## alternative hypothesis: true p is not equal to 0.14 ## 95 percent confidence interval: ## 0.1630867 0.5905104 ## sample estimates: ## p ## 0.35 You can also see if we can pipe into prop.test by summing up the number of successes (“1”: the person’s name starts with “C” or “S”). Because this is using an unsummarized form of the data, it lets us use some of the tidyverse tools more easily: library(purrr) library(broom) student_list %&gt;% mutate(c_or_s = first_letter %in% c(&quot;C&quot;, &quot;S&quot;)) %&gt;% pluck(&quot;c_or_s&quot;) %&gt;% sum() %&gt;% prop.test(n = 20, p = 0.14) %&gt;% tidy() ## Warning in prop.test(., n = 20, p = 0.14): Chi-squared approximation may be ## incorrect ## # A tibble: 1 x 8 ## estimate statistic p.value parameter conf.low conf.high method alternative ## &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;int&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;chr&gt; &lt;chr&gt; ## 1 0.35 5.69 0.0171 1 0.163 0.591 1-sample … two.sided Finally, when we run this test, we get the warning that “Chi-squared approximation may be incorrect”. Based on googling ‘r prop.test “Chi-squared approximation may be incorrect”’, it sounds like we might be getting this error because we have a pretty low number of people in the class. One recommendation is to use binom.test, which will run as an exact binomial test: binom.test(x = 7, n = 20, p = 0.14) ## ## Exact binomial test ## ## data: 7 and 20 ## number of successes = 7, number of trials = 20, p-value = 0.01534 ## alternative hypothesis: true probability of success is not equal to 0.14 ## 95 percent confidence interval: ## 0.1539092 0.5921885 ## sample estimates: ## probability of success ## 0.35 7.8.4 Using regression models to explore data #1 For this exercise, you will need the following packages. If do not have them already, you will need to install them. library(ggplot2) library(broom) library(ggfortify) For this part of the exercise, you’ll use a dataset on weather, air pollution, and mortality counts in Chicago, IL. This dataset is called chicagoNMMAPS and is part of the dlnm package. Change the name of the data frame to chic (this object name is shorter and will be easier to work with). Check out the data a bit to see what variables you have, and then perform the following tasks: Write out (on paper, not in R) the regression equation for regressing dewpoint temperature on temperature. Try fitting a linear regression of dew point temperature (dptp) regressed on temperature (temp). Save this model as the object mod_1 (i.e., is the dependent variable of dewpoint temperature linearly associated with the independent variable of temperature). Based on this regression, does there seem to be a relationship between temperature and dewpoint temperature in Chicago? (Hint: Try using glance and tidy from the broom package on the model object to get more information about the model you fit.) What is the coefficient for temperature (in other words, for every 1 degree increase in temperature, how much do we expect the dewpoint temperature to change?)? What is the p-value for the coefficient for temperature? Plot temperature (x-axis) versus dewpoint temperature (y-axis) for Chicago. Add in the regression line from the model you fit by using the results from augment. Use autoplot on the model object to generate some model diagnostic plots (make sure you have the ggfortify package loaded and installed). 7.8.4.1 Example R code: The regression equation for the model you want to fit, regressing dewpoint temperature on temperature, is: \\[ Y_t \\sim \\beta_0 + \\beta_1 X_t \\] where \\(Y_t\\) is the dewpoint temperature on day \\(t\\), \\(X_t\\) is the temperature on day \\(t\\), and \\(\\beta_0\\) and \\(\\beta_1\\) are model coefficients. Install and load the dlnm package and then load the chicagoNMMAPS data. Change the name of the data frame to chic, so it will be shorter to call for the rest of your work. # install.packages(&quot;dlnm&quot;) library(dlnm) data(&quot;chicagoNMMAPS&quot;) chic &lt;- chicagoNMMAPS Fit a linear regression of dptp on temp and save as the object mod_1: mod_1 &lt;- lm(dptp ~ temp, data = chic) mod_1 ## ## Call: ## lm(formula = dptp ~ temp, data = chic) ## ## Coefficients: ## (Intercept) temp ## 24.025 1.621 Use functions from the broom package to pull the same information about the model in a “tidy” format. To find out if the evidence for a linear association between temperature and dewpoint temperature, use the tidy function to get model coefficients in a tidy format: tidy(mod_1) ## # A tibble: 2 x 5 ## term estimate std.error statistic p.value ## &lt;chr&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; ## 1 (Intercept) 24.0 0.113 213. 0 ## 2 temp 1.62 0.00763 212. 0 There does seem to be an association between temperature and dewpoint temperature: a unit increase in temperature is associated with a 1.6 unit increase in dewpoint temperature. The p-value for the temperature coefficient is &lt;2e-16. This is far below 0.05, which suggests we would be very unlikely to see such a strong association by chance if the null hypothesis, that the two variables are not associated, were true. You can also check overall model summaries using the glance function: glance(mod_1) ## # A tibble: 1 x 12 ## r.squared adj.r.squared sigma statistic p.value df logLik AIC BIC ## &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; ## 1 0.898 0.898 5.90 45108. 0 1 -16332. 32670. 32690. ## # … with 3 more variables: deviance &lt;dbl&gt;, df.residual &lt;int&gt;, nobs &lt;int&gt; To create plots of the observations and the fit model, use the augment function to add model output (e.g., predictions, residuals) to the original data frame of observed temperatures and dew point temperatures: augment(mod_1) %&gt;% slice(1:3) ## # A tibble: 3 x 8 ## dptp temp .fitted .resid .std.resid .hat .sigma .cooksd ## &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; ## 1 31.5 -0.278 23.6 7.93 1.34 0.000376 5.90 0.000340 ## 2 29.9 0.556 24.9 4.95 0.839 0.000348 5.90 0.000123 ## 3 27.4 0.556 24.9 2.45 0.415 0.000348 5.90 0.0000300 Plot these two variables and add in the fitted line from the model (note: I’ve used the color option to make the color of the points gray). Use the output from augment to create a plot of the original data, with the predicted values used to plot a fitted line. augment(mod_1) %&gt;% ggplot(aes(x = temp, y = dptp)) + geom_point(size = 0.8, alpha = 0.5, col = &quot;gray&quot;) + geom_line(aes(x = temp, y = .fitted), color = &quot;red&quot;, size = 2) + theme_classic() Plot some plots to check model assumptions for the model you fit using the autoplot function on your model object: autoplot(mod_1) ## Warning: `arrange_()` is deprecated as of dplyr 0.7.0. ## Please use `arrange()` instead. ## See vignette(&#39;programming&#39;) for more help ## This warning is displayed once every 8 hours. ## Call `lifecycle::last_warnings()` to see where this warning was generated. 7.8.5 Using regression models to explore data #2 Try fitting the regression from the last part of the in-course exercise as a GLM, using glm() (but still assuming the outcome variable is normally distributed). Are your coefficients different? Does \\(PM_{10}\\) vary by day of the week? (Hint: The dow variable is a factor that gives day of the week. You can do an ANOVA analysis by fitting a linear model using this variable as the independent variable. Some of the overall model summaries will compare this model to an intercept-only model.) What day of the week is \\(PM_{10}\\) generally highest? (Check the model coefficients to figure this out.) Try to write out (on paper) the regression equation for the model you’re fitting. Try using glm() to run a Poisson regression of respiratory deaths (resp) on temperature during summer days. Start by creating a subset with just summer days called summer. (Hint: Use the month function with the argument label = TRUE from lubridate to do this—just pull out the subset where the month June, July, or August.) Try to write out the regression equation for the model you’re fitting. The coefficient for the temperature variable in this model is our best estimate (based on this model) of the log relative risk for a one degree Celcius increase in temperature. What is the relative risk associated with a one degree Celsius increase? 7.8.5.1 Example R code: Try fitting the model from the last part of the in-course exercise using glm(). Call it mod_1a. Compare the coefficients for the two models. You can use the tidy function on an lm or glm object to pull out just the model coefficients and associated model results. Here, I’ve used a pipeline of code to create a tidy data frame that merges these “tidy” coefficient outputs (from the two models) into a single data frame): mod_1a &lt;- glm(dptp ~ temp, data = chic) tidy(mod_1) %&gt;% select(term, estimate) %&gt;% inner_join(mod_1a %&gt;% tidy() %&gt;% select(term, estimate), by = &quot;term&quot;) %&gt;% rename(estimate_lm_mod = estimate.x, estimate_glm_mod = estimate.y) ## # A tibble: 2 x 3 ## term estimate_lm_mod estimate_glm_mod ## &lt;chr&gt; &lt;dbl&gt; &lt;dbl&gt; ## 1 (Intercept) 24.0 24.0 ## 2 temp 1.62 1.62 The results from the two models are identical. As a note, you could have also just run tidy on each model object, without merging them together into a single data frame: tidy(mod_1) ## # A tibble: 2 x 5 ## term estimate std.error statistic p.value ## &lt;chr&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; ## 1 (Intercept) 24.0 0.113 213. 0 ## 2 temp 1.62 0.00763 212. 0 tidy(mod_1a) ## # A tibble: 2 x 5 ## term estimate std.error statistic p.value ## &lt;chr&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; ## 1 (Intercept) 24.0 0.113 213. 0 ## 2 temp 1.62 0.00763 212. 0 Fit a model of \\(PM_{10}\\) regressed on day of week, where day of week is a factor. mod_2 &lt;- lm(pm10 ~ dow, data = chic) tidy(mod_2) ## # A tibble: 7 x 5 ## term estimate std.error statistic p.value ## &lt;chr&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; ## 1 (Intercept) 27.5 0.730 37.7 7.47e-273 ## 2 dowMonday 6.13 1.03 5.93 3.22e- 9 ## 3 dowTuesday 6.80 1.03 6.62 4.05e- 11 ## 4 dowWednesday 8.48 1.03 8.26 1.85e- 16 ## 5 dowThursday 8.80 1.02 8.60 1.08e- 17 ## 6 dowFriday 9.48 1.03 9.24 3.61e- 20 ## 7 dowSaturday 3.66 1.03 3.56 3.68e- 4 Use glance to check some of the overall summaries of this model. The statistic column here is the F statistic from test comparing this model to an intercept-only model. glance(mod_2) ## # A tibble: 1 x 12 ## r.squared adj.r.squared sigma statistic p.value df logLik AIC BIC ## &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; ## 1 0.0259 0.0247 19.1 21.5 4.61e-25 6 -21234. 42484. 42536. ## # … with 3 more variables: deviance &lt;dbl&gt;, df.residual &lt;int&gt;, nobs &lt;int&gt; As a note, you may have heard in previous statistics classes that you can use the anova() command to compare this model to a model with only an intercept (i.e., one that only fits a global mean and uses that as the expected value for all of the observations). Note that, in this case, the F value from anova for this model comparison is the same as the statistic you got in the overall summary statistics you get with glance in the previous code. anova(mod_2) ## Analysis of Variance Table ## ## Response: pm10 ## Df Sum Sq Mean Sq F value Pr(&gt;F) ## dow 6 46924 7820.6 21.5 &lt; 2.2e-16 *** ## Residuals 4856 1766407 363.8 ## --- ## Signif. codes: 0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1 The overall p-value from anova for with day-of-week coefficients versus the model that just has an intercept is &lt; 2.2e-16. This is well below 0.05, which suggests that day-of-week is associated with PM10 concentration, as a model that includes day-of-week does a much better job of explaining variation in PM10 than a model without it does. Use a boxplot to visually compare PM10 by day of week. ggplot(chic, aes(x = dow, y = pm10)) + geom_boxplot() Now try the same plot, but try using the ylim = option to change the limits on the y-axis for the graph, so you can get a better idea of the pattern by day of week (some of the extreme values are very high, which makes it hard to compare by eye when the y-axis extends to include them all). ggplot(chic, aes(x = dow, y = pm10)) + geom_boxplot() + ylim(c(0, 100)) ## Warning: Removed 292 rows containing non-finite values (stat_boxplot). Create a subset called summer with just the summer days: library(lubridate) summer &lt;- chic %&gt;% mutate(month = month(date, label = TRUE)) %&gt;% filter(month %in% c(&quot;Jun&quot;, &quot;Jul&quot;, &quot;Aug&quot;)) summer %&gt;% slice(1:3) ## date time year month doy dow death cvd resp temp dptp rhum ## 1 1987-06-01 152 1987 Jun 152 Monday 112 60 5 23.61111 68.50 71.875 ## 2 1987-06-02 153 1987 Jun 153 Tuesday 111 57 7 22.22222 64.75 95.250 ## 3 1987-06-03 154 1987 Jun 154 Wednesday 120 59 9 20.55556 47.25 47.125 ## pm10 o3 ## 1 22.95607 34.94623 ## 2 31.31339 18.96620 ## 3 34.95607 23.59270 Use glm() to fit a Poisson model of respiratory deaths regressed on temperature. Since you want to fit a Poisson model, use the option family = poisson(link = \"log\"). mod_3 &lt;- glm(resp ~ temp, data = summer, family = poisson(link = &quot;log&quot;)) glance(mod_3) ## # A tibble: 1 x 8 ## null.deviance df.null logLik AIC BIC deviance df.residual nobs ## &lt;dbl&gt; &lt;int&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;int&gt; &lt;int&gt; ## 1 1499. 1287 -3211. 6425. 6436. 1494. 1286 1288 tidy(mod_3) ## # A tibble: 2 x 5 ## term estimate std.error statistic p.value ## &lt;chr&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; ## 1 (Intercept) 1.91 0.0584 32.7 6.60e-235 ## 2 temp 0.00614 0.00258 2.38 1.74e- 2 Use the fitted model coefficient to determine the relative risk for a one degree Celsius increase in temperature. First, remember that you can use the tidy() function to read out the model coefficients. The second of these is the value for the temperature coefficient. That means that you can use indexing ([2]) to get just that value. That’s the log relative risk; take the exponent to get the relative risk. tidy(mod_3) %&gt;% filter(term == &quot;temp&quot;) %&gt;% mutate(log_rr = exp(estimate)) ## # A tibble: 1 x 6 ## term estimate std.error statistic p.value log_rr ## &lt;chr&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; ## 1 temp 0.00614 0.00258 2.38 0.0174 1.01 As a note, you can use the conf.int parameter in tidy to also pull confidence intervals: tidy(mod_3, conf.int = TRUE) ## # A tibble: 2 x 7 ## term estimate std.error statistic p.value conf.low conf.high ## &lt;chr&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; ## 1 (Intercept) 1.91 0.0584 32.7 6.60e-235 1.80 2.02 ## 2 temp 0.00614 0.00258 2.38 1.74e- 2 0.00108 0.0112 You could use this to get the confidence interval for relative risk (check out the mutate_at function if you haven’t seen it before): tidy(mod_3, conf.int = TRUE) %&gt;% select(term, estimate, conf.low, conf.high) %&gt;% filter(term == &quot;temp&quot;) %&gt;% mutate_at(vars(estimate:conf.high), funs(exp(.))) ## Warning: `funs()` is deprecated as of dplyr 0.8.0. ## Please use a list of either functions or lambdas: ## ## # Simple named list: ## list(mean = mean, median = median) ## ## # Auto named with `tibble::lst()`: ## tibble::lst(mean, median) ## ## # Using lambdas ## list(~ mean(., trim = .2), ~ median(., na.rm = TRUE)) ## This warning is displayed once every 8 hours. ## Call `lifecycle::last_warnings()` to see where this warning was generated. ## # A tibble: 1 x 4 ## term estimate conf.low conf.high ## &lt;chr&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; ## 1 temp 1.01 1.00 1.01 7.8.6 Trying out nesting and mapping We’ll be using data that’s in an R package called “nycflights13”. This data package can be installed from CRAN. Install the package and then load the package and its “flights” dataset. So that this data will be easier to work with, remove all columns except for those for the departure delay (dep_delay), the carrier (carrier), the hour the flight was supposed to leave (hour), and the airport the flight left from (origin). Also, limit the dataset to only flights that left from LaGuardia Airport (“LGA”). We want to figure out if the probability of a flight leaving 15 minutes late or more increases over the day for flights leaving LaGuardia. Filter out all the rows where the departure delay is missing and then create a new column called late_dep that is true if the flight left 15 minutes or more late and false otherwise. What proportion of all flights leaving from LaGuardia leave 15 minutes late or later? Next, determine what proportion of all flights are delayed base on the hour that the flight was scheduled to depart (hour). Create a plot showing how the probability of leaving 15 minutes or more late changes by hour. Fit a generalized linear model for the association between the binary variable of whether the flight was 15 minutes or more late (late_dep) and the hour the flight was scheduled to leave (hour). Use a binomial model (add family = binomial(link = \"logit\") in the glm call). The estimate from the model for hour will be an estimate of the log odds ratio for a one-hour increase in scheduled departure time. Take the exponent of this estimate with exp to get an estimated odds ratio for a one-hour increase in scheduled departure time. Is this estimate larger than 1.0? Next, we want to see if this association is similar across airlines. First, create a dataframe called nested_flights where the flights data is grouped by airline (carrier) and then nest the data, so that there is a “data” list-column where each item is a dataframe of flight delay data for a specific carrier. Use the map function from purrr inside a mutate statement to apply the glm code you used earlier for the whole dataset, but in this case for the data for each airline character. Then use the map function inside a mutate statement again to “tidy” the data. Clean the data up a bit. Remove the columns for data and glm_result and then unnest the dataframe list-column with the tidy version of the model results. Filter to get only the estimates for the “hour” term. Then calculate an odds ratio (or) by taking the exponent (check out the exp function) of the original estimate. The package has a dataframe with the full name of each carrier (airlines). Join this data into the data you’ve been working with, so you have the full names of airlines. Finally, create the following plot with each airline’s odds ratio for the change in the chance of a delay per one-hour increase in the scheduled hour of departure: 7.8.6.1 Example R code: Install the “nycflights13” package from CRAN. Load the package and its “flights” dataset. library(ggplot2) library(nycflights13) data(flights) So that this data will be easier to work with, remove all columns except for those for the departure dealy (dep_delay), the carrier (carrier), the hour the flight was supposed to leave (hour), and the airport the flight left from (origin). flights &lt;- flights %&gt;% select(dep_delay, carrier, hour, origin) %&gt;% filter(origin == &quot;LGA&quot;) flights ## # A tibble: 104,662 x 4 ## dep_delay carrier hour origin ## &lt;dbl&gt; &lt;chr&gt; &lt;dbl&gt; &lt;chr&gt; ## 1 4 UA 5 LGA ## 2 -6 DL 6 LGA ## 3 -3 EV 6 LGA ## 4 -2 AA 6 LGA ## 5 -1 AA 6 LGA ## 6 0 B6 6 LGA ## 7 0 MQ 6 LGA ## 8 -8 DL 6 LGA ## 9 -3 MQ 6 LGA ## 10 13 AA 6 LGA ## # … with 104,652 more rows Filter out all the rows where the departure delay is missing and then create a new column called late_dep that is true if the flight left 15 minutes or more late and false otherwise. flights &lt;- flights %&gt;% filter(!is.na(dep_delay)) %&gt;% mutate(late_dep = dep_delay &gt; 15) flights ## # A tibble: 101,509 x 5 ## dep_delay carrier hour origin late_dep ## &lt;dbl&gt; &lt;chr&gt; &lt;dbl&gt; &lt;chr&gt; &lt;lgl&gt; ## 1 4 UA 5 LGA FALSE ## 2 -6 DL 6 LGA FALSE ## 3 -3 EV 6 LGA FALSE ## 4 -2 AA 6 LGA FALSE ## 5 -1 AA 6 LGA FALSE ## 6 0 B6 6 LGA FALSE ## 7 0 MQ 6 LGA FALSE ## 8 -8 DL 6 LGA FALSE ## 9 -3 MQ 6 LGA FALSE ## 10 13 AA 6 LGA FALSE ## # … with 101,499 more rows What proportion of all flights leaving from LaGuardia leave 15 minutes late or later? To check this, remember that TRUE is saved as a “1” and FALSE is saved as a “0”. That means that we can take the mean of a logical vector to get the proportion of trials that are true. flights %&gt;% purrr::pluck(&quot;late_dep&quot;) %&gt;% mean() ## [1] 0.1889685 Determine what proportion of all flights are delayed base on the hour that the flight was scheduled to depart (hour). Create a plot showing how the probability of leaving 15 minutes or more late changes by hour. flights_late &lt;- flights %&gt;% group_by(hour) %&gt;% summarize(prob_late = mean(late_dep)) ## `summarise()` ungrouping output (override with `.groups` argument) flights_late ## # A tibble: 18 x 2 ## hour prob_late ## &lt;dbl&gt; &lt;dbl&gt; ## 1 5 0.0588 ## 2 6 0.0519 ## 3 7 0.0570 ## 4 8 0.0967 ## 5 9 0.105 ## 6 10 0.128 ## 7 11 0.159 ## 8 12 0.163 ## 9 13 0.200 ## 10 14 0.207 ## 11 15 0.236 ## 12 16 0.273 ## 13 17 0.292 ## 14 18 0.298 ## 15 19 0.314 ## 16 20 0.331 ## 17 21 0.309 ## 18 22 0.391 flights_late %&gt;% ggplot(aes(x = hour, y = prob_late)) + geom_line() Fit a generalized linear model for the association between the binary variable of whether the flight was 15 minutes or more late (late_dep) and the hour the flight was scheduled to leave (hour). Use a binomial model (add family = binomial(link = \"logit\") in the glm call). The estimate from the model for hour will be an estimate of the log odds ratio for a one-hour increase in scheduled departure time. Take the exponent of this estimate with exp to get an estimated odds ratio for a one-hour increase in scheduled departure time. Is this estimate larger than 1.0? glm(late_dep ~ hour, data = flights, family = binomial(link = &quot;logit&quot;)) ## ## Call: glm(formula = late_dep ~ hour, family = binomial(link = &quot;logit&quot;), ## data = flights) ## ## Coefficients: ## (Intercept) hour ## -3.3643 0.1399 ## ## Degrees of Freedom: 101508 Total (i.e. Null); 101507 Residual ## Null Deviance: 98410 ## Residual Deviance: 92810 AIC: 92810 library(broom) glm(late_dep ~ hour, data = flights, family = binomial(link = &quot;logit&quot;)) %&gt;% tidy() %&gt;% # Tidy the model results filter(term == &quot;hour&quot;) %&gt;% # Only look at the estimate for `hour` mutate(or = exp(estimate)) # Estimate is log odds ratio. Take exponent for odds ratio ## # A tibble: 1 x 6 ## term estimate std.error statistic p.value or ## &lt;chr&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; ## 1 hour 0.140 0.00195 71.7 0 1.15 Next, we want to see if this association is similar across airlines. First, create a dataframe called nested_flights where the flights data is grouped by airline (carrier) and then nest the data, so that there is a “data” list-column where each item is a dataframe of flight delay data for a specific carrier: nested_flights &lt;- flights %&gt;% group_by(carrier) %&gt;% nest() nested_flights ## # A tibble: 13 x 2 ## # Groups: carrier [13] ## carrier data ## &lt;chr&gt; &lt;list&gt; ## 1 UA &lt;tibble [7,837 × 4]&gt; ## 2 DL &lt;tibble [22,857 × 4]&gt; ## 3 EV &lt;tibble [8,255 × 4]&gt; ## 4 AA &lt;tibble [15,063 × 4]&gt; ## 5 B6 &lt;tibble [5,925 × 4]&gt; ## 6 MQ &lt;tibble [16,189 × 4]&gt; ## 7 WN &lt;tibble [6,000 × 4]&gt; ## 8 FL &lt;tibble [3,187 × 4]&gt; ## 9 US &lt;tibble [12,574 × 4]&gt; ## 10 F9 &lt;tibble [682 × 4]&gt; ## 11 9E &lt;tibble [2,372 × 4]&gt; ## 12 YV &lt;tibble [545 × 4]&gt; ## 13 OO &lt;tibble [23 × 4]&gt; To check the contents of the list-column, try: nested_flights$data[[1]] %&gt;% # Get the first element of the &quot;data&quot; column head() ## # A tibble: 6 x 4 ## dep_delay hour origin late_dep ## &lt;dbl&gt; &lt;dbl&gt; &lt;chr&gt; &lt;lgl&gt; ## 1 4 5 LGA FALSE ## 2 -4 6 LGA FALSE ## 3 1 6 LGA FALSE ## 4 9 7 LGA FALSE ## 5 -4 7 LGA FALSE ## 6 2 7 LGA FALSE Use the map function from purrr inside a mutate statement to apply the glm code you used earlier for the whole dataset, but in this case for the data for each airline character: library(purrr) library(tidyr) prob_late &lt;- nested_flights %&gt;% mutate(glm_result = purrr::map(data, ~ glm(late_dep ~ hour, data = .x, family = binomial(link = &quot;logit&quot;)))) # Check the results for the first element of the &quot;glm_result&quot; column: prob_late$glm_result[[1]] ## ## Call: glm(formula = late_dep ~ hour, family = binomial(link = &quot;logit&quot;), ## data = .x) ## ## Coefficients: ## (Intercept) hour ## -3.4305 0.1551 ## ## Degrees of Freedom: 7836 Total (i.e. Null); 7835 Residual ## Null Deviance: 7601 ## Residual Deviance: 7064 AIC: 7068 Then use the map function inside a mutate statement again to “tidy” the data. prob_late &lt;- prob_late %&gt;% mutate(glm_tidy = purrr::map(glm_result, ~ tidy(.x))) Remove the columns for data and glm_result: prob_late &lt;- prob_late %&gt;% select(-data, -glm_result) Then `unnest the dataframe list-column with the tidy version of the model results: prob_late &lt;- prob_late %&gt;% unnest(glm_tidy) Filter to get only the estimates for the “hour” term: prob_late &lt;- prob_late %&gt;% filter(term == &quot;hour&quot;) Then calculate an odds ratio (or) by taking the exponent (check the exp function) of the original estimate. prob_late &lt;- prob_late %&gt;% mutate(or = exp(estimate)) head(prob_late) ## # A tibble: 6 x 7 ## # Groups: carrier [6] ## carrier term estimate std.error statistic p.value or ## &lt;chr&gt; &lt;chr&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; ## 1 UA hour 0.155 0.00705 22.0 2.88e-107 1.17 ## 2 DL hour 0.148 0.00454 32.6 1.80e-232 1.16 ## 3 EV hour 0.0952 0.00572 16.7 2.79e- 62 1.10 ## 4 AA hour 0.146 0.00580 25.2 1.05e-139 1.16 ## 5 B6 hour 0.168 0.00702 23.9 7.13e-126 1.18 ## 6 MQ hour 0.110 0.00468 23.5 2.23e-122 1.12 The package has a dataframe with the full name of each carrier (airlines). Join this data into the data you’ve been working with, so you have the full names of airlines: prob_late &lt;- left_join(prob_late, airlines, by = &quot;carrier&quot;) Finally, create the following plot with each airline’s association between hour in the day and the chance of a delay: library(forcats) data(airlines) prob_late %&gt;% ungroup() %&gt;% mutate(estimate = exp(estimate)) %&gt;% mutate(name = fct_reorder(name, estimate)) %&gt;% ggplot(aes(x = estimate, y = name)) + geom_point() + geom_vline(xintercept = 1, linetype = 3) + labs(x = &quot;Odds ratio for one-hour increase in scheduled deparature time&quot;, y = &quot;&quot;) 7.8.7 Writing functions Say that you have a four-letter character string (e.g., “ling”) and that you want to move the last letter to the front of the string to create a new four-letter character string (e.g., “glin”). Write some R code to do this. Next, write a function named move_letter that does the same thing—takes a four-letter character string (e.g., “ling”) and creates a new four-letter character string where the last letter in the original string has been moved to the front of the string (e.g., “glin”). It should input one parameter (word, the original four-letter character string or a vector of four-letter character strings). Read the word list at https://raw.githubusercontent.com/dwyl/english-words/master/words.txt into an R dataframe called word_list. It will only have one column; name this column word. Write code that can take a vector of character strings (e.g., c('ling', 'scat', 'soil')) and return a logical vector that says whether that character string is a word in the word_list dataframe you created in the last step (e.g., c('FALSE', 'TRUE', 'TRUE') for a vector where the first value isn’t a word but the second and third are). You may find the pluck function in the purrr package useful in writing this code (to pull the word column out of the word_list dataframe). Write a function called is_word that inputs (1) a vector of character strings and (2) a dataframe with a column called “word” that lists real words. The function should return a logical vector saying whether each character string is a real word. The function should have two arguments: words_to_check, which is a vector of character strings (e.g., c('ling', 'scat', 'soil')), and real_word_list, which is a dataframe with a column called words of real English words (e.g., the word_list dataframe you created from the word list on GitHub). Set the real_word_list argument to have the default value of words, the dataframe you created earlier in this exercise by reading in the dataframe of English words from GitHub. Try using the function with a different word list. As an example, you could read in and use the word list of Google’s top 10,000 English words from https://raw.githubusercontent.com/first20hours/google-10000-english/master/google-10000-english-no-swears.txt. Try using these functions to solve the word puzzle problem in the last homework. 7.8.7.1 Example R code Say that you have a four-letter character string (e.g., “ling”) and that you want to move the last letter to the front of the string to create a new four-letter character string (e.g., “glin”). You can use functions from the stringr package to help with this. library(stringr) # Start with an example word. You can use the example word from # the problem statement (&#39;ling&#39;). word &lt;- &quot;ling&quot; # Break the word into two parts first_three_letters &lt;- str_sub(word, 1, 3) last_letter &lt;- str_sub(word, 4, 4) first_three_letters ## [1] &quot;lin&quot; last_letter ## [1] &quot;g&quot; # Put the parts back together in the right order new_word &lt;- str_c(last_letter, first_three_letters) # You could also use `paste` or `paste0` here new_word ## [1] &quot;glin&quot; Write a function named move_letter that takes a four-letter character string (e.g., “ling”) and creates a new four-letter character string where the last letter in the original string has been moved to the front of the string (e.g., “glin”). It should input one parameter (word, the original four-letter character string or a vector of four-letter character strings). To do this, take the code that you just wrote and put it inside a function named move_letter: move_letter &lt;- function(word){ # Break the word into two parts first_three_letters &lt;- str_sub(word, 1, 3) last_letter &lt;- str_sub(word, 4, 4) # Put the parts back together in the right order str_c(last_letter, first_three_letters) } # Check the function move_letter(word = &quot;ling&quot;) ## [1] &quot;glin&quot; # Try with some other four-letter strings move_letter(word = &quot;cats&quot;) ## [1] &quot;scat&quot; move_letter(word = &quot;oils&quot;) ## [1] &quot;soil&quot; # Check using with a vector of four-letter character strings move_letter(c(&quot;cats&quot;, &quot;oils&quot;)) ## [1] &quot;scat&quot; &quot;soil&quot; Notice that now word is being used as an argument in the function. It’s as if you assigned the string or vector of strings that you want to convert to the object name word, and then you run all the code. The output of the function is the last expression in the function code (str_c(last_letter, first_three_letters)). Also, note that you can add comments inside the function with #, just like you can with other R code. Read the word list at https://raw.githubusercontent.com/dwyl/english-words/master/words.txt into an R dataframe called words. It will only have one column; name this column word_list. library(readr) word_list &lt;- read_csv(&quot;https://raw.githubusercontent.com/dwyl/english-words/master/words.txt&quot;, col_names = &quot;word&quot;) ## Parsed with column specification: ## cols( ## word = col_character() ## ) ## Warning: 2 parsing failures. ## row col expected actual file ## 11 -- 1 columns 3 columns &#39;https://raw.githubusercontent.com/dwyl/english-words/master/words.txt&#39; ## 12 -- 1 columns 2 columns &#39;https://raw.githubusercontent.com/dwyl/english-words/master/words.txt&#39; Write code that can take a vector of character strings (e.g., c('ling', 'scat', 'soil')) and return a logical vector that says whether that character string is a word in the word_list dataframe you created in the last step (e.g., c('TRUE', 'FALSE', 'TRUE') for a vector where the second value isn’t a word but the first and third are). library(purrr) words_to_check &lt;- c(&quot;ling&quot;, &quot;scat&quot;, &quot;soil&quot;) words_to_check %in% pluck(word_list, &quot;word&quot;) ## [1] TRUE FALSE TRUE This code works because you can use pluck to extract a column (as a vector) from a dataframe, and then you can use the %in% operator to see if each value in one vector (the words_to_check vector in this example) is one of the values in the second vector (the word column from the words dataframe in this example). Write a function called is_word that inputs (1) a vector of character strings and (2) a dataframe with a column called “word” that lists real words. The function should return a logical vector saying whether each character string is a real word. The function should have two arguments: words_to_check, which is a vector of character strings and real_word_list which is a dataframe with a column called words of real English words. Set the real_word_list argument to have the default value of words, the dataframe you created earlier in this exercise by reading in the dataframe of English words from GitHub. # Put the code you wrote inside a function is_word &lt;- function(words_to_check, real_word_list = word_list){ words_to_check %in% pluck(real_word_list, &quot;word&quot;) } # Check the function is_word(words_to_check = c(&quot;ling&quot;, &quot;scat&quot;, &quot;soil&quot;)) ## [1] TRUE FALSE TRUE Note that, if you want to use the word_list dataframe for your real word list, you don’t have to specify that when you call the is_word function, since you set that as your default value. If you wanted to use a different word list, you can specify a different value for the real_word_list argument when you run the function. For example, to use Google’s top 10,000 English word list from GitHub instead, use: google_words &lt;- read_csv(&quot;https://raw.githubusercontent.com/first20hours/google-10000-english/master/google-10000-english-no-swears.txt&quot;, col_names = &quot;word&quot;) ## Parsed with column specification: ## cols( ## word = col_character() ## ) is_word(words_to_check = c(&quot;ling&quot;, &quot;scat&quot;, &quot;soil&quot;), real_word_list = google_words) ## [1] FALSE FALSE TRUE In this case, there was a rarer word (“ling”) that counted as a word when the original word list was used, but not when the Google top-10,000 words list was used. Try using these functions to solve the word puzzle problem in the last homework. word_list %&gt;% filter(str_detect(word, &quot;^m.{7}$&quot;)) %&gt;% # This is using regular expressions separate(word, into = c(&quot;first_word&quot;, &quot;second_word&quot;), # Note that you can use `separate` with a number to use position sep = 4, remove = FALSE) %&gt;% mutate(second_word = move_letter(second_word)) %&gt;% # Use the first function here filter(is_word(first_word) &amp; is_word(second_word)) %&gt;% # Use the second function here unite(&quot;new_word&quot;, c(&quot;first_word&quot;, &quot;second_word&quot;), sep = &quot; &quot;) # You can use `unite` to put the two new words in a phrase in one column. ## # A tibble: 103 x 2 ## word new_word ## &lt;chr&gt; &lt;chr&gt; ## 1 mailings mail sing ## 2 maillots mail slot ## 3 majorate majo erat ## 4 majoring majo grin ## 5 malmiest malm ties ## 6 maltiest malt ties ## 7 mandalas mand sala ## 8 mandated mand date ## 9 mandates mand sate ## 10 mangiest mang ties ## # … with 93 more rows You can get the choices down to even fewer options if you match the new words against the Google top-10,000 words list, by using the real_word_list option in the is_word function you wrote. (However, “maillots” is not a common enough word that you can also start from that shorter word list!) word_list %&gt;% filter(str_detect(word, &quot;^m.{7}$&quot;)) %&gt;% separate(word, into = c(&quot;first_word&quot;, &quot;second_word&quot;), sep = 4, remove = FALSE) %&gt;% mutate(second_word = move_letter(second_word)) %&gt;% filter(is_word(first_word, real_word_list = google_words) &amp; is_word(second_word, real_word_list = google_words)) %&gt;% unite(&quot;new_word&quot;, c(&quot;first_word&quot;, &quot;second_word&quot;), sep = &quot; &quot;) ## # A tibble: 43 x 2 ## word new_word ## &lt;chr&gt; &lt;chr&gt; ## 1 mailings mail sing ## 2 maillots mail slot ## 3 maintops main stop ## 4 markings mark sing ## 5 maskings mask sing ## 6 massager mass rage ## 7 massages mass sage ## 8 massiest mass ties ## 9 mattings matt sing ## 10 mealiest meal ties ## # … with 33 more rows "],
+["reproducible-research-1.html", "Chapter 5 Reproducible research #1 5.1 What is reproducible research? 5.2 Markdown 5.3 Literate programming in R 5.4 Style guidelines 5.5 More with knitr 5.6 In-course exercise", " Chapter 5 Reproducible research #1 Download a pdf of the lecture slides covering this topic. 5.1 What is reproducible research? A data analysis is reproducible if all the information (data, files, etc.) required is available for someone else to re-do your entire analysis. This includes: Data available All code for cleaning raw data All code and software (specific versions, packages) for analysis Some advantages of making your research reproducible are: You can (easily) figure out what you did six months from now. You can (easily) make adjustments to code or data, even early in the process, and re-run all analysis. When you’re ready to publish, you can (easily) do a last double-check of your full analysis, from cleaning the raw data through generating figures and tables for the paper. You can pass along or share a project with others. You can give useful code examples to people who want to extend your research. Here is a famous research example of the dangers of writing code that is hard to double-check or confirm: The Economist The New York Times Simply Statistics Some of the steps required to making research reproducible are: All your raw data should be saved in the project directory. You should have clear documentation on the source of all this data. Scripts should be included with all the code used to clean this data into the data set(s) used for final analyses and to create any figures and tables. You should include details on the versions of any software used in analysis (for R, this includes the version of R as well as versions of all packages used). If possible, there should be no “by hand” steps used in the analysis; instead, all steps should be done using code saved in scripts. For example, you should use a script to clean data, rather than cleaning it by hand in Excel. If any “non-scriptable” steps are unavoidable, you should very clearly document those steps. There are several software tools that can help you improve the reproducibility of your research: knitr: Create files that include both your code and text. These can be rendered to create final reports and papers. They keep code within the final file for the report. knitr complements: Create fancier tables and figures within RMarkdown documents. Packages include tikzDevice, animate, xtables, and pander. packrat: Save versions of each package used for the analysis, then load those package versions when code is run again in the future. In this section, I will focus on using knitr and RMarkdown files. 5.2 Markdown R Markdown files are mostly written using Markdown. To write R Markdown files, you need to understand what markup languages like Markdown are and how they work. In Word and other word processing programs you have used, you can add formatting using buttons and keyboard shortcuts (e.g., “Ctrl-B” for bold). The file saves the words you type. It also saves the formatting, but you see the final output, rather than the formatting markup, when you edit the file (WYSIWYG – what you see is what you get). In markup languages, on the other hand, you markup the document directly to show what formatting the final version should have (e.g., you type **bold** in the file to end up with a document with bold). Examples of markup languages include: HTML (HyperText Markup Language) LaTex Markdown (a “lightweight” markup language) For example, Figure 5.1 some marked-up HTML code from CSU’s website, while Figure 5.2 shows how that file looks when it’s rendered by a web browser. Figure 5.1: Example of the source of an HTML file. Figure 5.2: Example of a rendered HTML file. To write a file in Markdown, you’ll need to learn the conventions for creating formatting. This table shows what you would need to write in a flat file for some common formatting choices: Code Rendering Explanation **text** text boldface *text* text italicized [text](www.google.com) text hyperlink # text first-level header ## text second-level header Some other simple things you can do in Markdown include: Lists (ordered or bulleted) Equations Tables Figures from file Block quotes Superscripts For more Markdown conventions, see RStudio’s R Markdown Reference Guide (link also available through “Help” in RStudio). 5.3 Literate programming in R Literate programming, an idea developed by Donald Knuth, mixes code that can be executed with regular text. The files you create can then be rendered, to run any embedded code. The final output will have results from your code and the regular text. The knitr package can be used for literate programming in R. In essence, knitr allows you to write an R Markdown file that can be rendered into a pdf, Word, or HTML document. Here are the basics of opening and rendering an R Markdown file in RStudio: To open a new R Markdown file, go to “File” -&gt; “New File” -&gt; “RMarkdown…” -&gt; for now, chose a “Document” in “HTML” format. This will open a new R Markdown file in RStudio. The file extension for R Markdown files is “.Rmd”. The new file comes with some example code and text. You can run the file as-is to try out the example. You will ultimately delete this example code and text and replace it with your own. Once you “knit” the R Markdown file, R will render an HTML file with the output. This is automatically saved in the same directory where you saved your .Rmd file. Write everything besides R code using Markdown syntax. To include R code in an RMarkdown document, you need to separate off the code chunk using the following syntax: ```{r} my_vec &lt;- 1:10 ``` This syntax tells R how to find the start and end of pieces of R code when the file is rendered. R will walk through, find each piece of R code, run it and create output (printed output or figures, for example), and then pass the file along to another program to complete rendering (e.g., Tex for pdf files). You can specify a name for each chunk, if you’d like, by including it after “r” when you begin your chunk. For example, to give the name load_nepali to a code chunk that loads the nepali dataset, specify that name in the start of the code chunk: ```{r load_nepali} library(faraway) data(nepali) ``` Here are a couple of tips for naming code chunks: Chunk names must be unique across a document. Any chunks you don’t name are given numbers by knitr. You do not have to name each chunk. However, there are some advantages: It will be easier to find any errors. You can use the chunk labels in referencing for figure labels. You can reference chunks later by name. You can add options when you start a chunk. Many of these options can be set as TRUE / FALSE and include: Option Action echo Print out the R code? eval Run the R code? messages Print out messages? warnings Print out warnings? include If FALSE, run code, but don’t print code or results Other chunk options take values other than TRUE / FALSE. Some you might want to include are: Option Action results How to print results (e.g., hide runs the code, but doesn’t print the results) fig.width Width to print your figure, in inches (e.g., fig.width = 4) fig.height Height to print your figure Add these options in the opening brackets and separate multiple ones with commas: ```{r messages = FALSE, echo = FALSE} nepali[1, 1:3] ``` I will cover other chunk options later, once you’ve gotten the chance to try writting R Markdown files. You can set “global” options at the beginning of the document. This will create new defaults for all of the chunks in the document. For example, if you want echo, warning, and message to be FALSE by default in all code chunks, you can run: ```{r global_options} knitr::opts_chunk$set(echo = FALSE, message = FALSE, warning = FALSE) ``` If you set both global and local chunk options that you set specifically for a chunk will take precedence over global options. For example, running a document with: ```{r global_options} knitr::opts_chunk$set(echo = FALSE, message = FALSE, warning = FALSE) ``` ```{r check_nepali, echo = TRUE} head(nepali, 1) ``` would print the code for the check_nepali chunk, because the option specified for that specific chunk (echo = TRUE) would override the global option (echo = FALSE). You can also include R output directly in your text (“inline”) using backticks: “There are `r nrow(nepali)` observations in the nepali data set. The average age is `r mean(nepali$age, na.rm = TRUE)` months.” Once the file is rendered, this gives: “There are 1000 observations in the nepali data set. The average age is 37.662 months.” Here are two tips that will help you diagnose some problems rendering R Markdown files: Be sure to save your R Markdown file before you run it. All the code in the file will run “from scratch”– as if you just opened a new R session. The code will run using, as a working directory, the directory where you saved the R Markdown file. You’ll want to try out pieces of your code as you write an R Markdown document. There are a few ways you can do that: You can run code in chunks just like you can run code from a script (Ctrl-Return or the “Run” button). You can run all the code in a chunk (or all the code in all chunks) using the different options under the “Run” button in RStudio. All the “Run” options have keyboard shortcuts, so you can use those. You can render R Markdown documents to other formats: Word Pdf (requires that you’ve installed “Tex” on your computer.) Slides (ioslides) Click the button to the right of “Knit” to see different options for rendering on your computer. You can freely post your RMarkdown documents at RPubs. If you want to post to RPubs, you need to create an account. Once you do, you can click the “Publish” button on the window that pops up with your rendered file. RPubs can also be a great place to look for interesting example code, although it sometimes can be pretty overwhelmed with MOOC homework. If you’d like to find out more, here are two good how-to books on reproducible research in R (the CSU library has both in hard copy): Reproducible Research with R and RStudio, Christopher Gandrud Dynamic Documents with R and knitr, Yihui Xie 5.4 Style guidelines R style guidelines provide rules for how to format code in an R script. Some people develop their own style as they learn to code. However, it is easy to get in the habit of following style guidelines, and they offer some important advantages: Clean code is easier to read and interpret later. It’s easier to catch and fix mistakes when code is clear. Others can more easily follow and adapt your code if it’s clean. Some style guidelines will help prevent possible problems (e.g., avoiding . in function names). For this course, we will use R style guidelines from two sources: Google’s R style guidelines Hadley Wickham’s R style guidelines These two sets of style guidelines are very similar. Hear are a few guidelines we’ve already covered in class: Use &lt;-, not =, for assignment. Guidelines for naming objects: All lowercase letters or numbers Use underscore (_) to separate words, not camelCase or a dot (.) (this differs for Google and Wickham style guides) Have some consistent names to use for “throw-away” objects (e.g., df, ex, a, b) Make names meaningful Descriptive names for R scripts (“random_group_assignment.R”) Nouns for objects (todays_groups for an object with group assignments) Verbs for functions (make_groups for the function to assign groups) 5.4.1 Line length Google: Keep lines to 80 characters or less To set your script pane to be limited to 80 characters, go to “RStudio” -&gt; “Preferences” -&gt; “Code” -&gt; “Display”, and set “Margin Column” to 80. # Do my_df &lt;- data.frame(n = 1:3, letter = c(&quot;a&quot;, &quot;b&quot;, &quot;c&quot;), cap_letter = c(&quot;A&quot;, &quot;B&quot;, &quot;C&quot;)) # Don&#39;t my_df &lt;- data.frame(n = 1:3, letter = c(&quot;a&quot;, &quot;b&quot;, &quot;c&quot;), cap_letter = c(&quot;A&quot;, &quot;B&quot;, &quot;C&quot;)) This guideline helps ensure that your code is formatted in a way that you can see all of the code without scrolling horizontally (left and right). 5.4.2 Spacing Binary operators (e.g., &lt;-, +, -) should have a space on either side A comma should have a space after it, but not before. Colons should not have a space on either side. Put spaces before and after = when assigning parameter arguments # Do shots_per_min &lt;- worldcup$Shots / worldcup$Time #Don&#39;t shots_per_min&lt;-worldcup$Shots/worldcup$Time #Do ave_time &lt;- mean(worldcup[1:10 , &quot;Time&quot;]) #Don&#39;t ave_time&lt;-mean(worldcup[1 : 10 ,&quot;Time&quot;]) 5.4.3 Semicolons Although you can use a semicolon to put two lines of code on the same line, you should avoid it. # Do a &lt;- 1:10 b &lt;- 3 # Don&#39;t a &lt;- 1:10; b &lt;- 3 5.4.4 Commenting For a comment on its own line, use #. Follow with a space, then the comment. You can put a short comment at the end of a line of R code. In this case, put two spaces after the end of the code, one #, and one more space before the comment. If it helps make it easier to read your code, separate sections using a comment character followed by many hyphens (e.g., #------------). Anything after the comment character is “muted”. # Read in health data --------------------------- # Clean exposure data --------------------------- 5.4.5 Indentation Google: Within function calls, line up new lines with first letter after opening parenthesis for parameters to function calls: Example: # Relabel sex variable nepali$sex &lt;- factor(nepali$sex, levels = c(1, 2), labels = c(&quot;Male&quot;, &quot;Female&quot;)) 5.4.6 Code grouping Group related pieces of code together. Separate blocks of code by empty spaces. # Load data library(faraway) data(nepali) # Relabel sex variable nepali$sex &lt;- factor(nepali$sex, levels = c(1, 2), labels = c(&quot;Male&quot;, &quot;Female&quot;)) Note that this grouping often happens naturally when using tidyverse functions, since they encourage piping (%&gt;% and +). 5.4.7 Broader guidelines Omit needless code. Don’t repeat yourself. We’ll learn more about satisfying these guidelines when we talk about writing your own functions in the next part of the class. 5.5 More with knitr 5.5.1 Equations in knitr You can write equations in RMarkdown documents by setting them apart with dollar signs ($). For an equation on a line by itself (display equation), you two $s before and after the equation, on separate lines, then use LaTex syntax for writing the equations. To help with this, you may want to use this LaTex math cheat sheet.. You may also find an online LaTex equation editor like Codecogs.com helpful. Note: Equations denoted this way will always compile for pdf documents, but won’t always come through on Markdown files (for example, GitHub won’t compile math equations). For example, writing this in your R Markdown file: $$ E(Y_{t}) \\sim \\beta_{0} + \\beta_{1}X_{1} $$ will result in this rendered equation: \\[ E(Y_{t}) \\sim \\beta_{0} + \\beta_{1}X_{1} \\] To put math within a sentence (inline equation), just use one $ on either side of the math. For example, writing this in a R Markdown file: &quot;We are trying to model $E(Y_{t})$.&quot; The rendered document will show up as: “We are trying to model \\(E(Y_{t})\\).” 5.5.2 Figures from file You can include not only figures that you create with R, but also figures that you have saved on your computer. The best way to do that is with the include_graphics function in knitr: library(knitr) include_graphics(&quot;figures/CSU_ram.png&quot;) This example would include a figure with the filename “MyFigure.png” that is saved in the “figures” sub-directory of the parent directory of the directory where your .Rmd is saved. Don’t forget that you will need to give an absolute pathway or the relative pathway from the directory where the .Rmd file is saved. 5.5.3 Saving graphics files You can save figures that you create in R. Typically, you won’t need to save figures for an R Markdown file, since you can include figure code directly. However, you will sometimes want to save a figure from a script. You have two options: Use the “Export” choice in RStudio Write code to export the figure in your R script To make your research more reproducible, use the second choice. To use code export a figure you created in R, take three steps: Open a graphics device (e.g., pdf(\"MyFile.pdf\")). Write the code to print your plot. Close the graphics device using dev.off(). For example, the following code would save a scatterplot of time versus passes as a pdf named “MyFigure” in the “figures” subdirectory of the current working directory: pdf(&quot;figures/MyFigure.pdf&quot;, width = 8, height = 6) ggplot(worldcup, aes(x = Time, y = Passes)) + geom_point(aes(color = Position)) + theme_bw() dev.off() If you create multiple plots before you close the device, they’ll all save to different pages of the same pdf file. You can open a number of different graphics devices. Here are some of the functions you can use to open graphics devices: pdf png bmp jpeg tiff svg You will use a device-specific function to open a graphics device (e.g., pdf). However, you will always close these devices with dev.off. Most of the functions to open graphics devices include parameters like height and width. These can be used to specify the size of the output figure. The units for these depend on the device (e.g., inches for pdf, pixels by default for png). Use the helpfile for the function to determine these details. 5.5.4 Tables in R Markdown If you want to create a nice, formatted table from an R dataframe, you can do that using kable from the knitr package. my_df &lt;- data.frame(letters = c(&quot;a&quot;, &quot;b&quot;, &quot;c&quot;), numbers = 1:3) kable(my_df) letters numbers a 1 b 2 c 3 There are a few options for the kable function: arg expl colnames Column names (default: column name in the dataframe) align A vector giving the alignment for each column (‘l’, ‘c’, ‘r’) caption Table caption digits Number of digits to round to. If you want to round columns different amounts, use a vector with one element for each column. my.df &lt;- data.frame(letters = c(&quot;a&quot;, &quot;b&quot;, &quot;c&quot;), numbers = rnorm(3)) kable(my.df, digits = 2, align = c(&quot;r&quot;, &quot;c&quot;), caption = &quot;My new table&quot;, col.names = c(&quot;First 3 letters&quot;, &quot;First 3 numbers&quot;)) Table 5.1: My new table First 3 letters First 3 numbers a 1.28 b 0.49 c 0.96 From Yihui: “Want more features? No, that is all I have. You should turn to other packages for help. I’m not going to reinvent their wheels.” If you want to do fancier tables, you may want to explore the xtable and pander packages. As a note, these might both be more effective when compiling to pdf, rather than html. 5.6 In-course exercise For all of today’s tasks, you’ll use the code from last week’s in-course exercise to do the exercises. This week we are not focusing on writing new code, but rather on how to take R code and put it in an R Markdown file, so we can create reports from files that include the original code. 5.6.1 Creating a Markdown document First, you’ll create a Markdown document, without any R code in it yet. In RStudio, go to “File” -&gt; “New File” -&gt; “R Markdown”. From the window that brings up, choose “Document” on the left-hand column and “HTML” as the output format. A new file will open in the script pane of your RStudio session. Save this file (you may pick the name and directory). The file extension should be “.Rmd”. First, before you try to write your own Markdown, try rendering the example that the script includes by default. (This code is always included, as a template, when you first open a new RMarkdown file using the RStudio “New file” interface we used in this example.) Try rendering this default R Markdown example by clicking the “Knit” button at the top of the script file. For some of you, you may not yet have everything you need on your computer to be able to get this to work. If so, let me know. RStudio usually includes all the necessary tools when you install it, but there may be some exceptions. If you could get the document to knit, do the following tasks: Look through the HTML document that was created. Compare it to the R Markdown script that created it, and see if you can understand, at least broadly, what’s going on. Look in the directory where you saved the R Markdown file. You should now also see a new, .html file in that folder. Try opening it with a web browser like Safari. Go back to the R Markdown file. Delete everything after the initial header information (everything after the 6th line). In the header information, make sure the title, author, and date are things you’re happy with. If not, change them. Using Markdown syntax, write up a description of the data (worldcup) we used last week to create the fancier figure. Try to include the following elements: Bold and italic text Hyperlinks A list, either ordered or bulleted Headers 5.6.2 Adding in R code Now incorporate the R code from previous weeks’ exercises into your document. Once you get the document to render with some basic pieces of code in it, try the following: Try some different chunk options. For example, try setting echo = FALSE in some of your code chunks. Similarly, try using the options results = \"hide\" and include = FALSE. You should have at least one code chunk that generates figures. Try experimenting with the fig.width and fig.height options for the chunk to change the size of the figure. Try using the global commands. See if you can switch the echo default value for this document from TRUE (the usual default) to FALSE. 5.6.3 Working with R Markdown documents Finally, try the following tasks to get some experience working with R Markdown files in RStudio: Go to one of your code chunks. Locate the small gray arrow just to the left of the line where you initiate the code chunk. Click on it and see what happens. Then click on it again. Put your cursor inside one of your code chunks. Try using the “Run” button (or Ctrl-Return) to run code in that chunk at your R console. Did it work? Pick a code chunk in your document. Put your cursor somewhere in the code in that chunk. Click on the “Run” button and choose “Run All Chunks Above”. What did that do? If it did not work, what do you think might be going on? (Hint: Check getwd() and think about which directory you’ve used to save your R Markdown file.) Pick another chunk of code. Put the cursor somewhere in the code for that chunk. Click on the “Run” button and choose “Run Current Chunk”. Then try “Run Next Chunk”. Try to figure out all the options the “Run” button gives you and when each might be useful. Click on the small gray arrow to the right of the “Knit HTML” button. If the option is offered, select “Knit Word” and try it. What does this do? 5.6.4 R style guidelines Go through all the R code in your R Markdown file. Are there are places where your code is not following style conventions for R? Clean up your code to correct any of these issues. "],
