diff --git a/docs/01_lesson.html b/docs/01_lesson.html
index 3fb25d5..2330dc4 100644
--- a/docs/01_lesson.html
+++ b/docs/01_lesson.html
@@ -2,12 +2,11 @@
 <html xmlns="http://www.w3.org/1999/xhtml" lang="en" xml:lang="en"><head>
 
 <meta charset="utf-8">
-<meta name="generator" content="quarto-1.2.269">
+<meta name="generator" content="quarto-1.3.433">
 
 <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes">
 
 <meta name="author" content="Fay Walker">
-<meta name="dcterms.date" content="2023-08-15">
 
 <title>Intro to R - 1&nbsp; Data Visualization</title>
 <style>
@@ -19,7 +18,7 @@
 ul.task-list{list-style: none;}
 ul.task-list li input[type="checkbox"] {
   width: 0.8em;
-  margin: 0 0.8em 0.2em -1.6em;
+  margin: 0 0.8em 0.2em -1em; /* quarto-specific, see https://github.com/quarto-dev/quarto-cli/issues/4556 */ 
   vertical-align: middle;
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@@ -59,7 +58,8 @@
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     "search-clear-button-title": "Clear",
     "search-detached-cancel-button-title": "Cancel",
-    "search-submit-button-title": "Submit"
+    "search-submit-button-title": "Submit",
+    "search-label": "Search"
   }
 }</script>
 
@@ -71,11 +71,16 @@
 
 <div id="quarto-search-results"></div>
   <header id="quarto-header" class="headroom fixed-top">
-  <nav class="quarto-secondary-nav" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar" aria-controls="quarto-sidebar" aria-expanded="false" aria-label="Toggle sidebar navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">
-    <div class="container-fluid d-flex justify-content-between">
-      <h1 class="quarto-secondary-nav-title"><span class="chapter-number">1</span>&nbsp; <span class="chapter-title">Data Visualization</span></h1>
-      <button type="button" class="quarto-btn-toggle btn" aria-label="Show secondary navigation">
-        <i class="bi bi-chevron-right"></i>
+  <nav class="quarto-secondary-nav">
+    <div class="container-fluid d-flex">
+      <button type="button" class="quarto-btn-toggle btn" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar,#quarto-sidebar-glass" aria-controls="quarto-sidebar" aria-expanded="false" aria-label="Toggle sidebar navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">
+        <i class="bi bi-layout-text-sidebar-reverse"></i>
+      </button>
+      <nav class="quarto-page-breadcrumbs" aria-label="breadcrumb"><ol class="breadcrumb"><li class="breadcrumb-item"><a href="./01_lesson.html"><span class="chapter-number">1</span>&nbsp; <span class="chapter-title">Data Visualization</span></a></li></ol></nav>
+      <a class="flex-grow-1" role="button" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar,#quarto-sidebar-glass" aria-controls="quarto-sidebar" aria-expanded="false" aria-label="Toggle sidebar navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">      
+      </a>
+      <button type="button" class="btn quarto-search-button" aria-label="" onclick="window.quartoOpenSearch();">
+        <i class="bi bi-search"></i>
       </button>
     </div>
   </nav>
@@ -83,75 +88,83 @@ <h1 class="quarto-secondary-nav-title"><span class="chapter-number">1</span>&nbs
 <!-- content -->
 <div id="quarto-content" class="quarto-container page-columns page-rows-contents page-layout-article">
 <!-- sidebar -->
-  <nav id="quarto-sidebar" class="sidebar collapse sidebar-navigation floating overflow-auto">
+  <nav id="quarto-sidebar" class="sidebar collapse collapse-horizontal sidebar-navigation floating overflow-auto">
     <div class="pt-lg-2 mt-2 text-left sidebar-header">
     <div class="sidebar-title mb-0 py-0">
       <a href="./">Intro to R</a> 
     </div>
       </div>
-      <div class="mt-2 flex-shrink-0 align-items-center">
+        <div class="mt-2 flex-shrink-0 align-items-center">
         <div class="sidebar-search">
         <div id="quarto-search" class="" title="Search"></div>
         </div>
-      </div>
+        </div>
     <div class="sidebar-menu-container"> 
     <ul class="list-unstyled mt-1">
         <li class="sidebar-item">
   <div class="sidebar-item-container"> 
-  <a href="./index.html" class="sidebar-item-text sidebar-link">Intro to R: A Hands on Tutorial</a>
+  <a href="./index.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text">Intro to R: A Hands on Tutorial</span></a>
   </div>
 </li>
         <li class="sidebar-item">
   <div class="sidebar-item-container"> 
-  <a href="./01_lesson.html" class="sidebar-item-text sidebar-link active"><span class="chapter-number">1</span>&nbsp; <span class="chapter-title">Data Visualization</span></a>
+  <a href="./01_lesson.html" class="sidebar-item-text sidebar-link active">
+ <span class="menu-text"><span class="chapter-number">1</span>&nbsp; <span class="chapter-title">Data Visualization</span></span></a>
   </div>
 </li>
         <li class="sidebar-item">
   <div class="sidebar-item-container"> 
-  <a href="./02_lesson.html" class="sidebar-item-text sidebar-link"><span class="chapter-number">2</span>&nbsp; <span class="chapter-title">Data Munging 1</span></a>
+  <a href="./02_lesson.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text"><span class="chapter-number">2</span>&nbsp; <span class="chapter-title">Data Munging 1</span></span></a>
   </div>
 </li>
         <li class="sidebar-item">
   <div class="sidebar-item-container"> 
-  <a href="./03_lesson.html" class="sidebar-item-text sidebar-link"><span class="chapter-number">3</span>&nbsp; <span class="chapter-title">Data Munging (II)</span></a>
+  <a href="./03_lesson.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text"><span class="chapter-number">3</span>&nbsp; <span class="chapter-title">Data Munging (II)</span></span></a>
   </div>
 </li>
         <li class="sidebar-item">
   <div class="sidebar-item-container"> 
-  <a href="./04_lesson.html" class="sidebar-item-text sidebar-link"><span class="chapter-number">4</span>&nbsp; <span class="chapter-title">Data Import</span></a>
+  <a href="./04_lesson.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text"><span class="chapter-number">4</span>&nbsp; <span class="chapter-title">Data Import</span></span></a>
   </div>
 </li>
         <li class="sidebar-item">
   <div class="sidebar-item-container"> 
-  <a href="./05_lesson.html" class="sidebar-item-text sidebar-link"><span class="chapter-number">5</span>&nbsp; <span class="chapter-title">Iteration (or becoming an R Superhero)</span></a>
+  <a href="./05_lesson.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text"><span class="chapter-number">5</span>&nbsp; <span class="chapter-title">Iteration (or becoming an R Superhero)</span></span></a>
   </div>
 </li>
         <li class="sidebar-item">
   <div class="sidebar-item-container"> 
-  <a href="./06_lesson.html" class="sidebar-item-text sidebar-link"><span class="chapter-number">6</span>&nbsp; <span class="chapter-title">Putting it All Together (and into Markdown)</span></a>
+  <a href="./06_lesson.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text"><span class="chapter-number">6</span>&nbsp; <span class="chapter-title">Putting it All Together (and into Markdown)</span></span></a>
   </div>
 </li>
     </ul>
     </div>
 </nav>
+<div id="quarto-sidebar-glass" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar,#quarto-sidebar-glass"></div>
 <!-- margin-sidebar -->
     <div id="quarto-margin-sidebar" class="sidebar margin-sidebar">
         <nav id="TOC" role="doc-toc" class="toc-active">
     <h2 id="toc-title">Table of contents</h2>
    
   <ul>
-  <li><a href="#review" id="toc-review" class="nav-link active" data-scroll-target="#review"><span class="toc-section-number">1.1</span>  Review</a></li>
-  <li><a href="#exercise-0-set-up-a-project-and-load-packages" id="toc-exercise-0-set-up-a-project-and-load-packages" class="nav-link" data-scroll-target="#exercise-0-set-up-a-project-and-load-packages"><span class="toc-section-number">1.2</span>  Exercise 0 (Set up a project and load packages)</a></li>
-  <li><a href="#exercise-1-make-a-plot" id="toc-exercise-1-make-a-plot" class="nav-link" data-scroll-target="#exercise-1-make-a-plot"><span class="toc-section-number">1.3</span>  Exercise 1 (Make a plot)</a></li>
-  <li><a href="#exercise-2-change-some-of-the-plot-settings" id="toc-exercise-2-change-some-of-the-plot-settings" class="nav-link" data-scroll-target="#exercise-2-change-some-of-the-plot-settings"><span class="toc-section-number">1.4</span>  Exercise 2 (Change some of the plot settings)</a></li>
-  <li><a href="#exercise-3-add-a-regression-lineconfidence-interval" id="toc-exercise-3-add-a-regression-lineconfidence-interval" class="nav-link" data-scroll-target="#exercise-3-add-a-regression-lineconfidence-interval"><span class="toc-section-number">1.5</span>  Exercise 3 (Add a regression line/confidence interval)</a></li>
-  <li><a href="#exercise-4-scale-the-axes" id="toc-exercise-4-scale-the-axes" class="nav-link" data-scroll-target="#exercise-4-scale-the-axes"><span class="toc-section-number">1.6</span>  Exercise 4 (Scale the axes)</a></li>
-  <li><a href="#exercise-5-make-it-look-nice" id="toc-exercise-5-make-it-look-nice" class="nav-link" data-scroll-target="#exercise-5-make-it-look-nice"><span class="toc-section-number">1.7</span>  Exercise 5 (Make it look nice!!)</a></li>
-  <li><a href="#excercise-6-multiple-little-graphsfaceting" id="toc-excercise-6-multiple-little-graphsfaceting" class="nav-link" data-scroll-target="#excercise-6-multiple-little-graphsfaceting"><span class="toc-section-number">1.8</span>  Excercise 6 (Multiple little graphs/Faceting)</a></li>
-  <li><a href="#exercise-7-mapping" id="toc-exercise-7-mapping" class="nav-link" data-scroll-target="#exercise-7-mapping"><span class="toc-section-number">1.9</span>  Exercise 7 (Mapping)</a></li>
-  <li><a href="#functions" id="toc-functions" class="nav-link" data-scroll-target="#functions"><span class="toc-section-number">1.10</span>  Functions</a></li>
-  <li><a href="#theory" id="toc-theory" class="nav-link" data-scroll-target="#theory"><span class="toc-section-number">1.11</span>  Theory</a></li>
-  <li><a href="#resources" id="toc-resources" class="nav-link" data-scroll-target="#resources"><span class="toc-section-number">1.12</span>  Resources</a></li>
+  <li><a href="#review" id="toc-review" class="nav-link active" data-scroll-target="#review"><span class="header-section-number">1.1</span> Review</a></li>
+  <li><a href="#exercise-0-set-up-a-project-and-load-packages" id="toc-exercise-0-set-up-a-project-and-load-packages" class="nav-link" data-scroll-target="#exercise-0-set-up-a-project-and-load-packages"><span class="header-section-number">1.2</span> Exercise 0 (Set up a project and load packages)</a></li>
+  <li><a href="#exercise-1-make-a-plot" id="toc-exercise-1-make-a-plot" class="nav-link" data-scroll-target="#exercise-1-make-a-plot"><span class="header-section-number">1.3</span> Exercise 1 (Make a plot)</a></li>
+  <li><a href="#exercise-2-change-some-of-the-plot-settings" id="toc-exercise-2-change-some-of-the-plot-settings" class="nav-link" data-scroll-target="#exercise-2-change-some-of-the-plot-settings"><span class="header-section-number">1.4</span> Exercise 2 (Change some of the plot settings)</a></li>
+  <li><a href="#exercise-3-add-a-regression-lineconfidence-interval" id="toc-exercise-3-add-a-regression-lineconfidence-interval" class="nav-link" data-scroll-target="#exercise-3-add-a-regression-lineconfidence-interval"><span class="header-section-number">1.5</span> Exercise 3 (Add a regression line/confidence interval)</a></li>
+  <li><a href="#exercise-4-scale-the-axes" id="toc-exercise-4-scale-the-axes" class="nav-link" data-scroll-target="#exercise-4-scale-the-axes"><span class="header-section-number">1.6</span> Exercise 4 (Scale the axes)</a></li>
+  <li><a href="#exercise-5-make-it-look-nice" id="toc-exercise-5-make-it-look-nice" class="nav-link" data-scroll-target="#exercise-5-make-it-look-nice"><span class="header-section-number">1.7</span> Exercise 5 (Make it look nice!!)</a></li>
+  <li><a href="#excercise-6-multiple-little-graphsfaceting" id="toc-excercise-6-multiple-little-graphsfaceting" class="nav-link" data-scroll-target="#excercise-6-multiple-little-graphsfaceting"><span class="header-section-number">1.8</span> Excercise 6 (Multiple little graphs/Faceting)</a></li>
+  <li><a href="#exercise-7-mapping" id="toc-exercise-7-mapping" class="nav-link" data-scroll-target="#exercise-7-mapping"><span class="header-section-number">1.9</span> Exercise 7 (Mapping)</a></li>
+  <li><a href="#functions" id="toc-functions" class="nav-link" data-scroll-target="#functions"><span class="header-section-number">1.10</span> Functions</a></li>
+  <li><a href="#theory" id="toc-theory" class="nav-link" data-scroll-target="#theory"><span class="header-section-number">1.11</span> Theory</a></li>
+  <li><a href="#resources" id="toc-resources" class="nav-link" data-scroll-target="#resources"><span class="header-section-number">1.12</span> Resources</a></li>
   </ul>
 </nav>
     </div>
@@ -160,7 +173,7 @@ <h2 id="toc-title">Table of contents</h2>
 
 <header id="title-block-header" class="quarto-title-block default">
 <div class="quarto-title">
-<h1 class="title d-none d-lg-block"><span class="chapter-number">1</span>&nbsp; <span class="chapter-title">Data Visualization</span></h1>
+<h1 class="title"><span class="chapter-number">1</span>&nbsp; <span class="chapter-title">Data Visualization</span></h1>
 </div>
 
 
@@ -177,7 +190,7 @@ <h1 class="title d-none d-lg-block"><span class="chapter-number">1</span>&nbsp;
     <div>
     <div class="quarto-title-meta-heading">Published</div>
     <div class="quarto-title-meta-contents">
-      <p class="date">August 15, 2023</p>
+      <p class="date">Invalid Date</p>
     </div>
   </div>
   
@@ -377,9 +390,23 @@ <h2 data-number="1.12" class="anchored" data-anchor-id="resources"><span class="
     icon: icon
   };
   anchorJS.add('.anchored');
+  const isCodeAnnotation = (el) => {
+    for (const clz of el.classList) {
+      if (clz.startsWith('code-annotation-')) {                     
+        return true;
+      }
+    }
+    return false;
+  }
   const clipboard = new window.ClipboardJS('.code-copy-button', {
-    target: function(trigger) {
-      return trigger.previousElementSibling;
+    text: function(trigger) {
+      const codeEl = trigger.previousElementSibling.cloneNode(true);
+      for (const childEl of codeEl.children) {
+        if (isCodeAnnotation(childEl)) {
+          childEl.remove();
+        }
+      }
+      return codeEl.innerText;
     }
   });
   clipboard.on('success', function(e) {
@@ -444,6 +471,92 @@ <h2 data-number="1.12" class="anchored" data-anchor-id="resources"><span class="
       return note.innerHTML;
     });
   }
+      let selectedAnnoteEl;
+      const selectorForAnnotation = ( cell, annotation) => {
+        let cellAttr = 'data-code-cell="' + cell + '"';
+        let lineAttr = 'data-code-annotation="' +  annotation + '"';
+        const selector = 'span[' + cellAttr + '][' + lineAttr + ']';
+        return selector;
+      }
+      const selectCodeLines = (annoteEl) => {
+        const doc = window.document;
+        const targetCell = annoteEl.getAttribute("data-target-cell");
+        const targetAnnotation = annoteEl.getAttribute("data-target-annotation");
+        const annoteSpan = window.document.querySelector(selectorForAnnotation(targetCell, targetAnnotation));
+        const lines = annoteSpan.getAttribute("data-code-lines").split(",");
+        const lineIds = lines.map((line) => {
+          return targetCell + "-" + line;
+        })
+        let top = null;
+        let height = null;
+        let parent = null;
+        if (lineIds.length > 0) {
+            //compute the position of the single el (top and bottom and make a div)
+            const el = window.document.getElementById(lineIds[0]);
+            top = el.offsetTop;
+            height = el.offsetHeight;
+            parent = el.parentElement.parentElement;
+          if (lineIds.length > 1) {
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+            const bottom = lastEl.offsetTop + lastEl.offsetHeight;
+            height = bottom - top;
+          }
+          if (top !== null && height !== null && parent !== null) {
+            // cook up a div (if necessary) and position it 
+            let div = window.document.getElementById("code-annotation-line-highlight");
+            if (div === null) {
+              div = window.document.createElement("div");
+              div.setAttribute("id", "code-annotation-line-highlight");
+              div.style.position = 'absolute';
+              parent.appendChild(div);
+            }
+            div.style.top = top - 2 + "px";
+            div.style.height = height + 4 + "px";
+            let gutterDiv = window.document.getElementById("code-annotation-line-highlight-gutter");
+            if (gutterDiv === null) {
+              gutterDiv = window.document.createElement("div");
+              gutterDiv.setAttribute("id", "code-annotation-line-highlight-gutter");
+              gutterDiv.style.position = 'absolute';
+              const codeCell = window.document.getElementById(targetCell);
+              const gutter = codeCell.querySelector('.code-annotation-gutter');
+              gutter.appendChild(gutterDiv);
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+            gutterDiv.style.top = top - 2 + "px";
+            gutterDiv.style.height = height + 4 + "px";
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+          selectedAnnoteEl = annoteEl;
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+      const unselectCodeLines = () => {
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+        elementsIds.forEach((elId) => {
+          const div = window.document.getElementById(elId);
+          if (div) {
+            div.remove();
+          }
+        });
+        selectedAnnoteEl = undefined;
+      };
+      // Attach click handler to the DT
+      const annoteDls = window.document.querySelectorAll('dt[data-target-cell]');
+      for (const annoteDlNode of annoteDls) {
+        annoteDlNode.addEventListener('click', (event) => {
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+          if (clickedEl !== selectedAnnoteEl) {
+            unselectCodeLines();
+            const activeEl = window.document.querySelector('dt[data-target-cell].code-annotation-active');
+            if (activeEl) {
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+            selectCodeLines(clickedEl);
+            clickedEl.classList.add('code-annotation-active');
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+            clickedEl.classList.remove('code-annotation-active');
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   const findCites = (el) => {
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diff --git a/docs/02_lesson.html b/docs/02_lesson.html
index a161e04..0048c03 100644
--- a/docs/02_lesson.html
+++ b/docs/02_lesson.html
@@ -2,12 +2,12 @@
 <html xmlns="http://www.w3.org/1999/xhtml" lang="en" xml:lang="en"><head>
 
 <meta charset="utf-8">
-<meta name="generator" content="quarto-1.2.269">
+<meta name="generator" content="quarto-1.3.433">
 
 <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes">
 
-<meta name="author" content="Fay Walker">
-<meta name="dcterms.date" content="2023-08-15">
+<meta name="author" content="Aaron R. Williams">
+<meta name="dcterms.date" content="2023-08-22">
 
 <title>Intro to R - 2&nbsp; Data Munging 1</title>
 <style>
@@ -19,9 +19,43 @@
 ul.task-list{list-style: none;}
 ul.task-list li input[type="checkbox"] {
   width: 0.8em;
-  margin: 0 0.8em 0.2em -1.6em;
+  margin: 0 0.8em 0.2em -1em; /* quarto-specific, see https://github.com/quarto-dev/quarto-cli/issues/4556 */ 
   vertical-align: middle;
 }
+/* CSS for syntax highlighting */
+pre > code.sourceCode { white-space: pre; position: relative; }
+pre > code.sourceCode > span { display: inline-block; line-height: 1.25; }
+pre > code.sourceCode > span:empty { height: 1.2em; }
+.sourceCode { overflow: visible; }
+code.sourceCode > span { color: inherit; text-decoration: inherit; }
+div.sourceCode { margin: 1em 0; }
+pre.sourceCode { margin: 0; }
+@media screen {
+div.sourceCode { overflow: auto; }
+}
+@media print {
+pre > code.sourceCode { white-space: pre-wrap; }
+pre > code.sourceCode > span { text-indent: -5em; padding-left: 5em; }
+}
+pre.numberSource code
+  { counter-reset: source-line 0; }
+pre.numberSource code > span
+  { position: relative; left: -4em; counter-increment: source-line; }
+pre.numberSource code > span > a:first-child::before
+  { content: counter(source-line);
+    position: relative; left: -1em; text-align: right; vertical-align: baseline;
+    border: none; display: inline-block;
+    -webkit-touch-callout: none; -webkit-user-select: none;
+    -khtml-user-select: none; -moz-user-select: none;
+    -ms-user-select: none; user-select: none;
+    padding: 0 4px; width: 4em;
+  }
+pre.numberSource { margin-left: 3em;  padding-left: 4px; }
+div.sourceCode
+  {   }
+@media screen {
+pre > code.sourceCode > span > a:first-child::before { text-decoration: underline; }
+}
 </style>
 
 
@@ -59,10 +93,13 @@
     "search-more-matches-text": "more matches in this document",
     "search-clear-button-title": "Clear",
     "search-detached-cancel-button-title": "Cancel",
-    "search-submit-button-title": "Submit"
+    "search-submit-button-title": "Submit",
+    "search-label": "Search"
   }
 }</script>
 
+  <script src="https://polyfill.io/v3/polyfill.min.js?features=es6"></script>
+  <script src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-chtml-full.js" type="text/javascript"></script>
 
 <link rel="stylesheet" href="style.scss">
 </head>
@@ -71,11 +108,16 @@
 
 <div id="quarto-search-results"></div>
   <header id="quarto-header" class="headroom fixed-top">
-  <nav class="quarto-secondary-nav" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar" aria-controls="quarto-sidebar" aria-expanded="false" aria-label="Toggle sidebar navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">
-    <div class="container-fluid d-flex justify-content-between">
-      <h1 class="quarto-secondary-nav-title"><span class="chapter-number">2</span>&nbsp; <span class="chapter-title">Data Munging 1</span></h1>
-      <button type="button" class="quarto-btn-toggle btn" aria-label="Show secondary navigation">
-        <i class="bi bi-chevron-right"></i>
+  <nav class="quarto-secondary-nav">
+    <div class="container-fluid d-flex">
+      <button type="button" class="quarto-btn-toggle btn" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar,#quarto-sidebar-glass" aria-controls="quarto-sidebar" aria-expanded="false" aria-label="Toggle sidebar navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">
+        <i class="bi bi-layout-text-sidebar-reverse"></i>
+      </button>
+      <nav class="quarto-page-breadcrumbs" aria-label="breadcrumb"><ol class="breadcrumb"><li class="breadcrumb-item"><a href="./02_lesson.html"><span class="chapter-number">2</span>&nbsp; <span class="chapter-title">Data Munging 1</span></a></li></ol></nav>
+      <a class="flex-grow-1" role="button" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar,#quarto-sidebar-glass" aria-controls="quarto-sidebar" aria-expanded="false" aria-label="Toggle sidebar navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">      
+      </a>
+      <button type="button" class="btn quarto-search-button" aria-label="" onclick="window.quartoOpenSearch();">
+        <i class="bi bi-search"></i>
       </button>
     </div>
   </nav>
@@ -83,71 +125,103 @@ <h1 class="quarto-secondary-nav-title"><span class="chapter-number">2</span>&nbs
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 <div id="quarto-content" class="quarto-container page-columns page-rows-contents page-layout-article">
 <!-- sidebar -->
-  <nav id="quarto-sidebar" class="sidebar collapse sidebar-navigation floating overflow-auto">
+  <nav id="quarto-sidebar" class="sidebar collapse collapse-horizontal sidebar-navigation floating overflow-auto">
     <div class="pt-lg-2 mt-2 text-left sidebar-header">
     <div class="sidebar-title mb-0 py-0">
       <a href="./">Intro to R</a> 
     </div>
       </div>
-      <div class="mt-2 flex-shrink-0 align-items-center">
+        <div class="mt-2 flex-shrink-0 align-items-center">
         <div class="sidebar-search">
         <div id="quarto-search" class="" title="Search"></div>
         </div>
-      </div>
+        </div>
     <div class="sidebar-menu-container"> 
     <ul class="list-unstyled mt-1">
         <li class="sidebar-item">
   <div class="sidebar-item-container"> 
-  <a href="./index.html" class="sidebar-item-text sidebar-link">Intro to R: A Hands on Tutorial</a>
+  <a href="./index.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text">Intro to R: A Hands on Tutorial</span></a>
   </div>
 </li>
         <li class="sidebar-item">
   <div class="sidebar-item-container"> 
-  <a href="./01_lesson.html" class="sidebar-item-text sidebar-link"><span class="chapter-number">1</span>&nbsp; <span class="chapter-title">Data Visualization</span></a>
+  <a href="./01_lesson.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text"><span class="chapter-number">1</span>&nbsp; <span class="chapter-title">Data Visualization</span></span></a>
   </div>
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         <li class="sidebar-item">
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-  <a href="./02_lesson.html" class="sidebar-item-text sidebar-link active"><span class="chapter-number">2</span>&nbsp; <span class="chapter-title">Data Munging 1</span></a>
+  <a href="./02_lesson.html" class="sidebar-item-text sidebar-link active">
+ <span class="menu-text"><span class="chapter-number">2</span>&nbsp; <span class="chapter-title">Data Munging 1</span></span></a>
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         <li class="sidebar-item">
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-  <a href="./03_lesson.html" class="sidebar-item-text sidebar-link"><span class="chapter-number">3</span>&nbsp; <span class="chapter-title">Data Munging (II)</span></a>
+  <a href="./03_lesson.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text"><span class="chapter-number">3</span>&nbsp; <span class="chapter-title">Data Munging (II)</span></span></a>
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-  <a href="./04_lesson.html" class="sidebar-item-text sidebar-link"><span class="chapter-number">4</span>&nbsp; <span class="chapter-title">Data Import</span></a>
+  <a href="./04_lesson.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text"><span class="chapter-number">4</span>&nbsp; <span class="chapter-title">Data Import</span></span></a>
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-  <a href="./05_lesson.html" class="sidebar-item-text sidebar-link"><span class="chapter-number">5</span>&nbsp; <span class="chapter-title">Iteration (or becoming an R Superhero)</span></a>
+  <a href="./05_lesson.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text"><span class="chapter-number">5</span>&nbsp; <span class="chapter-title">Iteration (or becoming an R Superhero)</span></span></a>
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-  <a href="./06_lesson.html" class="sidebar-item-text sidebar-link"><span class="chapter-number">6</span>&nbsp; <span class="chapter-title">Putting it All Together (and into Markdown)</span></a>
+  <a href="./06_lesson.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text"><span class="chapter-number">6</span>&nbsp; <span class="chapter-title">Putting it All Together (and into Markdown)</span></span></a>
   </div>
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 </nav>
+<div id="quarto-sidebar-glass" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar,#quarto-sidebar-glass"></div>
 <!-- margin-sidebar -->
     <div id="quarto-margin-sidebar" class="sidebar margin-sidebar">
         <nav id="TOC" role="doc-toc" class="toc-active">
     <h2 id="toc-title">Table of contents</h2>
    
   <ul>
-  <li><a href="#review" id="toc-review" class="nav-link active" data-scroll-target="#review"><span class="toc-section-number">2.1</span>  Review</a></li>
-  <li><a href="#the-tidyverse-package" id="toc-the-tidyverse-package" class="nav-link" data-scroll-target="#the-tidyverse-package"><span class="toc-section-number">2.2</span>  The tidyverse package</a></li>
-  <li><a href="#primary-functions" id="toc-primary-functions" class="nav-link" data-scroll-target="#primary-functions"><span class="toc-section-number">2.3</span>  Primary functions</a></li>
-  <li><a href="#exercise-0-creating-a-project-and-loading-packages" id="toc-exercise-0-creating-a-project-and-loading-packages" class="nav-link" data-scroll-target="#exercise-0-creating-a-project-and-loading-packages"><span class="toc-section-number">2.4</span>  Exercise 0: Creating a Project and Loading Packages</a></li>
-  <li><a href="#exercise-1-tidyverse-function-overview" id="toc-exercise-1-tidyverse-function-overview" class="nav-link" data-scroll-target="#exercise-1-tidyverse-function-overview"><span class="toc-section-number">2.5</span>  Exercise 1: Tidyverse Function Overview</a></li>
-  <li><a href="#exercise-2-ifthen-and-summarizing" id="toc-exercise-2-ifthen-and-summarizing" class="nav-link" data-scroll-target="#exercise-2-ifthen-and-summarizing"><span class="toc-section-number">2.6</span>  Exercise 2: If/Then and Summarizing</a></li>
-  <li><a href="#exercise-3-put-it-all-together" id="toc-exercise-3-put-it-all-together" class="nav-link" data-scroll-target="#exercise-3-put-it-all-together"><span class="toc-section-number">2.7</span>  Exercise 3: Put it all together</a></li>
-  <li><a href="#resources" id="toc-resources" class="nav-link" data-scroll-target="#resources"><span class="toc-section-number">2.8</span>  Resources</a></li>
+  <li><a href="#review" id="toc-review" class="nav-link active" data-scroll-target="#review"><span class="header-section-number">2.1</span> Review</a></li>
+  <li><a href="#assignment" id="toc-assignment" class="nav-link" data-scroll-target="#assignment"><span class="header-section-number">2.2</span> Assignment</a></li>
+  <li><a href="#tidy-data" id="toc-tidy-data" class="nav-link" data-scroll-target="#tidy-data"><span class="header-section-number">2.3</span> Tidy Data</a>
+  <ul class="collapse">
+  <li><a href="#tidy-data-1" id="toc-tidy-data-1" class="nav-link" data-scroll-target="#tidy-data-1"><span class="header-section-number">2.3.1</span> Tidy data</a></li>
+  </ul></li>
+  <li><a href="#exercise-0-creating-a-project-and-loading-packages" id="toc-exercise-0-creating-a-project-and-loading-packages" class="nav-link" data-scroll-target="#exercise-0-creating-a-project-and-loading-packages"><span class="header-section-number">2.4</span> Exercise 0: Creating a Project and Loading Packages</a></li>
+  <li><a href="#select" id="toc-select" class="nav-link" data-scroll-target="#select"><span class="header-section-number">2.5</span> 1. <code>select()</code></a>
+  <ul class="collapse">
+  <li><a href="#exercise-1" id="toc-exercise-1" class="nav-link" data-scroll-target="#exercise-1"><span class="header-section-number">2.5.1</span> <span style="     color: #1696d2 !important;">Exercise 1</span></a></li>
+  </ul></li>
+  <li><a href="#rename" id="toc-rename" class="nav-link" data-scroll-target="#rename"><span class="header-section-number">2.6</span> 2. <code>rename()</code></a></li>
+  <li><a href="#filter" id="toc-filter" class="nav-link" data-scroll-target="#filter"><span class="header-section-number">2.7</span> 3. <code>filter()</code></a>
+  <ul class="collapse">
+  <li><a href="#exercise-2" id="toc-exercise-2" class="nav-link" data-scroll-target="#exercise-2"><span class="header-section-number">2.7.1</span> <span style="     color: #1696d2 !important;">Exercise 2</span></a></li>
+  </ul></li>
+  <li><a href="#arrange" id="toc-arrange" class="nav-link" data-scroll-target="#arrange"><span class="header-section-number">2.8</span> 4. <code>arrange()</code></a>
+  <ul class="collapse">
+  <li><a href="#exercise-3" id="toc-exercise-3" class="nav-link" data-scroll-target="#exercise-3"><span class="header-section-number">2.8.1</span> <span style="     color: #1696d2 !important;">Exercise 3</span></a></li>
+  </ul></li>
+  <li><a href="#mutate" id="toc-mutate" class="nav-link" data-scroll-target="#mutate"><span class="header-section-number">2.9</span> 5. <code>mutate()</code></a>
+  <ul class="collapse">
+  <li><a href="#exercise-4" id="toc-exercise-4" class="nav-link" data-scroll-target="#exercise-4"><span class="header-section-number">2.9.1</span> <span style="     color: #1696d2 !important;">Exercise 4</span></a></li>
+  </ul></li>
+  <li><a href="#section" id="toc-section" class="nav-link" data-scroll-target="#section"><span class="header-section-number">2.10</span> <code>%&gt;%</code></a></li>
+  <li><a href="#summarize" id="toc-summarize" class="nav-link" data-scroll-target="#summarize"><span class="header-section-number">2.11</span> 6. <code>summarize()</code></a></li>
+  <li><a href="#group_by" id="toc-group_by" class="nav-link" data-scroll-target="#group_by"><span class="header-section-number">2.12</span> 7. <code>group_by()</code></a>
+  <ul class="collapse">
+  <li><a href="#exercise-5" id="toc-exercise-5" class="nav-link" data-scroll-target="#exercise-5"><span class="header-section-number">2.12.1</span> <span style="     color: #1696d2 !important;">Exercise 5</span></a></li>
+  <li><a href="#exercise-6" id="toc-exercise-6" class="nav-link" data-scroll-target="#exercise-6"><span class="header-section-number">2.12.2</span> <span style="     color: #1696d2 !important;">Exercise 6</span></a></li>
+  </ul></li>
+  <li><a href="#resources" id="toc-resources" class="nav-link" data-scroll-target="#resources"><span class="header-section-number">2.13</span> Resources</a></li>
   </ul>
 </nav>
     </div>
@@ -156,7 +230,7 @@ <h2 id="toc-title">Table of contents</h2>
 
