Merge branch 'main' of https://github.com/jhudsl/intro_to_r into main

Author: jhudsl-robot

modules/Basic_R/Basic_R.pdf

@@ -291,16 +291,16 @@ <h1>Part 3</h1>
   replace = TRUE
 )
 my_responses</code></pre>
-<pre><code>##  [1] &quot;Neutral&quot;           &quot;Strongly Agree&quot;    &quot;Neutral&quot;          
-##  [4] &quot;Strongly Agree&quot;    &quot;Disagree&quot;          &quot;Disagree&quot;         
-##  [7] &quot;Strongly Agree&quot;    &quot;Agree&quot;             &quot;Neutral&quot;          
-## [10] &quot;Strongly Agree&quot;    &quot;Disagree&quot;          &quot;Neutral&quot;          
-## [13] &quot;Disagree&quot;          &quot;Neutral&quot;           &quot;Strongly Agree&quot;   
-## [16] &quot;Neutral&quot;           &quot;Neutral&quot;           &quot;Disagree&quot;         
-## [19] &quot;Neutral&quot;           &quot;Strongly Disagree&quot; &quot;Agree&quot;            
-## [22] &quot;Disagree&quot;          &quot;Strongly Disagree&quot; &quot;Neutral&quot;          
-## [25] &quot;Agree&quot;             &quot;Strongly Agree&quot;    &quot;Disagree&quot;         
-## [28] &quot;Disagree&quot;          &quot;Neutral&quot;           &quot;Agree&quot;</code></pre>
+<pre><code>##  [1] &quot;Strongly Disagree&quot; &quot;Neutral&quot;           &quot;Agree&quot;            
+##  [4] &quot;Strongly Agree&quot;    &quot;Strongly Disagree&quot; &quot;Strongly Disagree&quot;
+##  [7] &quot;Strongly Disagree&quot; &quot;Disagree&quot;          &quot;Strongly Disagree&quot;
+## [10] &quot;Strongly Disagree&quot; &quot;Strongly Agree&quot;    &quot;Strongly Agree&quot;   
+## [13] &quot;Disagree&quot;          &quot;Agree&quot;             &quot;Strongly Agree&quot;   
+## [16] &quot;Strongly Disagree&quot; &quot;Agree&quot;             &quot;Neutral&quot;          
+## [19] &quot;Disagree&quot;          &quot;Strongly Disagree&quot; &quot;Strongly Agree&quot;   
+## [22] &quot;Neutral&quot;           &quot;Strongly Agree&quot;    &quot;Strongly Agree&quot;   
+## [25] &quot;Strongly Agree&quot;    &quot;Disagree&quot;          &quot;Neutral&quot;          
+## [28] &quot;Strongly Agree&quot;    &quot;Strongly Disagree&quot; &quot;Strongly Disagree&quot;</code></pre>
 <p><strong>Bonus / Extra practice</strong>: Let’s say you change your survey so participants can rank their response 1-10 (inclusive). Create a randomly sampled vector of 30 survey responses. (hint use <code>seq()</code> and <code>sample()</code> and set the replace argument to <code>TRUE</code>). Store the output as <code>my_responses_2</code>. Examine the data by typing the name in the Console using a function.</p>
 <pre class="r"><code>my_responses_2 &lt;- sample(
   x = seq(from = 1, to = 10),

modules/Basic_R/lab/Basic_R_Lab_Key.html

@@ -215,7 +215,7 @@ <h1 data-config-title><!-- populated from slide_config.json --></h1>
 <li>Double</li>
 </ol>
 
-</article></slide><slide class=""><hgroup><h2>Integer</h2></hgroup><article  id="integer" class="small">
+</article></slide><slide class=""><hgroup><h2>Integer</h2></hgroup><article  class="small" id="integer">
 
 <p><code>Integer</code> is a special subset of <code>numeric</code> that contains only <span style="color: red;">
 whole numbers</span>.</p>

modules/Data_Classes/Data_Classes.html

@@ -204,7 +204,7 @@ <h1 data-config-title><!-- populated from slide_config.json --></h1>
 <li>The <code>lubridate</code> package is helpful for dates and times<br/>📃<a href='https://jhudatascience.org/intro_to_r/modules/cheatsheets/Day-4.pdf' title=''>Cheatsheet</a></li>
 </ul>
 
-</article></slide><slide class=""><hgroup><h2>Data Cleaning</h2></hgroup><article  class="emphasized" id="data-cleaning">
+</article></slide><slide class=""><hgroup><h2>Data Cleaning</h2></hgroup><article  id="data-cleaning" class="emphasized">
 
 <p>In general, data cleaning is a process of investigating your data for inaccuracies, or recoding it in a way that makes it more manageable.</p>
 
@@ -225,7 +225,7 @@ <h1 data-config-title><!-- populated from slide_config.json --></h1>
 <li><code>Inf</code> and <code>-Inf</code> - Infinity, happens when you divide a positive number (or negative number) by 0.</li>
 </ul>
 