+["entering-and-cleaning-data-2.html", "Chapter 6 Entering and cleaning data #2 6.1 Joining datasets 6.2 Tidy data 6.3 Longer data 6.4 Working with factors 6.5 String operations and regular expressions 6.6 Tidy select 6.7 In-course exercise", " Chapter 6 Entering and cleaning data #2 Download a pdf of the lecture slides covering this topic. 6.1 Joining datasets So far, you have only worked with a single data source at a time. When you work on your own projects, however, you typically will need to merge together two or more datasets to create the a data frame to answer your research question. For example, for air pollution epidemiology, you will often have to join several datasets: Health outcome data (e.g., number of deaths per day) Air pollution concentrations Weather measurements (since weather can be a confounder) Demographic data The dplyr package has a family of different functions to join two dataframes together, the *_join family of functions. All combine two dataframes, which I’ll call x and y here. The functions include: inner_join(x, y): Keep only rows where there are observations in both x and y. left_join(x, y): Keep all rows from x, whether they have a match in y or not. right_join(x, y): Keep all rows from y, whether they have a match in x or not. full_join(x, y): Keep all rows from both x and y, whether they have a match in the other dataset or not. In the examples, I’ll use two datasets, x and y. Both datasets include the column course. The other column in x is grade, while the other column in y is day. Observations exist for courses x and y in both datasets, but for w and z in only one dataset. x &lt;- data.frame(course = c(&quot;x&quot;, &quot;y&quot;, &quot;z&quot;), grade = c(90, 82, 78)) y &lt;- data.frame(course = c(&quot;w&quot;, &quot;x&quot;, &quot;y&quot;), day = c(&quot;Tues&quot;, &quot;Mon / Fri&quot;, &quot;Tue&quot;)) Here is what these two example datasets look like: x ## course grade ## 1 x 90 ## 2 y 82 ## 3 z 78 y ## course day ## 1 w Tues ## 2 x Mon / Fri ## 3 y Tue With inner_join, you’ll only get the observations that show up in both datasets. That means you’ll lose data on z (only in the first dataset) and w (only in the second dataset). inner_join(x, y) ## Joining, by = &quot;course&quot; ## course grade day ## 1 x 90 Mon / Fri ## 2 y 82 Tue With left_join, you’ll keep everything in x (the “left” dataset), but not keep things in y that don’t match something in x. That means that, here, you’ll lose w: left_join(x, y) ## Joining, by = &quot;course&quot; ## course grade day ## 1 x 90 Mon / Fri ## 2 y 82 Tue ## 3 z 78 &lt;NA&gt; right_join is the opposite: right_join(x, y) ## Joining, by = &quot;course&quot; ## course grade day ## 1 x 90 Mon / Fri ## 2 y 82 Tue ## 3 w NA Tues full_join keeps everything from both datasets: full_join(x, y) ## Joining, by = &quot;course&quot; ## course grade day ## 1 x 90 Mon / Fri ## 2 y 82 Tue ## 3 z 78 &lt;NA&gt; ## 4 w NA Tues 6.2 Tidy data All of the material in this section comes directly from Hadley Wickham’s paper on tidy data. You will need to read this paper to prepare for the quiz on this section. Getting your data into a “tidy” format makes it easier to model and plot. By taking the time to tidy your data at the start of an analysis, you will save yourself time, and make it easier to plan out later steps. Characteristics of tidy data are: Each variable forms a column. Each observation forms a row. Each type of observational unit forms a table. Here are five common problems that Hadley Wickham has identified that keep data from being tidy: Column headers are values, not variable names. Multiple variables are stored in one column. Variables are stored in both rows and columns. Multiple types of observational units are stored in the same table. A single observational unit is stored in multiple tables. Here are examples (again, from Hadley Wickham’s paper on tidy data, which is required reading for this week of the course) of each of these problems. Column headers are values, not variable names. Solution: Multiple variables are stored in one column. Solution: Variables are stored in both rows and columns. Solution: Multiple types of observational units are stored in the same table. Solution: A single observational unit is stored in multiple tables. Example: exposure and outcome data stored in different files: File 1: Daily mortality counts File 2: Daily air pollution measurements 6.3 Longer data There are two functions from the tidyr package (another member of the tidyverse) that you can use to change between wider and longr data: pivot_longer and pivot_wider. Here is a description of these two functions: pivot_longer: Takes several columns and pivots them down into two columns. One of the new columns contains the former column names and the other contains the former cell values. pivot_wider: Takes two columns and pivots them up into multiple columns. Column names for the new columns will come from one column and the cell values from the other. The following examples are show the effects of making a dataset longer or wider. Here is some simulated wide data: wide_stocks[1:3, ] ## # A tibble: 3 x 4 ## time X Y Z ## &lt;date&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; ## 1 2009-01-01 -0.141 2.00 6.45 ## 2 2009-01-02 -0.857 -0.286 2.43 ## 3 2009-01-03 -0.434 2.19 -6.25 In the wide_stocks dataset, there are separate columns for three different stocks (X, Y, and Z). Each cell gives the value for a certain stock on a certain day. This data isn’t “tidy”, because the identify of the stock (X, Y, or Z) is a variable, and you’ll probably want to include it as a variable in modeling. wide_stocks[1:3, ] ## # A tibble: 3 x 4 ## time X Y Z ## &lt;date&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; ## 1 2009-01-01 -0.141 2.00 6.45 ## 2 2009-01-02 -0.857 -0.286 2.43 ## 3 2009-01-03 -0.434 2.19 -6.25 If you want to convert the dataframe to have all stock values in a single column, you can use pivot_longer to convert wide data to long data: long_stocks &lt;- pivot_longer(data = wide_stocks, cols = -time, names_to = &quot;stock&quot;, values_to = &quot;price&quot;) long_stocks[1:5, ] ## # A tibble: 5 x 3 ## time stock price ## &lt;date&gt; &lt;chr&gt; &lt;dbl&gt; ## 1 2009-01-01 X -0.141 ## 2 2009-01-01 Y 2.00 ## 3 2009-01-01 Z 6.45 ## 4 2009-01-02 X -0.857 ## 5 2009-01-02 Y -0.286 In this “longer” dataframe, there is now one column that gives the identify of the stock (stock) and another column that gives the price of that stock that day (price): long_stocks[1:5, ] ## # A tibble: 5 x 3 ## time stock price ## &lt;date&gt; &lt;chr&gt; &lt;dbl&gt; ## 1 2009-01-01 X -0.141 ## 2 2009-01-01 Y 2.00 ## 3 2009-01-01 Z 6.45 ## 4 2009-01-02 X -0.857 ## 5 2009-01-02 Y -0.286 The format for a pivots_longer call is: ## Generic code new_df &lt;- pivot_longer(old_df, cols = [name(s) of the columns you want to make longer], names_to = [name of new column to store the old column names], values_to = [name of new column to store the old values]) Three important notes: Everything is pivoted into one of two columns – one column with the old column names, and one column with the old cell values With the names_to and values_to arguments, you are just providing column names for the two columns that everything’s pivoted into. If there is a column you don’t want to include in the pivot (date in the example), use - to exclude it in the cols argument. Notice how easy it is, now that the data is longer, to use stock for aesthetics of faceting in a ggplot2 call: ggplot(long_stocks, aes(x = time, y = price)) + geom_line() + facet_grid(. ~ stock) + theme_bw() If you have data in a “longer” format and would like to make it “wider”, you can use pivot_wider to do that: stocks &lt;- pivot_wider(long_stocks, names_from = &quot;stock&quot;, values_from = price) stocks[1:5, ] ## # A tibble: 5 x 4 ## time X Y Z ## &lt;date&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; ## 1 2009-01-01 -0.141 2.00 6.45 ## 2 2009-01-02 -0.857 -0.286 2.43 ## 3 2009-01-03 -0.434 2.19 -6.25 ## 4 2009-01-04 -1.78 1.29 -2.42 ## 5 2009-01-05 -0.0842 3.67 -2.05 Notice that this reverses the action of pivot_longer. The “wider” your data the less likely it is to be tidy, so won’t use pivot_wider frequently when you are preparing data for analysis. However, pivot_wider can be very helpful in creating tables for final reports and presentations. For example, if you wanted to create a table with means and standard deviations for each of the three stocks, you could use pivot_wider to rearrange the final summary to create an attractive table. stock_summary &lt;- long_stocks %&gt;% group_by(stock) %&gt;% summarize(N = n(), mean = mean(price), sd = sd(price)) ## `summarise()` ungrouping output (override with `.groups` argument) stock_summary ## # A tibble: 3 x 4 ## stock N mean sd ## &lt;chr&gt; &lt;int&gt; &lt;dbl&gt; &lt;dbl&gt; ## 1 X 10 -0.333 0.746 ## 2 Y 10 1.12 2.69 ## 3 Z 10 -1.11 3.63 stock_summary %&gt;% mutate(&quot;Mean (Std.dev.)&quot; = paste0(round(mean, 2), &quot; (&quot;, round(sd, 2), &quot;)&quot;)) %&gt;% dplyr::select(- mean, - sd) %&gt;% mutate(N = as.character(N)) %&gt;% # might be able to deal with this in pivot_longer call pivot_longer(cols = -stock, names_to = &quot;Statistic&quot;, values_to = &quot;Value&quot;) %&gt;% pivot_wider(names_from = &quot;stock&quot;, values_from = &quot;Value&quot;) %&gt;% knitr::kable() Statistic X Y Z N 10 10 10 Mean (Std.dev.) -0.33 (0.75) 1.12 (2.69) -1.11 (3.63) 6.4 Working with factors Hadley Wickham has developed a package called forcats that helps you work with categorical variables (factors). I’ll show some examples of its functions using the worldcup dataset: library(forcats) library(faraway) data(worldcup) The fct_recode function can be used to change the labels of a function (along the lines of using factor with levels and labels to reset factor labels). One big advantage is that fct_recode lets you change labels for some, but not all, levels. For example, here are the team names: library(stringr) worldcup %&gt;% filter(str_detect(Team, &quot;^US&quot;)) %&gt;% slice(1:3) %&gt;% select(Team, Position, Time) ## Team Position Time ## Beasley USA Midfielder 10 ## Bocanegra USA Defender 390 ## Bornstein USA Defender 200 If you just want to change “USA” to “United States,” you can run: worldcup &lt;- worldcup %&gt;% mutate(Team = fct_recode(Team, `United States` = &quot;USA&quot;)) worldcup %&gt;% filter(str_detect(Team, &quot;^Un&quot;)) %&gt;% slice(1:3) %&gt;% select(Team, Position, Time) ## Team Position Time ## 1 United States Midfielder 10 ## 2 United States Defender 390 ## 3 United States Defender 200 You can use the fct_lump function to lump uncommon factors into an “Other” category. For example, to lump the two least common positions together, you can run (n specifies how many categories to keep outside of “Other”): worldcup %&gt;% dplyr::mutate(Position = forcats::fct_lump(Position, n = 2)) %&gt;% dplyr::count(Position) ## Position n ## 1 Defender 188 ## 2 Midfielder 228 ## 3 Other 179 You can use the fct_infreq function to reorder the levels of a factor from most common to least common: levels(worldcup$Position) ## [1] &quot;Defender&quot; &quot;Forward&quot; &quot;Goalkeeper&quot; &quot;Midfielder&quot; worldcup &lt;- worldcup %&gt;% mutate(Position = fct_infreq(Position)) levels(worldcup$Position) ## [1] &quot;Midfielder&quot; &quot;Defender&quot; &quot;Forward&quot; &quot;Goalkeeper&quot; If you want to reorder one factor by another variable (ascending order), you can use fct_reorder (e.g., homework 3). For example, to relevel Position by the average shots on goals for each position, you can run: levels(worldcup$Position) ## [1] &quot;Midfielder&quot; &quot;Defender&quot; &quot;Forward&quot; &quot;Goalkeeper&quot; worldcup &lt;- worldcup %&gt;% group_by(Position) %&gt;% mutate(ave_shots = mean(Shots)) %&gt;% ungroup() %&gt;% mutate(Position = fct_reorder(Position, ave_shots)) levels(worldcup$Position) ## [1] &quot;Goalkeeper&quot; &quot;Defender&quot; &quot;Midfielder&quot; &quot;Forward&quot; 6.5 String operations and regular expressions For these examples, we’ll use some data on passengers of the Titanic. You can load this data using: # install.packages(&quot;titanic&quot;) library(titanic) data(&quot;titanic_train&quot;) We will be using the stringr package: library(stringr) This data includes a column called “Name” with passenger names. This column is somewhat messy and includes several elements that we might want to separate (last name, first name, title). Here are the first few values of “Name”: titanic_train %&gt;% select(Name) %&gt;% slice(1:3) ## Name ## 1 Braund, Mr. Owen Harris ## 2 Cumings, Mrs. John Bradley (Florence Briggs Thayer) ## 3 Heikkinen, Miss. Laina We’ve already done some things to manipulate strings. For example, if we wanted to separate “Name” into last name and first name (including title), we could actually do that with the separate function: titanic_train %&gt;% select(Name) %&gt;% slice(1:3) %&gt;% separate(Name, c(&quot;last_name&quot;, &quot;first_name&quot;), sep = &quot;, &quot;) ## last_name first_name ## 1 Braund Mr. Owen Harris ## 2 Cumings Mrs. John Bradley (Florence Briggs Thayer) ## 3 Heikkinen Miss. Laina Notice that separate is looking for a regular pattern (“,”) and then doing something based on the location of that pattern in each string (splitting the string). There are a variety of functions in R that can perform manipulations based on finding regular patterns in character strings. The str_detect function will look through each element of a character vector for a designated pattern. If the pattern is there, it will return TRUE, and otherwise FALSE. The convention is: ## Generic code str_detect(string = [vector you want to check], pattern = [pattern you want to check for]) For example, to create a logical vector specifying which of the Titanic passenger names include “Mrs.”, you can call: mrs &lt;- str_detect(titanic_train$Name, &quot;Mrs.&quot;) head(mrs) ## [1] FALSE TRUE FALSE TRUE FALSE FALSE The result is a logical vector, so str_detect can be used in filter to subset data to only rows where the passenger’s name includes “Mrs.”: titanic_train %&gt;% filter(str_detect(Name, &quot;Mrs.&quot;)) %&gt;% select(Name) %&gt;% slice(1:3) ## Name ## 1 Cumings, Mrs. John Bradley (Florence Briggs Thayer) ## 2 Futrelle, Mrs. Jacques Heath (Lily May Peel) ## 3 Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg) There is an older, base R function called grepl that does something very similar (although note that the order of the arguments is reversed). titanic_train %&gt;% filter(grepl(&quot;Mrs.&quot;, Name)) %&gt;% select(Name) %&gt;% slice(1:3) ## Name ## 1 Cumings, Mrs. John Bradley (Florence Briggs Thayer) ## 2 Futrelle, Mrs. Jacques Heath (Lily May Peel) ## 3 Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg) The str_extract function can be used to extract a string (if it exists) from each value in a character vector. It follows similar conventions to str_detect: ## Generic code str_extract(string = [vector you want to check], pattern = [pattern you want to check for]) For example, you might want to extract “Mrs.” if it exists in a passenger’s name: titanic_train %&gt;% mutate(mrs = str_extract(Name, &quot;Mrs.&quot;)) %&gt;% select(Name, mrs) %&gt;% slice(1:3) ## Name mrs ## 1 Braund, Mr. Owen Harris &lt;NA&gt; ## 2 Cumings, Mrs. John Bradley (Florence Briggs Thayer) Mrs. ## 3 Heikkinen, Miss. Laina &lt;NA&gt; Notice that now we’re creating a new column (mrs) that either has “Mrs.” (if there’s a match) or is missing (NA) if there’s not a match. For this first example, we were looking for an exact string (“Mrs”). However, you can use patterns that match a particular pattern, but not an exact string. For example, we could expand the regular expression to find “Mr.” or “Mrs.”: titanic_train %&gt;% mutate(title = str_extract(Name, &quot;Mr\\\\.|Mrs\\\\.&quot;)) %&gt;% select(Name, title) %&gt;% slice(1:3) ## Name title ## 1 Braund, Mr. Owen Harris Mr. ## 2 Cumings, Mrs. John Bradley (Florence Briggs Thayer) Mrs. ## 3 Heikkinen, Miss. Laina &lt;NA&gt; Note that this pattern uses a special operator (|) to find one pattern or another. Double backslashes (\\\\) escape the special character “.”. As a note, in regular expressions, all of the following characters are special characters that need to be escaped with backslashes if you want to use them literally: . * + ^ ? $ \\ | ( ) [ ] { } Notice that “Mr.” and “Mrs.” both start with “Mr”, end with “.”, and may or may not have an “s” in between. titanic_train %&gt;% mutate(title = str_extract(Name, &quot;Mr(s)*\\\\.&quot;)) %&gt;% select(Name, title) %&gt;% slice(1:3) ## Name title ## 1 Braund, Mr. Owen Harris Mr. ## 2 Cumings, Mrs. John Bradley (Florence Briggs Thayer) Mrs. ## 3 Heikkinen, Miss. Laina &lt;NA&gt; This pattern uses (s)* to match zero or more “s”s at this spot in the pattern. In the previous code, we found “Mr.” and “Mrs.”, but missed “Miss.”. We could tweak the pattern again to try to capture that, as well. For all three, we have the pattern that it starts with “M”, has some lowercase letters, and then ends with “.”. titanic_train %&gt;% mutate(title = str_extract(Name, &quot;M[a-z]+\\\\.&quot;)) %&gt;% select(Name, title) %&gt;% slice(1:3) ## Name title ## 1 Braund, Mr. Owen Harris Mr. ## 2 Cumings, Mrs. John Bradley (Florence Briggs Thayer) Mrs. ## 3 Heikkinen, Miss. Laina Miss. The last pattern used [a-z]+ to match one or more lowercase letters. The [a-z]is a character class. You can also match digits ([0-9]), uppercase letters ([A-Z]), just some letters ([aeiou]), etc. You can negate a character class by starting it with ^. For example, [^0-9] will match anything that isn’t a digit. Sometimes, you want to match a pattern, but then only subset a part of it. For example, each passenger seems to have a title (“Mr.”, “Mrs.”, etc.) that comes after “,” and before “.”. We can use this pattern to find the title, but then we get some extra stuff with the match: titanic_train %&gt;% mutate(title = str_extract(Name, &quot;,\\\\s[A-Za-z]*\\\\.\\\\s&quot;)) %&gt;% select(title) %&gt;% slice(1:3) ## title ## 1 , Mr. ## 2 , Mrs. ## 3 , Miss. As a note, in this pattern, \\\\s is used to match a space. We are getting things like “, Mr. ”, when we really want “Mr”. We can use the str_match function to do this. We group what we want to extract from the pattern in parentheses, and then the function returns a matrix. The first column is the full pattern match, and each following column gives just what matches within the groups. head(str_match(titanic_train$Name, pattern = &quot;,\\\\s([A-Za-z]*)\\\\.\\\\s&quot;)) ## [,1] [,2] ## [1,] &quot;, Mr. &quot; &quot;Mr&quot; ## [2,] &quot;, Mrs. &quot; &quot;Mrs&quot; ## [3,] &quot;, Miss. &quot; &quot;Miss&quot; ## [4,] &quot;, Mrs. &quot; &quot;Mrs&quot; ## [5,] &quot;, Mr. &quot; &quot;Mr&quot; ## [6,] &quot;, Mr. &quot; &quot;Mr&quot; To get just the title, then, we can run: titanic_train %&gt;% mutate(title = str_match(Name, &quot;,\\\\s([A-Za-z]*)\\\\.\\\\s&quot;)[ , 2]) %&gt;% select(Name, title) %&gt;% slice(1:3) ## Name title ## 1 Braund, Mr. Owen Harris Mr ## 2 Cumings, Mrs. John Bradley (Florence Briggs Thayer) Mrs ## 3 Heikkinen, Miss. Laina Miss The [ , 2] pulls out just the second column from the matrix returned by str_match. Here are some of the most common titles: titanic_train %&gt;% mutate(title = str_match(Name, &quot;,\\\\s([A-Za-z]*)\\\\.\\\\s&quot;)[ , 2]) %&gt;% group_by(title) %&gt;% summarize(n = n()) %&gt;% arrange(desc(n)) %&gt;% slice(1:5) ## `summarise()` ungrouping output (override with `.groups` argument) ## # A tibble: 5 x 2 ## title n ## &lt;chr&gt; &lt;int&gt; ## 1 Mr 517 ## 2 Miss 182 ## 3 Mrs 125 ## 4 Master 40 ## 5 Dr 7 Here are a few other examples of regular expressions in action with this dataset. Get just names that start with (“^”) the letter “A”: titanic_train %&gt;% filter(str_detect(Name, &quot;^A&quot;)) %&gt;% select(Name) %&gt;% slice(1:3) ## Name ## 1 Allen, Mr. William Henry ## 2 Andersson, Mr. Anders Johan ## 3 Asplund, Mrs. Carl Oscar (Selma Augusta Emilia Johansson) Get names with “II” or “III” ({2,} says to match at least two times): titanic_train %&gt;% filter(str_detect(Name, &quot;I{2,}&quot;)) %&gt;% select(Name) %&gt;% slice(1:3) ## Name ## 1 Carter, Master. William Thornton II ## 2 Roebling, Mr. Washington Augustus II Get names with “Andersen” or “Anderson” (alternatives in square brackets): titanic_train %&gt;% filter(str_detect(Name, &quot;Anders[eo]n&quot;)) %&gt;% select(Name) ## Name ## 1 Andersen-Jensen, Miss. Carla Christine Nielsine ## 2 Anderson, Mr. Harry ## 3 Walker, Mr. William Anderson ## 4 Olsvigen, Mr. Thor Anderson ## 5 Soholt, Mr. Peter Andreas Lauritz Andersen Get names that start with (“^” outside of brackets) the letters “A” and “B”: titanic_train %&gt;% filter(str_detect(Name, &quot;^[AB]&quot;)) %&gt;% select(Name) %&gt;% slice(1:3) ## Name ## 1 Braund, Mr. Owen Harris ## 2 Allen, Mr. William Henry ## 3 Bonnell, Miss. Elizabeth Get names that end with (“$”) the letter “b” (either lowercase or uppercase): titanic_train %&gt;% filter(str_detect(Name, &quot;[bB]$&quot;)) %&gt;% select(Name) ## Name ## 1 Emir, Mr. Farred Chehab ## 2 Goldschmidt, Mr. George B ## 3 Cook, Mr. Jacob ## 4 Pasic, Mr. Jakob Some useful regular expression operators include: Operator Meaning . Any character * Match 0 or more times (greedy) *? Match 0 or more times (non-greedy) + Match 1 or more times (greedy) +? Match 1 or more times (non-greedy) ^ Starts with (in brackets, negates) $ Ends with […] Character classes For more on these patterns, see: Help file for the stringi-search-regex function in the stringi package (which should install when you install stringr) Chapter 14 of R For Data Science http://gskinner.com/RegExr: Interactive tool for helping you build regular expression pattern strings 6.6 Tidy select There are tidyverse functions to make selecting variables more straightforwards. You can call these functions as arguments of the select function to streamline variable selection. Examples include: starts_with(), ends_with(), and contains(). Here we use starts_with(\"t\") to select all variables that begin with t. titanic_train %&gt;% select(starts_with(&quot;t&quot;)) %&gt;% slice(1:3) ## Ticket ## 1 A/5 21171 ## 2 PC 17599 ## 3 STON/O2. 3101282 The are also tidyverse functions that allow us to easily operate on a selection of variables. These functions are called scoped varients. You can identify these functions by these _all, _at, and _if suffixes. Here we use select_if to select all the numeric variables in a dataframe and covert their names to lower case (a handy function to tidy the variable names). titanic_train %&gt;% select_if(is.numeric, tolower) %&gt;% slice(1:3) ## passengerid survived pclass age sibsp parch fare ## 1 1 0 3 22 1 0 7.2500 ## 2 2 1 1 38 1 0 71.2833 ## 3 3 1 3 26 0 0 7.9250 The select_if function takes the following form. ## Generic code new_df &lt;- select_if(old_df, .predicate [selects the variable to keep], .funs = [the function to apply to the selected columns]) Here we use select_at to select all the variables that contain ss in their name and then covert their names to lower case (a handy function to tidy the variable names). titanic_train %&gt;% select_at(vars(contains(&quot;ss&quot;)), tolower) %&gt;% slice(1:3) ## passengerid pclass ## 1 1 3 ## 2 2 1 ## 3 3 3 6.7 In-course exercise For today’s exercise, we’ll be using the following three datasets (click on the file name to access the correct file for today’s class for each dataset): File name Description country_timeseries.csv Ebola cases by country for the 2014 outbreak mexico_exposure.csv and mexico_deaths.csv Daily death counts and environmental measurements for Mexico City, Mexico, for 2008 measles_data/ Number of cases of measles in CA since end of Dec. 2014 Note that you likely have already downloaded all the files in the measles_data folder, since we used them in an earlier in-course exercise. If so, there is no need to re-download those files. Here are the sources for this data: country_timeseries.csv : Caitlin Rivers’ Ebola repository (Caitlin originally collected this data from the WHO and WHO Situation reports) mexico_exposure.csv and mexico_deaths.csv : one of Hadley Wickham’s GitHub repos (Hadley got the data originally from the Secretaria de Salud of Mexico’s website, although it appears the link is now broken. I separated the data into two dataframes so students could practice merging.) measles_data/: one of scarpino’s GitHub repos (Data originally from pdfs from the California Department of Public Health) If you want to use these data further, you should go back and pull them from their original sources. They are here only for use in R code examples for this course. Here are some of the packages you will need for this exercise: library(dplyr) library(gridExtra) library(ggthemes) 6.7.1 Designing tidy data Check out the country_timeseries.csv file on Ebola for this week’s example data. Talk with your partner and decide what changes you would need to make to this dataset to turn it into a “tidy” dataset, in particular which of the five common “untidy” problems the data currently has and why. Do the same for the data on daily mortality and daily weather in Mexico. Do the same for the set of files with measles data. 6.7.2 Easier data wrangling Use read_csv to read the Mexico data (exposure and mortality) directly from GitHub into your R session. Call the dataframes mex_deaths and mex_exp. Are there any values of the day column in mex_deaths that is not present in the day column of mex_exp? How about vice-versa? (Hint: There are a few ways you could check this. One is to try filtering down to just rows in one dataframe where the day values are not present in the day values from the other dataframe. The %in% logical vector may be useful.) Merge the two datasets together to create the dataframe mexico. Exclude all columns except the outcome (deaths), day, and mean temperature. Convert the day to a Date class. If you did not already, try combining all the steps in the previous task into one “chained” pipeline of code using the pipe operator, %&gt;%. Use this new dataframe to plot deaths by date in Mexico using ggplot2. The final plot should look like this: 6.7.2.1 Example R code Use read_csv to read the mexico data (exposure and mortality) directly from GitHub into your R session. Call the dataframes mex_deaths and mex_exp: deaths_url &lt;- paste0(&quot;https://github.com/geanders/RProgrammingForResearch/&quot;, &quot;raw/master/data/mexico_deaths.csv&quot;) mex_deaths &lt;- read_csv(deaths_url) head(mex_deaths) ## # A tibble: 6 x 2 ## day deaths ## &lt;chr&gt; &lt;dbl&gt; ## 1 1/1/08 296 ## 2 1/2/08 274 ## 3 1/3/08 339 ## 4 1/4/08 300 ## 5 1/5/08 327 ## 6 1/6/08 332 exposure_url &lt;- paste0(&quot;https://github.com/geanders/RProgrammingForResearch/&quot;, &quot;raw/master/data/mexico_exposure.csv&quot;) mex_exp &lt;- read_csv(exposure_url) head(mex_exp) ## # A tibble: 6 x 14 ## day temp_min temp_max temp_mean humidity wind NO NO2 NOX O3 ## &lt;chr&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; ## 1 1/1/… 7.8 17.8 11.8 53.5 2.66 0.00925 0.0187 0.0278 NA ## 2 1/2/… 2.6 9.8 6.64 61.7 3.35 0.00542 0.0187 0.0241 0.0201 ## 3 1/3/… 1.1 15.6 7.04 59.9 1.89 0.0160 0.0381 0.0540 0.0184 ## 4 1/4/… 3.1 20.6 10.9 57.5 1.20 0.0408 0.0584 0.0993 0.0215 ## 5 1/5/… 6 21.3 13.4 45.7 0.988 0.0469 0.0602 0.107 0.0239 ## 6 1/6/… 7.2 22.1 14.3 40.8 0.854 0.0286 0.051 0.0795 0.0249 ## # … with 4 more variables: CO &lt;dbl&gt;, SO2 &lt;dbl&gt;, PM10 &lt;dbl&gt;, PM25 &lt;dbl&gt; Check if there are any values of the day column in mex_deaths that are not present in the day column of mex_exp and vice-versa. mex_deaths %&gt;% filter(!(day %in% mex_exp$day)) ## # A tibble: 0 x 2 ## # … with 2 variables: day &lt;chr&gt;, deaths &lt;dbl&gt; mex_exp %&gt;% filter(!(day %in% mex_deaths$day)) ## # A tibble: 0 x 14 ## # … with 14 variables: day &lt;chr&gt;, temp_min &lt;dbl&gt;, temp_max &lt;dbl&gt;, ## # temp_mean &lt;dbl&gt;, humidity &lt;dbl&gt;, wind &lt;dbl&gt;, NO &lt;dbl&gt;, NO2 &lt;dbl&gt;, ## # NOX &lt;dbl&gt;, O3 &lt;dbl&gt;, CO &lt;dbl&gt;, SO2 &lt;dbl&gt;, PM10 &lt;dbl&gt;, PM25 &lt;dbl&gt; One important note is that, when you’re doing this check, you do not want to overwrite your original dataframe, so be sure that you do not reassign this output to mex_deaths or mex_exp. An even quicker way to do check this is to create a logical vector that checks this and use sum to add up the values in the logical vector. If the sum is zero, that tells you that the logical check is never true, so there are no cases where there is a day value in one dataframe that is not also in the other dataframe. sum(!(mex_deaths$day %in% mex_exp$day)) ## [1] 0 sum(!(mex_exp$day %in% mex_deaths$day)) ## [1] 0 Merge the two datasets together to create the dataframe mexico. Exclude all columns except the outcome (deaths), date, and mean temperature. mexico &lt;- full_join(mex_deaths, mex_exp, by = &quot;day&quot;) mexico &lt;- select(mexico, day, deaths, temp_mean) Convert the date to a date class. library(lubridate) ## For parsing dates mexico &lt;- mutate(mexico, day = mdy(day)) Try combining all the steps in the previous task into one “chained” command: mexico &lt;- full_join(mex_deaths, mex_exp, by = &quot;day&quot;) %&gt;% select(day, deaths, temp_mean) %&gt;% mutate(day = mdy(day)) head(mexico) ## # A tibble: 6 x 3 ## day deaths temp_mean ## &lt;date&gt; &lt;dbl&gt; &lt;dbl&gt; ## 1 2008-01-01 296 11.8 ## 2 2008-01-02 274 6.64 ## 3 2008-01-03 339 7.04 ## 4 2008-01-04 300 10.9 ## 5 2008-01-05 327 13.4 ## 6 2008-01-06 332 14.3 Note that, in this case, all the values of day in mex_deaths have one and only one matching value in mex_exp, and vice-versa. Because of this, we would have gotten the same mexico dataframe if we’d used inner_join, left_join or right_join instead of full_join. The differences between these *_join functions come into play when you have some values of your matching column that aren’t in both of the dataframes you’re joining. Use this new dataframe to plot deaths by date using ggplot: ggplot(data = mexico) + geom_point(mapping = aes(x = day, y = deaths), size = 1.5, alpha = 0.5) + labs(x = &quot;Date in 2008&quot;, y = &quot;# of deaths&quot;) + ggtitle(&quot;Deaths by date&quot;) + theme_few() 6.7.3 More extensive data wrangling Read the Ebola data directly from GitHub into your R session. Call the dataframe ebola. Use dplyr functions to create a tidy dataset. First, change it from “wide” data to “long” data. Name the new column with the key variable and the new column with the values count. The first few lines of the “long” version of the dataset should look like this: ## # A tibble: 6 x 4 ## Date Day variable count ## &lt;chr&gt; &lt;dbl&gt; &lt;chr&gt; &lt;dbl&gt; ## 1 1/5/2015 289 Cases_Guinea 2776 ## 2 1/5/2015 289 Cases_Liberia NA ## 3 1/5/2015 289 Cases_SierraLeone 10030 ## 4 1/5/2015 289 Cases_Nigeria NA ## 5 1/5/2015 289 Cases_Senegal NA ## 6 1/5/2015 289 Cases_UnitedStates NA Convert the Date column to a Date class. Use the separate function to separate the variable column into two columns, type (“Cases” or “Deaths”) and country (“Guinea”, “Liberia”, etc.). At this point, the data should look like this: ## # A tibble: 6 x 5 ## Date Day type country count ## &lt;date&gt; &lt;dbl&gt; &lt;chr&gt; &lt;chr&gt; &lt;dbl&gt; ## 1 2015-01-05 289 Cases Guinea 2776 ## 2 2015-01-05 289 Cases Liberia NA ## 3 2015-01-05 289 Cases SierraLeone 10030 ## 4 2015-01-05 289 Cases Nigeria NA ## 5 2015-01-05 289 Cases Senegal NA ## 6 2015-01-05 289 Cases UnitedStates NA Use the pivot_wider function to convert the data so you have separate columns for the two variables of numbers of Cases and Deaths. At this point, the dataframe should look like this: ## # A tibble: 6 x 5 ## Date Day country Cases Deaths ## &lt;date&gt; &lt;dbl&gt; &lt;chr&gt; &lt;dbl&gt; &lt;dbl&gt; ## 1 2015-01-05 289 Guinea 2776 1786 ## 2 2015-01-05 289 Liberia NA NA ## 3 2015-01-05 289 SierraLeone 10030 2977 ## 4 2015-01-05 289 Nigeria NA NA ## 5 2015-01-05 289 Senegal NA NA ## 6 2015-01-05 289 UnitedStates NA NA Remove any observations where counts of both cases and deaths are missing for that country on that date. Now that your data is tidy, create one plot showing Ebola cases by date, faceted by country, and one showing Ebola deaths by date, also faceted by country. Try using the option scales = \"free_y\" in the facet_wrap function and see how that changes these graphs. Discuss with your group the advantages and disadvantages of using this option when creating these small multiple plots. The plots should look something like this (if you’re using the scales = \"free_y\" option): Based on these plots, what would your next questions be about this data before you used it for an analysis? Can you put all of the steps of this cleaning process into just a few “chained” code pipelines using %&gt;%? If you have extra time (super-challenge!): There is a function called fct_reorder in the forcats package that can be used to reorder the levels of a factor in a dataframe based on another column in the same dataframe. This function can be very useful for using a meaningful order when plotting. We’ll cover the forcats package in a later class, but today check out the help file for fct_reorder and see if you can figure out how to use it to reorder the small multiple plots in order of the maximum number of cases or deaths (for the two plots respectively) in each country. You’ll be able to do this by changing the code in facet_wrap from ~ country to ~ fct_reorder(country, ...), but with the ... replaced with certain arguments. If you’re getting stuck, try running the examples in the fct_reorder helpfile to get a feel for how this function can be used when plotting. The plots will look something like this: 6.7.3.1 Example R code Read the data in using read_csv. ebola_url &lt;- paste0(&quot;https://github.com/geanders/RProgrammingForResearch/&quot;, &quot;raw/master/data/country_timeseries.csv&quot;) ebola &lt;- read_csv(ebola_url) head(ebola) ## # A tibble: 6 x 18 ## Date Day Cases_Guinea Cases_Liberia Cases_SierraLeo… Cases_Nigeria ## &lt;chr&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; ## 1 1/5/… 289 2776 NA 10030 NA ## 2 1/4/… 288 2775 NA 9780 NA ## 3 1/3/… 287 2769 8166 9722 NA ## 4 1/2/… 286 NA 8157 NA NA ## 5 12/3… 284 2730 8115 9633 NA ## 6 12/2… 281 2706 8018 9446 NA ## # … with 12 more variables: Cases_Senegal &lt;dbl&gt;, Cases_UnitedStates &lt;dbl&gt;, ## # Cases_Spain &lt;dbl&gt;, Cases_Mali &lt;dbl&gt;, Deaths_Guinea &lt;dbl&gt;, ## # Deaths_Liberia &lt;dbl&gt;, Deaths_SierraLeone &lt;dbl&gt;, Deaths_Nigeria &lt;dbl&gt;, ## # Deaths_Senegal &lt;dbl&gt;, Deaths_UnitedStates &lt;dbl&gt;, Deaths_Spain &lt;dbl&gt;, ## # Deaths_Mali &lt;dbl&gt; Change the data to long data using the pivoter_longer function from tidyr: ebola &lt;- ebola %&gt;% pivot_longer(cols = c(-Date, -Day), names_to = &quot;variable&quot;, values_to = &quot;count&quot;) head(ebola) ## # A tibble: 6 x 4 ## Date Day variable count ## &lt;chr&gt; &lt;dbl&gt; &lt;chr&gt; &lt;dbl&gt; ## 1 1/5/2015 289 Cases_Guinea 2776 ## 2 1/5/2015 289 Cases_Liberia NA ## 3 1/5/2015 289 Cases_SierraLeone 10030 ## 4 1/5/2015 289 Cases_Nigeria NA ## 5 1/5/2015 289 Cases_Senegal NA ## 6 1/5/2015 289 Cases_UnitedStates NA Convert Date to a date class: ebola &lt;- ebola %&gt;% mutate(Date = mdy(Date)) head(ebola) ## # A tibble: 6 x 4 ## Date Day variable count ## &lt;date&gt; &lt;dbl&gt; &lt;chr&gt; &lt;dbl&gt; ## 1 2015-01-05 289 Cases_Guinea 2776 ## 2 2015-01-05 289 Cases_Liberia NA ## 3 2015-01-05 289 Cases_SierraLeone 10030 ## 4 2015-01-05 289 Cases_Nigeria NA ## 5 2015-01-05 289 Cases_Senegal NA ## 6 2015-01-05 289 Cases_UnitedStates NA Split variable into type and country: ebola &lt;- ebola %&gt;% separate(variable, c(&quot;type&quot;, &quot;country&quot;), sep = &quot;_&quot;) head(ebola) ## # A tibble: 6 x 5 ## Date Day type country count ## &lt;date&gt; &lt;dbl&gt; &lt;chr&gt; &lt;chr&gt; &lt;dbl&gt; ## 1 2015-01-05 289 Cases Guinea 2776 ## 2 2015-01-05 289 Cases Liberia NA ## 3 2015-01-05 289 Cases SierraLeone 10030 ## 4 2015-01-05 289 Cases Nigeria NA ## 5 2015-01-05 289 Cases Senegal NA ## 6 2015-01-05 289 Cases UnitedStates NA Convert the data so you have separate columns for the two variables of numbers of Cases and Deaths: ebola &lt;- pivot_wider(ebola, names_from = type, values_from = count) head(ebola) ## # A tibble: 6 x 5 ## Date Day country Cases Deaths ## &lt;date&gt; &lt;dbl&gt; &lt;chr&gt; &lt;dbl&gt; &lt;dbl&gt; ## 1 2015-01-05 289 Guinea 2776 1786 ## 2 2015-01-05 289 Liberia NA NA ## 3 2015-01-05 289 SierraLeone 10030 2977 ## 4 2015-01-05 289 Nigeria NA NA ## 5 2015-01-05 289 Senegal NA NA ## 6 2015-01-05 289 UnitedStates NA NA Remove any observations where counts of cases or deaths are missing for that country: ebola &lt;- filter(ebola, !is.na(Cases) &amp; !is.na(Deaths)) head(ebola) ## # A tibble: 6 x 5 ## Date Day country Cases Deaths ## &lt;date&gt; &lt;dbl&gt; &lt;chr&gt; &lt;dbl&gt; &lt;dbl&gt; ## 1 2015-01-05 289 Guinea 2776 1786 ## 2 2015-01-05 289 SierraLeone 10030 2977 ## 3 2015-01-04 288 Guinea 2775 1781 ## 4 2015-01-04 288 SierraLeone 9780 2943 ## 5 2015-01-03 287 Guinea 2769 1767 ## 6 2015-01-03 287 Liberia 8166 3496 Now that your data is tidy, create one plot showing ebola cases by date, faceted by country, and one showing ebola deaths by date, also faceted by country: ggplot(ebola, aes(x = Date, y = Cases)) + geom_line() + facet_wrap(~ country, ncol = 4) + theme_classic() ggplot(ebola, aes(x = Date, y = Deaths)) + geom_line() + facet_wrap(~ country, ncol = 4) + theme_classic() Try using the option scales = \"free_y\" in the facet_wrap() function (in the gridExtra package) and see how that changes these graphs: ggplot(ebola, aes(x = Date, y = Cases)) + geom_line() + facet_wrap(~ country, ncol = 4, scales = &quot;free_y&quot;) + theme_classic() ggplot(ebola, aes(x = Date, y = Deaths)) + geom_line() + facet_wrap(~ country, ncol = 4, scales = &quot;free_y&quot;) + theme_classic() Put all of the steps of this cleaning process into just a few “chaining” calls. ebola &lt;- read_csv(ebola_url) %&gt;% pivot_longer(cols = c(-Date, -Day), names_to = &quot;variable&quot;, values_to = &quot;count&quot;) %&gt;% mutate(Date = mdy(Date)) %&gt;% separate(variable, c(&quot;type&quot;, &quot;country&quot;), sep = &quot;_&quot;) %&gt;% pivot_wider(names_from = type, values_from = count) %&gt;% filter(!is.na(Cases) &amp; !is.na(Deaths)) ggplot(ebola, aes(x = Date, y = Cases)) + geom_line() + facet_wrap(~ country, ncol = 4) + theme_classic() ggplot(ebola, aes(x = Date, y = Deaths)) + geom_line() + facet_wrap(~ country, ncol = 4) + theme_classic() Use the fct_reorder function inside the facet_wrap function call to reorder the small-multiple graphs. library(forcats) ggplot(ebola, aes(x = Date, y = Cases)) + geom_line() + facet_wrap(~ fct_reorder(country, Cases, .fun = max, .desc = TRUE), ncol = 4) + theme_classic() ggplot(ebola, aes(x = Date, y = Deaths)) + geom_line() + facet_wrap(~ fct_reorder(country, Deaths, .fun = max, .desc = TRUE), ncol = 4) + theme_classic() 6.7.4 Tidying VADeaths data R comes with a dataset called VADeaths that gives death rates per 1,000 people in Virginia in 1940 by age, sex, and rural / urban. Use data(\"VADeaths\") to load this data. Make sure you understand what each column and row is showing – use the helpfile (?VADeaths) if you need. Go through the three characteristics of tidy data and the five common problems in untidy data that we talked about in class. Sketch out (you’re welcome to use the whiteboards) what a tidy version of this data would look like. Open a new R script file. Write R code to transform this dataset into a tidy dataset. Try using a pipe chain, with %&gt;% and tidyverse functions, to clean the data. Use the tidy data to create the following graph: There is no example R code for this – try to figure out the code yourselves. We will go over a solution in class. You may find the RStudio Data Wrangling cheatsheet helpful for remembering which tidyverse functions do what. 6.7.5 Baby names In the Fall 2018 session, we seem to have an unusually high percent of the class with names that start with an “A” or “K”. In this part of the exercise, we’ll see if we can figure out whether the proportion of “A” and “K” names is unusual. There is a package on CRAN called babynames with data on baby names by year in the United States, based on data from the U.S.’s Social Security Administration. We will use this data to compare the proportion of “A” and “K” names in our class with the proportion in these baby names. We’ll also do a few other things to explore this data. First, check out patterns in your own name. Is your name included in this dataset? Has your name been used for males and females? How have the patterns in the proportion of babies with your name, for both males and females, changed over time (use a plot to look at this)? In the year you were born, what were the 5 most popular baby names for males and females? Try to come up with some attractive ways (figures and tables) to show this. 6.7.5.1 Example R code Install and load the babynames package and its “babynames” dataframe: # install.packages(&quot;babynames&quot;) library(babynames) data(&quot;babynames&quot;) Remember that you can use ?babynames to find out more about this dataframe. Check out patterns in your own name. Is your name included in this dataset? You can use filter to create a subset of this data where you’ve filtered down to just the rows with your name. To see if your name ever shows up, you can use count on this dataframe—if your name never shows up, then you will have 0 rows in the new dataframe. As long as there’s at least one row, your name shows up somewhere. (If your name is not in here, try your middle or last name, or the name of a fictional character you like, for the rest of these exercises.) my_name &lt;- babynames %&gt;% filter(name == &quot;Brooke&quot;) my_name %&gt;% count() ## # A tibble: 1 x 1 ## n ## &lt;int&gt; ## 1 174 Has your name been used for males and females? To figure this out, you can group the dataset with rows with your name by the sex column and then use count to count the number of rows in the dataset for males and females. If your name has only been used for one gender, then only one row will result from running this code (for an example, try my first name, “Georgiana”). my_name %&gt;% group_by(sex) %&gt;% count() ## # A tibble: 2 x 2 ## # Groups: sex [2] ## sex n ## &lt;chr&gt; &lt;int&gt; ## 1 F 82 ## 2 M 92 How have the patterns in the proportion of babies with your name, for both males and females, changed over time (use a plot to look at this)? To check this out, I recommend you create a plot of the proportion of babies with your name (prop) versus year (year). You can use color to show these patterns for males and females separately. I’ve done some extra things here to (1) relabel the sex factor, so the label shows up with clearer names and (2) change the labels for the x-, y-, and color scales. library(forcats) my_name %&gt;% mutate(sex = fct_recode(sex, Male = &quot;M&quot;, Female = &quot;F&quot;)) %&gt;% ggplot(aes(x = year, y = prop, color = sex)) + geom_line() + labs(x = &quot;Year&quot;, y = &quot;Proportion of babies\\nof each sex named &#39;Brooke&#39;&quot;, color = &quot;&quot;) In the year you were born, what were the 5 most popular baby names for males and females? top_my_year &lt;- babynames %&gt;% filter(year == 1981) %&gt;% group_by(sex) %&gt;% arrange(desc(prop)) %&gt;% slice(1:5) top_my_year ## # A tibble: 10 x 5 ## # Groups: sex [2] ## year sex name n prop ## &lt;dbl&gt; &lt;chr&gt; &lt;chr&gt; &lt;int&gt; &lt;dbl&gt; ## 1 1981 F Jennifer 57049 0.0319 ## 2 1981 F Jessica 42532 0.0238 ## 3 1981 F Amanda 34374 0.0192 ## 4 1981 F Sarah 28173 0.0158 ## 5 1981 F Melissa 28000 0.0157 ## 6 1981 M Michael 68765 0.0369 ## 7 1981 M Christopher 50233 0.0270 ## 8 1981 M Matthew 43330 0.0233 ## 9 1981 M Jason 41932 0.0225 ## 10 1981 M David 40659 0.0218 If you’d like to show this in a prettier way, you could show this as a table: library(knitr) top_my_year %&gt;% mutate(rank = 1:n()) %&gt;% # Since the data is grouped by sex, this will rank # separately for females and males ungroup() %&gt;% # You have to ungroup before you can run `mutate` on `sex` mutate(sex = fct_recode(sex, Male = &quot;M&quot;, Female = &quot;F&quot;), percent = round(100 * prop, 1), percent = paste(percent, &quot;%&quot;, sep = &quot;&quot;)) %&gt;% select(sex, rank, name, percent) %&gt;% kable() sex rank name percent Female 1 Jennifer 3.2% Female 2 Jessica 2.4% Female 3 Amanda 1.9% Female 4 Sarah 1.6% Female 5 Melissa 1.6% Male 1 Michael 3.7% Male 2 Christopher 2.7% Male 3 Matthew 2.3% Male 4 Jason 2.3% Male 5 David 2.2% You could also show it as a figure: library(scales) top_my_year %&gt;% ungroup() %&gt;% mutate(name = fct_reorder(name, prop, .desc = TRUE), sex = fct_recode(sex, Male = &quot;M&quot;, Female = &quot;F&quot;)) %&gt;% ggplot(aes(x = name)) + geom_bar(aes(weight = prop)) + coord_flip() + labs(x = &quot;&quot;, y = &quot;Percent of babies with name in 1981&quot;) + theme(legend.position = &quot;top&quot;) + scale_y_continuous(labels = percent) + facet_wrap(~ sex, scales = &quot;free_y&quot;) + theme_classic() "],