 <header id="title-block-header" class="quarto-title-block default">
 <div class="quarto-title">
-<h1 class="title d-none d-lg-block"><span class="chapter-number">2</span>&nbsp; <span class="chapter-title">Data Munging 1</span></h1>
+<h1 class="title"><span class="chapter-number">2</span>&nbsp; <span class="chapter-title">Data Munging 1</span></h1>
 </div>
 
 
@@ -166,14 +240,14 @@ <h1 class="title d-none d-lg-block"><span class="chapter-number">2</span>&nbsp;
     <div>
     <div class="quarto-title-meta-heading">Author</div>
     <div class="quarto-title-meta-contents">
-             <p>Fay Walker </p>
+             <p>Aaron R. Williams </p>
           </div>
   </div>
     
     <div>
     <div class="quarto-title-meta-heading">Published</div>
     <div class="quarto-title-meta-contents">
-      <p class="date">August 15, 2023</p>
+      <p class="date">August 22, 2023</p>
     </div>
   </div>
   
@@ -183,50 +257,59 @@ <h1 class="title d-none d-lg-block"><span class="chapter-number">2</span>&nbsp;
 
 </header>
 
-<style>
-@import url('https://fonts.googleapis.com/css?family=Lato&display=swap');
-</style>
-<p><link href="https://fonts.googleapis.com/css?family=Lato&amp;display=swap" rel="stylesheet"></p>
 <section id="review" class="level2" data-number="2.1">
 <h2 data-number="2.1" class="anchored" data-anchor-id="review"><span class="header-section-number">2.1</span> Review</h2>
 <ul>
-<li>Assignment Operator</li>
 <li>Console/Environment/Script</li>
-<li>Comment your code and read your error messages</li>
+<li>Comment your code with <code>#</code> and read your error messages</li>
 </ul>
 </section>
-<section id="the-tidyverse-package" class="level2" data-number="2.2">
-<h2 data-number="2.2" class="anchored" data-anchor-id="the-tidyverse-package"><span class="header-section-number">2.2</span> The tidyverse package</h2>
-<ul>
-<li>Collection of packages using same syntax and grammar</li>
-<li>Works with “tidy” data</li>
-<li><a href="https://r4ds.had.co.nz/tibbles.html">Uses tibbles</a></li>
-<li>%&gt;%</li>
-</ul>
+<section id="assignment" class="level2" data-number="2.2">
+<h2 data-number="2.2" class="anchored" data-anchor-id="assignment"><span class="header-section-number">2.2</span> Assignment</h2>
+<p><code>&lt;-</code> is the assignment operator. An object created on the right side of an assignment operator is assigned to a name on the left side of an assignment operator. Assignment operators are important for saving the consequences of operations and functions. Without assignment, the result of a calculation is not saved for use in future calculations. Operations without assignment operators will typically be printed to the console but not saved for future use.</p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb1"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a>a <span class="ot">&lt;-</span> <span class="dv">1</span></span>
+<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a>b <span class="ot">&lt;-</span> <span class="dv">2</span></span>
+<span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb1-4"><a href="#cb1-4" aria-hidden="true" tabindex="-1"></a>c <span class="ot">&lt;-</span> a <span class="sc">+</span> b</span>
+<span id="cb1-5"><a href="#cb1-5" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb1-6"><a href="#cb1-6" aria-hidden="true" tabindex="-1"></a>c</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-stdout">
+<pre><code>[1] 3</code></pre>
+</div>
+</div>
 </section>
-<section id="primary-functions" class="level2" data-number="2.3">
-<h2 data-number="2.3" class="anchored" data-anchor-id="primary-functions"><span class="header-section-number">2.3</span> Primary functions</h2>
+<section id="tidy-data" class="level2" data-number="2.3">
+<h2 data-number="2.3" class="anchored" data-anchor-id="tidy-data"><span class="header-section-number">2.3</span> Tidy Data</h2>
+<blockquote class="blockquote">
+<p>The <a href="https://www.tidyverse.org/">tidyverse</a> is an opinionated collection of R packages designed for data science. All packages share an underlying design philosophy, grammar, and data structures. ~ tidyverse.org</p>
+</blockquote>
+<p><code>library(tidyverse)</code> contains:</p>
 <ul>
-<li><code>select()</code></li>
-<li><code>rename()</code></li>
-<li><code>filter()</code></li>
-<li><code>arrange()</code></li>
-<li><code>mutate()</code></li>
-<li><code>group_by()</code></li>
-<li><code>summarize()</code></li>
+<li><a href="https://ggplot2.tidyverse.org">ggplot2</a>, for data visualization.</li>
+<li><a href="https://dplyr.tidyverse.org">dplyr</a>, for data manipulation.</li>
+<li><a href="https://tidyr.tidyverse.org">tidyr</a>, for data tidying.</li>
+<li><a href="https://readr.tidyverse.org">readr</a>, for data import.</li>
+<li><a href="https://purrr.tidyverse.org">purrr</a>, for functional programming.</li>
+<li><a href="https://tibble.tidyverse.org">tibble</a>, for tibbles, a modern re-imagining of data frames.</li>
+<li><a href="https://github.com/tidyverse/stringr">stringr</a>, for strings.</li>
+<li><a href="https://github.com/hadley/forcats">forcats</a>, for factors.</li>
 </ul>
-<p>All of these functions have the same structure.</p>
+<section id="tidy-data-1" class="level3" data-number="2.3.1">
+<h3 data-number="2.3.1" class="anchored" data-anchor-id="tidy-data-1"><span class="header-section-number">2.3.1</span> Tidy data</h3>
+<p>The defining opinion of the tidyverse is its wholehearted adoption of tidy data. Tidy data has three features:</p>
 <ol type="1">
-<li>The first argument is the dataframe</li>
-<li>The subsequent arguments describe what to do with the data frame, using the variable names (without quotes because you’re already refering to that df)</li>
-<li>Separate functions by %&gt;%</li>
-<li>The result is a new data frame.</li>
+<li>Each variable forms a column.</li>
+<li>Each observation forms a row.</li>
+<li>Each type of observational unit forms a dataframe. (This is from the paper, not the book)</li>
 </ol>
-<p><img src="C:/Users/fwalker/Downloads/image.png" class="img-fluid"></p>
-<ul>
-<li>If you have hit an error, trying running it line by line (function by function)</li>
-</ul>
+<p><img src="www/images/tidy-data.png" class="img-fluid"></p>
 <p>Source: <a href="https://r4ds.had.co.nz/transform.html">R for data science</a></p>
+<blockquote class="blockquote">
+<p>Tidy datasets are all alike, but every messy dataset is messy in its own way. ~ <a href="https://r4ds.had.co.nz/tidy-data.html">Hadley Wickham</a></p>
+</blockquote>
+<p>The tidy approach to data science is powerful because it breaks data work into two distinct parts. First, get the data into a tidy format. Second, use tools optimized for tidy data. By standardizing the data structure for most community-created tools, the framework oriented diffuse development and reduced the friction of data work.</p>
+</section>
 </section>
 <section id="exercise-0-creating-a-project-and-loading-packages" class="level2" data-number="2.4">
 <h2 data-number="2.4" class="anchored" data-anchor-id="exercise-0-creating-a-project-and-loading-packages"><span class="header-section-number">2.4</span> Exercise 0: Creating a Project and Loading Packages</h2>
@@ -234,86 +317,608 @@ <h2 data-number="2.4" class="anchored" data-anchor-id="exercise-0-creating-a-pro
 <p><font color="#55b748"><strong>Step 1:</strong></font> Open RStudio. File &gt; New Project &gt; New Directory &gt; Select the location where you would like to create a new folder that houses your R Project. Call it <code>urbn101</code>.</p>
 <p><font color="#55b748"><strong>Step 2:</strong></font> Open an <code>.R</code> script with the button in the top left (sheet with a plus sign icon). Save the script as <code>02_data-munging1.R</code>.</p>
 <p><font color="#55b748"><strong>Step 3:</strong></font> If you have not previously installed <code>library(tidyverse)</code>: submit <code>install.packages("tidyverse")</code> to the Console (type and hit enter)</p>
-<p><font color="#55b748"><strong>Step 4:</strong></font> Write <code>library(tidyverse)</code> at the top of <code>02_data-munging1.R</code>. With the cursor on the line of text, click Control-Enter.</p>
+<p>We’ll focus on the key dplyr syntax using the March 2020 Annual Social and Economic Supplement (ASEC) to the Current Population Survey (CPS). Run the following code to load the data.</p>
+<p><font color="#55b748"><strong>Step 4:</strong></font> Add and run the following code to load ASEC data.</p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb3"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(tidyverse)</span>
+<span id="cb3-2"><a href="#cb3-2" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb3-3"><a href="#cb3-3" aria-hidden="true" tabindex="-1"></a>asec <span class="ot">&lt;-</span> <span class="fu">read_csv</span>(</span>
+<span id="cb3-4"><a href="#cb3-4" aria-hidden="true" tabindex="-1"></a>  <span class="fu">paste0</span>(</span>
+<span id="cb3-5"><a href="#cb3-5" aria-hidden="true" tabindex="-1"></a>    <span class="st">"https://raw.githubusercontent.com/awunderground/awunderground-data/"</span>,</span>
+<span id="cb3-6"><a href="#cb3-6" aria-hidden="true" tabindex="-1"></a>    <span class="st">"main/cps/cps-asec.csv"</span></span>
+<span id="cb3-7"><a href="#cb3-7" aria-hidden="true" tabindex="-1"></a>  )</span>
+<span id="cb3-8"><a href="#cb3-8" aria-hidden="true" tabindex="-1"></a>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</div>
+<p>We can use <code>glimpse(asec)</code> to quickly view the data. We can also use <code>View(asec)</code> to open up <code>asec</code> in RStudio.</p>
+<p><br></p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb4"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a><span class="fu">glimpse</span>(<span class="at">x =</span> asec)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-stdout">
+<pre><code>Rows: 157,959
+Columns: 17
+$ year       &lt;dbl&gt; 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020,…
+$ serial     &lt;dbl&gt; 1, 1, 2, 2, 3, 4, 4, 5, 5, 5, 5, 7, 8, 9, 10, 10, 10, 12, 1…
+$ month      &lt;chr&gt; "March", "March", "March", "March", "March", "March", "Marc…
+$ cpsid      &lt;dbl&gt; 2.01903e+13, 2.01903e+13, 2.01812e+13, 2.01812e+13, 2.01902…
+$ asecflag   &lt;dbl&gt; 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
+$ asecwth    &lt;dbl&gt; 1552.90, 1552.90, 990.49, 990.49, 1505.27, 1430.70, 1430.70…
+$ pernum     &lt;dbl&gt; 1, 2, 1, 2, 1, 1, 2, 1, 2, 3, 4, 1, 1, 1, 1, 2, 3, 1, 2, 3,…
+$ cpsidp     &lt;dbl&gt; 2.01903e+13, 2.01903e+13, 2.01812e+13, 2.01812e+13, 2.01902…
+$ asecwt     &lt;dbl&gt; 1552.90, 1552.90, 990.49, 990.49, 1505.27, 1430.70, 1196.57…
+$ ftype      &lt;chr&gt; "Primary family", "Primary family", "Primary family", "Prim…
+$ ftotval    &lt;dbl&gt; 127449, 127449, 64680, 64680, 40002, 8424, 8424, 59114, 591…
+$ inctot     &lt;dbl&gt; 52500, 74949, 44000, 20680, 40002, 0, 8424, 610, 58001, 503…
+$ incwage    &lt;dbl&gt; 52500, 56000, 34000, 0, 40000, 0, 8424, 0, 58000, 0, 0, 0, …
+$ offpov     &lt;chr&gt; "Above Poverty Line", "Above Poverty Line", "Above Poverty …
+$ offpovuniv &lt;chr&gt; "In Poverty Universe", "In Poverty Universe", "In Poverty U…
+$ offtotval  &lt;dbl&gt; 127449, 127449, 64680, 64680, 40002, 8424, 8424, 59114, 591…
+$ offcutoff  &lt;dbl&gt; 17120, 17120, 17120, 17120, 13300, 15453, 15453, 26370, 263…</code></pre>
+</div>
+</div>
+<p><br></p>
+<p>We’re going to learn seven functions and one new piece of syntax from <code>library(dplyr)</code> that will be our main tools for manipulating tidy frames. These functions and a few extensions outlined in the <a href="https://rstudio.com/resources/cheatsheets/">Data Transformation Cheat Sheet</a> are the core of data analysis in the Tidyverse.</p>
 </section>
-<section id="exercise-1-tidyverse-function-overview" class="level2" data-number="2.5">
-<h2 data-number="2.5" class="anchored" data-anchor-id="exercise-1-tidyverse-function-overview"><span class="header-section-number">2.5</span> Exercise 1: Tidyverse Function Overview</h2>
-<p><font color="#55b748"><strong>Step 1:</strong></font> Assign the <code>storms</code> dataset to be called storms. Submit <code>View(storms)</code> in the console.</p>
-<p>You can also deselect specific columns in your data by using the <code>-</code> operator</p>
-<p><font color="#ec008b"><strong>Select</strong></font></p>
-<p><code>select()</code> is used when you want to limit the number of <strong>columns</strong> in your dataframe. The arguments after the dataframe should be names of columns, without quotes. For example:</p>
-<pre><code>dataset %&gt;%
-  select(col1, col2)</code></pre>
-<p><font color="#55b748"><strong>Step 2:</strong></font> Create a new dataset with all the variables except for <code>ts_diameter</code> and <code>hu_diameter</code>, by using <code>select</code> and <code>-</code>.</p>
-<p><font color="#55b748"><strong>Step 3:</strong></font> Create a new dataset that only category 5 storms using <code>filter()</code>.</p>
-<p><font color="#ec008b"><strong>Filter</strong></font></p>
-<p><code>filter()</code> is used when you want to filter to specific rows in your dataframe, based on certain conditions. Filter works with the following comparison operators:</p>
-<ul>
-<li><code>&lt;</code>, <code>&gt;</code>, <code>&lt;=</code>, <code>&gt;=</code></li>
-<li><code>==</code>, <code>!=</code></li>
-</ul>
-<p>You can also chain together multiple condtions with <code>&amp;</code> (and) or <code>|</code> (or). The arguments after the dataframe should be conditions you want to filter your dataset on. For example</p>
-<pre><code>dataset %&gt;%
-filter(col1&gt;0)</code></pre>
-<p><font color="#55b748"><strong>Step 4:</strong></font> Chain all of these commands together with the <code>%&gt;%</code> operator. Then rename the column <code>pressure</code> to <code>air_pressure</code>.</p>
-<p><font color="#ec008b"><strong>Rename</strong></font></p>
-<p>The syntax for rename is:</p>
-<pre><code>dataset %&gt;% 
-  rename(new_name = current_name)</code></pre>
-<p>This same syntax can be used inside <code>select()</code>, and you can rename and select variables simultanously.</p>
-<p><font color="#55b748"><strong>Step 5:</strong></font> <code>wind</code> is measured in knots. Convert <code>wind</code> into miles per hour. Each knot is equal to 1.15078 miles per hour.</p>
-<p><font color="#ec008b"><strong>Mutate</strong></font></p>
-<p><code>mutate()</code> can make new variables and edit existing variables. New variables are always added to the end of the dataframe. You can use arithmetic arguments, such as <code>+</code>, <code>-</code>, <code>*</code>, <code>/</code>, and <code>^</code>. The syntax for mutate is:</p>
-<pre><code>dataset %&gt;% 
-  mutate(new_column = 100 * other_column)</code></pre>
-<p><font color="#55b748"><strong>Step 6:</strong></font> Make a dataset with storms that occured in the summer (June, July, August).</p>
-<p><font color="#ec008b"><strong>The <code>%in%</code> operator</strong></font></p>
-<p>The <code>%in%</code> operator is useful for filtering based on more than one option within one variable.</p>
-<pre><code>filter(dataset, var1 == "a" | var1 == "b" | var1 == "c")</code></pre>
-<p>is equivalent to</p>
-<pre><code>dataset %&gt;%
-filter(var1 %in% c("a", "b", "c"))</code></pre>
+<section id="select" class="level2" data-number="2.5">
+<h2 data-number="2.5" class="anchored" data-anchor-id="select"><span class="header-section-number">2.5</span> 1. <code>select()</code></h2>
+<p><code>select()</code> drops columns from a dataframe and/or reorders the columns in a dataframe. The arguments after the name of the dataframe should be the names of columns you wish to keep, without quotes. All other columns not listed are dropped.</p>
+<p><br></p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb6"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb6-1"><a href="#cb6-1" aria-hidden="true" tabindex="-1"></a><span class="fu">select</span>(<span class="at">.data =</span> asec, year, month, serial)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-stdout">
+<pre><code># A tibble: 157,959 × 3
+    year month serial
+   &lt;dbl&gt; &lt;chr&gt;  &lt;dbl&gt;
+ 1  2020 March      1
+ 2  2020 March      1
+ 3  2020 March      2
+ 4  2020 March      2
+ 5  2020 March      3
+ 6  2020 March      4
+ 7  2020 March      4
+ 8  2020 March      5
+ 9  2020 March      5
+10  2020 March      5
+# ℹ 157,949 more rows</code></pre>
+</div>
+</div>
+<p><br></p>
+<p>This works great until the goal is to select 99 of 100 variables. Fortunately, <code>-</code> can be used to remove variables. You can also select all but multiple variables by listing them with the <code>-</code> symbol separated by commas.</p>
+<p><br></p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb8"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb8-1"><a href="#cb8-1" aria-hidden="true" tabindex="-1"></a><span class="fu">select</span>(<span class="at">.data =</span> asec, <span class="sc">-</span>asecflag)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-stdout">
+<pre><code># A tibble: 157,959 × 16
+    year serial month   cpsid asecwth pernum  cpsidp asecwt ftype ftotval inctot
+   &lt;dbl&gt;  &lt;dbl&gt; &lt;chr&gt;   &lt;dbl&gt;   &lt;dbl&gt;  &lt;dbl&gt;   &lt;dbl&gt;  &lt;dbl&gt; &lt;chr&gt;   &lt;dbl&gt;  &lt;dbl&gt;
+ 1  2020      1 March 2.02e13   1553.      1 2.02e13  1553. Prim…  127449  52500
+ 2  2020      1 March 2.02e13   1553.      2 2.02e13  1553. Prim…  127449  74949
+ 3  2020      2 March 2.02e13    990.      1 2.02e13   990. Prim…   64680  44000
+ 4  2020      2 March 2.02e13    990.      2 2.02e13   990. Prim…   64680  20680
+ 5  2020      3 March 2.02e13   1505.      1 2.02e13  1505. Nonf…   40002  40002
+ 6  2020      4 March 2.02e13   1431.      1 2.02e13  1431. Prim…    8424      0
+ 7  2020      4 March 2.02e13   1431.      2 2.02e13  1197. Prim…    8424   8424
+ 8  2020      5 March 2.02e13   1133.      1 2.02e13  1133. Prim…   59114    610
+ 9  2020      5 March 2.02e13   1133.      2 2.02e13  1133. Prim…   59114  58001
+10  2020      5 March 2.02e13   1133.      3 2.02e13  1322. Prim…   59114    503
+# ℹ 157,949 more rows
+# ℹ 5 more variables: incwage &lt;dbl&gt;, offpov &lt;chr&gt;, offpovuniv &lt;chr&gt;,
+#   offtotval &lt;dbl&gt;, offcutoff &lt;dbl&gt;</code></pre>
+</div>
+</div>
+<p><br></p>
+<section id="exercise-1" class="level3" data-number="2.5.1">
+<h3 data-number="2.5.1" class="anchored" data-anchor-id="exercise-1"><span class="header-section-number">2.5.1</span> <span style="     color: #1696d2 !important;">Exercise 1</span></h3>
+<div class="tabset-margin-container"></div><div class="panel-tabset">
+<ul class="nav nav-tabs" role="tablist"><li class="nav-item" role="presentation"><a class="nav-link active" id="tabset-1-1-tab" data-bs-toggle="tab" data-bs-target="#tabset-1-1" role="tab" aria-controls="tabset-1-1" aria-selected="true">Prompt</a></li><li class="nav-item" role="presentation"><a class="nav-link" id="tabset-1-2-tab" data-bs-toggle="tab" data-bs-target="#tabset-1-2" role="tab" aria-controls="tabset-1-2" aria-selected="false">Solution</a></li></ul>
+<div class="tab-content">
+<div id="tabset-1-1" class="tab-pane active" role="tabpanel" aria-labelledby="tabset-1-1-tab">
+<ol type="1">
+<li>Select <code>pernum</code> and <code>inctot</code> from <code>asec</code>.</li>
+</ol>
+</div>
+<div id="tabset-1-2" class="tab-pane" role="tabpanel" aria-labelledby="tabset-1-2-tab">
+<ol type="1">
+<li>Select <code>pernum</code> and <code>inctot</code> from <code>asec</code>.</li>
+</ol>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb10"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb10-1"><a href="#cb10-1" aria-hidden="true" tabindex="-1"></a><span class="fu">select</span>(<span class="at">.data =</span> asec, inctot, asec)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</div>
+</div>
+</div>
+</div>
+<p><span class="math display">\[\cdots\]</span></p>
+</section>
+</section>
+<section id="rename" class="level2" data-number="2.6">
+<h2 data-number="2.6" class="anchored" data-anchor-id="rename"><span class="header-section-number">2.6</span> 2. <code>rename()</code></h2>
+<p><code>rename()</code> renames columns in a data frame. The pattern is <code>new_name = old_name</code>.</p>
+<p><br></p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb11"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb11-1"><a href="#cb11-1" aria-hidden="true" tabindex="-1"></a><span class="fu">rename</span>(<span class="at">.data =</span> asec, <span class="at">serial_number =</span> serial)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-stdout">
+<pre><code># A tibble: 157,959 × 17
+    year serial_number month   cpsid asecflag asecwth pernum  cpsidp asecwt
+   &lt;dbl&gt;         &lt;dbl&gt; &lt;chr&gt;   &lt;dbl&gt;    &lt;dbl&gt;   &lt;dbl&gt;  &lt;dbl&gt;   &lt;dbl&gt;  &lt;dbl&gt;
+ 1  2020             1 March 2.02e13        1   1553.      1 2.02e13  1553.
+ 2  2020             1 March 2.02e13        1   1553.      2 2.02e13  1553.
+ 3  2020             2 March 2.02e13        1    990.      1 2.02e13   990.
+ 4  2020             2 March 2.02e13        1    990.      2 2.02e13   990.
+ 5  2020             3 March 2.02e13        1   1505.      1 2.02e13  1505.
+ 6  2020             4 March 2.02e13        1   1431.      1 2.02e13  1431.
+ 7  2020             4 March 2.02e13        1   1431.      2 2.02e13  1197.
+ 8  2020             5 March 2.02e13        1   1133.      1 2.02e13  1133.
+ 9  2020             5 March 2.02e13        1   1133.      2 2.02e13  1133.
+10  2020             5 March 2.02e13        1   1133.      3 2.02e13  1322.
+# ℹ 157,949 more rows
+# ℹ 8 more variables: ftype &lt;chr&gt;, ftotval &lt;dbl&gt;, inctot &lt;dbl&gt;, incwage &lt;dbl&gt;,
+#   offpov &lt;chr&gt;, offpovuniv &lt;chr&gt;, offtotval &lt;dbl&gt;, offcutoff &lt;dbl&gt;</code></pre>
+</div>
+</div>
+<p><br></p>
+<p>You can also rename a selection of variables using <code>rename_with()</code>. The <code>.cols</code> argument is used to select the columns to rename and takes a <code>tidyselect</code> statement like those we introduced above. Here, we’re using the <code>where()</code> selection helper which selects all columns where a given condition is TRUE. The default value for the <code>.cols</code> argument is <code>everything()</code> which selects all columns in the dataset.</p>
+<p><br></p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb13"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb13-1"><a href="#cb13-1" aria-hidden="true" tabindex="-1"></a><span class="fu">rename_with</span>(<span class="at">.data =</span> asec, <span class="at">.fn =</span> toupper, <span class="at">.cols =</span> <span class="fu">where</span>(is.numeric))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-stdout">
+<pre><code># A tibble: 157,959 × 17
+    YEAR SERIAL month   CPSID ASECFLAG ASECWTH PERNUM  CPSIDP ASECWT ftype      
+   &lt;dbl&gt;  &lt;dbl&gt; &lt;chr&gt;   &lt;dbl&gt;    &lt;dbl&gt;   &lt;dbl&gt;  &lt;dbl&gt;   &lt;dbl&gt;  &lt;dbl&gt; &lt;chr&gt;      
+ 1  2020      1 March 2.02e13        1   1553.      1 2.02e13  1553. Primary fa…
+ 2  2020      1 March 2.02e13        1   1553.      2 2.02e13  1553. Primary fa…
+ 3  2020      2 March 2.02e13        1    990.      1 2.02e13   990. Primary fa…
+ 4  2020      2 March 2.02e13        1    990.      2 2.02e13   990. Primary fa…
+ 5  2020      3 March 2.02e13        1   1505.      1 2.02e13  1505. Nonfamily …
+ 6  2020      4 March 2.02e13        1   1431.      1 2.02e13  1431. Primary fa…
+ 7  2020      4 March 2.02e13        1   1431.      2 2.02e13  1197. Primary fa…
+ 8  2020      5 March 2.02e13        1   1133.      1 2.02e13  1133. Primary fa…
+ 9  2020      5 March 2.02e13        1   1133.      2 2.02e13  1133. Primary fa…
+10  2020      5 March 2.02e13        1   1133.      3 2.02e13  1322. Primary fa…
+# ℹ 157,949 more rows
+# ℹ 7 more variables: FTOTVAL &lt;dbl&gt;, INCTOT &lt;dbl&gt;, INCWAGE &lt;dbl&gt;, offpov &lt;chr&gt;,
+#   offpovuniv &lt;chr&gt;, OFFTOTVAL &lt;dbl&gt;, OFFCUTOFF &lt;dbl&gt;</code></pre>
+</div>
+</div>
+<p><br></p>
+<p>Most <code>dplyr</code> functions can rename columns simply by prefacing the operation with <code>new_name =</code>. For example, this can be done with <code>select()</code>:</p>
+<p><br></p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb15"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb15-1"><a href="#cb15-1" aria-hidden="true" tabindex="-1"></a><span class="fu">select</span>(<span class="at">.data =</span> asec, year, month, <span class="at">serial_number =</span> serial)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-stdout">
+<pre><code># A tibble: 157,959 × 3
+    year month serial_number
+   &lt;dbl&gt; &lt;chr&gt;         &lt;dbl&gt;
+ 1  2020 March             1
+ 2  2020 March             1
+ 3  2020 March             2
+ 4  2020 March             2
+ 5  2020 March             3
+ 6  2020 March             4
+ 7  2020 March             4
+ 8  2020 March             5
+ 9  2020 March             5
+10  2020 March             5
+# ℹ 157,949 more rows</code></pre>
+</div>
+</div>
+<p><br></p>
 </section>
-<section id="exercise-2-ifthen-and-summarizing" class="level2" data-number="2.6">
-<h2 data-number="2.6" class="anchored" data-anchor-id="exercise-2-ifthen-and-summarizing"><span class="header-section-number">2.6</span> Exercise 2: If/Then and Summarizing</h2>
-<p><font color="#55b748"><strong>Step 1:</strong></font> Use the <code>txhousing</code> dataset. Assign the dataset to an object in your global environment named <code>tx_housing</code>.</p>
-<p><font color="#55b748"><strong>Step 2:</strong></font> Compute a new variable, <code>average_price</code> by dividing <code>volume</code> by <code>sales</code>.</p>
-<p><font color="#55b748"><strong>Step 3:</strong></font> Use <code>if_else()</code> to create a variable called <code>flag</code> that is 1 if the average sales price is larger than the median sales price.</p>
-<p><font color="#ec008b"><strong>If/then logic</strong></font></p>
-<p><code>if_else()</code> is an important tool in combination with <code>mutate()</code>. It employs if-then logic: the first argument is a binary statement. The second statement says what to do if the first argument is evaluated to <code>TRUE</code>- the third is what to do if it is <code>FALSE</code> This is particularly useful for creating dummy or indicator variables. When you have more than two possible outcomes, it is more efficient to use <code>case_when()</code>.</p>
-<p><font color="#55b748"><strong>Step 3a:</strong></font> Type <code>?case_when</code> into the console.</p>
-<p><font color="#55b748"><strong>Step 4:</strong></font> Type the following code into your script.</p>
-<pre><code>tx_housing %&gt;% 
-  group_by(year) %&gt;% 
-  summarize(yearly_sales = sum(sales))</code></pre>
-<p><font color="#ec008b"><strong>Group By</strong></font></p>
-<p><code>group_by</code> groups your dataframe into discrete categories. It can be very powerful when used with <code>summarize</code>.</p>
-<p><font color="#ec008b"><strong>Summarize</strong></font></p>
-<p><code>summarize</code> computes summary statistics for as set of observations in your data. The syntax is:</p>
-<pre><code>dataset %&gt;% 
-  mutate(max = max(other_column))</code></pre>
-<p>or</p>
-<pre><code>dataset %&gt;%
-  group_by(col1) %&gt;%
-  summarize(count=n())</code></pre>
-<p><font color="#55b748"><strong>Step 5:</strong></font> Add the argument <code>na.rm = TRUE</code> to the <code>sum()</code> function and rerun the code. Add a comment to explain the code.</p>
-<p><font color="#55b748"><strong>Step 6:</strong></font> Sort the results of the previous code in order from most to least sales by adding <code>%&gt;% arrange(desc(yearly_sales))</code> to the end of the code.</p>
-<p><font color="#55b748"><strong>Step 7a:</strong></font> Which city had the most home sales in January 2008? (Hint: use <code>filter()</code> and <code>arrange()</code>).</p>
-<p><font color="#55b748"><strong>Step 7b:</strong></font> Which city had the least home sales in 2006? (Hint: group by the city and the year- then use <code>summarize()</code>).</p>
-<p><font color="#55b748"><strong>BONUS:</strong></font> Plot the number of annual home sales over time!</p>
+<section id="filter" class="level2" data-number="2.7">
+<h2 data-number="2.7" class="anchored" data-anchor-id="filter"><span class="header-section-number">2.7</span> 3. <code>filter()</code></h2>
+<p><code>filter()</code> reduces the number of observations in a dataframe. Every column in a dataframe has a name. Rows do not necessarily have names in a dataframe, so rows need to be filtered based on logical conditions.</p>
+<p><code>==</code>, <code>&lt;</code>, <code>&gt;</code>, <code>&lt;=</code>, <code>&gt;=</code>, <code>!=</code>, <code>%in%</code>, and <code>is.na()</code> are all operators that can be used for logical conditions. <code>!</code> can be used to negate a condition and <code>&amp;</code> and <code>|</code> can be used to combine conditions. <code>|</code> means or.</p>
+<p><br></p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb17"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb17-1"><a href="#cb17-1" aria-hidden="true" tabindex="-1"></a><span class="co"># return rows with pernum of 1 and incwage &gt; $100,000</span></span>
+<span id="cb17-2"><a href="#cb17-2" aria-hidden="true" tabindex="-1"></a><span class="fu">filter</span>(<span class="at">.data =</span> asec, pernum <span class="sc">==</span> <span class="dv">1</span> <span class="sc">&amp;</span> incwage <span class="sc">&gt;</span> <span class="dv">100000</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-stdout">
+<pre><code># A tibble: 5,551 × 17
+    year serial month   cpsid asecflag asecwth pernum  cpsidp asecwt ftype      
+   &lt;dbl&gt;  &lt;dbl&gt; &lt;chr&gt;   &lt;dbl&gt;    &lt;dbl&gt;   &lt;dbl&gt;  &lt;dbl&gt;   &lt;dbl&gt;  &lt;dbl&gt; &lt;chr&gt;      
+ 1  2020     28 March 2.02e13        1    678.      1 2.02e13   678. Primary fa…
+ 2  2020    134 March 0              1    923.      1 0         923. Primary fa…
+ 3  2020    136 March 2.02e13        1    906.      1 2.02e13   906. Primary fa…
+ 4  2020    137 March 2.02e13        1   1493.      1 2.02e13  1493. Nonfamily …
+ 5  2020    359 March 2.02e13        1    863.      1 2.02e13   863. Primary fa…
+ 6  2020    372 March 2.02e13        1   1338.      1 2.02e13  1338. Primary fa…
+ 7  2020    404 March 0              1    677.      1 0         677. Primary fa…
+ 8  2020    420 March 2.02e13        1    747.      1 2.02e13   747. Primary fa…
+ 9  2020    450 March 2.02e13        1   1309.      1 2.02e13  1309. Primary fa…
+10  2020    491 March 0              1   1130.      1 0        1130. Primary fa…
+# ℹ 5,541 more rows
+# ℹ 7 more variables: ftotval &lt;dbl&gt;, inctot &lt;dbl&gt;, incwage &lt;dbl&gt;, offpov &lt;chr&gt;,
+#   offpovuniv &lt;chr&gt;, offtotval &lt;dbl&gt;, offcutoff &lt;dbl&gt;</code></pre>
+</div>
+</div>
+<p><a href="https://cps.ipums.org/cps-action/variables/group">IPUMS CPS</a> contains full documentation with information about <code>pernum</code> and <code>incwage</code>.</p>
+<p><br></p>
+<section id="exercise-2" class="level3" data-number="2.7.1">
+<h3 data-number="2.7.1" class="anchored" data-anchor-id="exercise-2"><span class="header-section-number">2.7.1</span> <span style="     color: #1696d2 !important;">Exercise 2</span></h3>
+<div class="tabset-margin-container"></div><div class="panel-tabset">
+<ul class="nav nav-tabs" role="tablist"><li class="nav-item" role="presentation"><a class="nav-link active" id="tabset-2-1-tab" data-bs-toggle="tab" data-bs-target="#tabset-2-1" role="tab" aria-controls="tabset-2-1" aria-selected="true">Prompt</a></li><li class="nav-item" role="presentation"><a class="nav-link" id="tabset-2-2-tab" data-bs-toggle="tab" data-bs-target="#tabset-2-2" role="tab" aria-controls="tabset-2-2" aria-selected="false">Solution</a></li></ul>
+<div class="tab-content">
+<div id="tabset-2-1" class="tab-pane active" role="tabpanel" aria-labelledby="tabset-2-1-tab">
+<ol type="1">
+<li>Filter <code>asec</code> to rows with <code>month</code> equal to <code>"March"</code>.</li>
+<li>Filter <code>asec</code> to rows with <code>inctot</code> less than <code>999999999</code>.</li>
+<li>Filter <code>asec</code> to rows with <code>pernum</code> equal to <code>3</code> and <code>inctot</code> less than <code>999999999</code>.</li>
+</ol>
+</div>
+<div id="tabset-2-2" class="tab-pane" role="tabpanel" aria-labelledby="tabset-2-2-tab">
+<ol type="1">
+<li>Filter <code>asec</code> to rows with <code>month</code> equal to <code>"March"</code>.</li>
+<li>Filter <code>asec</code> to rows with <code>inctot</code> less than <code>999999999</code>.</li>
+<li>Filter <code>asec</code> to rows with <code>pernum</code> equal to <code>3</code> and <code>inctot</code> less than <code>999999999</code>.</li>
+</ol>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb19"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb19-1"><a href="#cb19-1" aria-hidden="true" tabindex="-1"></a><span class="fu">filter</span>(asec, month <span class="sc">==</span> <span class="st">"March"</span>)</span>
+<span id="cb19-2"><a href="#cb19-2" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb19-3"><a href="#cb19-3" aria-hidden="true" tabindex="-1"></a><span class="fu">filter</span>(asec, inctot <span class="sc">&lt;</span> <span class="dv">999999999</span>)</span>