-</article></slide><slide class=""><hgroup><h2>Finding Missing data</h2></hgroup><article  class="small" id="finding-missing-data">
+</article></slide><slide class=""><hgroup><h2>Finding Missing data</h2></hgroup><article  id="finding-missing-data" class="small">
 
 <ul>
 <li><code>is.na</code> - looks for <code>NAN</code> and <code>NA</code></li>
@@ -251,7 +251,7 @@ <h1 data-config-title><!-- populated from slide_config.json --></h1>
 
 <pre >[1] FALSE FALSE  TRUE</pre>
 
-</article></slide><slide class=""><hgroup><h2>Useful checking functions</h2></hgroup><article  class="small" id="useful-checking-functions">
+</article></slide><slide class=""><hgroup><h2>Useful checking functions</h2></hgroup><article  id="useful-checking-functions" class="small">
 
 <ul>
 <li><code>any</code> will be <code>TRUE</code> if ANY are true
@@ -538,7 +538,7 @@ <h1 data-config-title><!-- populated from slide_config.json --></h1>
 
 <p><strong><code>filter()</code> removes missing values by default.</strong> Because R can’t tell for sure if an <code>NA</code> value meets the condition. To keep them need to add <code>is.na()</code> conditional.</p>
 
-</article></slide><slide class=""><hgroup><h2>filter() and missing data</h2></hgroup><article  class="codesmall" id="filter-and-missing-data-1">
+</article></slide><slide class=""><hgroup><h2>filter() and missing data</h2></hgroup><article  id="filter-and-missing-data-1" class="codesmall">
 
 <pre class = 'prettyprint lang-r'>df</pre>
 
@@ -612,7 +612,7 @@ <h1 data-config-title><!-- populated from slide_config.json --></h1>
 1     2     6
 2     1     2</pre>
 
-</article></slide><slide class=""><hgroup><h2>Drop <strong>columns</strong> with any missing values</h2></hgroup><article  class="codesmall" id="drop-columns-with-any-missing-values">
+</article></slide><slide class=""><hgroup><h2>Drop <strong>columns</strong> with any missing values</h2></hgroup><article  id="drop-columns-with-any-missing-values" class="codesmall">
 
 <p>Use the <code>miss_var_which()</code> function from <code>naniar</code></p>
 
@@ -633,7 +633,7 @@ <h1 data-config-title><!-- populated from slide_config.json --></h1>
 
 <pre >[1] &quot;Dog&quot; &quot;Cat&quot;</pre>
 
-</article></slide><slide class=""><hgroup><h2>Drop <strong>columns</strong> with any missing values</h2></hgroup><article  class="codesmall" id="drop-columns-with-any-missing-values-1">
+</article></slide><slide class=""><hgroup><h2>Drop <strong>columns</strong> with any missing values</h2></hgroup><article  id="drop-columns-with-any-missing-values-1" class="codesmall">
 
 <pre class = 'prettyprint lang-r'>df %&gt;% select(!miss_var_which(df))</pre>
 
@@ -647,7 +647,7 @@ <h1 data-config-title><!-- populated from slide_config.json --></h1>
 5     5
 6     6</pre>
 
-</article></slide><slide class=""><hgroup><h2>Removing columns with threshold of percent missing row values</h2></hgroup><article  class="codesmall" id="removing-columns-with-threshold-of-percent-missing-row-values">
+</article></slide><slide class=""><hgroup><h2>Removing columns with threshold of percent missing row values</h2></hgroup><article  id="removing-columns-with-threshold-of-percent-missing-row-values" class="codesmall">
 
 <pre class = 'prettyprint lang-r'>is.na(df) %&gt;% head(n = 3)</pre>
 
@@ -712,7 +712,7 @@ <h1 data-config-title><!-- populated from slide_config.json --></h1>
 
 <p>You might want to keep the <code>NA</code> values so that you know the original sample size.</p>
 
-</article></slide><slide class=""><hgroup><h2>Word of caution</h2></hgroup><article  class="codesmall" id="word-of-caution">
+</article></slide><slide class=""><hgroup><h2>Word of caution</h2></hgroup><article  id="word-of-caution" class="codesmall">
 
 <p>Calculating percentages will give you a different result depending on your choice to include NA values.</p>
 

modules/Data_Cleaning/Data_Cleaning.html

@@ -248,7 +248,7 @@ <h1 data-config-title><!-- populated from slide_config.json --></h1>
 <pre >  0%  25%  50%  75% 100% 
  1.0  2.5  4.5  6.5  8.0 </pre>
 
-</article></slide><slide class=""><hgroup><h2>Statistical summarization</h2></hgroup><article  class="codesmall" id="statistical-summarization-2">
+</article></slide><slide class=""><hgroup><h2>Statistical summarization</h2></hgroup><article  id="statistical-summarization-2" class="codesmall">
 
 <p>We will talk more about data types later, but you can only do summarization on numeric or logical types. Not characters.</p>