+["exploring-data-2.html", "Chapter 7 Exploring data #2 7.1 Simple statistical tests in R 7.2 Matrices 7.3 Lists 7.4 Apply a test multiple times 7.5 Regression models 7.6 Handling model objects 7.7 Functions 7.8 In-course exercise", " Chapter 7 Exploring data #2 Download a pdf of the lecture slides covering this topic. 7.1 Simple statistical tests in R Let’s pull the fatal accident data just for the county that includes Las Vegas, NV. Each US county has a unique identifier (FIPS code), composed of a two-digit state FIPS and a three-digit county FIPS code. The state FIPS for Nevada is 32; the county FIPS for Clark County is 003. Therefore, we can filter down to Clark County data in the FARS data we collected with the following code: library(readr) library(dplyr) clark_co_accidents &lt;- read_csv(&quot;data/accident.csv&quot;) %&gt;% filter(STATE == 32 &amp; COUNTY == 3) We can also check the number of accidents: clark_co_accidents %&gt;% count() ## # A tibble: 1 x 1 ## n ## &lt;int&gt; ## 1 201 We want to test if the probability, on a Friday or Saturday, of a fatal accident occurring is higher than on other days of the week. Let’s clean the data up a bit as a start: library(tidyr) library(lubridate) clark_co_accidents &lt;- clark_co_accidents %&gt;% select(DAY, MONTH, YEAR) %&gt;% unite(date, DAY, MONTH, YEAR, sep = &quot;-&quot;) %&gt;% mutate(date = dmy(date)) Here’s what the data looks like now: clark_co_accidents %&gt;% slice(1:5) ## # A tibble: 5 x 1 ## date ## &lt;date&gt; ## 1 2016-01-10 ## 2 2016-02-21 ## 3 2016-01-06 ## 4 2016-01-13 ## 5 2016-02-18 Next, let’s get the count of accidents by date: clark_co_accidents &lt;- clark_co_accidents %&gt;% group_by(date) %&gt;% count() %&gt;% ungroup() clark_co_accidents %&gt;% slice(1:3) ## # A tibble: 3 x 2 ## date n ## &lt;date&gt; &lt;int&gt; ## 1 2016-01-03 1 ## 2 2016-01-06 1 ## 3 2016-01-09 3 We’re missing the dates without a fatal crash, so let’s add those. First, create a dataframe with all dates in 2016: all_dates &lt;- tibble(date = seq(ymd(&quot;2016-01-01&quot;), ymd(&quot;2016-12-31&quot;), by = 1)) all_dates %&gt;% slice(1:5) ## # A tibble: 5 x 1 ## date ## &lt;date&gt; ## 1 2016-01-01 ## 2 2016-01-02 ## 3 2016-01-03 ## 4 2016-01-04 ## 5 2016-01-05 Then merge this with the original dataset on Las Vegas fatal crashes and make any day missing from the fatal crashes dataset have a “0” for number of fatal accidents (n): clark_co_accidents &lt;- clark_co_accidents %&gt;% right_join(all_dates, by = &quot;date&quot;) %&gt;% # If `n` is missing, set to 0. Otherwise keep value. mutate(n = ifelse(is.na(n), 0, n)) clark_co_accidents %&gt;% slice(1:3) ## # A tibble: 3 x 2 ## date n ## &lt;date&gt; &lt;dbl&gt; ## 1 2016-01-03 1 ## 2 2016-01-06 1 ## 3 2016-01-09 3 Next, let’s add some information about day of week and weekend: clark_co_accidents &lt;- clark_co_accidents %&gt;% mutate(weekday = wday(date, label = TRUE), weekend = weekday %in% c(&quot;Fri&quot;, &quot;Sat&quot;)) clark_co_accidents %&gt;% slice(1:3) ## # A tibble: 3 x 4 ## date n weekday weekend ## &lt;date&gt; &lt;dbl&gt; &lt;ord&gt; &lt;lgl&gt; ## 1 2016-01-03 1 Sun FALSE ## 2 2016-01-06 1 Wed FALSE ## 3 2016-01-09 3 Sat TRUE Now let’s calculate the probability that a day has at least one fatal crash, separately for weekends and weekdays: clark_co_accidents &lt;- clark_co_accidents %&gt;% mutate(any_crash = n &gt; 0) crash_prob &lt;- clark_co_accidents %&gt;% group_by(weekend) %&gt;% summarize(n_days = n(), crash_days = sum(any_crash)) %&gt;% mutate(prob_crash_day = crash_days / n_days) ## `summarise()` ungrouping output (override with `.groups` argument) crash_prob ## # A tibble: 2 x 4 ## weekend n_days crash_days prob_crash_day ## &lt;lgl&gt; &lt;int&gt; &lt;int&gt; &lt;dbl&gt; ## 1 FALSE 260 107 0.412 ## 2 TRUE 106 43 0.406 In R, you can use prop.test to test if two proportions are equal. Inputs include the total number of trials in each group (n =) and the number of “successes”\" (x =): prop.test(x = crash_prob$crash_days, n = crash_prob$n_days) ## ## 2-sample test for equality of proportions with continuity correction ## ## data: crash_prob$crash_days out of crash_prob$n_days ## X-squared = 1.5978e-30, df = 1, p-value = 1 ## alternative hypothesis: two.sided ## 95 percent confidence interval: ## -0.1109757 0.1227318 ## sample estimates: ## prop 1 prop 2 ## 0.4115385 0.4056604 I won’t be teaching in this course how to find the correct statistical test. That’s something you’ll hopefully learn in a statistics course. There are also a variety of books that can help you with this, including some that you can access free online through CSU’s library. One servicable introduction is “Statistical Analysis with R for Dummies”. You can create an object from the output of any statistical test in R. Typically, this will be (at least at some level) in an object class called a “list”: vegas_test &lt;- prop.test(x = crash_prob$crash_days, n = crash_prob$n_days) is.list(vegas_test) ## [1] TRUE So far, we’ve mostly worked with two object types in R, dataframes and vectors. In the next subsection we’ll look more at two object classes we haven’t looked at much, matrices and lists. Both have important roles once you start applying more advanced methods to analyze your data. 7.2 Matrices A matrix is like a data frame, but all the values in all columns must be of the same class (e.g., numeric, character). R uses matrices a lot for its underlying math (e.g., for the linear algebra operations required for fitting regression models). R can do matrix operations quite quickly. You can create a matrix with the matrix function. Input a vector with the values to fill the matrix and ncol to set the number of columns: foo &lt;- matrix(1:10, ncol = 5) foo ## [,1] [,2] [,3] [,4] [,5] ## [1,] 1 3 5 7 9 ## [2,] 2 4 6 8 10 By default, the matrix will fill up by column. You can fill it by row with the byrow function: foo &lt;- matrix(1:10, ncol = 5, byrow = TRUE) foo ## [,1] [,2] [,3] [,4] [,5] ## [1,] 1 2 3 4 5 ## [2,] 6 7 8 9 10 In certain situations, you might want to work with a matrix instead of a data frame (for example, in cases where you were concerned about speed – a matrix is more memory efficient than the corresponding data frame). If you want to convert a data frame to a matrix, you can use the as.matrix function: foo &lt;- tibble(col_1 = 1:2, col_2 = 3:4, col_3 = 5:6, col_4 = 7:8, col_5 = 9:10) (foo &lt;- as.matrix(foo)) ## col_1 col_2 col_3 col_4 col_5 ## [1,] 1 3 5 7 9 ## [2,] 2 4 6 8 10 You can index matrices with square brackets, just like data frames: foo[1, 1:2] ## col_1 col_2 ## 1 3 You cannot, however, use dplyr functions with matrices: foo %&gt;% filter(col_1 == 1) All elements in a matrix must have the same class. The matrix will default to make all values the most general class of any of the values, in any column. For example, if we replaced one numeric value with the character “a”, everything would turn into a character: foo[1, 1] &lt;- &quot;a&quot; foo ## col_1 col_2 col_3 col_4 col_5 ## [1,] &quot;a&quot; &quot;3&quot; &quot;5&quot; &quot;7&quot; &quot;9&quot; ## [2,] &quot;2&quot; &quot;4&quot; &quot;6&quot; &quot;8&quot; &quot;10&quot; 7.3 Lists A list has different elements, just like a data frame has different columns. However, the different elements of a list can have different lengths (unlike the columns of a data frame). The different elements can also have different classes. bar &lt;- list(some_letters = letters[1:3], some_numbers = 1:5, some_logical_values = c(TRUE, FALSE)) bar ## $some_letters ## [1] &quot;a&quot; &quot;b&quot; &quot;c&quot; ## ## $some_numbers ## [1] 1 2 3 4 5 ## ## $some_logical_values ## [1] TRUE FALSE To index an element from a list, use double square brackets. You can use bracket indexing either with numbers (which element in the list?) or with names: bar[[1]] ## [1] &quot;a&quot; &quot;b&quot; &quot;c&quot; bar[[&quot;some_numbers&quot;]] ## [1] 1 2 3 4 5 You can also index lists with the $ operator: bar$some_logical_values ## [1] TRUE FALSE To access a specific value within a list element we can index the element e.g.: bar[[1]][[2]] ## [1] &quot;b&quot; Lists can be used to contain data with an unusual structure and / or lots of different components. For example, the information from fitting a regression is often stored as a list: my_mod &lt;- glm(rnorm(10) ~ c(1:10)) is.list(my_mod) ## [1] TRUE The names function returns the name of each element in the list: head(names(my_mod), 3) ## [1] &quot;coefficients&quot; &quot;residuals&quot; &quot;fitted.values&quot; my_mod[[&quot;coefficients&quot;]] ## (Intercept) c(1:10) ## -1.030100 0.163329 A list can even contain other lists. We can use the str function to see the structure of a list: a_list &lt;- list(list(&quot;a&quot;, &quot;b&quot;), list(1, 2)) str(a_list) ## List of 2 ## $ :List of 2 ## ..$ : chr &quot;a&quot; ## ..$ : chr &quot;b&quot; ## $ :List of 2 ## ..$ : num 1 ## ..$ : num 2 Sometimes you’ll see unnecessary lists-of-lists, perhaps when importing data into R created. Or a list with multiple elements that you would like to combine. You can remove a level of hierarchy from a list using the flatten function from the purrr package: library(purrr) a_list ## [[1]] ## [[1]][[1]] ## [1] &quot;a&quot; ## ## [[1]][[2]] ## [1] &quot;b&quot; ## ## ## [[2]] ## [[2]][[1]] ## [1] 1 ## ## [[2]][[2]] ## [1] 2 flatten(a_list) ## [[1]] ## [1] &quot;a&quot; ## ## [[2]] ## [1] &quot;b&quot; ## ## [[3]] ## [1] 1 ## ## [[4]] ## [1] 2 Let’s look at the list object from the statistical test we ran for Las Vegas: str(vegas_test) ## List of 9 ## $ statistic : Named num 1.6e-30 ## ..- attr(*, &quot;names&quot;)= chr &quot;X-squared&quot; ## $ parameter : Named num 1 ## ..- attr(*, &quot;names&quot;)= chr &quot;df&quot; ## $ p.value : num 1 ## $ estimate : Named num [1:2] 0.412 0.406 ## ..- attr(*, &quot;names&quot;)= chr [1:2] &quot;prop 1&quot; &quot;prop 2&quot; ## $ null.value : NULL ## $ conf.int : num [1:2] -0.111 0.123 ## ..- attr(*, &quot;conf.level&quot;)= num 0.95 ## $ alternative: chr &quot;two.sided&quot; ## $ method : chr &quot;2-sample test for equality of proportions with continuity correction&quot; ## $ data.name : chr &quot;crash_prob$crash_days out of crash_prob$n_days&quot; ## - attr(*, &quot;class&quot;)= chr &quot;htest&quot; Using str to print out the list’s structure doesn’t produce the easiest to digest output. We can use the jsonedit function from the listviewer package to create a widget in the viewer pane to more esily explore our list. library(listviewer) jsonedit(vegas_test) We can pull out an element using the $ notation: vegas_test$p.value ## [1] 1 Or using the [[ notation: vegas_test[[4]] ## prop 1 prop 2 ## 0.4115385 0.4056604 You may have noticed, though, that this output is not a tidy dataframe. Ack! That means we can’t use all the tidyverse tricks we’ve learned so far in the course! Fortunately, David Robinson noticed this problem and came up with a package called broom that can “tidy up” a lot of these kinds of objects. The broom package has three main functions: glance: Return a one-row, tidy dataframe from a model or other R object tidy: Return a tidy dataframe from a model or other R object augment: “Augment” the dataframe you input to the statistical function Here is the output for tidy for the vegas_test object (augment won’t work for this type of object, and glance gives the same thing as tidy): library(broom) tidy(vegas_test) ## # A tibble: 1 x 9 ## estimate1 estimate2 statistic p.value parameter conf.low conf.high method ## &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;chr&gt; ## 1 0.412 0.406 1.60e-30 1.00 1 -0.111 0.123 2-sam… ## # … with 1 more variable: alternative &lt;chr&gt; 7.4 Apply a test multiple times Often, we don’t want to just apply a statistical test to our entire data set. Let’s look at an example from the microbiome package. library(microbiome) data(peerj32) print(names(peerj32)) ## [1] &quot;lipids&quot; &quot;microbes&quot; &quot;meta&quot; &quot;phyloseq&quot; Like we saw before, the list-like phyloseq objects require a little tidying before we can use them easiliy. library(dplyr) peerj32_tibble &lt;- (psmelt(peerj32$phyloseq)) %&gt;% dplyr::select(-Sample) %&gt;% rename_all(tolower) %&gt;% as_tibble() ## # A tibble: 3 x 10 ## otu abundance time sex subject sample group phylum family genus ## &lt;chr&gt; &lt;dbl&gt; &lt;int&gt; &lt;fct&gt; &lt;fct&gt; &lt;chr&gt; &lt;fct&gt; &lt;chr&gt; &lt;chr&gt; &lt;chr&gt; ## 1 Bacteroi… 9734 1 male S19 sampl… LGG Bacter… Bacter… Bactero… ## 2 Bacteroi… 5883 1 female S14 sampl… Plac… Bacter… Bacter… Bactero… ## 3 Bacteroi… 5134 2 female S8 sampl… LGG Bacter… Bacter… Bactero… We can make a plot from the resulting tibble to help us explore the data. library(ggplot2) ggplot(peerj32_tibble, aes(x = subject, y = abundance, color = sex)) + geom_point() + theme_bw() + facet_wrap(~phylum, ncol = 4, scales = &quot;free&quot;) + theme(axis.text.x = element_blank(), legend.position = &quot;top&quot;) + xlab(&quot;Subject ID&quot;) We can group the data by phylum and group and then use the nest function from the tidyr package to create a new dataframe containing the nested data: library(tidyr) nested_data &lt;- peerj32_tibble %&gt;% group_by(phylum, group) %&gt;% nest() slice(nested_data, 1:3) ## # A tibble: 16 x 3 ## # Groups: group, phylum [16] ## group phylum data ## &lt;fct&gt; &lt;chr&gt; &lt;list&gt; ## 1 LGG Actinobacteria &lt;tibble [128 × 8]&gt; ## 2 LGG Bacteroidetes &lt;tibble [256 × 8]&gt; ## 3 LGG Cyanobacteria &lt;tibble [16 × 8]&gt; ## 4 LGG Firmicutes &lt;tibble [1,216 × 8]&gt; ## 5 LGG Fusobacteria &lt;tibble [16 × 8]&gt; ## 6 LGG Proteobacteria &lt;tibble [416 × 8]&gt; ## 7 LGG Spirochaetes &lt;tibble [16 × 8]&gt; ## 8 LGG Verrucomicrobia &lt;tibble [16 × 8]&gt; ## 9 Placebo Actinobacteria &lt;tibble [224 × 8]&gt; ## 10 Placebo Bacteroidetes &lt;tibble [448 × 8]&gt; ## 11 Placebo Cyanobacteria &lt;tibble [28 × 8]&gt; ## 12 Placebo Firmicutes &lt;tibble [2,128 × 8]&gt; ## 13 Placebo Fusobacteria &lt;tibble [28 × 8]&gt; ## 14 Placebo Proteobacteria &lt;tibble [728 × 8]&gt; ## 15 Placebo Spirochaetes &lt;tibble [28 × 8]&gt; ## 16 Placebo Verrucomicrobia &lt;tibble [28 × 8]&gt; We can then use the map function from the purrr package to apply functions to each nested dataframe. Let’s start by counting the rows in each nested dataframe and filtering out dataframes with less than 25 rows. library(purrr) filtered_data &lt;- nested_data %&gt;% mutate(n_rows = purrr::map(data, ~ nrow(.x))) %&gt;% filter(n_rows &gt; 25) Now let’s perform a t-test on each out the nested dataframes. Remember, each nested dataframe is one unique combination of phylum and group. t_tests &lt;- filtered_data %&gt;% mutate(t_test = purrr::map(data, ~t.test(abundance ~ sex, data = .x))) t_tests ## # A tibble: 12 x 5 ## # Groups: group, phylum [12] ## group phylum data n_rows t_test ## &lt;fct&gt; &lt;chr&gt; &lt;list&gt; &lt;list&gt; &lt;list&gt; ## 1 LGG Bacteroidetes &lt;tibble [256 × 8]&gt; &lt;int [1]&gt; &lt;htest&gt; ## 2 Placebo Bacteroidetes &lt;tibble [448 × 8]&gt; &lt;int [1]&gt; &lt;htest&gt; ## 3 Placebo Firmicutes &lt;tibble [2,128 × 8]&gt; &lt;int [1]&gt; &lt;htest&gt; ## 4 LGG Firmicutes &lt;tibble [1,216 × 8]&gt; &lt;int [1]&gt; &lt;htest&gt; ## 5 Placebo Verrucomicrobia &lt;tibble [28 × 8]&gt; &lt;int [1]&gt; &lt;htest&gt; ## 6 LGG Actinobacteria &lt;tibble [128 × 8]&gt; &lt;int [1]&gt; &lt;htest&gt; ## 7 Placebo Actinobacteria &lt;tibble [224 × 8]&gt; &lt;int [1]&gt; &lt;htest&gt; ## 8 Placebo Proteobacteria &lt;tibble [728 × 8]&gt; &lt;int [1]&gt; &lt;htest&gt; ## 9 LGG Proteobacteria &lt;tibble [416 × 8]&gt; &lt;int [1]&gt; &lt;htest&gt; ## 10 Placebo Cyanobacteria &lt;tibble [28 × 8]&gt; &lt;int [1]&gt; &lt;htest&gt; ## 11 Placebo Fusobacteria &lt;tibble [28 × 8]&gt; &lt;int [1]&gt; &lt;htest&gt; ## 12 Placebo Spirochaetes &lt;tibble [28 × 8]&gt; &lt;int [1]&gt; &lt;htest&gt; The resulting dataframe contains a new column, which contains the model objects. Just as we mapped the t.test function, we can map the tidying functions from the broom package to extract the information we want from the nested model objects. summary &lt;- t_tests %&gt;% mutate(summary = purrr::map(t_test, broom::glance)) summary ## # A tibble: 12 x 6 ## # Groups: group, phylum [12] ## group phylum data n_rows t_test summary ## &lt;fct&gt; &lt;chr&gt; &lt;list&gt; &lt;list&gt; &lt;list&gt; &lt;list&gt; ## 1 LGG Bacteroidetes &lt;tibble [256 × 8]&gt; &lt;int [1]&gt; &lt;htes… &lt;tibble [1 × 10… ## 2 Placebo Bacteroidetes &lt;tibble [448 × 8]&gt; &lt;int [1]&gt; &lt;htes… &lt;tibble [1 × 10… ## 3 Placebo Firmicutes &lt;tibble [2,128 × 8… &lt;int [1]&gt; &lt;htes… &lt;tibble [1 × 10… ## 4 LGG Firmicutes &lt;tibble [1,216 × 8… &lt;int [1]&gt; &lt;htes… &lt;tibble [1 × 10… ## 5 Placebo Verrucomicrobia &lt;tibble [28 × 8]&gt; &lt;int [1]&gt; &lt;htes… &lt;tibble [1 × 10… ## 6 LGG Actinobacteria &lt;tibble [128 × 8]&gt; &lt;int [1]&gt; &lt;htes… &lt;tibble [1 × 10… ## 7 Placebo Actinobacteria &lt;tibble [224 × 8]&gt; &lt;int [1]&gt; &lt;htes… &lt;tibble [1 × 10… ## 8 Placebo Proteobacteria &lt;tibble [728 × 8]&gt; &lt;int [1]&gt; &lt;htes… &lt;tibble [1 × 10… ## 9 LGG Proteobacteria &lt;tibble [416 × 8]&gt; &lt;int [1]&gt; &lt;htes… &lt;tibble [1 × 10… ## 10 Placebo Cyanobacteria &lt;tibble [28 × 8]&gt; &lt;int [1]&gt; &lt;htes… &lt;tibble [1 × 10… ## 11 Placebo Fusobacteria &lt;tibble [28 × 8]&gt; &lt;int [1]&gt; &lt;htes… &lt;tibble [1 × 10… ## 12 Placebo Spirochaetes &lt;tibble [28 × 8]&gt; &lt;int [1]&gt; &lt;htes… &lt;tibble [1 × 10… The final step is to return a tidy dataframe, we can do this using the unnest function from the tidyr package. Note: it is also wise to ungroup after you are done operating on your grouped variables - you may run into problems if you forget your dataframe is grouped later on! unnested &lt;- summary %&gt;% unnest(summary) %&gt;% ungroup() %&gt;% dplyr::select(group, phylum, p.value) unnested ## # A tibble: 12 x 3 ## group phylum p.value ## &lt;fct&gt; &lt;chr&gt; &lt;dbl&gt; ## 1 LGG Bacteroidetes 0.930 ## 2 Placebo Bacteroidetes 0.828 ## 3 Placebo Firmicutes 0.344 ## 4 LGG Firmicutes 0.131 ## 5 Placebo Verrucomicrobia 0.106 ## 6 LGG Actinobacteria 0.600 ## 7 Placebo Actinobacteria 0.997 ## 8 Placebo Proteobacteria 0.856 ## 9 LGG Proteobacteria 0.0111 ## 10 Placebo Cyanobacteria 0.193 ## 11 Placebo Fusobacteria 0.265 ## 12 Placebo Spirochaetes 0.728 We can then plot the results. Let’s do this for the “LGG” (non-placebo) group, using our principles for good graphics. p_data &lt;- unnested %&gt;% filter(group == &quot;LGG&quot;) %&gt;% mutate(phylum = fct_reorder(phylum, p.value)) ggplot(p_data, aes(y = p.value, x = phylum)) + facet_wrap(~group) + geom_bar(stat=&quot;identity&quot;, fill = &quot;grey90&quot;, color = &quot;grey20&quot;) + coord_flip() + theme_bw() + ggtitle(&quot;Difference in abundance by gender&quot;) + xlab(&quot;&quot;) + ylab(&quot;p-value from t-test&quot;) 7.5 Regression models 7.5.1 Formula structure Regression models can be used to estimate how the expected value of a dependent variable changes as independent variables change. In R, regression formulas take this structure: ## Generic code [response variable] ~ [indep. var. 1] + [indep. var. 2] + ... Notice that a tilde, ~, is used to separate the independent and dependent variables and that a plus sign, +, is used to join independent variables. This format mimics the statistical notation: \\[ Y_i \\sim X_1 + X_2 + X_3 \\] You will use this type of structure in R fo a lot of different function calls, including those for linear models (fit with the lm function) and generalized linear models (fit with the glm function). There are some conventions that can be used in R formulas. Common ones include: Convention Meaning I() evaluate the formula inside I() before fitting (e.g., I(x1 + x2)) : fit the interaction between x1 and x2 variables * fit the main effects and interaction for both variables (e.g., x1*x2 equals x1 + x2 + x1:x2) . include as independent variables all variables other than the response (e.g., y ~ .) 1 intercept (e.g., y ~ 1 for an intercept-only model) - do not include a variable in the data frame as an independent variables (e.g., y ~ . - x1); usually used in conjunction with . or 1 7.5.2 Linear models To fit a linear model, you can use the function lm(). This function is part of the stats package, which comes installed with base R. In this function, you can use the data option to specify the data frame from which to get the vectors. ## Warning: Problem with `mutate()` input `sex`. ## ℹ Unknown levels in `f`: 1, 2 ## ℹ Input `sex` is `fct_recode(factor(sex), Male = &quot;1&quot;, Female = &quot;2&quot;)`. ## Warning: Unknown levels in `f`: 1, 2 mod_a &lt;- lm(wt ~ ht, data = nepali) This previous call fits the model: \\[ Y_{i} = \\beta_{0} + \\beta_{1}X_{1,i} + \\epsilon_{i} \\] where: \\(Y_{i}\\) : weight of child \\(i\\) \\(X_{1,i}\\) : height of child \\(i\\) If you run the lm function without saving it as an object, R will fit the regression and print out the function call and the estimated model coefficients: lm(wt ~ ht, data = nepali) ## ## Call: ## lm(formula = wt ~ ht, data = nepali) ## ## Coefficients: ## (Intercept) ht ## -8.7581 0.2342 However, to be able to use the model later for things like predictions and model assessments, you should save the output of the function as an R object: mod_a &lt;- lm(wt ~ ht, data = nepali) This object has a special class, lm: class(mod_a) ## [1] &quot;lm&quot; This class is a special type of list object. If you use is.list to check, you can confirm that this object is a list: is.list(mod_a) ## [1] TRUE There are a number of functions that you can apply to an lm object. These include: Function Description summary Get a variety of information on the model, including coefficients and p-values for the coefficients coefficients Pull out just the coefficients for a model fitted Get the fitted values from the model (for the data used to fit the model) plot Create plots to help assess model assumptions residuals Get the model residuals For example, you can get the coefficients from the model by running: coefficients(mod_a) ## (Intercept) ht ## -8.7581466 0.2341969 The estimated coefficient for the intercept is always given under the name “(Intercept)”. Estimated coefficients for independent variables are given based on their column names in the original data (“ht” here, for \\(\\beta_1\\), or the estimated increase in expected weight for a one unit increase in height). You can use the output from a coefficients call to plot a regression line based on the model fit on top of points showing the original data (Figure 7.1). mod_coef &lt;- coefficients(mod_a) ggplot(nepali, aes(x = ht, y = wt)) + geom_point(size = 0.2) + xlab(&quot;Height (cm)&quot;) + ylab(&quot;Weight (kg)&quot;) + geom_abline(aes(intercept = mod_coef[1], slope = mod_coef[2]), col = &quot;blue&quot;) Figure 7.1: Example of using the output from a coefficients call to add a regression line to a scatterplot. You can also add a linear regression line to a scatterplot by adding the geom geom_smooth using the argument method = “lm”. You can use the function residuals on an lm object to pull out the residuals from the model fit: head(residuals(mod_a)) ## 1 2 3 4 6 7 ## 0.1993922 -0.4329393 -0.4373953 -0.1355300 -0.6749080 -1.0838199 The result of a residuals call is a vector with one element for each of the non-missing observations (rows) in the data frame you used to fit the model. Each value gives the different between the model fitted value and the observed value for each of these observations, in the same order the observations show up in the data frame. The residuals are in the same order as the observations in the original data frame. You can also use the shorter function coef as an alternative to coefficients and the shorter function resid as an alternative to residuals. As noted in the subsection on simple statistics functions, the summary function returns different output depending on the type of object that is input to the function. If you input a regression model object to summary, the function gives you a lot of information about the model. For example, here is the output returned by running summary for the linear regression model object we just created: summary(mod_a) ## ## Call: ## lm(formula = wt ~ ht, data = nepali) ## ## Residuals: ## Min 1Q Median 3Q Max ## -3.0077 -0.5479 -0.0293 0.4972 3.3514 ## ## Coefficients: ## Estimate Std. Error t value Pr(&gt;|t|) ## (Intercept) -8.758147 0.211529 -41.40 &lt;2e-16 *** ## ht 0.234197 0.002459 95.23 &lt;2e-16 *** ## --- ## Signif. codes: 0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1 ## ## Residual standard error: 0.8733 on 875 degrees of freedom ## (123 observations deleted due to missingness) ## Multiple R-squared: 0.912, Adjusted R-squared: 0.9119 ## F-statistic: 9068 on 1 and 875 DF, p-value: &lt; 2.2e-16 This output includes a lot of useful elements, including (1) basic summary statistics for the residuals (to meet model assumptions, the median should be around zero and the absolute values fairly similar for the first and third quantiles), (2) coefficient estimates, standard errors, and p-values, and (3) some model summary statistics, including residual standard error, degrees of freedom, number of missing observations, and F-statistic. The object returned by the summary() function when it is applied to an lm object is a list, which you can confirm using the is.list function: is.list(summary(mod_a)) ## [1] TRUE With any list, you can use the names function to get the names of all of the different elements of the object: names(summary(mod_a)) ## [1] &quot;call&quot; &quot;terms&quot; &quot;residuals&quot; &quot;coefficients&quot; ## [5] &quot;aliased&quot; &quot;sigma&quot; &quot;df&quot; &quot;r.squared&quot; ## [9] &quot;adj.r.squared&quot; &quot;fstatistic&quot; &quot;cov.unscaled&quot; &quot;na.action&quot; You can use the $ operator to pull out any element of the list. For example, to pull out the table with information on the estimated model coefficients, you can run: summary(mod_a)$coefficients ## Estimate Std. Error t value Pr(&gt;|t|) ## (Intercept) -8.7581466 0.211529182 -41.40396 2.411051e-208 ## ht 0.2341969 0.002459347 95.22726 0.000000e+00 The plot function, like the summary function, will give different output depending on the class of the object that you input. For an lm object, you can use the plot function to get a number of useful diagnostic plots that will help you check regression assumptions (Figure 7.2): plot(mod_a) Figure 7.2: Example output from running the plot function with an lm object as the input. You can also use binary variables or factors as independent variables in regression models. For example, in the nepali dataset, sex is a factor variable with the levels “Male” and “Female”. You can fit a linear model of weight regressed on sex for this data with the call: mod_b &lt;- lm(wt ~ sex, data = nepali) This call fits the model: \\[ Y_{i} = \\beta_{0} + \\beta_{1}X_{1,i} + \\epsilon_{i} \\] where \\(X_{1,i}\\) : sex of child \\(i\\), where 0 = male and 1 = female. Here are the estimated coefficients from fitting this model: summary(mod_b)$coefficients ## Estimate Std. Error t value Pr(&gt;|t|) ## (Intercept) 11.4226374 0.1375427 83.047920 0.00000000 ## sexFemale -0.4866184 0.1982810 -2.454185 0.01431429 You’ll notice that, in addition to an estimated intercept ((Intercept)), the other estimated coefficient is sexFemale rather than just sex, although the column name in the data frame input to lm for this variable is sex. This is because, when a factor or binary variable is input as an independent variable in a linear regression model, R will fit an estimated coefficient for all levels of factors except the first factor level. By default, this first factor level is used as the baseline level, and so its estimated mean is given by the estimated intercept, while the other model coefficients give the estimated difference from this baseline. For example, the model fit above tells us that the estimated mean weight of males is 11.4, while the estimated mean weight of females is 11.4 + -0.5 = 10.9. 7.6 Handling model objects The broom package contains tools for converting statistical objects into nice tidy data frames. The tools in the broom package make it easy to process statistical results in R using the tools of the tidyverse. 7.6.1 broom::tidy The tidy() function returns a data frame with information on the fitted model terms. For example, when applied to one of our linear models we get: library(broom) kable(tidy(mod_a), digits = 3) term estimate std.error statistic p.value (Intercept) -8.758 0.212 -41.404 0 ht 0.234 0.002 95.227 0 class(tidy(mod_a)) ## [1] &quot;tbl_df&quot; &quot;tbl&quot; &quot;data.frame&quot; You can pass arguments to the tidy() function. For example, include confidence intervals: kable(tidy(mod_a, conf.int = TRUE), digits = 3) term estimate std.error statistic p.value conf.low conf.high (Intercept) -8.758 0.212 -41.404 0 -9.173 -8.343 ht 0.234 0.002 95.227 0 0.229 0.239 7.6.2 broom::augment The augment() function adds information about a fitted model to the dataset used to fit the model. For example, when applied to one of our linear models we get information on the fitted values and residuals included in the output: kable(head(broom::augment(mod_a), 3), digits = 3) .rownames wt ht .fitted .resid .std.resid .hat .sigma .cooksd 1 12.8 91.2 12.601 0.199 0.228 0.001 0.874 0 2 12.8 93.9 13.233 -0.433 -0.496 0.002 0.874 0 3 13.1 95.2 13.537 -0.437 -0.501 0.002 0.874 0 7.6.3 broom::glance The glance() functions returns a one row summary of a fitted model object: For example: kable(glance(mod_a, conf.int = TRUE), digits = 3) r.squared adj.r.squared sigma statistic p.value df logLik AIC BIC deviance df.residual nobs 0.912 0.912 0.873 9068.23 0 1 -1124.615 2255.23 2269.56 667.351 875 877 7.6.4 References– statistics in R One great (and free online for CSU students through our library) book to find out more about using R for basic statistics is: Introductory Statistics with R If you want all the details about fitting linear models and GLMs in R, Julian Faraway’s books are fantastic. He has one on linear models and one on extensions including logistic and Poisson models: Linear Models with R (also free online through the CSU library) Extending the Linear Model with R 7.7 Functions As you move to larger projects, you will find yourself using the same code a lot. Examples include: Reading in data from a specific type of equipment (air pollution monitor, accelerometer) Running a specific type of analysis Creating a specific type of plot or map If you find yourself cutting and pasting a lot, convert the code to a function. Advantages of writing functions include: Coding is more efficient Easier to change your code (if you’ve cut and paste code and you want to change something, you have to change it everywhere - this is an easy way to accidentally create bugs in your code) Easier to share code with others You can name a function anything you want (although try to avoid names of preexisting-existing functions). You then define any inputs (arguments; separate multiple arguments with commas) and put the code to run in braces: ## Note: this code will not run [function name] &lt;- function([any arguments]){ [code to run] } Here is an example of a very basic function. This function takes a number as input and adds 1 to that number. add_one &lt;- function(number){ out &lt;- number + 1 return(out) } add_one(number = 3) ## [1] 4 add_one(number = -1) ## [1] 0 Functions can input any type of R object (for example, vectors, data frames, even other functions and ggplot objects) Similarly, functions can output any type of R object When defining a function, you can set default values for some of the parameters You can explicitly specify the value to return from the function For example, the following function inputs a data frame (datafr) and a one-element vector (child_id) and returns only rows in the data frame where it’s id column matches child_id. It includes a default value for datafr, but not for child_id. subset_nepali &lt;- function(datafr = nepali, child_id){ datafr &lt;- datafr %&gt;% filter(id == child_id) return(datafr) } If an argument is not given for a parameter with a default, the function will run using the default value for that parameter. For example: subset_nepali(child_id = &quot;120011&quot;) ## id sex wt ht mage lit died alive age ## 1 120011 Male 12.8 91.2 35 0 2 5 41 ## 2 120011 Male 12.8 93.9 35 0 2 5 45 ## 3 120011 Male 13.1 95.2 35 0 2 5 49 ## 4 120011 Male 13.8 96.9 35 0 2 5 53 ## 5 120011 Male NA NA 35 0 2 5 57 If an argument is not given for a parameter without a default, the function call will result in an error. For example: subset_nepali(datafr = nepali) ## Error: Problem with `filter()` input `..1`. ## x argument &quot;child_id&quot; is missing, with no default ## ℹ Input `..1` is `id == child_id`. By default, the function will return the last defined object, although the choice of using return can affect printing behavior when you run the function. For example, I could have written the subset_nepali function like this: subset_nepali &lt;- function(datafr = nepali, child_id){ datafr &lt;- datafr %&gt;% filter(id == child_id) } In this case, the output will not automatically print out when you call the function without assigning it to an R object: subset_nepali(child_id = &quot;120011&quot;) However, the output can be assigned to an R object in the same way as when the function was defined without return: first_childs_data &lt;- subset_nepali(child_id = &quot;120011&quot;) first_childs_data ## id sex wt ht mage lit died alive age ## 1 120011 Male 12.8 91.2 35 0 2 5 41 ## 2 120011 Male 12.8 93.9 35 0 2 5 45 ## 3 120011 Male 13.1 95.2 35 0 2 5 49 ## 4 120011 Male 13.8 96.9 35 0 2 5 53 ## 5 120011 Male NA NA 35 0 2 5 57 R is very “good” at running functions! It will look for (scope) the variables in your function in various places (environments). So your functions may run even when you don’t expect them to, potentially, with unexpected results! The return function can also be used to return an object other than the last defined object (although this doesn’t tend to be something you need to do very often). If you did not use return in the following code, it will output “Test output”: subset_nepali &lt;- function(datafr = nepali, child_id){ datafr &lt;- datafr %&gt;% filter(id == child_id) a &lt;- &quot;Test output&quot; } (subset_nepali(child_id = &quot;120011&quot;)) ## [1] &quot;Test output&quot; Conversely, you can use return to output datafr, even though it’s not the last object defined: subset_nepali &lt;- function(datafr = nepali, child_id){ datafr &lt;- datafr %&gt;% filter(id == child_id) a &lt;- &quot;Test output&quot; return(datafr) } subset_nepali(child_id = &quot;120011&quot;) ## id sex wt ht mage lit died alive age ## 1 120011 Male 12.8 91.2 35 0 2 5 41 ## 2 120011 Male 12.8 93.9 35 0 2 5 45 ## 3 120011 Male 13.1 95.2 35 0 2 5 49 ## 4 120011 Male 13.8 96.9 35 0 2 5 53 ## 5 120011 Male NA NA 35 0 2 5 57 7.7.1 if / else statements There are other control structures you can use in your R code. Two that you will commonly use within R functions are if and ifelse statements. An if statement tells R that, if a certain condition is true, do run some code. For example, if you wanted to print out only odd numbers between 1 and 5, one way to do that is with an if statement: (Note: the %% operator in R returns the remainder of the first value (i) divided by the second value (2).) for(i in 1:5){ if(i %% 2 == 1){ print(i) } } ## [1] 1 ## [1] 3 ## [1] 5 The if statement runs some code if a condition is true, but does nothing if it is false. If you’d like different code to run depending on whether the condition is true or false, you can us an if / else or an if / else if / else statement. for(i in 1:5){ if(i %% 2 == 1){ print(i) } else { print(paste(i, &quot;is even&quot;)) } } ## [1] 1 ## [1] &quot;2 is even&quot; ## [1] 3 ## [1] &quot;4 is even&quot; ## [1] 5 What would this code do? for(i in 1:100){ if(i %% 3 == 0 &amp; i %% 5 == 0){ print(&quot;FizzBuzz&quot;) } else if(i %% 3 == 0){ print(&quot;Fizz&quot;) } else if(i %% 5 == 0){ print(&quot;Buzz&quot;) } else { print(i) } } If / else statements are extremely useful in functions. In R, the if statement evaluates everything in the parentheses and, if that evaluates to TRUE, runs everything in the braces. This means that you can trigger code in an if statement with a single-value logical vector: weekend &lt;- TRUE if(weekend){ print(&quot;It&#39;s the weekend!&quot;) } ## [1] &quot;It&#39;s the weekend!&quot; This functionality can be useful with parameters you choose to include when writing your own functions (e.g., print = TRUE). 7.7.2 Some other control structures The control structure you are most likely to use in data analysis with R is the “if / else” statement. However, there are a few other control structures you may occasionally find useful: next break while You can use the next structure to skip to the next round of a loop when a certain condition is met. For example, we could have used this code to print out odd numbers between 1 and 5: i &lt;- 0 while(i &lt; 5){ i &lt;- 1 + i if(i %% 2 == 0){ next } print(i) } ## [1] 1 ## [1] 3 ## [1] 5 You can use break to break out of a loop if a certain condition is met. For example, the final code will break out of the loop once i is over 2, so it will only print the numbers 1 through 3: i &lt;- 0 while(i &lt;= 5){ if(i &gt; 2){ break } i &lt;- 1 + i print(i) } ## [1] 1 ## [1] 2 ## [1] 3 7.8 In-course exercise 7.8.1 Exploring taxonomic profiling data We’ll be using a package on Bioconductor called microbiome. You’ll need to install that package from Bioconductor. This uses code that’s different from the default you use to download a package from CRAN. Go to the Bioconductor page for the microbiome package and figure out how to install this package based on instructions on that page. The microbiome that includes tools for exploring and analysing microbiome profiling data. This package has a website with tutorial information here. We want to explore a dataset on genus-level microbiota profiling (atlas1006). Navigate to the tutorial webpage to figure out how you can get this example raw data loaded in your R session. Use the class and str functions to start exploring this data. Is it in a dataframe (tibble)? Is it in a tidy format? How is the data structured? On the microbiome page, find the documentation describing the atlas1006 data. Look through this documentation to figure out what information is included in the data. Also, check the helpfile for this dataset and look up the original article describing the data (you can find the article information in the help resources). The atlas1006 data is stored in a special object class called a “phyloseq” object (you should have seen this when you used class with the object). You can pull certain parts of this data using special functions called “accessors”. One is get_variable. Try running get_variable with the atlas1006 data. What do you think this data is showing? Which different nationalities are represented by the study subjects, based on the dataframe you extracted in the last step? How many samples have each nationality? Which different BMI groups are included? Does it look like the study was balanced among these groups? Based on the data you extracted, does it look like diversity varies much between males and females? Across BMI groups? Discuss what steps you would need to take to create the following plot. To start, don’t write any code, just develop a plan. Talk about what the dataset should look like right before you create the plot and what functions you could use to get the data from its current format to that format. Try to write the code to create this plot. This will include some code for cleaning the data and some code for plotting. I will add one example answer after class, but I’d like you to try to figure it out yourselves first. 7.8.1.1 Example R code Install the microbiome package from Bioconductor: if (!requireNamespace(&quot;BiocManager&quot;, quietly = TRUE)) install.packages(&quot;BiocManager&quot;) BiocManager::install(&quot;microbiome&quot;) Load the atlas1006 example data in the microbiome package and explore it with str and class: library(microbiome) data(atlas1006) class(atlas1006) ## [1] &quot;phyloseq&quot; ## attr(,&quot;package&quot;) ## [1] &quot;phyloseq&quot; str(atlas1006) ## Formal class &#39;phyloseq&#39; [package &quot;phyloseq&quot;] with 5 slots ## ..@ otu_table:Formal class &#39;otu_table&#39; [package &quot;phyloseq&quot;] with 2 slots ## .. .. ..@ .Data : num [1:130, 1:1151] 0 0 0 21 1 72 0 0 176 10 ... ## .. .. .. ..- attr(*, &quot;dimnames&quot;)=List of 2 ## .. .. .. .. ..$ : chr [1:130] &quot;Actinomycetaceae&quot; &quot;Aerococcus&quot; &quot;Aeromonas&quot; &quot;Akkermansia&quot; ... ## .. .. .. .. ..$ : chr [1:1151] &quot;Sample-1&quot; &quot;Sample-2&quot; &quot;Sample-3&quot; &quot;Sample-4&quot; ... ## .. .. ..@ taxa_are_rows: logi TRUE ## ..@ tax_table:Formal class &#39;taxonomyTable&#39; [package &quot;phyloseq&quot;] with 1 slot ## .. .. ..@ .Data: chr [1:130, 1:3] &quot;Actinobacteria&quot; &quot;Firmicutes&quot; &quot;Proteobacteria&quot; &quot;Verrucomicrobia&quot; ... ## .. .. .. ..- attr(*, &quot;dimnames&quot;)=List of 2 ## .. .. .. .. ..$ : chr [1:130] &quot;Actinomycetaceae&quot; &quot;Aerococcus&quot; &quot;Aeromonas&quot; &quot;Akkermansia&quot; ... ## .. .. .. .. ..$ : chr [1:3] &quot;Phylum&quot; &quot;Family&quot; &quot;Genus&quot; ## ..@ sam_data :&#39;data.frame&#39;: 1151 obs. of 10 variables: ## Formal class &#39;sample_data&#39; [package &quot;phyloseq&quot;] with 4 slots ## .. .. ..@ .Data :List of 10 ## .. .. .. ..$ : int [1:1151] 28 24 52 22 25 42 25 27 21 25 ... ## .. .. .. ..$ : Factor w/ 2 levels &quot;female&quot;,&quot;male&quot;: 2 1 2 1 1 2 1 1 1 1 ... ## .. .. .. ..$ : Factor w/ 6 levels &quot;CentralEurope&quot;,..: 6 6 6 6 6 6 6 6 6 6 ... ## .. .. .. ..$ : Factor w/ 3 levels &quot;o&quot;,&quot;p&quot;,&quot;r&quot;: NA NA NA NA NA NA NA NA NA NA ... ## .. .. .. ..$ : Factor w/ 40 levels &quot;1&quot;,&quot;2&quot;,&quot;3&quot;,&quot;4&quot;,..: 1 1 1 1 1 1 1 1 1 1 ... ## .. .. .. ..$ : num [1:1151] 5.76 6.06 5.5 5.87 5.89 5.53 5.49 5.38 5.34 5.64 ... ## .. .. .. ..$ : Factor w/ 6 levels &quot;underweight&quot;,..: 5 4 2 1 2 2 1 2 2 2 ... ## .. .. .. ..$ : Factor w/ 1006 levels &quot;1&quot;,&quot;2&quot;,&quot;3&quot;,&quot;4&quot;,..: 1 2 3 4 5 6 7 8 9 10 ... ## .. .. .. ..$ : num [1:1151] 0 0 0 0 0 0 0 0 0 0 ... ## .. .. .. ..$ : chr [1:1151] &quot;Sample-1&quot; &quot;Sample-2&quot; &quot;Sample-3&quot; &quot;Sample-4&quot; ... ## .. .. ..@ names : chr [1:10] &quot;age&quot; &quot;sex&quot; &quot;nationality&quot; &quot;DNA_extraction_method&quot; ... ## .. .. ..@ row.names: chr [1:1151] &quot;Sample-1&quot; &quot;Sample-2&quot; &quot;Sample-3&quot; &quot;Sample-4&quot; ... ## .. .. ..@ .S3Class : chr &quot;data.frame&quot; ## ..@ phy_tree : NULL ## ..@ refseq : NULL Pull out the data frame that contains information on each study subject in the atlas1006 data by using the get_variable accessor function: get_variable(atlas1006) %&gt;% head() ## age sex nationality DNA_extraction_method project diversity ## Sample-1 28 male US &lt;NA&gt; 1 5.76 ## Sample-2 24 female US &lt;NA&gt; 1 6.06 ## Sample-3 52 male US &lt;NA&gt; 1 5.50 ## Sample-4 22 female US &lt;NA&gt; 1 5.87 ## Sample-5 25 female US &lt;NA&gt; 1 5.89 ## Sample-6 42 male US &lt;NA&gt; 1 5.53 ## bmi_group subject time sample ## Sample-1 severeobese 1 0 Sample-1 ## Sample-2 obese 2 0 Sample-2 ## Sample-3 lean 3 0 Sample-3 ## Sample-4 underweight 4 0 Sample-4 ## Sample-5 lean 5 0 Sample-5 ## Sample-6 lean 6 0 Sample-6 Note: Since the first argument to get_variable is the phyloseq object (here, the atlas1006 data object), you can pipe into the function, like this: atlas1006 %&gt;% get_variable() %&gt;% head() Find out the different nationalities included in the data: atlas1006 %&gt;% get_variable() %&gt;% as_tibble() %&gt;% # Change output from a data.frame to a tibble group_by(nationality) %&gt;% count() ## # A tibble: 7 x 2 ## # Groups: nationality [7] ## nationality n ## &lt;fct&gt; &lt;int&gt; ## 1 CentralEurope 650 ## 2 EasternEurope 15 ## 3 Scandinavia 271 ## 4 SouthEurope 89 ## 5 UKIE 50 ## 6 US 44 ## 7 &lt;NA&gt; 32 It looks like most subjects were from Central Europe, with the next-largest group from Scandinavia. Note: If you wanted to rearrange this summary to give the nationalities in order of the number of subjects in each, you could add on a forcats function: atlas1006 %&gt;% get_variable() %&gt;% as_tibble() %&gt;% # Change output from a data.frame to a tibble mutate(nationality = fct_infreq(nationality)) %&gt;% group_by(nationality) %&gt;% count() ## # A tibble: 7 x 2 ## # Groups: nationality [7] ## nationality n ## &lt;fct&gt; &lt;int&gt; ## 1 CentralEurope 650 ## 2 Scandinavia 271 ## 3 SouthEurope 89 ## 4 UKIE 50 ## 5 US 44 ## 6 EasternEurope 15 ## 7 &lt;NA&gt; 32 Find out the different BMI groups included in the data and if the study seemed to be balanced across those groups: atlas1006 %&gt;% get_variable() %&gt;% as_tibble() %&gt;% # Change output from a data.frame to a tibble group_by(bmi_group) %&gt;% count() ## # A tibble: 7 x 2 ## # Groups: bmi_group [7] ## bmi_group n ## &lt;fct&gt; &lt;int&gt; ## 1 underweight 21 ## 2 lean 484 ## 3 overweight 197 ## 4 obese 222 ## 5 severeobese 99 ## 6 morbidobese 22 ## 7 &lt;NA&gt; 106 There were six different BMI groups. Over 100 study subjects had this information missing. The samples were not evenly distributed across these BMI groups. Instead, the most common (lean) had almost 500 subjects, while the smaller BMI-group samples were around 20 people. See if it looks like diversity varies much between males and females: atlas1006 %&gt;% get_variable() %&gt;% as_tibble() %&gt;% group_by(sex) %&gt;% summarize(mean_diversity = mean(diversity)) ## `summarise()` ungrouping output (override with `.groups` argument) ## # A tibble: 3 x 2 ## sex mean_diversity ## &lt;fct&gt; &lt;dbl&gt; ## 1 female 5.84 ## 2 male 5.85 ## 3 &lt;NA&gt; 5.76 See if it looks like diversity varies much across BMI groups: atlas1006 %&gt;% get_variable() %&gt;% as_tibble() %&gt;% group_by(bmi_group) %&gt;% summarize(mean_diversity = mean(diversity)) ## `summarise()` ungrouping output (override with `.groups` argument) ## # A tibble: 7 x 2 ## bmi_group mean_diversity ## &lt;fct&gt; &lt;dbl&gt; ## 1 underweight 5.84 ## 2 lean 5.89 ## 3 overweight 5.83 ## 4 obese 5.82 ## 5 severeobese 5.74 ## 6 morbidobese 5.67 ## 7 &lt;NA&gt; 5.81 Here is the code for the plot: library(tidyverse) library(ggthemes) library(microbiome) data(atlas1006) atlas1006 %&gt;% get_variable() %&gt;% ggplot(aes(x = bmi_group, y = diversity, color = sex)) + geom_boxplot(alpha = 0.2) + facet_wrap(~ nationality) 7.8.2 More with baby names Let’s look at baby names that we started looking at last class, based on the letter they start with. For the full dataframe, what proportion of baby names start with each letter? See if you can create a figure to help show this. Create the same plot using the names of people from our class. What proportion of names start with “C” or “S” across the full dataset? 7.8.2.1 Example R code For the full dataframe, what proportion of baby names start with “S”? To start, create a column with the first letter of each name. You can use functions in the stringr package to do this. The easiest might be to use the position of the first letter to pull that information. library(babynames) library(stringr) top_letters &lt;- babynames %&gt;% mutate(first_letter = str_sub(name, 1, 1)) top_letters %&gt;% select(name, first_letter) %&gt;% slice(1:5) ## # A tibble: 5 x 2 ## name first_letter ## &lt;chr&gt; &lt;chr&gt; ## 1 Mary M ## 2 Anna A ## 3 Emma E ## 4 Elizabeth E ## 5 Minnie M Now we can group by letter and figure out these proportions. First, while the data is grouped, count the number of names with each letter. Then, ungroup and use mutate to divide this by the total number of names: top_letters &lt;- top_letters %&gt;% group_by(first_letter) %&gt;% summarize(n = sum(n)) %&gt;% ungroup() %&gt;% mutate(prop = n / sum(n)) %&gt;% arrange(desc(prop)) ## `summarise()` ungrouping output (override with `.groups` argument) top_letters ## # A tibble: 26 x 3 ## first_letter n prop ## &lt;chr&gt; &lt;int&gt; &lt;dbl&gt; ## 1 J 44612175 0.128 ## 2 M 32864210 0.0944 ## 3 A 28855232 0.0829 ## 4 C 25533863 0.0733 ## 5 D 24240271 0.0696 ## 6 R 23702794 0.0681 ## 7 S 21373830 0.0614 ## 8 L 18942067 0.0544 ## 9 E 17033760 0.0489 ## 10 K 17006684 0.0489 ## # … with 16 more rows Here’s one way to visualize this: library(scales) top_letters %&gt;% mutate(first_letter = fct_reorder(first_letter, prop)) %&gt;% ggplot(aes(x = first_letter)) + geom_bar(aes(weight = prop)) + coord_flip() + scale_y_continuous(labels = percent) + labs(x = &quot;&quot;, y = &quot;Percent of names that start with ...&quot;) Create the same plot using the names of people in our class. First, create a vector with the names of people in our class: student_list &lt;- data_frame(name = c(&quot;Burton&quot;, &quot;Caroline&quot;, &quot;Chaoyu&quot;, &quot;Collin&quot;, &quot;Daniel&quot;, &quot;Eric&quot;, &quot;Erin&quot;, &quot;Heather&quot;, &quot;Jacob&quot;, &quot;Jessica&quot;, &quot;Khum&quot;, &quot;Kyle&quot;, &quot;Matthew&quot;, &quot;Molly&quot;, &quot;Nikki&quot;, &quot;Rachel&quot;, &quot;Sere&quot;, &quot;Shayna&quot;, &quot;Sherry&quot;, &quot;Sunny&quot;)) student_list &lt;- student_list %&gt;% mutate(first_letter = str_sub(name, 1, 1)) student_list ## # A tibble: 20 x 2 ## name first_letter ## &lt;chr&gt; &lt;chr&gt; ## 1 Burton B ## 2 Caroline C ## 3 Chaoyu C ## 4 Collin C ## 5 Daniel D ## 6 Eric E ## 7 Erin E ## 8 Heather H ## 9 Jacob J ## 10 Jessica J ## 11 Khum K ## 12 Kyle K ## 13 Matthew M ## 14 Molly M ## 15 Nikki N ## 16 Rachel R ## 17 Sere S ## 18 Shayna S ## 19 Sherry S ## 20 Sunny S library(scales) student_list %&gt;% group_by(first_letter) %&gt;% count() %&gt;% ungroup() %&gt;% mutate(prop = n / sum(n)) %&gt;% mutate(first_letter = fct_reorder(first_letter, prop)) %&gt;% ggplot(aes(x = first_letter)) + geom_bar(aes(weight = prop)) + coord_flip() + scale_y_continuous(labels = percent) + labs(x = &quot;&quot;, y = &quot;Percent of students with\\na name that starts with ...&quot;) What proportion of names start with “C” or “S” across the full dataset? You can create a dataframe that (1) pulls out the first letter of each name (just like we did for the last part of the question) and (2) tests whether that first letter is an “A” or a “K” (using a logical statement): c_or_s &lt;- babynames %&gt;% mutate(first_letter = str_sub(name, 1, 1), c_or_s = first_letter %in% c(&quot;C&quot;, &quot;S&quot;)) c_or_s %&gt;% select(name, first_letter, c_or_s) %&gt;% slice(1:5) ## # A tibble: 5 x 3 ## name first_letter c_or_s ## &lt;chr&gt; &lt;chr&gt; &lt;lgl&gt; ## 1 Mary M FALSE ## 2 Anna A FALSE ## 3 Emma E FALSE ## 4 Elizabeth E FALSE ## 5 Minnie M FALSE Next, group by this logical column (c_or_s) and figure out the number of baby names for each group. Then, to get the proportion of the total, ungroup and mutate to divide by the total number across the data: c_or_s %&gt;% group_by(c_or_s) %&gt;% count() %&gt;% ungroup() %&gt;% mutate(prop = n / sum(n)) ## # A tibble: 2 x 3 ## c_or_s n prop ## &lt;lgl&gt; &lt;int&gt; &lt;dbl&gt; ## 1 FALSE 1655755 0.860 ## 2 TRUE 268910 0.140 7.8.3 Running a simple statistical test In the last part of the in-course exercise, we found out that about 14% of babies born in the United States between 1980 and 1995 had names that started with an “C” or “S” (268,910 babies out of 1,924,665). What is the proportion of people with names that start with an “C” or “S” in our class? Use a simple statistical test to test the hypothesis that the class comes from a binomial distribution with the same distribution as babies born in the US over the time tracked by this data, in terms of chance of having a name that starts with “C” or “S”. (Hint: You will be comparing two proportions. Try googling for a statistical test in R that does that.) See if you can figure out a way to make a single “tidy” pipeline for the whole analysis (and output the result as a tidy dataframe). Does the tidy function from broom give different information from this test than the output we got for the Shapiro-Wilk test? You may get the warning “Chi-squared approximation may be incorrect”. See if you can figure out this warning if you get it with the test you used. 7.8.3.1 Example R code Here is a vector with names in our class: library(stringr) student_list &lt;- tibble(name = c(&quot;Burton&quot;, &quot;Caroline&quot;, &quot;Chaoyu&quot;, &quot;Collin&quot;, &quot;Daniel&quot;, &quot;Eric&quot;, &quot;Erin&quot;, &quot;Heather&quot;, &quot;Jacob&quot;, &quot;Jessica&quot;, &quot;Khum&quot;, &quot;Kyle&quot;, &quot;Matthew&quot;, &quot;Molly&quot;, &quot;Nikki&quot;, &quot;Rachel&quot;, &quot;Sere&quot;, &quot;Shayna&quot;, &quot;Sherry&quot;, &quot;Sunny&quot;)) student_list &lt;- student_list %&gt;% mutate(first_letter = str_sub(name, 1, 1)) student_list %&gt;% slice(1:3) ## # A tibble: 3 x 2 ## name first_letter ## &lt;chr&gt; &lt;chr&gt; ## 1 Burton B ## 2 Caroline C ## 3 Chaoyu C Let’s get the total number of students, and then the total number with a name that starts with “C” or “S”: tot_students &lt;- student_list %&gt;% count() tot_students ## # A tibble: 1 x 1 ## n ## &lt;int&gt; ## 1 20 c_or_s_students &lt;- student_list %&gt;% mutate(c_or_s = first_letter %in% c(&quot;C&quot;, &quot;S&quot;)) %&gt;% group_by(c_or_s) %&gt;% count() c_or_s_students ## # A tibble: 2 x 2 ## # Groups: c_or_s [2] ## c_or_s n ## &lt;lgl&gt; &lt;int&gt; ## 1 FALSE 13 ## 2 TRUE 7 The proportion of students with names starting with “A” or “K” are 7 / 20 = 0.35. You could run a statistical test comparing these two proportions (check the help file for the function to figure out where to include each piece): prop.test(x = c(268910, 7), n = c(1924665, 20)) ## Warning in prop.test(x = c(268910, 7), n = c(1924665, 20)): Chi-squared ## approximation may be incorrect ## ## 2-sample test for equality of proportions with continuity correction ## ## data: c(268910, 7) out of c(1924665, 20) ## X-squared = 5.7121, df = 1, p-value = 0.01685 ## alternative hypothesis: two.sided ## 95 percent confidence interval: ## -0.44432032 0.02375596 ## sample estimates: ## prop 1 prop 2 ## 0.1397178 0.3500000 There are a few different ways you could run this test. For example, you could also test whether the proportion in our class is consistent with the null hypothesis that you were drawn from a binomial distribution with a proportion of 0.14 (in-line with the national values): prop.test(x = 7, n = 20, p = 0.14) ## Warning in prop.test(x = 7, n = 20, p = 0.14): Chi-squared approximation may be ## incorrect ## ## 1-sample proportions test with continuity correction ## ## data: 7 out of 20, null probability 0.14 ## X-squared = 5.6852, df = 1, p-value = 0.01711 ## alternative hypothesis: true p is not equal to 0.14 ## 95 percent confidence interval: ## 0.1630867 0.5905104 ## sample estimates: ## p ## 0.35 You can also see if we can pipe into prop.test by summing up the number of successes (“1”: the person’s name starts with “C” or “S”). Because this is using an unsummarized form of the data, it lets us use some of the tidyverse tools more easily: library(purrr) library(broom) student_list %&gt;% mutate(c_or_s = first_letter %in% c(&quot;C&quot;, &quot;S&quot;)) %&gt;% pluck(&quot;c_or_s&quot;) %&gt;% sum() %&gt;% prop.test(n = 20, p = 0.14) %&gt;% tidy() ## Warning in prop.test(., n = 20, p = 0.14): Chi-squared approximation may be ## incorrect ## # A tibble: 1 x 8 ## estimate statistic p.value parameter conf.low conf.high method alternative ## &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;int&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;chr&gt; &lt;chr&gt; ## 1 0.35 5.69 0.0171 1 0.163 0.591 1-sample … two.sided Finally, when we run this test, we get the warning that “Chi-squared approximation may be incorrect”. Based on googling ‘r prop.test “Chi-squared approximation may be incorrect”’, it sounds like we might be getting this error because we have a pretty low number of people in the class. One recommendation is to use binom.test, which will run as an exact binomial test: binom.test(x = 7, n = 20, p = 0.14) ## ## Exact binomial test ## ## data: 7 and 20 ## number of successes = 7, number of trials = 20, p-value = 0.01534 ## alternative hypothesis: true probability of success is not equal to 0.14 ## 95 percent confidence interval: ## 0.1539092 0.5921885 ## sample estimates: ## probability of success ## 0.35 7.8.4 Using regression models to explore data #1 For this exercise, you will need the following packages. If do not have them already, you will need to install them. library(ggplot2) library(broom) library(ggfortify) For this part of the exercise, you’ll use a dataset on weather, air pollution, and mortality counts in Chicago, IL. This dataset is called chicagoNMMAPS and is part of the dlnm package. Change the name of the data frame to chic (this object name is shorter and will be easier to work with). Check out the data a bit to see what variables you have, and then perform the following tasks: Write out (on paper, not in R) the regression equation for regressing dewpoint temperature on temperature. Try fitting a linear regression of dew point temperature (dptp) regressed on temperature (temp). Save this model as the object mod_1 (i.e., is the dependent variable of dewpoint temperature linearly associated with the independent variable of temperature). Based on this regression, does there seem to be a relationship between temperature and dewpoint temperature in Chicago? (Hint: Try using glance and tidy from the broom package on the model object to get more information about the model you fit.) What is the coefficient for temperature (in other words, for every 1 degree increase in temperature, how much do we expect the dewpoint temperature to change?)? What is the p-value for the coefficient for temperature? Plot temperature (x-axis) versus dewpoint temperature (y-axis) for Chicago. Add in the regression line from the model you fit by using the results from augment. Use autoplot on the model object to generate some model diagnostic plots (make sure you have the ggfortify package loaded and installed). 7.8.4.1 Example R code: The regression equation for the model you want to fit, regressing dewpoint temperature on temperature, is: \\[ Y_t \\sim \\beta_0 + \\beta_1 X_t \\] where \\(Y_t\\) is the dewpoint temperature on day \\(t\\), \\(X_t\\) is the temperature on day \\(t\\), and \\(\\beta_0\\) and \\(\\beta_1\\) are model coefficients. Install and load the dlnm package and then load the chicagoNMMAPS data. Change the name of the data frame to chic, so it will be shorter to call for the rest of your work. # install.packages(&quot;dlnm&quot;) library(dlnm) data(&quot;chicagoNMMAPS&quot;) chic &lt;- chicagoNMMAPS Fit a linear regression of dptp on temp and save as the object mod_1: mod_1 &lt;- lm(dptp ~ temp, data = chic) mod_1 ## ## Call: ## lm(formula = dptp ~ temp, data = chic) ## ## Coefficients: ## (Intercept) temp ## 24.025 1.621 Use functions from the broom package to pull the same information about the model in a “tidy” format. To find out if the evidence for a linear association between temperature and dewpoint temperature, use the tidy function to get model coefficients in a tidy format: tidy(mod_1) ## # A tibble: 2 x 5 ## term estimate std.error statistic p.value ## &lt;chr&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; ## 1 (Intercept) 24.0 0.113 213. 0 ## 2 temp 1.62 0.00763 212. 0 There does seem to be an association between temperature and dewpoint temperature: a unit increase in temperature is associated with a 1.6 unit increase in dewpoint temperature. The p-value for the temperature coefficient is &lt;2e-16. This is far below 0.05, which suggests we would be very unlikely to see such a strong association by chance if the null hypothesis, that the two variables are not associated, were true. You can also check overall model summaries using the glance function: glance(mod_1) ## # A tibble: 1 x 12 ## r.squared adj.r.squared sigma statistic p.value df logLik AIC BIC ## &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; ## 1 0.898 0.898 5.90 45108. 0 1 -16332. 32670. 32690. ## # … with 3 more variables: deviance &lt;dbl&gt;, df.residual &lt;int&gt;, nobs &lt;int&gt; To create plots of the observations and the fit model, use the augment function to add model output (e.g., predictions, residuals) to the original data frame of observed temperatures and dew point temperatures: augment(mod_1) %&gt;% slice(1:3) ## # A tibble: 3 x 8 ## dptp temp .fitted .resid .std.resid .hat .sigma .cooksd ## &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; ## 1 31.5 -0.278 23.6 7.93 1.34 0.000376 5.90 0.000340 ## 2 29.9 0.556 24.9 4.95 0.839 0.000348 5.90 0.000123 ## 3 27.4 0.556 24.9 2.45 0.415 0.000348 5.90 0.0000300 Plot these two variables and add in the fitted line from the model (note: I’ve used the color option to make the color of the points gray). Use the output from augment to create a plot of the original data, with the predicted values used to plot a fitted line. augment(mod_1) %&gt;% ggplot(aes(x = temp, y = dptp)) + geom_point(size = 0.8, alpha = 0.5, col = &quot;gray&quot;) + geom_line(aes(x = temp, y = .fitted), color = &quot;red&quot;, size = 2) + theme_classic() Plot some plots to check model assumptions for the model you fit using the autoplot function on your model object: autoplot(mod_1) ## Warning: `arrange_()` is deprecated as of dplyr 0.7.0. ## Please use `arrange()` instead. ## See vignette(&#39;programming&#39;) for more help ## This warning is displayed once every 8 hours. ## Call `lifecycle::last_warnings()` to see where this warning was generated. 7.8.5 Using regression models to explore data #2 Try fitting the regression from the last part of the in-course exercise as a GLM, using glm() (but still assuming the outcome variable is normally distributed). Are your coefficients different? Does \\(PM_{10}\\) vary by day of the week? (Hint: The dow variable is a factor that gives day of the week. You can do an ANOVA analysis by fitting a linear model using this variable as the independent variable. Some of the overall model summaries will compare this model to an intercept-only model.) What day of the week is \\(PM_{10}\\) generally highest? (Check the model coefficients to figure this out.) Try to write out (on paper) the regression equation for the model you’re fitting. Try using glm() to run a Poisson regression of respiratory deaths (resp) on temperature during summer days. Start by creating a subset with just summer days called summer. (Hint: Use the month function with the argument label = TRUE from lubridate to do this—just pull out the subset where the month June, July, or August.) Try to write out the regression equation for the model you’re fitting. The coefficient for the temperature variable in this model is our best estimate (based on this model) of the log relative risk for a one degree Celcius increase in temperature. What is the relative risk associated with a one degree Celsius increase? 7.8.5.1 Example R code: Try fitting the model from the last part of the in-course exercise using glm(). Call it mod_1a. Compare the coefficients for the two models. You can use the tidy function on an lm or glm object to pull out just the model coefficients and associated model results. Here, I’ve used a pipeline of code to create a tidy data frame that merges these “tidy” coefficient outputs (from the two models) into a single data frame): mod_1a &lt;- glm(dptp ~ temp, data = chic) tidy(mod_1) %&gt;% select(term, estimate) %&gt;% inner_join(mod_1a %&gt;% tidy() %&gt;% select(term, estimate), by = &quot;term&quot;) %&gt;% rename(estimate_lm_mod = estimate.x, estimate_glm_mod = estimate.y) ## # A tibble: 2 x 3 ## term estimate_lm_mod estimate_glm_mod ## &lt;chr&gt; &lt;dbl&gt; &lt;dbl&gt; ## 1 (Intercept) 24.0 24.0 ## 2 temp 1.62 1.62 The results from the two models are identical. As a note, you could have also just run tidy on each model object, without merging them together into a single data frame: tidy(mod_1) ## # A tibble: 2 x 5 ## term estimate std.error statistic p.value ## &lt;chr&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; ## 1 (Intercept) 24.0 0.113 213. 