+<span id="cb19-4"><a href="#cb19-4" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb19-5"><a href="#cb19-5" aria-hidden="true" tabindex="-1"></a><span class="fu">filter</span>(asec, pernum <span class="sc">==</span> <span class="dv">3</span>, inctot <span class="sc">&lt;</span> <span class="dv">999999999</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</div>
+</div>
+</div>
+</div>
 </section>
-<section id="exercise-3-put-it-all-together" class="level2" data-number="2.7">
-<h2 data-number="2.7" class="anchored" data-anchor-id="exercise-3-put-it-all-together"><span class="header-section-number">2.7</span> Exercise 3: Put it all together</h2>
-<p><font color="#55b748"><strong>Step 1:</strong></font> Use the <code>starwars</code> dataframe. Type <code>count(starwars, homeworld)</code> into your script.</p>
-<p><font color="#55b748"><strong>Step 2:</strong></font> Filter out any characters that have a missing <code>homeworld</code>.</p>
-<p><font color="#55b748"><strong>Step 3:</strong></font> Find the average and median height and mass for humans vs.&nbsp;droids. (Hint: use <code>group_by()</code> and <code>summarize()</code>). Make sure that you are correcting for observations with missing values!</p>
-<p><font color="#55b748"><strong>Step 4:</strong></font> Make a dummy variable called <code>droid</code> that is <code>1</code> for droids and <code>0</code> for humans. Make a variable called <code>tall</code> that is <code>1</code> if the characters are more than 200 cm tall. How many tall humans are there?</p>
-<p><font color="#55b748"><strong>Step 5:</strong></font> What is the most common skin color for droids?</p>
 </section>
-<section id="resources" class="level2" data-number="2.8">
-<h2 data-number="2.8" class="anchored" data-anchor-id="resources"><span class="header-section-number">2.8</span> Resources</h2>
+<section id="arrange" class="level2" data-number="2.8">
+<h2 data-number="2.8" class="anchored" data-anchor-id="arrange"><span class="header-section-number">2.8</span> 4. <code>arrange()</code></h2>
+<p><code>arrange()</code> sorts the rows of a data frame in alpha-numeric order based on the values of a variable or variables. The dataframe is sorted by the first variable first and each subsequent variable is used to break ties. <code>desc()</code> is used to reverse the sort order for a given variable.</p>
+<p><br></p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb20"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb20-1"><a href="#cb20-1" aria-hidden="true" tabindex="-1"></a><span class="co"># sort pernum is descending order because high pernums are interesting</span></span>
+<span id="cb20-2"><a href="#cb20-2" aria-hidden="true" tabindex="-1"></a><span class="fu">arrange</span>(<span class="at">.data =</span> asec, <span class="fu">desc</span>(pernum))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-stdout">
+<pre><code># A tibble: 157,959 × 17
+    year serial month   cpsid asecflag asecwth pernum  cpsidp asecwt ftype      
+   &lt;dbl&gt;  &lt;dbl&gt; &lt;chr&gt;   &lt;dbl&gt;    &lt;dbl&gt;   &lt;dbl&gt;  &lt;dbl&gt;   &lt;dbl&gt;  &lt;dbl&gt; &lt;chr&gt;      
+ 1  2020  91430 March 0              1    505.     16 0         604. Secondary …
+ 2  2020  91430 March 0              1    505.     15 0         465. Secondary …
+ 3  2020  91430 March 0              1    505.     14 0         416. Secondary …
+ 4  2020  15037 March 2.02e13        1   2272.     13 2.02e13  2633. Primary fa…
+ 5  2020  78495 March 0              1   1279.     13 0        1424. Related su…
+ 6  2020  91430 March 0              1    505.     13 0         465. Secondary …
+ 7  2020  15037 March 2.02e13        1   2272.     12 2.02e13  1689. Primary fa…
+ 8  2020  18102 March 0              1   2468.     12 0        2871. Primary fa…
+ 9  2020  22282 March 0              1   2801.     12 0        3879. Related su…
+10  2020  30274 March 2.02e13        1    653.     12 2.02e13   858. Primary fa…
+# ℹ 157,949 more rows
+# ℹ 7 more variables: ftotval &lt;dbl&gt;, inctot &lt;dbl&gt;, incwage &lt;dbl&gt;, offpov &lt;chr&gt;,
+#   offpovuniv &lt;chr&gt;, offtotval &lt;dbl&gt;, offcutoff &lt;dbl&gt;</code></pre>
+</div>
+</div>
+<p><br></p>
+<section id="exercise-3" class="level3" data-number="2.8.1">
+<h3 data-number="2.8.1" class="anchored" data-anchor-id="exercise-3"><span class="header-section-number">2.8.1</span> <span style="     color: #1696d2 !important;">Exercise 3</span></h3>
+<div class="tabset-margin-container"></div><div class="panel-tabset">
+<ul class="nav nav-tabs" role="tablist"><li class="nav-item" role="presentation"><a class="nav-link active" id="tabset-3-1-tab" data-bs-toggle="tab" data-bs-target="#tabset-3-1" role="tab" aria-controls="tabset-3-1" aria-selected="true">Prompt</a></li><li class="nav-item" role="presentation"><a class="nav-link" id="tabset-3-2-tab" data-bs-toggle="tab" data-bs-target="#tabset-3-2" role="tab" aria-controls="tabset-3-2" aria-selected="false">Solution</a></li></ul>
+<div class="tab-content">
+<div id="tabset-3-1" class="tab-pane active" role="tabpanel" aria-labelledby="tabset-3-1-tab">
+<ol type="1">
+<li>Sort <code>asec</code> in descending order by <code>pernum</code> and ascending order by <code>inctot</code>.</li>
+</ol>
+</div>
+<div id="tabset-3-2" class="tab-pane" role="tabpanel" aria-labelledby="tabset-3-2-tab">
+<ol type="1">
+<li>Sort <code>asec</code> in descending order by <code>pernum</code> and ascending order by <code>inctot</code>.</li>
+</ol>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb22"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb22-1"><a href="#cb22-1" aria-hidden="true" tabindex="-1"></a><span class="fu">arrange</span>(asec, <span class="fu">desc</span>(pernum), inctot)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</div>
+</div>
+</div>
+</div>
+</section>
+</section>
+<section id="mutate" class="level2" data-number="2.9">
+<h2 data-number="2.9" class="anchored" data-anchor-id="mutate"><span class="header-section-number">2.9</span> 5. <code>mutate()</code></h2>
+<p><code>mutate()</code> creates new variables or edits existing variables. We can use arithmetic arguments, such as <code>+</code>, <code>-</code>, <code>*</code>, <code>/</code>, and <code>^</code>. We can also custom functions and functions from packages. For example, we can use <code>library(stringr)</code> for string manipulation and <code>library(lubridate)</code> for date manipulation.</p>
+<p><br></p>
+<p>Variables are created by adding a new column name, like <code>inctot_adjusted</code>, to the left of <code>=</code> in <code>mutate()</code>.</p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb23"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb23-1"><a href="#cb23-1" aria-hidden="true" tabindex="-1"></a><span class="co"># adjust inctot for underreporting</span></span>
+<span id="cb23-2"><a href="#cb23-2" aria-hidden="true" tabindex="-1"></a><span class="fu">mutate</span>(<span class="at">.data =</span> asec, <span class="at">inctot_adjusted =</span> inctot <span class="sc">*</span> <span class="fl">1.1</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-stdout">
+<pre><code># A tibble: 157,959 × 18
+    year serial month   cpsid asecflag asecwth pernum  cpsidp asecwt ftype      
+   &lt;dbl&gt;  &lt;dbl&gt; &lt;chr&gt;   &lt;dbl&gt;    &lt;dbl&gt;   &lt;dbl&gt;  &lt;dbl&gt;   &lt;dbl&gt;  &lt;dbl&gt; &lt;chr&gt;      
+ 1  2020      1 March 2.02e13        1   1553.      1 2.02e13  1553. Primary fa…
+ 2  2020      1 March 2.02e13        1   1553.      2 2.02e13  1553. Primary fa…
+ 3  2020      2 March 2.02e13        1    990.      1 2.02e13   990. Primary fa…
+ 4  2020      2 March 2.02e13        1    990.      2 2.02e13   990. Primary fa…
+ 5  2020      3 March 2.02e13        1   1505.      1 2.02e13  1505. Nonfamily …
+ 6  2020      4 March 2.02e13        1   1431.      1 2.02e13  1431. Primary fa…
+ 7  2020      4 March 2.02e13        1   1431.      2 2.02e13  1197. Primary fa…
+ 8  2020      5 March 2.02e13        1   1133.      1 2.02e13  1133. Primary fa…
+ 9  2020      5 March 2.02e13        1   1133.      2 2.02e13  1133. Primary fa…
+10  2020      5 March 2.02e13        1   1133.      3 2.02e13  1322. Primary fa…
+# ℹ 157,949 more rows
+# ℹ 8 more variables: ftotval &lt;dbl&gt;, inctot &lt;dbl&gt;, incwage &lt;dbl&gt;, offpov &lt;chr&gt;,
+#   offpovuniv &lt;chr&gt;, offtotval &lt;dbl&gt;, offcutoff &lt;dbl&gt;, inctot_adjusted &lt;dbl&gt;</code></pre>
+</div>
+</div>
+<p><br></p>
+<p>Variables are edited by including an existing column name, like <code>inctot</code>, to the left of <code>=</code> in <code>mutate()</code>.</p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb25"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb25-1"><a href="#cb25-1" aria-hidden="true" tabindex="-1"></a><span class="co"># adjust income because of underreporting</span></span>
+<span id="cb25-2"><a href="#cb25-2" aria-hidden="true" tabindex="-1"></a><span class="fu">mutate</span>(<span class="at">.data =</span> asec, <span class="at">inctot =</span> inctot <span class="sc">*</span> <span class="fl">1.1</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-stdout">
+<pre><code># A tibble: 157,959 × 17
+    year serial month   cpsid asecflag asecwth pernum  cpsidp asecwt ftype      
+   &lt;dbl&gt;  &lt;dbl&gt; &lt;chr&gt;   &lt;dbl&gt;    &lt;dbl&gt;   &lt;dbl&gt;  &lt;dbl&gt;   &lt;dbl&gt;  &lt;dbl&gt; &lt;chr&gt;      
+ 1  2020      1 March 2.02e13        1   1553.      1 2.02e13  1553. Primary fa…
+ 2  2020      1 March 2.02e13        1   1553.      2 2.02e13  1553. Primary fa…
+ 3  2020      2 March 2.02e13        1    990.      1 2.02e13   990. Primary fa…
+ 4  2020      2 March 2.02e13        1    990.      2 2.02e13   990. Primary fa…
+ 5  2020      3 March 2.02e13        1   1505.      1 2.02e13  1505. Nonfamily …
+ 6  2020      4 March 2.02e13        1   1431.      1 2.02e13  1431. Primary fa…
+ 7  2020      4 March 2.02e13        1   1431.      2 2.02e13  1197. Primary fa…
+ 8  2020      5 March 2.02e13        1   1133.      1 2.02e13  1133. Primary fa…
+ 9  2020      5 March 2.02e13        1   1133.      2 2.02e13  1133. Primary fa…
+10  2020      5 March 2.02e13        1   1133.      3 2.02e13  1322. Primary fa…
+# ℹ 157,949 more rows
+# ℹ 7 more variables: ftotval &lt;dbl&gt;, inctot &lt;dbl&gt;, incwage &lt;dbl&gt;, offpov &lt;chr&gt;,
+#   offpovuniv &lt;chr&gt;, offtotval &lt;dbl&gt;, offcutoff &lt;dbl&gt;</code></pre>
+</div>
+</div>
+<p><br></p>
+<p>Conditional logic inside of <code>mutate()</code> with functions like <code>if_else()</code> and <code>case_when()</code> is key to mastering data munging in R.</p>
+<section id="exercise-4" class="level3" data-number="2.9.1">
+<h3 data-number="2.9.1" class="anchored" data-anchor-id="exercise-4"><span class="header-section-number">2.9.1</span> <span style="     color: #1696d2 !important;">Exercise 4</span></h3>
+<div class="tabset-margin-container"></div><div class="panel-tabset">
+<ul class="nav nav-tabs" role="tablist"><li class="nav-item" role="presentation"><a class="nav-link active" id="tabset-4-1-tab" data-bs-toggle="tab" data-bs-target="#tabset-4-1" role="tab" aria-controls="tabset-4-1" aria-selected="true">Prompt</a></li><li class="nav-item" role="presentation"><a class="nav-link" id="tabset-4-2-tab" data-bs-toggle="tab" data-bs-target="#tabset-4-2" role="tab" aria-controls="tabset-4-2" aria-selected="false">Solution</a></li></ul>
+<div class="tab-content">
+<div id="tabset-4-1" class="tab-pane active" role="tabpanel" aria-labelledby="tabset-4-1-tab">
+<ol type="1">
+<li>Create a new variable called <code>in_poverty</code>. If <code>offtotval</code> is less than <code>offcutoff</code> then use <code>"Below Poverty Line"</code>. Otherwise, use <code>"Above Poverty Line"</code>. <em>Hint:</em> <code>if_else()</code> is useful and works like the IF command in Microsoft Excel.</li>
+</ol>
+</div>
+<div id="tabset-4-2" class="tab-pane" role="tabpanel" aria-labelledby="tabset-4-2-tab">
+<ol type="1">
+<li>Create a new variable called <code>in_poverty</code>. If <code>offtotval</code> is less than <code>offcutoff</code> then use <code>"Below Poverty Line"</code>. Otherwise, use <code>"Above Poverty Line"</code>. <em>Hint:</em> <code>if_else()</code> is useful and works like the IF command in Microsoft Excel.</li>
+</ol>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb27"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb27-1"><a href="#cb27-1" aria-hidden="true" tabindex="-1"></a><span class="fu">mutate</span>(</span>
+<span id="cb27-2"><a href="#cb27-2" aria-hidden="true" tabindex="-1"></a>  asec,</span>
+<span id="cb27-3"><a href="#cb27-3" aria-hidden="true" tabindex="-1"></a>  <span class="at">in_poverty =</span> <span class="fu">if_else</span>(</span>
+<span id="cb27-4"><a href="#cb27-4" aria-hidden="true" tabindex="-1"></a>    <span class="at">condition =</span> offtotval <span class="sc">&lt;</span> offcutoff, </span>
+<span id="cb27-5"><a href="#cb27-5" aria-hidden="true" tabindex="-1"></a>    <span class="at">true =</span> <span class="st">"Below Poverty Line"</span>, </span>
+<span id="cb27-6"><a href="#cb27-6" aria-hidden="true" tabindex="-1"></a>    <span class="at">false =</span> <span class="st">"Above Poverty Line"</span></span>
+<span id="cb27-7"><a href="#cb27-7" aria-hidden="true" tabindex="-1"></a>  )</span>
+<span id="cb27-8"><a href="#cb27-8" aria-hidden="true" tabindex="-1"></a>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</div>
+</div>
+</div>
+</div>
+</section>
+</section>
+<section id="section" class="level2" data-number="2.10">
+<h2 data-number="2.10" class="anchored" data-anchor-id="section"><span class="header-section-number">2.10</span> <code>%&gt;%</code></h2>
+<p>Data munging is tiring when each operation needs to be assigned to a name with <code>&lt;-</code>. The pipe, <code>%&gt;%</code>, allows lines of code to be chained together so the assignment operator only needs to be used once.</p>
+<p>Consider this fake code <a href="https://twitter.com/hadleywickham/status/1359852563726819332?lang=en">example from Hadley Wickham</a>:</p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb28"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb28-1"><a href="#cb28-1" aria-hidden="true" tabindex="-1"></a>I <span class="sc">%&gt;%</span> </span>
+<span id="cb28-2"><a href="#cb28-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">tumble</span>(<span class="at">out_of =</span> <span class="st">"bed"</span>) <span class="sc">%&gt;%</span> </span>
+<span id="cb28-3"><a href="#cb28-3" aria-hidden="true" tabindex="-1"></a>  <span class="fu">stumble</span>(<span class="at">to =</span> <span class="st">"the kitchen"</span>) <span class="sc">%&gt;%</span> </span>
+<span id="cb28-4"><a href="#cb28-4" aria-hidden="true" tabindex="-1"></a>  <span class="fu">pour</span>(<span class="at">who =</span> <span class="st">"myself"</span>, <span class="at">unit =</span> <span class="st">"cup"</span>, <span class="at">what =</span> <span class="st">"ambition"</span>) <span class="sc">%&gt;%</span> </span>
+<span id="cb28-5"><a href="#cb28-5" aria-hidden="true" tabindex="-1"></a>  <span class="fu">yawn</span>() <span class="sc">%&gt;%</span> </span>
+<span id="cb28-6"><a href="#cb28-6" aria-hidden="true" tabindex="-1"></a>  <span class="fu">stretch</span>() <span class="sc">%&gt;%</span> </span>
+<span id="cb28-7"><a href="#cb28-7" aria-hidden="true" tabindex="-1"></a>  <span class="fu">try</span>(<span class="fu">come_to_life</span>())</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</div>
+<p><br></p>
+<p><code>%&gt;%</code> passes the output from function as the first argument in a subsequent function. For example, this line can be rewritten:</p>
+<p><br></p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb29"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb29-1"><a href="#cb29-1" aria-hidden="true" tabindex="-1"></a><span class="co"># old way</span></span>
+<span id="cb29-2"><a href="#cb29-2" aria-hidden="true" tabindex="-1"></a><span class="fu">mutate</span>(<span class="at">.data =</span> asec, <span class="at">inctot_adjusted =</span> inctot <span class="sc">*</span> <span class="fl">1.1</span>)</span>
+<span id="cb29-3"><a href="#cb29-3" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb29-4"><a href="#cb29-4" aria-hidden="true" tabindex="-1"></a><span class="co"># new way</span></span>
+<span id="cb29-5"><a href="#cb29-5" aria-hidden="true" tabindex="-1"></a>asec <span class="sc">%&gt;%</span></span>
+<span id="cb29-6"><a href="#cb29-6" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">inctot_adjusted =</span> inctot <span class="sc">*</span> <span class="fl">1.1</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</div>
+<p><br></p>
+<p>See the power:</p>
+<p><br></p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb30"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb30-1"><a href="#cb30-1" aria-hidden="true" tabindex="-1"></a>new_asec <span class="ot">&lt;-</span> asec <span class="sc">%&gt;%</span></span>
+<span id="cb30-2"><a href="#cb30-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(pernum <span class="sc">==</span> <span class="dv">1</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb30-3"><a href="#cb30-3" aria-hidden="true" tabindex="-1"></a>  <span class="fu">select</span>(year, month, pernum, inctot) <span class="sc">%&gt;%</span></span>
+<span id="cb30-4"><a href="#cb30-4" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">inctot_adjusted =</span> inctot <span class="sc">*</span> <span class="fl">1.1</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb30-5"><a href="#cb30-5" aria-hidden="true" tabindex="-1"></a>  <span class="fu">select</span>(<span class="sc">-</span>inctot)</span>
+<span id="cb30-6"><a href="#cb30-6" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb30-7"><a href="#cb30-7" aria-hidden="true" tabindex="-1"></a>new_asec</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-stdout">
+<pre><code># A tibble: 60,460 × 4
+    year month pernum inctot_adjusted
+   &lt;dbl&gt; &lt;chr&gt;  &lt;dbl&gt;           &lt;dbl&gt;
+ 1  2020 March      1          57750 
+ 2  2020 March      1          48400 
+ 3  2020 March      1          44002.
+ 4  2020 March      1              0 
+ 5  2020 March      1            671 
+ 6  2020 March      1          19279.
+ 7  2020 March      1          12349.
+ 8  2020 March      1          21589.
+ 9  2020 March      1          47306.
+10  2020 March      1          10949.
+# ℹ 60,450 more rows</code></pre>
+</div>
+</div>
+<p><br></p>
+</section>
+<section id="summarize" class="level2" data-number="2.11">
+<h2 data-number="2.11" class="anchored" data-anchor-id="summarize"><span class="header-section-number">2.11</span> 6. <code>summarize()</code></h2>
+<p><code>summarize()</code> collapses many rows in a dataframe into fewer rows with summary statistics of the many rows. <code>n()</code>, <code>mean()</code>, and <code>sum()</code> are common summary statistics. Renaming is useful with <code>summarize()</code>!</p>
+<p><br></p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb32"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb32-1"><a href="#cb32-1" aria-hidden="true" tabindex="-1"></a><span class="co"># summarize without renaming the statistics</span></span>
+<span id="cb32-2"><a href="#cb32-2" aria-hidden="true" tabindex="-1"></a>asec <span class="sc">%&gt;%</span></span>
+<span id="cb32-3"><a href="#cb32-3" aria-hidden="true" tabindex="-1"></a>  <span class="fu">summarize</span>(<span class="fu">mean</span>(ftotval), <span class="fu">mean</span>(inctot))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-stdout">
+<pre><code># A tibble: 1 × 2
+  `mean(ftotval)` `mean(inctot)`
+            &lt;dbl&gt;          &lt;dbl&gt;
+1         105254.     209921275.</code></pre>
+</div>
+<div class="sourceCode cell-code" id="cb34"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb34-1"><a href="#cb34-1" aria-hidden="true" tabindex="-1"></a><span class="co"># summarize and rename the statistics</span></span>
+<span id="cb34-2"><a href="#cb34-2" aria-hidden="true" tabindex="-1"></a>asec <span class="sc">%&gt;%</span></span>
+<span id="cb34-3"><a href="#cb34-3" aria-hidden="true" tabindex="-1"></a>  <span class="fu">summarize</span>(</span>
+<span id="cb34-4"><a href="#cb34-4" aria-hidden="true" tabindex="-1"></a>    <span class="at">mean_ftotval =</span> <span class="fu">mean</span>(ftotval), </span>
+<span id="cb34-5"><a href="#cb34-5" aria-hidden="true" tabindex="-1"></a>    <span class="at">mean_inctot =</span> <span class="fu">mean</span>(inctot)</span>
+<span id="cb34-6"><a href="#cb34-6" aria-hidden="true" tabindex="-1"></a>  )</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-stdout">
+<pre><code># A tibble: 1 × 2
+  mean_ftotval mean_inctot
+         &lt;dbl&gt;       &lt;dbl&gt;
+1      105254.  209921275.</code></pre>
+</div>
+</div>
+<p><br></p>
+<p><code>summarize()</code> returns a data frame. This means all dplyr functions can be used on the output of <code>summarize()</code>. This is powerful! Manipulating summary statistics in Stata and SAS can be a chore. Here, it’s just another dataframe that can be manipulated with a tool set optimized for dataframes: dplyr.</p>
+</section>
+<section id="group_by" class="level2" data-number="2.12">
+<h2 data-number="2.12" class="anchored" data-anchor-id="group_by"><span class="header-section-number">2.12</span> 7. <code>group_by()</code></h2>
+<p><code>group_by()</code> groups a dataframe based on specified variables. <code>summarize()</code> with grouped dataframes creates subgroup summary statistics. <code>mutate()</code> with <code>group_by()</code> calculates grouped summaries for each row.</p>
+<p><br></p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb36"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb36-1"><a href="#cb36-1" aria-hidden="true" tabindex="-1"></a>asec <span class="sc">%&gt;%</span></span>
+<span id="cb36-2"><a href="#cb36-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(pernum) <span class="sc">%&gt;%</span></span>
+<span id="cb36-3"><a href="#cb36-3" aria-hidden="true" tabindex="-1"></a>  <span class="fu">summarize</span>(</span>
+<span id="cb36-4"><a href="#cb36-4" aria-hidden="true" tabindex="-1"></a>    <span class="at">n =</span> <span class="fu">n</span>(),</span>
+<span id="cb36-5"><a href="#cb36-5" aria-hidden="true" tabindex="-1"></a>    <span class="at">mean_ftotval =</span> <span class="fu">mean</span>(ftotval), </span>
+<span id="cb36-6"><a href="#cb36-6" aria-hidden="true" tabindex="-1"></a>    <span class="at">mean_inctot =</span> <span class="fu">mean</span>(inctot)</span>
+<span id="cb36-7"><a href="#cb36-7" aria-hidden="true" tabindex="-1"></a>  )</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-stdout">
+<pre><code># A tibble: 16 × 4
+   pernum     n mean_ftotval mean_inctot
+    &lt;dbl&gt; &lt;int&gt;        &lt;dbl&gt;       &lt;dbl&gt;
+ 1      1 60460       94094.      57508.
+ 2      2 45151      108700.   77497357.
+ 3      3 25650      117966.  473030618.
+ 4      4 15797      121815.  634999933.
+ 5      5  6752      108609.  691504650.
+ 6      6  2582       89448.  682810446.
+ 7      7   922       78889.  682218196.
+ 8      8   353       72284.  682725646.
+ 9      9   158       54599.  632917559.
+10     10    73       58145.  657543632.
+11     11    37       61847.  702708584 
+12     12    18       50249.  777780725.
+13     13     3       25152   666666666 
+14     14     1       18000       18000 
+15     15     1       25000       25000 
+16     16     1       15000       15000 </code></pre>
+</div>
+</div>
+<p><br></p>
+<p>Dataframes can be grouped by multiple variables.</p>
+<p>Grouped tibbles include metadata about groups. For example, <code>Groups:   pernum, offpov [40]</code>. One grouping is dropped each time <code>summarize()</code> is used. It is easy to forget if a dataframe is grouped, so it is safe to include <code>ungroup()</code> at the end of a section of functions.</p>
+<p><br></p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb38"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb38-1"><a href="#cb38-1" aria-hidden="true" tabindex="-1"></a>asec <span class="sc">%&gt;%</span></span>
+<span id="cb38-2"><a href="#cb38-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(pernum, offpov) <span class="sc">%&gt;%</span></span>
+<span id="cb38-3"><a href="#cb38-3" aria-hidden="true" tabindex="-1"></a>  <span class="fu">summarize</span>(</span>
+<span id="cb38-4"><a href="#cb38-4" aria-hidden="true" tabindex="-1"></a>    <span class="at">n =</span> <span class="fu">n</span>(),</span>
+<span id="cb38-5"><a href="#cb38-5" aria-hidden="true" tabindex="-1"></a>    <span class="at">mean_ftotval =</span> <span class="fu">mean</span>(ftotval), </span>
+<span id="cb38-6"><a href="#cb38-6" aria-hidden="true" tabindex="-1"></a>    <span class="at">mean_inctot =</span> <span class="fu">mean</span>(inctot)</span>
+<span id="cb38-7"><a href="#cb38-7" aria-hidden="true" tabindex="-1"></a>  ) <span class="sc">%&gt;%</span></span>
+<span id="cb38-8"><a href="#cb38-8" aria-hidden="true" tabindex="-1"></a>  <span class="fu">arrange</span>(offpov) <span class="sc">%&gt;%</span></span>
+<span id="cb38-9"><a href="#cb38-9" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ungroup</span>()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-stderr">
+<pre><code>`summarise()` has grouped output by 'pernum'. You can override using the
+`.groups` argument.</code></pre>
+</div>
+<div class="cell-output cell-output-stdout">
+<pre><code># A tibble: 40 × 5
+   pernum offpov                 n mean_ftotval mean_inctot
+    &lt;dbl&gt; &lt;chr&gt;              &lt;int&gt;        &lt;dbl&gt;       &lt;dbl&gt;
+ 1      1 Above Poverty Line 53872      104451.      63642.
+ 2      2 Above Poverty Line 40978      118691.   59082162.
+ 3      3 Above Poverty Line 23052      129891.  463440562.
+ 4      4 Above Poverty Line 14076      135039.  631720097.
+ 5      5 Above Poverty Line  5805      123937.  688206447.
+ 6      6 Above Poverty Line  2118      105867.  683199297.
+ 7      7 Above Poverty Line   724       96817.  697520661.
+ 8      8 Above Poverty Line   269       90328.  672870019.
+ 9      9 Above Poverty Line   114       70438.  622815186.
+10     10 Above Poverty Line    57       71483.  666678408.
+# ℹ 30 more rows</code></pre>
+</div>
+</div>
+<p><br></p>
+<section id="exercise-5" class="level3" data-number="2.12.1">
+<h3 data-number="2.12.1" class="anchored" data-anchor-id="exercise-5"><span class="header-section-number">2.12.1</span> <span style="     color: #1696d2 !important;">Exercise 5</span></h3>
+<div class="tabset-margin-container"></div><div class="panel-tabset">
+<ul class="nav nav-tabs" role="tablist"><li class="nav-item" role="presentation"><a class="nav-link active" id="tabset-5-1-tab" data-bs-toggle="tab" data-bs-target="#tabset-5-1" role="tab" aria-controls="tabset-5-1" aria-selected="true">Prompt</a></li><li class="nav-item" role="presentation"><a class="nav-link" id="tabset-5-2-tab" data-bs-toggle="tab" data-bs-target="#tabset-5-2" role="tab" aria-controls="tabset-5-2" aria-selected="false">Solution</a></li></ul>
+<div class="tab-content">
+<div id="tabset-5-1" class="tab-pane active" role="tabpanel" aria-labelledby="tabset-5-1-tab">
+<ol type="1">
+<li><code>filter()</code> to only include observations with <code>"In Poverty Universe"</code> in <code>offpovuniv</code>.</li>
+<li><code>group_by()</code> <code>offpov</code>.</li>
+<li>Use <code>summarize()</code> and <code>n()</code> to count the number of observations in poverty.</li>
+</ol>
+</div>
+<div id="tabset-5-2" class="tab-pane" role="tabpanel" aria-labelledby="tabset-5-2-tab">
+<ol type="1">
+<li><code>filter()</code> to only include observations with <code>"In Poverty Universe"</code> in <code>offpovuniv</code>.</li>
+<li><code>group_by()</code> <code>offpov</code>.</li>
+<li>Use <code>summarize()</code> and <code>n()</code> to count the number of observations in poverty.</li>
+</ol>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb41"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb41-1"><a href="#cb41-1" aria-hidden="true" tabindex="-1"></a>asec <span class="sc">%&gt;%</span></span>
+<span id="cb41-2"><a href="#cb41-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(offpovuniv <span class="sc">==</span> <span class="st">"In Poverty Universe"</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb41-3"><a href="#cb41-3" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(offpov) <span class="sc">%&gt;%</span></span>
+<span id="cb41-4"><a href="#cb41-4" aria-hidden="true" tabindex="-1"></a>  <span class="fu">summarize</span>(<span class="fu">n</span>())</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</div>
+</div>
+</div>
+</div>
+</section>
+<section id="exercise-6" class="level3" data-number="2.12.2">
+<h3 data-number="2.12.2" class="anchored" data-anchor-id="exercise-6"><span class="header-section-number">2.12.2</span> <span style="     color: #1696d2 !important;">Exercise 6</span></h3>
+<div class="tabset-margin-container"></div><div class="panel-tabset">
+<ul class="nav nav-tabs" role="tablist"><li class="nav-item" role="presentation"><a class="nav-link active" id="tabset-6-1-tab" data-bs-toggle="tab" data-bs-target="#tabset-6-1" role="tab" aria-controls="tabset-6-1" aria-selected="true">Prompt</a></li><li class="nav-item" role="presentation"><a class="nav-link" id="tabset-6-2-tab" data-bs-toggle="tab" data-bs-target="#tabset-6-2" role="tab" aria-controls="tabset-6-2" aria-selected="false">Solution</a></li></ul>
+<div class="tab-content">
+<div id="tabset-6-1" class="tab-pane active" role="tabpanel" aria-labelledby="tabset-6-1-tab">
+<ol type="1">
+<li><code>filter()</code> to only include observations with <code>"In Poverty Universe"</code>.</li>
+<li><code>group_by()</code> <code>cpsid</code>.</li>
+<li>Use <code>mutate(family_size = n())</code> to calculate the family size for each observation in <code>asec</code>.</li>
+<li><code>ungroup()</code></li>
+<li><code>group_by()</code> <code>family_size</code>, and <code>offpov</code>.</li>
+<li>Use <code>summarize()</code> and <code>n()</code> to see how many families of each size are experiencing poverty.</li>
+</ol>
+</div>
+<div id="tabset-6-2" class="tab-pane" role="tabpanel" aria-labelledby="tabset-6-2-tab">
+<ol type="1">
+<li><code>filter()</code> to only include observations with <code>"In Poverty Universe"</code>.</li>
+<li><code>group_by()</code> <code>cpsid</code>.</li>
+<li>Use <code>mutate(family_size = n())</code> to calculate the family size for each observation in <code>asec</code>.</li>
+<li><code>ungroup()</code></li>
+<li><code>group_by()</code> <code>family_size</code>, and <code>offpov</code>.</li>
+<li>Use <code>summarize()</code> and <code>n()</code> to see how many families of each size are experiencing poverty.</li>
+</ol>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb42"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb42-1"><a href="#cb42-1" aria-hidden="true" tabindex="-1"></a>asec <span class="sc">%&gt;%</span></span>
+<span id="cb42-2"><a href="#cb42-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(offpovuniv <span class="sc">==</span> <span class="st">"In Poverty Universe"</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb42-3"><a href="#cb42-3" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(cpsid) <span class="sc">%&gt;%</span></span>
+<span id="cb42-4"><a href="#cb42-4" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">family_size =</span> <span class="fu">n</span>()) <span class="sc">%&gt;%</span></span>
+<span id="cb42-5"><a href="#cb42-5" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ungroup</span>() <span class="sc">%&gt;%</span></span>
+<span id="cb42-6"><a href="#cb42-6" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(family_size, offpov) <span class="sc">%&gt;%</span></span>
+<span id="cb42-7"><a href="#cb42-7" aria-hidden="true" tabindex="-1"></a>  <span class="fu">summarize</span>(<span class="fu">n</span>())</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</div>
+</div>
+</div>
+</div>
+<p><strong>Are the estimates from the previous two exercises correct?</strong></p>
+<p>Let’s look at a <a href="https://www.census.gov/library/publications/2020/demo/p60-270.html">Census Report</a> to see how many people were in poverty in 2019. We estimated about 16,500 people. The Census Bureau says 34.0 million people.</p>
+<p>No! We did not account for sampling weights, so our estimates are incorrect. <a href="https://cran.r-project.org/web/packages/srvyr/index.html"><code>library(srvyr)</code></a> has tools for weighted estimation with complex surveys.</p>
+</section>
+</section>
+<section id="resources" class="level2" data-number="2.13">
+<h2 data-number="2.13" class="anchored" data-anchor-id="resources"><span class="header-section-number">2.13</span> Resources</h2>
 <ul>
-<li><a href="https://r4ds.had.co.nz/transform.html">R for Data Science: data transformation</a></li>
+<li><a href="https://r4ds.hadley.nz/data-transform">R for Data Science: data transformation</a></li>
 <li><a href="https://www.rstudio.com/wp-content/uploads/2015/02/data-wrangling-cheatsheet.pdf">Data wrangling cheat sheet</a></li>
 </ul>
 
