From 3f2543527d774791058183b9b5be896964383bd2 Mon Sep 17 00:00:00 2001 From: Nics-Github Date: Mon, 11 Nov 2024 12:05:26 -0500 Subject: [PATCH] day 30 --- .../site_libs/revealjs/dist/theme/quarto.css | 7 +- chapter 7 examples.qmd | 13 +- .../execute-results/html.json | 16 + course-materials/lectures/24_data_masking.qmd | 177 ++ course-materials/lectures/26_maps-ggplot.qmd | 12 +- .../lectures/30_anonymous_functions.qmd | 129 ++ course-materials/lectures/30_map_review.qmd | 94 + docs/30_anonymous_functions.html | 725 +++++++ .../lectures/24_data_masking.html | 987 ++++++++++ .../figure-revealjs/unnamed-chunk-14-1.png | Bin 0 -> 61794 bytes .../lectures/26_maps-ggplot.html | 1709 ++++++++--------- .../figure-html/unnamed-chunk-2-1.png | Bin 15862 -> 0 bytes .../figure-html/unnamed-chunk-4-1.png | Bin 59536 -> 0 bytes .../figure-html/unnamed-chunk-6-1.png | Bin 46545 -> 0 bytes .../figure-html/unnamed-chunk-8-1.png | Bin 33589 -> 0 bytes .../figure-revealjs/unnamed-chunk-2-1.png | Bin 0 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examples.qmd @@ -45,12 +45,13 @@ library(tidyverse) library(mosaicData) my_func <- function(data,variable2){ - my_plot <- data |> - ggplot(aes(x = cesd))+ - aes_string(y = variable2)+ + data |> + ggplot(aes(x = cesd, y = .data[[variable2]]))+ geom_point() } +my_func(HELPrct, age) + numeric_names_only <- HELPrct |> select("age":"d1")|> names() @@ -69,12 +70,12 @@ library(tidyverse) library(mosaicData) my_func <- function(data,variable2){ - my_plot <- data |> - ggplot(aes(x = cesd, y = {{variable2}}))+ + data |> + ggplot(aes(x = cesd, y = variable2 ))+ geom_point() } -my_func(HELPrct, age) + ``` diff --git a/chapter 7 examples_files/execute-results/html.json b/chapter 7 examples_files/execute-results/html.json new file mode 100644 index 0000000..e535d8a --- /dev/null +++ b/chapter 7 examples_files/execute-results/html.json @@ -0,0 +1,16 @@ +{ + "hash": "037e0265372063b7a6475c0e653169e0", + "result": { + "markdown": "---\ntitle: \"map() practice\"\nauthor: \"Schwab\"\neditor: visual\n---\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(tidyverse)\n```\n\n::: {.cell-output .cell-output-stderr}\n```\n── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──\n✔ dplyr 1.1.4 ✔ readr 2.1.5\n✔ forcats 1.0.0 ✔ stringr 1.5.1\n✔ ggplot2 3.5.1 ✔ tibble 3.2.1\n✔ lubridate 1.9.3 ✔ tidyr 1.3.1\n✔ purrr 1.0.2 \n── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──\n✖ dplyr::filter() masks stats::filter()\n✖ dplyr::lag() masks stats::lag()\nℹ Use the conflicted package () to force all conflicts to become errors\n```\n:::\n\n```{.r .cell-code}\nlibrary(mosaicData)\n```\n:::\n\n\nDoing problems 1 and 3 from [here](https://mdsr-book.github.io/mdsr3e/07-iteration.html#iteration-exercises)\n\n**Problem 1 (Easy)**: Use the `HELPrct` data from the `mosaicData` to calculate the mean of all numeric variables (be sure to exclude missing values).\\\n\n\n::: {.cell}\n\n```{.r .cell-code}\nHELPrct |>\n summarise(\n across(.cols = where(is.numeric),\n .fns = ~mean(. , na.rm = TRUE))\n )\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n age anysubstatus cesd d1 daysanysub dayslink drugrisk e2b\n1 35.65342 0.7723577 32.84768 3.059603 75.30738 255.6056 1.887168 2.504673\n female i1 i2 id indtot linkstatus mcs pcs\n1 0.2362031 17.90728 24.54746 233.4018 35.72848 0.3781903 31.67668 48.04854\n pss_fr sexrisk avg_drinks max_drinks hospitalizations\n1 6.706402 4.642384 17.90728 24.54746 3.059603\n```\n:::\n:::\n\n\n**Problem 3 (Medium)**: Use routines from the `purrr` package and the `HELPrct` data frame from the `mosaicData` package to fit a regression model predicting `cesd` as a function of `age` separately for each of the levels of the `substance` variable. Generate a formatted table (with suitable caption) of results\n(estimates and confidence intervals) for the slope parameter for each level of the grouping variable.\n\n(Hint: Use `group_by()` and `group_modify()` to fit the regression model on each part. Note that `broom::tidy()` is useful in having the output of the model be a data frame.)\n\n\n::: {.cell}\n\n```{.r .cell-code}\nHELPrct |> select(substance, cesd, age) |>\ngroup_by(substance)|>\n group_modify(.f = ~broom::tidy( lm(cesd~age, data = .))) |> \n filter(term ==\"age\") |>\n mutate(CI_low = estimate - 2*std.error, CI_high = estimate + 2*std.error) |>\n select(-statistic, - p.value, - std.error) |>\n kableExtra::kable(caption = \"The likelyhood someone uses a substance increates with age.\", digits = 1)\n```\n\n::: {.cell-output-display}\nTable: The likelyhood someone uses a substance increates with age.\n\n|substance |term | estimate| CI_low| CI_high|\n|:---------|:----|--------:|------:|-------:|\n|alcohol |age | 0.3| 0.1| 0.6|\n|cocaine |age | -0.3| -0.6| 0.0|\n|heroin |age | -0.2| -0.5| 0.0|\n:::\n:::\n\n\nActivity 2\n\nMake a function that takes a df and a variable then produces a scatter plot of that variable vs cesd. Use it to plot multiple graphs of the first 6 numeric variables HELPrct. [Hint: check out the extended example from 7.7](https://mdsr-book.github.io/mdsr3e/07-iteration.html#extended-example-factors-associated-with-bmi)\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(tidyverse)\nlibrary(mosaicData)\n\nmy_func <- function(data,variable2){\n my_plot <- data |>\n ggplot(aes(x = cesd))+\n aes_string(y = variable2)+\n geom_point()\n}\n\nnumeric_names_only <- HELPrct |>\n select(\"age\":\"d1\")|>\n names()\n\n\nmap(.x = numeric_names_only,\n .f = my_func,\n data= HELPrct\n )|>\n patchwork::wrap_plots(ncol = 2)\n```\n\n::: {.cell-output .cell-output-stderr}\n```\nWarning: `aes_string()` was deprecated in ggplot2 3.0.0.