0 ## 2 temp 1.62 0.00763 212. 0 tidy(mod_1a) ## # A tibble: 2 x 5 ## term estimate std.error statistic p.value ## &lt;chr&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; ## 1 (Intercept) 24.0 0.113 213. 0 ## 2 temp 1.62 0.00763 212. 0 Fit a model of \\(PM_{10}\\) regressed on day of week, where day of week is a factor. mod_2 &lt;- lm(pm10 ~ dow, data = chic) tidy(mod_2) ## # A tibble: 7 x 5 ## term estimate std.error statistic p.value ## &lt;chr&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; ## 1 (Intercept) 27.5 0.730 37.7 7.47e-273 ## 2 dowMonday 6.13 1.03 5.93 3.22e- 9 ## 3 dowTuesday 6.80 1.03 6.62 4.05e- 11 ## 4 dowWednesday 8.48 1.03 8.26 1.85e- 16 ## 5 dowThursday 8.80 1.02 8.60 1.08e- 17 ## 6 dowFriday 9.48 1.03 9.24 3.61e- 20 ## 7 dowSaturday 3.66 1.03 3.56 3.68e- 4 Use glance to check some of the overall summaries of this model. The statistic column here is the F statistic from test comparing this model to an intercept-only model. glance(mod_2) ## # A tibble: 1 x 12 ## r.squared adj.r.squared sigma statistic p.value df logLik AIC BIC ## &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; ## 1 0.0259 0.0247 19.1 21.5 4.61e-25 6 -21234. 42484. 42536. ## # … with 3 more variables: deviance &lt;dbl&gt;, df.residual &lt;int&gt;, nobs &lt;int&gt; As a note, you may have heard in previous statistics classes that you can use the anova() command to compare this model to a model with only an intercept (i.e., one that only fits a global mean and uses that as the expected value for all of the observations). Note that, in this case, the F value from anova for this model comparison is the same as the statistic you got in the overall summary statistics you get with glance in the previous code. anova(mod_2) ## Analysis of Variance Table ## ## Response: pm10 ## Df Sum Sq Mean Sq F value Pr(&gt;F) ## dow 6 46924 7820.6 21.5 &lt; 2.2e-16 *** ## Residuals 4856 1766407 363.8 ## --- ## Signif. codes: 0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1 The overall p-value from anova for with day-of-week coefficients versus the model that just has an intercept is &lt; 2.2e-16. This is well below 0.05, which suggests that day-of-week is associated with PM10 concentration, as a model that includes day-of-week does a much better job of explaining variation in PM10 than a model without it does. Use a boxplot to visually compare PM10 by day of week. ggplot(chic, aes(x = dow, y = pm10)) + geom_boxplot() Now try the same plot, but try using the ylim = option to change the limits on the y-axis for the graph, so you can get a better idea of the pattern by day of week (some of the extreme values are very high, which makes it hard to compare by eye when the y-axis extends to include them all). ggplot(chic, aes(x = dow, y = pm10)) + geom_boxplot() + ylim(c(0, 100)) ## Warning: Removed 292 rows containing non-finite values (stat_boxplot). Create a subset called summer with just the summer days: library(lubridate) summer &lt;- chic %&gt;% mutate(month = month(date, label = TRUE)) %&gt;% filter(month %in% c(&quot;Jun&quot;, &quot;Jul&quot;, &quot;Aug&quot;)) summer %&gt;% slice(1:3) ## date time year month doy dow death cvd resp temp dptp rhum ## 1 1987-06-01 152 1987 Jun 152 Monday 112 60 5 23.61111 68.50 71.875 ## 2 1987-06-02 153 1987 Jun 153 Tuesday 111 57 7 22.22222 64.75 95.250 ## 3 1987-06-03 154 1987 Jun 154 Wednesday 120 59 9 20.55556 47.25 47.125 ## pm10 o3 ## 1 22.95607 34.94623 ## 2 31.31339 18.96620 ## 3 34.95607 23.59270 Use glm() to fit a Poisson model of respiratory deaths regressed on temperature. Since you want to fit a Poisson model, use the option family = poisson(link = \"log\"). mod_3 &lt;- glm(resp ~ temp, data = summer, family = poisson(link = &quot;log&quot;)) glance(mod_3) ## # A tibble: 1 x 8 ## null.deviance df.null logLik AIC BIC deviance df.residual nobs ## &lt;dbl&gt; &lt;int&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;int&gt; &lt;int&gt; ## 1 1499. 1287 -3211. 6425. 6436. 1494. 1286 1288 tidy(mod_3) ## # A tibble: 2 x 5 ## term estimate std.error statistic p.value ## &lt;chr&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; ## 1 (Intercept) 1.91 0.0584 32.7 6.60e-235 ## 2 temp 0.00614 0.00258 2.38 1.74e- 2 Use the fitted model coefficient to determine the relative risk for a one degree Celsius increase in temperature. First, remember that you can use the tidy() function to read out the model coefficients. The second of these is the value for the temperature coefficient. That means that you can use indexing ([2]) to get just that value. That’s the log relative risk; take the exponent to get the relative risk. tidy(mod_3) %&gt;% filter(term == &quot;temp&quot;) %&gt;% mutate(log_rr = exp(estimate)) ## # A tibble: 1 x 6 ## term estimate std.error statistic p.value log_rr ## &lt;chr&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; ## 1 temp 0.00614 0.00258 2.38 0.0174 1.01 As a note, you can use the conf.int parameter in tidy to also pull confidence intervals: tidy(mod_3, conf.int = TRUE) ## # A tibble: 2 x 7 ## term estimate std.error statistic p.value conf.low conf.high ## &lt;chr&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; ## 1 (Intercept) 1.91 0.0584 32.7 6.60e-235 1.80 2.02 ## 2 temp 0.00614 0.00258 2.38 1.74e- 2 0.00108 0.0112 You could use this to get the confidence interval for relative risk (check out the mutate_at function if you haven’t seen it before): tidy(mod_3, conf.int = TRUE) %&gt;% select(term, estimate, conf.low, conf.high) %&gt;% filter(term == &quot;temp&quot;) %&gt;% mutate_at(vars(estimate:conf.high), funs(exp(.))) ## Warning: `funs()` is deprecated as of dplyr 0.8.0. ## Please use a list of either functions or lambdas: ## ## # Simple named list: ## list(mean = mean, median = median) ## ## # Auto named with `tibble::lst()`: ## tibble::lst(mean, median) ## ## # Using lambdas ## list(~ mean(., trim = .2), ~ median(., na.rm = TRUE)) ## This warning is displayed once every 8 hours. ## Call `lifecycle::last_warnings()` to see where this warning was generated. ## # A tibble: 1 x 4 ## term estimate conf.low conf.high ## &lt;chr&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; ## 1 temp 1.01 1.00 1.01 7.8.6 Trying out nesting and mapping We’ll be using data that’s in an R package called “nycflights13”. This data package can be installed from CRAN. Install the package and then load the package and its “flights” dataset. So that this data will be easier to work with, remove all columns except for those for the departure delay (dep_delay), the carrier (carrier), the hour the flight was supposed to leave (hour), and the airport the flight left from (origin). Also, limit the dataset to only flights that left from LaGuardia Airport (“LGA”). We want to figure out if the probability of a flight leaving 15 minutes late or more increases over the day for flights leaving LaGuardia. Filter out all the rows where the departure delay is missing and then create a new column called late_dep that is true if the flight left 15 minutes or more late and false otherwise. What proportion of all flights leaving from LaGuardia leave 15 minutes late or later? Next, determine what proportion of all flights are delayed base on the hour that the flight was scheduled to depart (hour). Create a plot showing how the probability of leaving 15 minutes or more late changes by hour. Fit a generalized linear model for the association between the binary variable of whether the flight was 15 minutes or more late (late_dep) and the hour the flight was scheduled to leave (hour). Use a binomial model (add family = binomial(link = \"logit\") in the glm call). The estimate from the model for hour will be an estimate of the log odds ratio for a one-hour increase in scheduled departure time. Take the exponent of this estimate with exp to get an estimated odds ratio for a one-hour increase in scheduled departure time. Is this estimate larger than 1.0? Next, we want to see if this association is similar across airlines. First, create a dataframe called nested_flights where the flights data is grouped by airline (carrier) and then nest the data, so that there is a “data” list-column where each item is a dataframe of flight delay data for a specific carrier. Use the map function from purrr inside a mutate statement to apply the glm code you used earlier for the whole dataset, but in this case for the data for each airline character. Then use the map function inside a mutate statement again to “tidy” the data. Clean the data up a bit. Remove the columns for data and glm_result and then unnest the dataframe list-column with the tidy version of the model results. Filter to get only the estimates for the “hour” term. Then calculate an odds ratio (or) by taking the exponent (check out the exp function) of the original estimate. The package has a dataframe with the full name of each carrier (airlines). Join this data into the data you’ve been working with, so you have the full names of airlines. Finally, create the following plot with each airline’s odds ratio for the change in the chance of a delay per one-hour increase in the scheduled hour of departure: 7.8.6.1 Example R code: Install the “nycflights13” package from CRAN. Load the package and its “flights” dataset. library(ggplot2) library(nycflights13) data(flights) So that this data will be easier to work with, remove all columns except for those for the departure dealy (dep_delay), the carrier (carrier), the hour the flight was supposed to leave (hour), and the airport the flight left from (origin). flights &lt;- flights %&gt;% select(dep_delay, carrier, hour, origin) %&gt;% filter(origin == &quot;LGA&quot;) flights ## # A tibble: 104,662 x 4 ## dep_delay carrier hour origin ## &lt;dbl&gt; &lt;chr&gt; &lt;dbl&gt; &lt;chr&gt; ## 1 4 UA 5 LGA ## 2 -6 DL 6 LGA ## 3 -3 EV 6 LGA ## 4 -2 AA 6 LGA ## 5 -1 AA 6 LGA ## 6 0 B6 6 LGA ## 7 0 MQ 6 LGA ## 8 -8 DL 6 LGA ## 9 -3 MQ 6 LGA ## 10 13 AA 6 LGA ## # … with 104,652 more rows Filter out all the rows where the departure delay is missing and then create a new column called late_dep that is true if the flight left 15 minutes or more late and false otherwise. flights &lt;- flights %&gt;% filter(!is.na(dep_delay)) %&gt;% mutate(late_dep = dep_delay &gt; 15) flights ## # A tibble: 101,509 x 5 ## dep_delay carrier hour origin late_dep ## &lt;dbl&gt; &lt;chr&gt; &lt;dbl&gt; &lt;chr&gt; &lt;lgl&gt; ## 1 4 UA 5 LGA FALSE ## 2 -6 DL 6 LGA FALSE ## 3 -3 EV 6 LGA FALSE ## 4 -2 AA 6 LGA FALSE ## 5 -1 AA 6 LGA FALSE ## 6 0 B6 6 LGA FALSE ## 7 0 MQ 6 LGA FALSE ## 8 -8 DL 6 LGA FALSE ## 9 -3 MQ 6 LGA FALSE ## 10 13 AA 6 LGA FALSE ## # … with 101,499 more rows What proportion of all flights leaving from LaGuardia leave 15 minutes late or later? To check this, remember that TRUE is saved as a “1” and FALSE is saved as a “0”. That means that we can take the mean of a logical vector to get the proportion of trials that are true. flights %&gt;% purrr::pluck(&quot;late_dep&quot;) %&gt;% mean() ## [1] 0.1889685 Determine what proportion of all flights are delayed base on the hour that the flight was scheduled to depart (hour). Create a plot showing how the probability of leaving 15 minutes or more late changes by hour. flights_late &lt;- flights %&gt;% group_by(hour) %&gt;% summarize(prob_late = mean(late_dep)) ## `summarise()` ungrouping output (override with `.groups` argument) flights_late ## # A tibble: 18 x 2 ## hour prob_late ## &lt;dbl&gt; &lt;dbl&gt; ## 1 5 0.0588 ## 2 6 0.0519 ## 3 7 0.0570 ## 4 8 0.0967 ## 5 9 0.105 ## 6 10 0.128 ## 7 11 0.159 ## 8 12 0.163 ## 9 13 0.200 ## 10 14 0.207 ## 11 15 0.236 ## 12 16 0.273 ## 13 17 0.292 ## 14 18 0.298 ## 15 19 0.314 ## 16 20 0.331 ## 17 21 0.309 ## 18 22 0.391 flights_late %&gt;% ggplot(aes(x = hour, y = prob_late)) + geom_line() Fit a generalized linear model for the association between the binary variable of whether the flight was 15 minutes or more late (late_dep) and the hour the flight was scheduled to leave (hour). Use a binomial model (add family = binomial(link = \"logit\") in the glm call). The estimate from the model for hour will be an estimate of the log odds ratio for a one-hour increase in scheduled departure time. Take the exponent of this estimate with exp to get an estimated odds ratio for a one-hour increase in scheduled departure time. Is this estimate larger than 1.0? glm(late_dep ~ hour, data = flights, family = binomial(link = &quot;logit&quot;)) ## ## Call: glm(formula = late_dep ~ hour, family = binomial(link = &quot;logit&quot;), ## data = flights) ## ## Coefficients: ## (Intercept) hour ## -3.3643 0.1399 ## ## Degrees of Freedom: 101508 Total (i.e. Null); 101507 Residual ## Null Deviance: 98410 ## Residual Deviance: 92810 AIC: 92810 library(broom) glm(late_dep ~ hour, data = flights, family = binomial(link = &quot;logit&quot;)) %&gt;% tidy() %&gt;% # Tidy the model results filter(term == &quot;hour&quot;) %&gt;% # Only look at the estimate for `hour` mutate(or = exp(estimate)) # Estimate is log odds ratio. Take exponent for odds ratio ## # A tibble: 1 x 6 ## term estimate std.error statistic p.value or ## &lt;chr&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; ## 1 hour 0.140 0.00195 71.7 0 1.15 Next, we want to see if this association is similar across airlines. First, create a dataframe called nested_flights where the flights data is grouped by airline (carrier) and then nest the data, so that there is a “data” list-column where each item is a dataframe of flight delay data for a specific carrier: nested_flights &lt;- flights %&gt;% group_by(carrier) %&gt;% nest() nested_flights ## # A tibble: 13 x 2 ## # Groups: carrier [13] ## carrier data ## &lt;chr&gt; &lt;list&gt; ## 1 UA &lt;tibble [7,837 × 4]&gt; ## 2 DL &lt;tibble [22,857 × 4]&gt; ## 3 EV &lt;tibble [8,255 × 4]&gt; ## 4 AA &lt;tibble [15,063 × 4]&gt; ## 5 B6 &lt;tibble [5,925 × 4]&gt; ## 6 MQ &lt;tibble [16,189 × 4]&gt; ## 7 WN &lt;tibble [6,000 × 4]&gt; ## 8 FL &lt;tibble [3,187 × 4]&gt; ## 9 US &lt;tibble [12,574 × 4]&gt; ## 10 F9 &lt;tibble [682 × 4]&gt; ## 11 9E &lt;tibble [2,372 × 4]&gt; ## 12 YV &lt;tibble [545 × 4]&gt; ## 13 OO &lt;tibble [23 × 4]&gt; To check the contents of the list-column, try: nested_flights$data[[1]] %&gt;% # Get the first element of the &quot;data&quot; column head() ## # A tibble: 6 x 4 ## dep_delay hour origin late_dep ## &lt;dbl&gt; &lt;dbl&gt; &lt;chr&gt; &lt;lgl&gt; ## 1 4 5 LGA FALSE ## 2 -4 6 LGA FALSE ## 3 1 6 LGA FALSE ## 4 9 7 LGA FALSE ## 5 -4 7 LGA FALSE ## 6 2 7 LGA FALSE Use the map function from purrr inside a mutate statement to apply the glm code you used earlier for the whole dataset, but in this case for the data for each airline character: library(purrr) library(tidyr) prob_late &lt;- nested_flights %&gt;% mutate(glm_result = purrr::map(data, ~ glm(late_dep ~ hour, data = .x, family = binomial(link = &quot;logit&quot;)))) # Check the results for the first element of the &quot;glm_result&quot; column: prob_late$glm_result[[1]] ## ## Call: glm(formula = late_dep ~ hour, family = binomial(link = &quot;logit&quot;), ## data = .x) ## ## Coefficients: ## (Intercept) hour ## -3.4305 0.1551 ## ## Degrees of Freedom: 7836 Total (i.e. Null); 7835 Residual ## Null Deviance: 7601 ## Residual Deviance: 7064 AIC: 7068 Then use the map function inside a mutate statement again to “tidy” the data. prob_late &lt;- prob_late %&gt;% mutate(glm_tidy = purrr::map(glm_result, ~ tidy(.x))) Remove the columns for data and glm_result: prob_late &lt;- prob_late %&gt;% select(-data, -glm_result) Then `unnest the dataframe list-column with the tidy version of the model results: prob_late &lt;- prob_late %&gt;% unnest(glm_tidy) Filter to get only the estimates for the “hour” term: prob_late &lt;- prob_late %&gt;% filter(term == &quot;hour&quot;) Then calculate an odds ratio (or) by taking the exponent (check the exp function) of the original estimate. prob_late &lt;- prob_late %&gt;% mutate(or = exp(estimate)) head(prob_late) ## # A tibble: 6 x 7 ## # Groups: carrier [6] ## carrier term estimate std.error statistic p.value or ## &lt;chr&gt; &lt;chr&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; ## 1 UA hour 0.155 0.00705 22.0 2.88e-107 1.17 ## 2 DL hour 0.148 0.00454 32.6 1.80e-232 1.16 ## 3 EV hour 0.0952 0.00572 16.7 2.79e- 62 1.10 ## 4 AA hour 0.146 0.00580 25.2 1.05e-139 1.16 ## 5 B6 hour 0.168 0.00702 23.9 7.13e-126 1.18 ## 6 MQ hour 0.110 0.00468 23.5 2.23e-122 1.12 The package has a dataframe with the full name of each carrier (airlines). Join this data into the data you’ve been working with, so you have the full names of airlines: prob_late &lt;- left_join(prob_late, airlines, by = &quot;carrier&quot;) Finally, create the following plot with each airline’s association between hour in the day and the chance of a delay: library(forcats) data(airlines) prob_late %&gt;% ungroup() %&gt;% mutate(estimate = exp(estimate)) %&gt;% mutate(name = fct_reorder(name, estimate)) %&gt;% ggplot(aes(x = estimate, y = name)) + geom_point() + geom_vline(xintercept = 1, linetype = 3) + labs(x = &quot;Odds ratio for one-hour increase in scheduled deparature time&quot;, y = &quot;&quot;) 7.8.7 Writing functions Say that you have a four-letter character string (e.g., “ling”) and that you want to move the last letter to the front of the string to create a new four-letter character string (e.g., “glin”). Write some R code to do this. Next, write a function named move_letter that does the same thing—takes a four-letter character string (e.g., “ling”) and creates a new four-letter character string where the last letter in the original string has been moved to the front of the string (e.g., “glin”). It should input one parameter (word, the original four-letter character string or a vector of four-letter character strings). Read the word list at https://raw.githubusercontent.com/dwyl/english-words/master/words.txt into an R dataframe called word_list. It will only have one column; name this column word. Write code that can take a vector of character strings (e.g., c('ling', 'scat', 'soil')) and return a logical vector that says whether that character string is a word in the word_list dataframe you created in the last step (e.g., c('FALSE', 'TRUE', 'TRUE') for a vector where the first value isn’t a word but the second and third are). You may find the pluck function in the purrr package useful in writing this code (to pull the word column out of the word_list dataframe). Write a function called is_word that inputs (1) a vector of character strings and (2) a dataframe with a column called “word” that lists real words. The function should return a logical vector saying whether each character string is a real word. The function should have two arguments: words_to_check, which is a vector of character strings (e.g., c('ling', 'scat', 'soil')), and real_word_list, which is a dataframe with a column called words of real English words (e.g., the word_list dataframe you created from the word list on GitHub). Set the real_word_list argument to have the default value of words, the dataframe you created earlier in this exercise by reading in the dataframe of English words from GitHub. Try using the function with a different word list. As an example, you could read in and use the word list of Google’s top 10,000 English words from https://raw.githubusercontent.com/first20hours/google-10000-english/master/google-10000-english-no-swears.txt. Try using these functions to solve the word puzzle problem in the last homework. 7.8.7.1 Example R code Say that you have a four-letter character string (e.g., “ling”) and that you want to move the last letter to the front of the string to create a new four-letter character string (e.g., “glin”). You can use functions from the stringr package to help with this. library(stringr) # Start with an example word. You can use the example word from # the problem statement (&#39;ling&#39;). word &lt;- &quot;ling&quot; # Break the word into two parts first_three_letters &lt;- str_sub(word, 1, 3) last_letter &lt;- str_sub(word, 4, 4) first_three_letters ## [1] &quot;lin&quot; last_letter ## [1] &quot;g&quot; # Put the parts back together in the right order new_word &lt;- str_c(last_letter, first_three_letters) # You could also use `paste` or `paste0` here new_word ## [1] &quot;glin&quot; Write a function named move_letter that takes a four-letter character string (e.g., “ling”) and creates a new four-letter character string where the last letter in the original string has been moved to the front of the string (e.g., “glin”). It should input one parameter (word, the original four-letter character string or a vector of four-letter character strings). To do this, take the code that you just wrote and put it inside a function named move_letter: move_letter &lt;- function(word){ # Break the word into two parts first_three_letters &lt;- str_sub(word, 1, 3) last_letter &lt;- str_sub(word, 4, 4) # Put the parts back together in the right order str_c(last_letter, first_three_letters) } # Check the function move_letter(word = &quot;ling&quot;) ## [1] &quot;glin&quot; # Try with some other four-letter strings move_letter(word = &quot;cats&quot;) ## [1] &quot;scat&quot; move_letter(word = &quot;oils&quot;) ## [1] &quot;soil&quot; # Check using with a vector of four-letter character strings move_letter(c(&quot;cats&quot;, &quot;oils&quot;)) ## [1] &quot;scat&quot; &quot;soil&quot; Notice that now word is being used as an argument in the function. It’s as if you assigned the string or vector of strings that you want to convert to the object name word, and then you run all the code. The output of the function is the last expression in the function code (str_c(last_letter, first_three_letters)). Also, note that you can add comments inside the function with #, just like you can with other R code. Read the word list at https://raw.githubusercontent.com/dwyl/english-words/master/words.txt into an R dataframe called words. It will only have one column; name this column word_list. library(readr) word_list &lt;- read_csv(&quot;https://raw.githubusercontent.com/dwyl/english-words/master/words.txt&quot;, col_names = &quot;word&quot;) ## Parsed with column specification: ## cols( ## word = col_character() ## ) ## Warning: 2 parsing failures. ## row col expected actual file ## 11 -- 1 columns 3 columns &#39;https://raw.githubusercontent.com/dwyl/english-words/master/words.txt&#39; ## 12 -- 1 columns 2 columns &#39;https://raw.githubusercontent.com/dwyl/english-words/master/words.txt&#39; Write code that can take a vector of character strings (e.g., c('ling', 'scat', 'soil')) and return a logical vector that says whether that character string is a word in the word_list dataframe you created in the last step (e.g., c('TRUE', 'FALSE', 'TRUE') for a vector where the second value isn’t a word but the first and third are). library(purrr) words_to_check &lt;- c(&quot;ling&quot;, &quot;scat&quot;, &quot;soil&quot;) words_to_check %in% pluck(word_list, &quot;word&quot;) ## [1] TRUE FALSE TRUE This code works because you can use pluck to extract a column (as a vector) from a dataframe, and then you can use the %in% operator to see if each value in one vector (the words_to_check vector in this example) is one of the values in the second vector (the word column from the words dataframe in this example). Write a function called is_word that inputs (1) a vector of character strings and (2) a dataframe with a column called “word” that lists real words. The function should return a logical vector saying whether each character string is a real word. The function should have two arguments: words_to_check, which is a vector of character strings and real_word_list which is a dataframe with a column called words of real English words. Set the real_word_list argument to have the default value of words, the dataframe you created earlier in this exercise by reading in the dataframe of English words from GitHub. # Put the code you wrote inside a function is_word &lt;- function(words_to_check, real_word_list = word_list){ words_to_check %in% pluck(real_word_list, &quot;word&quot;) } # Check the function is_word(words_to_check = c(&quot;ling&quot;, &quot;scat&quot;, &quot;soil&quot;)) ## [1] TRUE FALSE TRUE Note that, if you want to use the word_list dataframe for your real word list, you don’t have to specify that when you call the is_word function, since you set that as your default value. If you wanted to use a different word list, you can specify a different value for the real_word_list argument when you run the function. For example, to use Google’s top 10,000 English word list from GitHub instead, use: google_words &lt;- read_csv(&quot;https://raw.githubusercontent.com/first20hours/google-10000-english/master/google-10000-english-no-swears.txt&quot;, col_names = &quot;word&quot;) ## Parsed with column specification: ## cols( ## word = col_character() ## ) is_word(words_to_check = c(&quot;ling&quot;, &quot;scat&quot;, &quot;soil&quot;), real_word_list = google_words) ## [1] FALSE FALSE TRUE In this case, there was a rarer word (“ling”) that counted as a word when the original word list was used, but not when the Google top-10,000 words list was used. Try using these functions to solve the word puzzle problem in the last homework. word_list %&gt;% filter(str_detect(word, &quot;^m.{7}$&quot;)) %&gt;% # This is using regular expressions separate(word, into = c(&quot;first_word&quot;, &quot;second_word&quot;), # Note that you can use `separate` with a number to use position sep = 4, remove = FALSE) %&gt;% mutate(second_word = move_letter(second_word)) %&gt;% # Use the first function here filter(is_word(first_word) &amp; is_word(second_word)) %&gt;% # Use the second function here unite(&quot;new_word&quot;, c(&quot;first_word&quot;, &quot;second_word&quot;), sep = &quot; &quot;) # You can use `unite` to put the two new words in a phrase in one column. ## # A tibble: 103 x 2 ## word new_word ## &lt;chr&gt; &lt;chr&gt; ## 1 mailings mail sing ## 2 maillots mail slot ## 3 majorate majo erat ## 4 majoring majo grin ## 5 malmiest malm ties ## 6 maltiest malt ties ## 7 mandalas mand sala ## 8 mandated mand date ## 9 mandates mand sate ## 10 mangiest mang ties ## # … with 93 more rows You can get the choices down to even fewer options if you match the new words against the Google top-10,000 words list, by using the real_word_list option in the is_word function you wrote. (However, “maillots” is not a common enough word that you can also start from that shorter word list!) word_list %&gt;% filter(str_detect(word, &quot;^m.{7}$&quot;)) %&gt;% separate(word, into = c(&quot;first_word&quot;, &quot;second_word&quot;), sep = 4, remove = FALSE) %&gt;% mutate(second_word = move_letter(second_word)) %&gt;% filter(is_word(first_word, real_word_list = google_words) &amp; is_word(second_word, real_word_list = google_words)) %&gt;% unite(&quot;new_word&quot;, c(&quot;first_word&quot;, &quot;second_word&quot;), sep = &quot; &quot;) ## # A tibble: 43 x 2 ## word new_word ## &lt;chr&gt; &lt;chr&gt; ## 1 mailings mail sing ## 2 maillots mail slot ## 3 maintops main stop ## 4 markings mark sing ## 5 maskings mask sing ## 6 massager mass rage ## 7 massages mass sage ## 8 massiest mass ties ## 9 mattings matt sing ## 10 mealiest meal ties ## # … with 33 more rows "],
 ["reporting-results-2.html", "Chapter 8 Reporting results #2 8.1 Course lectures 8.2 Example data 8.3 ggplot2 extras and extensions 8.4 Simple features 8.5 In-course exercise", " Chapter 8 Reporting results #2 Download a pdf of the lecture slides covering this topic. 8.1 Course lectures In 2019, we cancelled our in-course meeting for bad weather. There are videos of the lecture available, covering ggplot2 extensions and mapping in ggplot: Example commit. 8.2 Example data This week, we’ll be using some example data from NOAA’s Storm Events Database. This data lists major weather-related storm events during 2017. For each event, it includes information like the start and end dates, where it happened, associated deaths, injuries, and property damage, and some other characteristics. See the in-course exercises for this week for more on getting and cleaning this data. As part of the in-course exercise, you’ll be making the following plot and saving it as the object storm_plot: We’ll be using the data and this plot in the next sections. 8.3 ggplot2 extras and extensions 8.3.1 scales package The scales package gives you a few more options for labeling with your ggplot scales. For example, if you wanted to change the notation for the axes in the plot of state area versus number of storm events, you could use the scales package to add commas to the numeric axis values. For the rest of these slides, I’ve saved the ggplot object with out plot to the object named storm_plot, so we don’t have to repeat that code every time. library(scales) storm_plot + scale_x_continuous(labels = comma) + scale_y_continuous(labels = comma) The scales package also includes labeling functions for: dollars (labels = dollar) percent (labels = percent) 8.3.2 ggplot2 extensions The ggplot2 framework is set up so that others can create packages that “extend” the system, creating functions that can be added on as layers to a ggplot object. Some of the types of extensions available include: More themes Useful additions (things that you may be able to do without the package, but that the package makes easier) Tools for plotting different types of data There is a gallery with links to ggplot2 extensions at https://www.ggplot2-exts.org. This list may not be exhaustive—there may be other extensions on CRAN or on GitHub that the package maintainer did not submit for this gallery. 8.3.3 More ggplot2 themes You have already played around a lot with using ggplot themes to change how your graphs look. Several people have created packages with additional themes: ggthemes ggthemr ggtech ggsci library(ggthemes) library(gridExtra) a &lt;- storm_plot + theme_fivethirtyeight() + ggtitle(&quot;Five Thirty Eight&quot;) b &lt;- storm_plot + theme_economist() + ggtitle(&quot;Economist&quot;) c &lt;- storm_plot + theme_excel() + ggtitle(&quot;Excel&quot;) d &lt;- storm_plot + theme_few() + ggtitle(&quot;Stephen Few&quot;) grid.arrange(a, b, c, d, ncol = 2) 8.3.4 Other useful ggplot2 extensions Other ggplot2 extensions do things you might have been able to figure out how to do without the extension, but the extension makes it much easier to do. These tasks include: Highlighting interesting points “Repelling” text labels Arranging plots 8.3.4.1 Repelling / highlighting with text labels The first is repelling text labels. When you add labels to points on a plot, they often overlap: storm_plot + facet_wrap(~ region) + geom_label(aes(label = state)) The ggrepel package helps make sure that these labels don’t overlap: library(ggrepel) storm_plot + facet_wrap(~ region) + geom_label_repel(aes(label = state)) It may be too much to label every point. Instead, you may just want to highlight notable point. You can use the gghighlight package to do that. library(gghighlight) storm_plot + facet_wrap(~ region) + gghighlight(area &gt; 150000 | n &gt; 1500, label_key = state) The gghighlight package also works for things like histograms. For example, you could create a dataset with the count by day-of-year of certain types of events: storms_by_month &lt;- storms_2017 %&gt;% filter(event_type %in% c(&quot;Flood&quot;, &quot;Flash Flood&quot;, &quot;Heavy Rain&quot;)) %&gt;% mutate(month = month(begin_date_time, label = TRUE)) %&gt;% group_by(month, event_type) %&gt;% count() %&gt;% ungroup() storms_by_month %&gt;% slice(1:4) ## # A tibble: 4 x 3 ## month event_type n ## &lt;ord&gt; &lt;chr&gt; &lt;int&gt; ## 1 Jan Flash Flood 113 ## 2 Jan Flood 255 ## 3 Jan Heavy Rain 80 ## 4 Feb Flash Flood 65 ggplot(storms_by_month, aes(x = month, y = n, group = event_type)) + geom_bar(stat = &quot;identity&quot;) + labs(x = &quot;Month&quot;, y = &quot;# of events&quot;) + gghighlight(max(n) &gt; 400, label_key = event_type) + facet_wrap(~ event_type, ncol = 1) 8.3.4.2 Arranging plots You may have multiple related plots you want to have as multiple panels of a single figure. There are a few packages that help with this. One very good one is patchwork. You need to install this from GitHub: devtools::install_github(&quot;thomasp85/patchwork&quot;) Find out more: https://github.com/thomasp85/patchwork#patchwork Say we want to plot seasonal patterns in events in the five counties with the highest number of events in 2017. We can use dplyr to figure out these counties: top_counties &lt;- storms_2017 %&gt;% group_by(fips, state, cz_name) %&gt;% count() %&gt;% ungroup() %&gt;% top_n(5, wt = n) Then create a plot with the time patterns: library(forcats) top_counties_month &lt;- storms_2017 %&gt;% semi_join(top_counties, by = &quot;fips&quot;) %&gt;% mutate(month = month(begin_date_time), county = paste(cz_name, &quot; County, &quot;, state, sep = &quot;&quot;)) %&gt;% count(county, month) %&gt;% ggplot(aes(x = month, y = n)) + geom_bar(stat = &quot;identity&quot;) + facet_wrap(~ fct_reorder(county, n, .fun = sum, .desc = TRUE), nrow = 2) + scale_x_continuous(name = &quot;&quot;, breaks = c(1, 4, 7, 10), labels = c(&quot;Jan&quot;, &quot;Apr&quot;, &quot;Jul&quot;, &quot;Oct&quot;)) + scale_y_continuous(name = &quot;Frequency&quot;, breaks = c(0, 20)) Here’s this plot: top_counties_month Now that you have two ggplot objects (storm_plot and top_counties_month), you can use patchwork to put them together: library(patchwork) storm_plot + top_counties_month + plot_layout(ncol = 1, heights = c(2, 1)) A slightly fancier version: (storm_plot + theme(legend.position = &quot;top&quot;) + gghighlight(n &gt; 1500 | area &gt; 200000, label_key = state)) + top_counties_month + plot_layout(ncol = 1, heights = c(2, 1)) Other packages for arranging ggplot objects include: gridExtra cowplot 8.4 Simple features 8.4.1 Introduction to simple features sf objects: “Simple features” R framework that is in active development There will likely be changes in the near future Plays very well with tidyverse functions, including dplyr and ggplot2 tools library(sf) To show simple features, we’ll pull in the Colorado county boundaries from the U.S. Census. To do this, we’ll use the tigris package, which accesses the U.S. Census API. It allows you to pull geographic data for U.S. counties, states, tracts, voting districts, roads, rails, and a number of other geographies. To learn more about the tigris package, check out this article: https://journal.r-project.org/archive/2016/RJ-2016-043/index.html With tigris, you can read in data for county boundaries using the counties function. We’ll use the option class = \"sf\" to read these spatial dataframes in as sf objects. library(tigris) co_counties &lt;- counties(state = &quot;CO&quot;, cb = TRUE, class = &quot;sf&quot;) class(co_counties) ## [1] &quot;sf&quot; &quot;data.frame&quot; You can think of an sf object as a dataframe, but with one special column called geometry. co_counties %&gt;% slice(1:3) ## Simple feature collection with 3 features and 9 fields ## geometry type: MULTIPOLYGON ## dimension: XY ## bbox: xmin: -109.0603 ymin: 37.28912 xmax: -104.3511 ymax: 41.00344 ## geographic CRS: NAD83 ## STATEFP COUNTYFP COUNTYNS AFFGEOID GEOID NAME LSAD ALAND ## 1 08 077 00198154 0500000US08077 08077 Mesa 06 8621849401 ## 2 08 107 00198169 0500000US08107 08107 Routt 06 6117602807 ## 3 08 055 00198143 0500000US08055 08055 Huerfano 06 4120756304 ## AWATER geometry ## 1 31490395 MULTIPOLYGON (((-109.0603 3... ## 2 15831744 MULTIPOLYGON (((-107.4426 4... ## 3 5792101 MULTIPOLYGON (((-105.5013 3... The geometry column has a special class (sfc): class(co_counties$geometry) ## [1] &quot;sfc_MULTIPOLYGON&quot; &quot;sfc&quot; You’ll notice there’s some extra stuff up at the top, too: Geometry type: Points, polygons, lines Dimension: Often two-dimensional, but can go up to four (if you have, for example, time for each measurement and some measure of measurement error / uncertainty) Bounding box (bbox): The x- and y-range of the data included EPSG: The EPSG Geodetic Parameter Dataset code for the Coordinate Reference Systems Projection (proj4string): How the data is currently projected, includes projection (“+proj”) and datum (“+datum”) You can pull some of this information out of the geometry column. For example, you can pull out the coordinates of the bounding box: st_bbox(co_counties$geometry) # For all counties ## xmin ymin xmax ymax ## -109.06025 36.99243 -102.04152 41.00344 st_bbox(co_counties$geometry[1]) # Just for first county ## xmin ymin xmax ymax ## -109.06025 38.49999 -107.37748 39.36671 You can add sf objects to ggplot objects using geom_sf: library(ggplot2) ggplot() + geom_sf(data = co_counties) You can map one of the columns in the sf object to the fill aesthetic to make a choropleth: ggplot() + geom_sf(data = co_counties, aes(fill = ALAND)) You can use all your usual ggplot tricks with this: library(viridis) ggplot() + geom_sf(data = co_counties, aes(fill = ALAND)) + scale_fill_viridis(name = &quot;Land area&quot;, label = comma) + ggtitle(&quot;Land areas of Colorado counties&quot;) + theme_dark() Because simple features are a special type of dataframe, you can also use a lot of dplyr tricks. For example, you could pull out just Larimer County, CO: larimer &lt;- co_counties %&gt;% filter(NAME == &quot;Larimer&quot;) larimer ## Simple feature collection with 1 feature and 9 fields ## geometry type: MULTIPOLYGON ## dimension: XY ## bbox: xmin: -106.1954 ymin: 40.25788 xmax: -104.9431 ymax: 40.99821 ## geographic CRS: NAD83 ## STATEFP COUNTYFP COUNTYNS AFFGEOID GEOID NAME LSAD ALAND ## 1 08 069 00198150 0500000US08069 08069 Larimer 06 6723025059 ## AWATER geometry ## 1 99007869 MULTIPOLYGON (((-106.1954 4... Note: You may need the development version of ggplot2 for the next piece of code to work (devtools::install_github(\"tidyverse/ggplot2\")). ggplot() + geom_sf(data = co_counties, color = &quot;lightgray&quot;) + geom_sf(data = larimer, fill = &quot;darkcyan&quot;) + geom_sf_text(data = larimer, aes(label = NAME), color = &quot;white&quot;) + theme_dark() + labs(x = &quot;&quot;, y = &quot;&quot;) This operability with tidyverse functions means that you should now be able to figure out how to create a map of the number of events listed in the NOAA Storm Events database (of those listed by county) for each county in Colorado (for the code, see the in-course exercise): 8.4.2 State boundaries The tigris package allows you to pull state boundaries, as well, but on some computers mapping these seems to take a really long time. Instead, for now I recommend that you pull the state boundaries using base R’s maps package and convert that to an sf object: library(maps) ## ## Attaching package: &#39;maps&#39; ## The following object is masked from &#39;package:purrr&#39;: ## ## map ## The following object is masked from &#39;package:faraway&#39;: ## ## ozone us_states &lt;- map(&quot;state&quot;, plot = FALSE, fill = TRUE) %&gt;% st_as_sf() You can see these borders include an ID column that you can use to join by state: head(us_states) ## Simple feature collection with 6 features and 1 field ## geometry type: MULTIPOLYGON ## dimension: XY ## bbox: xmin: -124.3834 ymin: 30.24071 xmax: -71.78015 ymax: 42.04937 ## geographic CRS: WGS 84 ## ID geom ## 1 alabama MULTIPOLYGON (((-87.46201 3... ## 2 arizona MULTIPOLYGON (((-114.6374 3... ## 3 arkansas MULTIPOLYGON (((-94.05103 3... ## 4 california MULTIPOLYGON (((-120.006 42... ## 5 colorado MULTIPOLYGON (((-102.0552 4... ## 6 connecticut MULTIPOLYGON (((-73.49902 4... As with other sf objects, you can map these state boundaries using ggplot: ggplot() + geom_sf(data = us_states, color = &quot;white&quot;, fill = &quot;darkcyan&quot;, alpha = 0.5) As a note, you can use xlim and ylim with these plots, but remember that the x-axis is longitude in degrees West, which are negative: ggplot() + geom_sf(data = us_states, color = &quot;white&quot;, fill = &quot;darkcyan&quot;, alpha = 0.5) + xlim(c(-90, -75)) + ylim(c(24, 38)) 8.4.3 Creating an sf object You can create an sf object from a regular dataframe. You just need to specify: The coordinate information (which columns are longitudes and latitudes) The Coordinate Reference System (CRS) (how to translate your coordinates to places in the world) For the CRS, if you are mapping the new sf object with other, existing sf objects, make sure that you use the same CRS for all sf objects. Spatial objects can have different Coordinate Reference Systems (CRSs). CRSs can be geographic (e.g., WGS84, for longitude-latitude data) or projected (e.g., UTM, NADS83). There is a website that lists projection strings and can be useful in setting projection information or re-projecting data: http://www.spatialreference.org Here is an excellent resource on projections and maps in R from Melanie Frazier: https://www.nceas.ucsb.edu/~frazier/RSpatialGuides/OverviewCoordinateReferenceSystems.pdf Let’s look at floods in Colorado. First, clean up the data: co_floods &lt;- storms_2017 %&gt;% filter(state == &quot;Colorado&quot; &amp; event_type %in% c(&quot;Flood&quot;, &quot;Flash Flood&quot;)) %&gt;% select(begin_date_time, event_id, begin_lat:end_lon) %&gt;% gather(key = &quot;key&quot;, value = &quot;value&quot;, -begin_date_time, -event_id) %&gt;% separate(key, c(&quot;time&quot;, &quot;key&quot;)) %&gt;% spread(key = key, value = value) There are now two rows per event, one with the starting location and one with the ending location: co_floods %&gt;% slice(1:5) ## # A tibble: 5 x 5 ## begin_date_time event_id time lat lon ## &lt;dttm&gt; &lt;dbl&gt; &lt;chr&gt; &lt;dbl&gt; &lt;dbl&gt; ## 1 2017-05-08 16:00:00 693374 begin 40.3 -105. ## 2 2017-05-08 16:00:00 693374 end 40.5 -104. ## 3 2017-05-10 15:00:00 686479 begin 38.1 -105. ## 4 2017-05-10 15:00:00 686479 end 38.1 -105. ## 5 2017-05-10 15:20:00 686480 begin 38.2 -105. Change to an sf object by saying which columns are the coordinates and setting a CRS: co_floods &lt;- st_as_sf(co_floods, coords = c(&quot;lon&quot;, &quot;lat&quot;)) %&gt;% st_set_crs(4269) co_floods %&gt;% slice(1:3) ## Simple feature collection with 3 features and 3 fields ## geometry type: POINT ## dimension: XY ## bbox: xmin: -105.0496 ymin: 38.1167 xmax: -104.39 ymax: 40.49 ## geographic CRS: NAD83 ## # A tibble: 3 x 4 ## begin_date_time event_id time geometry ## &lt;dttm&gt; &lt;dbl&gt; &lt;chr&gt; &lt;POINT [°]&gt; ## 1 2017-05-08 16:00:00 693374 begin (-104.76 40.32) ## 2 2017-05-08 16:00:00 693374 end (-104.39 40.49) ## 3 2017-05-10 15:00:00 686479 begin (-105.0496 38.1167) Now you can map the data: ggplot() + geom_sf(data = co_counties, color = &quot;lightgray&quot;) + geom_sf(data = co_floods, aes(color = month(begin_date_time), shape = time)) + scale_color_viridis(name = &quot;Month&quot;) If you want to show lines instead of points, group by the appropriate ID and then summarize within each event to get a line: co_floods &lt;- co_floods %&gt;% group_by(event_id) %&gt;% summarize(month = month(first(begin_date_time)), do_union = FALSE) %&gt;% st_cast(&quot;LINESTRING&quot;) ## `summarise()` ungrouping output (override with `.groups` argument) head(co_floods) ## Simple feature collection with 6 features and 2 fields ## geometry type: LINESTRING ## dimension: XY ## bbox: xmin: -105.8286 ymin: 38.0708 xmax: -104.39 ymax: 40.49 ## geographic CRS: NAD83 ## # A tibble: 6 x 3 ## event_id month geometry ## &lt;dbl&gt; &lt;dbl&gt; &lt;LINESTRING [°]&gt; ## 1 686479 5 (-105.0496 38.1167, -104.9687 38.0708) ## 2 686480 5 (-104.8425 38.2275, -104.8137 38.1854) ## 3 693306 6 (-104.8947 38.999, -104.8734 38.8783) ## 4 693374 5 (-104.76 40.32, -104.39 40.49) ## 5 693444 6 (-105.7688 38.3753, -105.8286 38.3127) ## 6 693449 6 (-105.07 38.15, -105.0973 38.1524) Now this data will map as lines: ggplot() + geom_sf(data = co_counties, color = &quot;lightgray&quot;) + geom_sf(data = co_floods, aes(color = factor(month), fill = factor(month))) + scale_fill_viridis(name = &quot;Month&quot;, discrete = TRUE) + scale_color_viridis(name = &quot;Month&quot;, discrete = TRUE) 8.4.4 Reading in from GIS files You can also create sf objects by reading in data from files you would normally use for GIS. For example, you can read in an sf object from a shapefile, which is a format often used for GIS in which a collection of several files jointly store geographic data. The files making up a shapefile can include: “.shp”: The coordinates defining the shape of each geographic object. For a point, this would be a single coordinate (e.g., latitude and longitude). For lines and polygons, there will be multiple coordinates per geographic object. “.prf”: Information on the projection of the data (how to get from the coordinates to a place in the world). “.dbf”: Data that goes along with each geographical object. For example, earlier we looked at data on counties, and one thing measured for each county was its land area. Characteristics like that would be included in the “.dbf” file in a shapefile. Often, with geographic data, you will be given the option to downloaded a compressed file (e.g., a zipped file). When you unzip the folder, it will include a number of files in these types of formats (“.shp”, “prf”, “.dbf”, etc.). Sometimes, that single folder will include multiple files from each extension. For example, it might have several files that end with “.shp”. In this case, you have multiple layers of geographic information you can read in. We’ve been looking at data on storms from NOAA for 2017. As an example, let’s try to pair that data up with some from the National Hurricane Center for the same year. The National Hurricane Center allows you to access a variety of GIS data through the webpage https://www.nhc.noaa.gov/gis/?text. Let’s pull some data on Hurricane Harvey in 2017 and map it with information from the NOAA Storm Events database. On https://www.nhc.noaa.gov/gis/?text, go to the section called “Preliminary Best Track”. Select the year 2017. Then select “Hurricane Harvey” and download “al092017_best_track.zip”. Depending on your computer, you may then need to unzip this file (many computers will unzip it automatically). Base R has a function called unzip that can help with this. You’ll then have a folder with a number of different files in it. Move this folder somewhere that is convenient for the working directory you use for class. For example, I moved it into the “data” subdirectory of the working directory I use for the class. You can use list.files to see all the files in this unzipped folder: list.files(&quot;data/al092017_best_track/&quot;) ## [1] &quot;al092017_lin.dbf&quot; &quot;al092017_lin.prj&quot; ## [3] &quot;al092017_lin.shp&quot; &quot;al092017_lin.shp.xml&quot; ## [5] &quot;al092017_lin.shx&quot; &quot;al092017_pts.dbf&quot; ## [7] &quot;al092017_pts.prj&quot; &quot;al092017_pts.shp&quot; ## [9] &quot;al092017_pts.shp.xml&quot; &quot;al092017_pts.shx&quot; ## [11] &quot;al092017_radii.dbf&quot; &quot;al092017_radii.prj&quot; ## [13] &quot;al092017_radii.shp&quot; &quot;al092017_radii.shp.xml&quot; ## [15] &quot;al092017_radii.shx&quot; &quot;al092017_windswath.dbf&quot; ## [17] &quot;al092017_windswath.prj&quot; &quot;al092017_windswath.shp&quot; ## [19] &quot;al092017_windswath.shp.xml&quot; &quot;al092017_windswath.shx&quot; You can use st_layers to find out the available layers in a shapefile directory: st_layers(&quot;data/al092017_best_track/&quot;) ## Driver: ESRI Shapefile ## Available layers: ## layer_name geometry_type features fields ## 1 al092017_windswath Polygon 4 6 ## 2 al092017_radii Polygon 61 9 ## 3 al092017_lin Line String 17 3 ## 4 al092017_pts Point 74 15 Once you know which layer you want, you can use read_sf to read it in as an sf object: harvey_track &lt;- read_sf(&quot;data/al092017_best_track/&quot;, layer = &quot;al092017_lin&quot;) head(harvey_track) ## Simple feature collection with 6 features and 3 fields ## geometry type: LINESTRING ## dimension: XY ## bbox: xmin: -92.3 ymin: 13 xmax: -45.8 ymax: 21.4 ## geographic CRS: Unknown datum based upon the Authalic Sphere ## # A tibble: 6 x 4 ## STORMNUM STORMTYPE SS geometry ## &lt;dbl&gt; &lt;chr&gt; &lt;int&gt; &lt;LINESTRING [°]&gt; ## 1 9 Low 0 (-45.8 13.7, -47.4 13.7, -49 13.6, -50.6 13.6, … ## 2 9 Tropical Depr… 0 (-52 13.4, -53.4 13.1, -55 13) ## 3 9 Tropical Storm 0 (-55 13, -56.6 13, -58.4 13, -59.6 13.1, -60.3 … ## 4 9 Tropical Depr… 0 (-67.5 13.7, -69.2 13.8) ## 5 9 Tropical Wave 0 (-69.2 13.8, -71 14, -72.9 14.2, -75 14.4, -76.… ## 6 9 Low 0 (-89.7 20, -90.7 20.5, -91.6 20.9, -92.3 21.4) ggplot() + geom_sf(data = filter(us_states, ID %in% c(&quot;texas&quot;, &quot;louisiana&quot;))) + geom_sf(data = harvey_track, aes(color = STORMTYPE)) + xlim(c(-107, -89)) + ylim(c(25, 37)) You can read in other layers: harvey_windswath &lt;- read_sf(&quot;data/al092017_best_track/&quot;, layer = &quot;al092017_windswath&quot;) head(harvey_windswath) ## Simple feature collection with 4 features and 6 fields ## geometry type: POLYGON ## dimension: XY ## bbox: xmin: -98.66872 ymin: 12.94564 xmax: -54.58527 ymax: 31.15894 ## geographic CRS: Unknown datum based upon the Authalic Sphere ## # A tibble: 4 x 7 ## RADII STORMID BASIN STORMNUM STARTDTG ENDDTG geometry ## &lt;dbl&gt; &lt;chr&gt; &lt;chr&gt; &lt;dbl&gt; &lt;chr&gt; &lt;chr&gt; &lt;POLYGON [°]&gt; ## 1 34 al092017 AL 9 2017081… 20170… ((-65.68199 14.50528, -65.66399… ## 2 34 al092017 AL 9 2017082… 20170… ((-96.22456 31.15752, -96.17218… ## 3 50 al092017 AL 9 2017082… 20170… ((-97.32707 29.60604, -97.31424… ## 4 64 al092017 AL 9 2017082… 20170… ((-97.20689 29.08475, -97.19893… ggplot() + geom_sf(data = filter(us_states, ID %in% c(&quot;texas&quot;, &quot;louisiana&quot;))) + geom_sf(data = harvey_windswath, aes(fill = factor(RADII)), alpha = 0.2) + xlim(c(-107, -89)) + ylim(c(25, 37)) + scale_fill_viridis(name = &quot;Wind (kts)&quot;, discrete = TRUE, option = &quot;B&quot;, begin = 0.6, direction = -1) The read_sf function is very powerful and can read in data from lots of different formats. See Section 2 of the sf manual (https://cran.r-project.org/web/packages/sf/vignettes/sf2.html) for more on this function. You can find (much, much) more on working with spatial data in R online: R Spatial: http://rspatial.org/index.html Geocomputation with R: https://geocompr.robinlovelace.net 8.5 In-course exercise 8.5.1 Getting and cleaning the example data This week, we’ll be using some example data from NOAA’s Storm Events Database. This data lists major weather-related storm events during 2017. For each event, it includes information like the start and end dates, where it happened, associated deaths, injuries, and property damage, and some other characteristics. Each row is a separate event. However, often several events are grouped together within the same episode. Some of the event types are listed by their county ID (FIPS code) (“C”), but some are listed by a forecast zone ID (“Z”). Which ID is used is given in the column CZ_TYPE. Go to https://www1.ncdc.noaa.gov/pub/data/swdi/stormevents/csvfiles/ and download the bulk storm details data for 2017, in the file that starts “StormEvents_details” and includes “d2017”. Move this into a good directory for your current working directory and read it in using read_csv from the readr package. Limit the dataframe to: the beginning and ending dates and times, the episode ID, the event ID, the state name and FIPS, the “CZ” name, type, and FIPS, the event type, the source, and the begining latitude and longitude and ending latitude and longitude Convert the beginning and ending dates to a “date-time” class (there should be one column for the beginning date-time and one for the ending date-time) Change state and county names to title case (e.g., “New Jersey” instead of “NEW JERSEY”) Limit to the events listed by county FIPS (CZ_TYPE of “C”) and then remove the CZ_TYPE column Pad the state and county FIPS with a “0” at the beginning (hint: there’s a function in stringr to do this) and then unite the two columns to make one fips column with the 5-digit county FIPS code Change all the column names to lower case (you may want to try the rename_all function for this) There is data that comes with R on U.S. states (data(\"state\")). Use that to create a dataframe with the state name, area, and region Create a dataframe with the number of events per state in 2017. Merge in the state information dataframe you just created. Remove any states that are not in the state information dataframe Create the following plot: 8.5.1.1 Example R code Read in the data using read_csv. Here’s the code I used. Yours might be a bit different, depending on the current name of the file and where you moved it. library(readr) library(dplyr) storms_2017 &lt;- read_csv(&quot;data/StormEvents_details-ftp_v1.0_d2017_c20180918.csv&quot;) ## Parsed with column specification: ## cols( ## .default = col_character(), ## BEGIN_YEARMONTH = col_double(), ## BEGIN_DAY = col_double(), ## BEGIN_TIME = col_double(), ## END_YEARMONTH = col_double(), ## END_DAY = col_double(), ## END_TIME = col_double(), ## EPISODE_ID = col_double(), ## EVENT_ID = col_double(), ## STATE_FIPS = col_double(), ## YEAR = col_double(), ## CZ_FIPS = col_double(), ## INJURIES_DIRECT = col_double(), ## INJURIES_INDIRECT = col_double(), ## DEATHS_DIRECT = col_double(), ## DEATHS_INDIRECT = col_double(), ## MAGNITUDE = col_double(), ## CATEGORY = col_double(), ## TOR_LENGTH = col_double(), ## TOR_WIDTH = col_double(), ## BEGIN_RANGE = col_double() ## # ... with 5 more columns ## ) ## See spec(...) for full column specifications. Here’s what the first few columns and rows should look like: storms_2017 %&gt;% select(1:3) %&gt;% slice(1:3) ## # A tibble: 3 x 3 ## BEGIN_YEARMONTH BEGIN_DAY BEGIN_TIME ## &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; ## 1 201704 6 1509 ## 2 201704 6 930 ## 3 201704 5 1749 Once you’ve read the data in, here’s the code that I used to clean the data: library(lubridate) library(stringr) library(tidyr) storms_2017 &lt;- storms_2017 %&gt;% select(BEGIN_DATE_TIME, END_DATE_TIME, EPISODE_ID:STATE_FIPS, EVENT_TYPE:CZ_NAME, SOURCE, BEGIN_LAT:END_LON) %&gt;% mutate(BEGIN_DATE_TIME = dmy_hms(BEGIN_DATE_TIME), END_DATE_TIME = dmy_hms(END_DATE_TIME), STATE = str_to_title(STATE), CZ_NAME = str_to_title(CZ_NAME)) %&gt;% filter(CZ_TYPE == &quot;C&quot;) %&gt;% select(-CZ_TYPE) %&gt;% mutate(STATE_FIPS = str_pad(STATE_FIPS, 2, side = &quot;left&quot;, pad = &quot;0&quot;), CZ_FIPS = str_pad(CZ_FIPS, 3, side = &quot;left&quot;, pad = &quot;0&quot;)) %&gt;% unite(fips, STATE_FIPS, CZ_FIPS, sep = &quot;&quot;) %&gt;% rename_all(funs(str_to_lower(.))) Here’s what the data looks like now: storms_2017 %&gt;% slice(1:3) ## # A tibble: 3 x 13 ## begin_date_time end_date_time episode_id event_id state fips ## &lt;dttm&gt; &lt;dttm&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;chr&gt; &lt;chr&gt; ## 1 2017-04-06 15:09:00 2017-04-06 15:09:00 113355 678791 New … 34015 ## 2 2017-04-06 09:30:00 2017-04-06 09:40:00 113459 679228 Flor… 12071 ## 3 2017-04-05 17:49:00 2017-04-05 17:53:00 113448 679268 Ohio 39057 ## # … with 7 more variables: event_type &lt;chr&gt;, cz_name &lt;chr&gt;, source &lt;chr&gt;, ## # begin_lat &lt;dbl&gt;, begin_lon &lt;dbl&gt;, end_lat &lt;dbl&gt;, end_lon &lt;dbl&gt; There is data that comes with R on U.S. states (data(\"state\")). Use that to create a dataframe with the state name, area, and region: data(&quot;state&quot;) us_state_info &lt;- data_frame(state = state.name, area = state.area, region = state.region) Create a dataframe with the number of events per state in 2017. Merge in the state information dataframe you just created. Remove any states that are not in the state information dataframe: state_storms &lt;- storms_2017 %&gt;% group_by(state) %&gt;% count() %&gt;% ungroup() %&gt;% right_join(us_state_info, by = &quot;state&quot;) To create the plot: Ultimately, in this group exercise, you will create a plot of state land area versus the number of storm events in the state: library(ggplot2) storm_plot &lt;- ggplot(state_storms, aes(x = area, y = n)) + geom_point(aes(color = region)) + labs(x = &quot;Land area (square miles)&quot;, y = &quot;# of storm events in 2017&quot;) storm_plot 8.5.2 Trying out ggplot2 extensions Go back through the notes so far for this week. Pick your favorite plot that’s been shown so far and recreate it. All the code is in the notes, but you’ll need to work through it closely to make sure that you understand how to add code from the extension into the rest of the ggplot2 code. 8.5.3 Using simple features to map Re-create the following map of the number of events listed in the NOAA Storm Events database (of those listed by county) for each county in Colorado: If you have time, try this one, too. It shows the number of three certain types of events by county. If a county had no events, it’s shown in gray (as having a missing value when you count up the events that did happen). 8.5.3.1 Example R code Here is some R code that could be used to create the figure. Note that the code to create storms_2017 and co_counties is available in the course notes. library(viridis) co_event_counts &lt;- storms_2017 %&gt;% filter(state == &quot;Colorado&quot;) %&gt;% group_by(fips) %&gt;% count() %&gt;% ungroup() co_county_events &lt;- co_counties %&gt;% mutate(fips = paste(STATEFP, COUNTYFP, sep = &quot;&quot;)) %&gt;% full_join(co_event_counts, by = &quot;fips&quot;) %&gt;% mutate(n = ifelse(!is.na(n), n, 0)) ggplot() + geom_sf(data = co_county_events, aes(fill = n)) + scale_fill_viridis(name = &quot;Number of events\\n(2017)&quot;) Example code for second plot: co_event_counts &lt;- storms_2017 %&gt;% filter(state == &quot;Colorado&quot;) %&gt;% filter(event_type %in% c(&quot;Tornado&quot;, &quot;Heavy Rain&quot;, &quot;Hail&quot;)) %&gt;% group_by(fips, event_type) %&gt;% count() %&gt;% ungroup() co_county_events &lt;- co_counties %&gt;% mutate(fips = paste(STATEFP, COUNTYFP, sep = &quot;&quot;)) %&gt;% right_join(co_event_counts, by = &quot;fips&quot;) ggplot() + geom_sf(data = co_counties, color = &quot;lightgray&quot;) + geom_sf(data = co_county_events, aes(fill = n)) + scale_fill_viridis(name = &quot;Number of events\\n(2017)&quot;) + theme(legend.position = &quot;top&quot;) + facet_wrap(~ event_type, ncol = 3) + theme(axis.line = element_blank(), axis.text = element_blank(), axis.ticks = element_blank()) 8.5.4 More on mapping See if you can put everything we’ve talked about together to create the following map. Note that you can get the county boundaries using the tigris package and the hurricane data from the NHC website mentioned in the text. The storm events data is in the storms_2017 dataframe created in code in the text. See how close you can get to this figure. 8.5.4.1 Example R code Here is the code I used to create the figure: harvey_events &lt;- storms_2017 %&gt;% filter(state %in% c(&quot;Texas&quot;, &quot;Louisiana&quot;) &amp; ymd_hms(&quot;2017-08-25 00:00:00&quot;) &lt; begin_date_time &amp; begin_date_time &lt; ymd_hms(&quot;2017-10-05 00:00:00&quot;)) %&gt;% group_by(fips) %&gt;% count() %&gt;% ungroup() tx_la_counties &lt;- counties(state = c(&quot;TX&quot;, &quot;LA&quot;), cb = TRUE, class = &quot;sf&quot;) %&gt;% mutate(fips = paste(STATEFP, COUNTYFP, sep = &quot;&quot;)) %&gt;% full_join(harvey_events, by = &quot;fips&quot;) %&gt;% mutate(n = ifelse(is.na(n), 0, n)) ggplot() + geom_sf(data = tx_la_counties, color = NA, aes(fill = n)) + geom_sf(data = filter(us_states, ID %in% c(&quot;texas&quot;, &quot;louisiana&quot;)), fill = NA, color = &quot;lightgray&quot;) + geom_sf(data = harvey_windswath, aes(color = factor(RADII)), alpha = 0.4) + geom_sf(data = harvey_track, color = &quot;red&quot;, alpha = 0.1, size = 1) + xlim(c(-107, -89)) + ylim(c(25, 37)) + scale_fill_viridis(name = &quot;# of events&quot;, option = &quot;A&quot;, direction = -1) + scale_color_viridis(name = &quot;Wind (kts)&quot;, discrete = TRUE, option = &quot;B&quot;, direction = -1) + theme_dark() + ggtitle(&quot;Number of events near Harvey&#39;s landfall&quot;, subtitle = &quot;# events starting Aug. 25 to Sept. 5, 2017&quot;) "],