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diff --git a/docs/03_lesson.html b/docs/03_lesson.html
index d1b1570..68775e5 100644
--- a/docs/03_lesson.html
+++ b/docs/03_lesson.html
@@ -2,12 +2,12 @@
 <html xmlns="http://www.w3.org/1999/xhtml" lang="en" xml:lang="en"><head>
 
 <meta charset="utf-8">
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 <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes">
 
 <meta name="author" content="R Users Group">
-<meta name="dcterms.date" content="2023-08-15">
+<meta name="dcterms.date" content="2023-08-22">
 
 <title>Intro to R - 3&nbsp; Data Munging (II)</title>
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 ul.task-list{list-style: none;}
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+pre.numberSource { margin-left: 3em;  padding-left: 4px; }
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-    "search-submit-button-title": "Submit"
+    "search-submit-button-title": "Submit",
+    "search-label": "Search"
   }
 }</script>
 
 
-<link rel="stylesheet" href="../style.css">
+<link rel="stylesheet" href="style.scss">
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 <div id="quarto-search-results"></div>
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-    <div class="container-fluid d-flex justify-content-between">
-      <h1 class="quarto-secondary-nav-title"><span class="chapter-number">3</span>&nbsp; <span class="chapter-title">Data Munging (II)</span></h1>
-      <button type="button" class="quarto-btn-toggle btn" aria-label="Show secondary navigation">
-        <i class="bi bi-chevron-right"></i>
+  <nav class="quarto-secondary-nav">
+    <div class="container-fluid d-flex">
+      <button type="button" class="quarto-btn-toggle btn" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar,#quarto-sidebar-glass" aria-controls="quarto-sidebar" aria-expanded="false" aria-label="Toggle sidebar navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">
+        <i class="bi bi-layout-text-sidebar-reverse"></i>
+      </button>
+      <nav class="quarto-page-breadcrumbs" aria-label="breadcrumb"><ol class="breadcrumb"><li class="breadcrumb-item"><a href="./03_lesson.html"><span class="chapter-number">3</span>&nbsp; <span class="chapter-title">Data Munging (II)</span></a></li></ol></nav>
+      <a class="flex-grow-1" role="button" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar,#quarto-sidebar-glass" aria-controls="quarto-sidebar" aria-expanded="false" aria-label="Toggle sidebar navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">      
+      </a>
+      <button type="button" class="btn quarto-search-button" aria-label="" onclick="window.quartoOpenSearch();">
+        <i class="bi bi-search"></i>
       </button>
     </div>
   </nav>
@@ -146,98 +123,106 @@ <h1 class="quarto-secondary-nav-title"><span class="chapter-number">3</span>&nbs
 <!-- content -->
 <div id="quarto-content" class="quarto-container page-columns page-rows-contents page-layout-article">
 <!-- sidebar -->
-  <nav id="quarto-sidebar" class="sidebar collapse sidebar-navigation floating overflow-auto">
+  <nav id="quarto-sidebar" class="sidebar collapse collapse-horizontal sidebar-navigation floating overflow-auto">
     <div class="pt-lg-2 mt-2 text-left sidebar-header">
     <div class="sidebar-title mb-0 py-0">
       <a href="./">Intro to R</a> 
     </div>
       </div>
-      <div class="mt-2 flex-shrink-0 align-items-center">
+        <div class="mt-2 flex-shrink-0 align-items-center">
         <div class="sidebar-search">
         <div id="quarto-search" class="" title="Search"></div>
         </div>
-      </div>
+        </div>
     <div class="sidebar-menu-container"> 
     <ul class="list-unstyled mt-1">
         <li class="sidebar-item">
   <div class="sidebar-item-container"> 
-  <a href="./index.html" class="sidebar-item-text sidebar-link">Intro to R: A Hands on Tutorial</a>
+  <a href="./index.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text">Intro to R: A Hands on Tutorial</span></a>
   </div>
 </li>
         <li class="sidebar-item">
   <div class="sidebar-item-container"> 
-  <a href="./01_lesson.html" class="sidebar-item-text sidebar-link"><span class="chapter-number">1</span>&nbsp; <span class="chapter-title">Data Visualization</span></a>
+  <a href="./01_lesson.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text"><span class="chapter-number">1</span>&nbsp; <span class="chapter-title">Data Visualization</span></span></a>
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         <li class="sidebar-item">
   <div class="sidebar-item-container"> 
-  <a href="./02_lesson.html" class="sidebar-item-text sidebar-link"><span class="chapter-number">2</span>&nbsp; <span class="chapter-title">Data Munging 1</span></a>
+  <a href="./02_lesson.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text"><span class="chapter-number">2</span>&nbsp; <span class="chapter-title">Data Munging 1</span></span></a>
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         <li class="sidebar-item">
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-  <a href="./03_lesson.html" class="sidebar-item-text sidebar-link active"><span class="chapter-number">3</span>&nbsp; <span class="chapter-title">Data Munging (II)</span></a>
+  <a href="./03_lesson.html" class="sidebar-item-text sidebar-link active">
+ <span class="menu-text"><span class="chapter-number">3</span>&nbsp; <span class="chapter-title">Data Munging (II)</span></span></a>
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-  <a href="./04_lesson.html" class="sidebar-item-text sidebar-link"><span class="chapter-number">4</span>&nbsp; <span class="chapter-title">Data Import</span></a>
+  <a href="./04_lesson.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text"><span class="chapter-number">4</span>&nbsp; <span class="chapter-title">Data Import</span></span></a>
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         <li class="sidebar-item">
   <div class="sidebar-item-container"> 
-  <a href="./05_lesson.html" class="sidebar-item-text sidebar-link"><span class="chapter-number">5</span>&nbsp; <span class="chapter-title">Iteration (or becoming an R Superhero)</span></a>
+  <a href="./05_lesson.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text"><span class="chapter-number">5</span>&nbsp; <span class="chapter-title">Iteration (or becoming an R Superhero)</span></span></a>
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   <div class="sidebar-item-container"> 
-  <a href="./06_lesson.html" class="sidebar-item-text sidebar-link"><span class="chapter-number">6</span>&nbsp; <span class="chapter-title">Putting it All Together (and into Markdown)</span></a>
+  <a href="./06_lesson.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text"><span class="chapter-number">6</span>&nbsp; <span class="chapter-title">Putting it All Together (and into Markdown)</span></span></a>
   </div>
 </li>
     </ul>
     </div>
 </nav>
+<div id="quarto-sidebar-glass" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar,#quarto-sidebar-glass"></div>
 <!-- margin-sidebar -->
     <div id="quarto-margin-sidebar" class="sidebar margin-sidebar">
         <nav id="TOC" role="doc-toc" class="toc-active">
     <h2 id="toc-title">Table of contents</h2>
    
   <ul>
-  <li><a href="#review" id="toc-review" class="nav-link active" data-scroll-target="#review"><span class="toc-section-number">3.1</span>  Review</a></li>
-  <li><a href="#motivation" id="toc-motivation" class="nav-link" data-scroll-target="#motivation"><span class="toc-section-number">3.2</span>  Motivation</a>
+  <li><a href="#review" id="toc-review" class="nav-link active" data-scroll-target="#review"><span class="header-section-number">3.1</span> Review</a></li>
+  <li><a href="#motivation" id="toc-motivation" class="nav-link" data-scroll-target="#motivation"><span class="header-section-number">3.2</span> Motivation</a>
   <ul class="collapse">
-  <li><a href="#exercise-0" id="toc-exercise-0" class="nav-link" data-scroll-target="#exercise-0"><span class="toc-section-number">3.2.1</span>  Exercise 0</a></li>
+  <li><a href="#exercise-0" id="toc-exercise-0" class="nav-link" data-scroll-target="#exercise-0"><span class="header-section-number">3.2.1</span> Exercise 0</a></li>
   </ul></li>
-  <li><a href="#conditional-transformation" id="toc-conditional-transformation" class="nav-link" data-scroll-target="#conditional-transformation"><span class="toc-section-number">3.3</span>  Conditional transformation</a></li>
-  <li><a href="#strings" id="toc-strings" class="nav-link" data-scroll-target="#strings"><span class="toc-section-number">3.4</span>  Strings</a>
+  <li><a href="#conditional-transformation" id="toc-conditional-transformation" class="nav-link" data-scroll-target="#conditional-transformation"><span class="header-section-number">3.3</span> Conditional transformation</a></li>
+  <li><a href="#strings" id="toc-strings" class="nav-link" data-scroll-target="#strings"><span class="header-section-number">3.4</span> Strings</a>
   <ul class="collapse">
-  <li><a href="#exercise-1" id="toc-exercise-1" class="nav-link" data-scroll-target="#exercise-1"><span class="toc-section-number">3.4.1</span>  Exercise 1</a></li>
+  <li><a href="#exercise-1" id="toc-exercise-1" class="nav-link" data-scroll-target="#exercise-1"><span class="header-section-number">3.4.1</span> Exercise 1</a></li>
   </ul></li>
-  <li><a href="#dates" id="toc-dates" class="nav-link" data-scroll-target="#dates"><span class="toc-section-number">3.5</span>  Dates</a>
+  <li><a href="#dates" id="toc-dates" class="nav-link" data-scroll-target="#dates"><span class="header-section-number">3.5</span> Dates</a>
   <ul class="collapse">
-  <li><a href="#exercise-2" id="toc-exercise-2" class="nav-link" data-scroll-target="#exercise-2"><span class="toc-section-number">3.5.1</span>  Exercise 2</a></li>
+  <li><a href="#exercise-2" id="toc-exercise-2" class="nav-link" data-scroll-target="#exercise-2"><span class="header-section-number">3.5.1</span> Exercise 2</a></li>
   </ul></li>
-  <li><a href="#group_by-and-summarize" id="toc-group_by-and-summarize" class="nav-link" data-scroll-target="#group_by-and-summarize"><span class="toc-section-number">3.6</span>  <code>group_by</code> and <code>summarize()</code></a>
+  <li><a href="#group_by-and-summarize" id="toc-group_by-and-summarize" class="nav-link" data-scroll-target="#group_by-and-summarize"><span class="header-section-number">3.6</span> <code>group_by</code> and <code>summarize()</code></a>
   <ul class="collapse">
-  <li><a href="#exercise-3" id="toc-exercise-3" class="nav-link" data-scroll-target="#exercise-3"><span class="toc-section-number">3.6.1</span>  Exercise 3</a></li>
+  <li><a href="#exercise-3" id="toc-exercise-3" class="nav-link" data-scroll-target="#exercise-3"><span class="header-section-number">3.6.1</span> Exercise 3</a></li>
   </ul></li>
-  <li><a href="#left_join" id="toc-left_join" class="nav-link" data-scroll-target="#left_join"><span class="toc-section-number">3.7</span>  <code>left_join()</code></a>
+  <li><a href="#left_join" id="toc-left_join" class="nav-link" data-scroll-target="#left_join"><span class="header-section-number">3.7</span> <code>left_join()</code></a>
   <ul class="collapse">
-  <li><a href="#exercise-4" id="toc-exercise-4" class="nav-link" data-scroll-target="#exercise-4"><span class="toc-section-number">3.7.1</span>  Exercise 4</a></li>
+  <li><a href="#exercise-4" id="toc-exercise-4" class="nav-link" data-scroll-target="#exercise-4"><span class="header-section-number">3.7.1</span> Exercise 4</a></li>
   </ul></li>
-  <li><a href="#custom-functions" id="toc-custom-functions" class="nav-link" data-scroll-target="#custom-functions"><span class="toc-section-number">3.8</span>  Custom functions</a>
+  <li><a href="#custom-functions" id="toc-custom-functions" class="nav-link" data-scroll-target="#custom-functions"><span class="header-section-number">3.8</span> Custom functions</a>
   <ul class="collapse">
-  <li><a href="#exercise-5" id="toc-exercise-5" class="nav-link" data-scroll-target="#exercise-5"><span class="toc-section-number">3.8.1</span>  Exercise 5</a></li>
+  <li><a href="#exercise-5" id="toc-exercise-5" class="nav-link" data-scroll-target="#exercise-5"><span class="header-section-number">3.8.1</span> Exercise 5</a></li>
   </ul></li>
-  <li><a href="#system-functions" id="toc-system-functions" class="nav-link" data-scroll-target="#system-functions"><span class="toc-section-number">3.9</span>  System functions</a>
+  <li><a href="#system-functions" id="toc-system-functions" class="nav-link" data-scroll-target="#system-functions"><span class="header-section-number">3.9</span> System functions</a>
   <ul class="collapse">
-  <li><a href="#exercise-6" id="toc-exercise-6" class="nav-link" data-scroll-target="#exercise-6"><span class="toc-section-number">3.9.1</span>  Exercise 6</a></li>
+  <li><a href="#exercise-6" id="toc-exercise-6" class="nav-link" data-scroll-target="#exercise-6"><span class="header-section-number">3.9.1</span> Exercise 6</a></li>
   </ul></li>
-  <li><a href="#conditional-logic" id="toc-conditional-logic" class="nav-link" data-scroll-target="#conditional-logic"><span class="toc-section-number">3.10</span>  Conditional logic</a>
+  <li><a href="#conditional-logic" id="toc-conditional-logic" class="nav-link" data-scroll-target="#conditional-logic"><span class="header-section-number">3.10</span> Conditional logic</a>
   <ul class="collapse">
-  <li><a href="#example" id="toc-example" class="nav-link" data-scroll-target="#example"><span class="toc-section-number">3.10.1</span>  Example</a></li>
+  <li><a href="#example" id="toc-example" class="nav-link" data-scroll-target="#example"><span class="header-section-number">3.10.1</span> Example</a></li>
   </ul></li>
-  <li><a href="#resources" id="toc-resources" class="nav-link" data-scroll-target="#resources"><span class="toc-section-number">3.11</span>  Resources</a></li>
+  <li><a href="#resources" id="toc-resources" class="nav-link" data-scroll-target="#resources"><span class="header-section-number">3.11</span> Resources</a></li>
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@@ -246,7 +231,7 @@ <h2 id="toc-title">Table of contents</h2>
 
 <header id="title-block-header" class="quarto-title-block default">
 <div class="quarto-title">
-<h1 class="title d-none d-lg-block"><span class="chapter-number">3</span>&nbsp; <span class="chapter-title">Data Munging (II)</span></h1>
+<h1 class="title"><span class="chapter-number">3</span>&nbsp; <span class="chapter-title">Data Munging (II)</span></h1>
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@@ -263,7 +248,7 @@ <h1 class="title d-none d-lg-block"><span class="chapter-number">3</span>&nbsp;
     <div>
     <div class="quarto-title-meta-heading">Published</div>
     <div class="quarto-title-meta-contents">
-      <p class="date">August 15, 2023</p>
+      <p class="date">August 22, 2023</p>
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   </div>
   
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@@ -531,9 +513,23 @@ <h2 data-number="3.11" class="anchored" data-anchor-id="resources"><span class="
     icon: icon
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diff --git a/docs/04_lesson.html b/docs/04_lesson.html
index 9432a53..654911c 100644
--- a/docs/04_lesson.html
+++ b/docs/04_lesson.html
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 <html xmlns="http://www.w3.org/1999/xhtml" lang="en" xml:lang="en"><head>
 
 <meta charset="utf-8">
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 <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes">
 
 <meta name="author" content="R Users Group">
-<meta name="dcterms.date" content="2023-08-15">
+<meta name="dcterms.date" content="2023-08-22">
 
 <title>Intro to R - 4&nbsp; Data Import</title>
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 ul.task-list{list-style: none;}
 ul.task-list li input[type="checkbox"] {
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+  margin: 0 0.8em 0.2em -1em; /* quarto-specific, see https://github.com/quarto-dev/quarto-cli/issues/4556 */ 
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     "search-clear-button-title": "Clear",
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-    "search-submit-button-title": "Submit"
+    "search-submit-button-title": "Submit",
+    "search-label": "Search"
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 }</script>
 
@@ -134,11 +106,16 @@
 
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-    <div class="container-fluid d-flex justify-content-between">
-      <h1 class="quarto-secondary-nav-title"><span class="chapter-number">4</span>&nbsp; <span class="chapter-title">Data Import</span></h1>
-      <button type="button" class="quarto-btn-toggle btn" aria-label="Show secondary navigation">
-        <i class="bi bi-chevron-right"></i>
+  <nav class="quarto-secondary-nav">
+    <div class="container-fluid d-flex">
+      <button type="button" class="quarto-btn-toggle btn" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar,#quarto-sidebar-glass" aria-controls="quarto-sidebar" aria-expanded="false" aria-label="Toggle sidebar navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">
+        <i class="bi bi-layout-text-sidebar-reverse"></i>
+      </button>
+      <nav class="quarto-page-breadcrumbs" aria-label="breadcrumb"><ol class="breadcrumb"><li class="breadcrumb-item"><a href="./04_lesson.html"><span class="chapter-number">4</span>&nbsp; <span class="chapter-title">Data Import</span></a></li></ol></nav>
+      <a class="flex-grow-1" role="button" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar,#quarto-sidebar-glass" aria-controls="quarto-sidebar" aria-expanded="false" aria-label="Toggle sidebar navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">      
+      </a>
+      <button type="button" class="btn quarto-search-button" aria-label="" onclick="window.quartoOpenSearch();">
+        <i class="bi bi-search"></i>
       </button>
     </div>
   </nav>
@@ -146,90 +123,98 @@ <h1 class="quarto-secondary-nav-title"><span class="chapter-number">4</span>&nbs
 <!-- content -->
 <div id="quarto-content" class="quarto-container page-columns page-rows-contents page-layout-article">
 <!-- sidebar -->
-  <nav id="quarto-sidebar" class="sidebar collapse sidebar-navigation floating overflow-auto">
+  <nav id="quarto-sidebar" class="sidebar collapse collapse-horizontal sidebar-navigation floating overflow-auto">
     <div class="pt-lg-2 mt-2 text-left sidebar-header">
     <div class="sidebar-title mb-0 py-0">
       <a href="./">Intro to R</a> 
     </div>
       </div>
-      <div class="mt-2 flex-shrink-0 align-items-center">
+        <div class="mt-2 flex-shrink-0 align-items-center">
         <div class="sidebar-search">
         <div id="quarto-search" class="" title="Search"></div>
         </div>
-      </div>
+        </div>
     <div class="sidebar-menu-container"> 
     <ul class="list-unstyled mt-1">
         <li class="sidebar-item">
   <div class="sidebar-item-container"> 
-  <a href="./index.html" class="sidebar-item-text sidebar-link">Intro to R: A Hands on Tutorial</a>
+  <a href="./index.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text">Intro to R: A Hands on Tutorial</span></a>
   </div>
 </li>
         <li class="sidebar-item">
   <div class="sidebar-item-container"> 
-  <a href="./01_lesson.html" class="sidebar-item-text sidebar-link"><span class="chapter-number">1</span>&nbsp; <span class="chapter-title">Data Visualization</span></a>
+  <a href="./01_lesson.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text"><span class="chapter-number">1</span>&nbsp; <span class="chapter-title">Data Visualization</span></span></a>
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   <div class="sidebar-item-container"> 
-  <a href="./02_lesson.html" class="sidebar-item-text sidebar-link"><span class="chapter-number">2</span>&nbsp; <span class="chapter-title">Data Munging 1</span></a>
+  <a href="./02_lesson.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text"><span class="chapter-number">2</span>&nbsp; <span class="chapter-title">Data Munging 1</span></span></a>
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         <li class="sidebar-item">
   <div class="sidebar-item-container"> 
-  <a href="./03_lesson.html" class="sidebar-item-text sidebar-link"><span class="chapter-number">3</span>&nbsp; <span class="chapter-title">Data Munging (II)</span></a>
+  <a href="./03_lesson.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text"><span class="chapter-number">3</span>&nbsp; <span class="chapter-title">Data Munging (II)</span></span></a>
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-  <a href="./04_lesson.html" class="sidebar-item-text sidebar-link active"><span class="chapter-number">4</span>&nbsp; <span class="chapter-title">Data Import</span></a>
+  <a href="./04_lesson.html" class="sidebar-item-text sidebar-link active">
+ <span class="menu-text"><span class="chapter-number">4</span>&nbsp; <span class="chapter-title">Data Import</span></span></a>
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   <div class="sidebar-item-container"> 
-  <a href="./05_lesson.html" class="sidebar-item-text sidebar-link"><span class="chapter-number">5</span>&nbsp; <span class="chapter-title">Iteration (or becoming an R Superhero)</span></a>
+  <a href="./05_lesson.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text"><span class="chapter-number">5</span>&nbsp; <span class="chapter-title">Iteration (or becoming an R Superhero)</span></span></a>
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-  <a href="./06_lesson.html" class="sidebar-item-text sidebar-link"><span class="chapter-number">6</span>&nbsp; <span class="chapter-title">Putting it All Together (and into Markdown)</span></a>
+  <a href="./06_lesson.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text"><span class="chapter-number">6</span>&nbsp; <span class="chapter-title">Putting it All Together (and into Markdown)</span></span></a>
   </div>
 </li>
     </ul>
     </div>
 </nav>
+<div id="quarto-sidebar-glass" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar,#quarto-sidebar-glass"></div>
 <!-- margin-sidebar -->
     <div id="quarto-margin-sidebar" class="sidebar margin-sidebar">
         <nav id="TOC" role="doc-toc" class="toc-active">
     <h2 id="toc-title">Table of contents</h2>
    