\nℹ Please use tidy evaluation idioms with `aes()`.\nℹ See also `vignette(\"ggplot2-in-packages\")` for more information.\n```\n:::\n\n::: {.cell-output .cell-output-stderr}\n```\nWarning: Removed 207 rows containing missing values or values outside the scale range\n(`geom_point()`).\n```\n:::\n\n::: {.cell-output-display}\n![](chapter-7-examples_files/figure-html/unnamed-chunk-4-1.png){width=672}\n:::\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(tidyverse)\nlibrary(mosaicData)\n\nmy_func <- function(data,variable2){\n my_plot <- data |>\n ggplot(aes(x = cesd, y = {{variable2}}))+\n geom_point()\n}\n\nmy_func(HELPrct, age)\n```\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(NHANES)\n\nbmi_plot <- function(.data, x_var) {\n ggplot(.data, aes(y = BMI)) +\n aes_string(x = x_var) + \n geom_jitter(alpha = 0.3) + \n geom_smooth() + \n labs(\n title = paste(\"BMI by\", x_var),\n subtitle = \"NHANES\",\n caption = \"US National Center for Health Statistics (NCHS)\"\n )\n}\nbmi_plot(NHANES, \"Age\")\n```\n\n::: {.cell-output .cell-output-stderr}\n```\n`geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = \"cs\")'\n```\n:::\n\n::: {.cell-output .cell-output-stderr}\n```\nWarning: Removed 366 rows containing non-finite outside the scale range\n(`stat_smooth()`).\n```\n:::\n\n::: {.cell-output .cell-output-stderr}\n```\nWarning: Removed 366 rows containing missing values or values outside the scale range\n(`geom_point()`).\n```\n:::\n\n::: {.cell-output-display}\n![](chapter-7-examples_files/figure-html/unnamed-chunk-6-1.png){width=672}\n:::\n\n```{.r .cell-code}\nc(\"Age\", \"HHIncomeMid\", \"PhysActiveDays\", \n \"TVHrsDay\", \"AlcoholDay\", \"Pulse\") |>\n map(bmi_plot, .data = NHANES) |>\n patchwork::wrap_plots(ncol = 2)\n```\n\n::: {.cell-output .cell-output-stderr}\n```\n`geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = \"cs\")'\n```\n:::\n\n::: {.cell-output .cell-output-stderr}\n```\nWarning: Removed 366 rows containing non-finite outside the scale range\n(`stat_smooth()`).\nRemoved 366 rows containing missing values or values outside the scale range\n(`geom_point()`).\n```\n:::\n\n::: {.cell-output .cell-output-stderr}\n```\n`geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = \"cs\")'\n```\n:::\n\n::: {.cell-output .cell-output-stderr}\n```\nWarning: Removed 1148 rows containing non-finite outside the scale range\n(`stat_smooth()`).\n```\n:::\n\n::: {.cell-output .cell-output-stderr}\n```\nWarning: Removed 1148 rows containing missing values or values outside the scale range\n(`geom_point()`).\n```\n:::\n\n::: {.cell-output .cell-output-stderr}\n```\n`geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = \"cs\")'\n```\n:::\n\n::: {.cell-output .cell-output-stderr}\n```\nWarning: Removed 5442 rows containing non-finite outside the scale range\n(`stat_smooth()`).\n```\n:::\n\n::: {.cell-output .cell-output-stderr}\n```\nWarning: Failed to fit group -1.\nCaused by error in `smooth.construct.cr.smooth.spec()`:\n! x has insufficient unique values to support 10 knots: reduce k.\n```\n:::\n\n::: {.cell-output .cell-output-stderr}\n```\nWarning: Removed 5442 rows containing missing values or values outside the scale range\n(`geom_point()`).\n```\n:::\n\n::: {.cell-output .cell-output-stderr}\n```\n`geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = \"cs\")'\n```\n:::\n\n::: {.cell-output .cell-output-stderr}\n```\nWarning: Removed 366 rows containing non-finite outside the scale range\n(`stat_smooth()`).\n```\n:::\n\n::: {.cell-output .cell-output-stderr}\n```\nWarning: Removed 366 rows containing missing values or values outside the scale range\n(`geom_point()`).\n```\n:::\n\n::: {.cell-output .cell-output-stderr}\n```\n`geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = \"cs\")'\n```\n:::\n\n::: {.cell-output .cell-output-stderr}\n```\nWarning: Removed 5113 rows containing non-finite outside the scale range\n(`stat_smooth()`).\n```\n:::\n\n::: {.cell-output .cell-output-stderr}\n```\nWarning: Removed 5113 rows containing missing values or values outside the scale range\n(`geom_point()`).\n```\n:::\n\n::: {.cell-output .cell-output-stderr}\n```\n`geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = \"cs\")'\n```\n:::\n\n::: {.cell-output .cell-output-stderr}\n```\nWarning: Removed 1502 rows containing non-finite outside the scale range\n(`stat_smooth()`).\n```\n:::\n\n::: {.cell-output .cell-output-stderr}\n```\nWarning: Removed 1502 rows containing missing values or values outside the scale range\n(`geom_point()`).