 ["reproducible-research-2.html", "Chapter 9 Reproducible research #2 9.1 Templates 9.2 R Projects 9.3 git 9.4 In-course exercise", " Chapter 9 Reproducible research #2 Download slides for Nichole Monhait’s guest lecture Nichole’s full repo for the lecture is at: https://github.com/nmonhait/erhs_535. Download a pdf of the lecture slides covering this topic. 9.1 Templates R Markdown templates can be used to change multiple elements of the style of a rendered document. You can think of these as being the document-level analog to the themes we’ve used with ggplot objects. To do this, some kind of style file is applied when rendering document. For HTML documents, Cascading Style Sheets (CSS) (.css) can be used to change the style of different elements. For pdf files, LaTeX package (style) files (.sty) are used. To open a new R Markdown file that uses a template, in RStudio, go to “File” -&gt; “New File” -&gt; “R Markdown” -&gt; “From Template”. Different templates come with different R packages. A couple of templates come with the rmarkdown package, which you likely already have. Many of these templates will only render to pdf. To render a pdf from R Markdown, you need to have a version of TeX installed on your computer. Like R, TeX is open source software. RStudio recommends the following installations by system: For Macs: MacTeX For PCs: MiKTeX Links for installing both can be found at http://www.latex-project.org/ftp.html Current version of TeX: 3.14159265. The tufte package has templates for creating handouts typeset like Edward Tufte’s books. This package includes templates for creating both pdf and HTML documents in this style. The package includes special functions like newthought, special chunk options like fig.fullwidth, and special knitr engines like marginfigure. Special features available in the tufte template include: Margin references Margin figures Side notes Full width figures The rticles package has templates for several journals: Journal of Statistical Software The R Journal Association for Computing Machinery ACS publications (Journal of the American Chemical Society, Environmental Science &amp; Technology) Elsevier publications Some of these templates create a whole directory, with several files besides the .Rmd file. For example, the template for The R Journal includes: The R Markdown file in which you write your article “RJournal.sty”: A LaTeX package (style) file specific to The R Journal. This file tells LaTeX how to render all the elements in your article in the style desired by this journal. “RJreferences.bib”: A BibTeX file, where you can save citation information for all references in your article. “Rlogo.png”: An example figure (the R logo). Once you render the R Markdown document from this template, you’ll end up with some new files in the directory: “[your file name].tex”: A TeX file with the content from your R Markdown file. This will be “wrapped” with some additional formatting input to create “RJwrapper.tex”. “RJwrapper.tex”: A TeX file that includes both the content from your R Markdown file and additional formatting input. Typically, you will submit this file (along with the BibTeX, any figure and image files, and possibly the style file) to the journal. “RJwrapper.pdf”: The rendered pdf file (what the published article would look like) This template files will often require some syntax that looks more like LaTeX than Markdown. For example, for the template for The R Journal, you need to use \\citep{} and \\citet{} to include citations. These details will depend on the style file of the template. As a note, you can always use raw LaTeX in R Markdown documents, not just in documents you’re creating with a template. You just need to be careful not to mix the two. For example, if you use a LaTeX environment to begin an itemized list (e.g., with begin{itemize}), you must start each item with item, not -. You can create your own template. You create it as part of a custom R package, and then will have access to the template once you’ve installed the package. This can be useful if you often write documents in a certain style, or if you ever work somewhere with certain formatting requirements for reports. RStudio has full instructions for creating your own template: http://rmarkdown.rstudio.com/developer_document_templates.html 9.2 R Projects 9.2.1 Organization So far, you have run much of your analysis within a single R script or R Markdown file. Often, any associated data are within the same working directory as your script or R Markdown file, but the files for one project are not separated from files for other projects. As you move to larger projects, this kind of set-up won’t work as well. Instead, you’ll want to start keeping all materials for a project in a single and exclusive directory. Often, it helps to organize the files in a project directory into subdirectories. Common subdirectories include: data-raw: Raw data and R scripts to clean the raw data. data: Cleaned data, often saved as .RData after being generated by a script in data-raw. R: Code for any functions used in analysis. reports: Any final products rendered from R Markdown and their original R Markdown files (e.g., paper drafts, reports, presentations). 9.2.2 Creating R Projects RStudio allows you to create “Projects” to organize code, data, and results within a directory. When you create a project, RStudio adds a file with the extension “.Rproj” to the directory. There are some advantages to setting a directory to be an R Project. The project: Automatically uses the directory as your current working directory when you open the project. Coordinates well with git version control and GitHub repository system. Opens a “Files” window for navigating project files in an RStudio pane when you open the project. You can create a new project from scratch or from an existing directory. To create an R project from a working directory, in RStudio go to “File” -&gt; “New Project” -&gt; “New Directory”. You can then choose where you want to save the new project directory. 9.3 git Git is a version control system. It saves information about all changes you make on all files in a repository. This allows you to revert back to previous versions and search through the history for all files in the repository. Git is open source. You can download it for different operating systems here: https://git-scm.com/downloads You will need git on your computer to use git with RStudio and create local git repositories you can sync with GitHub repositories. Before you use git, you should configure it. For example, you should make sure it has your name and email address. You can configure git with commands at the shell. For example, I would run the following code at a shell to configure git to have my proper user name and email: git config --global user.name &quot;Brooke Anderson&quot; git config --global user.email &quot;brooke.anderson@colostate.edu&quot; Sometimes, RStudio will automatically find git (once you’ve installed git) when you start RStudio. However, in some cases, you may need to take some more steps to activate git in RStudio. To do this, go to “RStudio” -&gt; “Preferences” -&gt; “Git/SVN”. Choose “Enable version control”. If RStudio doesn’t find your version of git in the “Git executable” box, browse for it. 9.3.1 Initializing a git repository You can initialize a git repository using commands from the shell. To do that, take the following steps (first check that it is not already a git repository): Use a shell (“Terminal” on Macs) to navigate to to that directory. You can use cd to do that (similar to setwd in R). Once you are in the directory, run git status. If you get the message fatal: Not a git repository (or any of the parent directories): .git, it is not yet a git repository. If you do not get an error from git status, the directory is already a repository. If you do get an error, run git init to initialize it as a repository. For example, if I wanted to make the “fars_analysis” directory, which is a direct subdirectory of my home directory, a git repository, I could open a shell and run: cd ~/fars_analysis git init You can also initialize a git repository for a directory that is an R Project directory through R Studio. Open the Project. Go to “Tools” -&gt; “Version Control” -&gt; “Project Setup”. In the box for “Version control system”, choose “Git”. Note: If you have just installed git, and have not restarted RStudio, you’ll need to do that before RStudio will recognize git. If you do not see “Git” in the box for “Version control system”, it means either that you do not have git installed on your computer or that RStudio was unable to find it. Once you initialize the project as a git repository, you should have a “Git” window in one of your RStudio panes (top right pane by default). As you make and save changes to files, they will show up in this window for you to commit. For example, this is what the Git window for our coursebook looks like when I have changes to the slides for week 9 that I need to commit: 9.3.2 Committing When you want git to record changes, you commit the files with the changes. Each time you commit, you have to include a short commit message with some information about the changes. You can make commits from a shell. However, in this course we’ll just make commits from the RStudio environment. To make a commit from RStudio, click on the “Commit” button in the Git window. That will open a separate commit window that looks like this: In this window, to commit changes: Click on the files you want to commit to select them. If you’d like, you can use the bottom part of the window to look through the changes you are committing in each file. Write a message in the “Commit message” box. Keep the message to one line in this box if you can. If you need to explain more, write a short one-line message, skip a line, and then write a longer explanation. Click on the “Commit” button on the right. Once you commit changes to files, they will disappear from the Git window until you make and save more changes in them. 9.3.3 Browsing history On the top left of the Commit window, you can toggle to “History”. This window allows you to explore the history of commits for the repository. GitHub allows you to host git repositories online. This allows you to: Work collaboratively on a shared repository Fork someone else’s repository to create your own copy that you can use and change as you want Suggest changes to other people’s repositories through pull requests To push local repositories to GitHub and fork other people’s repositories, you will need a GitHub account. You can sign up at https://github.com. A free account is fine. The basic unit for working in GitHub is the repository. You can think of a repository as very similar to an R Project— it’s a directory of files with some supplemental files saving some additional information about the directory. While R Projects have this additional information saved as an “.RProj” file, git repositories have this information in a directory called “.git”. Because this pathname starts with a dot, it won’t show up in many of the ways you list files in a directory. From a shell, you can see files that start with . by running ls -a from within that directory. 9.3.4 Linking local repo to GitHub repo If you have a local directory that you would like to push to GitHub, these are the steps to do it. First, you need to make sure that the directory is under git version control. See the notes on initializing a repository. Next, you need to create an empty repository on GitHub to sync with your local repository. Do that by: In GitHub, click on the “+” in the upper right corner (“Create new”). Choose “Create new repository”. Give your repository the same name as the local directory you’d like to connect it to. For example, if you want to connect it to a directory called “fars_analysis” on your computer, name the repository “fars_analysis”. Leave everything else as-is (unless you’d like to add a short description in the “Description” box). Click on “Create repository” at the bottom of the page. Now you are ready to connect the two repositories. First, you’ll want to change some settings in RStudio so GitHub will recognize that your local repository belongs to you, rather than asking for you password every time. In RStudio, go to “RStudio” -&gt; “Preferences” -&gt; “Git / svn”. Choose to “Create RSA key”. Click on “View public key”. Copy what shows up. Go to your GitHub account and navigate to “Settings”. Click on “SSH and GPG keys”. Click on “New SSH key”. Name the key something like “RStudio” (you might want to include the device name if you’ll have SSH keys from RStudio on several computers). Past in your public key in the “Key box”. 9.3.5 Syncing RStudio and GitHub Now you’re ready to push your local repository to the empty GitHub repository you created. Open a shell and navigate to the directory you want to push. (You can open a shell from RStudio using the gear button in the Git window.) Add the GitHub repository as a remote branch with the following command (this gives an example for adding a GitHub repository named “ex_repo” in my GitHub account, “geanders”): git remote add origin git@github.com:geanders/ex_repo.git Push the contents of the local repository to the GitHub repository. git push -u origin master To pull a repository that already exists on GitHub and to which you have access (or that you’ve forked), first use cd to change a shell into the directory where you want to put the repository then run git clone to clone the repository locally. For example, if I wanted to clone a GitHub repository called “ex_repo” in my GitHub account, I would run: git clone git@github.com:geanders/ex_repo.git Once you have linked a local R project with a GitHub repository, you can push and pull commits using the blue down arrow (pull from GitHub) and green up arrow (push to GitHub) in the Git window in RStudio. GitHub helps you work with others on code. There are two main ways you can do this: Collaborating: Different people have the ability to push and pull directly to and from the same repository. When one person pushes a change to the repository, other collaborators can immediately get the changes by pulling the latest GitHub commits to their local repository. Forking: Different people have their own GitHub repositories, with each linked to their own local repository. When a person pushes changes to GitHub, it only makes changes to his own repository. The person must issue a pull request to another person’s fork of the repository to share the changes. 9.3.6 Issues Each original GitHub repository (i.e., not a fork of another repository) has a tab for “Issues”. This page works like a Discussion Forum. You can create new “Issue” threads to describe and discuss things that you want to change about the repository. Issues can be closed once the problem has been resolved. You can close issues on the “Issue” page with the “Close issue” button. If a commit you make in RStudio closes an issue, you can automatically close the issue on GitHub by including “Close #[issue number]” in your commit message and then pushing to GitHub. For example, if issue #5 is “Fix typo in section 3”, and you make a change to fix that typo, you could make and save the change locally, commit that change with the commit message “Close #5”, and then push to GitHub, and issue #5 in “Issues” for that GitHub repository will automatically be closed, with a link to the commit that fixed the issue. 9.3.7 Pull request You can use a pull request to suggest changes to a repository that you do not own or otherwise have the permission to directly change. You can also use pull requests within your own repositories. Some people will create a pull request every time they have a big issue they want to fix in one of their repositories. In GitHub, each repository has a “Pull requests” tab where you can manage pull requests (submit a pull request to another fork or merge in someone else’s pull request for your fork). Take the following steps to suggest changes to someone else’s repository: Fork the repository Make changes (locally or on GitHub) Save your changes and commit them Submit a pull request to the original repository If there are not any conflicts and the owner of the original repository likes your changes, he or she can merge them directly into the original repository. If there are conflicts, these need to be resolved before the pull request can be merged. 9.3.8 Merge conflicts At some point, you will get merge conflicts. These happen when two people have changed the same piece of code in two different ways at the same time. For example, say Rachel and are both working on local versions of the same repository, and I change a line to mtcars[1, ] while Rachel changes the same line to head(mtcars, 1). Rachel pushes to the GitHub version of the repository before I do. When I pull the latest commits to the GitHub repository, I will have a merge conflict for this line. To be able to commit a final version, I’ll need to decide which version of the code to use and commit a version of the file with that code. Merge conflicts can come up in a few situations: You pull in commits from the GitHub branch of a repository you’ve been working on locally. Someone sends a pull request for one of your repositories. If there are merge conflicts, they’ll show up like this in the file: &lt;&lt;&lt;&lt;&lt;&lt;&lt; HEAD mtcars[1, ] ======= head(mtcars, 1) &gt;&gt;&gt;&gt;&gt;&gt;&gt; remote-branch To fix them, search for all these spots in files with conflicts, pick the code you want to use, and delete everything else. For the example conflict, I might change the file from this: &lt;&lt;&lt;&lt;&lt;&lt;&lt; HEAD mtcars[1, ] ======= head(mtcars, 1) &gt;&gt;&gt;&gt;&gt;&gt;&gt; remote-branch To this: head(mtcars, 1) Then you can save and commit the file. 9.3.9 Find out more If you’d like to find out more, Hadley Wickham has a great chapter on using git and GitHub with RStudio in his R Packages book: http://r-pkgs.had.co.nz/git.html 9.4 In-course exercise 9.4.1 Organizing a project In this part of the group exercise, you will set up an R Project to use for the next homework assignment. You will want to set up a similar project for your final group project. First, you need to create a new project. In RStudio, go to “File” -&gt; “New Project” -&gt; “New Directory”. Choose where you want to save this directory and what you want to name it. Once you open the project, one of the RStudio panes should have a tab called “Files”. This shows the files in this project directory and allows you to navigate through them. Currently, you won’t have any files other than the R project file (“.Rproj”). As a next step, create several subdirectories. We’ll use these to structure the data and R Markdown files for your homework. Create the following subdirectories (you can use the “New Folder” button in the RStudio “Files” pane): data writing Download the data for the homework from the Washington Post’s GitHub page: https://github.com/washingtonpost/data-homicides. Save this data inside your R project in the data subdirectory 9.4.2 Initializing git for an R Project If you do not already have one, sign up for a GitHub account. The free option is fine. If you do not already have git installed on your computer, install it: https://git-scm.com/downloads Restart RStudio. go to “RStudio” -&gt; “Preferences” -&gt;“Git/SVN”. Choose “Enable version control”. If RStudio doesn’t find yourversion of git in the “Git executable” box, browse for it. Open your homework project in RStudio. Change your Project settings to initialize git for this project (see the course notes for tips on how to do that). Open a shell from R using the gear symbol in the “Git” pane you should now see in RStudio. Configure git from this shell. For example, I would open a shell and run: git config --global user.name &quot;Brooke Anderson&quot; git config --global user.email &quot;brooke.anderson@colostate.edu&quot; Note that you only need to do this once (until you get a new computer or, maybe, update git). - Go to the “Commit” window. Click on all of the files you see there, and then make an initial commit using “Initial commit” as your commit message. - The writing subdirectory will have your R Markdown file and its output. Create a new R Markdown file (“File” -&gt; “New File” -&gt; “R Markdown”) and save it to this subdirectory. You can change the name and date for the file if you’d like. Delete all the text that comes as a default. Write a piece of code that lists the files you saved in the data subdirectory. Remember that the working directory for an R Markdown file is the directory in which it’s saved, so you may need to use a relative pathname that goes up one directory (..) and then goes into data subdirectory. - Commit this change using the “Commit” window. After you commit the changes, look at the “History” window to see the history of your commits. 9.4.3 Linking your project with a GitHub repository (See course notes for more on these steps.) Login to your GitHub account online. Create an empty GitHub repository for the project. Give it the same name as the name of your R project directory. If you do not already have an RSA kay, create one in RStudio and add it as an SSH key in your GitHub settings. If you already have a key (you almost certainly know if you do), see if you can copy it and submit it in GitHub. Set this empty online GitHub repository as the remote branch of your local git repository for the project. Push your local repository to this GitHub repository. Go to your GitHub account and make sure the repository was pushed. Try making some more changes to your local repository. Commit the changes, then use the green up arrow in the Git window to push the changes to your GitHub repository. "],
 ["entering-and-cleaning-data-3.html", "Chapter 10 Entering and cleaning data #3 10.1 Pulling online data 10.2 Example R API wrapper packages 10.3 tigris package 10.4 countyweather 10.5 Cleaning very messy data 10.6 In-course exercise", " Chapter 10 Entering and cleaning data #3 Download a pdf of the lecture slides covering this topic. 10.1 Pulling online data 10.1.1 APIs APIs are “Application Program Interfaces”. An API provides the rules for software applications to interact. In the case of open data APIs, they provide the rules you need to know to write R code to request and pull data from the organization’s web server into your R session. Often, an API can help you avoid downloading all available data, and instead only download the subset you need. The basic strategy for using APIs from R is: Figure out the API rules for HTTP requests Write R code to create a request in the proper format Send the request using GET or POST HTTP methods Once you get back data from the request, parse it into an easier-to-use format if necessary Start by reading any documentation available for the API. This will often give information on what data is available and how to put together requests. Source: https://api.nasa.gov/api.html#EONET Many organizations will require you to get an API key and use this key in each of your API requests. This key allows the organization to control API access, including enforcing rate limits per user. API rate limits restrict how often you can request data (e.g., an hourly limit of 1,000 requests per user for NASA APIs). You should keep this key private. In particular, make sure you do not include it in code that is posted to GitHub. The riem package, developed by Maelle Salmon and an ROpenSci package, is an excellent and straightforward example of how you can use R to pull open data through a web API. This package allows you to pull weather data from airports around the world directly from the Iowa Environmental Mesonet. To get a certain set of weather data from the Iowa Environmental Mesonet, you can send an HTTP request specifying a base URL, “https://mesonet.agron.iastate.edu/cgi-bin/request/asos.py/”, as well as some parameters describing the subset of dataset you want (e.g., date ranges, weather variables, output format). Once you know the rules for the names and possible values of these parameters (more on that below), you can submit an HTTP GET request using the GET function from the httr package. https://mesonet.agron.iastate.edu/cgi-bin/request/asos.py?station=DEN&amp;data=sknt&amp;year1=2016&amp;month1=6&amp;day1=1&amp;year2=2016&amp;month2=6&amp;day2=30&amp;tz=America%2FDenver&amp;format=comma&amp;latlon=no&amp;direct=no&amp;report_type=1&amp;report_type=2 When you are making an HTTP request using the GET or POST functions from the httr package, you can include the key-value pairs for any query parameters as a list object in the query argurment of the function. library(httr) meso_url &lt;- paste0(&quot;https://mesonet.agron.iastate.edu/&quot;, &quot;cgi-bin/request/asos.py/&quot;) denver &lt;- GET(url = meso_url, query = list(station = &quot;DEN&quot;, data = &quot;sped&quot;, year1 = &quot;2016&quot;, month1 = &quot;6&quot;, day1 = &quot;1&quot;, year2 = &quot;2016&quot;, month2 = &quot;6&quot;, day2 = &quot;30&quot;, tz = &quot;America/Denver&quot;, format = &quot;comma&quot;)) The GET call will return a special type of list object with elements that include the url you queried and the content of the page at that url: str(denver, max.level = 1, list.len = 6) ## List of 10 ## $ url : chr &quot;https://mesonet.agron.iastate.edu/cgi-bin/request/asos.py/?station=DEN&amp;data=sped&amp;year1=2016&amp;month1=6&amp;day1=1&amp;yea&quot;| __truncated__ ## $ status_code: int 200 ## $ headers :List of 6 ## ..- attr(*, &quot;class&quot;)= chr [1:2] &quot;insensitive&quot; &quot;list&quot; ## $ all_headers:List of 1 ## $ cookies :&#39;data.frame&#39;: 0 obs. of 7 variables: ## $ content : raw [1:239447] 23 44 45 42 ... ## [list output truncated] ## - attr(*, &quot;class&quot;)= chr &quot;response&quot; The httr package includes functions to pull out elements of this list object, including: headers: Pull out the header information content: Pull out the content returned from the page status_code: Pull out the status code from the GET request (e.g., 200: okay; 404: not found) Note: For some fun examples of 404 pages, see https://www.creativebloq.com/web-design/best-404-pages-812505 You can use content from httr to retrieve the contents of the HTTP request we made. For this particular web data, the requested data is a comma-separated file, so you can convert it to a dataframe with read_csv: denver %&gt;% content() %&gt;% read_csv(skip = 5, na = &quot;M&quot;) %&gt;% slice(1:3) ## # A tibble: 3 x 3 ## station valid sped ## &lt;chr&gt; &lt;dttm&gt; &lt;dbl&gt; ## 1 DEN 2016-06-01 00:00:00 9.2 ## 2 DEN 2016-06-01 00:05:00 9.2 ## 3 DEN 2016-06-01 00:10:00 6.9 The riem package wraps up this whole process, so you can call a single function to get in the data you want from the API: library(riem) denver_2 &lt;- riem_measures(station = &quot;DEN&quot;, date_start = &quot;2016-06-01&quot;, date_end = &quot;2016-06-30&quot;) denver_2 %&gt;% slice(1:3) ## # A tibble: 3 x 31 ## station valid lon lat tmpf dwpf relh drct sknt p01i ## &lt;chr&gt; &lt;dttm&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; ## 1 DEN 2016-06-01 00:00:00 -105. 39.8 NA NA NA 70 7 NA ## 2 DEN 2016-06-01 00:05:00 -105. 39.8 NA NA NA 80 8 NA ## 3 DEN 2016-06-01 00:10:00 -105. 39.8 NA NA NA 80 9 NA ## # … with 21 more variables: alti &lt;dbl&gt;, mslp &lt;dbl&gt;, vsby &lt;dbl&gt;, gust &lt;dbl&gt;, ## # skyc1 &lt;chr&gt;, skyc2 &lt;chr&gt;, skyc3 &lt;chr&gt;, skyc4 &lt;chr&gt;, skyl1 &lt;dbl&gt;, ## # skyl2 &lt;dbl&gt;, skyl3 &lt;dbl&gt;, skyl4 &lt;dbl&gt;, wxcodes &lt;chr&gt;, ## # ice_accretion_1hr &lt;lgl&gt;, ice_accretion_3hr &lt;lgl&gt;, ice_accretion_6hr &lt;lgl&gt;, ## # peak_wind_gust &lt;dbl&gt;, peak_wind_drct &lt;dbl&gt;, peak_wind_time &lt;chr&gt;, ## # feel &lt;dbl&gt;, metar &lt;chr&gt; 10.2 Example R API wrapper packages The tigris package is a very useful example of an API wrapper. It retrieves geographic boundary data from the U.S. Census for a number of different geographies: Location boundaries States Counties Blocks Tracks School districts Congressional districts Roads Primary roads Primary and secondary roads Water Area-water Linear-water Coastline Other Landmarks Military 10.3 tigris package The following plot is an example of the kinds of maps you can create using the tigris package. This map comes from: Kyle Walker. 2016. “tigris: An R Package to Access and Work with Geographic Data from the US Census Bureau”. The R Journal. This is a great article to read to find out more about tigris. A number of other R packages also help you access and use data from the U.S. Census: acs: Download, manipulate, and present American Community Survey and Decennial data from the US Census (see “Working with the American Community Survey in R: A Guide to Using the acs Package”, a book available free online through the CSU library) USABoundaries: Historical and contemporary boundaries of the United States of America idbr: R interface to the US Census Bureau International Data Base API (e.g., populations of other countries) The organization rOpenSci (https://ropensci.org) has the following mission: “At rOpenSci we are creating packages that allow access to data repositories through the R statistical programming environment that is already a familiar part of the workflow of many scientists. Our tools not only facilitate drawing data into an environment where it can readily be manipulated, but also one in which those analyses and methods can be easily shared, replicated, and extended by other researchers.” rOpenSci collects a number of packages for tapping into open data for research. These are listed at https://ropensci.org/packages. Many of these packages are wrappers for APIs with data useful for scientific research Some examples (all descriptions from rOpenSci): AntWeb: Access data from the world’s largest ant database chromer: Interact with the chromosome counts database (CCDB) gender: Encodes gender based on names and dates of birth musemeta: R Client for Scraping Museum Metadata, including The Metropolitan Museum of Art, the Canadian Science &amp; Technology Museum Corporation, the National Gallery of Art, and the Getty Museum, and more to come. rusda: Interface to some USDA databases webchem: Retrieve chemical information from many sources. Currently includes: Chemical Identifier Resolver, ChemSpider, PubChem, and Chemical Translation Service. As an example, one ROpenSci package, rnoaa, allows you to: “Access climate data from NOAA, including temperature and precipitation, as well as sea ice cover data, and extreme weather events” It includes access to: Buoy data from the National Buoy Data Center Historical Observing Metadata Repository (HOMR))— climate station metadata National Climatic Data Center weather station data Sea ice data International Best Track Archive for Climate Stewardship (IBTrACS)— tropical cyclone tracking data Severe Weather Data Inventory (SWDI) 10.4 countyweather The countyweather package, developed by a student here at CSU, wraps the rnoaa package to let you pull and aggregate weather at the county level in the U.S. For example, you can pull all data from Miami during Hurricane Andrew: When you pull the data for a county, the package also maps the contributing weather stations: The USGS also has a very nice collection of R packages that wrap USGS open data APIs, which can be accessed through: https://owi.usgs.gov/R/ “USGS-R is a community of support for users of the R scientific programming language. USGS-R resources include R training materials, R tools for the retrieval and analysis of USGS data, and support for a growing group of USGS-R developers.” USGS R packages include: dataRetrieval: Obtain water quality sample data, streamflow data, and metadata directly from either the USGS or EPA EGRET: Analysis of long-term changes in water quality and streamflow, including the water-quality method Weighted Regressions on Time, Discharge, and Season (WRTDS) laketemps: Lake temperature data package for Global Lake Temperature Collaboration Project lakeattributes: Common useful lake attribute data soilmoisturetools: Tools for soil moisture data retrieval and visualization Here are some examples of other R packages that faciliate use of an API for open data: twitteR: Twitter Quandl: Quandl (financial data) RGoogleAnalytics: Google Analytics WDI, wbstats: World Bank GuardianR, rdian: The Guardian Media Group blsAPI: Bureau of Labor Statistics rtimes: New York Times Find out more about writing API packages with this vignette for the httr package: https://cran.r-project.org/web/packages/httr/vignettes/api-packages.html. This document includes advice on error handling within R code that accesses data through an open API. 10.5 Cleaning very messy data One version of Atlantic basin hurricane tracks is available here: https://www.nhc.noaa.gov/data/hurdat/hurdat2-1851-2017-050118.txt. The data is not in a classic delimited format: This data is formatted in the following way: Data for many storms are included in one file. Data for a storm starts with a shorter line, with values for the storm ID, name, and number of observations for the storm. These values are comma separated. Observations for each storm are longer lines. There are multiple observations for each storm, where each observation gives values like the location and maximum winds for the storm at that time. Strategy for reading in very messy data: Read in all lines individually. Use regular expressions to split each line into the elements you’d like to use to fill columns. Write functions and / or map calls to process lines and use the contents to fill a data frame. Once you have the data in a data frame, do any remaining cleaning to create a data frame that is easy to use to answer research questions. Because the data is not nicely formatted, you can’t use read_csv or similar functions to read it in. However, the read_lines function from readr allows you to read a text file in one line at a time. You can then write code and functions to parse the file one line at a time, to turn it into a dataframe you can use. Note: Base R has readLines, which is very similar. The read_lines function from readr will read in lines from a text file directly, without trying to separate into columns. You can use the n_max argument to specify the number of lines to read it. For example, to read in three lines from the hurricane tracking data, you can run: tracks_url &lt;- paste0(&quot;http://www.nhc.noaa.gov/data/hurdat/&quot;, &quot;hurdat2-1851-2017-050118.txt&quot;) hurr_tracks &lt;- read_lines(tracks_url, n_max = 3) hurr_tracks ## [1] &quot;AL011851, UNNAMED, 14,&quot; ## [2] &quot;18510625, 0000, , HU, 28.0N, 94.8W, 80, -999, -999, -999, -999, -999, -999, -999, -999, -999, -999, -999, -999, -999,&quot; ## [3] &quot;18510625, 0600, , HU, 28.0N, 95.4W, 80, -999, -999, -999, -999, -999, -999, -999, -999, -999, -999, -999, -999, -999,&quot; The data has been read in as a vector, rather than a dataframe: class(hurr_tracks) ## [1] &quot;character&quot; length(hurr_tracks) ## [1] 3 hurr_tracks[1] ## [1] &quot;AL011851, UNNAMED, 14,&quot; You can use regular expressions to break each line up. For example, you can use str_split from the stringr package to break the first line of the hurricane track data into its three separate components: library(stringr) str_split(hurr_tracks[1], pattern = &quot;,&quot;) ## [[1]] ## [1] &quot;AL011851&quot; &quot; UNNAMED&quot; &quot; 14&quot; ## [4] &quot;&quot; You can use this to create a list where each element of the list has the split-up version of a line of the original data. First, read in all of the data: tracks_url &lt;- paste0(&quot;http://www.nhc.noaa.gov/data/hurdat/&quot;, &quot;hurdat2-1851-2017-050118.txt&quot;) hurr_tracks &lt;- read_lines(tracks_url) length(hurr_tracks) ## [1] 52151 Next, use map with str_split to split each line of the data at the commas: library(purrr) hurr_tracks &lt;- purrr::map(hurr_tracks, str_split, pattern = &quot;,&quot;, simplify = TRUE) hurr_tracks[[1]] ## [,1] [,2] [,3] [,4] ## [1,] &quot;AL011851&quot; &quot; UNNAMED&quot; &quot; 14&quot; &quot;&quot; hurr_tracks[[2]][1:2] ## [1] &quot;18510625&quot; &quot; 0000&quot; Next, you want to split this list into two lists, one with the shorter “meta-data” lines and one with the longer “observation” lines. You can use map_int to create a vector with the length of each line. You will later use this to identify which lines are short or long. hurr_lengths &lt;- map_int(hurr_tracks, length) hurr_lengths[1:17] ## [1] 4 21 21 21 21 21 21 21 21 21 21 21 21 21 21 4 21 unique(hurr_lengths) ## [1] 4 21 You can use bracket indexing to split the hurr_tracks into two lists: one with the shorter lines that start each observation (hurr_meta) and one with the storm observations (hurr_obs). Use bracket indexing with the hurr_lengths vector you just created to make that split. hurr_meta &lt;- hurr_tracks[hurr_lengths == 4] hurr_obs &lt;- hurr_tracks[hurr_lengths == 21] hurr_meta[1:3] ## [[1]] ## [,1] [,2] [,3] [,4] ## [1,] &quot;AL011851&quot; &quot; UNNAMED&quot; &quot; 14&quot; &quot;&quot; ## ## [[2]] ## [,1] [,2] [,3] [,4] ## [1,] &quot;AL021851&quot; &quot; UNNAMED&quot; &quot; 1&quot; &quot;&quot; ## ## [[3]] ## [,1] [,2] [,3] [,4] ## [1,] &quot;AL031851&quot; &quot; UNNAMED&quot; &quot; 1&quot; &quot;&quot; hurr_obs[1:2] ## [[1]] ## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] ## [1,] &quot;18510625&quot; &quot; 0000&quot; &quot; &quot; &quot; HU&quot; &quot; 28.0N&quot; &quot; 94.8W&quot; &quot; 80&quot; &quot; -999&quot; &quot; -999&quot; ## [,10] [,11] [,12] [,13] [,14] [,15] [,16] [,17] [,18] ## [1,] &quot; -999&quot; &quot; -999&quot; &quot; -999&quot; &quot; -999&quot; &quot; -999&quot; &quot; -999&quot; &quot; -999&quot; &quot; -999&quot; &quot; -999&quot; ## [,19] [,20] [,21] ## [1,] &quot; -999&quot; &quot; -999&quot; &quot;&quot; ## ## [[2]] ## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] ## [1,] &quot;18510625&quot; &quot; 0600&quot; &quot; &quot; &quot; HU&quot; &quot; 28.0N&quot; &quot; 95.4W&quot; &quot; 80&quot; &quot; -999&quot; &quot; -999&quot; ## [,10] [,11] [,12] [,13] [,14] [,15] [,16] [,17] [,18] ## [1,] &quot; -999&quot; &quot; -999&quot; &quot; -999&quot; &quot; -999&quot; &quot; -999&quot; &quot; -999&quot; &quot; -999&quot; &quot; -999&quot; &quot; -999&quot; ## [,19] [,20] [,21] ## [1,] &quot; -999&quot; &quot; -999&quot; &quot;&quot; Now, you can use bind_rows from dplyr to change the list of metadata into a dataframe. (You first need to use as_tibble with map to convert all elements of the list from matrices to dataframes.) library(dplyr); library(tibble) hurr_meta &lt;- hurr_meta %&gt;% purrr::map(as_tibble) %&gt;% bind_rows() ## Warning: The `x` argument of `as_tibble.matrix()` must have unique column names if `.name_repair` is omitted as of tibble 2.0.0. ## Using compatibility `.name_repair`. ## This warning is displayed once every 8 hours. ## Call `lifecycle::last_warnings()` to see where this warning was generated. hurr_meta %&gt;% slice(1:3) ## # A tibble: 3 x 4 ## V1 V2 V3 V4 ## &lt;chr&gt; &lt;chr&gt; &lt;chr&gt; &lt;chr&gt; ## 1 AL011851 &quot; UNNAMED&quot; &quot; 14&quot; &quot;&quot; ## 2 AL021851 &quot; UNNAMED&quot; &quot; 1&quot; &quot;&quot; ## 3 AL031851 &quot; UNNAMED&quot; &quot; 1&quot; &quot;&quot; You can clean up the data a bit more. First, the fourth column doesn’t have any non-missing values, so you can get rid of it: unique(hurr_meta$V4) ## [1] &quot;&quot; Second, the second and third columns include a lot of leading whitespace: hurr_meta$V2[1:2] ## [1] &quot; UNNAMED&quot; &quot; UNNAMED&quot; Last, we want to name the columns. hurr_meta &lt;- hurr_meta %&gt;% select(-V4) %&gt;% rename(storm_id = V1, storm_name = V2, n_obs = V3) %&gt;% mutate(storm_name = str_trim(storm_name), n_obs = as.numeric(n_obs)) hurr_meta %&gt;% slice(1:3) ## # A tibble: 3 x 3 ## storm_id storm_name n_obs ## &lt;chr&gt; &lt;chr&gt; &lt;dbl&gt; ## 1 AL011851 UNNAMED 14 ## 2 AL021851 UNNAMED 1 ## 3 AL031851 UNNAMED 1 Now you can do the same idea with the hurricane observations. First, we’ll want to add storm identifiers to that data. The “meta” data includes storm ids and the number of observations per storm. We can take advantage of that to make a storm_id vector that will line up with the storm observations. storm_id &lt;- rep(hurr_meta$storm_id, times = hurr_meta$n_obs) head(storm_id, 3) ## [1] &quot;AL011851&quot; &quot;AL011851&quot; &quot;AL011851&quot; length(storm_id) ## [1] 50303 length(hurr_obs) ## [1] 50303 hurr_obs &lt;- hurr_obs %&gt;% purrr::map(as_tibble) %&gt;% bind_rows() %&gt;% mutate(storm_id = storm_id) hurr_obs %&gt;% select(V1:V2, V5:V6, storm_id) %&gt;% slice(1:3) ## # A tibble: 3 x 5 ## V1 V2 V5 V6 storm_id ## &lt;chr&gt; &lt;chr&gt; &lt;chr&gt; &lt;chr&gt; &lt;chr&gt; ## 1 18510625 &quot; 0000&quot; &quot; 28.0N&quot; &quot; 94.8W&quot; AL011851 ## 2 18510625 &quot; 0600&quot; &quot; 28.0N&quot; &quot; 95.4W&quot; AL011851 ## 3 18510625 &quot; 1200&quot; &quot; 28.0N&quot; &quot; 96.0W&quot; AL011851 10.6 In-course exercise 10.6.1 Working with an API wrapper package The rplos package provides a wrapper to the Public Library of Science (PLoS)’s API. PLOS has a collection of academic journals spanning a variety of topics. Check out this page of documentation for this API: http://api.plos.org/solr/search-fields/ Look through the potential search terms. Use the searchplos function to search articles in the PLoS collection for the term “West Nile”. Pull the publication date, title, abstract, article type, subject, and journal of each matching article and save the result to an R object called wn_papers. You may find it helpful to look at the examples in the helpfile for searchplos or the tutorial available at: https://ropensci.org/tutorials/rplos_tutorial/ The object returned by searchplos will be a list with two top levels, meta and data. Confirm that this is true for the wn_papers object you created. Look at the meta part of the list (you can use $ indexing to pull this out). How many articles were found with “West Nile” in them? Does the query seem case-sensitive (i.e., do you get the same number of papers when you query “west nile” rather than “West Nile”)? Re-run your query (save the results to wn_papers_titles) looking only for papers with “West Nile” in the title of the paper. How many papers are returned by this query? By default the limit of the number of papers returned by a query will be 10. You can change this (to a certain degree) by using the limit option. For the call you ran to create wn_papers_titles, set the limit to the number of articles that match this query, as identified in the meta element of the first run of the call. Check the number of rows in the data element that was returned to make sure it has the same number of rows as the number of articles that match the query. Create a plot of the number of articles published per year. Use color to show which articles are Research Articles versus other types of articles. Determine which journals have published these articles and the number of articles published in each journal. You may notice that sometimes “PLoS” is used and sometimes “PLOS”. See if you can fix this in R to get the count of articles per journal without this capitalization difference causing problems. Explore the list of packages on ROpenSci, those through the USGS, and those related to the U.S. Census. See if you can identify any other packages that might provide access to data relevant to West Nile virus. 10.6.1.1 Example R code library(rplos) wn_papers &lt;- searchplos(q = &quot;West Nile&quot;, fl = c(&quot;publication_date&quot;, &quot;title&quot;, &quot;journal&quot;, &quot;subject&quot;, &quot;abstract&quot;, &quot;article_type&quot;)) Confirm the structure of the returned object: str(wn_papers) ## List of 2 ## $ meta: tibble [1 × 2] (S3: tbl_df/tbl/data.frame) ## ..$ numFound: int 3331 ## ..$ start : int 0 ## $ data: tibble [10 × 6] (S3: tbl_df/tbl/data.frame) ## ..$ journal : chr [1:10] &quot;PLOS Pathogens&quot; &quot;PLOS ONE&quot; &quot;PLOS Neglected Tropical Diseases&quot; &quot;PLOS ONE&quot; ... ## ..$ publication_date: chr [1:10] &quot;2019-10-31T00:00:00Z&quot; &quot;2015-12-30T00:00:00Z&quot; &quot;2017-11-08T00:00:00Z&quot; &quot;2017-10-12T00:00:00Z&quot; ... ## ..$ article_type : chr [1:10] &quot;Review&quot; &quot;Research Article&quot; &quot;Research Article&quot; &quot;Research Article&quot; ... ## ..$ abstract : chr [1:10] &quot;\\nIt has been 20 years since West Nile virus first emerged in the Americas, and since then, little progress has&quot;| __truncated__ &quot;\\nWest Nile virus (WNV) is a mosquito-transmitted Flavivirus belonging to the Japanese encephalitis antigenic c&quot;| __truncated__ &quot;\\nThe West Nile virus (WNV), isolated in 1937, is an arbovirus (arthropod-borne virus) that infects thousands o&quot;| __truncated__ &quot;\\nThe structural risk of West Nile Disease results from the usual functioning of the socio-ecological system, w&quot;| __truncated__ ... ## ..$ title : chr [1:10] &quot;Twenty years of West Nile virus spread and evolution in the Americas visualized by Nextstrain&quot; &quot;Spatio-Temporal Identification of Areas Suitable for West Nile Disease in the Mediterranean Basin and Central Europe&quot; &quot;Biological and phylogenetic characteristics of West African lineages of West Nile virus&quot; &quot;Geographic variations of the bird-borne structural risk of West Nile virus circulation in Europe&quot; ... ## ..$ subject : chr [1:10] &quot;/Biology and life sciences/Biogeography/Phylogeography,/Biology and life sciences/Evolutionary biology/Evolutio&quot;| __truncated__ &quot;/Biology and life sciences/Developmental biology/Life cycles/Larvae,/Biology and life sciences/Microbiology/Med&quot;| __truncated__ &quot;/Biology and life sciences/Evolutionary biology/Evolutionary systematics/Phylogenetics/Phylogenetic analysis,/B&quot;| __truncated__ &quot;/Biology and life sciences/Ecology/Community ecology/Ecological risk,/Biology and life sciences/Microbiology/Me&quot;| __truncated__ ... ## ..- attr(*, &quot;numFound&quot;)= int 3331 ## ..- attr(*, &quot;start&quot;)= int 0 Look at the meta data: wn_papers$meta ## # A tibble: 1 x 2 ## numFound start ## &lt;int&gt; &lt;int&gt; ## 1 3331 0 Query for just articles with “West Nile” in the title: wn_papers_titles &lt;- searchplos(q = &quot;title:West Nile&quot;, fl = c(&quot;publication_date&quot;, &quot;title&quot;, &quot;journal&quot;, &quot;subject&quot;, &quot;abstract&quot;, &quot;article_type&quot;)) Look at the metadata: wn_papers_titles$meta ## # A tibble: 1 x 2 ## numFound start ## &lt;int&gt; &lt;int&gt; ## 1 217 0 Re-run the query using an appropriate limit to get all the matching articles: wn_papers_titles &lt;- searchplos(q = &quot;title:West Nile&quot;, fl = c(&quot;publication_date&quot;, &quot;title&quot;, &quot;journal&quot;, &quot;subject&quot;, &quot;abstract&quot;, &quot;article_type&quot;), limit = 190) Check the number of rows: nrow(wn_papers_titles$data) ## [1] 190 Create a plot of the number of articles in the PLoS journals with “West Nile” in the title by year: library(lubridate) library(dplyr) library(ggplot2) wn_papers_titles$data %&gt;% mutate(publication_date = ymd_hms(publication_date), pub_year = year(publication_date), research_article = article_type == &quot;Research Article&quot;) %&gt;% group_by(pub_year, research_article) %&gt;% count() %&gt;% ggplot(aes(x = pub_year, y = n, fill = research_article)) + geom_col() + labs(x = &quot;Year of publication&quot;, y = &quot;Number of articles published&quot;, fill = &quot;Research article&quot;) + ggtitle(&quot;West Nile articles published in PLoS journals&quot;, subtitle = &quot;Based on articles with &#39;West Nile&#39; in the title&quot;) Determine which journals have published these articles and the number of articles published in each journal: library(forcats) library(stringr) wn_papers_titles$data %&gt;% mutate(journal = str_replace(journal, &quot;PLOS&quot;, &quot;PLoS&quot;)) %&gt;% group_by(journal) %&gt;% count() %&gt;% ungroup() %&gt;% filter(!is.na(journal)) %&gt;% arrange(desc(n)) ## # A tibble: 6 x 2 ## journal n ## &lt;chr&gt; &lt;int&gt; ## 1 PLoS ONE 104 ## 2 PLoS Neglected Tropical Diseases 41 ## 3 PLoS Pathogens 30 ## 4 PLoS Computational Biology 3 ## 5 PLoS Medicine 3 ## 6 PLoS Biology 2 10.6.2 Cleaning very messy data With your groups, create an R script that does all the steps described so far to pull the messy hurricane tracks data from online and clean it. Then try the following further cleaning steps: Select only the columns with date, time, storm status, location (latitude and longitude), maximum sustained winds, and minimum pressure and renames them Create a column with the date-time of each observation, in a date-time class Clean up the latitude and longitude so that you have separate columns for the numeric values and for the direction indicator (e.g., N, S, E, W) Clean up the wind column, so it gives wind speed as a number and NA in cases where wind speed is missing If you have time, try to figure out what the status abbreviations stand for. Create a new factor column named status_long with the status spelled out. "],
