Merge pull request #118 from fhdsl/functions-changes

Tweaks and re-render

modules/Functions/lab/Functions_Lab.Rmd

@@ -14,14 +14,12 @@ knitr::opts_chunk$set(echo = TRUE)
 Load all the libraries we will use in this lab. 
 
 ```{r message=FALSE}
-library(readr)
-library(dplyr)
-library(ggplot2)
+library(tidyverse)
 ```
 
 ### 1.1
 
-Create a function that takes one argument, a vector, and returns the sum of the vector and squares the result. Call it "sum_squared". Test your function on the vector `c(2,7,21,30,90)` - you should get the answer 22500.
+Create a function that takes one argument, a vector, and returns the sum of the vector and then squares the result. Call it "sum_squared". Test your function on the vector `c(2,7,21,30,90)` - you should get the answer 22500.
 
 ```
 # General format
@@ -73,9 +71,7 @@ Create a new number `b_num` that is not contained with `nums`. Use your updated
 Read in the CalEnviroScreen from https://daseh.org/data/CalEnviroScreen_data.csv. Assign the data the name "ces".
 
 ```{r message = FALSE, label = '2.1response'}
-ces <- read_csv("https://daseh.org/data/CalEnviroScreen_data.csv")
-# If downloaded
-# ces <- read_csv("CalEnviroScreen_data.csv")
+
 ```
 
 ### 2.2
@@ -86,9 +82,8 @@ We want to get some summary statistics on water contamination. Use `across` insi
 # General format
 data %>%
   summarize(across(
-    .cols = {vector or tidyselect},
-    .fns = {some function},
-    {additional arguments}
+    {vector or tidyselect},
+    {some function}
   ))
 ```
 
@@ -99,14 +94,14 @@ data %>%
 
 ### 2.3
 
-Use `across` and `mutate` to convert all columns containing the word "Pctl" into proportions (i.e., divide that value by 100). **Hint**: use `contains()` to select the right columns within `across()`. Use a "function on the fly" to divide by 100 (`function(x) x / 100`). It will also be easier to check your work if you `select()` columns that match "Pctl".
+Use `across` and `mutate` to convert all columns containing the word "Pctl" into proportions (i.e., divide that value by 100). **Hint**: use `contains()` to select the right columns within `across()`. Use an anonymous function ("function on the fly") to divide by 100 (`function(x) x / 100`). It will also be easier to check your work if you `select()` columns that match "Pctl".
 
 ```
 # General format
 data %>%
   mutate(across(
-    .cols = {vector or tidyselect},
-    .fns = {some function}
+    {vector or tidyselect},
+    {some function}
   ))
 ```
 
@@ -118,16 +113,14 @@ data %>%
 
 ### P.2
 
-
-
-Use `across` and `mutate` to convert all columns starting with the string "PM" into a binary variable: TRUE if the value is greater than 10 and FALSE if less than or equal to 10. **Hint**: use `starts_with()` to select the columns that start with "PM". Use a "function on the fly" to do a logical test if the value is greater than 10.
+Use `across` and `mutate` to convert all columns starting with the string "PM" into a binary variable: TRUE if the value is greater than 10 and FALSE if less than or equal to 10. **Hint**: use `starts_with()` to select the columns that start with "PM". Use an anonymous function ("function on the fly") to do a logical test if the value is greater than 10.
 
 ```{r P.2response}
 
 ```
 
 
-### P.3 
+### P.3
 
 Take your code from question 2.4 and assign it to the variable `ces_dat`. 
 