\n```\n:::\n\n::: {.cell-output-display}\n![](chapter-7-examples_files/figure-html/unnamed-chunk-6-2.png){width=672}\n:::\n:::\n", + "supporting": [ + "chapter-7-examples_files" + ], + "filters": [ + "rmarkdown/pagebreak.lua" + ], + "includes": {}, + "engineDependencies": {}, + "preserve": {}, + "postProcess": true + } +} \ No newline at end of file diff --git a/course-materials/lectures/24_data_masking.qmd b/course-materials/lectures/24_data_masking.qmd new file mode 100644 index 0000000..5479eb8 --- /dev/null +++ b/course-materials/lectures/24_data_masking.qmd @@ -0,0 +1,177 @@ +--- +title: "data masking practice" +format: + revealjs: + theme: beige + +editor: visual +execute: + echo: true +--- + +```{r} +library(tidyverse) +``` + +# Scope + +Local vs. Global variables + +```{r} +x <- 10 + +df <- tribble( + ~x, ~y, ~ z , + 5, 0 , 0 +) +``` + +x and df are global. They are referred to as environment variables. + +df#x and df\$y are local to the dataframe. They are often referred to as stats-variables. + +## The dollar sign operator + +We don't use the dollar sign operator often, because we use the tidyverse which makes it unnecessary. + +`$` selects a variable. + +```{r} +df$x +# or +df[1:3] +``` + +We can select a variable with tidyr from the tidyverse. + +```{r} +df |> + select(x,y,z) +``` + +This makes things easier to read and less redundant. + +## Make a function + +```{r} +my_printer <- function(variable){ + print(variable) +} +``` + +To print out 5 we need to tell R where to find the variable 5. + +```{r} +my_printer(x) +my_printer(df$x) +``` + +my_printer() looks through the environment first. + +So my_printer(x)=10 and not 5. + +## tidyverse problems + +The tidyverse uses a process called masking to blur the lines between environment and data variables. + +This is why we like it so much. + +However it makes programming with the tidyverse functions more challenging. + +## Use summarize to calculate a mean. + +The code below works as expected because tidyr is working under the hood to select x from df. + +```{r} +df |> + summarise(mean(x)) +``` + +## A mean calculating function + +We get into problems when using tidyverse functions within homemade functions: + +```{r} +my_mean_maker <- function(data, variable){ + data |> + summarise(mean(variable)) +} +``` + +```{r} +my_mean_maker(data= df, variable = x) +``` + +We're still getting 10 in our output! It should be 5, because that is the value from the data frame. + +my_mean_maker() goes to the global environment because it doesn't recognize that it should be looking in the data frame. + +## When is tidyr working? + +We can check to see if tidyr is working under the hood by checking the arguments of a function. + +We are looking for the words "data-masking" or "tidy-select". + +If these words are not present we will not need to inject the data (aka unmask the data). + +```{r} +?summarise +``` + +## Solution + +We need to inject the data into the sumarize function. We can do this by **unmasking** the data with {{}}. + +This tells R to look for the variable within the dataframe, not the global environment. + +```{r} +my_mean_maker <- function(data, variable){ + data |> + summarise(mean({{variable}})) +} +``` + +```{r} +my_mean_maker(data= df, variable = x) +``` + +## Problem 1 + +Make a function that will calculate the mean, median, and standard deviation of any variable from a data frame using tidyverse functions. + +Use `filter( !is.na() )` or `na.rm = TRUE` to remove missing variables. + +Test your function on the `starwars` data `height` variable. + +## Solution 1 + +```{r} +centers_of_data <- function(data, variable){ + data|> + filter(!is.na({{variable}})) |> + summarise(mean = mean({{variable}}), median = median({{variable}}), sd= sd({{variable}})) +} + + +centers_of_data(starwars, height) +``` + +## Problem 2 + +Make a function that makes a bargraph of a categorical variable. + +Test your function on the `starwars` `eye_color` variable. + +## Solution 2 + +```{r} +my_bar_grapher <- function(data, variable){ + data |> + ggplot(aes({{variable}}))+ + geom_bar() + + theme_minimal() +} + +my_bar_grapher(starwars, eye_color) + +``` diff --git a/course-materials/lectures/26_maps-ggplot.qmd b/course-materials/lectures/26_maps-ggplot.qmd index 1076934..1fc660f 100644 --- a/course-materials/lectures/26_maps-ggplot.qmd +++ b/course-materials/lectures/26_maps-ggplot.qmd @@ -78,7 +78,7 @@ Coordinate Reference Systems (CRS) - Is the type of map projection employed To g Which one will coord_sf default to? ```{r} -# ?coord_sf +?coord_sf ``` [This is the crs commonly used in gps.](https://epsg.io/4326) @@ -132,13 +132,3 @@ ma_counties |> ggplot(aes(long,lat))+ Often times map data is saved as a shape file. The US government has shape files [here](https://www.census.gov/geographies/mapping-files/time-series/geo/carto-boundary-file.html). - -```{r} -map_url <- "https://www2.census.gov/geo/tiger/GENZ2018/shp/cb_2018_us_region_500k.zip" -us_region <- download.file(url = map_url, - destfile = basename(map_url)) - -#regions <- read_sf("us_region_unzipped") |> -# sf::st_transform('+proj=longlat +datum=WGS84') - -``` diff --git a/course-materials/lectures/30_anonymous_functions.qmd