-["exploring-data-3.html", "Chapter 11 Exploring data #3 11.1 Simulations 11.2 Other computationally-intensive approaches", " Chapter 11 Exploring data #3 Download a pdf of the lecture slides covering this topic. 11.1 Simulations 11.1.1 The lady tasting tea Source: Flikr commons, https://www.flickr.com/photos/internetarchivebookimages/20150531109/ “Dr. Muriel Bristol, a colleague of Fisher’s, claimed that when drinking tea she could distinguish whether milk or tea was added to the cup first (she preferred milk first). To test her claim, Fisher asked her to taste eight cups of tea, four of which had milk added first and four of which had tea added first.” — Agresti, Categorical Data Analysis, p.91 Research questions: If she just guesses, what is the probability she will get all cups right? What if more or fewer cups are used in the experiment? One way to figure this out is to run a simulation. In R, sample can be a very helpful function for simulations. It lets you randomly draw values from a vector, with or without replacement. ## Generic code sample(x = [vector to sample from], size = [number of samples to take], replace = [logical-- should values in the vector be replaced?], prob = [vector of probability weights]) Create vectors of the true and guessed values, in order, for the cups of tea: n_cups &lt;- 8 cups &lt;- sample(rep(c(&quot;milk&quot;, &quot;tea&quot;), each = n_cups / 2)) cups ## [1] &quot;tea&quot; &quot;tea&quot; &quot;milk&quot; &quot;milk&quot; &quot;tea&quot; &quot;tea&quot; &quot;milk&quot; &quot;milk&quot; guesses &lt;- sample(rep(c(&quot;milk&quot;, &quot;tea&quot;), each = n_cups / 2)) guesses ## [1] &quot;tea&quot; &quot;tea&quot; &quot;milk&quot; &quot;milk&quot; &quot;tea&quot; &quot;milk&quot; &quot;milk&quot; &quot;tea&quot; For this simulation, determine how many cups she got right (i.e., guess equals the true value): cup_results &lt;- cups == guesses cup_results ## [1] TRUE TRUE TRUE TRUE TRUE FALSE TRUE FALSE n_right &lt;- sum(cup_results) n_right ## [1] 6 Write a function that will run one simulation. It takes the argument n_cups— in real life, they used eight cups (n_cups = 8). Note that this function just wraps the code we just walked through. sim_null_tea &lt;- function(n_cups){ cups &lt;- sample(rep(c(&quot;milk&quot;, &quot;tea&quot;), each = n_cups / 2)) guesses &lt;- sample(rep(c(&quot;milk&quot;, &quot;tea&quot;), each = n_cups / 2)) cup_results &lt;- cups == guesses n_right &lt;- sum(cup_results) return(n_right) } sim_null_tea(n_cups = 8) ## [1] 4 Now, we need to run a lot of simulations, to see what happens on average if she guesses. You can use the replicate function to do that. ## Generic code replicate(n = [number of replications to run], eval = [code to replicate each time]) tea_sims &lt;- replicate(5, sim_null_tea(n_cups = 8)) tea_sims ## [1] 8 4 4 4 6 This call gives a vector with the number of cups she got right for each simulation. You can replicate the simulation many times to get a better idea of what to expect if she just guesses, including what percent of the time she gets all cups right. tea_sims &lt;- replicate(1000, sim_null_tea(n_cups = 8)) mean(tea_sims) ## [1] 3.902 quantile(tea_sims, probs = c(0.025, 0.975)) ## 2.5% 97.5% ## 2 6 mean(tea_sims == 8) ## [1] 0.012 Now we’d like to know, for different numbers of cups of tea, what is the probability that the lady will get all cups right? For this, we can apply the replication code across different values of n_cups: n_cups &lt;- seq(from = 2, to = 14, by = 2) perc_all_right &lt;- sapply(n_cups, FUN = function(n_cups){ cups_right &lt;- replicate(1000, sim_null_tea(n_cups)) out &lt;- mean(cups_right == n_cups) return(out) }) perc_all_right ## [1] 0.488 0.166 0.051 0.019 0.003 0.000 0.000 tea_sims &lt;- data_frame(n_cups, perc_all_right) ggplot(tea_sims, aes(x = n_cups, y = perc_all_right)) + geom_point() + xlab(&quot;# of cups tested&quot;) + ylab(&quot;Probability of getting\\nall cups right if guessing&quot;) You can answer this question analytically using the hypergeometric distribution: \\[ P(n_{11} = t) = \\frac{{n_{1+} \\choose t} {n_{2+} \\choose n_{+1}-t}}{{n \\choose n_{+1}}} \\] Guessed milk Guessed tea Total Really milk \\(n_{11}\\) \\(n_{12}\\) \\(n_{1+} = 4\\) Really tea \\(n_{21}\\) \\(n_{22}\\) \\(n_{2+} = 4\\) Total \\(n_{+1} = 4\\) \\(n_{+2} = 4\\) \\(n = 8\\) In R, you can use dhyper to get the density of the hypergeometric function: dhyper(x = [# of cups she guesses have milk first that do], m = [# of cups with milk first], n = [# of cups with tea first], k = [# of cups she guesses have milk first]) Probability she gets three “milk” cups right if she’s just guessing and there are eight cups, four with milk first and four with tea first: dhyper(x = 3, m = 4, n = 4, k = 4) ## [1] 0.2285714 Probability she gets three or more “milk” cups right if she’s just guessing: dhyper(x = 3, m = 4, n = 4, k = 4) + dhyper(x = 4, m = 4, n = 4, k = 4) ## [1] 0.2428571 Other density functions: dnorm: Normal dpois: Poisson dbinom: Binomial dchisq: Chi-squared dt: Student’s t dunif: Uniform You can get the analytical result for each of the number of cups we simulated and compare those values to our simulations: analytical_results &lt;- data_frame(n_cups = seq(2, 14, 2)) %&gt;% mutate(perc_all_right = dhyper(x = n_cups / 2, m = n_cups / 2, n = n_cups / 2, k = n_cups / 2)) ggplot(analytical_results, aes(x = n_cups, y = perc_all_right)) + geom_line(color = &quot;darkgray&quot;) + geom_point(data = tea_sims) + xlab(&quot;# of cups tested&quot;) + ylab(&quot;Probability of getting\\nall cups right if guessing&quot;) For more on this story (and R.A. Fisher), see: The Lady Tasting Tea: How Statistics Revolutionized Science in the Twentieth Century. David Salsburg. The Design of Experiments. Ronald Fisher. https://priceonomics.com/why-the-father-of-modern-statistics-didnt-believe/ 11.1.2 Playing darts Research question: Is a person skilled at playing darts? Here’s our dart board– the numbers are the number of points you win for a hit in each area. ## ## Attaching package: &#39;plotrix&#39; ## The following object is masked from &#39;package:scales&#39;: ## ## rescale First, what would we expect to see if the person we test has no skill at playing darts? Questions to consider: What would the dart board look like under the null (say the person throws 20 darts for the experiment)? About what do you think the person’s mean score would be if they had no skill at darts? What are some ways to estimate or calculate the expected mean score under the null? Let’s use R to answer the first question: what would the null look like? First, create some random throws (the square goes from -1 to 1 on both sides): n.throws &lt;- 20 throw.x &lt;- runif(n.throws, min = -1, max = 1) throw.y &lt;- runif(n.throws, min = -1, max = 1) head(cbind(throw.x, throw.y)) ## throw.x throw.y ## [1,] 0.5524726 -0.8217305 ## [2,] -0.1481548 -0.2206223 ## [3,] 0.9452591 0.6049874 ## [4,] -0.5267736 -0.7348108 ## [5,] -0.7088718 0.5798019 ## [6,] 0.9778563 -0.8479374 plot(c(-1, 1), c(-1,1), type = &quot;n&quot;, asp=1, xlab = &quot;&quot;, ylab = &quot;&quot;, axes = FALSE) rect( -1, -1, 1, 1) draw.circle( 0, 0, .75, col = &quot;red&quot;) draw.circle( 0, 0, .5, col = &quot;white&quot;) draw.circle( 0, 0, .25, col = &quot;red&quot;) points(throw.x, throw.y, col = &quot;black&quot;, pch = 19) Next, let’s tally up the score for this simulation of what would happen under the null. To score each throw, we calculate how far the point is from (0, 0), and then use the following rules: 20 points: \\(0.00 \\le \\sqrt{x^2 + y^2} \\le .25\\) 15 points: \\(0.25 &lt; \\sqrt{x^2 + y^2} \\le .50\\) 10 points: \\(0.50 &lt; \\sqrt{x^2 + y^2} \\le .75\\) 0 points: \\(0.75 &lt; \\sqrt{x^2 + y^2} \\le 1.41\\) Use these rules to “score” each random throw: throw.dist &lt;- sqrt(throw.x^2 + throw.y^2) head(throw.dist) ## [1] 0.9901853 0.2657518 1.1222854 0.9041225 0.9157890 1.2942955 throw.score &lt;- cut(throw.dist, breaks = c(0, .25, .5, .75, 1.5), labels = c(&quot;20&quot;, &quot;15&quot;, &quot;10&quot;, &quot;0&quot;), right = FALSE) head(throw.score) ## [1] 0 15 0 0 0 0 ## Levels: 20 15 10 0 Now that we’ve scored each throw, let’s tally up the total: table(throw.score) ## throw.score ## 20 15 10 0 ## 0 3 3 14 mean(as.numeric(as.character(throw.score))) ## [1] 3.75 So, this just showed one example of what might happen under the null. If we had a lot of examples like this (someone with no skill throwing 20 darts), what would we expect the mean scores to be? Questions to consider: How can you figure out the expected value of the mean scores under the null (that the person has no skill)? Do you think that 20 throws will be enough to figure out if a person’s mean score is different from this value, if he or she is pretty good at darts? What steps do you think you could take to figure out the last question? What could you change about the experiment to make it easier to tell if someone’s skilled at darts? How can we figure this out? Theory. Calculate the expected mean value using the expectation formula. Simulation. Simulate a lot of examples using R and calculate the mean of the mean score from these. The expected value of the mean, \\(E[\\bar{X}]\\), is the expected value of \\(X\\), \\(E[X]\\). To calculate the expected value of \\(X\\), use the formula: \\[ E[X] = \\sum_x xp(x) \\] \\[ E[X] = 20 * p(X = 20) + 15 * p(X = 15) + 10 * p(X = 10) + 0 * p(X = 0) \\] So we just need to figure out \\(p(X = x)\\) for \\(x = 20, 15, 10\\). (In all cases, we’re dividing by 4 because that’s the area of the full square, \\(2^2\\).) \\(p(X = 20)\\): Proportional to area of the smallest circle, \\((\\pi * 0.25^2) / 4 = 0.049\\) \\(p(X = 15)\\): Proportional to area of the middle circle minus area of the smallest circle, \\(\\pi(0.50^2 - 0.25^2) / 4 = 0.147\\) \\(p(X = 10)\\): Proportional to area of the largest circle minus area of the middle circle, \\(\\pi(0.75^2 - 0.50^2) / 4 = 0.245\\) \\(p(X = 0)\\): Proportional to area of the square minus area of the largest circle, \\((2^2 - \\pi * 0.75^2) / 4 = 0.558\\) As a double check, if we’ve done this right, the probabilities should sum to 1: \\[0.049 + 0.147 + 0.245 + 0.558 = 0.999\\] \\[ E[X] = \\sum_x xp(x)\\] \\[ E[X] = 20 * 0.049 + 15 * 0.147 + 10 * 0.245 + 0 * 0.558\\] \\[ E[X] = 5.635 \\] Remember, this also gives us \\(E[\\bar{X}]\\). Now it’s pretty easy to also calculate \\(var(X)\\) and \\(var(\\bar{X})\\): \\[ Var(X) = E[(X - \\mu)^2] = E[X^2] - E[X]^2 \\] \\[ E[X^2] = 20^2 * 0.049 + 15^2 * 0.147 + 10^2 * 0.245 + 0^2 * 0.558 = 77.18 \\] \\[ Var(X) = 77.175 - (5.635)^2 = 45.42 \\] \\[ Var(\\bar X) = \\sigma^2 / n = 45.42 / 20 = 2.27 \\] Note that we can use the Central Limit Theorem to calculate a 95% confidence interval for the mean score when someone with no skill (null hypothesis) throws 20 darts: 5.635 + c(-1, 1) * qnorm(.975) * sqrt(2.27) ## [1] 2.682017 8.587983 We can check our math by running simulations– we should get the same values of \\(E[\\bar{X}]\\) and \\(Var(\\bar{X})\\) (which we can calculate directly from the simulations using R). n.throws &lt;- 20 n.sims &lt;- 10000 x.throws &lt;- matrix(runif(n.throws * n.sims, -1, 1), ncol = n.throws, nrow = n.sims) y.throws &lt;- matrix(runif(n.throws * n.sims, -1, 1), ncol = n.throws, nrow = n.sims) dist.throws &lt;- sqrt(x.throws^2 + y.throws^2) score.throws &lt;- apply(dist.throws, 2, cut, breaks = c(0, .25, .5, .75, 1.5), labels = c(&quot;20&quot;, &quot;15&quot;, &quot;10&quot;, &quot;0&quot;), right = FALSE) dist.throws[1:3,1:5] ## [,1] [,2] [,3] [,4] [,5] ## [1,] 0.8316972 0.8431826 0.8722920 0.5525720 1.3335355 ## [2,] 0.6574708 1.3203518 0.8397787 0.4103512 0.4317604 ## [3,] 0.6139829 0.2888675 0.4662491 1.0717015 0.8835623 score.throws[1:3,1:5] ## [,1] [,2] [,3] [,4] [,5] ## [1,] &quot;0&quot; &quot;0&quot; &quot;0&quot; &quot;10&quot; &quot;0&quot; ## [2,] &quot;10&quot; &quot;0&quot; &quot;0&quot; &quot;15&quot; &quot;15&quot; ## [3,] &quot;10&quot; &quot;15&quot; &quot;15&quot; &quot;0&quot; &quot;0&quot; mean.scores &lt;- apply(score.throws, MARGIN = 1, function(x){ out &lt;- mean(as.numeric( as.character(x))) return(out) }) head(mean.scores) ## [1] 1.25 6.75 3.75 5.25 7.25 6.25 Let’s check the simulated mean and variance against the theoretical values: mean(mean.scores) ## Theoretical: 5.635 ## [1] 5.648225 var(mean.scores) ## Theoretical: 2.27 ## [1] 2.301028 11.1.3 Simulations in research Simulations in the wild (just a few examples): The Manhattan Project US Coast Guard search and rescue Infectious disease modeling 11.2 Other computationally-intensive approaches 11.2.1 Bootstrap and friends Bootstraping: Sample the dataset with replacement and reestimate the statistical parameter(s) each time. Jackknifing: Rake out one observation at a time and reestimate the statistical parameter(s) with the rest of the data. Permutation tests: See how unusual the result from the data is compared to if you shuffle your data (and so remove any relationship in observed data between variables). Cross-validation: See how well your model performs if you pick a subset of the data, build the model just on that subset, and then test how well it predicts for the rest of the data, and repeat that many times. 11.2.2 Bayesian analysis Suggested books for learning more about Bayesian analysis in R: Doing Bayesian Data Analysis, Second Edition: A Tutorial with R, JAGS, and Stan. John Kruschke. Statistical Rethinking: A Bayesian Course with Examples in R and Stan. Richard McElreath. Bayesian Data Analysis, Third Edition. Andrew Gelman et al. R can tap into software for Bayesian analysis: BUGS JAGS STAN 11.2.3 Ensemble models and friends Bagging: Sample data with replacement and build a tree model. Repeat many times. To predict, predict from all models and take the majority vote. Random forest: Same as bagging, for picking each node of a tree, only consider a random subset of variables. Boosting: Same as bagging, but “learn” from previous models as you build new models. Stacked models: Build many different models (e.g., generalized linear regression, Naive Bayes, k-nearest neighbors, random forest, …), determine weights for each, and predict using weighted predictions combined from all models. For more on these and other machine learning topics, see: An Introduction to Statistical Learning. Gareth James, Robert Tibshirani, and Trevor Hastie. The caret package: http://topepo.github.io/caret/index.html For many examples of predictive models like this built with R (and Python): https://www.kaggle.com "],
-["reporting-data-results-3.html", "Chapter 12 Reporting data results #3 12.1 Shiny web apps 12.2 htmlWidgets", " Chapter 12 Reporting data results #3 Download a pdf of the lecture slides covering this topic. Slides for the second half of these week are available to download in an HTML format. Save this file as HTML, and then you should be able to open the file in a web browser to see the presentation. 12.1 Shiny web apps 12.1.1 Resources for learning Shiny There is an excellent tutorial to get you started here at RStudio. There are also several great sites that show you both Shiny examples and their code, here and here. Many of the examples and ideas in the course notes this week come directly or are adapted from RStudio’s Shiny tutorial. To start, Shiny has several example apps that you can try out. These are all available through your R session once you install the Shiny package. You can make them available to your R session using the command system.file(): install.packages(&quot;shiny&quot;) library(shiny) system.file(&quot;examples&quot;, package = &quot;shiny&quot;) 12.1.2 Basics of Shiny apps Once you have Shiny installed, you can run the examples using the runExample() command. For example, to run the first example, you would run: runExample(&quot;01_hello&quot;) This is a histogram that lets you adjust the number of bins using a slider bar. Other examples are: 02_text, 03_reactivity, 04_mpg, 05_sliders, 06_tabsets, 07_widgets, 08_html, 09_upload, 10_download, and 11_timer. When you run any of these, a window will come up in your R session that shows the Shiny App, and your R session will pay attention to commands it gets from that application until you close the window. Notice that if you scroll down, you’ll be able to see the code that’s running behind the application: Generally, each application takes two files: (1) a user interface file and (2) a server file. You can kind of think of the two elements of an R shiny app, the user interface and the server, as two parts of a restaurant. The user interface is the dining area. This is the only place the customer every sees. It’s where the customer makes his order, and it’s also where the final product comes out for him to consume. The server is the kitchen. It takes the order, does all the stuff to make it happen, and then sends out the final product back to the dining area. At its heart, an R shiny app is just a directory on your computer or a server with these two files (as well as any necessary data files) in it. For example, here’s a visual of an App I wrote to go with a paper: This has the heart of the application (server.R and ui.R) plus a couple of R helper files and subdirectories with some figures and data that I’m using in the that application. If I open either of the main files in RStudio, I can run the application locally using a button at the top of the file called “Run App”. Once I have the App running, if I have an account for the Shiny server, I can choose to “Publish” the application to the Shiny server, and then anyone can access and use it online (this service is free up to a certain number of visitors per time– unless you make something that is very popular, you should be well within the free limit). 12.1.3 server.R file The server file will be named server.R. This file tells R what code to run with the inputs it gets from a user making certain selections. For example, for the histogram example, this file tells R how to re-draw a histogram of the data with the number of bins that the user specified on the slider on the application. Once you get through all the code for what to do, this file also will have code telling R what to send back to the application for the user to see (in this case, a picture of a histogram made with the specified number of bars). Here is the code in the server.R file for the histogram example: library(shiny) # Define server logic required to draw a histogram shinyServer(function(input, output) { # Expression that generates a histogram. The expression is # wrapped in a call to renderPlot to indicate that: # # 1) It is &quot;reactive&quot; and therefore should be automatically # re-executed when inputs change # 2) Its output type is a plot output$distPlot &lt;- renderPlot({ x &lt;- faithful[, 2] # Old Faithful Geyser data bins &lt;- seq(min(x), max(x), length.out = input$bins + 1) # draw the histogram with the specified number of bins hist(x, breaks = bins, col = &#39;darkgray&#39;, border = &#39;white&#39;) }) }) Notice that some of the “interior” code here looks very familiar and should remind you of code we’ve been learning about this class. For example, this file has within it some code to figure out the breaks for histogram bins, based on how many total bins you want, and draw a histogram with those bin breaks: x &lt;- faithful[, 2] # Old Faithful Geyser data bins &lt;- seq(min(x), max(x), length.out = input$bins + 1) # draw the histogram with the specified number of bins hist(x, breaks = bins, col = &#39;darkgray&#39;, border = &#39;white&#39;) This code is then “wrapped” in two other functions. First, this code is generating a plot that will be posted to the application, so it’s wrapped in a renderPlot function to send that plot as output back to the application: output$distPlot &lt;- renderPlot({ x &lt;- faithful[, 2] # Old Faithful Geyser data bins &lt;- seq(min(x), max(x), length.out = input$bins + 1) # draw the histogram with the specified number of bins hist(x, breaks = bins, col = &#39;darkgray&#39;, border = &#39;white&#39;) }) Notice that this code is putting the results of renderPlot into a slot of the object output named distPlot. We could have used any name we wanted to here, not just distPlot, for the name of the slot where we’re putting this plot, but it is important to put everything into an object called output. Now that we’ve rendered the plot and put it in that slot of the output object, we’ll be able to refer to it by its name in the user interface file, when we want to draw it somewhere there. All of this is wrapped up in another wrapper: # Define server logic required to draw a histogram shinyServer(function(input, output) { # Expression that generates a histogram. The expression is # wrapped in a call to renderPlot to indicate that: # # 1) It is &quot;reactive&quot; and therefore should be automatically # re-executed when inputs change # 2) Its output type is a plot output$distPlot &lt;- renderPlot({ x &lt;- faithful[, 2] # Old Faithful Geyser data bins &lt;- seq(min(x), max(x), length.out = input$bins + 1) # draw the histogram with the specified number of bins hist(x, breaks = bins, col = &#39;darkgray&#39;, border = &#39;white&#39;) }) }) The server.R file also has a line to load the shiny package. You should think of apps as being like Rmd files– if there are any packages or datasets that you need to use in the code in that file, you need to load it within the file, because R won’t check in your current R session to find it when it runs the file. 12.1.4 ui.R file The other file that a Shiny app needs is the user interface file (ui.R). This is the file that describes how the application should look. It will write all the buttons and sliders and all that you want for the application interface. This is also where you specify what you want to go where and put in any text that you want to show up. For example, here is the ui.R file for the histogram example: library(shiny) # Define UI for application that draws a histogram shinyUI(fluidPage( # Application title titlePanel(&quot;Hello Shiny!&quot;), # Sidebar with a slider input for the number of bins sidebarLayout( sidebarPanel( sliderInput(&quot;bins&quot;, &quot;Number of bins:&quot;, min = 1, max = 50, value = 30) ), # Show a plot of the generated distribution mainPanel( plotOutput(&quot;distPlot&quot;) ) ) )) There are a few things to notice with this code. First, there is some code that tells the application to show the results from the server.R code. For example, the following code tells R to show the histogram that we put into the output object in the distPlot slot and to put that graph in the main panel of the application: mainPanel( plotOutput(&quot;distPlot&quot;) ) Other parts of the ui.R code will tell the application what kinds of choice boxes and sliders to have on the application, and what default value to set each to. For example, the following code tells the application that it should have a slider bar that can take a minimum value of 1 and a maximum value of 50. When you first open the application, its default value should be 30. It should be annotated with the text “Number of bins:”. Whatever value is selected should be saved to the bins slot of the input object (just like we’re using the output object to get things out of the server and printed to the application, we’re using the input object to get things that the user chooses from the application interface to the server where we can run R code). sliderInput(&quot;bins&quot;, &quot;Number of bins:&quot;, min = 1, max = 50, value = 30) 12.1.5 Making a Shiny app The first step in making a Shiny app is to make a new directory somewhere and to create R scripts for that directory called server.R and ui.R. You can just make these two files the normal way– within RStudio, do “New File”, “R Script”, and then just save them with the correct names to the directory you created for the App. Once you save a file as ui.R, notice that you’ll have a button in the top right of the file called “Run App”. When you’re ready to run your application, you can either use this button or use the command runApp. 12.1.6 Starting with the ui.R file Next, you’ll need to put code in these files. I would suggest starting with the ui.R files. This file is where you get to set up how the application looks and how people will be able to interact with it. That means that this is a good place to start because it’s both quickly fulfilling (I made something pretty!) and also because you need to have an idea of what inputs and outputs you need before you can effectively make the server file to tell R what to do. In truth, though, you’ll be going back and forth quite a bit between these two files as you edit your application. In the ui.R file, everything needs to be wrapped in a shinyUI() function, and then most things will be wrapped in other functions within that to set up different panels. For example, here’s a very basic ui.R file (adapted directly from the RStudio tutorial) that shows a very basic set up for a user interface: shinyUI(fluidPage( titlePanel(&quot;Tweets during Paris Attack&quot;), sidebarLayout( sidebarPanel(&quot;Select hashtag to display&quot;), mainPanel(&quot;Map of tweets&quot;) ) )) Notice that everything that I want to go in certain panels of the page are wrapped in functions like sidebarPanel and mainPanel and titlePanel. Everything in this file will be divided up by the place you want it to go in the final version. As a note, the sidebar layout (a sidebar on one side and one main panel) is the simplest possible Shiny layout. You can do fancier layouts if you want by using different functions like fluidRow() and navBarPage(). RStudio has a layout help page with very detailed instructions and examples to help you figure out how to do other layouts. If I run this ui.R, even if my server.R file only includes the line shinyServer(function(input, output) { }), I’ll get the following version application: This doesn’t have anything interactive on it, and it isn’t using R at all, but it shows the basics of how the syntax of the ui.R file works. As a note, I don’t have all of the functions for this, like fluidPage and titlePanel memorized. When I’m working on this file, I’ll either look to example code from other Shiny apps of look at RStudio’s help for Shiny applications until I can figure out what syntax to use to do what I want to do. 12.1.7 Adding in widgets Next, I’ll add in some cool things that will let the user interact with the application. In this case, I’d like to have a slider bar so people can chose the range of time for the tweets that are shown. I’d also like to have a selection box so that users can look at maps of specific hashtags or terms. To add these on (they won’t be functional, yet, but they’ll be there!), I can edit the ui.R script to the following: shinyUI(fluidPage( titlePanel(&quot;Tweets during Paris Attack&quot;), sidebarLayout(position = &quot;right&quot;, sidebarPanel(&quot;Choose what to display&quot;, sliderInput(inputId = &quot;time_range&quot;, label = &quot;Select the time range: &quot;, value = c(as.POSIXct(&quot;2015-11-13 00:00:00&quot;, tz = &quot;CET&quot;), as.POSIXct(&quot;2015-11-14 12:00:00&quot;, tz = &quot;CET&quot;)), min = as.POSIXct(&quot;2015-11-13 00:00:00&quot;, tz = &quot;CET&quot;), max = as.POSIXct(&quot;2015-11-14 12:00:00&quot;, tz = &quot;CET&quot;), step = 60, timeFormat = &quot;%dth %H:%M&quot;, timezone = &quot;+0100&quot;)), mainPanel(&quot;Map of tweets&quot;) ) )) The important part of this is the new sliderInput call, which sets up a slider bar that users can use to specify certain time ranges to look at. Here is what the interface of the app looks like now: If I open this application, I can move the slider bar around, but I it isn’t actually sending any information to R yet. The heart of this new addition to the ui.R file is this: sliderInput(inputId = &quot;time_range&quot;, label = &quot;Select the time range: &quot;, value = c(as.POSIXct(&quot;2015-11-13 00:00:00&quot;, tz = &quot;CET&quot;), as.POSIXct(&quot;2015-11-14 12:00:00&quot;, tz = &quot;CET&quot;)), min = as.POSIXct(&quot;2015-11-13 00:00:00&quot;, tz = &quot;CET&quot;), max = as.POSIXct(&quot;2015-11-14 12:00:00&quot;, tz = &quot;CET&quot;), step = 60, timeFormat = &quot;%dth %H:%M&quot;, timezone = &quot;+0100&quot;) This is all within the function sliderInput and is all being used to set up the slider bar. \"time_range\" is the name I’m giving the input I get from this. Later, when I write my server code, I’ll be able to pull the values that the user suggested from the time_range slot of the input object. The next thing is the label. This is what I want R to print right before it gives the slider bar. The value object says which values I want to be the defaults on the slider. This is where the slider positions will be when someone initially opens the application. min and max give the highest and lowest values that will show up on the slider bar. For this application, I’m calling them as POSIXct objects because I want to do this for times rather than numbers. step says how big of an increment I want the slider to advance by when someone is pulling it. If the values of the slider are times, then the default unit for this is seconds, so I’m saying to have a step size of one minute. The timeFormat says how I want the time to print out at the interface and the timezone says what time zone I want time values to display in. Things like this slider bar are called “Control Widgets”, and there’s a whole list of them in the third lesson of RStudio’s Shiny tutorial. There are also examples online in the Shiny Gallery. 12.1.8 Creating output in server.R Next, I’ll put some R code in the server.R file to create a figure and pass it through to the ui.R file to print out to the application interface. At first, I won’t make this figure “reactive”; that is, it won’t change at all when the user changes the slider bar. However, I will eventually add in that reactivity so that the plot changes everytime a user changes the slider bar. I am going to create a map of all the Tweets that included certain hashtags or phrases and that were Tweeted (and geolocated) from within a five-mile radius of the center of Paris during the attacks last Friday. For this, I’m going to use data on Tweets I pulled using the TwitteR package, which syncs up with Twitter’s API. Here’s an example of what the data looks like (I’ve cleared out some of the extra columns I won’t use): paris_twitter &lt;- read.csv(&quot;data/App-1/data/final_tweets.csv&quot;, as.is = TRUE) %&gt;% mutate(tag = factor(tag), created = ymd_hms(created, tz = &quot;Europe/Paris&quot;), text = iconv(text, to=&#39;ASCII//TRANSLIT&#39;)) paris_twitter[1:2, ] ## text ## 1 RT @forza_will2006: My heart aches for the people of France. #PorteOuverte #PrayForParis #DownWithTerrorism #Pompidou... https://t.co/KwtNPsQ... ## 2 Ensemble contre la haine #jesuisparis #porteouverte #paris @ Place de la Republique https://t.co/TZykpVJHwd ## created longitude latitude tag ## 1 2015-11-15 01:27:56 NA NA #PorteOuverte ## 2 2015-11-14 22:19:47 2.364184 48.86747 #PorteOuverte About an equal number of these have and don’t have location data: table(!is.na(paris_twitter$longitude)) ## ## FALSE TRUE ## 10478 10683 Here is a table of the number of tweets under the five most-tweeted tags: head(paris_twitter) ## text ## 1 RT @forza_will2006: My heart aches for the people of France. #PorteOuverte #PrayForParis #DownWithTerrorism #Pompidou... https://t.co/KwtNPsQ... ## 2 Ensemble contre la haine #jesuisparis #porteouverte #paris @ Place de la Republique https://t.co/TZykpVJHwd ## 3 Libert&#39;e, &#39;egalit&#39;e, fraternit&#39;e #Par&#39;is #TodosSomosPar&#39;is #JeSuisParis #PorteOuverte #peace #paix #paz... https://t.co/AjYVrmCCnL ## 4 Stay With My French #prayforparis #parisattacks #porteouverte #france #riphumanity #liberte #egalit&#39;e... https://t.co/9DH5diHmyz ## 5 &lt;NA&gt; ## 6 RT @bodoi_music: J&#39;ai peur je cherche un abri #porteouverte https://t.co/1DIr4VLOEa ## created longitude latitude tag ## 1 2015-11-15 01:27:56 NA NA #PorteOuverte ## 2 2015-11-14 22:19:47 2.364184 48.86747 #PorteOuverte ## 3 2015-11-14 21:36:06 2.350800 48.85670 #PorteOuverte ## 4 2015-11-14 21:00:49 2.350800 48.85670 #PorteOuverte ## 5 2015-11-14 20:11:01 2.350800 48.85670 #PorteOuverte ## 6 2015-11-14 19:52:43 NA NA #PorteOuverte tweet_sum &lt;- paris_twitter %&gt;% dplyr::group_by_(~ tag) %&gt;% dplyr::summarize_(n = ~ n(), example = ~ gsub(&quot;[[:punct:]]&quot;, &quot; &quot;, base::sample(text, 1))) %&gt;% dplyr::arrange_(~ dplyr::desc(n)) ## Warning: `group_by_()` is deprecated as of dplyr 0.7.0. ## Please use `group_by()` instead. ## See vignette(&#39;programming&#39;) for more help ## This warning is displayed once every 8 hours. ## Call `lifecycle::last_warnings()` to see where this warning was generated. ## Warning: `summarise_()` is deprecated as of dplyr 0.7.0. ## Please use `summarise()` instead. ## This warning is displayed once every 8 hours. ## Call `lifecycle::last_warnings()` to see where this warning was generated. knitr::kable(tweet_sum[1:5, ], col.names = c(&quot;Tag&quot;, &quot;# of Tweets&quot;, &quot;Example Tweet&quot;)) Tag # of Tweets Example Tweet #Paris 7502 NA #PrayForParis 7250 RT french sultan paris attaque Paris10 france PrayForParis https t co TOKfFUfxT4 | |#13novembre | 1255|RT taimaz Enorme dispositif policier boulevard Voltaire AFP paris 13novembre https t co acgmB89EY5 | |#PorteOuverte | 1147|RT hiyadisoufiane1 porteouverte Eiffel st Charles 37 rue Saint Charles si Jamais vs avez besoin | |#fusillade | 765|RT Vince66240 Rue Bichat on mesure la violence de l attaque sur lepetitcambodge et LeCarillon ParisAttacks fusillade https t co 9E | For the Tweets that are geolocated, it’s possible to map the tweet locations using the following code: paris_map &lt;- get_map(&quot;paris&quot;, zoom = 12, color = &quot;bw&quot;) paris_locations &lt;- c(&quot;Stade de France&quot;, &quot;18 Rue Alibert&quot;, &quot;50 Boulevard Voltaire&quot;, &quot;92 Rue de Charonne&quot;, &quot;Place de la Republique&quot;) paris_locations &lt;- paste(paris_locations, &quot;paris france&quot;) paris_locations &lt;- cbind(paris_locations, geocode(paris_locations)) # Plot contour map of tweet locations plot_map &lt;- function(tag = &quot;all&quot;, df = paris_twitter){ library(ggmap) library(dplyr) df &lt;- dplyr::select(df, tag, latitude, longitude) %&gt;% filter(!is.na(longitude)) %&gt;% mutate(tag = as.character(tag)) if(tag != &quot;all&quot;){ if(!(tag %in% df$tag)){ stop(paste(&quot;That tag is not in the data. Try one of the following tags instead: &quot;, paste(unique(df$tag), collapse = &quot;, &quot;))) } to_plot &lt;- df[df$tag == tag, ] } else { to_plot &lt;- df } hotel_de_ville &lt;- to_plot$latitude == 48.85670 &amp; to_plot$longitude == 2.350800 n_hotel_de_ville &lt;- sum(hotel_de_ville) if(n_hotel_de_ville == max(table(to_plot$latitude))){ hdv_index &lt;- sample((1:nrow(to_plot))[hotel_de_ville], round(n_hotel_de_ville / 2)) to_plot &lt;- to_plot[-hdv_index, ] } my_map &lt;- ggmap(paris_map, extent = &quot;device&quot;) + geom_point(data = to_plot, aes(x = longitude, y = latitude), color = &quot;darkgreen&quot;, alpha = 0.75) + geom_density2d(data = to_plot, aes(x = longitude, latitude), size = 0.3) + stat_density2d(data = to_plot, aes(x = longitude, y = latitude, fill = ..level.., alpha = ..level..), size = 0.01, bins = round(nrow(to_plot) / 3.3), geom = &quot;polygon&quot;) + scale_fill_gradient(low = &quot;green&quot;, high = &quot;yellow&quot;, guide = FALSE) + scale_alpha(guide = FALSE) + geom_point(data = paris_locations, aes(x = lon, y = lat), color = &quot;red&quot;, size = 5, alpha = 0.75) return(my_map) } To print this out in the application, I’ll put all the code for the mapping function in a file called helper.R, source this file in the server.R file, and then I can just call the function within the server file. The application will look as follows after this step: To complete this, I first changed the server.R file to look like this: library(dplyr) library(lubridate) source(&quot;helper.R&quot;) paris_twitter &lt;- read.csv(&quot;data/final_tweets.csv&quot;, as.is = TRUE) %&gt;% mutate(tag = factor(tag), created = ymd_hms(created, tz = &quot;Europe/Paris&quot;)) shinyServer(function(input, output) { output$twitter_map &lt;- renderPlot({ plot_map() }) }) Notice a few things here: I’m loading the packages I’ll need for the code. I’m running all the code in the helper.R file (which includes the function I created to plot this map) using the source() command. I put the code to plot the map (plot_map()) inside the renderPlot({}) function. I’m putting the plot in a twitter_map slot of the output object. All of this is going inside the call shinyServer(function(input, output){ }). One other change is necessary to get the map to print on the app. I need to add code to the ui.R file to tell R where to plot this map on the final interface. The full file now looks like this: shinyUI(fluidPage( titlePanel(&quot;Tweets during Paris Attack&quot;), sidebarLayout(position = &quot;right&quot;, sidebarPanel(&quot;Choose what to display&quot;, sliderInput(inputId = &quot;time_range&quot;, label = &quot;Select the time range: &quot;, value = c(as.POSIXct(&quot;2015-11-13 00:00:00&quot;, tz = &quot;CET&quot;), as.POSIXct(&quot;2015-11-15 06:00:00&quot;, tz = &quot;CET&quot;)), min = as.POSIXct(&quot;2015-11-13 00:00:00&quot;, tz = &quot;CET&quot;), max = as.POSIXct(&quot;2015-11-14 12:00:00&quot;, tz = &quot;CET&quot;), step = 60, timeFormat = &quot;%dth %H:%M&quot;, timezone = &quot;+0100&quot;)), mainPanel(&quot;Map of tweets&quot;, plotOutput(&quot;twitter_map&quot;)) ) )) The new part is where I’ve added the code: mainPanel(&quot;Map of tweets&quot;, plotOutput(&quot;twitter_map&quot;)) This tells R to put the plot output twitter_map from the output object in the main panel of the Shiny app. 12.1.9 Making the output reactive Now almost all of the pieces are in place to make this graphic reactive. First, I added some options to the function in helper.R to let it input time ranges and only plot the tweets within that range. Next, I need to use the values that the user selects from the slider in the call for plotting the map. To do this, I can use the values passed from the slider bar in the input object into the code in the server.R file. Here is the new code for the server.R file: library(ggmap) library(ggplot2) library(dplyr) library(lubridate) source(&quot;helper.R&quot;) paris_twitter &lt;- read.csv(&quot;data/final_tweets.csv&quot;, as.is = TRUE) %&gt;% mutate(tag = factor(tag)) paris_twitter$created &lt;- as.POSIXct(paris_twitter$created, tz = &quot;CET&quot;) shinyServer(function(input, output) { output$twitter_map &lt;- renderPlot({ plot_map(start.time = input$time_range[1], end.time = input$time_range[2]) }) }) The only addition from before is to use the start.time and end.time options in the plot_map function and to set them to the first, [1], and second, [2], values in the time_range slot of the input object. Remember that we chose to label the input from the slider bar time_range when we set up the ui.R file. This app is saved in the directory App-1 in this week’s directory if you’d like to play around with the code. I’ve deployed it on shinyapps here. 12.1.10 Fancier version I’ve also created a (much) fancier version of a Shiny App looking at this Twitter data that you can check out here. All the code for that is here. 12.2 htmlWidgets 12.2.1 Overview of htmlWidgets Very smart people have been working on creating interactive graphics in R for a long time. So far, nothing coded in R has taken off in a big way. JavaScript has developed a number of interactive graphics libraries that can be for documents viewed in a web browser. There is now a series of R packages that allow you to create plots from these JavaScript libraries from within R. There is a website with much more on these htmlWidgets at http://www.htmlwidgets.org. Some of the packages availabe to help you create interactive graphics from R using JavaScript graphics libraries: leaflet: Mapping (we’ll cover this next week) dygraphs: Time series plotly: A variety of plots, including maps rbokeh: A variety of plots, including maps networkD3: Network data d3heatmap: Heatmaps DT: Data tables DiagrammeR: Diagrams and flowcharts These packages can be used to make some pretty cool interactive visualizations for HTML output from R Markdown or Shiny (you can also render any of theme in RStudio). There are, however, a few limitations: Written by different people. The different packages have different styles as well as different interfaces. Learning how to use one package may not help you much with other of these packages. Many are still in development, often in early development. 12.2.2 plotly package From the package documentation: “Easily translate ggplot2 graphs to an interactive web-based version and / or create custom web-based visualizations directly from R.” Like many of the packages today, draws on functionality external to R, but within a package that allows you to work exclusively within R. Allows you to create interactive graphs from R. Some of the functions extend the ggplot2 code you’ve learned. Interactivity will only work within RStudio or on documents rendered to HTML. The plotly package allows an interface to let you work with plotly.js code directly using R code. plotly.js is an open source library for creating interactive graphs in JavaScript. This JavaScript library is built on d3.js (Data-Driven Documents), which is a key driver in interactive web-based data graphics today. There are two main ways of create plots within plotly: Use one of the functions to create a customized interactive graphic: plot_ly: Workhorse of plotly, renders most non-map types of graphs plot_geo, plot_mapbax: Specific functions for creating plotly maps. Create a ggplot object and then convert it to a plotly object using the ggplotly function. library(faraway) data(worldcup) library(plotly) plot_ly(worldcup, type = &quot;scatter&quot;, x = ~ Time, y = ~ Shots) Just like with ggplot2, the mappings you need depend on the type of plot you are creating. For example, scatterplots (type = \"scatter\") need x and y defined, while a surface plot (type = \"surface\") can be created with a single vector of elevation (we’ll see an example in a few slides). The help file for plot_ly includes a link with more documentation on the types of plots that can be made and the required mappings for each. plot_ly(worldcup, type = &quot;scatter&quot;, x = ~ Time, y = ~ Shots, color = ~ Position) The plotly package is designed so you can pipe data into plot_ly and add elements by piping into add_* functions (this idea is similar to adding elements to a ggplot object with +). worldcup %&gt;% plot_ly(x = ~ Time, y = ~ Shots, color = ~ Position) %&gt;% add_markers() Some of the add_* functions include: add_markers add_lines add_paths add_polygons add_segments add_histogram If you pipe to the rangeslider function, it allows the viewer to zoom in on part of the x range. (This can be particularly nice for time series.) You should have a dataset available through your R session named USAccDeaths. This gives a montly county of accidental deaths in the US for 1973 to 1978. This code will plot it and add a range slider on the lower x-axis. plot_ly(x = time(USAccDeaths), y = USAccDeaths) %&gt;% add_lines() %&gt;% rangeslider() For a 3-D scatterplot, add a mapping to the z variable: worldcup %&gt;% plot_ly(x = ~ Time, y = ~ Shots, z = ~ Passes, color = ~ Position, size = I(3)) %&gt;% add_markers() The volcano data comes with R and is in a matrix format. Each value gives the elevation for a particular pair of x- and y-coordinates. dim(volcano) ## [1] 87 61 volcano[1:4, 1:4] ## [,1] [,2] [,3] [,4] ## [1,] 100 100 101 101 ## [2,] 101 101 102 102 ## [3,] 102 102 103 103 ## [4,] 103 103 104 104 plot_ly(z = ~ volcano, type = &quot;surface&quot;) Mapping with plotly can build on some data that comes with base R or other packages you’ve likely added (or can add easily, as with the map_data function from ggplot2). For example, we can map state capitals and cities with &gt; 40,000 people using data in the us.cities dataframe in the maps package: head(maps::us.cities, 3) ## name country.etc pop lat long capital ## 1 Abilene TX TX 113888 32.45 -99.74 0 ## 2 Akron OH OH 206634 41.08 -81.52 0 ## 3 Alameda CA CA 70069 37.77 -122.26 0 Here is code you can use to map all of these cities on a US map: ggplot2::map_data(&quot;world&quot;, &quot;usa&quot;) %&gt;% group_by(group) %&gt;% filter(-125 &lt; long &amp; long &lt; -60 &amp; 25 &lt; lat &amp; lat &lt; 52) %&gt;% plot_ly(x = ~long, y = ~lat) %&gt;% add_polygons(hoverinfo = &quot;none&quot;) %&gt;% add_markers(text = ~paste(name, &quot;&lt;br /&gt;&quot;, pop), hoverinfo = &quot;text&quot;, alpha = 0.25, data = filter(maps::us.cities, -125 &lt; long &amp; long &lt; -60 &amp; 25 &lt; lat &amp; lat &lt; 52)) %&gt;% layout(showlegend = FALSE) You can also make choropleths interactive. Remember that we earlier created a choropleth of US state populations with the following code: library(choroplethr) data(df_pop_state) state_choropleth(df_pop_state) You can use the following code with plotly to make an interactive choropleth instead: us_map &lt;- list(scope = &#39;usa&#39;, projection = list(type = &#39;albers usa&#39;), lakecolor = toRGB(&#39;white&#39;)) plot_geo() %&gt;% add_trace(z = df_pop_state$value[df_pop_state$region != &quot;district of columbia&quot;], text = state.name, locations = state.abb, locationmode = &#39;USA-states&#39;) %&gt;% add_markers(x = state.center[[&quot;x&quot;]], y = state.center[[&quot;y&quot;]], size = I(2), symbol = I(8), color = I(&quot;white&quot;), hoverinfo = &quot;none&quot;) %&gt;% layout(geo = us_map) The other way to create a plotly graph is to first create a ggplot object and then transform it into an interactive graphic using the ggplotly function. The following code can be used to plot Time versus Shots for the World Cup date in a regular, non-interactive plot: shots_vs_time &lt;- worldcup %&gt;% mutate(Name = rownames(worldcup)) %&gt;% filter(Team %in% c(&quot;Netherlands&quot;, &quot;Germany&quot;, &quot;Spain&quot;, &quot;Uruguay&quot;)) %&gt;% ggplot(aes(x = Time, y = Shots, color = Position, group = Name)) + geom_point() + facet_wrap(~ Team) shots_vs_time To make the plot interactive, just pass the ggplot object to ggplotly: ggplotly(shots_vs_time) With R, not only can you pull things from another website using an API, you can also upload or submit things. There is a function in the plotly library, plotly_POST, that lets you post a plot you create in R to https://plot.ly. You need a plot.ly account to do that, but there are free accounts available. The creator of the R plotly package has written a bookdown book on the package that you can read here. It provides extensive details and examples for using plotly. Getting Started with D3 by Mike Dewar (a short book on D3 in JavaScript) is available for free here. 12.2.3 rbokeh package The rbokeh package provides an R interface to a Python interactive visualization library, Bokeh. There is a website with many more details on using the rbokeh package: https://hafen.github.io/rbokeh/ You can find out more about the original Python library, Bokeh, at http://bokeh.pydata.org/en/latest/. Here is an example of an interactive scatterplot of the World Cup data made with the rbokeh package: library(rbokeh) ## Registered S3 method overwritten by &#39;pryr&#39;: ## method from ## print.bytes Rcpp figure(width = 600, height = 300) %&gt;% ly_points(Time, Shots, data = worldcup, color = Position, hover = list(Time, Shots)) This package can also be used to create interactive maps. For example, the following dataset has data on Oregon climate stations, including locations: orstationc &lt;- read.csv(&quot;data/orstationc.csv&quot;) head(orstationc, 3) ## station lat lon elev tjan tjul tann pjan pjul pann idnum ## 1 ANT 44.917 -120.717 846 0.0 20.2 9.6 41 9 322 350197 ## 2 ARL 45.717 -120.200 96 0.9 24.6 12.5 40 6 228 350265 ## 3 ASH 42.217 -122.717 543 3.1 20.8 11.1 70 7 480 350304 ## Name ## 1 ANTELOPE 1 N USA-OR ## 2 ARLINGTON USA-OR ## 3 ASHLAND 1 N USA-OR You can use the following code to create an interactive map of these climate stations: gmap(lat = 44.1, lng = -120.767, zoom = 5, width = 500, height = 428) %&gt;% ly_points(lon, lat, data = orstationc, alpha = 0.8, col = &quot;red&quot;, hover = c(station, Name, elev, tann)) You can get very creative with this package. The following code comes directly from the help documentation for the package and shows how to use this to create an interactive version of the periodic table: # prepare data elements &lt;- subset(elements, !is.na(group)) elements$group &lt;- as.character(elements$group) elements$period &lt;- as.character(elements$period) # add colors for groups metals &lt;- c(&quot;alkali metal&quot;, &quot;alkaline earth metal&quot;, &quot;halogen&quot;, &quot;metal&quot;, &quot;metalloid&quot;, &quot;noble gas&quot;, &quot;nonmetal&quot;, &quot;transition metal&quot;) colors &lt;- c(&quot;#a6cee3&quot;, &quot;#1f78b4&quot;, &quot;#fdbf6f&quot;, &quot;#b2df8a&quot;, &quot;#33a02c&quot;, &quot;#bbbb88&quot;, &quot;#baa2a6&quot;, &quot;#e08e79&quot;) elements$color &lt;- colors[match(elements$metal, metals)] elements$type &lt;- elements$metal # make coordinates for labels elements$symx &lt;- paste(elements$group, &quot;:0.1&quot;, sep = &quot;&quot;) elements$numbery &lt;- paste(elements$period, &quot;:0.8&quot;, sep = &quot;&quot;) elements$massy &lt;- paste(elements$period, &quot;:0.15&quot;, sep = &quot;&quot;) elements$namey &lt;- paste(elements$period, &quot;:0.3&quot;, sep = &quot;&quot;) # create figure p &lt;- figure(title = &quot;Periodic Table&quot;, tools = c(&quot;resize&quot;, &quot;hover&quot;), ylim = as.character(c(7:1)), xlim = as.character(1:18), xgrid = FALSE, ygrid = FALSE, xlab = &quot;&quot;, ylab = &quot;&quot;, height = 445, width = 800) %&gt;% # plot rectangles ly_crect(group, period, data = elements, 0.9, 0.9, fill_color = color, line_color = color, fill_alpha = 0.6, hover = list(name, atomic.number, type, atomic.mass, electronic.configuration)) %&gt;% # add symbol text ly_text(symx, period, text = symbol, data = elements, font_style = &quot;bold&quot;, font_size = &quot;10pt&quot;, align = &quot;left&quot;, baseline = &quot;middle&quot;) %&gt;% # add atomic number text ly_text(symx, numbery, text = atomic.number, data = elements, font_size = &quot;6pt&quot;, align = &quot;left&quot;, baseline = &quot;middle&quot;) %&gt;% # add name text ly_text(symx, namey, text = name, data = elements, font_size = &quot;4pt&quot;, align = &quot;left&quot;, baseline = &quot;middle&quot;) %&gt;% # add atomic mass text ly_text(symx, massy, text = atomic.mass, data = elements, font_size = &quot;4pt&quot;, align = &quot;left&quot;, baseline = &quot;middle&quot;) p 12.2.4 dygraphs package The dygraphs package lets you create interactive time series plots from R using the dygraphs JavaScript library. The main function syntax is fairly straightforward. Like many of these packages, it allows piping. There is a website with more information on using dygraphs available at http://rstudio.github.io/dygraphs/index.html. For example, here is the code to plot monthly deaths from lung diseases in the UK in the 1970s. library(dygraphs) lungDeaths &lt;- cbind(mdeaths, fdeaths) dygraph(lungDeaths) %&gt;% dySeries(&quot;mdeaths&quot;, label = &quot;Male&quot;) %&gt;% dySeries(&quot;fdeaths&quot;, label = &quot;Female&quot;) 12.2.5 DT package The DT package provides a way to create interactive tables in R using the JavaScript DataTables library. We’ve already seen some examples of this output in some of the Shiny apps I showed last week. You can also use this package to include interactive tables in R Markdown documents you plan to render to HTML. There is a website with more information on this package at http://rstudio.github.io/DT/. library(DT) datatable(worldcup) 12.2.6 Creating your own widget If you find a JavaScript visualization library and would like to create bindings to R, you can create your own package for a new htmlWidget. There is advice on creating your own widget for R available at http://www.htmlwidgets.org/develop_intro.html. "],