modules/Functions/lab/Functions_Lab.html

@@ -356,15 +356,13 @@ <h1 class="title toc-ignore">Functions Lab</h1>
 <div id="part-1" class="section level1">
 <h1>Part 1</h1>
 <p>Load all the libraries we will use in this lab.</p>
-<pre class="r"><code>library(readr)
-library(dplyr)
-library(ggplot2)</code></pre>
+<pre class="r"><code>library(tidyverse)</code></pre>
 <div id="section" class="section level3">
 <h3>1.1</h3>
 <p>Create a function that takes one argument, a vector, and returns the
-sum of the vector and squares the result. Call it “sum_squared”. Test
-your function on the vector <code>c(2,7,21,30,90)</code> - you should
-get the answer 22500.</p>
+sum of the vector and then squares the result. Call it “sum_squared”.
+Test your function on the vector <code>c(2,7,21,30,90)</code> - you
+should get the answer 22500.</p>
 <pre><code># General format
 NEW_FUNCTION &lt;- function(x, y) x + y </code></pre>
 <p>or</p>
@@ -388,8 +386,8 @@ <h3>1.3</h3>
 <p>Amend the function <code>has_n</code> from question 1.2 so that it
 takes a default value of <code>21</code> for the numeric argument.</p>
 </div>
-<div id="section-3" class="section level3">
-<h3>1.4</h3>
+<div id="p.1" class="section level3">
+<h3>P.1</h3>
 <p>Create a new number <code>b_num</code> that is not contained with
 <code>nums</code>. Use your updated <code>has_n</code> function with the
 default value and add <code>b_num</code> as the <code>n</code> argument
@@ -398,12 +396,12 @@ <h3>1.4</h3>
 </div>
 <div id="part-2" class="section level1">
 <h1>Part 2</h1>
-<div id="section-4" class="section level3">
+<div id="section-3" class="section level3">
 <h3>2.1</h3>
 <p>Read in the CalEnviroScreen from <a href="https://daseh.org/data/CalEnviroScreen_data.csv" class="uri">https://daseh.org/data/CalEnviroScreen_data.csv</a>. Assign
 the data the name “ces”.</p>
 </div>
-<div id="section-5" class="section level3">
+<div id="section-4" class="section level3">
 <h3>2.2</h3>
 <p>We want to get some summary statistics on water contamination. Use
 <code>across</code> inside <code>summarize</code> to get the sum total
@@ -415,34 +413,40 @@ <h3>2.2</h3>
 <pre><code># General format
 data %&gt;%
   summarize(across(
-    .cols = {vector or tidyselect},
-    .fns = {some function},
-    {additional arguments}
+    {vector or tidyselect},
+    {some function}
   ))</code></pre>
 </div>
-<div id="section-6" class="section level3">
+<div id="section-5" class="section level3">
 <h3>2.3</h3>
 <p>Use <code>across</code> and <code>mutate</code> to convert all
 columns containing the word “Pctl” into proportions (i.e., divide that
 value by 100). <strong>Hint</strong>: use <code>contains()</code> to
-select the right columns within <code>across()</code>. Use a “function
-on the fly” to divide by 100. It will also be easier to check your work
-if you <code>select()</code> columns that match “Pctl”.</p>
+select the right columns within <code>across()</code>. Use an anonymous
+function (“function on the fly”) to divide by 100
+(<code>function(x) x / 100</code>). It will also be easier to check your
+work if you <code>select()</code> columns that match “Pctl”.</p>
+<pre><code># General format
+data %&gt;%
+  mutate(across(
+    {vector or tidyselect},
+    {some function}
+  ))</code></pre>
+</div>
 </div>
-<div id="section-7" class="section level3">
-<h3>2.4</h3>
+<div id="practice-on-your-own" class="section level1">
+<h1>Practice on Your Own!</h1>
+<div id="p.2" class="section level3">
+<h3>P.2</h3>
 <p>Use <code>across</code> and <code>mutate</code> to convert all
 columns starting with the string “PM” into a binary variable: TRUE if
 the value is greater than 10 and FALSE if less than or equal to 10.
 <strong>Hint</strong>: use <code>starts_with()</code> to select the
-columns that start with “PM”. Use a “function on the fly” to do a
-logical test if the value is greater than 10.</p>
-</div>
+columns that start with “PM”. Use an anonymous function (“function on
+the fly”) to do a logical test if the value is greater than 10.</p>
 </div>
-<div id="practice-on-your-own" class="section level1">
-<h1>Practice on Your Own!</h1>
-<div id="p.1" class="section level3">
-<h3>P.1</h3>
+<div id="p.3" class="section level3">
+<h3>P.3</h3>
 <p>Take your code from question 2.4 and assign it to the variable
 <code>ces_dat</code>.</p>
 <ul>

modules/Functions/lab/Functions_Lab_Key.Rmd

@@ -14,9 +14,7 @@ knitr::opts_chunk$set(echo = TRUE)
 Load all the libraries we will use in this lab. 
 
 ```{r message=FALSE}
-library(readr)
-library(dplyr)
-library(ggplot2)
+library(tidyverse)
 ```
 