b/course-materials/lectures/30_anonymous_functions.qmd new file mode 100644 index 0000000..98b15db --- /dev/null +++ b/course-materials/lectures/30_anonymous_functions.qmd @@ -0,0 +1,129 @@ +--- +title: "Anonymous Functions" +author: "Schwab" +editor: visual +format: + revealjs: + theme: beige +execute: + echo: true +--- + +## Digression + +Anonymous functions are functions without a name. + +## A named function + +```{r} +library(stringr) + +#This function has a name, upper_case + +upper_case <- function(word){ + str_to_upper(word) +} + +upper_case("hi") +``` + +## Anonymous functions + +This is the same function without a name + +```{r} +function(word) str_to_upper(word) +``` + +Its harder to use by itself, we often use them in combination with other functions. + +## Using the anonymous function + +The function after .f will only be used once, we don't need to name it. + +```{r} +words <- c("I", "Like", "to", "eat", "cheese", "!") +purrr::map_chr( + .x = words, + .f = function(word) str_to_upper(word) +) +``` + +## Name an anonymous function + +You can name an anonymous function. + +```{r} +# This works with map_chhr() because str_to_upper is vectorized + +upper_case_anon <- function(word) str_to_upper(word) +upper_case_anon(words) +``` + +## Does this work? + +```{r} +# This works because str_to_upper is vectorized + +upper_case_anon <- function(word) + str_to_upper(word) + +upper_case_anon(words) +``` + +Yes, but it can get confusing. + +## This does not work + +```{r, eval=FALSE} +library(tidyverse) + +confusing_function <- function(word) + upper_case_words<- + str_to_upper(word) +## This should lower the word's case, but it doesn't because its not part of the function. + str_to_lower(upper_case_words) + +confusing_function(words) +``` + +## Last example fixed with + +```{r} +confusing_function <- function(word){ + upper_case_words<- + str_to_upper(word) + ## Now it is part of the function. + str_to_lower(upper_case_words) +} + +confusing_function(words) +``` + +## Summary + +Use an anonymous function to do one thing. + +If it get complicated give some {} + +## More anonymous? + +Since R version 4.1, May 2021. + +Just use a `\(argument) operations` + +```{r, eval=FALSE} +# This is also an anonymous function. +\(word) str_to_upper(word) + +map_chr(.x = words, + .f = \(word) str_to_upper(word)) +``` + +## Deep dive + +There are actually lots of ways of making anonymous functions. + +[Here are more.](https://stackoverflow.com/questions/52492462/define-an-anonymous-function-without-using-the-function-keyword) I like the idea of the lambda package. + +## SDS 270 for more on functions in R diff --git a/course-materials/lectures/30_map_review.qmd b/course-materials/lectures/30_map_review.qmd new file mode 100644 index 0000000..7b4cfb3 --- /dev/null +++ b/course-materials/lectures/30_map_review.qmd @@ -0,0 +1,94 @@ +--- +title: "Map Review" +author: "Schwab" +format: + revealjs: + theme: beige +editor: visual +--- + +## Map code + +Your map code is probably in lab 1 or the website project. + +## Quick Review + +Quick Review of maps from the beginning of the semester. + +Simple Maps with the `maps` package + +```{r} +library(maps) +map(database = "state", regions = "Massachusetts") +``` + +```{r} +west_coast <-c('California',"Oregon","Washington") + +colors <- c('blue', 'pink','orange','yellow') + +map("state", regions = west_coast, col = colors, fill = TRUE) +``` + +## Get into trouble + +R gets confused when we have two packages loaded with the same name. + +Note that in the case below the maps maps wins out. We even get a message. + +```{r} +detach("package:maps", unload = TRUE) +library(maps) +library(purrr) + +west_coast <-c('California',"Oregon","Washington") + +colors <- c('blue', 'pink','orange','yellow') + +map("state", regions = west_coast, col = colors, fill = TRUE) + +``` + +## Now try the map from purr + +This is a silly example. Just to show we sometimes have to be explicit with R when choosing our functions + +```{r} +US_city_names <- maps::us.cities |> + select(name) + +# R is still thinking map() is from maps we have to be explicit +map(.x = US_city_names, + .f = ~str_detect(.x, "a")) + +# Explicitly tell R we want the map() from purrr +purrr::map(.x = US_city_names, + .f = ~str_detect(.x, "a")) + +``` + +## More geospacial map review + +We also made fancier maps + +```{r} +library(leaflet) +home_icon <- awesomeIcons() +school_icon <- awesomeIcons(icon = "education") + +# Put my awesome icons on the map. +leaflet()|> + addTiles() |> + addAwesomeMarkers( + lng = -122.1569, + lat = 37.7258, + popup = "I lived here once", + label = "SAN LEANDRO", + icon = home_icon + ) |> + addAwesomeMarkers(lng = -72.6403, + lat = 42.3163, + popup = "Home of the Pioneers", + label = "Smith College", + icon = school_icon) +``` diff --git a/docs/30_anonymous_functions.html b/docs/30_anonymous_functions.html new file mode 100644 index 0000000..bb78e41 --- /dev/null +++ b/docs/30_anonymous_functions.html @@ -0,0 +1,725 @@ + + + + + + + + + + +SDS 192 - Anonymous Functions + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Anonymous Functions