   <ul>
-  <li><a href="#review" id="toc-review" class="nav-link active" data-scroll-target="#review"><span class="toc-section-number">4.1</span>  Review</a></li>
-  <li><a href="#motivation" id="toc-motivation" class="nav-link" data-scroll-target="#motivation"><span class="toc-section-number">4.2</span>  Motivation</a></li>
-  <li><a href="#packages" id="toc-packages" class="nav-link" data-scroll-target="#packages"><span class="toc-section-number">4.3</span>  Packages</a></li>
-  <li><a href="#primary-functions" id="toc-primary-functions" class="nav-link" data-scroll-target="#primary-functions"><span class="toc-section-number">4.4</span>  Primary Functions</a></li>
-  <li><a href="#setup" id="toc-setup" class="nav-link" data-scroll-target="#setup"><span class="toc-section-number">4.5</span>  Setup</a>
+  <li><a href="#review" id="toc-review" class="nav-link active" data-scroll-target="#review"><span class="header-section-number">4.1</span> Review</a></li>
+  <li><a href="#motivation" id="toc-motivation" class="nav-link" data-scroll-target="#motivation"><span class="header-section-number">4.2</span> Motivation</a></li>
+  <li><a href="#packages" id="toc-packages" class="nav-link" data-scroll-target="#packages"><span class="header-section-number">4.3</span> Packages</a></li>
+  <li><a href="#primary-functions" id="toc-primary-functions" class="nav-link" data-scroll-target="#primary-functions"><span class="header-section-number">4.4</span> Primary Functions</a></li>
+  <li><a href="#setup" id="toc-setup" class="nav-link" data-scroll-target="#setup"><span class="header-section-number">4.5</span> Setup</a>
   <ul class="collapse">
-  <li><a href="#exercise" id="toc-exercise" class="nav-link" data-scroll-target="#exercise"><span class="toc-section-number">4.5.1</span>  Exercise</a></li>
+  <li><a href="#exercise" id="toc-exercise" class="nav-link" data-scroll-target="#exercise"><span class="header-section-number">4.5.1</span> Exercise</a></li>
   </ul></li>
-  <li><a href="#the-readr-package" id="toc-the-readr-package" class="nav-link" data-scroll-target="#the-readr-package"><span class="toc-section-number">4.6</span>  The <code>readr</code> Package</a>
+  <li><a href="#the-readr-package" id="toc-the-readr-package" class="nav-link" data-scroll-target="#the-readr-package"><span class="header-section-number">4.6</span> The <code>readr</code> Package</a>
   <ul class="collapse">
-  <li><a href="#csv-files" id="toc-csv-files" class="nav-link" data-scroll-target="#csv-files"><span class="toc-section-number">4.6.1</span>  CSV Files</a></li>
-  <li><a href="#other-delimiters" id="toc-other-delimiters" class="nav-link" data-scroll-target="#other-delimiters"><span class="toc-section-number">4.6.2</span>  Other Delimiters</a></li>
-  <li><a href="#common-issues" id="toc-common-issues" class="nav-link" data-scroll-target="#common-issues"><span class="toc-section-number">4.6.3</span>  Common Issues</a></li>
-  <li><a href="#exercise-3" id="toc-exercise-3" class="nav-link" data-scroll-target="#exercise-3"><span class="toc-section-number">4.6.4</span>  Exercise</a></li>
+  <li><a href="#csv-files" id="toc-csv-files" class="nav-link" data-scroll-target="#csv-files"><span class="header-section-number">4.6.1</span> CSV Files</a></li>
+  <li><a href="#other-delimiters" id="toc-other-delimiters" class="nav-link" data-scroll-target="#other-delimiters"><span class="header-section-number">4.6.2</span> Other Delimiters</a></li>
+  <li><a href="#common-issues" id="toc-common-issues" class="nav-link" data-scroll-target="#common-issues"><span class="header-section-number">4.6.3</span> Common Issues</a></li>
+  <li><a href="#exercise-3" id="toc-exercise-3" class="nav-link" data-scroll-target="#exercise-3"><span class="header-section-number">4.6.4</span> Exercise</a></li>
   </ul></li>
-  <li><a href="#the-readxl-package" id="toc-the-readxl-package" class="nav-link" data-scroll-target="#the-readxl-package"><span class="toc-section-number">4.7</span>  The <code>readxl</code> Package</a>
+  <li><a href="#the-readxl-package" id="toc-the-readxl-package" class="nav-link" data-scroll-target="#the-readxl-package"><span class="header-section-number">4.7</span> The <code>readxl</code> Package</a>
   <ul class="collapse">
-  <li><a href="#example-3" id="toc-example-3" class="nav-link" data-scroll-target="#example-3"><span class="toc-section-number">4.7.1</span>  Example</a></li>
-  <li><a href="#exercise-4" id="toc-exercise-4" class="nav-link" data-scroll-target="#exercise-4"><span class="toc-section-number">4.7.2</span>  Exercise</a></li>
+  <li><a href="#example-3" id="toc-example-3" class="nav-link" data-scroll-target="#example-3"><span class="header-section-number">4.7.1</span> Example</a></li>
+  <li><a href="#exercise-4" id="toc-exercise-4" class="nav-link" data-scroll-target="#exercise-4"><span class="header-section-number">4.7.2</span> Exercise</a></li>
   </ul></li>
-  <li><a href="#the-haven-package" id="toc-the-haven-package" class="nav-link" data-scroll-target="#the-haven-package"><span class="toc-section-number">4.8</span>  The <code>haven</code> Package</a>
+  <li><a href="#the-haven-package" id="toc-the-haven-package" class="nav-link" data-scroll-target="#the-haven-package"><span class="header-section-number">4.8</span> The <code>haven</code> Package</a>
   <ul class="collapse">
-  <li><a href="#stata-data-.dta" id="toc-stata-data-.dta" class="nav-link" data-scroll-target="#stata-data-.dta"><span class="toc-section-number">4.8.1</span>  Stata Data (.dta)</a></li>
-  <li><a href="#sas-data-.sas7bdat" id="toc-sas-data-.sas7bdat" class="nav-link" data-scroll-target="#sas-data-.sas7bdat"><span class="toc-section-number">4.8.2</span>  SAS Data (.sas7bdat)</a></li>
+  <li><a href="#stata-data-.dta" id="toc-stata-data-.dta" class="nav-link" data-scroll-target="#stata-data-.dta"><span class="header-section-number">4.8.1</span> Stata Data (.dta)</a></li>
+  <li><a href="#sas-data-.sas7bdat" id="toc-sas-data-.sas7bdat" class="nav-link" data-scroll-target="#sas-data-.sas7bdat"><span class="header-section-number">4.8.2</span> SAS Data (.sas7bdat)</a></li>
   </ul></li>
-  <li><a href="#the-arrow-package" id="toc-the-arrow-package" class="nav-link" data-scroll-target="#the-arrow-package"><span class="toc-section-number">4.9</span>  The <code>arrow</code> Package</a></li>
-  <li><a href="#resources" id="toc-resources" class="nav-link" data-scroll-target="#resources"><span class="toc-section-number">4.10</span>  Resources</a></li>
+  <li><a href="#the-arrow-package" id="toc-the-arrow-package" class="nav-link" data-scroll-target="#the-arrow-package"><span class="header-section-number">4.9</span> The <code>arrow</code> Package</a></li>
+  <li><a href="#resources" id="toc-resources" class="nav-link" data-scroll-target="#resources"><span class="header-section-number">4.10</span> Resources</a></li>
   </ul>
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     </div>
@@ -238,7 +223,7 @@ <h2 id="toc-title">Table of contents</h2>
 
 <header id="title-block-header" class="quarto-title-block default">
 <div class="quarto-title">
-<h1 class="title d-none d-lg-block"><span class="chapter-number">4</span>&nbsp; <span class="chapter-title">Data Import</span></h1>
+<h1 class="title"><span class="chapter-number">4</span>&nbsp; <span class="chapter-title">Data Import</span></h1>
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@@ -255,7 +240,7 @@ <h1 class="title d-none d-lg-block"><span class="chapter-number">4</span>&nbsp;
     <div>
     <div class="quarto-title-meta-heading">Published</div>
     <div class="quarto-title-meta-contents">
-      <p class="date">August 15, 2023</p>
+      <p class="date">August 22, 2023</p>
     </div>
   </div>
   
@@ -280,7 +265,7 @@ <h2 data-number="4.1" class="anchored" data-anchor-id="review"><span class="head
 <div class="quarto-figure quarto-figure-center">
 <figure class="figure">
 <p><img src="www/images/cracked_setwd.png" class="img-fluid figure-img" width="291"></p>
-<p></p><figcaption class="figure-caption">Artwork by @allison_horst</figcaption><p></p>
+<figcaption class="figure-caption">Artwork by @allison_horst</figcaption>
 </figure>
 </div></li>
 <li><p>Stay DRY: if you find yourself copying and pasting chunks of code, write a function instead.</p></li>
@@ -560,9 +545,23 @@ <h2 data-number="4.10" class="anchored" data-anchor-id="resources"><span class="
     icon: icon
   };
   anchorJS.add('.anchored');
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diff --git a/docs/05_lesson.html b/docs/05_lesson.html
index 20cf0f8..66ac662 100644
--- a/docs/05_lesson.html
+++ b/docs/05_lesson.html
@@ -2,12 +2,12 @@
 <html xmlns="http://www.w3.org/1999/xhtml" lang="en" xml:lang="en"><head>
 
 <meta charset="utf-8">
-<meta name="generator" content="quarto-1.2.269">
+<meta name="generator" content="quarto-1.3.433">
 
 <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes">
 
 <meta name="author" content="R Users Group">
-<meta name="dcterms.date" content="2023-08-15">
+<meta name="dcterms.date" content="2023-08-22">
 
 <title>Intro to R - 5&nbsp; Iteration (or becoming an R Superhero)</title>
 <style>
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 ul.task-list{list-style: none;}
 ul.task-list li input[type="checkbox"] {
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-  margin: 0 0.8em 0.2em -1.6em;
+  margin: 0 0.8em 0.2em -1em; /* quarto-specific, see https://github.com/quarto-dev/quarto-cli/issues/4556 */ 
   vertical-align: middle;
 }
+/* CSS for syntax highlighting */
 pre > code.sourceCode { white-space: pre; position: relative; }
 pre > code.sourceCode > span { display: inline-block; line-height: 1.25; }
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     padding: 0 4px; width: 4em;
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-pre.numberSource { margin-left: 3em; border-left: 1px solid #aaaaaa;  padding-left: 4px; }
+pre.numberSource { margin-left: 3em;  padding-left: 4px; }
 div.sourceCode
   {   }
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     "search-clear-button-title": "Clear",
     "search-detached-cancel-button-title": "Cancel",
-    "search-submit-button-title": "Submit"
+    "search-submit-button-title": "Submit",
+    "search-label": "Search"
   }
 }</script>
 
@@ -134,11 +106,16 @@
 
 <div id="quarto-search-results"></div>
   <header id="quarto-header" class="headroom fixed-top">
-  <nav class="quarto-secondary-nav" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar" aria-controls="quarto-sidebar" aria-expanded="false" aria-label="Toggle sidebar navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">
-    <div class="container-fluid d-flex justify-content-between">
-      <h1 class="quarto-secondary-nav-title"><span class="chapter-number">5</span>&nbsp; <span class="chapter-title">Iteration (or becoming an R Superhero)</span></h1>
-      <button type="button" class="quarto-btn-toggle btn" aria-label="Show secondary navigation">
-        <i class="bi bi-chevron-right"></i>
+  <nav class="quarto-secondary-nav">
+    <div class="container-fluid d-flex">
+      <button type="button" class="quarto-btn-toggle btn" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar,#quarto-sidebar-glass" aria-controls="quarto-sidebar" aria-expanded="false" aria-label="Toggle sidebar navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">
+        <i class="bi bi-layout-text-sidebar-reverse"></i>
+      </button>
+      <nav class="quarto-page-breadcrumbs" aria-label="breadcrumb"><ol class="breadcrumb"><li class="breadcrumb-item"><a href="./05_lesson.html"><span class="chapter-number">5</span>&nbsp; <span class="chapter-title">Iteration (or becoming an R Superhero)</span></a></li></ol></nav>
+      <a class="flex-grow-1" role="button" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar,#quarto-sidebar-glass" aria-controls="quarto-sidebar" aria-expanded="false" aria-label="Toggle sidebar navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">      
+      </a>
+      <button type="button" class="btn quarto-search-button" aria-label="" onclick="window.quartoOpenSearch();">
+        <i class="bi bi-search"></i>
       </button>
     </div>
   </nav>
@@ -146,88 +123,96 @@ <h1 class="quarto-secondary-nav-title"><span class="chapter-number">5</span>&nbs
 <!-- content -->
 <div id="quarto-content" class="quarto-container page-columns page-rows-contents page-layout-article">
 <!-- sidebar -->
-  <nav id="quarto-sidebar" class="sidebar collapse sidebar-navigation floating overflow-auto">
+  <nav id="quarto-sidebar" class="sidebar collapse collapse-horizontal sidebar-navigation floating overflow-auto">
     <div class="pt-lg-2 mt-2 text-left sidebar-header">
     <div class="sidebar-title mb-0 py-0">
       <a href="./">Intro to R</a> 
     </div>
       </div>
-      <div class="mt-2 flex-shrink-0 align-items-center">
+        <div class="mt-2 flex-shrink-0 align-items-center">
         <div class="sidebar-search">
         <div id="quarto-search" class="" title="Search"></div>
         </div>
-      </div>
+        </div>
     <div class="sidebar-menu-container"> 
     <ul class="list-unstyled mt-1">
         <li class="sidebar-item">
   <div class="sidebar-item-container"> 
-  <a href="./index.html" class="sidebar-item-text sidebar-link">Intro to R: A Hands on Tutorial</a>
+  <a href="./index.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text">Intro to R: A Hands on Tutorial</span></a>
   </div>
 </li>
         <li class="sidebar-item">
   <div class="sidebar-item-container"> 
-  <a href="./01_lesson.html" class="sidebar-item-text sidebar-link"><span class="chapter-number">1</span>&nbsp; <span class="chapter-title">Data Visualization</span></a>
+  <a href="./01_lesson.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text"><span class="chapter-number">1</span>&nbsp; <span class="chapter-title">Data Visualization</span></span></a>
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         <li class="sidebar-item">
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-  <a href="./02_lesson.html" class="sidebar-item-text sidebar-link"><span class="chapter-number">2</span>&nbsp; <span class="chapter-title">Data Munging 1</span></a>
+  <a href="./02_lesson.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text"><span class="chapter-number">2</span>&nbsp; <span class="chapter-title">Data Munging 1</span></span></a>
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         <li class="sidebar-item">
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-  <a href="./03_lesson.html" class="sidebar-item-text sidebar-link"><span class="chapter-number">3</span>&nbsp; <span class="chapter-title">Data Munging (II)</span></a>
+  <a href="./03_lesson.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text"><span class="chapter-number">3</span>&nbsp; <span class="chapter-title">Data Munging (II)</span></span></a>
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-  <a href="./04_lesson.html" class="sidebar-item-text sidebar-link"><span class="chapter-number">4</span>&nbsp; <span class="chapter-title">Data Import</span></a>
+  <a href="./04_lesson.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text"><span class="chapter-number">4</span>&nbsp; <span class="chapter-title">Data Import</span></span></a>
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         <li class="sidebar-item">
   <div class="sidebar-item-container"> 
-  <a href="./05_lesson.html" class="sidebar-item-text sidebar-link active"><span class="chapter-number">5</span>&nbsp; <span class="chapter-title">Iteration (or becoming an R Superhero)</span></a>
+  <a href="./05_lesson.html" class="sidebar-item-text sidebar-link active">
+ <span class="menu-text"><span class="chapter-number">5</span>&nbsp; <span class="chapter-title">Iteration (or becoming an R Superhero)</span></span></a>
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         <li class="sidebar-item">
   <div class="sidebar-item-container"> 
-  <a href="./06_lesson.html" class="sidebar-item-text sidebar-link"><span class="chapter-number">6</span>&nbsp; <span class="chapter-title">Putting it All Together (and into Markdown)</span></a>
+  <a href="./06_lesson.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text"><span class="chapter-number">6</span>&nbsp; <span class="chapter-title">Putting it All Together (and into Markdown)</span></span></a>
   </div>
 </li>
     </ul>
     </div>
 </nav>
+<div id="quarto-sidebar-glass" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar,#quarto-sidebar-glass"></div>
 <!-- margin-sidebar -->
     <div id="quarto-margin-sidebar" class="sidebar margin-sidebar">
         <nav id="TOC" role="doc-toc" class="toc-active">
     <h2 id="toc-title">Table of contents</h2>
    
   <ul>
-  <li><a href="#review" id="toc-review" class="nav-link active" data-scroll-target="#review"><span class="toc-section-number">5.1</span>  Review</a></li>
-  <li><a href="#motivation" id="toc-motivation" class="nav-link" data-scroll-target="#motivation"><span class="toc-section-number">5.2</span>  Motivation</a></li>
-  <li><a href="#vectorization" id="toc-vectorization" class="nav-link" data-scroll-target="#vectorization"><span class="toc-section-number">5.3</span>  Vectorization</a></li>
-  <li><a href="#across" id="toc-across" class="nav-link" data-scroll-target="#across"><span class="toc-section-number">5.4</span>  <code>across()</code></a>
+  <li><a href="#review" id="toc-review" class="nav-link active" data-scroll-target="#review"><span class="header-section-number">5.1</span> Review</a></li>
+  <li><a href="#motivation" id="toc-motivation" class="nav-link" data-scroll-target="#motivation"><span class="header-section-number">5.2</span> Motivation</a></li>
+  <li><a href="#vectorization" id="toc-vectorization" class="nav-link" data-scroll-target="#vectorization"><span class="header-section-number">5.3</span> Vectorization</a></li>
+  <li><a href="#across" id="toc-across" class="nav-link" data-scroll-target="#across"><span class="header-section-number">5.4</span> <code>across()</code></a>
   <ul class="collapse">
-  <li><a href="#mutate" id="toc-mutate" class="nav-link" data-scroll-target="#mutate"><span class="toc-section-number">5.4.1</span>  <code>mutate()</code></a></li>
-  <li><a href="#summarize" id="toc-summarize" class="nav-link" data-scroll-target="#summarize"><span class="toc-section-number">5.4.2</span>  <code>summarize()</code></a></li>
-  <li><a href="#exercise-1" id="toc-exercise-1" class="nav-link" data-scroll-target="#exercise-1"><span class="toc-section-number">5.4.3</span>  Exercise 1</a></li>
+  <li><a href="#mutate" id="toc-mutate" class="nav-link" data-scroll-target="#mutate"><span class="header-section-number">5.4.1</span> <code>mutate()</code></a></li>
+  <li><a href="#summarize" id="toc-summarize" class="nav-link" data-scroll-target="#summarize"><span class="header-section-number">5.4.2</span> <code>summarize()</code></a></li>
+  <li><a href="#exercise-1" id="toc-exercise-1" class="nav-link" data-scroll-target="#exercise-1"><span class="header-section-number">5.4.3</span> Exercise 1</a></li>
   </ul></li>
-  <li><a href="#where" id="toc-where" class="nav-link" data-scroll-target="#where"><span class="toc-section-number">5.5</span>  <code>where()</code></a>
+  <li><a href="#where" id="toc-where" class="nav-link" data-scroll-target="#where"><span class="header-section-number">5.5</span> <code>where()</code></a>
   <ul class="collapse">
-  <li><a href="#example" id="toc-example" class="nav-link" data-scroll-target="#example"><span class="toc-section-number">5.5.1</span>  Example</a></li>
-  <li><a href="#exercise-2" id="toc-exercise-2" class="nav-link" data-scroll-target="#exercise-2"><span class="toc-section-number">5.5.2</span>  Exercise 2</a></li>
+  <li><a href="#example" id="toc-example" class="nav-link" data-scroll-target="#example"><span class="header-section-number">5.5.1</span> Example</a></li>
+  <li><a href="#exercise-2" id="toc-exercise-2" class="nav-link" data-scroll-target="#exercise-2"><span class="header-section-number">5.5.2</span> Exercise 2</a></li>
   </ul></li>
-  <li><a href="#selector-helpers" id="toc-selector-helpers" class="nav-link" data-scroll-target="#selector-helpers"><span class="toc-section-number">5.6</span>  Selector Helpers</a></li>
-  <li><a href="#pivot_" id="toc-pivot_" class="nav-link" data-scroll-target="#pivot_"><span class="toc-section-number">5.7</span>  <code>pivot_*</code></a>
+  <li><a href="#selector-helpers" id="toc-selector-helpers" class="nav-link" data-scroll-target="#selector-helpers"><span class="header-section-number">5.6</span> Selector Helpers</a></li>
+  <li><a href="#pivot_" id="toc-pivot_" class="nav-link" data-scroll-target="#pivot_"><span class="header-section-number">5.7</span> <code>pivot_*</code></a>
   <ul class="collapse">
-  <li><a href="#tidy-data" id="toc-tidy-data" class="nav-link" data-scroll-target="#tidy-data"><span class="toc-section-number">5.7.1</span>  Tidy data</a></li>
+  <li><a href="#tidy-data" id="toc-tidy-data" class="nav-link" data-scroll-target="#tidy-data"><span class="header-section-number">5.7.1</span> Tidy data</a></li>
   </ul></li>
-  <li><a href="#librarypurrr" id="toc-librarypurrr" class="nav-link" data-scroll-target="#librarypurrr"><span class="toc-section-number">5.8</span>  <code>library(purrr)</code></a>
+  <li><a href="#librarypurrr" id="toc-librarypurrr" class="nav-link" data-scroll-target="#librarypurrr"><span class="header-section-number">5.8</span> <code>library(purrr)</code></a>
   <ul class="collapse">
-  <li><a href="#many-files" id="toc-many-files" class="nav-link" data-scroll-target="#many-files"><span class="toc-section-number">5.8.1</span>  Many files</a></li>
-  <li><a href="#many-models" id="toc-many-models" class="nav-link" data-scroll-target="#many-models"><span class="toc-section-number">5.8.2</span>  Many models</a></li>
+  <li><a href="#many-files" id="toc-many-files" class="nav-link" data-scroll-target="#many-files"><span class="header-section-number">5.8.1</span> Many files</a></li>
+  <li><a href="#many-models" id="toc-many-models" class="nav-link" data-scroll-target="#many-models"><span class="header-section-number">5.8.2</span> Many models</a></li>
   </ul></li>
-  <li><a href="#resources" id="toc-resources" class="nav-link" data-scroll-target="#resources"><span class="toc-section-number">5.9</span>  Resources</a></li>
+  <li><a href="#resources" id="toc-resources" class="nav-link" data-scroll-target="#resources"><span class="header-section-number">5.9</span> Resources</a></li>
   </ul>
 </nav>
     </div>
@@ -236,7 +221,7 @@ <h2 id="toc-title">Table of contents</h2>
 
 <header id="title-block-header" class="quarto-title-block default">
 <div class="quarto-title">
-<h1 class="title d-none d-lg-block"><span class="chapter-number">5</span>&nbsp; <span class="chapter-title">Iteration (or becoming an R Superhero)</span></h1>
+<h1 class="title"><span class="chapter-number">5</span>&nbsp; <span class="chapter-title">Iteration (or becoming an R Superhero)</span></h1>
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@@ -253,7 +238,7 @@ <h1 class="title d-none d-lg-block"><span class="chapter-number">5</span>&nbsp;
     <div>
     <div class="quarto-title-meta-heading">Published</div>
     <div class="quarto-title-meta-contents">
-      <p class="date">August 15, 2023</p>
+      <p class="date">August 22, 2023</p>
     </div>
   </div>
   
@@ -279,9 +264,6 @@ <h2 data-number="5.1" class="anchored" data-anchor-id="review"><span class="head
 <li>Don’t use <code>haven::write_sas()</code> to try to create a <code>sas7bdat</code> file!</li>
 <li>Tidy data</li>
 </ul>
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-</div>
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@@ -800,9 +782,23 @@ <h2 data-number="5.9" class="anchored" data-anchor-id="resources"><span class="h
     icon: icon
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+        let height = null;
+        let parent = null;
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+        annoteDlNode.addEventListener('click', (event) => {
+          const clickedEl = event.target;
+          if (clickedEl !== selectedAnnoteEl) {
+            unselectCodeLines();
+            const activeEl = window.document.querySelector('dt[data-target-cell].code-annotation-active');
+            if (activeEl) {
+              activeEl.classList.remove('code-annotation-active');
+            }
+            selectCodeLines(clickedEl);
+            clickedEl.classList.add('code-annotation-active');
+          } else {
+            // Unselect the line
+            unselectCodeLines();
+            clickedEl.classList.remove('code-annotation-active');
+          }
+        });
+      }
   const findCites = (el) => {
     const parentEl = el.parentElement;
     if (parentEl) {
diff --git a/docs/06_lesson.html b/docs/06_lesson.html
index c8febc9..a1b56b0 100644
--- a/docs/06_lesson.html
+++ b/docs/06_lesson.html
@@ -2,12 +2,12 @@
 <html xmlns="http://www.w3.org/1999/xhtml" lang="en" xml:lang="en"><head>
 
 <meta charset="utf-8">
-<meta name="generator" content="quarto-1.2.269">
+<meta name="generator" content="quarto-1.3.433">
 
 <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes">
 
 <meta name="author" content="Fay Walker (MET) and Aaron Williams (IBP/TECH)">
-<meta name="dcterms.date" content="2023-08-15">
+<meta name="dcterms.date" content="2023-08-22">
 
 <title>Intro to R - 6&nbsp; Putting it All Together (and into Markdown)</title>
 <style>
@@ -19,7 +19,7 @@
 ul.task-list{list-style: none;}
 ul.task-list li input[type="checkbox"] {
   width: 0.8em;
-  margin: 0 0.8em 0.2em -1.6em;
+  margin: 0 0.8em 0.2em -1em; /* quarto-specific, see https://github.com/quarto-dev/quarto-cli/issues/4556 */ 
   vertical-align: middle;
 }
 </style>
@@ -58,7 +58,8 @@
     "search-more-matches-text": "more matches in this document",
     "search-clear-button-title": "Clear",
     "search-detached-cancel-button-title": "Cancel",
-    "search-submit-button-title": "Submit"
+    "search-submit-button-title": "Submit",
+    "search-label": "Search"
   }
 }</script>
 
@@ -70,11 +71,16 @@
 
 <div id="quarto-search-results"></div>
   <header id="quarto-header" class="headroom fixed-top">
-  <nav class="quarto-secondary-nav" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar" aria-controls="quarto-sidebar" aria-expanded="false" aria-label="Toggle sidebar navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">
-    <div class="container-fluid d-flex justify-content-between">
-      <h1 class="quarto-secondary-nav-title"><span class="chapter-number">6</span>&nbsp; <span class="chapter-title">Putting it All Together (and into Markdown)</span></h1>
-      <button type="button" class="quarto-btn-toggle btn" aria-label="Show secondary navigation">
-        <i class="bi bi-chevron-right"></i>
+  <nav class="quarto-secondary-nav">
+    <div class="container-fluid d-flex">
+      <button type="button" class="quarto-btn-toggle btn" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar,#quarto-sidebar-glass" aria-controls="quarto-sidebar" aria-expanded="false" aria-label="Toggle sidebar navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">
+        <i class="bi bi-layout-text-sidebar-reverse"></i>
+      </button>
+      <nav class="quarto-page-breadcrumbs" aria-label="breadcrumb"><ol class="breadcrumb"><li class="breadcrumb-item"><a href="./06_lesson.html"><span class="chapter-number">6</span>&nbsp; <span class="chapter-title">Putting it All Together (and into Markdown)</span></a></li></ol></nav>
+      <a class="flex-grow-1" role="button" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar,#quarto-sidebar-glass" aria-controls="quarto-sidebar" aria-expanded="false" aria-label="Toggle sidebar navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">      
+      </a>
+      <button type="button" class="btn quarto-search-button" aria-label="" onclick="window.quartoOpenSearch();">
+        <i class="bi bi-search"></i>
       </button>
     </div>
   </nav>
@@ -82,75 +88,83 @@ <h1 class="quarto-secondary-nav-title"><span class="chapter-number">6</span>&nbs
 <!-- content -->
 <div id="quarto-content" class="quarto-container page-columns page-rows-contents page-layout-article">
 <!-- sidebar -->
-  <nav id="quarto-sidebar" class="sidebar collapse sidebar-navigation floating overflow-auto">
+  <nav id="quarto-sidebar" class="sidebar collapse collapse-horizontal sidebar-navigation floating overflow-auto">
     <div class="pt-lg-2 mt-2 text-left sidebar-header">
     <div class="sidebar-title mb-0 py-0">
       <a href="./">Intro to R</a> 
     </div>
       </div>
-      <div class="mt-2 flex-shrink-0 align-items-center">
+        <div class="mt-2 flex-shrink-0 align-items-center">
         <div class="sidebar-search">
         <div id="quarto-search" class="" title="Search"></div>
         </div>
-      </div>
+        </div>
     <div class="sidebar-menu-container"> 
     <ul class="list-unstyled mt-1">
         <li class="sidebar-item">
   <div class="sidebar-item-container"> 
-  <a href="./index.html" class="sidebar-item-text sidebar-link">Intro to R: A Hands on Tutorial</a>
+  <a href="./index.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text">Intro to R: A Hands on Tutorial</span></a>
   </div>
 </li>
         <li class="sidebar-item">
   <div class="sidebar-item-container"> 
-  <a href="./01_lesson.html" class="sidebar-item-text sidebar-link"><span class="chapter-number">1</span>&nbsp; <span class="chapter-title">Data Visualization</span></a>
+  <a href="./01_lesson.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text"><span class="chapter-number">1</span>&nbsp; <span class="chapter-title">Data Visualization</span></span></a>
   </div>
 </li>
         <li class="sidebar-item">
   <div class="sidebar-item-container"> 
-  <a href="./02_lesson.html" class="sidebar-item-text sidebar-link"><span class="chapter-number">2</span>&nbsp; <span class="chapter-title">Data Munging 1</span></a>
+  <a href="./02_lesson.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text"><span class="chapter-number">2</span>&nbsp; <span class="chapter-title">Data Munging 1</span></span></a>
   </div>
 </li>
         <li class="sidebar-item">
   <div class="sidebar-item-container"> 
-  <a href="./03_lesson.html" class="sidebar-item-text sidebar-link"><span class="chapter-number">3</span>&nbsp; <span class="chapter-title">Data Munging (II)</span></a>
+  <a href="./03_lesson.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text"><span class="chapter-number">3</span>&nbsp; <span class="chapter-title">Data Munging (II)</span></span></a>
   </div>
 </li>
         <li class="sidebar-item">
   <div class="sidebar-item-container"> 
-  <a href="./04_lesson.html" class="sidebar-item-text sidebar-link"><span class="chapter-number">4</span>&nbsp; <span class="chapter-title">Data Import</span></a>
+  <a href="./04_lesson.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text"><span class="chapter-number">4</span>&nbsp; <span class="chapter-title">Data Import</span></span></a>
   </div>
 </li>
         <li class="sidebar-item">
   <div class="sidebar-item-container"> 
-  <a href="./05_lesson.html" class="sidebar-item-text sidebar-link"><span class="chapter-number">5</span>&nbsp; <span class="chapter-title">Iteration (or becoming an R Superhero)</span></a>
+  <a href="./05_lesson.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text"><span class="chapter-number">5</span>&nbsp; <span class="chapter-title">Iteration (or becoming an R Superhero)</span></span></a>
   </div>
 </li>
         <li class="sidebar-item">
   <div class="sidebar-item-container"> 
-  <a href="./06_lesson.html" class="sidebar-item-text sidebar-link active"><span class="chapter-number">6</span>&nbsp; <span class="chapter-title">Putting it All Together (and into Markdown)</span></a>
+  <a href="./06_lesson.html" class="sidebar-item-text sidebar-link active">
+ <span class="menu-text"><span class="chapter-number">6</span>&nbsp; <span class="chapter-title">Putting it All Together (and into Markdown)</span></span></a>
   </div>
 </li>
     </ul>
     </div>
 </nav>
+<div id="quarto-sidebar-glass" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar,#quarto-sidebar-glass"></div>
 <!-- margin-sidebar -->
     <div id="quarto-margin-sidebar" class="sidebar margin-sidebar">
         <nav id="TOC" role="doc-toc" class="toc-active">
     <h2 id="toc-title">Table of contents</h2>
    
   <ul>
-  <li><a href="#key-takeaways" id="toc-key-takeaways" class="nav-link active" data-scroll-target="#key-takeaways"><span class="toc-section-number">6.1</span>  Key Takeaways</a></li>
-  <li><a href="#helpful-packages" id="toc-helpful-packages" class="nav-link" data-scroll-target="#helpful-packages"><span class="toc-section-number">6.2</span>  Helpful Packages</a></li>
-  <li><a href="#what-is-r-markdown" id="toc-what-is-r-markdown" class="nav-link" data-scroll-target="#what-is-r-markdown"><span class="toc-section-number">6.3</span>  What is R Markdown?</a></li>
-  <li><a href="#urbntemplates" id="toc-urbntemplates" class="nav-link" data-scroll-target="#urbntemplates"><span class="toc-section-number">6.4</span>  urbntemplates</a>
+  <li><a href="#key-takeaways" id="toc-key-takeaways" class="nav-link active" data-scroll-target="#key-takeaways"><span class="header-section-number">6.1</span> Key Takeaways</a></li>
+  <li><a href="#helpful-packages" id="toc-helpful-packages" class="nav-link" data-scroll-target="#helpful-packages"><span class="header-section-number">6.2</span> Helpful Packages</a></li>
+  <li><a href="#what-is-r-markdown" id="toc-what-is-r-markdown" class="nav-link" data-scroll-target="#what-is-r-markdown"><span class="header-section-number">6.3</span> What is R Markdown?</a></li>
+  <li><a href="#urbntemplates" id="toc-urbntemplates" class="nav-link" data-scroll-target="#urbntemplates"><span class="header-section-number">6.4</span> urbntemplates</a>
   <ul class="collapse">
-  <li><a href="#yaml-header" id="toc-yaml-header" class="nav-link" data-scroll-target="#yaml-header"><span class="toc-section-number">6.4.1</span>  YAML Header</a></li>
-  <li><a href="#markdown" id="toc-markdown" class="nav-link" data-scroll-target="#markdown"><span class="toc-section-number">6.4.2</span>  Markdown</a></li>
-  <li><a href="#code-chunks" id="toc-code-chunks" class="nav-link" data-scroll-target="#code-chunks"><span class="toc-section-number">6.4.3</span>  Code Chunks</a></li>
-  <li><a href="#create-a-markdown-html-fact-sheet---piece-by-piece" id="toc-create-a-markdown-html-fact-sheet---piece-by-piece" class="nav-link" data-scroll-target="#create-a-markdown-html-fact-sheet---piece-by-piece"><span class="toc-section-number">6.4.4</span>  Create a Markdown HTML Fact Sheet - Piece by Piece</a></li>
-  <li><a href="#step-1-install-libraryurbntemplates" id="toc-step-1-install-libraryurbntemplates" class="nav-link" data-scroll-target="#step-1-install-libraryurbntemplates"><span class="toc-section-number">6.4.5</span>  <font color="#55b748"><strong>Step 1:</strong></font> Install <code>library(urbntemplates)</code></a></li>
+  <li><a href="#yaml-header" id="toc-yaml-header" class="nav-link" data-scroll-target="#yaml-header"><span class="header-section-number">6.4.1</span> YAML Header</a></li>
+  <li><a href="#markdown" id="toc-markdown" class="nav-link" data-scroll-target="#markdown"><span class="header-section-number">6.4.2</span> Markdown</a></li>
+  <li><a href="#code-chunks" id="toc-code-chunks" class="nav-link" data-scroll-target="#code-chunks"><span class="header-section-number">6.4.3</span> Code Chunks</a></li>
+  <li><a href="#create-a-markdown-html-fact-sheet---piece-by-piece" id="toc-create-a-markdown-html-fact-sheet---piece-by-piece" class="nav-link" data-scroll-target="#create-a-markdown-html-fact-sheet---piece-by-piece"><span class="header-section-number">6.4.4</span> Create a Markdown HTML Fact Sheet - Piece by Piece</a></li>
+  <li><a href="#step-1-install-libraryurbntemplates" id="toc-step-1-install-libraryurbntemplates" class="nav-link" data-scroll-target="#step-1-install-libraryurbntemplates"><span class="header-section-number">6.4.5</span> <font color="#55b748"><strong>Step 1:</strong></font> Install <code>library(urbntemplates)</code></a></li>
   </ul></li>
-  <li><a href="#resources" id="toc-resources" class="nav-link" data-scroll-target="#resources"><span class="toc-section-number">6.5</span>  Resources</a></li>
+  <li><a href="#resources" id="toc-resources" class="nav-link" data-scroll-target="#resources"><span class="header-section-number">6.5</span> Resources</a></li>
   </ul>
 </nav>
     </div>
@@ -159,7 +173,7 @@ <h2 id="toc-title">Table of contents</h2>
 
