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berkeley-stat243 · Dec 5, 2024 · 0836b1f · 0836b1f
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diff --git a/.nojekyll b/.nojekyll
@@ -1 +1 @@
-f0eb44d4
+ff7d4a77
diff --git a/labs/lab0-setup.pdf b/labs/lab0-setup.pdf
diff --git a/labs/lab1-submission.pdf b/labs/lab1-submission.pdf
diff --git a/labs/lab2-testing.pdf b/labs/lab2-testing.pdf
diff --git a/labs/lab3-debugging.pdf b/labs/lab3-debugging.pdf
diff --git a/labs/lab4-reproducibility.pdf b/labs/lab4-reproducibility.pdf
diff --git a/labs/lab5-codereview.pdf b/labs/lab5-codereview.pdf
diff --git a/labs/lab6-scfparallel.pdf b/labs/lab6-scfparallel.pdf
diff --git a/labs/lab7-pythonvsr.pdf b/labs/lab7-pythonvsr.pdf
diff --git a/labs/lab8-simulation.pdf b/labs/lab8-simulation.pdf
diff --git a/labs/lab9-git.pdf b/labs/lab9-git.pdf
diff --git a/project/project.pdf b/project/project.pdf
diff --git a/ps/ps1.pdf b/ps/ps1.pdf
diff --git a/ps/ps2.pdf b/ps/ps2.pdf
diff --git a/ps/ps3.pdf b/ps/ps3.pdf
diff --git a/ps/ps4.pdf b/ps/ps4.pdf
diff --git a/ps/ps5.pdf b/ps/ps5.pdf
diff --git a/ps/ps6.pdf b/ps/ps6.pdf
diff --git a/ps/ps7.pdf b/ps/ps7.pdf
diff --git a/ps/ps8.pdf b/ps/ps8.pdf
diff --git a/sitemap.xml b/sitemap.xml
@@ -122,7 +122,7 @@
   </url>
   <url>
     <loc>https://stat243.berkeley.edu/fall-2024/units/unit12-graphics.html</loc>
-    <lastmod>2024-12-05T18:35:26.891Z</lastmod>
+    <lastmod>2024-12-05T18:48:39.325Z</lastmod>
   </url>
   <url>
     <loc>https://stat243.berkeley.edu/fall-2024/syllabus.html</loc>

diff --git a/units/graphics_files/life_exp.R b/units/graphics_files/life_exp.R
@@ -0,0 +1,25 @@
+library(tidyr)
+tmp <- read.csv('life_exp.csv') %>% pivot_longer(c(starts_with(c('le','sd')))) %>%
+    separate(name, into = c('type','sex','education'), sep = "_")
+
+## Hacky: this manipulation to get separate columns for life expectancy and sd
+## could be done more elegantly.
+data <- tmp[tmp$type == 'le', ]
+sd <- tmp[tmp$type == 'sd', ]
+
+names(data)[5] <- 'lifeexp'
+data$sd <- sd$value
+
+plot(data$lifeexp, data$sd)
+
+## Some different ways to display the relationships.
+
+library(ggplot2)
+
+ggplot(data, aes(x = lifeexp, y = sd)) + geom_point() + facet_wrap(~country)
+ggplot(data, aes(x = lifeexp, y = sd)) + geom_point(aes(color = sex, shape = education)) + facet_wrap(~country)
+
+ggplot(data, aes(x = lifeexp, y = sd)) + geom_point(aes(color = education)) + facet_wrap(~sex)
+
+ggplot(data, aes(x = lifeexp, y = sd)) + geom_point(aes(color = sex)) + facet_wrap(~education)
+
diff --git a/units/regex.pdf b/units/regex.pdf
diff --git a/units/unit1-intro.pdf b/units/unit1-intro.pdf
diff --git a/units/unit10-linalg.pdf b/units/unit10-linalg.pdf
diff --git a/units/unit11-optim.pdf b/units/unit11-optim.pdf
diff --git a/units/unit12-graphics.html b/units/unit12-graphics.html
@@ -564,7 +564,7 @@ <h2 class="anchored" data-anchor-id="some-example-graphics">Some example graphic
 <li>This <a href="https://www.nytimes.com/interactive/2024/08/02/climate/electricity-generation-us-states.html">NY Times article</a> shows the sources of electricity for the US and specific states.</li>
 <li>This <a href="http://www.nytimes.com/interactive/2016/08/08/sports/olympics/history-olympic-dominance-charts.html">NY Times article</a> presents time-series graphics about how many Olympic medals have been won by different countries.</li>
 </ul></li>
-<li><p>Consider <a href="https://academic.oup.com/view-large/figure/81018073/dyr146f2.gif">this scatterplot of life expectancy statistics</a> from <a href="https://academic.oup.com/ije/article/40/6/1703/801755">this article</a> on variation in lifespan in European countries in the International Journal of Epidemiology. What are you able to learn from this presentation of the data. What other ways could you plot the data to better illustrate patterns in the data? The data are in <a href="graphics_files/life_exp.csv">this CSV</a> with <a href="graphics_files/liffe_exp.R">some initial exploration of plotting approaches using <code>ggplot</code> in R</a>.</p></li>
+<li><p>Consider <a href="https://academic.oup.com/view-large/figure/81018073/dyr146f2.gif">this scatterplot of life expectancy statistics</a> from <a href="https://academic.oup.com/ije/article/40/6/1703/801755">this article</a> on variation in lifespan in European countries in the International Journal of Epidemiology. What are you able to learn from this presentation of the data. What other ways could you plot the data to better illustrate patterns in the data? The data are in <a href="graphics_files/life_exp.csv">this CSV</a> with <a href="graphics_files/life_exp.R">some initial exploration of plotting approaches using <code>ggplot</code> in R</a>.</p></li>
 <li><p>Here’s <a href="graphics_files/exampleGraphic.png">another online graphic</a>. What aspects of the graph could be improved? What aspects do you like?</p></li>
 <li><p><a href="graphics_files/shell.pdf">This file</a> in the repository has an example of a crazy pie chart from an advertisement in the NY Times from December 2014.</p></li>
 <li><p><a href="graphics_files/wainer1984.pdf">This article</a> in the repository is rather old but the ideas are still relevant and while the example figures are dated in terms of appearance, the same issues arise with more modern-looking graphics.</p></li>

diff --git a/units/unit12-graphics.pdf b/units/unit12-graphics.pdf
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diff --git a/units/unit2-dataTech.pdf b/units/unit2-dataTech.pdf
diff --git a/units/unit3-bash.pdf b/units/unit3-bash.pdf
diff --git a/units/unit4-goodPractices.pdf b/units/unit4-goodPractices.pdf
diff --git a/units/unit5-programming.pdf b/units/unit5-programming.pdf
diff --git a/units/unit6-parallel.pdf b/units/unit6-parallel.pdf
diff --git a/units/unit7-bigData.pdf b/units/unit7-bigData.pdf
diff --git a/units/unit8-numbers.pdf b/units/unit8-numbers.pdf
diff --git a/units/unit9-sim.pdf b/units/unit9-sim.pdf