+
+ + + +
+ +
+
Author
+
+

Schwab

+
+
+ + + +
+ + +
+ +
+

Digression

+

Anonymous functions are functions without a name.

+
+
+

A named function

+
+
library(stringr)
+
+#This function has a name, upper_case
+
+upper_case <-  function(word){
+  str_to_upper(word)
+}
+
+upper_case("hi")
+
+
[1] "HI"
+
+
+
+
+

Anonymous functions

+

This is the same function without a name

+
+
function(word) str_to_upper(word)
+
+
function(word) str_to_upper(word)
+
+
+

Its harder to use by itself, we often use them in combination with other functions.

+
+
+

Using the anonymous function

+

The function after .f will only be used once, we don’t need to name it.

+
+
words <- c("I", "Like", "to", "eat", "cheese", "!")
+purrr::map_chr(
+  .x = words,
+  .f = function(word) str_to_upper(word)
+)
+
+
[1] "I"      "LIKE"   "TO"     "EAT"    "CHEESE" "!"     
+
+
+
+
+

Name an anonymous function

+

You can name an anonymous function.

+
+
# This works with map_chhr() because str_to_upper is vectorized
+
+upper_case_anon <- function(word) str_to_upper(word)
+upper_case_anon(words)
+
+
[1] "I"      "LIKE"   "TO"     "EAT"    "CHEESE" "!"     
+
+
+
+
+

Does this work?

+
+
# This works because str_to_upper is vectorized
+
+upper_case_anon <- function(word) 
+  str_to_upper(word)
+
+upper_case_anon(words)
+
+
[1] "I"      "LIKE"   "TO"     "EAT"    "CHEESE" "!"     
+
+
+

Yes, but it can get confusing.

+
+
+

This does not work

+
+
library(tidyverse)
+
+confusing_function <- function(word) 
+  upper_case_words<- 
+    str_to_upper(word)
+## This should lower the word's case, but it doesn't because its not part of the function.  
+  str_to_lower(upper_case_words)
+
+confusing_function(words)
+
+
+
+

Last example fixed with

+
+
confusing_function <- function(word){ 
+  upper_case_words<- 
+    str_to_upper(word)
+  ## Now it is part of the function.  
+  str_to_lower(upper_case_words)
+}
+  
+confusing_function(words)
+
+
[1] "i"      "like"   "to"     "eat"    "cheese" "!"     
+
+
+
+
+

Summary

+

Use an anonymous function to do one thing.

+

If it get complicated give some {}

+
+
+

More anonymous?

+

Since R version 4.1, May 2021.

+

Just use a \(argument) operations

+
+
# This is also an anonymous function. 
+\(word) str_to_upper(word)
+
+map_chr(.x = words,
+    .f = \(word) str_to_upper(word))
+
+
+
+

Deep dive

+

There are actually lots of ways of making anonymous functions.

+

Here are more. I like the idea of the lambda package.