 <header id="title-block-header" class="quarto-title-block default">
 <div class="quarto-title">
-<h1 class="title d-none d-lg-block"><span class="chapter-number">6</span>&nbsp; <span class="chapter-title">Putting it All Together (and into Markdown)</span></h1>
+<h1 class="title"><span class="chapter-number">6</span>&nbsp; <span class="chapter-title">Putting it All Together (and into Markdown)</span></h1>
 </div>
 
 
@@ -176,7 +190,7 @@ <h1 class="title d-none d-lg-block"><span class="chapter-number">6</span>&nbsp;
     <div>
     <div class="quarto-title-meta-heading">Published</div>
     <div class="quarto-title-meta-contents">
-      <p class="date">August 15, 2023</p>
+      <p class="date">August 22, 2023</p>
     </div>
   </div>
   
@@ -352,9 +366,23 @@ <h2 data-number="6.5" class="anchored" data-anchor-id="resources"><span class="h
     icon: icon
   };
   anchorJS.add('.anchored');
+  const isCodeAnnotation = (el) => {
+    for (const clz of el.classList) {
+      if (clz.startsWith('code-annotation-')) {                     
+        return true;
+      }
+    }
+    return false;
+  }
   const clipboard = new window.ClipboardJS('.code-copy-button', {
-    target: function(trigger) {
-      return trigger.previousElementSibling;
+    text: function(trigger) {
+      const codeEl = trigger.previousElementSibling.cloneNode(true);
+      for (const childEl of codeEl.children) {
+        if (isCodeAnnotation(childEl)) {
+          childEl.remove();
+        }
+      }
+      return codeEl.innerText;
     }
   });
   clipboard.on('success', function(e) {
@@ -419,6 +447,92 @@ <h2 data-number="6.5" class="anchored" data-anchor-id="resources"><span class="h
       return note.innerHTML;
     });
   }
+      let selectedAnnoteEl;
+      const selectorForAnnotation = ( cell, annotation) => {
+        let cellAttr = 'data-code-cell="' + cell + '"';
+        let lineAttr = 'data-code-annotation="' +  annotation + '"';
+        const selector = 'span[' + cellAttr + '][' + lineAttr + ']';
+        return selector;
+      }
+      const selectCodeLines = (annoteEl) => {
+        const doc = window.document;
+        const targetCell = annoteEl.getAttribute("data-target-cell");
+        const targetAnnotation = annoteEl.getAttribute("data-target-annotation");
+        const annoteSpan = window.document.querySelector(selectorForAnnotation(targetCell, targetAnnotation));
+        const lines = annoteSpan.getAttribute("data-code-lines").split(",");
+        const lineIds = lines.map((line) => {
+          return targetCell + "-" + line;
+        })
+        let top = null;
+        let height = null;
+        let parent = null;
+        if (lineIds.length > 0) {
+            //compute the position of the single el (top and bottom and make a div)
+            const el = window.document.getElementById(lineIds[0]);
+            top = el.offsetTop;
+            height = el.offsetHeight;
+            parent = el.parentElement.parentElement;
+          if (lineIds.length > 1) {
+            const lastEl = window.document.getElementById(lineIds[lineIds.length - 1]);
+            const bottom = lastEl.offsetTop + lastEl.offsetHeight;
+            height = bottom - top;
+          }
+          if (top !== null && height !== null && parent !== null) {
+            // cook up a div (if necessary) and position it 
+            let div = window.document.getElementById("code-annotation-line-highlight");
+            if (div === null) {
+              div = window.document.createElement("div");
+              div.setAttribute("id", "code-annotation-line-highlight");
+              div.style.position = 'absolute';
+              parent.appendChild(div);
+            }
+            div.style.top = top - 2 + "px";
+            div.style.height = height + 4 + "px";
+            let gutterDiv = window.document.getElementById("code-annotation-line-highlight-gutter");
+            if (gutterDiv === null) {
+              gutterDiv = window.document.createElement("div");
+              gutterDiv.setAttribute("id", "code-annotation-line-highlight-gutter");
+              gutterDiv.style.position = 'absolute';
+              const codeCell = window.document.getElementById(targetCell);
+              const gutter = codeCell.querySelector('.code-annotation-gutter');
+              gutter.appendChild(gutterDiv);
+            }
+            gutterDiv.style.top = top - 2 + "px";
+            gutterDiv.style.height = height + 4 + "px";
+          }
+          selectedAnnoteEl = annoteEl;
+        }
+      };
+      const unselectCodeLines = () => {
+        const elementsIds = ["code-annotation-line-highlight", "code-annotation-line-highlight-gutter"];
+        elementsIds.forEach((elId) => {
+          const div = window.document.getElementById(elId);
+          if (div) {
+            div.remove();
+          }
+        });
+        selectedAnnoteEl = undefined;
+      };
+      // Attach click handler to the DT
+      const annoteDls = window.document.querySelectorAll('dt[data-target-cell]');
+      for (const annoteDlNode of annoteDls) {
+        annoteDlNode.addEventListener('click', (event) => {
+          const clickedEl = event.target;
+          if (clickedEl !== selectedAnnoteEl) {
+            unselectCodeLines();
+            const activeEl = window.document.querySelector('dt[data-target-cell].code-annotation-active');
+            if (activeEl) {
+              activeEl.classList.remove('code-annotation-active');
+            }
+            selectCodeLines(clickedEl);
+            clickedEl.classList.add('code-annotation-active');
+          } else {
+            // Unselect the line
+            unselectCodeLines();
+            clickedEl.classList.remove('code-annotation-active');
+          }
+        });
+      }
   const findCites = (el) => {
     const parentEl = el.parentElement;
     if (parentEl) {
diff --git a/docs/index.html b/docs/index.html
index ab2ca5f..01b2f5c 100644
--- a/docs/index.html
+++ b/docs/index.html
@@ -2,12 +2,12 @@
 <html xmlns="http://www.w3.org/1999/xhtml" lang="en" xml:lang="en"><head>
 
 <meta charset="utf-8">
-<meta name="generator" content="quarto-1.2.269">
+<meta name="generator" content="quarto-1.3.433">
 
 <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes">
 
-<meta name="author" content="Amy Rogin and Aaron Williams">
-<meta name="dcterms.date" content="2023-08-11">
+<meta name="author" content="Amy Rogin and Aaron R. Williams">
+<meta name="dcterms.date" content="2023-08-22">
 
 <title>Intro to R</title>
 <style>
@@ -19,7 +19,7 @@
 ul.task-list{list-style: none;}
 ul.task-list li input[type="checkbox"] {
   width: 0.8em;
-  margin: 0 0.8em 0.2em -1.6em;
+  margin: 0 0.8em 0.2em -1em; /* quarto-specific, see https://github.com/quarto-dev/quarto-cli/issues/4556 */ 
   vertical-align: middle;
 }
 </style>
@@ -58,7 +58,8 @@
     "search-more-matches-text": "more matches in this document",
     "search-clear-button-title": "Clear",
     "search-detached-cancel-button-title": "Cancel",
-    "search-submit-button-title": "Submit"
+    "search-submit-button-title": "Submit",
+    "search-label": "Search"
   }
 }</script>
 
@@ -70,11 +71,16 @@
 
 <div id="quarto-search-results"></div>
   <header id="quarto-header" class="headroom fixed-top">
-  <nav class="quarto-secondary-nav" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar" aria-controls="quarto-sidebar" aria-expanded="false" aria-label="Toggle sidebar navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">
-    <div class="container-fluid d-flex justify-content-between">
-      <h1 class="quarto-secondary-nav-title">Intro to R</h1>
-      <button type="button" class="quarto-btn-toggle btn" aria-label="Show secondary navigation">
-        <i class="bi bi-chevron-right"></i>
+  <nav class="quarto-secondary-nav">
+    <div class="container-fluid d-flex">
+      <button type="button" class="quarto-btn-toggle btn" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar,#quarto-sidebar-glass" aria-controls="quarto-sidebar" aria-expanded="false" aria-label="Toggle sidebar navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">
+        <i class="bi bi-layout-text-sidebar-reverse"></i>
+      </button>
+      <nav class="quarto-page-breadcrumbs" aria-label="breadcrumb"><ol class="breadcrumb"><li class="breadcrumb-item"><a href="./index.html">Intro to R: A Hands on Tutorial</a></li></ol></nav>
+      <a class="flex-grow-1" role="button" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar,#quarto-sidebar-glass" aria-controls="quarto-sidebar" aria-expanded="false" aria-label="Toggle sidebar navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">      
+      </a>
+      <button type="button" class="btn quarto-search-button" aria-label="" onclick="window.quartoOpenSearch();">
+        <i class="bi bi-search"></i>
       </button>
     </div>
   </nav>
@@ -82,57 +88,65 @@ <h1 class="quarto-secondary-nav-title">Intro to R</h1>
 <!-- content -->
 <div id="quarto-content" class="quarto-container page-columns page-rows-contents page-layout-article">
 <!-- sidebar -->
-  <nav id="quarto-sidebar" class="sidebar collapse sidebar-navigation floating overflow-auto">
+  <nav id="quarto-sidebar" class="sidebar collapse collapse-horizontal sidebar-navigation floating overflow-auto">
     <div class="pt-lg-2 mt-2 text-left sidebar-header">
     <div class="sidebar-title mb-0 py-0">
       <a href="./">Intro to R</a> 
     </div>
       </div>
-      <div class="mt-2 flex-shrink-0 align-items-center">
+        <div class="mt-2 flex-shrink-0 align-items-center">
         <div class="sidebar-search">
         <div id="quarto-search" class="" title="Search"></div>
         </div>
-      </div>
+        </div>
     <div class="sidebar-menu-container"> 
     <ul class="list-unstyled mt-1">
         <li class="sidebar-item">
   <div class="sidebar-item-container"> 
-  <a href="./index.html" class="sidebar-item-text sidebar-link active">Intro to R: A Hands on Tutorial</a>
+  <a href="./index.html" class="sidebar-item-text sidebar-link active">
+ <span class="menu-text">Intro to R: A Hands on Tutorial</span></a>
   </div>
 </li>
         <li class="sidebar-item">
   <div class="sidebar-item-container"> 
-  <a href="./01_lesson.html" class="sidebar-item-text sidebar-link"><span class="chapter-number">1</span>&nbsp; <span class="chapter-title">Data Visualization</span></a>
+  <a href="./01_lesson.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text"><span class="chapter-number">1</span>&nbsp; <span class="chapter-title">Data Visualization</span></span></a>
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-  <a href="./02_lesson.html" class="sidebar-item-text sidebar-link"><span class="chapter-number">2</span>&nbsp; <span class="chapter-title">Data Munging 1</span></a>
+  <a href="./02_lesson.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text"><span class="chapter-number">2</span>&nbsp; <span class="chapter-title">Data Munging 1</span></span></a>
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-  <a href="./03_lesson.html" class="sidebar-item-text sidebar-link"><span class="chapter-number">3</span>&nbsp; <span class="chapter-title">Data Munging (II)</span></a>
+  <a href="./03_lesson.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text"><span class="chapter-number">3</span>&nbsp; <span class="chapter-title">Data Munging (II)</span></span></a>
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         <li class="sidebar-item">
   <div class="sidebar-item-container"> 
-  <a href="./04_lesson.html" class="sidebar-item-text sidebar-link"><span class="chapter-number">4</span>&nbsp; <span class="chapter-title">Data Import</span></a>
+  <a href="./04_lesson.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text"><span class="chapter-number">4</span>&nbsp; <span class="chapter-title">Data Import</span></span></a>
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-  <a href="./05_lesson.html" class="sidebar-item-text sidebar-link"><span class="chapter-number">5</span>&nbsp; <span class="chapter-title">Iteration (or becoming an R Superhero)</span></a>
+  <a href="./05_lesson.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text"><span class="chapter-number">5</span>&nbsp; <span class="chapter-title">Iteration (or becoming an R Superhero)</span></span></a>
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-  <a href="./06_lesson.html" class="sidebar-item-text sidebar-link"><span class="chapter-number">6</span>&nbsp; <span class="chapter-title">Putting it All Together (and into Markdown)</span></a>
+  <a href="./06_lesson.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text"><span class="chapter-number">6</span>&nbsp; <span class="chapter-title">Putting it All Together (and into Markdown)</span></span></a>
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@@ -154,7 +168,7 @@ <h2 id="toc-title">Table of contents</h2>
 
 <header id="title-block-header" class="quarto-title-block default">
 <div class="quarto-title">
-<h1 class="title d-none d-lg-block">Intro to R</h1>
+<h1 class="title">Intro to R</h1>
 </div>
 
 
@@ -164,14 +178,14 @@ <h1 class="title d-none d-lg-block">Intro to R</h1>
     <div>
     <div class="quarto-title-meta-heading">Author</div>
     <div class="quarto-title-meta-contents">
-             <p>Amy Rogin and Aaron Williams </p>
+             <p>Amy Rogin and Aaron R. Williams </p>
           </div>
   </div>
     
     <div>
     <div class="quarto-title-meta-heading">Published</div>
     <div class="quarto-title-meta-contents">
-      <p class="date">August 11, 2023</p>
+      <p class="date">August 22, 2023</p>
     </div>
   </div>
   