 ### 1.1
@@ -104,45 +102,43 @@ We want to get some summary statistics on water contamination. Use `across` insi
 # General format
 data %>%
   summarize(across(
-    .cols = {vector or tidyselect},
-    .fns = {some function},
-    {additional arguments}
+    {vector or tidyselect},
+    {some function}
   ))
 ```
 
 ```{r 2.2response}
 ces %>%
   summarize(across(
-    .cols = contains("Water") & ends_with("Pctl"),
-    .fns = sum
+    contains("Water") & ends_with("Pctl"),
+    sum
   ))
 ces %>%
   summarize(across(
-    .cols = contains("Water") & ends_with("Pctl"),
-    .fns = sum,
-    na.rm = TRUE
+    contains("Water") & ends_with("Pctl"),
+    function(x) sum(x, na.rm = T)
   ))
 ```
 
 
 ### 2.3
 
-Use `across` and `mutate` to convert all columns containing the word "Pctl" into proportions (i.e., divide that value by 100). **Hint**: use `contains()` to select the right columns within `across()`. Use a "function on the fly" to divide by 100 (`function(x) x / 100`). It will also be easier to check your work if you `select()` columns that match "Pctl".
+Use `across` and `mutate` to convert all columns containing the word "Pctl" into proportions (i.e., divide that value by 100). **Hint**: use `contains()` to select the right columns within `across()`. Use an anonymous function ("function on the fly") to divide by 100 (`function(x) x / 100`). It will also be easier to check your work if you `select()` columns that match "Pctl".
 
 ```
 # General format
 data %>%
   mutate(across(
-    .cols = {vector or tidyselect},
-    .fns = {some function}
+    {vector or tidyselect},
+    {some function}
   ))
 ```
 
 ```{r 2.3response}
 ces %>%
   mutate(across(
-    .cols = contains("Pctl"),
-    .fns = function(x) x / 100
+    contains("Pctl"),
+    function(x) x / 100
   )) %>%
   select(contains("Pctl"))
 ```
@@ -151,15 +147,13 @@ ces %>%
 
 ### P.2
 
-
-
-Use `across` and `mutate` to convert all columns starting with the string "PM" into a binary variable: TRUE if the value is greater than 10 and FALSE if less than or equal to 10. **Hint**: use `starts_with()` to select the columns that start with "PM". Use a "function on the fly" to do a logical test if the value is greater than 10.
+Use `across` and `mutate` to convert all columns starting with the string "PM" into a binary variable: TRUE if the value is greater than 10 and FALSE if less than or equal to 10. **Hint**: use `starts_with()` to select the columns that start with "PM". Use an anonymous function ("function on the fly") to do a logical test if the value is greater than 10.
 