+
+
+

SDS 270 for more on functions in R

+ + +
+ +
+ +
+
+ +
+ + + + \ No newline at end of file diff --git a/docs/course-materials/lectures/24_data_masking.html b/docs/course-materials/lectures/24_data_masking.html new file mode 100644 index 0000000..a5201a2 --- /dev/null +++ b/docs/course-materials/lectures/24_data_masking.html @@ -0,0 +1,987 @@ + + + + + + + + + + + + + + SDS 192 - data masking practice + + + + + + + + + + + + + + + +
+
+ +
+

data masking practice

+ +
+
+ +
+
+ +
+
library(tidyverse)
+
+
+
+
+

Scope

+

Local vs. Global variables

+
+
x <- 10
+
+df <- tribble(
+  ~x, ~y, ~ z ,
+  5, 0 , 0
+)
+
+

x and df are global. They are referred to as environment variables.

+

df#x and df$y are local to the dataframe. They are often referred to as stats-variables.

+
+
+

The dollar sign operator

+

We don’t use the dollar sign operator often, because we use the tidyverse which makes it unnecessary.

+

$ selects a variable.

+
+
df$x
+
+
[1] 5
+
+
# or 
+df[1:3]
+
+
# A tibble: 1 × 3
+      x     y     z
+  <dbl> <dbl> <dbl>
+1     5     0     0
+
+
+

We can select a variable with tidyr from the tidyverse.

+
+
df |>
+  select(x,y,z)
+
+
# A tibble: 1 × 3
+      x     y     z
+  <dbl> <dbl> <dbl>
+1     5     0     0
+
+
+

This makes things easier to read and less redundant.

+
+
+

Make a function

+
+
my_printer <- function(variable){
+  print(variable)
+}
+
+

To print out 5 we need to tell R where to find the variable 5.

+
+
my_printer(x)
+
+
[1] 10
+
+
my_printer(df$x)
+
+
[1] 5
+
+
+

my_printer() looks through the environment first.

+

So my_printer(x)=10 and not 5.

+
+
+

tidyverse problems

+

The tidyverse uses a process called masking to blur the lines between environment and data variables.

+

This is why we like it so much.

+

However it makes programming with the tidyverse functions more challenging.

+
+
+

Use summarize to calculate a mean.

+

The code below works as expected because tidyr is working under the hood to select x from df.

+
+
df |>
+    summarise(mean(x))
+
+
# A tibble: 1 × 1
+  `mean(x)`
+      <dbl>
+1         5
+
+
+
+
+

A mean calculating function

+

We get into problems when using tidyverse functions within homemade functions:

+
+
my_mean_maker <- function(data, variable){
+  data |>
+    summarise(mean(variable))
+}
+
+
+
my_mean_maker(data= df, variable = x)
+
+
# A tibble: 1 × 1
+  `mean(variable)`
+             <dbl>
+1               10
+
+
+

We’re still getting 10 in our output! It should be 5, because that is the value from the data frame.

+

my_mean_maker() goes to the global environment because it doesn’t recognize that it should be looking in the data frame.

+
+
+

When is tidyr working?

+

We can check to see if tidyr is working under the hood by checking the arguments of a function.

+

We are looking for the words “data-masking” or “tidy-select”.

+

If these words are not present we will not need to inject the data (aka unmask the data).

+
+
?summarise
+
+
+
+

Solution

+

We need to inject the data into the sumarize function. We can do this by unmasking the data with {{}}.

+

This tells R to look for the variable within the dataframe, not the global environment.

+
+
my_mean_maker <- function(data, variable){
+  data |>
+    summarise(mean({{variable}}))
+}
+
+
+
my_mean_maker(data= df, variable = x)
+
+
# A tibble: 1 × 1
+  `mean(x)`
+      <dbl>
+1         5
+
+
+
+
+

Problem 1

+

Make a function that will calculate the mean, median, and standard deviation of any variable from a data frame using tidyverse functions.

+

Use filter( !is.na() ) or na.rm = TRUE to remove missing variables.

+

Test your function on the starwars data height variable.

+
+
+

Solution 1

+
+
centers_of_data <- function(data, variable){
+  data|>
+    filter(!is.na({{variable}})) |>
+    summarise(mean = mean({{variable}}), median = median({{variable}}), sd= sd({{variable}}))
+}
+
+
+centers_of_data(starwars, height)
+
+
# A tibble: 1 × 3
+   mean median    sd
+  <dbl>  <int> <dbl>
+1  175.    180  34.8
+
+
+
+
+

Problem 2

+

Make a function that makes a bargraph of a categorical variable.

+

Test your function on the starwars eye_color variable.