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diff --git a/docs/search.json b/docs/search.json
index f8bc63e..22f4322 100644
--- a/docs/search.json
+++ b/docs/search.json
@@ -14,235 +14,186 @@
     "text": "Instructors (across all years)\n\nAaron R. Williams (IBP/TECH)\nAmy Rogin (MET)\nAjjit Narayanan (TECH)\nFay Walker (MET)\nSarah Strochak (HFPC)\nKyle Ueyama (TECH)\nOlivia Fiol (MET)\n\n\nQuestions?\nContact Amy Rogin (arogin@urban.org) or Aaron Williams (awilliams@urban.org)"
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-    "text": "3 Review\n\nAssignment Operator\nConsole/Environment/Script\nComment your code and read your error messages\n\n\n\n4 The tidyverse package\n\nCollection of packages using same syntax and grammar\nWorks with “tidy” data\nUses tibbles\n%>%\n\n\n\n5 Primary functions\n\nselect()\nrename()\nfilter()\narrange()\nmutate()\ngroup_by()\nsummarize()\n\nAll of these functions have the same structure.\n\nThe first argument is the dataframe\nThe subsequent arguments describe what to do with the data frame, using the variable names (without quotes because you’re already refering to that df)\nSeparate functions by %>%\nThe result is a new data frame.\n\n\n\nIf you have hit an error, trying running it line by line (function by function)\n\nSource: R for data science\n\n\n6 Exercise 0: Creating a Project and Loading Packages\nIf you are using a different computer or didn’t attend sessions 0 or 1, follow steps 1 and 2. If not- skip to step 3.\nStep 1: Open RStudio. File > New Project > New Directory > Select the location where you would like to create a new folder that houses your R Project. Call it urbn101.\nStep 2: Open an .R script with the button in the top left (sheet with a plus sign icon). Save the script as 02_data-munging1.R.\nStep 3: If you have not previously installed library(tidyverse): submit install.packages(\"tidyverse\") to the Console (type and hit enter)\nStep 4: Write library(tidyverse) at the top of 02_data-munging1.R. With the cursor on the line of text, click Control-Enter.\n\n\n7 Exercise 1: Tidyverse Function Overview\nStep 1: Assign the storms dataset to be called storms. Submit View(storms) in the console.\nYou can also deselect specific columns in your data by using the - operator\nSelect\nselect() is used when you want to limit the number of columns in your dataframe. The arguments after the dataframe should be names of columns, without quotes. For example:\ndataset %>%\n  select(col1, col2)\nStep 2: Create a new dataset with all the variables except for ts_diameter and hu_diameter, by using select and -.\nStep 3: Create a new dataset that only category 5 storms using filter().\nFilter\nfilter() is used when you want to filter to specific rows in your dataframe, based on certain conditions. Filter works with the following comparison operators:\n\n<, >, <=, >=\n==, !=\n\nYou can also chain together multiple condtions with & (and) or | (or). The arguments after the dataframe should be conditions you want to filter your dataset on. For example\ndataset %>%\nfilter(col1>0)\nStep 4: Chain all of these commands together with the %>% operator. Then rename the column pressure to air_pressure.\nRename\nThe syntax for rename is:\ndataset %>% \n  rename(new_name = current_name)\nThis same syntax can be used inside select(), and you can rename and select variables simultanously.\nStep 5: wind is measured in knots. Convert wind into miles per hour. Each knot is equal to 1.15078 miles per hour.\nMutate\nmutate() can make new variables and edit existing variables. New variables are always added to the end of the dataframe. You can use arithmetic arguments, such as +, -, *, /, and ^. The syntax for mutate is:\ndataset %>% \n  mutate(new_column = 100 * other_column)\nStep 6: Make a dataset with storms that occured in the summer (June, July, August).\nThe %in% operator\nThe %in% operator is useful for filtering based on more than one option within one variable.\nfilter(dataset, var1 == \"a\" | var1 == \"b\" | var1 == \"c\")\nis equivalent to\ndataset %>%\nfilter(var1 %in% c(\"a\", \"b\", \"c\"))\n\n\n8 Exercise 2: If/Then and Summarizing\nStep 1: Use the txhousing dataset. Assign the dataset to an object in your global environment named tx_housing.\nStep 2: Compute a new variable, average_price by dividing volume by sales.\nStep 3: Use if_else() to create a variable called flag that is 1 if the average sales price is larger than the median sales price.\nIf/then logic\nif_else() is an important tool in combination with mutate(). It employs if-then logic: the first argument is a binary statement. The second statement says what to do if the first argument is evaluated to TRUE- the third is what to do if it is FALSE This is particularly useful for creating dummy or indicator variables. When you have more than two possible outcomes, it is more efficient to use case_when().\nStep 3a: Type ?case_when into the console.\nStep 4: Type the following code into your script.\ntx_housing %>% \n  group_by(year) %>% \n  summarize(yearly_sales = sum(sales))\nGroup By\ngroup_by groups your dataframe into discrete categories. It can be very powerful when used with summarize.\nSummarize\nsummarize computes summary statistics for as set of observations in your data. The syntax is:\ndataset %>% \n  mutate(max = max(other_column))\nor\ndataset %>%\n  group_by(col1) %>%\n  summarize(count=n())\nStep 5: Add the argument na.rm = TRUE to the sum() function and rerun the code. Add a comment to explain the code.\nStep 6: Sort the results of the previous code in order from most to least sales by adding %>% arrange(desc(yearly_sales)) to the end of the code.\nStep 7a: Which city had the most home sales in January 2008? (Hint: use filter() and arrange()).\nStep 7b: Which city had the least home sales in 2006? (Hint: group by the city and the year- then use summarize()).\nBONUS: Plot the number of annual home sales over time!\n\n\n9 Exercise 3: Put it all together\nStep 1: Use the starwars dataframe. Type count(starwars, homeworld) into your script.\nStep 2: Filter out any characters that have a missing homeworld.\nStep 3: Find the average and median height and mass for humans vs. droids. (Hint: use group_by() and summarize()). Make sure that you are correcting for observations with missing values!\nStep 4: Make a dummy variable called droid that is 1 for droids and 0 for humans. Make a variable called tall that is 1 if the characters are more than 200 cm tall. How many tall humans are there?\nStep 5: What is the most common skin color for droids?\n\n\n10 Resources\n\nR for Data Science: data transformation\nData wrangling cheat sheet"
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-    "text": "7 Key Takeaways\nSource: RStudio\nlibrary(urbntemplates) contains three families of functions:\nA sensible workflow is: 1. Start a new project and create a .Rproj by submitting urbntemplates::start_project(). This will create and open a new .Rproj. 2. Inside the .Rproj, add the necessary documents for a part of a project, like a Shiny application, with a construct_() function. 3. Add any desired remaining templates or documents with use_() functions.\nStep 1: Install library(urbntemplates)\nStep 2: Create a new project using start_project()(Parenthesis should have directory and name of new project). Once you create the project, open a new script and create an html fact sheet using urbntemplates::construct_fact_sheet_html()\nStep 3: Resave the .Rmd with a better name.\nStep 4: Knit the R Markdown into a pdf document.\nStep 5: Add some text into the markdown, some code that appears as code, and some code that shows the result but not the code. Every code chunk should start with {r name} and then set the parameters (T/F, separated by comma. See full factsheet for more parameters."
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-    "text": "2 Review\nWhy Use R instead of Excel?\n\nReproducibility\nScalability\nFlexibility\nIterative\nOpen Source\n\nRemember\n\nEnvironment/scripts/console\nComment your code as you go\nRead your error messages\nWhere possible, type, don’t copy and paste - you will remember better!\nThe assignment operator\n\n\n\n3 Exercise 0 (Set up a project and load packages)\nStep 1: Open RStudio. File > New Project > New Directory > Select the location where you would like to create a new folder that houses your R Project. Call it urbn101\nStep 2: Open an .R script with the button in the top left (sheet with a plus sign icon). Save the script as 01_data-visualization.R.\nStep 3: Type install.packages(\"tidyverse\") and hit enter (submit) to the Console.\nStep 4: Write library(tidyverse) and at the top of 01_data-visualization.R. With the cursor on the line of text, hit Control-Enter (at the same time).\nStep 5: Repeat steps 3 & 4 with install.packages(\"ggplot2\") and library(ggplot2), respectively.\n\ntidyverse - a collection of libraries that use the same syntax/grammar (more on this on Monday)\nggplot2 - for making plots/graphs\n\n\n\n4 Exercise 1 (Make a plot)\nStep 1: Submit data() to the console. We will use the airquality dataset.\nStep 2: Type the following in your script:\nggplot(data=airquality)+\n  geom_point(mapping=aes(x=Temp, y=Ozone))\n\nData frames are the only appropriate input for library(ggplot2).\n\nStep 3: Add a comment above the ggplot2 code that describes the plot we created.\nStep 4: Add comments below the data visualization code that describes the argument or function that corresponds to each of the first three components of the grammar of graphics.\nData are the values represented in the visualization.\nAesthetic mappings are directions for how data are mapped in a plot in a way that we can perceive. Aesthetic mappings include linking variables to the x-position, y-position, color, fill, shape, transparency, and size.\nGeometric objects are representations of the data, including points, lines, and polygons.\n\n\n5 Exercise 2 (Change some of the plot settings)\nStep 1: Duplicate the code from your first chart. Inside aes(), add color = \"red\" (separated by a comma)\nStep 2: Move color = \"red\" from aes() to geom_point(). What changed?\nStep 3: Remove color = \"red\" and add color = Month inside aes().\nStep 4: This is a little cluttered. Add alpha = 0.2 inside geom_point().\nStep 5: Add a plus sign to the end of the geom_point line, type labs(title=\"Air Quality Temperature and Ozone Readings\")\nAesthetic mappings like x and y almost always vary with the data. Aesthetic mappings like color, fill, shape, transparency, and size can vary with the data. But those arguments can also be added as styles that don’t vary with the data. If you include those arguments in aes(), they will show up in the legend (which can be annoying!).\n\n\n6 Exercise 3 (Add a regression line/confidence interval)\nStep 1: Reconfigure the data so that the geompoint parentheses are empty (move mapping=aes(x=Temp, y=Ozone) to the ggplot line. Add + to the labs line and add geom_smooth()\n\n\n7 Exercise 4 (Scale the axes)\nStep 1: Create a new scatter plot using the msleep data set. Use bodywt on the x-axis and sleep_total on the y-axis.\nStep 2: The y-axis doesn’t contain zero. Below geom_point(), add scale_y_continuous(lim = c(0, NA)). Hint: add + after geom_point().\nStep 3: The x-axis is clustered near zero. Add scale_x_log10() above scale_y_continuous(lim = c(0, NA)).\nScales Turn data values, which are quantitative or categorical, into aesthetic values. This includes not only the x-axis and y-axis, but the ranges of sizes, shapes, and colors of aesthetics.\n\n\n8 Exercise 5 (Make it look nice!!)\nStep 1: Add the following code to your script. Submit it!\nggplot(storms)+ \ngeom_bar(mapping=aes(category))\nStep 2: Run install.packages(\"remotes\") and remotes::install_github(\"UrbanInstitute/urbnthemes\") in the console.\nStep 3: In the lines preceding the chart add and run the following code:\nlibrary(urbnthemes)\nset_urbn_defaults(style = \"print\")\nStep 4: Run the code to make the chart.\nStep 5: Add scale_y_continuous(expand = expand_scale(mult = c(0, 0.1))) and rerun the code.\nTheme controls the visual style of plot with font types, font sizes, background colors, margins, and positioning.\n\n\n9 Excercise 6 (Multiple little graphs/Faceting)\nStep 1: Read in Zillow Observed Rent Index (ZORI) data using read_csv('https://raw.githubusercontent.com/UI-Research/urbn101-intro-r/master/homework/zillow_clean.csv')\nStep 2: Clean the zillow data to just the most expensive ciites using code below:\nzillow_clean <- zillow %>%\n  arrange(desc(Avg_price)) %>%\n  slice(1:10) %>%\n  ggplot()+\n  geom_point(mapping=aes(x=Year, y=Avg_price))\nStep 3: In ggplot, plot the zillow_clean data as a scatter plot with year on the x axis and avg_price on the y axis.\nStep 4: Add facet_wrap(~RegionName) after the geom_point(mapping=aes(x=Year, y=Avg_price)) line.\n\n\n10 Exercise 7 (Mapping)\nStep 1: Read in UFO sighting data (source: National UFO Reporting Center), link: https://raw.githubusercontent.com/UI-Research/urbn101-intro-r/master/homework/ufo_state.csv\nStep 1: Install urbnmapr devtools::install_github(\"UrbanInstitute/urbnmapr\"), load the urbnmapr library, and update the urbnthemes style to map set_urbn_defaults(style = \"map\").\nStep 2:: Pull a shapefile of US States using urbnmapr and join the UFO counts by state to the shapefile using the code below:\nstates_sf <- get_urbn_map(\"states\", sf = TRUE)\n\nstates_ufo <- states_sf %>% \n  left_join(ufo, by=c(\"state_abbv\"=\"state\"))\nStep 3:: In ggplot, plot the states_ufo dataset, fill it in using the “count” column, add labels, change the outline (colour). Instead of using geom_point or geom_bar using geom_sf.\n Mapping Resources \n\nHow to Create State and County Maps Easily in R\nlibrary(urbnmapr)\nUrban Institute R Users Group website: mapping\n\n\n\n11 Functions\n\nggplot(): Create a plot, pull in data\naes(): The aesthetics that show up in the legend\ngeom_*(): What kind of graph\n\ngeom_point()\ngeom_line()\ngeom_bar()\ngeom_col()\ngeom_sf()\n\nscale_*(): The units on the x/y axis (discrete/continuous)\n\nscale_y_continuous()\n\nlabs(): Labels\n\nx/y/title/fill/colour\n\n\n\n\n12 Theory\n\nData\nAesthetic mappings\nGeometric objects\nScales\nCoordinate systems\nFacets\nStatistical transformations\nTheme\n\n\n\n13 Resources\n\nUrban Institute R Users Group website\nWhy the Urban Institute visualizes data with ggplot2\nR for Data Science: data visualization\nawunderground themes\nR Graph Gallery\n“A Layered Grammar of Graphics” by Hadley Wickham"
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-    "text": "4 Review\nLast week, we showed how mutate() can be used to create new variables or to transform new variables. For example, we converted knots into miles per hour. We also learned about if_else last week which works great for one condition. But what if you have multiple conditions? case_when() allows for a sequence of conditional logic that can be used to transform or create variables with mutate(). For example, here I create a variable called lateness that creates a character variable with the levels \"very late\", \"late\", and \"on time\". The logic is evaluated from top to bottom and TRUE is used to refer to all other remaining cases.\nThe syntax may look a little weird at first but it’s easy to pick up! The logical condition goes to the left of ~ and the output results goes to the right. & and | can be used to combine logical statements.\nlibrary(stringr) contains powerful and concise functions manipulating character variables. Reference the cheat sheet for an overview of the different string maniuplation functions.\nlibrary(lubridate) contains powerful and concise functions for creating and manipulating dates, times, and date-times. It is aware of leap days and leap seconds and is useful for calculating periods, durations, intervals, and more.\nThis is often the hardest concept to fully grasp, but it’s also the most powerful!\nJoins are the main method for combining two datasets with a commmon key column together. There are many types of joins, and we highly recommend you read this chapter on joins in R4DS if you want more info. Below is a quick visual summary of the types of joins you can perform in R.\nFor now we will focus on the “left” join, which merges observations from the right data set to the left data set. This is the join type I use 90% of the time in R. Below is an example of how the left_join() function works.\nSometimes functions don’t exist for desired calculations or we want to combine many calculations into one function to reduce copying-and-pasting.\n“You should consider writing a function whenever you’ve copied and pasted a block of code more than twice (i.e. you now have three copies of the same code).” ~ R4DS\nR has a flexible function system that makes it very easy to define custom functions!\nThree ingredients\nNote: Using tidyverse functions inside of custom functions often requires non-standard evaluation. Please reach out for help when this is your goal.\nR contains a full set of functions for managing files and folders on your computer\nWhat do you think this below chunk of code is doing in plain English?\nConditional logic is an important concept in computer programming. We already used ifelse() and case_when() to create indicator variables and conditional variables. Sometimes it’s also useful to run entire chunks of code conditionally.\nThree ingredients"
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-    "text": "7.1 Exercise 1\nThe month and day columns are currently integer variables. We want to turn them into character variables with leading zeros. For example, 1 should be \"01\".\nStep 1: Use mutate() to overwrite month and day with str_pad(). The first argument should be month or day. The second argument, width, should be 2.\nStep 2: The padding character is currently a space, but we want it to be \"0\". Use ?str_pad to figure out how to switch the padding character.\nStep 3: Pipe the result into the following line mutate(flight_date = paste(year, month, day, sep = \"-\"))\nStep 4: Drop all variables except flight_date, distance, and air_time.\nStep 5: Assign the result to flights_subset."
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+    "text": "1.1 Review\nWhy Use R instead of Excel?\n\nReproducibility\nScalability\nFlexibility\nIterative\nOpen Source\n\nRemember\n\nEnvironment/scripts/console\nComment your code as you go\nRead your error messages\nWhere possible, type, don’t copy and paste - you will remember better!\nThe assignment operator"
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-    "text": "8.1 Exercise 2\nStep 1: Add library(lubridate) after library(nycflight13) in your script.\nStep 2: library(lubridate) is powerful but it needs variables in the correct format. Use ymd() inside of mutate() to turn the flight_date variable into a date rather than a character vector.\nStep 3: Inside the previous mutate statement, add another column called weekday for the weekday of the flight. You can use wday(flight_date) to find the day of the week for each date.\nStep 4: Assign the result to flights_subset.\nStep 5: Use count() to count the number of flights by day of the week."
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+    "section": "1.2 Exercise 0 (Set up a project and load packages)",
+    "text": "1.2 Exercise 0 (Set up a project and load packages)\nStep 1: Open RStudio. File &gt; New Project &gt; New Directory &gt; Select the location where you would like to create a new folder that houses your R Project. Call it urbn101\nStep 2: Open an .R script with the button in the top left (sheet with a plus sign icon). Save the script as 01_data-visualization.R.\nStep 3: Type install.packages(\"tidyverse\") and hit enter (submit) to the Console.\nStep 4: Write library(tidyverse) and at the top of 01_data-visualization.R. With the cursor on the line of text, hit Control-Enter (at the same time).\nStep 5: Repeat steps 3 & 4 with install.packages(\"ggplot2\") and library(ggplot2), respectively.\n\ntidyverse - a collection of libraries that use the same syntax/grammar (more on this on Monday)\nggplot2 - for making plots/graphs"
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-    "text": "9.1 Exercise 3\nWe are going to summarize flights_subset from the previous example by week_day\nStep 1: group_by() week_day and use n() in summarize() to count the number of observations. This should match Step 5 from the previous exercise.\nStep 2: In the same summarize(), calculate mean(), and max() distance.\nStep 3: In the same summarize(), calculate median air_time.\nStep 4: Rename the resulting variables inside summarize() so they have more useful names."
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+    "text": "1.3 Exercise 1 (Make a plot)\nStep 1: Submit data() to the console. We will use the airquality dataset.\nStep 2: Type the following in your script:\nggplot(data=airquality)+\n  geom_point(mapping=aes(x=Temp, y=Ozone))\n\nData frames are the only appropriate input for library(ggplot2).\n\nStep 3: Add a comment above the ggplot2 code that describes the plot we created.\nStep 4: Add comments below the data visualization code that describes the argument or function that corresponds to each of the first three components of the grammar of graphics.\nData are the values represented in the visualization.\nAesthetic mappings are directions for how data are mapped in a plot in a way that we can perceive. Aesthetic mappings include linking variables to the x-position, y-position, color, fill, shape, transparency, and size.\nGeometric objects are representations of the data, including points, lines, and polygons."
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-    "text": "10.1 Exercise 4\nflights contains information about flights and the unit of observation is airplane flights. planes contains information about the airplanes and the unit of observation is the airplane. The common column between these tables is the tailnum column.\nWe want to add information about planes to the flights data set. This is a left join because for every flight in the dataset, we want to append flight information. This is also called a many-to-one join because we are joining many rows from the flights data to one row from the planes data.\nStep 1: Use left_join() to join planes to flights. The common key is tailnum.\nStep 2: Use anti_join() to see observations from flights that don’t have a match in planes and call the output object unmatched_flights. The common key is tailnum.\nStep 2: Use slice() to extract the tailnum value in the first row of unmatched_flights. Call this variable first_unmatched_tailnum\nStep 3: Use filter() to see if first_unmatched_tailnum is in planes. Hint: it shouldn’t be!"
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+    "section": "1.4 Exercise 2 (Change some of the plot settings)",
+    "text": "1.4 Exercise 2 (Change some of the plot settings)\nStep 1: Duplicate the code from your first chart. Inside aes(), add color = \"red\" (separated by a comma)\nStep 2: Move color = \"red\" from aes() to geom_point(). What changed?\nStep 3: Remove color = \"red\" and add color = Month inside aes().\nStep 4: This is a little cluttered. Add alpha = 0.2 inside geom_point().\nStep 5: Add a plus sign to the end of the geom_point line, type labs(title=\"Air Quality Temperature and Ozone Readings\")\nAesthetic mappings like x and y almost always vary with the data. Aesthetic mappings like color, fill, shape, transparency, and size can vary with the data. But those arguments can also be added as styles that don’t vary with the data. If you include those arguments in aes(), they will show up in the legend (which can be annoying!)."
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-    "text": "11.1 Exercise 5\nStep 1: Write a function called multiply_xy() that takes arguments x and y and multiplies them together.\nStep 2: Add your favorite number as the default for x and your least favorite number as the default for y.\nStep 3: Call the function and overwrite the default for y with your favorite number."
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+    "section": "1.5 Exercise 3 (Add a regression line/confidence interval)",
+    "text": "1.5 Exercise 3 (Add a regression line/confidence interval)\nStep 1: Reconfigure the data so that the geompoint parentheses are empty (move mapping=aes(x=Temp, y=Ozone) to the ggplot line. Add + to the labs line and add geom_smooth()"
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-    "text": "12.1 Exercise 6\nStep 1: Check to see if you are in an R project. Use dir.create() to create a folder called final-data/.\nStep 1: Use file.copy() to duplicate your script and then delete the copy with file.remove(). CAREFUL! There is no undo button!"
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+    "text": "1.6 Exercise 4 (Scale the axes)\nStep 1: Create a new scatter plot using the msleep data set. Use bodywt on the x-axis and sleep_total on the y-axis.\nStep 2: The y-axis doesn’t contain zero. Below geom_point(), add scale_y_continuous(lim = c(0, NA)). Hint: add + after geom_point().\nStep 3: The x-axis is clustered near zero. Add scale_x_log10() above scale_y_continuous(lim = c(0, NA)).\nScales Turn data values, which are quantitative or categorical, into aesthetic values. This includes not only the x-axis and y-axis, but the ranges of sizes, shapes, and colors of aesthetics."
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-    "text": "13.1 Example\n\nx <- 1000\n\nif (x > 999) {\n  print(\"x is big\")\n} else {\n  print(\"x is small\")\n}\n\n[1] \"x is big\""
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+    "text": "1.7 Exercise 5 (Make it look nice!!)\nStep 1: Add the following code to your script. Submit it!\nggplot(storms)+ \ngeom_bar(mapping=aes(category))\nStep 2: Run install.packages(\"remotes\") and remotes::install_github(\"UrbanInstitute/urbnthemes\") in the console.\nStep 3: In the lines preceding the chart add and run the following code:\nlibrary(urbnthemes)\nset_urbn_defaults(style = \"print\")\nStep 4: Run the code to make the chart.\nStep 5: Add scale_y_continuous(expand = expand_scale(mult = c(0, 0.1))) and rerun the code.\nTheme controls the visual style of plot with font types, font sizes, background colors, margins, and positioning."
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-    "text": "5 Review\nWe’ll be using three packages from the tidyverse for data import and export. These three should cover just about all of your use cases at Urban.\nOne note - while all three packages are installed with the tidyverse, only readr is loaded when you run library(tidyverse) in your R session. The other packages must be loaded separately.\nIn general, readr, readxl, and haven will come with some variant of a read function for data import, and a variant of write (except for Excel and SAS!) for data export.\nFor the simplest use cases, the various read functions will need just one argument: the path to the file you want to load.\nThe various write functions will need two arguments: the data frame you want to export, and the file path to save it to.\nreadr provides functions to read plain text rectangular data - think csv, txt or tsv files.\nFor data that is saved as .xls or .xlsx, use the readxl package.\nFinally, your SAS, SPSS, or Stata data can find safe haven in R.\nSometimes when you save text files, the column types (ie numeric, date, time, text, etc.) get lost and won’t be read back in upon reimporting In this case you may want to save your files as a binary file. One binary file format we suggest is .feather files, which you can read more about. The main benefits of storing data in a binary format is:\nThe cons are that:\nSo think before exercising this option! If you mainly will be working with your data in R and have large file sizes or complicated column types, .feather files may be a good option.\nYou can read and write these files with read_feather() and write_feather respectively."
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+    "section": "1.8 Excercise 6 (Multiple little graphs/Faceting)",
+    "text": "1.8 Excercise 6 (Multiple little graphs/Faceting)\nStep 1: Read in Zillow Observed Rent Index (ZORI) data using read_csv('https://raw.githubusercontent.com/UI-Research/urbn101-intro-r/master/homework/zillow_clean.csv')\nStep 2: Clean the zillow data to just the most expensive ciites using code below:\nzillow_clean &lt;- zillow %&gt;%\n  arrange(desc(Avg_price)) %&gt;%\n  slice(1:10) %&gt;%\n  ggplot()+\n  geom_point(mapping=aes(x=Year, y=Avg_price))\nStep 3: In ggplot, plot the zillow_clean data as a scatter plot with year on the x axis and avg_price on the y axis.\nStep 4: Add facet_wrap(~RegionName) after the geom_point(mapping=aes(x=Year, y=Avg_price)) line."
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-    "text": "9.1 Exercise\nStep 1: Open up your RStudio project, open a R script, save it with a meaningful name, and load the tidyverse, readxl, haven, and arrow.\nStep 2: Use the dir.create() function to create subfolders called data and modified in your R Project.\nStep 3: We’ll be downloading a couple different datasets for today’s examples. Staying DRY, let’s rework the example code from Wednesday into a function we can reuse. Copy and paste the following into your file. Make sure to run the code so the function becomes a part of your environment.\n\ndownload_data <- function(url, path) {\n  if (!file.exists(path)) {\n    download.file(url, path, mode = \"wb\")\n  }\n}"
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+    "text": "1.9 Exercise 7 (Mapping)\nStep 1: Read in UFO sighting data (source: National UFO Reporting Center), link: https://raw.githubusercontent.com/UI-Research/urbn101-intro-r/master/homework/ufo_state.csv\nStep 1: Install urbnmapr devtools::install_github(\"UrbanInstitute/urbnmapr\"), load the urbnmapr library, and update the urbnthemes style to map set_urbn_defaults(style = \"map\").\nStep 2:: Pull a shapefile of US States using urbnmapr and join the UFO counts by state to the shapefile using the code below:\nstates_sf &lt;- get_urbn_map(\"states\", sf = TRUE)\n\nstates_ufo &lt;- states_sf %&gt;% \n  left_join(ufo, by=c(\"state_abbv\"=\"state\"))\nStep 3:: In ggplot, plot the states_ufo dataset, fill it in using the “count” column, add labels, change the outline (colour). Instead of using geom_point or geom_bar using geom_sf.\n Mapping Resources \n\nHow to Create State and County Maps Easily in R\nlibrary(urbnmapr)\nUrban Institute R Users Group website: mapping"
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-    "text": "10.1 CSV Files\n\n10.1.1 Example\nFirst we’ll download an example csv file from the Urban Institute’s Education data portal:\n\n# Download ipeds data into data/ folder\ndownload_data(\"https://educationdata.urban.org/csv/ipeds/colleges_ipeds_completers.csv\",\n              \"data/colleges_ipeds_completers.csv\")\n\nAnd then use read_csv from the readr package to load the data in.\n\nipeds <- read_csv(\"data/colleges_ipeds_completers.csv\")\n\nWe now have a tibble that can be used with all the ggplot2 and dplyr functions you’ve learned so far. Let’s filter the data to only 2011 and write it out as a separate file:\n\nipeds_2011 <- ipeds %>%\n  filter(year == 2011)\n\nwrite_csv(ipeds_2011, \"modified/colleges_ipeds_completers_2011.csv\")\n\n\n\n10.1.2 Exercise\nStep 1: Filter the ipeds data frame to years 2014-2015 for the state of California (HINT: fips code of 6). Be sure to use <- to save it to a new object!\nStep 2: Write the filtered data frame to a file called “ipeds_completers_ca.csv” in your modified folder."
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-    "text": "10.2 Other Delimiters\nMost plain text files will use a comma to separate values. Sometimes you’ll see other delimiters used - the most common are tabs \\t and vertical bar | (which is often called a pipe, not to be confused with the tidyverse pipe %>%).\nTo read in a plain text file that uses another delimiter, use the read_delim function. This function will take two arguments: the path to the file you want to load and the character to use as a delimiter.\n\n10.2.1 Example\nFirst we’ll download a tab delimited file from the North Carolina Board of Elections:\n\ndownload_data(\"https://s3.amazonaws.com/dl.ncsbe.gov/data/ncvhis30.zip\",\n              \"data/ncvhis30.zip\")\n\nThen load in the file with the correct delimiter set. It may be helpful to open delimtied file with a program like Notepad so you can see the plain text in the file and decide what delimiter to use. In this case we know its a tab:\n\nncvhis <- read_delim(\"data/ncvhis30.zip\", delim = \"\\t\")\n\nNote that readr was able to read the data directly from the zipfile!\nSimilarly, the write_delim() function can be used to write out a plain text file with a specified delim argument.\n\n\n10.2.2 Exercise\nStep 1: Use write_delim() to write out ncvhis to a pipe | separated file."
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-    "text": "10.3 Common Issues\nNot all data import will be quick and easy. Often times you will run into issues - these can be daunting at first but are often not as bad as they seem.\n\n10.3.1 Example\nThe readr package provides a challenge.csv file that will throw numerous parsing issues.\n\nchallenge <- read_csv(readr_example(\"challenge.csv\"))\n\nYou can use the problems function to get a tibble of these issues.\n\nissues <- problems(challenge)\n\nA thousand parsing failures sounds like a lot! But it’s really just one error a thousand times, and not a thousand unique errors to work through.\nThe issue is that readr will read the first thousand lines of a file and use that as the column specification. This file changes data types at line 1001. This is a very common issue when reading text files into R. We can use the guess_max argument to increase the number of lines readr will use to set the column types.\n\nchallenge <- read_csv(readr_example(\"challenge.csv\"),\n                      guess_max = Inf)\n\nYou will run into other problems when importing data like:\n\nthe columns not being in the first row\nweird NA values\nblank rows at the top of files\n\nLuckily read_csv() and its friends have a lot of built in arguments to help deal with those issues. Look at the arguments in the docs using ?read_csv or go to the R4DS data import page for examples of how to deal with all the above problems.\n\n\n10.3.2 Exercise\nStep 1: Pull up the documentation for read_csv and take a look of some of the function arguments. There are a lot! You don’t need to know how everything works, but it’s good to have an idea of some of the options available to you."
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+    "text": "1.12 Resources\n\nUrban Institute R Users Group website\nWhy the Urban Institute visualizes data with ggplot2\nR for Data Science: data visualization\nawunderground themes\nR Graph Gallery\n“A Layered Grammar of Graphics” by Hadley Wickham"
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-    "text": "11.1 Example\nFor this example we’ll download data from the HUD FHA Single Family Portfolio Snap Shot.\n\ndownload_data(\"https://www.hud.gov/sites/dfiles/Housing/documents/FHA_SFSnapshot_Apr2019.xlsx\",\n              \"data/sfsnap.xlsx\")\n\nThis Excel file contains a number of tables on different sheets of the workbook. We can see a listing of the sheets using the excel_sheets function. As usual, the first argument is the path to the data file.\n\nexcel_sheets(\"data/sfsnap.xlsx\")\n\nWe can then use the read_excel function to load our data into our R session. Again, the first argument is the path to the .xlsx file. We also use the sheet argument to specify which sheet of the workbook we want.\n\npurchases <- read_excel(\"data/sfsnap.xlsx\", sheet = \"Purchase Data April 2019\")\n\nNote that the readxl package only provides functionality to, as the name implies, read Excel files. If you need to write your data out to .xls or .xlsx formats - don’t! We highly recommend saving data files as .csv files wherever possible. But if you you really need to, look into the writexl or openxlsx packages."
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+    "text": "2.2 Assignment\n&lt;- is the assignment operator. An object created on the right side of an assignment operator is assigned to a name on the left side of an assignment operator. Assignment operators are important for saving the consequences of operations and functions. Without assignment, the result of a calculation is not saved for use in future calculations. Operations without assignment operators will typically be printed to the console but not saved for future use.\n\na &lt;- 1\nb &lt;- 2\n\nc &lt;- a + b\n\nc\n\n[1] 3"
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-    "text": "12.1 Stata Data (.dta)\n\n12.1.1 Example\nFirst we’ll download and unzip the 2018 General Social Survey, which comes as a zipped .dta file.\n\ndownload_data(\"http://gss.norc.org/Documents/stata/2018_stata.zip\",\n              \"data/gss.zip\")\n\nunzip(\"data/gss.zip\", exdir = \"data/\")\n\nTo read a .dta file, we’ll use the aptly named read_dta function. As usual, the first argument to the function is the path to the data we want to import.\n\ngss <- read_dta(\"data/GSS2018.dta\")\n\n# We can also read in the zip file directly!\ngss <- read_dta(\"data/gss.zip\")"
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+    "text": "2.3 Tidy Data\n\nThe tidyverse is an opinionated collection of R packages designed for data science. All packages share an underlying design philosophy, grammar, and data structures. ~ tidyverse.org\n\nlibrary(tidyverse) contains:\n\nggplot2, for data visualization.\ndplyr, for data manipulation.\ntidyr, for data tidying.\nreadr, for data import.\npurrr, for functional programming.\ntibble, for tibbles, a modern re-imagining of data frames.\nstringr, for strings.\nforcats, for factors.\n\n\n2.3.1 Tidy data\nThe defining opinion of the tidyverse is its wholehearted adoption of tidy data. Tidy data has three features:\n\nEach variable forms a column.\nEach observation forms a row.\nEach type of observational unit forms a dataframe. (This is from the paper, not the book)\n\n\nSource: R for data science\n\nTidy datasets are all alike, but every messy dataset is messy in its own way. ~ Hadley Wickham\n\nThe tidy approach to data science is powerful because it breaks data work into two distinct parts. First, get the data into a tidy format. Second, use tools optimized for tidy data. By standardizing the data structure for most community-created tools, the framework oriented diffuse development and reduced the friction of data work."
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-    "text": "12.2 SAS Data (.sas7bdat)\nWe use read_sas to read in SAS files. This works mostly identical to read_stata, and you can read in .sas7bdat + .sas7bcat, or zipped folders with sas files. Note that while the write_sas() function exists, it doesn’t appear to actually work at the current version of haven.\n\n12.2.1 Example\nDownload and read in the 2017 1-year Public Use Microsample for Wyoming from the Census:\n\ndownload_data(\"https://www2.census.gov/programs-surveys/acs/data/pums/2017/1-Year/unix_pwy.zip\",\n              \"data/pums_wy.zip\")\n\n# Unzip the files\nunzip(\"data/pums_wy.zip\", exdir = \"data/\")\n\n# Read in sas file\npums_data = read_sas(\"data/pums_wy.zip\")\n\n# Write out as dta file\npums_data %>% write_dta(\"modified/pums_wy.dta\")"
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+    "section": "2.4 Exercise 0: Creating a Project and Loading Packages",