-["reproducible-research-3.html", "Chapter 13 Reproducible research #3 13.1 Overview of R packages 13.2 Basic example package: weathermetrics 13.3 Elements of an R package 13.4 Creating an R package", " Chapter 13 Reproducible research #3 Download a pdf of the lecture slides covering this topic. 13.1 Overview of R packages 13.1.1 What is an R package? From Writing R Extensions: “A directory of files which extend R”. Files bundled together using tar and compressed using gzip. The file extension is .tar.gz. These are the source files for the package, which then must be installed from this source code locally prior to use. Sometimes also called an extension of R. Example R package: You can also have “binary packages” for a certain operating system. From Writing R Extensions: A binary package is “a zip file or tarball containing the files of an installed package which can be unpacked rather than installing from sources.” Consider developing software when: You have developed a new method you want to share You have data you’d like to make publicly available You find yourself doing the same task repeatedly Why create an R package? Share some functions broadly Share some functions with a small group Create a version of code for yourself that’s more organized and easier to use Includes documentation (vignettes, help files) Function names linked to package namespace Once library is installed, can load easily 13.1.2 Example: NMMAPS package Source: www.ihapss.jhsph.edu Contents of NMMAPS package: Research impacts of NMMAPS package (Source: Barnett, Huang, and Turner, “Benefits of Publicly Available Data”, Epidemiology 2012): As of November 2011, 67 publications had been published using this data, with 1,781 citations to these papers Research using NMMAPS has been used by the US EPA in creating regulatory impact statements for air pollution (particulates and ozone) “Thanks to NMMAPS, there is probably no other country in the world with a greater understanding of the health effects of air pollution and heat waves in its population.” 13.1.3 Sharing an R package If you want to share your R package, there are a number of ways you can do that: CRAN GitHub Bioconductor: “Bioconductor provides tools for the analysis and comprehension of high-throughput genomic data.” (from the Bioconductor website.) Other repositories Private(-ish) repositories: e.g., ROpenSci’s repository (for more, see https://ropensci.org/blog/blog/2015/08/04/a-drat-repository-for-ropensci) drat repository: Make your own R package repository, including through GitHub pages. Compressed file: You can save a source tarball or binary package file with others without posting to a repository. Sharing on CRAN: Traditional way to share an R package widely Easiest way for others to get your package (install.packages) Some barriers: Size constraint on packages (5 MB) Must follow CRAN policies All packages must pass a submission process. This is not a guarantee that a package does what it says, just a check that required files are where they should be and that the package more or less doesn’t break things. Sharing on GitHub: GitHub is becoming more and more common as a place to share R packages, both development packages that eventually are posted to CRAN and packages that are never submitted to CRAN. No restrictions / submission requirements GitHub repository size restrictions (1 GB, no files over 100 MB) much larger than CRAN package size restrictions (5 MB) GitHub packages can be installed using install_github from the devtools package Requires devtools package, which has some set-up requirements (XCode for Mac, Rtools for Windows) Packages on CRAN cannot depend on packages available only on GitHub 13.1.4 Package names The format requirements for a package name are, based on Writing R Extensions: “This should contain only (ASCII) letters, numbers and dot, have at least two characters and start with a letter and not end in a dot.” Hadley Wickham’s additional guidelines: Make it easy to Google. Make it all uppercase or all lower case Base it on a word that’s easy to remember, but then tweak the spelling to make it unique (and easier to Google). Abbreviate. Add an “r”. 13.1.5 Package maintainer A package can have many authors, but only one maintainer. The maintainer is in charge of fixing any problems that come up with CRAN checks over time to keep the package on CRAN. The maintainer is also the person who will be emailed about bugs, etc., by other users. The package can have other authors, as well as people in other roles (e.g., contributor). See the helpfile for the person function for more on the codes used for different roles. 13.1.6 Find out more To find out more about writing R packages, useful sources are: Writing R Extensions: Guidelines for R packages from the R Core Team. R Packages by Hadley Wickham R package development cheatsheet 13.2 Basic example package: weathermetrics The key functions in this package are: convert_temperature: Convert between temperature metrics convert_precip: Convert between precipitation metrics convert_wind_speed: Convert between wind speed metrics heat.index: Calculates heat index from air temperature and a measure of air moisture (dew point temperature or relative humidity) 13.2.1 Contents of weathermetrics package Equation to convert from Celsius to Fahrenheit: \\[ T_F = \\frac{9}{5} T_C + 32 \\] Here is the associated R function: celsius.to.fahrenheit ## function (T.celsius, round = 2) ## { ## T.fahrenheit &lt;- (9/5) * T.celsius + 32 ## T.fahrenheit &lt;- round(T.fahrenheit, digits = round) ## return(T.fahrenheit) ## } ## &lt;bytecode: 0x7fea9ce1cef0&gt; ## &lt;environment: namespace:weathermetrics&gt; Here is part of the help file for this function: Here is the start of the function to calculate the heat index: head(heat.index.algorithm, 10) ## ## 1 function (t = NA, rh = NA) ## 2 { ## 3 if (is.na(rh) | is.na(t)) { ## 4 hi &lt;- NA ## 5 } ## 6 else if (t &lt;= 40) { ## 7 hi &lt;- t ## 8 } ## 9 else { ## 10 alpha &lt;- 61 + ((t - 68) * 1.2) + (rh * 0.094) Here is an example of using this function: data(suffolk) suffolk %&gt;% mutate(heat_index = heat.index(t = TemperatureF, rh = Relative.Humidity)) %&gt;% slice(1:5) ## Date TemperatureF Relative.Humidity heat_index ## 1 1998-07-12 72 69 72 ## 2 1998-07-13 73 66 73 ## 3 1998-07-14 74 74 75 ## 4 1998-07-15 78 86 80 ## 5 1998-07-16 78 100 81 13.3 Elements of an R package 13.3.1 Basic elements Things you edit directly: DESCRIPTION file: The package’s “Title page”. Metadata on the package, including names and contacts of authors, package name, and description. This file also lists all the package dependencies (other packages with functions this package uses). R folder: R code defining functions in the package. All code is included in one or more R scripts. If you use Roxygen for help with documentation, all of that is also included in these files. Things that are automatically written: man folder: Help documentation for each function. This files are automatically rendered if you use Roxygen. NAMESPACE file: Helps R find functions in your package you want others to use. 13.3.2 DESCRIPTION file Required elements: Package: Name of the package Version: Number of the current version of the package (e.g., 0.1.0) Title: Short title for the package, in title case and in 65 characters or less. Author and Maintainer (these two sections can be replaced with Authors@R section that uses the person function) Description: Paragraph describing the package License: Name of the license the package is under. If necessary, you can also refer to a LICENSE file included as another file in the package. Only some licenses are easily accepted by CRAN. Other elements that are common but not required: Date: Release date of this version of the package. Imports: A list of the packages on which this package depends: other packages with functions used by the code in this package. URL: If there is a webpage associated with the package, the address for it. Often, this is the web address of the package’s GitHub repository. BugReports: Where users can submit problems they’ve had. Often, the web address of the “Issues” page of the GitHub repository for the package. Package: weathermetrics Type: Package Title: Functions to Convert Between Weather Metrics Version: 1.2.2 Date: 2016-05-19 Authors@R: c(person(&quot;Brooke&quot;, &quot;Anderson&quot;, email = &quot;brooke.anderson@colostate.edu&quot;, role = c(&quot;aut&quot;, &quot;cre&quot;)), person(&quot;Roger&quot;, &quot;Peng&quot;, email = &quot;rdpeng@gmail.com&quot;, role = c(&quot;aut&quot;)), person(&quot;Joshua&quot;, &quot;Ferreri&quot;, email = &quot;joshua.m.ferreri@gmail.com&quot;, role = c(&quot;aut&quot;))) Description: Functions to convert between weather metrics, including conversions for metrics of temperature, air moisture, wind speed, and precipitation. This package also includes functions to calculate the heat index from air temperature and air moisture. URL: https://github.com/geanders/weathermetrics/ BugReports: https://github.com/geanders/weathermetrics/issues License: GPL-2 LazyData: true RoxygenNote: 5.0.1 Depends: R (&gt;= 2.10) Suggests: knitr, rmarkdown VignetteBuilder: knitr 13.3.3 R folder The R folder of the package includes: R scripts with code defining all functions for the package Help documentation for each function (if using Roxygen) Help documentation for the package data in “data.R” You define functions in the R scripts just as you would anytime you want to define a function in R. For example, “temperature_conversions.R” includes the following code to define converting from Celsius to Fahrenheit: celsius.to.fahrenheit &lt;- function (T.celsius, round = 2) { T.fahrenheit &lt;- (9/5) * T.celsius + 32 T.fahrenheit &lt;- round(T.fahrenheit, digits = round) return(T.fahrenheit) } Only exception: use package::function syntax to call functions from other packages (e.g., dplyr::mutate()). Using roxygen2, you put all information for the help files directly into a special type of code comments right before defining the function. Start each line with #'. To render into help files, use the document function from the devtools package. This will write out help files in the man folder of the package. Use these comments to specify which functions should be exported from the package using the @export tag. This information will be used to render the NAMESPACE file for the package. #&#39; Convert from Celsius to Fahrenheit. #&#39; #&#39; \\code{celsius.to.fahrenheit} creates a numeric vector of #&#39; temperatures in Fahrenheit from a numeric vector of #&#39; temperatures in Celsius. #&#39; #&#39; @param T.celsius Numeric vector of temperatures in Celsius. #&#39; @inheritParams convert_temperature #&#39; #&#39; @return A numeric vector of temperature values in Fahrenheit. #&#39; #&#39; @note Equations are from the source code for the US National #&#39; Weather Service&#39;s #&#39; \\href{http://www.wpc.ncep.noaa.gov/html/heatindex.shtml} #&#39; {online heat index calculator}. #&#39; @author #&#39; Brooke Anderson \\email{brooke.anderson@@colostate.edu}, #&#39; Roger Peng \\email{rdpeng@@gmail.com} #&#39; #&#39; @seealso \\code{\\link{fahrenheit.to.celsius}} #&#39; #&#39; @examples # Convert from Celsius to Fahrenheit. #&#39; data(lyon) #&#39; lyon$TemperatureF &lt;- celsius.to.fahrenheit(lyon$TemperatureC) #&#39; lyon #&#39; #&#39; @export Some of the most common tags you’ll use for roxygen2 are: @param: Use to explain parameters for the function. @inheritParam: If you have already explained a parameter for the help file for a different function, you can use this tag to use the same definition for this function. @return: Explanation of the object returned by the function. @examples: One or more examples of using the function. @export: Export the function, so it’s available when users load the package. By default, the first line in the roxygen2 comments is the function title and the next section is the function description. For more on roxygen2, see: https://cran.r-project.org/web/packages/roxygen2/vignettes/roxygen2.html Once you run document, this is all rendered as a help file. Now, when you run ?celsius.to.fahrenheit, you’ll get: The start of the NAMESPACE file will be automatically written when you run document and will look like: # Generated by roxygen2: do not edit by hand export(celsius.to.fahrenheit) export(celsius.to.kelvin) export(convert_precip) export(convert_temperature) export(convert_wind_speed) export(dewpoint.to.humidity) If you are automating helpfile documentation, you must also include an R script with the doumentation for each data set that comes with the package. This file will include roxygen2 documentation for each data set, followed by the name of the dataset in quotation marks. As an example, the next slide has the documentation in the “data.R” file for the “lyon” data set. #&#39; Weather in Lyon, France #&#39; #&#39; Daily values of mean temperature (Celsius) and mean dew #&#39; point temperature (Celsius) for the week of June 18, 2000, #&#39; in Lyon, France. #&#39; #&#39; @source \\href{http://www.wunderground.com/} #&#39; {Weather Underground} #&#39; #&#39; @format A data frame with columns: #&#39; \\describe{ #&#39; \\item{Date}{Date of weather observation} #&#39; \\item{TemperatureC}{Daily mean temperature in Celsius} #&#39; \\item{DewpointC}{Daily mean dewpoint temperature in #&#39; Celsius} #&#39; } &quot;lyon&quot; 13.3.4 Other common elements Some other elements, while not required, are common in many R packages: data folder: R objects with data that goes with the package. Often, these are small-ish data files for examples of how to use package functions. However, more “scientific” packages may include more substantive data in this folder. Some packages are created solely to deliver data. vignettes folder: One or more tutorials on why the package was created and how to use it. These can be written in RMarkdown. NEWS file: Information about changes in later versions of the package. .Rbuildignore file: Lists files and directories that should not be included in the package build LICENSE file: With certain licenses (MIT is a common example), you need a separate LICENSE file, to supplement the license information in the DESCRIPTION file. 13.3.5 Less common elements src folder: Sources and headers for compiled code (e.g., C++). demo folder: R scripts that give demonstrations of using the package. tests folder: Test code for the package. Currently, the best way to create tests for a package are with the testthat package. inst folder: Various and sundries, including a CITATION file to tell others how to cite your package and executable scripts not in R (e.g., shell scripts, Perl or Python code). 13.4 Creating an R package Invaluable tools when creating an R package: The devtools package: Various utility functions that help you develop an R package. R Packages by Hadley Wickham. Available from O’Reilly or free online at http://r-pkgs.had.co.nz GitHub: When in doubt of how to structure something, look for examples in code for other R packages. GitHub is currently the easiest way to browse through the code for many R packages. 13.4.1 Initializing an R package The easiest way to start a new R package project is through R Studio. Go to “File” -&gt; “New Project” -&gt; “Empty Directory”. One of the options is “R Package”. You’ll need to specify where you want to save the directory and the package name. You can also select if you’d like to use git (you’ll still need to set-up and sync with GitHub if you want to post the package to GitHub). Once you choose this, R Studio will create a new “skeleton” directory for you, with some of the default files and directories you need (kind of like how it starts with a template for RMarkdown documents). You can add and edit files within this structure to create your package. 13.4.2 Working on an R package Once you set-up the package, most of your work will be in writing the code for the package’s functions and creating documentation. The devtools package has some functions that are very useful for this process: load_all: Loads the last saved version of all functions in the package. You can use this to change and check functions without rebuilding the whole package and restarting R each time. document: Parse all roxygen2 comments to create the helpfiles in the man directory and the NAMESPACE file. As soon as you’ve loaded an documented the last saved version of your package, you can access the help file for each function using ?, as with other R functions. Control-. : This is a keyboard shortcut rather than a function, but it allows you to search the package for the code where a certain function is defined. As a package grows larger, this functionality is very useful for navigating the R code in the package. The devtools package also has some functions that set up useful infrastructure for the package. For example, if you want to include a vignette written in RMarkdown, you need to do a few things: Add a new folder called vignettes. Add inst/doc to the .gitignore file. (The built pdf is written into this folder, but typically you don’t want to include rendered files in git, just the code with which they were generated.) Make a few changes to the DESCRIPTION file. Rather than having to remember how to do all this yourself, you can use the use_vignette function, which adds this infrastructure to the package at once. Typically, you will only use these infrastructure calls once per package. Other useful infrastructure functions are: use_cran_comments: Add a text file with comments for the people who check the package when it’s submitted to CRAN. use_readme_rmd: Create an RMarkdown “README” file that you can use to provide information on the package (similar to the vignette, but this will show up on the first page of the GitHub repo if you have one for the package). use_news_md: Create a text file to provide details of changes in later package versions. use_travis: Add the infrastructure needed to check the package on Travis when you push to GitHub. use_rcpp: Add an src directory and other infrastructure needed to use C++ code within the package. use_testthat: Add infrastructure for using package tests based on testthat. There are a few infrastructure-type functions you might use more often: use_data: Save data currently in an R object in your working session to use as data within the package. This function saves that data as an .rda file in the data folder. use_build_ignore: Add a file or files to the “.Rbuildignore” file, so they won’t cause an error with CRAN checks (one of the checks is that there aren’t any unrecognized files or directories in the top level of the package). use_package: Add a package that your package depends on to the DESCRIPTION file. 13.4.3 Finalizing an R package Once you have included all the functions and documentation for a package, there are a few more steps before that version is ready to be shared: Create a vignette and / or README file to explain how others can use the package. Run the package through CRAN checks and resolve all ERRORS, WARNINGS, and NOTES. This is required if you are submitting to CRAN. It’s usually a good idea and improves the package even if you’re not. Change the version number to a stable version (typically, development versions end in .9000, like 0.0.0.9000). When you have a stable version of the package, you’ll change this to a three-part number (e.g., 0.1.0). Build the package locally. For this, you can use the “Build” tab in the upper right RStudio tab (it will show up once you have a package project open). Build the package on other systems. You can use Travis (Unix / Linux) and build_win (Windows) to do this for those systems. Create a pdf of all help files to proofread. To do this, open a bash shell in the parent directory of the package and run R CMD Rd2pdf &lt;packagename&gt; (for example, if the package were csupackage, you’d run R CMD Rd2pdf csupackage). This will create a pdf in that directory with all helpfiles for all functions. If you want the package to be on CRAN, submit to CRAN. There is a function called submit_cran that will build the package and submit it to CRAN. "],
+["exploring-data-3.html", "Chapter 11 Exploring data #3 11.1 Simulations 11.2 Other computationally-intensive approaches", " Chapter 11 Exploring data #3 Download a pdf of the lecture slides covering this topic. 11.1 Simulations 11.1.1 The lady tasting tea Source: Flikr commons, https://www.flickr.com/photos/internetarchivebookimages/20150531109/ “Dr. Muriel Bristol, a colleague of Fisher’s, claimed that when drinking tea she could distinguish whether milk or tea was added to the cup first (she preferred milk first). To test her claim, Fisher asked her to taste eight cups of tea, four of which had milk added first and four of which had tea added first.” — Agresti, Categorical Data Analysis, p.91 Research questions: If she just guesses, what is the probability she will get all cups right? What if more or fewer cups are used in the experiment? One way to figure this out is to run a simulation. In R, sample can be a very helpful function for simulations. It lets you randomly draw values from a vector, with or without replacement. ## Generic code sample(x = [vector to sample from], size = [number of samples to take], replace = [logical-- should values in the vector be replaced?], prob = [vector of probability weights]) Create vectors of the true and guessed values, in order, for the cups of tea: n_cups &lt;- 8 cups &lt;- sample(rep(c(&quot;milk&quot;, &quot;tea&quot;), each = n_cups / 2)) cups ## [1] &quot;tea&quot; &quot;milk&quot; &quot;milk&quot; &quot;tea&quot; &quot;milk&quot; &quot;milk&quot; &quot;tea&quot; &quot;tea&quot; guesses &lt;- sample(rep(c(&quot;milk&quot;, &quot;tea&quot;), each = n_cups / 2)) guesses ## [1] &quot;tea&quot; &quot;milk&quot; &quot;milk&quot; &quot;tea&quot; &quot;milk&quot; &quot;milk&quot; &quot;tea&quot; &quot;tea&quot; For this simulation, determine how many cups she got right (i.e., guess equals the true value): cup_results &lt;- cups == guesses cup_results ## [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE n_right &lt;- sum(cup_results) n_right ## [1] 8 Write a function that will run one simulation. It takes the argument n_cups— in real life, they used eight cups (n_cups = 8). Note that this function just wraps the code we just walked through. sim_null_tea &lt;- function(n_cups){ cups &lt;- sample(rep(c(&quot;milk&quot;, &quot;tea&quot;), each = n_cups / 2)) guesses &lt;- sample(rep(c(&quot;milk&quot;, &quot;tea&quot;), each = n_cups / 2)) cup_results &lt;- cups == guesses n_right &lt;- sum(cup_results) return(n_right) } sim_null_tea(n_cups = 8) ## [1] 6 Now, we need to run a lot of simulations, to see what happens on average if she guesses. You can use the replicate function to do that. ## Generic code replicate(n = [number of replications to run], eval = [code to replicate each time]) tea_sims &lt;- replicate(5, sim_null_tea(n_cups = 8)) tea_sims ## [1] 4 2 4 6 4 This call gives a vector with the number of cups she got right for each simulation. You can replicate the simulation many times to get a better idea of what to expect if she just guesses, including what percent of the time she gets all cups right. tea_sims &lt;- replicate(1000, sim_null_tea(n_cups = 8)) mean(tea_sims) ## [1] 4.062 quantile(tea_sims, probs = c(0.025, 0.975)) ## 2.5% 97.5% ## 2 6 mean(tea_sims == 8) ## [1] 0.016 Now we’d like to know, for different numbers of cups of tea, what is the probability that the lady will get all cups right? For this, we can apply the replication code across different values of n_cups: n_cups &lt;- seq(from = 2, to = 14, by = 2) perc_all_right &lt;- sapply(n_cups, FUN = function(n_cups){ cups_right &lt;- replicate(1000, sim_null_tea(n_cups)) out &lt;- mean(cups_right == n_cups) return(out) }) perc_all_right ## [1] 0.477 0.182 0.046 0.020 0.003 0.001 0.001 tea_sims &lt;- data_frame(n_cups, perc_all_right) ggplot(tea_sims, aes(x = n_cups, y = perc_all_right)) + geom_point() + xlab(&quot;# of cups tested&quot;) + ylab(&quot;Probability of getting\\nall cups right if guessing&quot;) You can answer this question analytically using the hypergeometric distribution: \\[ P(n_{11} = t) = \\frac{{n_{1+} \\choose t} {n_{2+} \\choose n_{+1}-t}}{{n \\choose n_{+1}}} \\] Guessed milk Guessed tea Total Really milk \\(n_{11}\\) \\(n_{12}\\) \\(n_{1+} = 4\\) Really tea \\(n_{21}\\) \\(n_{22}\\) \\(n_{2+} = 4\\) Total \\(n_{+1} = 4\\) \\(n_{+2} = 4\\) \\(n = 8\\) In R, you can use dhyper to get the density of the hypergeometric function: dhyper(x = [# of cups she guesses have milk first that do], m = [# of cups with milk first], n = [# of cups with tea first], k = [# of cups she guesses have milk first]) Probability she gets three “milk” cups right if she’s just guessing and there are eight cups, four with milk first and four with tea first: dhyper(x = 3, m = 4, n = 4, k = 4) ## [1] 0.2285714 Probability she gets three or more “milk” cups right if she’s just guessing: dhyper(x = 3, m = 4, n = 4, k = 4) + dhyper(x = 4, m = 4, n = 4, k = 4) ## [1] 0.2428571 Other density functions: dnorm: Normal dpois: Poisson dbinom: Binomial dchisq: Chi-squared dt: Student’s t dunif: Uniform You can get the analytical result for each of the number of cups we simulated and compare those values to our simulations: analytical_results &lt;- data_frame(n_cups = seq(2, 14, 2)) %&gt;% mutate(perc_all_right = dhyper(x = n_cups / 2, m = n_cups / 2, n = n_cups / 2, k = n_cups / 2)) ggplot(analytical_results, aes(x = n_cups, y = perc_all_right)) + geom_line(color = &quot;darkgray&quot;) + geom_point(data = tea_sims) + xlab(&quot;# of cups tested&quot;) + ylab(&quot;Probability of getting\\nall cups right if guessing&quot;) For more on this story (and R.A. Fisher), see: The Lady Tasting Tea: How Statistics Revolutionized Science in the Twentieth Century. David Salsburg. The Design of Experiments. Ronald Fisher. https://priceonomics.com/why-the-father-of-modern-statistics-didnt-believe/ 11.1.2 Playing darts Research question: Is a person skilled at playing darts? Here’s our dart board– the numbers are the number of points you win for a hit in each area. ## ## Attaching package: &#39;plotrix&#39; ## The following object is masked from &#39;package:scales&#39;: ## ## rescale First, what would we expect to see if the person we test has no skill at playing darts? Questions to consider: What would the dart board look like under the null (say the person throws 20 darts for the experiment)? About what do you think the person’s mean score would be if they had no skill at darts? What are some ways to estimate or calculate the expected mean score under the null? Let’s use R to answer the first question: what would the null look like? First, create some random throws (the square goes from -1 to 1 on both sides): n.throws &lt;- 20 throw.x &lt;- runif(n.throws, min = -1, max = 1) throw.y &lt;- runif(n.throws, min = -1, max = 1) head(cbind(throw.x, throw.y)) ## throw.x throw.y ## [1,] 0.7683825 -0.913790450 ## [2,] -0.4551363 0.534574261 ## [3,] -0.8513250 -0.723720532 ## [4,] -0.6361512 0.516585769 ## [5,] 0.5396098 0.981598570 ## [6,] 0.6168388 0.006258808 plot(c(-1, 1), c(-1,1), type = &quot;n&quot;, asp=1, xlab = &quot;&quot;, ylab = &quot;&quot;, axes = FALSE) rect( -1, -1, 1, 1) draw.circle( 0, 0, .75, col = &quot;red&quot;) draw.circle( 0, 0, .5, col = &quot;white&quot;) draw.circle( 0, 0, .25, col = &quot;red&quot;) points(throw.x, throw.y, col = &quot;black&quot;, pch = 19) Next, let’s tally up the score for this simulation of what would happen under the null. To score each throw, we calculate how far the point is from (0, 0), and then use the following rules: 20 points: \\(0.00 \\le \\sqrt{x^2 + y^2} \\le .25\\) 15 points: \\(0.25 &lt; \\sqrt{x^2 + y^2} \\le .50\\) 10 points: \\(0.50 &lt; \\sqrt{x^2 + y^2} \\le .75\\) 0 points: \\(0.75 &lt; \\sqrt{x^2 + y^2} \\le 1.41\\) Use these rules to “score” each random throw: throw.dist &lt;- sqrt(throw.x^2 + throw.y^2) head(throw.dist) ## [1] 1.1939115 0.7020817 1.1173744 0.8194810 1.1201404 0.6168706 throw.score &lt;- cut(throw.dist, breaks = c(0, .25, .5, .75, 1.5), labels = c(&quot;20&quot;, &quot;15&quot;, &quot;10&quot;, &quot;0&quot;), right = FALSE) head(throw.score) ## [1] 0 10 0 0 0 10 ## Levels: 20 15 10 0 Now that we’ve scored each throw, let’s tally up the total: table(throw.score) ## throw.score ## 20 15 10 0 ## 0 1 5 14 mean(as.numeric(as.character(throw.score))) ## [1] 3.25 So, this just showed one example of what might happen under the null. If we had a lot of examples like this (someone with no skill throwing 20 darts), what would we expect the mean scores to be? Questions to consider: How can you figure out the expected value of the mean scores under the null (that the person has no skill)? Do you think that 20 throws will be enough to figure out if a person’s mean score is different from this value, if he or she is pretty good at darts? What steps do you think you could take to figure out the last question? What could you change about the experiment to make it easier to tell if someone’s skilled at darts? How can we figure this out? Theory. Calculate the expected mean value using the expectation formula. Simulation. Simulate a lot of examples using R and calculate the mean of the mean score from these. The expected value of the mean, \\(E[\\bar{X}]\\), is the expected value of \\(X\\), \\(E[X]\\). To calculate the expected value of \\(X\\), use the formula: \\[ E[X] = \\sum_x xp(x) \\] \\[ E[X] = 20 * p(X = 20) + 15 * p(X = 15) + 10 * p(X = 10) + 0 * p(X = 0) \\] So we just need to figure out \\(p(X = x)\\) for \\(x = 20, 15, 10\\). (In all cases, we’re dividing by 4 because that’s the area of the full square, \\(2^2\\).) \\(p(X = 20)\\): Proportional to area of the smallest circle, \\((\\pi * 0.25^2) / 4 = 0.049\\) \\(p(X = 15)\\): Proportional to area of the middle circle minus area of the smallest circle, \\(\\pi(0.50^2 - 0.25^2) / 4 = 0.147\\) \\(p(X = 10)\\): Proportional to area of the largest circle minus area of the middle circle, \\(\\pi(0.75^2 - 0.50^2) / 4 = 0.245\\) \\(p(X = 0)\\): Proportional to area of the square minus area of the largest circle, \\((2^2 - \\pi * 0.75^2) / 4 = 0.558\\) As a double check, if we’ve done this right, the probabilities should sum to 1: \\[0.049 + 0.147 + 0.245 + 0.558 = 0.999\\] \\[ E[X] = \\sum_x xp(x)\\] \\[ E[X] = 20 * 0.049 + 15 * 0.147 + 10 * 0.245 + 0 * 0.558\\] \\[ E[X] = 5.635 \\] Remember, this also gives us \\(E[\\bar{X}]\\). Now it’s pretty easy to also calculate \\(var(X)\\) and \\(var(\\bar{X})\\): \\[ Var(X) = E[(X - \\mu)^2] = E[X^2] - E[X]^2 \\] \\[ E[X^2] = 20^2 * 0.049 + 15^2 * 0.147 + 10^2 * 0.245 + 0^2 * 0.558 = 77.18 \\] \\[ Var(X) = 77.175 - (5.635)^2 = 45.42 \\] \\[ Var(\\bar X) = \\sigma^2 / n = 45.42 / 20 = 2.27 \\] Note that we can use the Central Limit Theorem to calculate a 95% confidence interval for the mean score when someone with no skill (null hypothesis) throws 20 darts: 5.635 + c(-1, 1) * qnorm(.975) * sqrt(2.27) ## [1] 2.682017 8.587983 We can check our math by running simulations– we should get the same values of \\(E[\\bar{X}]\\) and \\(Var(\\bar{X})\\) (which we can calculate directly from the simulations using R). n.throws &lt;- 20 n.sims &lt;- 10000 x.throws &lt;- matrix(runif(n.throws * n.sims, -1, 1), ncol = n.throws, nrow = n.sims) y.throws &lt;- matrix(runif(n.throws * n.sims, -1, 1), ncol = n.throws, nrow = n.sims) dist.throws &lt;- sqrt(x.throws^2 + y.throws^2) score.throws &lt;- apply(dist.throws, 2, cut, breaks = c(0, .25, .5, .75, 1.5), labels = c(&quot;20&quot;, &quot;15&quot;, &quot;10&quot;, &quot;0&quot;), right = FALSE) dist.throws[1:3,1:5] ## [,1] [,2] [,3] [,4] [,5] ## [1,] 0.5478433 0.8309107 1.03368286 0.5909183 0.3177004 ## [2,] 0.5192013 0.5977724 0.97286645 0.3394507 0.3402281 ## [3,] 0.6441868 0.3762599 0.03928263 0.3861539 0.2078762 score.throws[1:3,1:5] ## [,1] [,2] [,3] [,4] [,5] ## [1,] &quot;10&quot; &quot;0&quot; &quot;0&quot; &quot;10&quot; &quot;15&quot; ## [2,] &quot;10&quot; &quot;10&quot; &quot;0&quot; &quot;15&quot; &quot;15&quot; ## [3,] &quot;10&quot; &quot;15&quot; &quot;20&quot; &quot;15&quot; &quot;20&quot; mean.scores &lt;- apply(score.throws, MARGIN = 1, function(x){ out &lt;- mean(as.numeric( as.character(x))) return(out) }) head(mean.scores) ## [1] 5.00 6.25 7.25 5.50 6.25 4.00 Let’s check the simulated mean and variance against the theoretical values: mean(mean.scores) ## Theoretical: 5.635 ## [1] 5.632425 var(mean.scores) ## Theoretical: 2.27 ## [1] 2.260558 11.1.3 Simulations in research Simulations in the wild (just a few examples): The Manhattan Project US Coast Guard search and rescue Infectious disease modeling 11.2 Other computationally-intensive approaches 11.2.1 Bootstrap and friends Bootstraping: Sample the dataset with replacement and reestimate the statistical parameter(s) each time. Jackknifing: Rake out one observation at a time and reestimate the statistical parameter(s) with the rest of the data. Permutation tests: See how unusual the result from the data is compared to if you shuffle your data (and so remove any relationship in observed data between variables). Cross-validation: See how well your model performs if you pick a subset of the data, build the model just on that subset, and then test how well it predicts for the rest of the data, and repeat that many times. 11.2.2 Bayesian analysis Suggested books for learning more about Bayesian analysis in R: Doing Bayesian Data Analysis, Second Edition: A Tutorial with R, JAGS, and Stan. John Kruschke. Statistical Rethinking: A Bayesian Course with Examples in R and Stan. Richard McElreath. Bayesian Data Analysis, Third Edition. Andrew Gelman et al. R can tap into software for Bayesian analysis: BUGS JAGS STAN 11.2.3 Ensemble models and friends Bagging: Sample data with replacement and build a tree model. Repeat many times. To predict, predict from all models and take the majority vote. Random forest: Same as bagging, for picking each node of a tree, only consider a random subset of variables. Boosting: Same as bagging, but “learn” from previous models as you build new models. Stacked models: Build many different models (e.g., generalized linear regression, Naive Bayes, k-nearest neighbors, random forest, …), determine weights for each, and predict using weighted predictions combined from all models. For more on these and other machine learning topics, see: An Introduction to Statistical Learning. Gareth James, Robert Tibshirani, and Trevor Hastie. The caret package: http://topepo.github.io/caret/index.html For many examples of predictive models like this built with R (and Python): https://www.kaggle.com "],
+["reporting-data-results-3.html", "Chapter 12 Reporting data results #3 12.1 Shiny web apps 12.2 htmlWidgets", " Chapter 12 Reporting data results #3 Download a pdf of the lecture slides covering this topic. Slides for the second half of these week are available to download in an HTML format. Save this file as HTML, and then you should be able to open the file in a web browser to see the presentation. 12.1 Shiny web apps 12.1.1 Resources for learning Shiny There is an excellent tutorial to get you started here at RStudio. There are also several great sites that show you both Shiny examples and their code, here and here. Many of the examples and ideas in the course notes this week come directly or are adapted from RStudio’s Shiny tutorial. To start, Shiny has several example apps that you can try out. These are all available through your R session once you install the Shiny package. You can make them available to your R session using the command system.file(): install.packages(&quot;shiny&quot;) library(shiny) system.file(&quot;examples&quot;, package = &quot;shiny&quot;) 12.1.2 Basics of Shiny apps Once you have Shiny installed, you can run the examples using the runExample() command. For example, to run the first example, you would run: runExample(&quot;01_hello&quot;) This is a histogram that lets you adjust the number of bins using a slider bar. Other examples are: 02_text, 03_reactivity, 04_mpg, 05_sliders, 06_tabsets, 07_widgets, 08_html, 09_upload, 10_download, and 11_timer. When you run any of these, a window will come up in your R session that shows the Shiny App, and your R session will pay attention to commands it gets from that application until you close the window. Notice that if you scroll down, you’ll be able to see the code that’s running behind the application: Generally, each application takes two files: (1) a user interface file and (2) a server file. You can kind of think of the two elements of an R shiny app, the user interface and the server, as two parts of a restaurant. The user interface is the dining area. This is the only place the customer every sees. It’s where the customer makes his order, and it’s also where the final product comes out for him to consume. The server is the kitchen. It takes the order, does all the stuff to make it happen, and then sends out the final product back to the dining area. At its heart, an R shiny app is just a directory on your computer or a server with these two files (as well as any necessary data files) in it. For example, here’s a visual of an App I wrote to go with a paper: This has the heart of the application (server.R and ui.R) plus a couple of R helper files and subdirectories with some figures and data that I’m using in the that application. If I open either of the main files in RStudio, I can run the application locally using a button at the top of the file called “Run App”. Once I have the App running, if I have an account for the Shiny server, I can choose to “Publish” the application to the Shiny server, and then anyone can access and use it online (this service is free up to a certain number of visitors per time– unless you make something that is very popular, you should be well within the free limit). 12.1.3 server.R file The server file will be named server.R. This file tells R what code to run with the inputs it gets from a user making certain selections. For example, for the histogram example, this file tells R how to re-draw a histogram of the data with the number of bins that the user specified on the slider on the application. Once you get through all the code for what to do, this file also will have code telling R what to send back to the application for the user to see (in this case, a picture of a histogram made with the specified number of bars). Here is the code in the server.R file for the histogram example: library(shiny) # Define server logic required to draw a histogram shinyServer(function(input, output) { # Expression that generates a histogram. The expression is # wrapped in a call to renderPlot to indicate that: # # 1) It is &quot;reactive&quot; and therefore should be automatically # re-executed when inputs change # 2) Its output type is a plot output$distPlot &lt;- renderPlot({ x &lt;- faithful[, 2] # Old Faithful Geyser data bins &lt;- seq(min(x), max(x), length.out = input$bins + 1) # draw the histogram with the specified number of bins hist(x, breaks = bins, col = &#39;darkgray&#39;, border = &#39;white&#39;) }) }) Notice that some of the “interior” code here looks very familiar and should remind you of code we’ve been learning about this class. For example, this file has within it some code to figure out the breaks for histogram bins, based on how many total bins you want, and draw a histogram with those bin breaks: x &lt;- faithful[, 2] # Old Faithful Geyser data bins &lt;- seq(min(x), max(x), length.out = input$bins + 1) # draw the histogram with the specified number of bins hist(x, breaks = bins, col = &#39;darkgray&#39;, border = &#39;white&#39;) This code is then “wrapped” in two other functions. First, this code is generating a plot that will be posted to the application, so it’s wrapped in a renderPlot function to send that plot as output back to the application: output$distPlot &lt;- renderPlot({ x &lt;- faithful[, 2] # Old Faithful Geyser data bins &lt;- seq(min(x), max(x), length.out = input$bins + 1) # draw the histogram with the specified number of bins hist(x, breaks = bins, col = &#39;darkgray&#39;, border = &#39;white&#39;) }) Notice that this code is putting the results of renderPlot into a slot of the object output named distPlot. We could have used any name we wanted to here, not just distPlot, for the name of the slot where we’re putting this plot, but it is important to put everything into an object called output. Now that we’ve rendered the plot and put it in that slot of the output object, we’ll be able to refer to it by its name in the user interface file, when we want to draw it somewhere there. All of this is wrapped up in another wrapper: # Define server logic required to draw a histogram shinyServer(function(input, output) { # Expression that generates a histogram. The expression is # wrapped in a call to renderPlot to indicate that: # # 1) It is &quot;reactive&quot; and therefore should be automatically # re-executed when inputs change # 2) Its output type is a plot output$distPlot &lt;- renderPlot({ x &lt;- faithful[, 2] # Old Faithful Geyser data bins &lt;- seq(min(x), max(x), length.out = input$bins + 1) # draw the histogram with the specified number of bins hist(x, breaks = bins, col = &#39;darkgray&#39;, border = &#39;white&#39;) }) }) The server.R file also has a line to load the shiny package. You should think of apps as being like Rmd files– if there are any packages or datasets that you need to use in the code in that file, you need to load it within the file, because R won’t check in your current R session to find it when it runs the file. 12.1.4 ui.R file The other file that a Shiny app needs is the user interface file (ui.R). This is the file that describes how the application should look. It will write all the buttons and sliders and all that you want for the application interface. This is also where you specify what you want to go where and put in any text that you want to show up. For example, here is the ui.R file for the histogram example: library(shiny) # Define UI for application that draws a histogram shinyUI(fluidPage( # Application title titlePanel(&quot;Hello Shiny!&quot;), # Sidebar with a slider input for the number of bins sidebarLayout( sidebarPanel( sliderInput(&quot;bins&quot;, &quot;Number of bins:&quot;, min = 1, max = 50, value = 30) ), # Show a plot of the generated distribution mainPanel( plotOutput(&quot;distPlot&quot;) ) ) )) There are a few things to notice with this code. First, there is some code that tells the application to show the results from the server.R code. For example, the following code tells R to show the histogram that we put into the output object in the distPlot slot and to put that graph in the main panel of the application: mainPanel( plotOutput(&quot;distPlot&quot;) ) Other parts of the ui.R code will tell the application what kinds of choice boxes and sliders to have on the application, and what default value to set each to. For example, the following code tells the application that it should have a slider bar that can take a minimum value of 1 and a maximum value of 50. When you first open the application, its default value should be 30. It should be annotated with the text “Number of bins:”. Whatever value is selected should be saved to the bins slot of the input object (just like we’re using the output object to get things out of the server and printed to the application, we’re using the input object to get things that the user chooses from the application interface to the server where we can run R code). sliderInput(&quot;bins&quot;, &quot;Number of bins:&quot;, min = 1, max = 50, value = 30) 12.1.5 Making a Shiny app The first step in making a Shiny app is to make a new directory somewhere and to create R scripts for that directory called server.R and ui.R. You can just make these two files the normal way– within RStudio, do “New File”, “R Script”, and then just save them with the correct names to the directory you created for the App. Once you save a file as ui.R, notice that you’ll have a button in the top right of the file called “Run App”. When you’re ready to run your application, you can either use this button or use the command runApp. 12.1.6 Starting with the ui.R file Next, you’ll need to put code in these files. I would suggest starting with the ui.R files. This file is where you get to set up how the application looks and how people will be able to interact with it. That means that this is a good place to start because it’s both quickly fulfilling (I made something pretty!) and also because you need to have an idea of what inputs and outputs you need before you can effectively make the server file to tell R what to do. In truth, though, you’ll be going back and forth quite a bit between these two files as you edit your application. In the ui.R file, everything needs to be wrapped in a shinyUI() function, and then most things will be wrapped in other functions within that to set up different panels. For example, here’s a very basic ui.R file (adapted directly from the RStudio tutorial) that shows a very basic set up for a user interface: shinyUI(fluidPage( titlePanel(&quot;Tweets during Paris Attack&quot;), sidebarLayout( sidebarPanel(&quot;Select hashtag to display&quot;), mainPanel(&quot;Map of tweets&quot;) ) )) Notice that everything that I want to go in certain panels of the page are wrapped in functions like sidebarPanel and mainPanel and titlePanel. Everything in this file will be divided up by the place you want it to go in the final version. As a note, the sidebar layout (a sidebar on one side and one main panel) is the simplest possible Shiny layout. You can do fancier layouts if you want by using different functions like fluidRow() and navBarPage(). RStudio has a layout help page with very detailed instructions and examples to help you figure out how to do other layouts. If I run this ui.R, even if my server.R file only includes the line shinyServer(function(input, output) { }), I’ll get the following version application: This doesn’t have anything interactive on it, and it isn’t using R at all, but it shows the basics of how the syntax of the ui.R file works. As a note, I don’t have all of the functions for this, like fluidPage and titlePanel memorized. When I’m working on this file, I’ll either look to example code from other Shiny apps of look at RStudio’s help for Shiny applications until I can figure out what syntax to use to do what I want to do. 12.1.7 Adding in widgets Next, I’ll add in some cool things that will let the user interact with the application. In this case, I’d like to have a slider bar so people can chose the range of time for the tweets that are shown. I’d also like to have a selection box so that users can look at maps of specific hashtags or terms. To add these on (they won’t be functional, yet, but they’ll be there!), I can edit the ui.R script to the following: shinyUI(fluidPage( titlePanel(&quot;Tweets during Paris Attack&quot;), sidebarLayout(position = &quot;right&quot;, sidebarPanel(&quot;Choose what to display&quot;, sliderInput(inputId = &quot;time_range&quot;, label = &quot;Select the time range: &quot;, value = c(as.POSIXct(&quot;2015-11-13 00:00:00&quot;, tz = &quot;CET&quot;), as.POSIXct(&quot;2015-11-14 12:00:00&quot;, tz = &quot;CET&quot;)), min = as.POSIXct(&quot;2015-11-13 00:00:00&quot;, tz = &quot;CET&quot;), max = as.POSIXct(&quot;2015-11-14 12:00:00&quot;, tz = &quot;CET&quot;), step = 60, timeFormat = &quot;%dth %H:%M&quot;, timezone = &quot;+0100&quot;)), mainPanel(&quot;Map of tweets&quot;) ) )) The important part of this is the new sliderInput call, which sets up a slider bar that users can use to specify certain time ranges to look at. Here is what the interface of the app looks like now: If I open this application, I can move the slider bar around, but I it isn’t actually sending any information to R yet. The heart of this new addition to the ui.R file is this: sliderInput(inputId = &quot;time_range&quot;, label = &quot;Select the time range: &quot;, value = c(as.POSIXct(&quot;2015-11-13 00:00:00&quot;, tz = &quot;CET&quot;), as.POSIXct(&quot;2015-11-14 12:00:00&quot;, tz = &quot;CET&quot;)), min = as.POSIXct(&quot;2015-11-13 00:00:00&quot;, tz = &quot;CET&quot;), max = as.POSIXct(&quot;2015-11-14 12:00:00&quot;, tz = &quot;CET&quot;), step = 60, timeFormat = &quot;%dth %H:%M&quot;, timezone = &quot;+0100&quot;) This is all within the function sliderInput and is all being used to set up the slider bar. \"time_range\" is the name I’m giving the input I get from this. Later, when I write my server code, I’ll be able to pull the values that the user suggested from the time_range slot of the input object. The next thing is the label. This is what I want R to print right before it gives the slider bar. The value object says which values I want to be the defaults on the slider. This is where the slider positions will be when someone initially opens the application. min and max give the highest and lowest values that will show up on the slider bar. For this application, I’m calling them as POSIXct objects because I want to do this for times rather than numbers. step says how big of an increment I want the slider to advance by when someone is pulling it. If the values of the slider are times, then the default unit for this is seconds, so I’m saying to have a step size of one minute. The timeFormat says how I want the time to print out at the interface and the timezone says what time zone I want time values to display in. Things like this slider bar are called “Control Widgets”, and there’s a whole list of them in the third lesson of RStudio’s Shiny tutorial. There are also examples online in the Shiny Gallery. 12.1.8 Creating output in server.R Next, I’ll put some R code in the server.R file to create a figure and pass it through to the ui.R file to print out to the application interface. At first, I won’t make this figure “reactive”; that is, it won’t change at all when the user changes the slider bar. However, I will eventually add in that reactivity so that the plot changes everytime a user changes the slider bar. I am going to create a map of all the Tweets that included certain hashtags or phrases and that were Tweeted (and geolocated) from within a five-mile radius of the center of Paris during the attacks last Friday. For this, I’m going to use data on Tweets I pulled using the TwitteR package, which syncs up with Twitter’s API. Here’s an example of what the data looks like (I’ve cleared out some of the extra columns I won’t use): paris_twitter &lt;- read.csv(&quot;data/App-1/data/final_tweets.csv&quot;, as.is = TRUE) %&gt;% mutate(tag = factor(tag), created = ymd_hms(created, tz = &quot;Europe/Paris&quot;), text = iconv(text, to=&#39;ASCII//TRANSLIT&#39;)) paris_twitter[1:2, ] ## text ## 1 RT @forza_will2006: My heart aches for the people of France. #PorteOuverte #PrayForParis #DownWithTerrorism #Pompidou... https://t.co/KwtNPsQ... ## 2 Ensemble contre la haine #jesuisparis #porteouverte #paris @ Place de la Republique https://t.co/TZykpVJHwd ## created longitude latitude tag ## 1 2015-11-15 01:27:56 NA NA #PorteOuverte ## 2 2015-11-14 22:19:47 2.364184 48.86747 #PorteOuverte About an equal number of these have and don’t have location data: table(!is.na(paris_twitter$longitude)) ## ## FALSE TRUE ## 10478 10683 Here is a table of the number of tweets under the five most-tweeted tags: head(paris_twitter) ## text ## 1 RT @forza_will2006: My heart aches for the people of France. #PorteOuverte #PrayForParis #DownWithTerrorism #Pompidou... https://t.co/KwtNPsQ... ## 2 Ensemble contre la haine #jesuisparis #porteouverte #paris @ Place de la Republique https://t.co/TZykpVJHwd ## 3 Libert&#39;e, &#39;egalit&#39;e, fraternit&#39;e #Par&#39;is #TodosSomosPar&#39;is #JeSuisParis #PorteOuverte #peace #paix #paz... https://t.co/AjYVrmCCnL ## 4 Stay With My French #prayforparis #parisattacks #porteouverte #france #riphumanity #liberte #egalit&#39;e... https://t.co/9DH5diHmyz ## 5 &lt;NA&gt; ## 6 RT @bodoi_music: J&#39;ai peur je cherche un abri #porteouverte https://t.co/1DIr4VLOEa ## created longitude latitude tag ## 1 2015-11-15 01:27:56 NA NA #PorteOuverte ## 2 2015-11-14 22:19:47 2.364184 48.86747 #PorteOuverte ## 3 2015-11-14 21:36:06 2.350800 48.85670 #PorteOuverte ## 4 2015-11-14 21:00:49 2.350800 48.85670 #PorteOuverte ## 5 2015-11-14 20:11:01 2.350800 48.85670 #PorteOuverte ## 6 2015-11-14 19:52:43 NA NA #PorteOuverte tweet_sum &lt;- paris_twitter %&gt;% dplyr::group_by_(~ tag) %&gt;% dplyr::summarize_(n = ~ n(), example = ~ gsub(&quot;[[:punct:]]&quot;, &quot; &quot;, base::sample(text, 1))) %&gt;% dplyr::arrange_(~ dplyr::desc(n)) ## Warning: `group_by_()` is deprecated as of dplyr 0.7.0. ## Please use `group_by()` instead. ## See vignette(&#39;programming&#39;) for more help ## This warning is displayed once every 8 hours. ## Call `lifecycle::last_warnings()` to see where this warning was generated. ## Warning: `summarise_()` is deprecated as of dplyr 0.7.0. ## Please use `summarise()` instead. ## This warning is displayed once every 8 hours. ## Call `lifecycle::last_warnings()` to see where this warning was generated. knitr::kable(tweet_sum[1:5, ], col.names = c(&quot;Tag&quot;, &quot;# of Tweets&quot;, &quot;Example Tweet&quot;)) Tag # of Tweets Example Tweet #Paris 7502 RT Vince66240 Tirs a la Kalash au petit Cambodge dans le 10 eme a Paris plusieurs morts Secours et police sur place tir https t co B #PrayForParis 7250 NA #13novembre 1255 RT taimaz 3 personnes a terre devant le Bataclan soign ees par les pompiers AFP 13novembre Paris #PorteOuverte 1147 RT hiyadisoufiane1 porteouverte Eiffel st Charles 37 rue Saint Charles si Jamais vs avez besoin #fusillade 765 RT jacques lefort Coups de feu et haute tension a l angle rues de Charonne amp Keller Paris11 Gendarmes en position fusillade https t For the Tweets that are geolocated, it’s possible to map the tweet locations using the following code: paris_map &lt;- get_map(&quot;paris&quot;, zoom = 12, color = &quot;bw&quot;) paris_locations &lt;- c(&quot;Stade de France&quot;, &quot;18 Rue Alibert&quot;, &quot;50 Boulevard Voltaire&quot;, &quot;92 Rue de Charonne&quot;, &quot;Place de la Republique&quot;) paris_locations &lt;- paste(paris_locations, &quot;paris france&quot;) paris_locations &lt;- cbind(paris_locations, geocode(paris_locations)) # Plot contour map of tweet locations plot_map &lt;- function(tag = &quot;all&quot;, df = paris_twitter){ library(ggmap) library(dplyr) df &lt;- dplyr::select(df, tag, latitude, longitude) %&gt;% filter(!is.na(longitude)) %&gt;% mutate(tag = as.character(tag)) if(tag != &quot;all&quot;){ if(!(tag %in% df$tag)){ stop(paste(&quot;That tag is not in the data. Try one of the following tags instead: &quot;, paste(unique(df$tag), collapse = &quot;, &quot;))) } to_plot &lt;- df[df$tag == tag, ] } else { to_plot &lt;- df } hotel_de_ville &lt;- to_plot$latitude == 48.85670 &amp; to_plot$longitude == 2.350800 n_hotel_de_ville &lt;- sum(hotel_de_ville) if(n_hotel_de_ville == max(table(to_plot$latitude))){ hdv_index &lt;- sample((1:nrow(to_plot))[hotel_de_ville], round(n_hotel_de_ville / 2)) to_plot &lt;- to_plot[-hdv_index, ] } my_map &lt;- ggmap(paris_map, extent = &quot;device&quot;) + geom_point(data = to_plot, aes(x = longitude, y = latitude), color = &quot;darkgreen&quot;, alpha = 0.75) + geom_density2d(data = to_plot, aes(x = longitude, latitude), size = 0.3) + stat_density2d(data = to_plot, aes(x = longitude, y = latitude, fill = ..level.., alpha = ..level..), size = 0.01, bins = round(nrow(to_plot) / 3.3), geom = &quot;polygon&quot;) + scale_fill_gradient(low = &quot;green&quot;, high = &quot;yellow&quot;, guide = FALSE) + scale_alpha(guide = FALSE) + geom_point(data = paris_locations, aes(x = lon, y = lat), color = &quot;red&quot;, size = 5, alpha = 0.75) return(my_map) } To print this out in the application, I’ll put all the code for the mapping function in a file called helper.R, source this file in the server.R file, and then I can just call the function within the server file. The application will look as follows after this step: To complete this, I first changed the server.R file to look like this: library(dplyr) library(lubridate) source(&quot;helper.R&quot;) paris_twitter &lt;- read.csv(&quot;data/final_tweets.csv&quot;, as.is = TRUE) %&gt;% mutate(tag = factor(tag), created = ymd_hms(created, tz = &quot;Europe/Paris&quot;)) shinyServer(function(input, output) { output$twitter_map &lt;- renderPlot({ plot_map() }) }) Notice a few things here: I’m loading the packages I’ll need for the code. I’m running all the code in the helper.R file (which includes the function I created to plot this map) using the source() command. I put the code to plot the map (plot_map()) inside the renderPlot({}) function. I’m putting the plot in a twitter_map slot of the output object. All of this is going inside the call shinyServer(function(input, output){ }). One other change is necessary to get the map to print on the app. I need to add code to the ui.R file to tell R where to plot this map on the final interface. The full file now looks like this: shinyUI(fluidPage( titlePanel(&quot;Tweets during Paris Attack&quot;), sidebarLayout(position = &quot;right&quot;, sidebarPanel(&quot;Choose what to display&quot;, sliderInput(inputId = &quot;time_range&quot;, label = &quot;Select the time range: &quot;, value = c(as.POSIXct(&quot;2015-11-13 00:00:00&quot;, tz = &quot;CET&quot;), as.POSIXct(&quot;2015-11-15 06:00:00&quot;, tz = &quot;CET&quot;)), min = as.POSIXct(&quot;2015-11-13 00:00:00&quot;, tz = &quot;CET&quot;), max = as.POSIXct(&quot;2015-11-14 12:00:00&quot;, tz = &quot;CET&quot;), step = 60, timeFormat = &quot;%dth %H:%M&quot;, timezone = &quot;+0100&quot;)), mainPanel(&quot;Map of tweets&quot;, plotOutput(&quot;twitter_map&quot;)) ) )) The new part is where I’ve added the code: mainPanel(&quot;Map of tweets&quot;, plotOutput(&quot;twitter_map&quot;)) This tells R to put the plot output twitter_map from the output object in the main panel of the Shiny app. 12.1.9 Making the output reactive Now almost all of the pieces are in place to make this graphic reactive. First, I added some options to the function in helper.R to let it input time ranges and only plot the tweets within that range. Next, I need to use the values that the user selects from the slider in the call for plotting the map. To do this, I can use the values passed from the slider bar in the input object into the code in the server.R file. Here is the new code for the server.R file: library(ggmap) library(ggplot2) library(dplyr) library(lubridate) source(&quot;helper.R&quot;) paris_twitter &lt;- read.csv(&quot;data/final_tweets.csv&quot;, as.is = TRUE) %&gt;% mutate(tag = factor(tag)) paris_twitter$created &lt;- as.POSIXct(paris_twitter$created, tz = &quot;CET&quot;) shinyServer(function(input, output) { output$twitter_map &lt;- renderPlot({ plot_map(start.time = input$time_range[1], end.time = input$time_range[2]) }) }) The only addition from before is to use the start.time and end.time options in the plot_map function and to set them to the first, [1], and second, [2], values in the time_range slot of the input object. Remember that we chose to label the input from the slider bar time_range when we set up the ui.R file. This app is saved in the directory App-1 in this week’s directory if you’d like to play around with the code. I’ve deployed it on shinyapps here. 12.1.10 Fancier version I’ve also created a (much) fancier version of a Shiny App looking at this Twitter data that you can check out here. All the code for that is here. 12.2 htmlWidgets 12.2.1 Overview of htmlWidgets Very smart people have been working on creating interactive graphics in R for a long time. So far, nothing coded in R has taken off in a big way. JavaScript has developed a number of interactive graphics libraries that can be for documents viewed in a web browser. There is now a series of R packages that allow you to create plots from these JavaScript libraries from within R. There is a website with much more on these htmlWidgets at http://www.htmlwidgets.org. Some of the packages availabe to help you create interactive graphics from R using JavaScript graphics libraries: leaflet: Mapping (we’ll cover this next week) dygraphs: Time series plotly: A variety of plots, including maps rbokeh: A variety of plots, including maps networkD3: Network data d3heatmap: Heatmaps DT: Data tables DiagrammeR: Diagrams and flowcharts These packages can be used to make some pretty cool interactive visualizations for HTML output from R Markdown or Shiny (you can also render any of theme in RStudio). There are, however, a few limitations: Written by different people. The different packages have different styles as well as different interfaces. Learning how to use one package may not help you much with other of these packages. Many are still in development, often in early development. 12.2.2 plotly package From the package documentation: “Easily translate ggplot2 graphs to an interactive web-based version and / or create custom web-based visualizations directly from R.” Like many of the packages today, draws on functionality external to R, but within a package that allows you to work exclusively within R. Allows you to create interactive graphs from R. Some of the functions extend the ggplot2 code you’ve learned. Interactivity will only work within RStudio or on documents rendered to HTML. The plotly package allows an interface to let you work with plotly.js code directly using R code. plotly.js is an open source library for creating interactive graphs in JavaScript. This JavaScript library is built on d3.js (Data-Driven Documents), which is a key driver in interactive web-based data graphics today. There are two main ways of create plots within plotly: Use one of the functions to create a customized interactive graphic: plot_ly: Workhorse of plotly, renders most non-map types of graphs plot_geo, plot_mapbax: Specific functions for creating plotly maps. Create a ggplot object and then convert it to a plotly object using the ggplotly function. library(faraway) data(worldcup) library(plotly) plot_ly(worldcup, type = &quot;scatter&quot;, x = ~ Time, y = ~ Shots) Just like with ggplot2, the mappings you need depend on the type of plot you are creating. For example, scatterplots (type = \"scatter\") need x and y defined, while a surface plot (type = \"surface\") can be created with a single vector of elevation (we’ll see an example in a few slides). The help file for plot_ly includes a link with more documentation on the types of plots that can be made and the required mappings for each. plot_ly(worldcup, type = &quot;scatter&quot;, x = ~ Time, y = ~ Shots, color = ~ Position) The plotly package is designed so you can pipe data into plot_ly and add elements by piping into add_* functions (this idea is similar to adding elements to a ggplot object with +). worldcup %&gt;% plot_ly(x = ~ Time, y = ~ Shots, color = ~ Position) %&gt;% add_markers() Some of the add_* functions include: add_markers add_lines add_paths add_polygons add_segments add_histogram If you pipe to the rangeslider function, it allows the viewer to zoom in on part of the x range. (This can be particularly nice for time series.) You should have a dataset available through your R session named USAccDeaths. This gives a montly county of accidental deaths in the US for 1973 to 1978. This code will plot it and add a range slider on the lower x-axis. plot_ly(x = time(USAccDeaths), y = USAccDeaths) %&gt;% add_lines() %&gt;% rangeslider() For a 3-D scatterplot, add a mapping to the z variable: worldcup %&gt;% plot_ly(x = ~ Time, y = ~ Shots, z = ~ Passes, color = ~ Position, size = I(3)) %&gt;% add_markers() The volcano data comes with R and is in a matrix format. Each value gives the elevation for a particular pair of x- and y-coordinates. dim(volcano) ## [1] 87 61 volcano[1:4, 1:4] ## [,1] [,2] [,3] [,4] ## [1,] 100 100 101 101 ## [2,] 101 101 102 102 ## [3,] 102 102 103 103 ## [4,] 103 103 104 104 plot_ly(z = ~ volcano, type = &quot;surface&quot;) Mapping with plotly can build on some data that comes with base R or other packages you’ve likely added (or can add easily, as with the map_data function from ggplot2). For example, we can map state capitals and cities with &gt; 40,000 people using data in the us.cities dataframe in the maps package: head(maps::us.cities, 3) ## name country.etc pop lat long capital ## 1 Abilene TX TX 113888 32.45 -99.74 0 ## 2 Akron OH OH 206634 41.08 -81.52 0 ## 3 Alameda CA CA 70069 37.77 -122.26 0 Here is code you can use to map all of these cities on a US map: ggplot2::map_data(&quot;world&quot;, &quot;usa&quot;) %&gt;% group_by(group) %&gt;% filter(-125 &lt; long &amp; long &lt; -60 &amp; 25 &lt; lat &amp; lat &lt; 52) %&gt;% plot_ly(x = ~long, y = ~lat) %&gt;% add_polygons(hoverinfo = &quot;none&quot;) %&gt;% add_markers(text = ~paste(name, &quot;&lt;br /&gt;&quot;, pop), hoverinfo = &quot;text&quot;, alpha = 0.25, data = filter(maps::us.cities, -125 &lt; long &amp; long &lt; -60 &amp; 25 &lt; lat &amp; lat &lt; 52)) %&gt;% layout(showlegend = FALSE) You can also make choropleths interactive. Remember that we earlier created a choropleth of US state populations with the following code: library(choroplethr) data(df_pop_state) state_choropleth(df_pop_state) You can use the following code with plotly to make an interactive choropleth instead: us_map &lt;- list(scope = &#39;usa&#39;, projection = list(type = &#39;albers usa&#39;), lakecolor = toRGB(&#39;white&#39;)) plot_geo() %&gt;% add_trace(z = df_pop_state$value[df_pop_state$region != &quot;district of columbia&quot;], text = state.name, locations = state.abb, locationmode = &#39;USA-states&#39;) %&gt;% add_markers(x = state.center[[&quot;x&quot;]], y = state.center[[&quot;y&quot;]], size = I(2), symbol = I(8), color = I(&quot;white&quot;), hoverinfo = &quot;none&quot;) %&gt;% layout(geo = us_map) The other way to create a plotly graph is to first create a ggplot object and then transform it into an interactive graphic using the ggplotly function. The following code can be used to plot Time versus Shots for the World Cup date in a regular, non-interactive plot: shots_vs_time &lt;- worldcup %&gt;% mutate(Name = rownames(worldcup)) %&gt;% filter(Team %in% c(&quot;Netherlands&quot;, &quot;Germany&quot;, &quot;Spain&quot;, &quot;Uruguay&quot;)) %&gt;% ggplot(aes(x = Time, y = Shots, color = Position, group = Name)) + geom_point() + facet_wrap(~ Team) shots_vs_time To make the plot interactive, just pass the ggplot object to ggplotly: ggplotly(shots_vs_time) With R, not only can you pull things from another website using an API, you can also upload or submit things. There is a function in the plotly library, plotly_POST, that lets you post a plot you create in R to https://plot.ly. You need a plot.ly account to do that, but there are free accounts available. The creator of the R plotly package has written a bookdown book on the package that you can read here. It provides extensive details and examples for using plotly. Getting Started with D3 by Mike Dewar (a short book on D3 in JavaScript) is available for free here. 12.2.3 rbokeh package The rbokeh package provides an R interface to a Python interactive visualization library, Bokeh. There is a website with many more details on using the rbokeh package: https://hafen.github.io/rbokeh/ You can find out more about the original Python library, Bokeh, at http://bokeh.pydata.org/en/latest/. Here is an example of an interactive scatterplot of the World Cup data made with the rbokeh package: library(rbokeh) ## Registered S3 method overwritten by &#39;pryr&#39;: ## method from ## print.bytes Rcpp figure(width = 600, height = 300) %&gt;% ly_points(Time, Shots, data = worldcup, color = Position, hover = list(Time, Shots)) This package can also be used to create interactive maps. For example, the following dataset has data on Oregon climate stations, including locations: orstationc &lt;- read.csv(&quot;data/orstationc.csv&quot;) head(orstationc, 3) ## station lat lon elev tjan tjul tann pjan pjul pann idnum ## 1 ANT 44.917 -120.717 846 0.0 20.2 9.6 41 9 322 350197 ## 2 ARL 45.717 -120.200 96 0.9 24.6 12.5 40 6 228 350265 ## 3 ASH 42.217 -122.717 543 3.1 20.8 11.1 70 7 480 350304 ## Name ## 1 ANTELOPE 1 N USA-OR ## 2 ARLINGTON USA-OR ## 3 ASHLAND 1 N USA-OR You can use the following code to create an interactive map of these climate stations: gmap(lat = 44.1, lng = -120.767, zoom = 5, width = 500, height = 428) %&gt;% ly_points(lon, lat, data = orstationc, alpha = 0.8, col = &quot;red&quot;, hover = c(station, Name, elev, tann)) You can get very creative with this package. The following code comes directly from the help documentation for the package and shows how to use this to create an interactive version of the periodic table: # prepare data elements &lt;- subset(elements, !is.na(group)) elements$group &lt;- as.character(elements$group) elements$period &lt;- as.character(elements$period) # add colors for groups metals &lt;- c(&quot;alkali metal&quot;, &quot;alkaline earth metal&quot;, &quot;halogen&quot;, &quot;metal&quot;, &quot;metalloid&quot;, &quot;noble gas&quot;, &quot;nonmetal&quot;, &quot;transition metal&quot;) colors &lt;- c(&quot;#a6cee3&quot;, &quot;#1f78b4&quot;, &quot;#fdbf6f&quot;, &quot;#b2df8a&quot;, &quot;#33a02c&quot;, &quot;#bbbb88&quot;, &quot;#baa2a6&quot;, &quot;#e08e79&quot;) elements$color &lt;- colors[match(elements$metal, metals)] elements$type &lt;- elements$metal # make coordinates for labels elements$symx &lt;- paste(elements$group, &quot;:0.1&quot;, sep = &quot;&quot;) elements$numbery &lt;- paste(elements$period, &quot;:0.8&quot;, sep = &quot;&quot;) elements$massy &lt;- paste(elements$period, &quot;:0.15&quot;, sep = &quot;&quot;) elements$namey &lt;- paste(elements$period, &quot;:0.3&quot;, sep = &quot;&quot;) # create figure p &lt;- figure(title = &quot;Periodic Table&quot;, tools = c(&quot;resize&quot;, &quot;hover&quot;), ylim = as.character(c(7:1)), xlim = as.character(1:18), xgrid = FALSE, ygrid = FALSE, xlab = &quot;&quot;, ylab = &quot;&quot;, height = 445, width = 800) %&gt;% # plot rectangles ly_crect(group, period, data = elements, 0.9, 0.9, fill_color = color, line_color = color, fill_alpha = 0.6, hover = list(name, atomic.number, type, atomic.mass, electronic.configuration)) %&gt;% # add symbol text ly_text(symx, period, text = symbol, data = elements, font_style = &quot;bold&quot;, font_size = &quot;10pt&quot;, align = &quot;left&quot;, baseline = &quot;middle&quot;) %&gt;% # add atomic number text ly_text(symx, numbery, text = atomic.number, data = elements, font_size = &quot;6pt&quot;, align = &quot;left&quot;, baseline = &quot;middle&quot;) %&gt;% # add name text ly_text(symx, namey, text = name, data = elements, font_size = &quot;4pt&quot;, align = &quot;left&quot;, baseline = &quot;middle&quot;) %&gt;% # add atomic mass text ly_text(symx, massy, text = atomic.mass, data = elements, font_size = &quot;4pt&quot;, align = &quot;left&quot;, baseline = &quot;middle&quot;) p 12.2.4 dygraphs package The dygraphs package lets you create interactive time series plots from R using the dygraphs JavaScript library. The main function syntax is fairly straightforward. Like many of these packages, it allows piping. There is a website with more information on using dygraphs available at http://rstudio.github.io/dygraphs/index.html. For example, here is the code to plot monthly deaths from lung diseases in the UK in the 1970s. library(dygraphs) lungDeaths &lt;- cbind(mdeaths, fdeaths) dygraph(lungDeaths) %&gt;% dySeries(&quot;mdeaths&quot;, label = &quot;Male&quot;) %&gt;% dySeries(&quot;fdeaths&quot;, label = &quot;Female&quot;) 12.2.5 DT package The DT package provides a way to create interactive tables in R using the JavaScript DataTables library. We’ve already seen some examples of this output in some of the Shiny apps I showed last week. You can also use this package to include interactive tables in R Markdown documents you plan to render to HTML. There is a website with more information on this package at http://rstudio.github.io/DT/. library(DT) datatable(worldcup) 12.2.6 Creating your own widget If you find a JavaScript visualization library and would like to create bindings to R, you can create your own package for a new htmlWidget. There is advice on creating your own widget for R available at http://www.htmlwidgets.org/develop_intro.html. "],