+
+
+

Solution 2

+
+
my_bar_grapher <- function(data, variable){
+  data |>
+    ggplot(aes({{variable}}))+
+    geom_bar() +
+    theme_minimal()
+}
+
+my_bar_grapher(starwars, eye_color)
+ +
+ + +
+
+
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zD;;H-tqo%Q&h4dnYz1x3r$zt;S-@Ac*qcJ&{T?#%6*Fw}plm+?9T6S!enR2#Yw=~I zEK0A2%EsGyP&>e?I1E#)k=?BzW1(vVstK(_8C@UXyVUPAqp2 zP$=AfjiZa;Ei9`rH1@l~z9u4hD?;i<&^8W(m#T9g>0eHO0h8 zECCUr2Foh-TCE&nM@@oem#8&hFigD*p~t_(<{Qedi}@mgq+G(vdHl3pYk7jbppfM3 zpi?5)ruwH4JA4ufsZm>rmXn}m(FV$ZQMp4!H!v2!q`n#?%F<1c5M$;p%1ERb+~yQO z`_jWa%bd1B?4yRLUA({@1j|t9GO@cnukADJ5L|ZtD52U@DCNe7M^gs&7OPs zN3jKR4ZR`9{-Rf7y#HxhkiI$72WX?0EprtwUiktE7MiQtzzx&`**`X*#YW5@gpw~F zwwr#x@jydpn}G1aMHh1KRWszo;+vR4y`TPFo;Ry9^0upbz%#Tie$gYg8A`xG5}daI z4s6i3Y`M-51DnbpttgI?@hTi2aN62fGramW5B|u?N=@3{;y>*B`_ec;H0gz)*9(8D zi)jYBwwcCiw#^qkd~!AGjmND9FTTWcSDv^AQ(NHLH6B`MOhNKOla?!#*6ajg7o+*h z%gD5Y*_$lPe?&^T3=6*bFru-_g=I+x=Sr-bMNqOGM@K zUojCtE#T$^dXKNIy>@DTw=zt|SebKfpTcZ}nfV^MVC2eT7B}=wp1l`H!x| r2}-ikUSG}o>yZ4num3;wzz=-Q_+IYw-lg=%0RQA=l% - - - - - - - - - -SDS 192 - Static Maps w/ ggplot - - - - - - - - - - + - + + - - - - - - - - + + + + + + SDS 192 - Static Maps w/ ggplot + + + + + + + + + + + + + + + +
+
+ +
+

Static Maps w/ ggplot

+ +
+
+
+Schwab +
+
-
-

tidygeocoder

-

Let’s find the latitude and longitude for a couple of places.

+
+

Libraries

-
addresses<- tibble(address=c("Smith College", "2 Tyler Ct, Northampton, MA 01060", "Holyoke Community College")) |>
-geocode(address, method="osm") |>
-  mutate(name = c("Smith College","McConnell Hall","Holyoke Community College"))
-
-
Passing 3 addresses to the Nominatim single address geocoder
+
library(tidyverse)
+library(sf)
+library(tidygeocoder)
+library(maps)
-
-
Query completed in: 3 seconds
-
-
addresses
+
+
+

tidygeocoder

+

Let’s find the latitude and longitude for a couple of places.

+
+
addresses<- tibble(address=c("Smith College", "2 Tyler Ct, Northampton, MA 01060", "Holyoke Community College")) |>
+geocode(address, method="osm") |>
+  mutate(name = c("Smith College","McConnell Hall","Holyoke Community College"))
+
+addresses
# A tibble: 3 × 4
   address                             lat  long name                     
@@ -265,32 +430,26 @@ 

tidygeocoder

-
-

My first map

+
+

My first map

Let’s plot those with ggplot.

-
ggplot(addresses) +
-  geom_point(aes(long,lat)) 
-
-
-
-

-
-
-
+
ggplot(addresses) +
+  geom_point(aes(long,lat)) 
+
-

Not very exciting. We’ll add it it in a moment.

+

Not very exciting. We’ll add it it in a moment.

-
-

Mass Map data

+
+

Mass Map data

Let’s build a map of Massachusetts with the map_data() function

-
# map_data comes from ggplot2
-
-ma_counties <- map_data("county", "massachusetts") |>
-  select( long, lat, group, id = subregion)
-
-head(ma_counties)
+
# map_data comes from ggplot2
+
+ma_counties <- map_data("county", "massachusetts") |>
+  select( long, lat, group, id = subregion)
+
+head(ma_counties)
       long      lat group         id
 1 -70.67435 41.73997     1 barnstable
@@ -302,63 +461,51 @@ 

Mass Map data

-
-

Mass Map

+
+

Mass Map

Notice there are a lot of points for barnstable. Those are the vertices of a polygon. Let’s draw the polygon with the points.

-
ma_counties |> ggplot(aes(long,lat,group=group))+
-  geom_polygon(fill = "white", colour = "grey50")+
-  geom_point()  
-
-
-
-

-
-
-
-
  #coord_sf()
+
ma_counties |> ggplot(aes(long,lat,group=group))+
+  geom_polygon(fill = "white", colour = "grey50")+
+  geom_point()  
+ +
  #coord_sf()
-
-
-

Mapping projections

+
+
+

Mapping projections

Coordinate Reference Systems (CRS) - Is the type of map projection employed To get a 3D Earth to a 2D surface.

Which one will coord_sf default to?

-
# ?coord_sf
+
?coord_sf

This is the crs commonly used in gps.

-
-

West wing explanation

+
+

West wing explanation

West Wing

-
-

Adding the 4326 crs

+
+

Adding the 4326 crs

-
ma_counties |> ggplot(aes(long,lat,group=group))+
-  geom_polygon(fill = "white", colour = "grey50")+
-  geom_point()+
-  coord_sf()
-
-
-
-

-
-
-
+
ma_counties |> ggplot(aes(long,lat,group=group))+
+  geom_polygon(fill = "white", colour = "grey50")+
+  geom_point()+
+  coord_sf()
+
-
-
-

Add some cities

+
+
+

Add some cities

Let’s grab just Massachusetts cities from the data frame us.cities.