 ```{r P.2response}
 ces %>%
   mutate(across(
-    .cols = starts_with("PM"),
-    .fns = function(x) x > 10
+    starts_with("PM"),
+    function(x) x > 10
   ))
 ```
 
@@ -176,8 +170,8 @@ Take your code from question 2.4 and assign it to the variable `ces_dat`.
 ces_dat <-
   ces %>%
   mutate(across(
-    .cols = starts_with("PM"),
-    .fns = function(x) x > 10
+    starts_with("PM"),
+    function(x) x > 10
   )) %>%
   filter(ApproxLocation != "Oakland")
 

modules/Functions/lab/Functions_Lab_Key.html

@@ -356,9 +356,7 @@ <h1 class="title toc-ignore">Functions Lab - Key</h1>
 <div id="part-1" class="section level1">
 <h1>Part 1</h1>
 <p>Load all the libraries we will use in this lab.</p>
-<pre class="r"><code>library(readr)
-library(dplyr)
-library(ggplot2)</code></pre>
+<pre class="r"><code>library(tidyverse)</code></pre>
 <div id="section" class="section level3">
 <h3>1.1</h3>
 <p>Create a function that takes one argument, a vector, and returns the
@@ -444,36 +442,23 @@ <h3>2.2</h3>
 <pre><code># General format
 data %&gt;%
   summarize(across(
-    .cols = {vector or tidyselect},
-    .fns = {some function},
-    {additional arguments}
+    {vector or tidyselect},
+    {some function}
   ))</code></pre>
 <pre class="r"><code>ces %&gt;%
   summarize(across(
-    .cols = contains(&quot;Water&quot;) &amp; ends_with(&quot;Pctl&quot;),
-    .fns = sum
+    contains(&quot;Water&quot;) &amp; ends_with(&quot;Pctl&quot;),
+    sum
   ))</code></pre>
 <pre><code>## # A tibble: 1 × 3
 ##   DrinkingWaterPctl GroundwaterThreatsPctl ImpWaterBodiesPctl
 ##               &lt;dbl&gt;                  &lt;dbl&gt;              &lt;dbl&gt;
 ## 1                NA                304029.            256802.</code></pre>
 <pre class="r"><code>ces %&gt;%
   summarize(across(
-    .cols = contains(&quot;Water&quot;) &amp; ends_with(&quot;Pctl&quot;),
-    .fns = sum,
-    na.rm = TRUE
+    contains(&quot;Water&quot;) &amp; ends_with(&quot;Pctl&quot;),
+    function(x) sum(x, na.rm = T)
   ))</code></pre>
-<pre><code>## Warning: There was 1 warning in `summarize()`.
-## ℹ In argument: `across(...)`.
-## Caused by warning:
-## ! The `...` argument of `across()` is deprecated as of dplyr 1.1.0.
-## Supply arguments directly to `.fns` through an anonymous function instead.
-## 
-##   # Previously
-##   across(a:b, mean, na.rm = TRUE)
-## 
-##   # Now
-##   across(a:b, \(x) mean(x, na.rm = TRUE))</code></pre>
 <pre><code>## # A tibble: 1 × 3
 ##   DrinkingWaterPctl GroundwaterThreatsPctl ImpWaterBodiesPctl
 ##               &lt;dbl&gt;                  &lt;dbl&gt;              &lt;dbl&gt;
@@ -484,20 +469,20 @@ <h3>2.3</h3>
 <p>Use <code>across</code> and <code>mutate</code> to convert all
 columns containing the word “Pctl” into proportions (i.e., divide that
 value by 100). <strong>Hint</strong>: use <code>contains()</code> to
-select the right columns within <code>across()</code>. Use a “function
-on the fly” to divide by 100 (<code>function(x) x / 100</code>). It will
-also be easier to check your work if you <code>select()</code> columns
-that match “Pctl”.</p>
+select the right columns within <code>across()</code>. Use an anonymous
+function (“function on the fly”) to divide by 100
+(<code>function(x) x / 100</code>). It will also be easier to check your
+work if you <code>select()</code> columns that match “Pctl”.</p>
 <pre><code># General format
 data %&gt;%
   mutate(across(
-    .cols = {vector or tidyselect},
-    .fns = {some function}
+    {vector or tidyselect},
+    {some function}
   ))</code></pre>
 <pre class="r"><code>ces %&gt;%
   mutate(across(
-    .cols = contains(&quot;Pctl&quot;),
-    .fns = function(x) x / 100
+    contains(&quot;Pctl&quot;),
+    function(x) x / 100
   )) %&gt;%
   select(contains(&quot;Pctl&quot;))</code></pre>
 <pre><code>## # A tibble: 8,035 × 23
@@ -530,12 +515,12 @@ <h3>P.2</h3>
 columns starting with the string “PM” into a binary variable: TRUE if
 the value is greater than 10 and FALSE if less than or equal to 10.
 <strong>Hint</strong>: use <code>starts_with()</code> to select the
-columns that start with “PM”. Use a “function on the fly” to do a
-logical test if the value is greater than 10.</p>
+columns that start with “PM”. Use an anonymous function (“function on
+the fly”) to do a logical test if the value is greater than 10.</p>
 <pre class="r"><code>ces %&gt;%
   mutate(across(
-    .cols = starts_with(&quot;PM&quot;),
-    .fns = function(x) x &gt; 10
+    starts_with(&quot;PM&quot;),
+    function(x) x &gt; 10
   ))</code></pre>
 <pre><code>## # A tibble: 8,035 × 68
 ##     ...1 CensusTract CaliforniaCounty   ZIP Longitude Latitude ApproxLocation
@@ -575,8 +560,8 @@ <h3>P.3</h3>
 <pre class="r"><code>ces_dat &lt;-
   ces %&gt;%
   mutate(across(
-    .cols = starts_with(&quot;PM&quot;),
-    .fns = function(x) x &gt; 10
+    starts_with(&quot;PM&quot;),
+    function(x) x &gt; 10
   )) %&gt;%
   filter(ApproxLocation != &quot;Oakland&quot;)