+["reproducible-research-3.html", "Chapter 13 Reproducible research #3 13.1 Overview of R packages 13.2 Basic example package: weathermetrics 13.3 Elements of an R package 13.4 Creating an R package", " Chapter 13 Reproducible research #3 Download a pdf of the lecture slides covering this topic. 13.1 Overview of R packages 13.1.1 What is an R package? From Writing R Extensions: “A directory of files which extend R”. Files bundled together using tar and compressed using gzip. The file extension is .tar.gz. These are the source files for the package, which then must be installed from this source code locally prior to use. Sometimes also called an extension of R. Example R package: You can also have “binary packages” for a certain operating system. From Writing R Extensions: A binary package is “a zip file or tarball containing the files of an installed package which can be unpacked rather than installing from sources.” Consider developing software when: You have developed a new method you want to share You have data you’d like to make publicly available You find yourself doing the same task repeatedly Why create an R package? Share some functions broadly Share some functions with a small group Create a version of code for yourself that’s more organized and easier to use Includes documentation (vignettes, help files) Function names linked to package namespace Once library is installed, can load easily 13.1.2 Example: NMMAPS package Source: www.ihapss.jhsph.edu Contents of NMMAPS package: Research impacts of NMMAPS package (Source: Barnett, Huang, and Turner, “Benefits of Publicly Available Data”, Epidemiology 2012): As of November 2011, 67 publications had been published using this data, with 1,781 citations to these papers Research using NMMAPS has been used by the US EPA in creating regulatory impact statements for air pollution (particulates and ozone) “Thanks to NMMAPS, there is probably no other country in the world with a greater understanding of the health effects of air pollution and heat waves in its population.” 13.1.3 Sharing an R package If you want to share your R package, there are a number of ways you can do that: CRAN GitHub Bioconductor: “Bioconductor provides tools for the analysis and comprehension of high-throughput genomic data.” (from the Bioconductor website.) Other repositories Private(-ish) repositories: e.g., ROpenSci’s repository (for more, see https://ropensci.org/blog/blog/2015/08/04/a-drat-repository-for-ropensci) drat repository: Make your own R package repository, including through GitHub pages. Compressed file: You can save a source tarball or binary package file with others without posting to a repository. Sharing on CRAN: Traditional way to share an R package widely Easiest way for others to get your package (install.packages) Some barriers: Size constraint on packages (5 MB) Must follow CRAN policies All packages must pass a submission process. This is not a guarantee that a package does what it says, just a check that required files are where they should be and that the package more or less doesn’t break things. Sharing on GitHub: GitHub is becoming more and more common as a place to share R packages, both development packages that eventually are posted to CRAN and packages that are never submitted to CRAN. No restrictions / submission requirements GitHub repository size restrictions (1 GB, no files over 100 MB) much larger than CRAN package size restrictions (5 MB) GitHub packages can be installed using install_github from the devtools package Requires devtools package, which has some set-up requirements (XCode for Mac, Rtools for Windows) Packages on CRAN cannot depend on packages available only on GitHub 13.1.4 Package names The format requirements for a package name are, based on Writing R Extensions: “This should contain only (ASCII) letters, numbers and dot, have at least two characters and start with a letter and not end in a dot.” Hadley Wickham’s additional guidelines: Make it easy to Google. Make it all uppercase or all lower case Base it on a word that’s easy to remember, but then tweak the spelling to make it unique (and easier to Google). Abbreviate. Add an “r”. 13.1.5 Package maintainer A package can have many authors, but only one maintainer. The maintainer is in charge of fixing any problems that come up with CRAN checks over time to keep the package on CRAN. The maintainer is also the person who will be emailed about bugs, etc., by other users. The package can have other authors, as well as people in other roles (e.g., contributor). See the helpfile for the person function for more on the codes used for different roles. 13.1.6 Find out more To find out more about writing R packages, useful sources are: Writing R Extensions: Guidelines for R packages from the R Core Team. R Packages by Hadley Wickham R package development cheatsheet 13.2 Basic example package: weathermetrics The key functions in this package are: convert_temperature: Convert between temperature metrics convert_precip: Convert between precipitation metrics convert_wind_speed: Convert between wind speed metrics heat.index: Calculates heat index from air temperature and a measure of air moisture (dew point temperature or relative humidity) 13.2.1 Contents of weathermetrics package Equation to convert from Celsius to Fahrenheit: \\[ T_F = \\frac{9}{5} T_C + 32 \\] Here is the associated R function: celsius.to.fahrenheit ## function (T.celsius, round = 2) ## { ## T.fahrenheit &lt;- (9/5) * T.celsius + 32 ## T.fahrenheit &lt;- round(T.fahrenheit, digits = round) ## return(T.fahrenheit) ## } ## &lt;bytecode: 0x7fd2106b3ae8&gt; ## &lt;environment: namespace:weathermetrics&gt; Here is part of the help file for this function: Here is the start of the function to calculate the heat index: head(heat.index.algorithm, 10) ## ## 1 function (t = NA, rh = NA) ## 2 { ## 3 if (is.na(rh) | is.na(t)) { ## 4 hi &lt;- NA ## 5 } ## 6 else if (t &lt;= 40) { ## 7 hi &lt;- t ## 8 } ## 9 else { ## 10 alpha &lt;- 61 + ((t - 68) * 1.2) + (rh * 0.094) Here is an example of using this function: data(suffolk) suffolk %&gt;% mutate(heat_index = heat.index(t = TemperatureF, rh = Relative.Humidity)) %&gt;% slice(1:5) ## Date TemperatureF Relative.Humidity heat_index ## 1 1998-07-12 72 69 72 ## 2 1998-07-13 73 66 73 ## 3 1998-07-14 74 74 75 ## 4 1998-07-15 78 86 80 ## 5 1998-07-16 78 100 81 13.3 Elements of an R package 13.3.1 Basic elements Things you edit directly: DESCRIPTION file: The package’s “Title page”. Metadata on the package, including names and contacts of authors, package name, and description. This file also lists all the package dependencies (other packages with functions this package uses). R folder: R code defining functions in the package. All code is included in one or more R scripts. If you use Roxygen for help with documentation, all of that is also included in these files. Things that are automatically written: man folder: Help documentation for each function. This files are automatically rendered if you use Roxygen. NAMESPACE file: Helps R find functions in your package you want others to use. 13.3.2 DESCRIPTION file Required elements: Package: Name of the package Version: Number of the current version of the package (e.g., 0.1.0) Title: Short title for the package, in title case and in 65 characters or less. Author and Maintainer (these two sections can be replaced with Authors@R section that uses the person function) Description: Paragraph describing the package License: Name of the license the package is under. If necessary, you can also refer to a LICENSE file included as another file in the package. Only some licenses are easily accepted by CRAN. Other elements that are common but not required: Date: Release date of this version of the package. Imports: A list of the packages on which this package depends: other packages with functions used by the code in this package. URL: If there is a webpage associated with the package, the address for it. Often, this is the web address of the package’s GitHub repository. BugReports: Where users can submit problems they’ve had. Often, the web address of the “Issues” page of the GitHub repository for the package. Package: weathermetrics Type: Package Title: Functions to Convert Between Weather Metrics Version: 1.2.2 Date: 2016-05-19 Authors@R: c(person(&quot;Brooke&quot;, &quot;Anderson&quot;, email = &quot;brooke.anderson@colostate.edu&quot;, role = c(&quot;aut&quot;, &quot;cre&quot;)), person(&quot;Roger&quot;, &quot;Peng&quot;, email = &quot;rdpeng@gmail.com&quot;, role = c(&quot;aut&quot;)), person(&quot;Joshua&quot;, &quot;Ferreri&quot;, email = &quot;joshua.m.ferreri@gmail.com&quot;, role = c(&quot;aut&quot;))) Description: Functions to convert between weather metrics, including conversions for metrics of temperature, air moisture, wind speed, and precipitation. This package also includes functions to calculate the heat index from air temperature and air moisture. URL: https://github.com/geanders/weathermetrics/ BugReports: https://github.com/geanders/weathermetrics/issues License: GPL-2 LazyData: true RoxygenNote: 5.0.1 Depends: R (&gt;= 2.10) Suggests: knitr, rmarkdown VignetteBuilder: knitr 13.3.3 R folder The R folder of the package includes: R scripts with code defining all functions for the package Help documentation for each function (if using Roxygen) Help documentation for the package data in “data.R” You define functions in the R scripts just as you would anytime you want to define a function in R. For example, “temperature_conversions.R” includes the following code to define converting from Celsius to Fahrenheit: celsius.to.fahrenheit &lt;- function (T.celsius, round = 2) { T.fahrenheit &lt;- (9/5) * T.celsius + 32 T.fahrenheit &lt;- round(T.fahrenheit, digits = round) return(T.fahrenheit) } Only exception: use package::function syntax to call functions from other packages (e.g., dplyr::mutate()). Using roxygen2, you put all information for the help files directly into a special type of code comments right before defining the function. Start each line with #'. To render into help files, use the document function from the devtools package. This will write out help files in the man folder of the package. Use these comments to specify which functions should be exported from the package using the @export tag. This information will be used to render the NAMESPACE file for the package. #&#39; Convert from Celsius to Fahrenheit. #&#39; #&#39; \\code{celsius.to.fahrenheit} creates a numeric vector of #&#39; temperatures in Fahrenheit from a numeric vector of #&#39; temperatures in Celsius. #&#39; #&#39; @param T.celsius Numeric vector of temperatures in Celsius. #&#39; @inheritParams convert_temperature #&#39; #&#39; @return A numeric vector of temperature values in Fahrenheit. #&#39; #&#39; @note Equations are from the source code for the US National #&#39; Weather Service&#39;s #&#39; \\href{http://www.wpc.ncep.noaa.gov/html/heatindex.shtml} #&#39; {online heat index calculator}. #&#39; @author #&#39; Brooke Anderson \\email{brooke.anderson@@colostate.edu}, #&#39; Roger Peng \\email{rdpeng@@gmail.com} #&#39; #&#39; @seealso \\code{\\link{fahrenheit.to.celsius}} #&#39; #&#39; @examples # Convert from Celsius to Fahrenheit. #&#39; data(lyon) #&#39; lyon$TemperatureF &lt;- celsius.to.fahrenheit(lyon$TemperatureC) #&#39; lyon #&#39; #&#39; @export Some of the most common tags you’ll use for roxygen2 are: @param: Use to explain parameters for the function. @inheritParam: If you have already explained a parameter for the help file for a different function, you can use this tag to use the same definition for this function. @return: Explanation of the object returned by the function. @examples: One or more examples of using the function. @export: Export the function, so it’s available when users load the package. By default, the first line in the roxygen2 comments is the function title and the next section is the function description. For more on roxygen2, see: https://cran.r-project.org/web/packages/roxygen2/vignettes/roxygen2.html Once you run document, this is all rendered as a help file. Now, when you run ?celsius.to.fahrenheit, you’ll get: The start of the NAMESPACE file will be automatically written when you run document and will look like: # Generated by roxygen2: do not edit by hand export(celsius.to.fahrenheit) export(celsius.to.kelvin) export(convert_precip) export(convert_temperature) export(convert_wind_speed) export(dewpoint.to.humidity) If you are automating helpfile documentation, you must also include an R script with the doumentation for each data set that comes with the package. This file will include roxygen2 documentation for each data set, followed by the name of the dataset in quotation marks. As an example, the next slide has the documentation in the “data.R” file for the “lyon” data set. #&#39; Weather in Lyon, France #&#39; #&#39; Daily values of mean temperature (Celsius) and mean dew #&#39; point temperature (Celsius) for the week of June 18, 2000, #&#39; in Lyon, France. #&#39; #&#39; @source \\href{http://www.wunderground.com/} #&#39; {Weather Underground} #&#39; #&#39; @format A data frame with columns: #&#39; \\describe{ #&#39; \\item{Date}{Date of weather observation} #&#39; \\item{TemperatureC}{Daily mean temperature in Celsius} #&#39; \\item{DewpointC}{Daily mean dewpoint temperature in #&#39; Celsius} #&#39; } &quot;lyon&quot; 13.3.4 Other common elements Some other elements, while not required, are common in many R packages: data folder: R objects with data that goes with the package. Often, these are small-ish data files for examples of how to use package functions. However, more “scientific” packages may include more substantive data in this folder. Some packages are created solely to deliver data. vignettes folder: One or more tutorials on why the package was created and how to use it. These can be written in RMarkdown. NEWS file: Information about changes in later versions of the package. .Rbuildignore file: Lists files and directories that should not be included in the package build LICENSE file: With certain licenses (MIT is a common example), you need a separate LICENSE file, to supplement the license information in the DESCRIPTION file. 13.3.5 Less common elements src folder: Sources and headers for compiled code (e.g., C++). demo folder: R scripts that give demonstrations of using the package. tests folder: Test code for the package. Currently, the best way to create tests for a package are with the testthat package. inst folder: Various and sundries, including a CITATION file to tell others how to cite your package and executable scripts not in R (e.g., shell scripts, Perl or Python code). 13.4 Creating an R package Invaluable tools when creating an R package: The devtools package: Various utility functions that help you develop an R package. R Packages by Hadley Wickham. Available from O’Reilly or free online at http://r-pkgs.had.co.nz GitHub: When in doubt of how to structure something, look for examples in code for other R packages. GitHub is currently the easiest way to browse through the code for many R packages. 13.4.1 Initializing an R package The easiest way to start a new R package project is through R Studio. Go to “File” -&gt; “New Project” -&gt; “Empty Directory”. One of the options is “R Package”. You’ll need to specify where you want to save the directory and the package name. You can also select if you’d like to use git (you’ll still need to set-up and sync with GitHub if you want to post the package to GitHub). Once you choose this, R Studio will create a new “skeleton” directory for you, with some of the default files and directories you need (kind of like how it starts with a template for RMarkdown documents). You can add and edit files within this structure to create your package. 13.4.2 Working on an R package Once you set-up the package, most of your work will be in writing the code for the package’s functions and creating documentation. The devtools package has some functions that are very useful for this process: load_all: Loads the last saved version of all functions in the package. You can use this to change and check functions without rebuilding the whole package and restarting R each time. document: Parse all roxygen2 comments to create the helpfiles in the man directory and the NAMESPACE file. As soon as you’ve loaded an documented the last saved version of your package, you can access the help file for each function using ?, as with other R functions. Control-. : This is a keyboard shortcut rather than a function, but it allows you to search the package for the code where a certain function is defined. As a package grows larger, this functionality is very useful for navigating the R code in the package. The devtools package also has some functions that set up useful infrastructure for the package. For example, if you want to include a vignette written in RMarkdown, you need to do a few things: Add a new folder called vignettes. Add inst/doc to the .gitignore file. (The built pdf is written into this folder, but typically you don’t want to include rendered files in git, just the code with which they were generated.) Make a few changes to the DESCRIPTION file. Rather than having to remember how to do all this yourself, you can use the use_vignette function, which adds this infrastructure to the package at once. Typically, you will only use these infrastructure calls once per package. Other useful infrastructure functions are: use_cran_comments: Add a text file with comments for the people who check the package when it’s submitted to CRAN. use_readme_rmd: Create an RMarkdown “README” file that you can use to provide information on the package (similar to the vignette, but this will show up on the first page of the GitHub repo if you have one for the package). use_news_md: Create a text file to provide details of changes in later package versions. use_travis: Add the infrastructure needed to check the package on Travis when you push to GitHub. use_rcpp: Add an src directory and other infrastructure needed to use C++ code within the package. use_testthat: Add infrastructure for using package tests based on testthat. There are a few infrastructure-type functions you might use more often: use_data: Save data currently in an R object in your working session to use as data within the package. This function saves that data as an .rda file in the data folder. use_build_ignore: Add a file or files to the “.Rbuildignore” file, so they won’t cause an error with CRAN checks (one of the checks is that there aren’t any unrecognized files or directories in the top level of the package). use_package: Add a package that your package depends on to the DESCRIPTION file. 13.4.3 Finalizing an R package Once you have included all the functions and documentation for a package, there are a few more steps before that version is ready to be shared: Create a vignette and / or README file to explain how others can use the package. Run the package through CRAN checks and resolve all ERRORS, WARNINGS, and NOTES. This is required if you are submitting to CRAN. It’s usually a good idea and improves the package even if you’re not. Change the version number to a stable version (typically, development versions end in .9000, like 0.0.0.9000). When you have a stable version of the package, you’ll change this to a three-part number (e.g., 0.1.0). Build the package locally. For this, you can use the “Build” tab in the upper right RStudio tab (it will show up once you have a package project open). Build the package on other systems. You can use Travis (Unix / Linux) and build_win (Windows) to do this for those systems. Create a pdf of all help files to proofread. To do this, open a bash shell in the parent directory of the package and run R CMD Rd2pdf &lt;packagename&gt; (for example, if the package were csupackage, you’d run R CMD Rd2pdf csupackage). This will create a pdf in that directory with all helpfiles for all functions. If you want the package to be on CRAN, submit to CRAN. There is a function called submit_cran that will build the package and submit it to CRAN. "],
 ["appendix-a-vocabulary.html", "A Appendix A: Vocabulary A.1 Quiz 1—R Preliminaries A.2 Quiz 2—Entering / cleaning data #1 A.3 Quiz 3 A.4 Quiz 4 A.5 Quiz 5 A.6 Quiz 6 A.7 Quiz 7 A.8 Quiz 8 A.9 Quiz 9", " A Appendix A: Vocabulary You will be responsible for knowing the following functions and vocabulary for the weekly quizzes. A.1 Quiz 1—R Preliminaries Grading policies for the course Course requirements / policies for in-class quizzes Free and open source software “Free as in beer” versus “free as in speech” Advantages and disadvantages of interpreted languages compared to “Point-and-click” software Advantages and of interpreted languages compared to compiled languages and assembly languages Difference between R and RStudio R session R console Function arguments (including required versus optional arguments) Accessing a function’s helpfile using ? Mathematical operators: +, -, *, - R objects and object names “gets arrow”: &lt;- Rules and style guidelines for naming objects ls() R scripts # comment character R packages CRAN Installing packages install.packages() Loading a package library() Types of package documentation: vignettes and helpfiles vignette(), option package = Vectors c() Two of the basic classes of vectors: character and numeric class() Square bracket indexing for vectors: [...] Dataframes tibble() function from the dplyr package select and slice functions to extract values from dataframes read_csv, option skip = str() summary() dim() ncol() nrow() Nate Silver FiveThirtyEight NA for missing values $ to get a column from a dataframe paste(), option sep = paste0() = vs. &lt;- for assignment expressions package::function() notation A.2 Quiz 2—Entering / cleaning data #1 What kinds of data can be read into R? delimited files (csv, tsv) fixed width files delimiter read_delim, options delim =, skip =, n_max =, col_names = read_fwf read_csv, options skip =, n_max =, col_names = readxl package and its read_excel() function haven package and its read_sas() function NA Computer directory structure working directory getwd() list.files() relative pathnames absolute pathnames shorthand for pathnames: ., .., ../data, etc. Reading in data from either a local or online flat file paste(), option sep = paste0() How to read flat files of data that are online directly into R dplyr package rename() Why you might want to rename column names (e.g., uppercase, long, unusual characters) select() slice() mutate() filter() arrange(), including with desc() %&gt;%, advantages of piping A.3 Quiz 3 Main types of vector classes in R: character, numeric, factor, date, logical lubridate functions, including ymd, ymd_hm, mdy, wday, and mday Common logical operators in R (==, !=, %in%, is.na(), &amp;, |) data() (with and without the name of a dataset as an option) library() (with and without an argument in the parentheses) logical vectors, including running sum on a logical vector What the bang operator (!) does to a logical operator The tidyverse min() max() mean() median() summarize() Special functions to use with summarize(): n(), n_distinct(), first(), last() Using group_by() before using summarize() The three basic elements of a ggplot plot: data, aesthetics, and geoms aes function and common aesthetics, including color, shape, x, y, alpha, size, and fill Mapping an aesthetic to a column in the data versus setting it to a constant value Some common geoms: geom_histogram, geom_points, geom_lines, geom_boxplot() The difference between “statistical” geoms (e.g., geom_boxplot, geom_smooth) and “non-statistical” (e.g., geom_point, geom_line) Common additions to ggplot objects: ggtitle, labs, xlim, ylim, expand_limits A.4 Quiz 4 Guidelines for good graphics Data density / data-to-ink ratio Small multiples Edward Tufte Hadley Wickham Where to put the + in ggplot statements to avoid problems (ends of lines instead of starts of new lines) Can you save a ggplot object as an R object that you can reference later? If so, how would you add elements on to that object? How would you print it when you were ready to print the graph to your RStudio graphics window? geom_hline(), geom_vline() geom_text() facet_grid(), facet_wrap() grid.arrange() from the gridExtra package ggthemes package, including theme_few() and theme_tufte() Setting point color for geom_point() both as a constant (all points red) and as a way to show the level of a factor for each observation size, alpha, color Re-naming and re-ordering factors Note: If you read this and find and bring in an example of a “small multiples” graph (from a newspaper, a website, an academic paper), you can get one extra point on this quiz A.5 Quiz 5 Reproducible research, including what it is and advantages to aiming to make your research reproducible R style guidelines on variable names, &lt;- vs. =, line length, spacing, semicolons, commenting, indentation, and code grouping Markup languages (concept and examples) Basic conventions for Markdown (bold, italics, links, headers, lists) Literate programming What working directory R uses for code in an .Rmd document Basic syntax for RMarkdown chunks, including how to name them Options for RMarkdown chunks: echo, eval, messages, warnings, include, fig.width, fig.height, results Difference between global options and chunk options, and which takes precendence What inline code is and how to write it in RMarkdown How to set global options Why style is important in coding RPubs A.6 Quiz 6 Three characteristics of tidy data Five common problems with tidy data and how to resolve them (make sure you understand the examples shown, which you can find out more about in the Hadley Wickham paper I reference in the slides) group_by with mutate, slice, and arrange separate and unite pivot_longer and pivot_wider The *_join family of functions (left_join, right_join, inner_join, full_join, anti_join, semi_join) A.7 Quiz 7 regular expressions difference in the output between str_extract and str_detect str_to_lower, str_to_upper, str_to_title, str_trim lists indexing from lists ([[ and $) exploring lists (class, names, str functions) exploring lists with jsonedit from the listviewer package tidy function from broom applied to the output from a statistical test function forcats package, including fct_recode, fct_infreq, fct_reorder, and fct_lump Bioconductor “accessor” functions for Bioconductor (e.g., get_variable) select with starts_with, select_if A.8 Quiz 8 Review of previous weeks (especially tidyverse packages: dplyr, tidyr, ggplot2, stringr, and forcats) Writing a formula with ~ syntax Regression modeling with lm, glm Using functions from broom to tidy model output (augment, tidy, glance) Nesting with group_by and nest and unnesting with unnest A list-column in a dataframe A.9 Quiz 9 Review of previous quizzes "],
 ["appendix-b-homework.html", "B Appendix B: Homework B.1 Homework #1 B.2 Homework #2 B.3 Homework #3 B.4 Homework #4 B.5 Homework #5 B.6 Homework #6", " B Appendix B: Homework This section provides the homework assignments for the course. B.1 Homework #1 Due date: Sept. 11 by 5:00 pm For this assignment, you will submit the assignment to me by email by the due date. You should include one file in your submission: A Word document with seven paragraphs. Each paragraph should be headed with the name of one swirl lesson and the body of the paragraph should describe that lesson and what you learned from it. For this homework assignment, you’ll be working through a few swirl lessons that are relevant to the material we’ve covered so far. Swirl is a platform that helps you learn R in R—you can complete the lessons right in your R console. Depending on your familiarity with R, you can either work through seven lessons of your choice in the R Programming: The basics of programming in R and Getting and Cleaning Data courses (suggested lessons are listed further below) (Option #1), or you can work through seven lessons of your choice taken from any number of swirl’s available courses (Option #2) . For each lesson completed, please write a few sentences that cover: 1. A summary of the topic(s) covered in that lesson, and 2. The most interesting thing that you learned from that lesson. Turn in a hardcopy of this (with your first and last name at the top) during class on the due date. To begin, you’ll first need to install the swirl package: install.packages(&quot;swirl&quot;) If you’ve never run swirl() before, you will be prompted to install a course. You can do that with the install_course function. For example, to install the R Programming course, you would run: library(swirl) install_course(&quot;R Programming&quot;) Once you’ve installed a course, every time you enter the swirl environment with swirl(), R Progamming should show up as a course option to select. You can enter R Programming to start lessons in that course by typing the number in fromt of it when you run swirl(). Once you have at least one course installed, you call the swirl() function to enter the interactive platform in RStudio. The console will take you through a few prompts: you’ll give swirl a name to call you, and take a look at some commands that are useful in the swirl environment. Those commands are listed further below. library(swirl) swirl() After calling swirl(), you may be prompted to clear your workspace variables by running rm=(list=ls()). Running this code will clear any variables you already have saved in your global environment. While swirl recommends that you do this, it’s not necessary. Some of these lessons complement online courses through Coursera, so sometimes you will be asked after you complete a lesson if you want to report your results to Coursera. You should select “No” for that option each time. B.1.1 Option 1 For Option 1 of this homework, you will need to work through seven of the 15 available lessons in the R Programming course. Here are some suggestions for particularly uesful lessons that you could choose (the lesson number within the course is in parentheses): R Programming course: Basic Building Blocks (1) Sequences of Numbers (3) Vectors (4) Missing Values (5) Subsetting Vectors (6) Logic (8) Looking at Data (12) Dates and Times (14) Getting and Cleaning Data course: Manipulating Data with dplyr (1) Grouping and Chaining with dplyr (2) Dates and Times with lubridate (4) Each lesson should take at most 10–15 minutes, but some are much shorter. You can complete the lessons in any order you want, but you may find it easiest to start with the lowest-numbered lessons and work your way up, in the order we’ve listed the lessons here. You’ll be able to get started on some of these lessons after your first day in class (“Basic Building Blocks”“, for example), but others cover topics that we’ll get to in weeks 2 and 3. Whether or not we’ve covered a swirl topic in class, you should be able to successfully work through the lesson. At the end of each lesson, you may be asked if you would like to receive credit for completing this course on Coursera.org. Always choose”no\" for this option. Again, you’ll need to compose and turn in a few sentences for each lesson. Make sure to include a summary of what each lesson was about, and the most interesting thing about that lesson. B.1.2 Option 2 If you’re already somewhat familiar with R, you might want to choose your seven lessons from other swirl courses instead of or in addition to those available in the R Programming and Getting and Cleaning Data courses. Check out the list of available Swirl Courses to see which ones you would like to install and check out available lessons for. For example, to choose a lesson in the Exploratory Data Analysis course, you would run: library(swirl) install_course(&quot;Exploratory Data Analysis&quot;) swirl() After entering the Exploratory Data Analysis course, you could choose from any one of its available lessons. In your written summary for each lesson (again, a few sentences that cover a summary of the lesson and the most interesting thing you learned), make sure to specify which course each lesson you completed was from. B.1.3 Special swirl commands In the swirl environment, knowing about the following commands will be helpful: Within each lesson, the prompt ... indicates that you should hit Enter to move on to the next section. skip(): skip the current question. play(): temporarily exit swirl. It can be useful during a swirl lesson to play around in the R console to try things out. nxt(): regain swirl’s attention after play()ing around in the console. main(): return to swirl’s main menu. bye(): exit swirl. Swirl will save your progress if you exit in the middle of a lesson. You can also hit the Esc. key to exit. (To re-enter swirl, run swirl(). In a new R session you will have to first load the swirl library: library(swirl).) B.1.3.1 For fun While they aren’t required for class, you should consider trying out some other swirl lessons later in the course. You can look through the course directory to see what other courses and lessons are available. For the first part of our course, you might find the “Exploratory Data Analysis” course helpful. If you would like to learn more about using R for statistical analysis, you might find the “Regression Models” course helpful. B.2 Homework #2 Due date: Sept. 25 by 5:00 p.m. For this assignment, you will submit the assignment to me by email by the due date. You should include three files in your submission: The final rendered Word document (rendered from an RMarkdown file). The original RMarkdown file used to create that final document. The dataset you used for the assignment. For this assignment, start by picking a dataset either from your own research or something interesting available online (if you’re struggling to find something, check out Five Thirty Eight’s GitHub data repository). You will then use this dataset to practice what you’ve learned with R so far. This will also be a chance, if you’re using a dataset from your own research for me to get an idea of how you might be planning to use R in future research. Very important note: Some research datasets have privacy constraints. This includes any datasets collected from human subjects, but can also include other datasets. For some projects, the principal investigator may prefer to keep the data private until publication of results. Before using a dataset for this project, please confirm with your research advisor that there are no constraints on the data. I do not plan to make the results of this assignment public, but I also do not want to us to be emailing back and forth a dataset with any constraints. Using your dataset, create an RMarkdown document with the content listed below. Be sure to set the echo option to TRUE so all of your code will also print out to the Word document. Include in the RMarkdown document: (1) your full name; (2) the due date of the assignment; and (3) a title that includes “Homework 2”. These should all be set in RMarkdown, not changed in the Word output. Each section I’ve listed below should be included in the RMarkdown document as its own section, with a section header created using Markdown code. Section 1: Description of the data: Describe the dataset you are using, both in terms of the content (what is this data measuring? how was it collected? what kinds of research questions are you hoping to use it to answer?) and in terms of its format (what type of file is it saved in? what if it is in a flat file, is it fixed width or delimited? if it is delimited, what is the delimiter? if it is binary, what is the program that would normally be used to open it?). (20 points) Section 2: Reading the data into R: Include code that reads the data into R and assigns it to a dataframe object that you can use later in the document. Explain in the text which R function you used to read in the data (e.g., read_csv) and which package it came from (if it was not a base R function). If there were any special options you needed to use (e.g., skip to skip some rows without data), list those and explain why you used them. Next, include some code to clean the data (e.g., rename columns, convert any dates into a “Date” format). You can filter to certain rows if you would like, but do not filter out missing values, as we’ll want to learn more about those later. (20 points) Section 3: Characteristics of the data: Describe the dataframe you just read in. How many rows does it have? How many columns? (Use inline code in the RMarkdown document to put this information into a sentence that reads “This dataframe has … rows and … columns.”.) What are the names of the columns? What does each row measure (i.e., what is the unit of observation)? Include a table (using Markdown directly or kable) explaining what each column measures. This table should have three columns: (1) the column name in the R dataframe; a very brief description of what each column measures; and (3) the units (if any) of the measurement in the column. (20 points) Section 4: Summary statistics: Pick three columns of the dataframe. Use the summarize function to get the following summaries of these columns: (1) minimum value; (2) maximum value; (3) mean value; (4) number of missing values. If there are missing values, make sure you use the appropriate options in summarizing these values to exclude those when calculating the minimum, maximum, and mean. Assign the result of this summarize call to a new R object, and print it out, so these summaries show up in your final, rendered Word document. (20 points) Section 5: Visualizing the data: Create two plots of your dataframe. One should use a “statistical” geom (e.g., histogram, bar chart, boxplot) and one a “non-statistical” geom (e.g., scatterplot, line plot for time series). Explain why these plots help you learn more about this data and about the interesting research questions you’re hoping to explore with the data. Be sure to customize the final size of each plot in the Word document using the fig.width and fig.height commands. For each plot, also be sure to customize the x- and y-axis labels. Finally, explain how each plot is following at least two of the principles of “good graphics” covered in week 4 of the course (Chapter 4 of the book)—if necessary, use ggplot functions and options to make the plots comply with some of these principles. (20 points) B.3 Homework #3 Due date: Oct. 16 This homework includes several parts. Make sure you complete each part for full credit (100 points). Complete your homework in an RMarkdown document and knit it to either Word or PDF to submit. Change your global options so that no warnings, messages, or errors are printed out, but that so all code and results are printed. (10 points) Select one of the figures you created for the last homework. You will be improving that figure in this part of the homework. Go through the six guidelines for good graphics we discussed in Chapter 6. Re-create the same plot you created in the last homework and, in a paragraph or two, list which of these elements you currently have in the plot and how these elements help make the plot more effective. (10 points) Select three of these guidelines for which you either aren’t using them in the current graph or could add to what you currently have in the plot. Create a new version of the plot based on your choice. Include a paragraph explaining how these changes make the plot more effective and how you added them. (20 points) In the titanic package (available on CRAN), there’s a titanic_train dataset we’ll use for this question. Load that dataset and create a dataframe object limited to the columns Survived and Age. Change the labeling for the Survived column so that it says “Survived” instead of “1” and “Died” instead of “0”. Print out the oldest five people who survived and who died (so, you’ll be printing out 10 rows total). (10 points) Create a histogram of the ages of people in this dataset, faceted by whether or not they survived. Arrange these so that they are vertically aligned rather than side by side (Hint: check the nrow option when faceting). Change the label on the y-axis to be something that more precisely describes what’s being shown (rather than “count”) and add a title to the plot. (10 points) Determine the mean age for each group (survived and died) for everyone who has a non-missing age, as well as the total number of people in the group and the number of people with age information missing for the group. Print out a two-row dataframe with this summary information (it should have columns for (1) which group (survived / died); (2) the mean age of people in the group with non-missing age data; (3) the total number of people in the group; and (4) the number of people in the group with missing age data). (15 points) Run a statistical test of whether there is a difference in the mean ages between the two groups (Hint: you might want to google something like “test difference two means”). You can run this test either using a simple statistical test or by fitting a linear regression—you may take either approach. (15 points) There’s a radio show on National Public Radio called “Weekend Edition”, and each week they have a “Sunday Puzzle”. The puzzle for the week ending August 5, 2018 was this: “I said think of a familiar two-word phrase in eight letters with four letters in each word. The first word starts with M. And I said move the first letter of the second word to the end, and you get a regular eight-letter word, which, amazingly, other than the M, doesn’t share any sounds with the original two-word phrase. What phrase is it?”. The winner had this to say about how he solved the problem: \"I went to the National Puzzlers’ League website and pulled up a list of eight-letter words that start with the letter M and just started scanning through it…’. You can solve this puzzle using R (some of the stringr functions, like str_count and str_sub, will be particularly helpful). Write R code to solve this puzzle. (10 points) Hint 1: Work backwords. Start with eight-letter words, and apply a backwards transform to the one described to transform them into two four-letter words. Hint 2: This link might prove very useful: https://github.com/dwyl/english-words. B.4 Homework #4 Due date: Oct. 30 For this homework, you will use a data set collected by the Washington Post on homicides in 50 large U.S. cities. The data is available through their GitHub repository: https://github.com/washingtonpost/data-homicides. You can read their accompanying article at https://www.washingtonpost.com/graphics/2018/investigations/where-murders-go-unsolved/. Submit your homework by email. You should include the Rmarkdown file (.Rmd) and the output Word document. Read in the data as an R object named homicidesand create a new column called city_name that combines the city and state like this “Baltimore, MD”. (15 points) Create a dataframe called unsolved with one row per city that gives the total number of homicides for the city and the number of unsolved homicides (those for which the disposition is “Closed without arrest” or “Open/No arrest”). (15 points) For the city of Baltimore, MD, use the prop.test function to estimate the proportion of homicides that are unsolved, as well as the 95% confidence interval for this proportion (we’re assuming that the data from the years covered by this dataset is a representative sample of Baltimore in a larger set of years). Print the output of the prop.test directly in your RMarkdown, and then save the output of prop.test as an R object and apply the tidy function from the broom package to this object and pull the estimated proportion and confidence intervals from the resulting tidy dataframe. (20 points) Now use what you learned from running prop.test for one city to run prop.test for all the cities. Your goal is to create the dataframe you need to create the figure shown below, where the points show the estimated proportions of unsolved homicides in each city and the horizontal lines show the estimated 95% confidence intervals. Do this all within a “tidy” pipeline, starting from the unsolved dataframe that you created for step 3. Use map2 from purrr to apply prop.test within each city and then map from purrr to apply tidy to this output. Use the unnest function from the tidyr package on the resulting list-column (from mapping tidy to the prop.test output list-column), with the option .drop = TRUE, to get your estimates back into a regular tidy data frame before plotting. (25 points) Create the plot shown below. Hint: Check out the geom_errorbarh geom with the height = 0 option to get the horizontal lines for the confidence intervals. To get full points, be very careful to make sure that all labeling, color choices, figure dimensions, etc., in your figure match the figure shown here. (25 points) B.5 Homework #5 Due date: Nov. 13 For this homework, you will continue using the dataset used for Homework #4. You will submit this project by sending me the link to a GitHub repository. For the write-up of this homework, you’ll need to write an RMarkdown document and render it to a pdf file within the R Project for the GitHub repository. B.5.1 Setting up a GitHub repository for this project Take the following steps to set up your GitHub repository for the homework (we will work on this as an in-course exercise): Set up a local (i.e., on your computer) R project with subdirectories for the data, writing (this is where you’ll put your Rmarkdown file and the output file), and figures. Download the Washington Post data and save it in an appropriate place in this R project directory. Initialize git for the R project and make your initial commit. Create an RMarkdown file for your homework answers in the appropriate subdirectory and save it. Commit this change with an appropriate commit message. (At this stage, you can look at your “History” in the git commit window to see the changes you’ve made so far.) Login to your GitHub account and create a new, blank repo with the same name as your R project. (If you have not already set up an SSH key to make it easier to push back and forth between your computer and GitHub, do so now.) On your computer, specify your GitHub repository as the remote for your project and do an initial push to send all the files to the GitHub repo. Look at your repository on GitHub to make sure it worked. Add a README.md file to the top level of your R project (on your local computer) using Markdown syntax and then commit the change locally and push to your GitHub repository. For the README.md file, you can create a text file in RStudio and save it as “README.md”. You can then write this file using Markdown syntax (like RMarkdown, but without any code chunks). As you work on your homework, make sure you commit regularly (with helpful commit messages). You should have at least 15 commit messages in your history for the repo by the time you turn in the homework. Select one of the following two figures to create for the homework: Choice 1: Pick one city in the data. Create a map showing the locations of the homicides in that city, using the sf framework discussed in class. Use tigris to download boundaries for some sub-city geography (e.g., tracts, block groups, county subdivisions) to show as a layer underneath the points showing homicides. Use different facets for solved versus unsolved homicides and different colors to show the three race groups with the highest number of homicides for that city (you may find the fct_lump function from forcats useful for this). Choice 2: Recreate the graph shown below. It shows monthly homicides in Baltimore, with a reference added for the date of the arrest of Freddie Gray and color used to show colder months (November through April) versus warmer months (May through October). There is a smooth line added to help show seasonal and long-term trends in this data. B.6 Homework #6 Due date: Wednesday, Dec. 4 Read the article Good Enough Practices in Scientific Computing by Wilson et al. (available here). In a half page, describe which of these “pretty good practices” you are incorporating for your group project. Also list one or two practices that you are not following but that would have made sense and how you could change your practices to follow them. Find an article in The R Journal that describes an R package that you could use in your own research or otherwise find interesting. Describe why the package was created and what you think it’s most interesting features are. In an R Markdown document, run one or two of the R examples included in the article. "]
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