-
# us.cities comes from the maps package
-
-ma_cities <- us.cities |>
-  filter(country.etc =="MA") |>
-  select(name, long, lat)
-
-head(ma_cities)
+
# us.cities comes from the maps package
+
+ma_cities <- us.cities |>
+  filter(country.etc =="MA") |>
+  select(name, long, lat)
+
+head(ma_cities)
                name   long   lat
 1         Andover MA -71.14 42.65
@@ -370,619 +517,425 @@ 

Add some cities

-
-

Exercise:

+
+

Exercise:

Plot MA cities onto our county map. Then clean up our map.

-
ma_counties |> ggplot(aes(long,lat))+
-  geom_polygon(fill = "white", colour = "grey50",aes(group=group)) +
-  #geom_point(data=ma_cities,.......)
-  
-  #This is the default mapping. Think GPS
- coord_sf(default_crs= st_crs(4326) )
-
-
-
-

-
-
-
-
          # This is Mercator
-          # st_crs(3857))
+
ma_counties |> ggplot(aes(long,lat))+
+  geom_polygon(fill = "white", colour = "grey50",aes(group=group)) +
+  #geom_point(data=ma_cities,.......)
+  
+  #This is the default mapping. Think GPS
+ coord_sf(default_crs= st_crs(4326) )
+ +
          # This is Mercator
+          # st_crs(3857))
-
-
-

Shape files

+
+
+

Shape files

Often times map data is saved as a shape file.

The US government has shape files here.

-
-
map_url <- "https://www2.census.gov/geo/tiger/GENZ2018/shp/cb_2018_us_region_500k.zip"
-us_region <- download.file(url = map_url,
-                           destfile = basename(map_url))
-
-#regions <- read_sf("us_region_unzipped") |>
-#  sf::st_transform('+proj=longlat +datum=WGS84')
-
+
+
+
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+
+ +
+

Anonymous Functions

+ +
+
+
+Schwab +
+
+
+ +
+
+

Digression

+

Anonymous functions are functions without a name.

+
+
+

A named function

+
+
library(stringr)
+
+#This function has a name, upper_case
+
+upper_case <-  function(word){
+  str_to_upper(word)
+}
+
+upper_case("hi")
+
+
[1] "HI"
+
+
+
+
+

Anonymous functions

+

This is the same function without a name

+
+
function(word) str_to_upper(word)
+
+
function(word) str_to_upper(word)
+
+
+

Its harder to use by itself, we often use them in combination with other functions.

+
+
+

Using the anonymous function

+

The function after .f will only be used once, we don’t need to name it.

+
+
words <- c("I", "Like", "to", "eat", "cheese", "!")
+purrr::map_chr(
+  .x = words,
+  .f = function(word) str_to_upper(word)
+)
+
+
[1] "I"      "LIKE"   "TO"     "EAT"    "CHEESE" "!"     
+
+
+
+
+

Name an anonymous function

+

You can name an anonymous function.

+
+
# This works with map_chhr() because str_to_upper is vectorized
+
+upper_case_anon <- function(word) str_to_upper(word)
+upper_case_anon(words)
+
+
[1] "I"      "LIKE"   "TO"     "EAT"    "CHEESE" "!"     
+
+
+
+
+

Does this work?

+
+
# This works because str_to_upper is vectorized
+
+upper_case_anon <- function(word) 
+  str_to_upper(word)
+
+upper_case_anon(words)
+
+
[1] "I"      "LIKE"   "TO"     "EAT"    "CHEESE" "!"     
+
+
+

Yes, but it can get confusing.

+
+
+

This does not work

+
+
library(tidyverse)
+
+confusing_function <- function(word) 
+  upper_case_words<- 
+    str_to_upper(word)
+## This should lower the word's case, but it doesn't because its not part of the function.  
+  str_to_lower(upper_case_words)
+
+confusing_function(words)
+
+
+
+

Last example fixed with

+
+
confusing_function <- function(word){ 
+  upper_case_words<- 
+    str_to_upper(word)
+  ## Now it is part of the function.  
+  str_to_lower(upper_case_words)
+}
+  
+confusing_function(words)
+
+
[1] "i"      "like"   "to"     "eat"    "cheese" "!"     
+
+
+
+
+

Summary

+

Use an anonymous function to do one thing.

+

If it get complicated give some {}

+
+
+

More anonymous?

+

Since R version 4.1, May 2021.

+

Just use a \(argument) operations

+
+
# This is also an anonymous function. 
+\(word) str_to_upper(word)
+
+map_chr(.x = words,
+    .f = \(word) str_to_upper(word))
+
+
+
+

Deep dive

+

There are actually lots of ways of making anonymous functions.

+

Here are more. I like the idea of the lambda package.

+
+
+

SDS 270 for more on functions in R

+ + +
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/index.html b/docs/index.html index a028617..e21ea11 100644 --- a/docs/index.html +++ b/docs/index.html @@ -7,7 +7,7 @@ - + SDS 192 - SDS 192: Intro to Data Science

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