+    "text": "2.4 Exercise 0: Creating a Project and Loading Packages\nIf you are using a different computer or didn’t attend sessions 0 or 1, follow steps 1 and 2. If not- skip to step 3.\nStep 1: Open RStudio. File &gt; New Project &gt; New Directory &gt; Select the location where you would like to create a new folder that houses your R Project. Call it urbn101.\nStep 2: Open an .R script with the button in the top left (sheet with a plus sign icon). Save the script as 02_data-munging1.R.\nStep 3: If you have not previously installed library(tidyverse): submit install.packages(\"tidyverse\") to the Console (type and hit enter)\nWe’ll focus on the key dplyr syntax using the March 2020 Annual Social and Economic Supplement (ASEC) to the Current Population Survey (CPS). Run the following code to load the data.\nStep 4: Add and run the following code to load ASEC data.\n\nlibrary(tidyverse)\n\nasec &lt;- read_csv(\n  paste0(\n    \"https://raw.githubusercontent.com/awunderground/awunderground-data/\",\n    \"main/cps/cps-asec.csv\"\n  )\n)\n\nWe can use glimpse(asec) to quickly view the data. We can also use View(asec) to open up asec in RStudio.\n\n\nglimpse(x = asec)\n\nRows: 157,959\nColumns: 17\n$ year       &lt;dbl&gt; 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020,…\n$ serial     &lt;dbl&gt; 1, 1, 2, 2, 3, 4, 4, 5, 5, 5, 5, 7, 8, 9, 10, 10, 10, 12, 1…\n$ month      &lt;chr&gt; \"March\", \"March\", \"March\", \"March\", \"March\", \"March\", \"Marc…\n$ cpsid      &lt;dbl&gt; 2.01903e+13, 2.01903e+13, 2.01812e+13, 2.01812e+13, 2.01902…\n$ asecflag   &lt;dbl&gt; 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…\n$ asecwth    &lt;dbl&gt; 1552.90, 1552.90, 990.49, 990.49, 1505.27, 1430.70, 1430.70…\n$ pernum     &lt;dbl&gt; 1, 2, 1, 2, 1, 1, 2, 1, 2, 3, 4, 1, 1, 1, 1, 2, 3, 1, 2, 3,…\n$ cpsidp     &lt;dbl&gt; 2.01903e+13, 2.01903e+13, 2.01812e+13, 2.01812e+13, 2.01902…\n$ asecwt     &lt;dbl&gt; 1552.90, 1552.90, 990.49, 990.49, 1505.27, 1430.70, 1196.57…\n$ ftype      &lt;chr&gt; \"Primary family\", \"Primary family\", \"Primary family\", \"Prim…\n$ ftotval    &lt;dbl&gt; 127449, 127449, 64680, 64680, 40002, 8424, 8424, 59114, 591…\n$ inctot     &lt;dbl&gt; 52500, 74949, 44000, 20680, 40002, 0, 8424, 610, 58001, 503…\n$ incwage    &lt;dbl&gt; 52500, 56000, 34000, 0, 40000, 0, 8424, 0, 58000, 0, 0, 0, …\n$ offpov     &lt;chr&gt; \"Above Poverty Line\", \"Above Poverty Line\", \"Above Poverty …\n$ offpovuniv &lt;chr&gt; \"In Poverty Universe\", \"In Poverty Universe\", \"In Poverty U…\n$ offtotval  &lt;dbl&gt; 127449, 127449, 64680, 64680, 40002, 8424, 8424, 59114, 591…\n$ offcutoff  &lt;dbl&gt; 17120, 17120, 17120, 17120, 13300, 15453, 15453, 26370, 263…\n\n\n\nWe’re going to learn seven functions and one new piece of syntax from library(dplyr) that will be our main tools for manipulating tidy frames. These functions and a few extensions outlined in the Data Transformation Cheat Sheet are the core of data analysis in the Tidyverse."
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-    "text": "6 Review\nThere is less need to write code for control flow in R. First, everything is vectorized so there is rarely a need to iterate to manipulate a vector.\nOn Day 2 we introduced a number of dplyr functions:\nSometimes we will want to apply these functions many times. For example, what if we want to manipulate many variables. For this, we will use the new across() function. To demonstrate across(), we’ll use the built-in mtcars data frame:\nSometimes we want to use across() with many variables but not all variables or not all variables of a certain type. We can use a character vector to pick a subset of columns.\nSometimes this is cumbersome. Fortunately, library(dplyr) has helpers that can create character vectors for use. For instance, if all of the variables we care about start with “e”, then we can\nThere are several selector helpers:\nWarning: across() is not an excuse to avoid tidy data! For example, the following is bad. Why does this violate tidy data? How much pain would we experience if we wanted to plot this data?\nWe use pivot_longer() to transform data from wide to long and pivot_wider() to transform data from long to wide.\nWe can fix the above data with pivot_longer()\nWe can unfix the data with pivot_wider()\nSometimes, we’re going to want to iterate on a data structure other than a tibble or iterate a function that isn’t vectorized."
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+    "text": "2.5 1. select()\nselect() drops columns from a dataframe and/or reorders the columns in a dataframe. The arguments after the name of the dataframe should be the names of columns you wish to keep, without quotes. All other columns not listed are dropped.\n\n\nselect(.data = asec, year, month, serial)\n\n# A tibble: 157,959 × 3\n    year month serial\n   &lt;dbl&gt; &lt;chr&gt;  &lt;dbl&gt;\n 1  2020 March      1\n 2  2020 March      1\n 3  2020 March      2\n 4  2020 March      2\n 5  2020 March      3\n 6  2020 March      4\n 7  2020 March      4\n 8  2020 March      5\n 9  2020 March      5\n10  2020 March      5\n# ℹ 157,949 more rows\n\n\n\nThis works great until the goal is to select 99 of 100 variables. Fortunately, - can be used to remove variables. You can also select all but multiple variables by listing them with the - symbol separated by commas.\n\n\nselect(.data = asec, -asecflag)\n\n# A tibble: 157,959 × 16\n    year serial month   cpsid asecwth pernum  cpsidp asecwt ftype ftotval inctot\n   &lt;dbl&gt;  &lt;dbl&gt; &lt;chr&gt;   &lt;dbl&gt;   &lt;dbl&gt;  &lt;dbl&gt;   &lt;dbl&gt;  &lt;dbl&gt; &lt;chr&gt;   &lt;dbl&gt;  &lt;dbl&gt;\n 1  2020      1 March 2.02e13   1553.      1 2.02e13  1553. Prim…  127449  52500\n 2  2020      1 March 2.02e13   1553.      2 2.02e13  1553. Prim…  127449  74949\n 3  2020      2 March 2.02e13    990.      1 2.02e13   990. Prim…   64680  44000\n 4  2020      2 March 2.02e13    990.      2 2.02e13   990. Prim…   64680  20680\n 5  2020      3 March 2.02e13   1505.      1 2.02e13  1505. Nonf…   40002  40002\n 6  2020      4 March 2.02e13   1431.      1 2.02e13  1431. Prim…    8424      0\n 7  2020      4 March 2.02e13   1431.      2 2.02e13  1197. Prim…    8424   8424\n 8  2020      5 March 2.02e13   1133.      1 2.02e13  1133. Prim…   59114    610\n 9  2020      5 March 2.02e13   1133.      2 2.02e13  1133. Prim…   59114  58001\n10  2020      5 March 2.02e13   1133.      3 2.02e13  1322. Prim…   59114    503\n# ℹ 157,949 more rows\n# ℹ 5 more variables: incwage &lt;dbl&gt;, offpov &lt;chr&gt;, offpovuniv &lt;chr&gt;,\n#   offtotval &lt;dbl&gt;, offcutoff &lt;dbl&gt;\n\n\n\n\n2.5.1 Exercise 1\n\nPromptSolution\n\n\n\nSelect pernum and inctot from asec.\n\n\n\n\nSelect pernum and inctot from asec.\n\n\nselect(.data = asec, inctot, asec)\n\n\n\n\n\\[\\cdots\\]"
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-    "text": "9.1 mutate()\nSay we wanted to round all the variables of this data frame. We could write some very repetitive code:\n\nmtcars %>%\n  mutate(\n    mpg = round(mpg),\n    cyl = round(cyl),\n    disp = round(disp),\n    hp = round(hp),\n    drat = round(drat),\n    wt = round(wt),\n    qsec = round(qsec),\n    vs = round(vs),\n    am = round(am),\n    gear = round(gear),\n    carb = round(carb)\n  )\n\n# A tibble: 32 × 11\n     mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb\n   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>\n 1    21     6   160   110     4     3    16     0     1     4     4\n 2    21     6   160   110     4     3    17     0     1     4     4\n 3    23     4   108    93     4     2    19     1     1     4     1\n 4    21     6   258   110     3     3    19     1     0     3     1\n 5    19     8   360   175     3     3    17     0     0     3     2\n 6    18     6   225   105     3     3    20     1     0     3     1\n 7    14     8   360   245     3     4    16     0     0     3     4\n 8    24     4   147    62     4     3    20     1     0     4     2\n 9    23     4   141    95     4     3    23     1     0     4     2\n10    19     6   168   123     4     3    18     1     0     4     4\n# ℹ 22 more rows\n\n\nOr we can let across() do the work for us. All we have to do is pass the function we want applied to all columns of our data frame:\n\nmtcars %>%\n  mutate(across(.cols = everything(), .fns = round))\n\n# A tibble: 32 × 11\n     mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb\n   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>\n 1    21     6   160   110     4     3    16     0     1     4     4\n 2    21     6   160   110     4     3    17     0     1     4     4\n 3    23     4   108    93     4     2    19     1     1     4     1\n 4    21     6   258   110     3     3    19     1     0     3     1\n 5    19     8   360   175     3     3    17     0     0     3     2\n 6    18     6   225   105     3     3    20     1     0     3     1\n 7    14     8   360   245     3     4    16     0     0     3     4\n 8    24     4   147    62     4     3    20     1     0     4     2\n 9    23     4   141    95     4     3    23     1     0     4     2\n10    19     6   168   123     4     3    18     1     0     4     4\n# ℹ 22 more rows\n\n\nTo supply your own expression, we can write our own function and pass it through to across():\n\ndivide_by_ten <- function(x) {\n  new_x <- x / 10\n\n  return(new_x)\n}\n\nmtcars %>%\n  mutate(across(.cols = everything(), .fns = divide_by_ten))\n\n# A tibble: 32 × 11\n     mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb\n   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>\n 1  2.1    0.6  16    11   0.39  0.262  1.65   0     0.1   0.4   0.4\n 2  2.1    0.6  16    11   0.39  0.288  1.70   0     0.1   0.4   0.4\n 3  2.28   0.4  10.8   9.3 0.385 0.232  1.86   0.1   0.1   0.4   0.1\n 4  2.14   0.6  25.8  11   0.308 0.322  1.94   0.1   0     0.3   0.1\n 5  1.87   0.8  36    17.5 0.315 0.344  1.70   0     0     0.3   0.2\n 6  1.81   0.6  22.5  10.5 0.276 0.346  2.02   0.1   0     0.3   0.1\n 7  1.43   0.8  36    24.5 0.321 0.357  1.58   0     0     0.3   0.4\n 8  2.44   0.4  14.7   6.2 0.369 0.319  2      0.1   0     0.4   0.2\n 9  2.28   0.4  14.1   9.5 0.392 0.315  2.29   0.1   0     0.4   0.2\n10  1.92   0.6  16.8  12.3 0.392 0.344  1.83   0.1   0     0.4   0.4\n# ℹ 22 more rows\n\n\nOr we can write it inline using the following syntax where ~ lets dplyr know to expect an inline function, and . is used as a placeholder for the variable.\n\nmtcars %>%\n  mutate(across(.cols = everything(), .fns = ~ . / 10))\n\n# A tibble: 32 × 11\n     mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb\n   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>\n 1  2.1    0.6  16    11   0.39  0.262  1.65   0     0.1   0.4   0.4\n 2  2.1    0.6  16    11   0.39  0.288  1.70   0     0.1   0.4   0.4\n 3  2.28   0.4  10.8   9.3 0.385 0.232  1.86   0.1   0.1   0.4   0.1\n 4  2.14   0.6  25.8  11   0.308 0.322  1.94   0.1   0     0.3   0.1\n 5  1.87   0.8  36    17.5 0.315 0.344  1.70   0     0     0.3   0.2\n 6  1.81   0.6  22.5  10.5 0.276 0.346  2.02   0.1   0     0.3   0.1\n 7  1.43   0.8  36    24.5 0.321 0.357  1.58   0     0     0.3   0.4\n 8  2.44   0.4  14.7   6.2 0.369 0.319  2      0.1   0     0.4   0.2\n 9  2.28   0.4  14.1   9.5 0.392 0.315  2.29   0.1   0     0.4   0.2\n10  1.92   0.6  16.8  12.3 0.392 0.344  1.83   0.1   0     0.4   0.4\n# ℹ 22 more rows"
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+    "text": "2.6 2. rename()\nrename() renames columns in a data frame. The pattern is new_name = old_name.\n\n\nrename(.data = asec, serial_number = serial)\n\n# A tibble: 157,959 × 17\n    year serial_number month   cpsid asecflag asecwth pernum  cpsidp asecwt\n   &lt;dbl&gt;         &lt;dbl&gt; &lt;chr&gt;   &lt;dbl&gt;    &lt;dbl&gt;   &lt;dbl&gt;  &lt;dbl&gt;   &lt;dbl&gt;  &lt;dbl&gt;\n 1  2020             1 March 2.02e13        1   1553.      1 2.02e13  1553.\n 2  2020             1 March 2.02e13        1   1553.      2 2.02e13  1553.\n 3  2020             2 March 2.02e13        1    990.      1 2.02e13   990.\n 4  2020             2 March 2.02e13        1    990.      2 2.02e13   990.\n 5  2020             3 March 2.02e13        1   1505.      1 2.02e13  1505.\n 6  2020             4 March 2.02e13        1   1431.      1 2.02e13  1431.\n 7  2020             4 March 2.02e13        1   1431.      2 2.02e13  1197.\n 8  2020             5 March 2.02e13        1   1133.      1 2.02e13  1133.\n 9  2020             5 March 2.02e13        1   1133.      2 2.02e13  1133.\n10  2020             5 March 2.02e13        1   1133.      3 2.02e13  1322.\n# ℹ 157,949 more rows\n# ℹ 8 more variables: ftype &lt;chr&gt;, ftotval &lt;dbl&gt;, inctot &lt;dbl&gt;, incwage &lt;dbl&gt;,\n#   offpov &lt;chr&gt;, offpovuniv &lt;chr&gt;, offtotval &lt;dbl&gt;, offcutoff &lt;dbl&gt;\n\n\n\nYou can also rename a selection of variables using rename_with(). The .cols argument is used to select the columns to rename and takes a tidyselect statement like those we introduced above. Here, we’re using the where() selection helper which selects all columns where a given condition is TRUE. The default value for the .cols argument is everything() which selects all columns in the dataset.\n\n\nrename_with(.data = asec, .fn = toupper, .cols = where(is.numeric))\n\n# A tibble: 157,959 × 17\n    YEAR SERIAL month   CPSID ASECFLAG ASECWTH PERNUM  CPSIDP ASECWT ftype      \n   &lt;dbl&gt;  &lt;dbl&gt; &lt;chr&gt;   &lt;dbl&gt;    &lt;dbl&gt;   &lt;dbl&gt;  &lt;dbl&gt;   &lt;dbl&gt;  &lt;dbl&gt; &lt;chr&gt;      \n 1  2020      1 March 2.02e13        1   1553.      1 2.02e13  1553. Primary fa…\n 2  2020      1 March 2.02e13        1   1553.      2 2.02e13  1553. Primary fa…\n 3  2020      2 March 2.02e13        1    990.      1 2.02e13   990. Primary fa…\n 4  2020      2 March 2.02e13        1    990.      2 2.02e13   990. Primary fa…\n 5  2020      3 March 2.02e13        1   1505.      1 2.02e13  1505. Nonfamily …\n 6  2020      4 March 2.02e13        1   1431.      1 2.02e13  1431. Primary fa…\n 7  2020      4 March 2.02e13        1   1431.      2 2.02e13  1197. Primary fa…\n 8  2020      5 March 2.02e13        1   1133.      1 2.02e13  1133. Primary fa…\n 9  2020      5 March 2.02e13        1   1133.      2 2.02e13  1133. Primary fa…\n10  2020      5 March 2.02e13        1   1133.      3 2.02e13  1322. Primary fa…\n# ℹ 157,949 more rows\n# ℹ 7 more variables: FTOTVAL &lt;dbl&gt;, INCTOT &lt;dbl&gt;, INCWAGE &lt;dbl&gt;, offpov &lt;chr&gt;,\n#   offpovuniv &lt;chr&gt;, OFFTOTVAL &lt;dbl&gt;, OFFCUTOFF &lt;dbl&gt;\n\n\n\nMost dplyr functions can rename columns simply by prefacing the operation with new_name =. For example, this can be done with select():\n\n\nselect(.data = asec, year, month, serial_number = serial)\n\n# A tibble: 157,959 × 3\n    year month serial_number\n   &lt;dbl&gt; &lt;chr&gt;         &lt;dbl&gt;\n 1  2020 March             1\n 2  2020 March             1\n 3  2020 March             2\n 4  2020 March             2\n 5  2020 March             3\n 6  2020 March             4\n 7  2020 March             4\n 8  2020 March             5\n 9  2020 March             5\n10  2020 March             5\n# ℹ 157,949 more rows"
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-    "text": "9.2 summarize()\nSay we wanted to find the mean of every variable in the mtcars data. In this case we can still use across()!\n\nmtcars %>%\n  summarize(across(.cols = everything(), .fns = mean))\n\n# A tibble: 1 × 11\n    mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb\n  <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>\n1  20.1  6.19  231.  147.  3.60  3.22  17.8 0.438 0.406  3.69  2.81\n\n\nWe can calculate multiple summary statistics by passing in a list() of the statistics we want:\n\nmtcars %>%\n  summarize(\n    n = n(),\n    across(\n      .cols = everything(),\n      .fns = list(mean = mean, median = median)\n    )\n  )\n\n# A tibble: 1 × 25\n      n mpg_mean mpg_median cyl_mean cyl_median disp_mean disp_median hp_mean\n  <int>    <dbl>      <dbl>    <dbl>      <dbl>     <dbl>       <dbl>   <dbl>\n1    32     20.1       19.2     6.19          6      231.        196.    147.\n# ℹ 17 more variables: hp_median <dbl>, drat_mean <dbl>, drat_median <dbl>,\n#   wt_mean <dbl>, wt_median <dbl>, qsec_mean <dbl>, qsec_median <dbl>,\n#   vs_mean <dbl>, vs_median <dbl>, am_mean <dbl>, am_median <dbl>,\n#   gear_mean <dbl>, gear_median <dbl>, carb_mean <dbl>, carb_median <dbl>,\n#   n_mean <dbl>, n_median <int>"
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+    "text": "2.7 3. filter()\nfilter() reduces the number of observations in a dataframe. Every column in a dataframe has a name. Rows do not necessarily have names in a dataframe, so rows need to be filtered based on logical conditions.\n==, &lt;, &gt;, &lt;=, &gt;=, !=, %in%, and is.na() are all operators that can be used for logical conditions. ! can be used to negate a condition and & and | can be used to combine conditions. | means or.\n\n\n# return rows with pernum of 1 and incwage &gt; $100,000\nfilter(.data = asec, pernum == 1 & incwage &gt; 100000)\n\n# A tibble: 5,551 × 17\n    year serial month   cpsid asecflag asecwth pernum  cpsidp asecwt ftype      \n   &lt;dbl&gt;  &lt;dbl&gt; &lt;chr&gt;   &lt;dbl&gt;    &lt;dbl&gt;   &lt;dbl&gt;  &lt;dbl&gt;   &lt;dbl&gt;  &lt;dbl&gt; &lt;chr&gt;      \n 1  2020     28 March 2.02e13        1    678.      1 2.02e13   678. Primary fa…\n 2  2020    134 March 0              1    923.      1 0         923. Primary fa…\n 3  2020    136 March 2.02e13        1    906.      1 2.02e13   906. Primary fa…\n 4  2020    137 March 2.02e13        1   1493.      1 2.02e13  1493. Nonfamily …\n 5  2020    359 March 2.02e13        1    863.      1 2.02e13   863. Primary fa…\n 6  2020    372 March 2.02e13        1   1338.      1 2.02e13  1338. Primary fa…\n 7  2020    404 March 0              1    677.      1 0         677. Primary fa…\n 8  2020    420 March 2.02e13        1    747.      1 2.02e13   747. Primary fa…\n 9  2020    450 March 2.02e13        1   1309.      1 2.02e13  1309. Primary fa…\n10  2020    491 March 0              1   1130.      1 0        1130. Primary fa…\n# ℹ 5,541 more rows\n# ℹ 7 more variables: ftotval &lt;dbl&gt;, inctot &lt;dbl&gt;, incwage &lt;dbl&gt;, offpov &lt;chr&gt;,\n#   offpovuniv &lt;chr&gt;, offtotval &lt;dbl&gt;, offcutoff &lt;dbl&gt;\n\n\nIPUMS CPS contains full documentation with information about pernum and incwage.\n\n\n2.7.1 Exercise 2\n\nPromptSolution\n\n\n\nFilter asec to rows with month equal to \"March\".\nFilter asec to rows with inctot less than 999999999.\nFilter asec to rows with pernum equal to 3 and inctot less than 999999999.\n\n\n\n\nFilter asec to rows with month equal to \"March\".\nFilter asec to rows with inctot less than 999999999.\nFilter asec to rows with pernum equal to 3 and inctot less than 999999999.\n\n\nfilter(asec, month == \"March\")\n\nfilter(asec, inctot &lt; 999999999)\n\nfilter(asec, pernum == 3, inctot &lt; 999999999)"
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-    "text": "9.3 Exercise 1\nStep 1: Open up a script, save it with a meaningful name, and load the tidyverse.\nStep 2: Use the mutate() and across() to take the log of each variable in mtcars.\nStep 3: Use the summarise() and across() to find the standard deviation (sd) of each variable in mtcars."
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+    "text": "2.8 4. arrange()\narrange() sorts the rows of a data frame in alpha-numeric order based on the values of a variable or variables. The dataframe is sorted by the first variable first and each subsequent variable is used to break ties. desc() is used to reverse the sort order for a given variable.\n\n\n# sort pernum is descending order because high pernums are interesting\narrange(.data = asec, desc(pernum))\n\n# A tibble: 157,959 × 17\n    year serial month   cpsid asecflag asecwth pernum  cpsidp asecwt ftype      \n   &lt;dbl&gt;  &lt;dbl&gt; &lt;chr&gt;   &lt;dbl&gt;    &lt;dbl&gt;   &lt;dbl&gt;  &lt;dbl&gt;   &lt;dbl&gt;  &lt;dbl&gt; &lt;chr&gt;      \n 1  2020  91430 March 0              1    505.     16 0         604. Secondary …\n 2  2020  91430 March 0              1    505.     15 0         465. Secondary …\n 3  2020  91430 March 0              1    505.     14 0         416. Secondary …\n 4  2020  15037 March 2.02e13        1   2272.     13 2.02e13  2633. Primary fa…\n 5  2020  78495 March 0              1   1279.     13 0        1424. Related su…\n 6  2020  91430 March 0              1    505.     13 0         465. Secondary …\n 7  2020  15037 March 2.02e13        1   2272.     12 2.02e13  1689. Primary fa…\n 8  2020  18102 March 0              1   2468.     12 0        2871. Primary fa…\n 9  2020  22282 March 0              1   2801.     12 0        3879. Related su…\n10  2020  30274 March 2.02e13        1    653.     12 2.02e13   858. Primary fa…\n# ℹ 157,949 more rows\n# ℹ 7 more variables: ftotval &lt;dbl&gt;, inctot &lt;dbl&gt;, incwage &lt;dbl&gt;, offpov &lt;chr&gt;,\n#   offpovuniv &lt;chr&gt;, offtotval &lt;dbl&gt;, offcutoff &lt;dbl&gt;\n\n\n\n\n2.8.1 Exercise 3\n\nPromptSolution\n\n\n\nSort asec in descending order by pernum and ascending order by inctot.\n\n\n\n\nSort asec in descending order by pernum and ascending order by inctot.\n\n\narrange(asec, desc(pernum), inctot)"
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-    "text": "10.1 Example\nTo demonstrate where() we’ll use the diamonds data frame from ggplot2:\n\nhead(diamonds)\n\n# A tibble: 6 × 10\n  carat cut       color clarity depth table price     x     y     z\n  <dbl> <ord>     <ord> <ord>   <dbl> <dbl> <int> <dbl> <dbl> <dbl>\n1  0.23 Ideal     E     SI2      61.5    55   326  3.95  3.98  2.43\n2  0.21 Premium   E     SI1      59.8    61   326  3.89  3.84  2.31\n3  0.23 Good      E     VS1      56.9    65   327  4.05  4.07  2.31\n4  0.29 Premium   I     VS2      62.4    58   334  4.2   4.23  2.63\n5  0.31 Good      J     SI2      63.3    58   335  4.34  4.35  2.75\n6  0.24 Very Good J     VVS2     62.8    57   336  3.94  3.96  2.48\n\n\nWhat happens if we try to use mutate(acoss(.cols = everything(), .fns = log)) to log transform each variable in this data frame?\n\ndiamonds %>%\n  mutate(across(.cols = everything(), .fns = log))\n\nError in `mutate()`:\nℹ In argument: `across(.cols = everything(), .fns = log)`.\nCaused by error in `across()`:\n! Can't compute column `cut`.\nCaused by error in `Math.factor()`:\n! 'log' not meaningful for factors\n\n\nIt doesn’t work because diamonds has some non-numeric columns. We could find all the columns that are numeric:\n\nglimpse(diamonds)\n\nRows: 53,940\nColumns: 10\n$ carat   <dbl> 0.23, 0.21, 0.23, 0.29, 0.31, 0.24, 0.24, 0.26, 0.22, 0.23, 0.…\n$ cut     <ord> Ideal, Premium, Good, Premium, Good, Very Good, Very Good, Ver…\n$ color   <ord> E, E, E, I, J, J, I, H, E, H, J, J, F, J, E, E, I, J, J, J, I,…\n$ clarity <ord> SI2, SI1, VS1, VS2, SI2, VVS2, VVS1, SI1, VS2, VS1, SI1, VS1, …\n$ depth   <dbl> 61.5, 59.8, 56.9, 62.4, 63.3, 62.8, 62.3, 61.9, 65.1, 59.4, 64…\n$ table   <dbl> 55, 61, 65, 58, 58, 57, 57, 55, 61, 61, 55, 56, 61, 54, 62, 58…\n$ price   <int> 326, 326, 327, 334, 335, 336, 336, 337, 337, 338, 339, 340, 34…\n$ x       <dbl> 3.95, 3.89, 4.05, 4.20, 4.34, 3.94, 3.95, 4.07, 3.87, 4.00, 4.…\n$ y       <dbl> 3.98, 3.84, 4.07, 4.23, 4.35, 3.96, 3.98, 4.11, 3.78, 4.05, 4.…\n$ z       <dbl> 2.43, 2.31, 2.31, 2.63, 2.75, 2.48, 2.47, 2.53, 2.49, 2.39, 2.…\n\n\nAnd then write a lot of repetitive mutate() statements again. Instead let’s use where() as follows:\n\ndiamonds %>%\n  mutate(across(.cols = where(is.numeric), .fns = log))\n\n# A tibble: 53,940 × 10\n   carat cut       color clarity depth table price     x     y     z\n   <dbl> <ord>     <ord> <ord>   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>\n 1 -1.47 Ideal     E     SI2      4.12  4.01  5.79  1.37  1.38 0.888\n 2 -1.56 Premium   E     SI1      4.09  4.11  5.79  1.36  1.35 0.837\n 3 -1.47 Good      E     VS1      4.04  4.17  5.79  1.40  1.40 0.837\n 4 -1.24 Premium   I     VS2      4.13  4.06  5.81  1.44  1.44 0.967\n 5 -1.17 Good      J     SI2      4.15  4.06  5.81  1.47  1.47 1.01 \n 6 -1.43 Very Good J     VVS2     4.14  4.04  5.82  1.37  1.38 0.908\n 7 -1.43 Very Good I     VVS1     4.13  4.04  5.82  1.37  1.38 0.904\n 8 -1.35 Very Good H     SI1      4.13  4.01  5.82  1.40  1.41 0.928\n 9 -1.51 Fair      E     VS2      4.18  4.11  5.82  1.35  1.33 0.912\n10 -1.47 Very Good H     VS1      4.08  4.11  5.82  1.39  1.40 0.871\n# ℹ 53,930 more rows\n\n\nWhen using the where() with across(), we first supply the condition that must be met, and then the function to apply to the columns that meet that condition. So in the above example, we first test if a column is.numeric, and then apply log to those that are.\nWe can use select() and where() to subset only those columns that are numeric:\n\ndiamonds %>%\n  select(where(is.numeric))\n\n# A tibble: 53,940 × 7\n   carat depth table price     x     y     z\n   <dbl> <dbl> <dbl> <int> <dbl> <dbl> <dbl>\n 1  0.23  61.5    55   326  3.95  3.98  2.43\n 2  0.21  59.8    61   326  3.89  3.84  2.31\n 3  0.23  56.9    65   327  4.05  4.07  2.31\n 4  0.29  62.4    58   334  4.2   4.23  2.63\n 5  0.31  63.3    58   335  4.34  4.35  2.75\n 6  0.24  62.8    57   336  3.94  3.96  2.48\n 7  0.24  62.3    57   336  3.95  3.98  2.47\n 8  0.26  61.9    55   337  4.07  4.11  2.53\n 9  0.22  65.1    61   337  3.87  3.78  2.49\n10  0.23  59.4    61   338  4     4.05  2.39\n# ℹ 53,930 more rows"
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+    "text": "2.9 5. mutate()\nmutate() creates new variables or edits existing variables. We can use arithmetic arguments, such as +, -, *, /, and ^. We can also custom functions and functions from packages. For example, we can use library(stringr) for string manipulation and library(lubridate) for date manipulation.\n\nVariables are created by adding a new column name, like inctot_adjusted, to the left of = in mutate().\n\n# adjust inctot for underreporting\nmutate(.data = asec, inctot_adjusted = inctot * 1.1)\n\n# A tibble: 157,959 × 18\n    year serial month   cpsid asecflag asecwth pernum  cpsidp asecwt ftype      \n   &lt;dbl&gt;  &lt;dbl&gt; &lt;chr&gt;   &lt;dbl&gt;    &lt;dbl&gt;   &lt;dbl&gt;  &lt;dbl&gt;   &lt;dbl&gt;  &lt;dbl&gt; &lt;chr&gt;      \n 1  2020      1 March 2.02e13        1   1553.      1 2.02e13  1553. Primary fa…\n 2  2020      1 March 2.02e13        1   1553.      2 2.02e13  1553. Primary fa…\n 3  2020      2 March 2.02e13        1    990.      1 2.02e13   990. Primary fa…\n 4  2020      2 March 2.02e13        1    990.      2 2.02e13   990. Primary fa…\n 5  2020      3 March 2.02e13        1   1505.      1 2.02e13  1505. Nonfamily …\n 6  2020      4 March 2.02e13        1   1431.      1 2.02e13  1431. Primary fa…\n 7  2020      4 March 2.02e13        1   1431.      2 2.02e13  1197. Primary fa…\n 8  2020      5 March 2.02e13        1   1133.      1 2.02e13  1133. Primary fa…\n 9  2020      5 March 2.02e13        1   1133.      2 2.02e13  1133. Primary fa…\n10  2020      5 March 2.02e13        1   1133.      3 2.02e13  1322. Primary fa…\n# ℹ 157,949 more rows\n# ℹ 8 more variables: ftotval &lt;dbl&gt;, inctot &lt;dbl&gt;, incwage &lt;dbl&gt;, offpov &lt;chr&gt;,\n#   offpovuniv &lt;chr&gt;, offtotval &lt;dbl&gt;, offcutoff &lt;dbl&gt;, inctot_adjusted &lt;dbl&gt;\n\n\n\nVariables are edited by including an existing column name, like inctot, to the left of = in mutate().\n\n# adjust income because of underreporting\nmutate(.data = asec, inctot = inctot * 1.1)\n\n# A tibble: 157,959 × 17\n    year serial month   cpsid asecflag asecwth pernum  cpsidp asecwt ftype      \n   &lt;dbl&gt;  &lt;dbl&gt; &lt;chr&gt;   &lt;dbl&gt;    &lt;dbl&gt;   &lt;dbl&gt;  &lt;dbl&gt;   &lt;dbl&gt;  &lt;dbl&gt; &lt;chr&gt;      \n 1  2020      1 March 2.02e13        1   1553.      1 2.02e13  1553. Primary fa…\n 2  2020      1 March 2.02e13        1   1553.      2 2.02e13  1553. Primary fa…\n 3  2020      2 March 2.02e13        1    990.      1 2.02e13   990. Primary fa…\n 4  2020      2 March 2.02e13        1    990.      2 2.02e13   990. Primary fa…\n 5  2020      3 March 2.02e13        1   1505.      1 2.02e13  1505. Nonfamily …\n 6  2020      4 March 2.02e13        1   1431.      1 2.02e13  1431. Primary fa…\n 7  2020      4 March 2.02e13        1   1431.      2 2.02e13  1197. Primary fa…\n 8  2020      5 March 2.02e13        1   1133.      1 2.02e13  1133. Primary fa…\n 9  2020      5 March 2.02e13        1   1133.      2 2.02e13  1133. Primary fa…\n10  2020      5 March 2.02e13        1   1133.      3 2.02e13  1322. Primary fa…\n# ℹ 157,949 more rows\n# ℹ 7 more variables: ftotval &lt;dbl&gt;, inctot &lt;dbl&gt;, incwage &lt;dbl&gt;, offpov &lt;chr&gt;,\n#   offpovuniv &lt;chr&gt;, offtotval &lt;dbl&gt;, offcutoff &lt;dbl&gt;\n\n\n\nConditional logic inside of mutate() with functions like if_else() and case_when() is key to mastering data munging in R.\n\n2.9.1 Exercise 4\n\nPromptSolution\n\n\n\nCreate a new variable called in_poverty. If offtotval is less than offcutoff then use \"Below Poverty Line\". Otherwise, use \"Above Poverty Line\". Hint: if_else() is useful and works like the IF command in Microsoft Excel.\n\n\n\n\nCreate a new variable called in_poverty. If offtotval is less than offcutoff then use \"Below Poverty Line\". Otherwise, use \"Above Poverty Line\". Hint: if_else() is useful and works like the IF command in Microsoft Excel.\n\n\nmutate(\n  asec,\n  in_poverty = if_else(\n    condition = offtotval &lt; offcutoff, \n    true = \"Below Poverty Line\", \n    false = \"Above Poverty Line\"\n  )\n)"
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-    "text": "10.2 Exercise 2\nNote: Each step is a separate task. They need not be piped together.\nStep 1: Use select() and where() to subset all the columns from diamonds that are factor variables.\nStep 2: Use the mutate(), across(), and where() to take the sqrt of each numeric variable in diamonds.\nStep 3: Use the summarise, across(), and where() to find the mean and standard deviation of each numeric variable in diamonds.\nStep 4: Use summarise() and across() to find the mean and standard deviation of each numeric variable in diamonds, first using group_by(), across(), and where() to group by all factor variables."
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+    "text": "2.10 %&gt;%\nData munging is tiring when each operation needs to be assigned to a name with &lt;-. The pipe, %&gt;%, allows lines of code to be chained together so the assignment operator only needs to be used once.\nConsider this fake code example from Hadley Wickham:\n\nI %&gt;% \n  tumble(out_of = \"bed\") %&gt;% \n  stumble(to = \"the kitchen\") %&gt;% \n  pour(who = \"myself\", unit = \"cup\", what = \"ambition\") %&gt;% \n  yawn() %&gt;% \n  stretch() %&gt;% \n  try(come_to_life())\n\n\n%&gt;% passes the output from function as the first argument in a subsequent function. For example, this line can be rewritten:\n\n\n# old way\nmutate(.data = asec, inctot_adjusted = inctot * 1.1)\n\n# new way\nasec %&gt;%\n  mutate(inctot_adjusted = inctot * 1.1)\n\n\nSee the power:\n\n\nnew_asec &lt;- asec %&gt;%\n  filter(pernum == 1) %&gt;%\n  select(year, month, pernum, inctot) %&gt;%\n  mutate(inctot_adjusted = inctot * 1.1) %&gt;%\n  select(-inctot)\n\nnew_asec\n\n# A tibble: 60,460 × 4\n    year month pernum inctot_adjusted\n   &lt;dbl&gt; &lt;chr&gt;  &lt;dbl&gt;           &lt;dbl&gt;\n 1  2020 March      1          57750 \n 2  2020 March      1          48400 \n 3  2020 March      1          44002.\n 4  2020 March      1              0 \n 5  2020 March      1            671 \n 6  2020 March      1          19279.\n 7  2020 March      1          12349.\n 8  2020 March      1          21589.\n 9  2020 March      1          47306.\n10  2020 March      1          10949.\n# ℹ 60,450 more rows"
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-    "text": "12.1 Tidy data\n\nHappy families are all alike; every unhappy family is unhappy in its own way. ~ Leo Tolstoy\n\n\ntidy datasets are all alike but every messy dataset is messy in its own way ~ Hadley Wickham\n\nTools optimized for one data format are going to be more powerful than tools optimized for any data format. If we can standardize our data, then we will have really powerful tools.\nUnfortunately, most people do not pay enough attention to the structure, storage, and dissemination of data."
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+    "text": "2.11 6. summarize()\nsummarize() collapses many rows in a dataframe into fewer rows with summary statistics of the many rows. n(), mean(), and sum() are common summary statistics. Renaming is useful with summarize()!\n\n\n# summarize without renaming the statistics\nasec %&gt;%\n  summarize(mean(ftotval), mean(inctot))\n\n# A tibble: 1 × 2\n  `mean(ftotval)` `mean(inctot)`\n            &lt;dbl&gt;          &lt;dbl&gt;\n1         105254.     209921275.\n\n# summarize and rename the statistics\nasec %&gt;%\n  summarize(\n    mean_ftotval = mean(ftotval), \n    mean_inctot = mean(inctot)\n  )\n\n# A tibble: 1 × 2\n  mean_ftotval mean_inctot\n         &lt;dbl&gt;       &lt;dbl&gt;\n1      105254.  209921275.\n\n\n\nsummarize() returns a data frame. This means all dplyr functions can be used on the output of summarize(). This is powerful! Manipulating summary statistics in Stata and SAS can be a chore. Here, it’s just another dataframe that can be manipulated with a tool set optimized for dataframes: dplyr."
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-    "text": "13.1 Many files\nOne approach is to use for loops. For loops are useful and should be understood. This code writes nine .csv files with 1000 random draws from a standard normal distribution.\n\ndir.create(\"data\")\n\nfor (i in 1:9) {\n  tibble(\n    id = 1:1000,\n    value = rnorm(1000)\n  ) %>%\n    write_csv(path = paste0(\"data/data\", i, \".csv\"))\n}\n\nlibrary(purrr) is a powerful alternative to for loops that is designed for functional programming. This code reads in the nine .csv files!\n\nfiles <- list.files(path = \"data\", full.names = TRUE)\n\nmap(.x = files, .f = read_csv)\n\nmap() always returns a list! map_*() functions can be used to return different data structures. For instance, map_chr() will always return a character vector. map_df() is like the code above but returns one data frame instead of a list of smaller data frames.\n\nmap_df(.x = files, .f = read_csv)"
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+    "text": "2.12 7. group_by()\ngroup_by() groups a dataframe based on specified variables. summarize() with grouped dataframes creates subgroup summary statistics. mutate() with group_by() calculates grouped summaries for each row.\n\n\nasec %&gt;%\n  group_by(pernum) %&gt;%\n  summarize(\n    n = n(),\n    mean_ftotval = mean(ftotval), \n    mean_inctot = mean(inctot)\n  )\n\n# A tibble: 16 × 4\n   pernum     n mean_ftotval mean_inctot\n    &lt;dbl&gt; &lt;int&gt;        &lt;dbl&gt;       &lt;dbl&gt;\n 1      1 60460       94094.      57508.\n 2      2 45151      108700.   77497357.\n 3      3 25650      117966.  473030618.\n 4      4 15797      121815.  634999933.\n 5      5  6752      108609.  691504650.\n 6      6  2582       89448.  682810446.\n 7      7   922       78889.  682218196.\n 8      8   353       72284.  682725646.\n 9      9   158       54599.  632917559.\n10     10    73       58145.  657543632.\n11     11    37       61847.  702708584 \n12     12    18       50249.  777780725.\n13     13     3       25152   666666666 \n14     14     1       18000       18000 \n15     15     1       25000       25000 \n16     16     1       15000       15000 \n\n\n\nDataframes can be grouped by multiple variables.\nGrouped tibbles include metadata about groups. For example, Groups:   pernum, offpov [40]. One grouping is dropped each time summarize() is used. It is easy to forget if a dataframe is grouped, so it is safe to include ungroup() at the end of a section of functions.\n\n\nasec %&gt;%\n  group_by(pernum, offpov) %&gt;%\n  summarize(\n    n = n(),\n    mean_ftotval = mean(ftotval), \n    mean_inctot = mean(inctot)\n  ) %&gt;%\n  arrange(offpov) %&gt;%\n  ungroup()\n\n`summarise()` has grouped output by 'pernum'. You can override using the\n`.groups` argument.\n\n\n# A tibble: 40 × 5\n   pernum offpov                 n mean_ftotval mean_inctot\n    &lt;dbl&gt; &lt;chr&gt;              &lt;int&gt;        &lt;dbl&gt;       &lt;dbl&gt;\n 1      1 Above Poverty Line 53872      104451.      63642.\n 2      2 Above Poverty Line 40978      118691.   59082162.\n 3      3 Above Poverty Line 23052      129891.  463440562.\n 4      4 Above Poverty Line 14076      135039.  631720097.\n 5      5 Above Poverty Line  5805      123937.  688206447.\n 6      6 Above Poverty Line  2118      105867.  683199297.\n 7      7 Above Poverty Line   724       96817.  697520661.\n 8      8 Above Poverty Line   269       90328.  672870019.\n 9      9 Above Poverty Line   114       70438.  622815186.\n10     10 Above Poverty Line    57       71483.  666678408.\n# ℹ 30 more rows\n\n\n\n\n2.12.1 Exercise 5\n\nPromptSolution\n\n\n\nfilter() to only include observations with \"In Poverty Universe\" in offpovuniv.\ngroup_by() offpov.\nUse summarize() and n() to count the number of observations in poverty.\n\n\n\n\nfilter() to only include observations with \"In Poverty Universe\" in offpovuniv.\ngroup_by() offpov.\nUse summarize() and n() to count the number of observations in poverty.\n\n\nasec %&gt;%\n  filter(offpovuniv == \"In Poverty Universe\") %&gt;%\n  group_by(offpov) %&gt;%\n  summarize(n())\n\n\n\n\n\n\n2.12.2 Exercise 6\n\nPromptSolution\n\n\n\nfilter() to only include observations with \"In Poverty Universe\".\ngroup_by() cpsid.\nUse mutate(family_size = n()) to calculate the family size for each observation in asec.\nungroup()\ngroup_by() family_size, and offpov.\nUse summarize() and n() to see how many families of each size are experiencing poverty.\n\n\n\n\nfilter() to only include observations with \"In Poverty Universe\".\ngroup_by() cpsid.\nUse mutate(family_size = n()) to calculate the family size for each observation in asec.\nungroup()\ngroup_by() family_size, and offpov.\nUse summarize() and n() to see how many families of each size are experiencing poverty.\n\n\nasec %&gt;%\n  filter(offpovuniv == \"In Poverty Universe\") %&gt;%\n  group_by(cpsid) %&gt;%\n  mutate(family_size = n()) %&gt;%\n  ungroup() %&gt;%\n  group_by(family_size, offpov) %&gt;%\n  summarize(n())\n\n\n\n\nAre the estimates from the previous two exercises correct?\nLet’s look at a Census Report to see how many people were in poverty in 2019. We estimated about 16,500 people. The Census Bureau says 34.0 million people.\nNo! We did not account for sampling weights, so our estimates are incorrect. library(srvyr) has tools for weighted estimation with complex surveys."
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-    "section": "13.2 Many models",
-    "text": "13.2 Many models\n\nlibrary(broom)\n\n# estimate a linear model for each of seven colors\nmany_models <- diamonds %>%\n  split(diamonds$color) %>%\n  map(~ lm(formula = price ~ carat + cut, data = .))\n\n# extract model diagnostics from each model\nmany_models_results <- bind_cols(\n  color = names(many_models),\n  map_df(many_models, glance)\n)\n\n# plot\nmany_models_results %>%\n  ggplot(aes(color, r.squared)) +\n  geom_col() +\n  scale_y_continuous(\n    expand = c(0, 0),\n    limits = c(0, 1)\n  ) +\n  labs(title = \"R-Squared for linear models estimated on subsets by color\") +\n  theme_minimal()\n\n\n\n\nThese examples are beyond the scope of an introductory class but I wanted to demonstrate the power of iteration in R. These tools can be used iterate data visualizations, R Markdown reports, statistical models, computer operations like file creation and transfer, and more."
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+    "text": "2.13 Resources\n\nR for Data Science: data transformation\nData wrangling cheat sheet"
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-    "text": "3.1 Review\n\n%>%: The pipe operator\ndplyr verbs for data manipulation\n\nselect()\nfilter()\nrename()\narrange()\nmutate()\nsummarize()\ngroup_by()"
+    "text": "3.1 Review\n\n%&gt;%: The pipe operator\ndplyr verbs for data manipulation\n\nselect()\nfilter()\nrename()\narrange()\nmutate()\nsummarize()\ngroup_by()"
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     "title": "3  Data Munging (II)",
     "section": "3.3 Conditional transformation",
-    "text": "3.3 Conditional transformation\nLast week, we showed how mutate() can be used to create new variables or to transform new variables. For example, we converted knots into miles per hour. We also learned about if_else last week which works great for one condition. But what if you have multiple conditions? case_when() allows for a sequence of conditional logic that can be used to transform or create variables with mutate(). For example, here I create a variable called lateness that creates a character variable with the levels \"very late\", \"late\", and \"on time\". The logic is evaluated from top to bottom and TRUE is used to refer to all other remaining cases.\nThe syntax may look a little weird at first but it’s easy to pick up! The logical condition goes to the left of ~ and the output results goes to the right. & and | can be used to combine logical statements.\n\nflights %>%\n  mutate(\n    lateness = case_when(\n      arr_delay > 30 ~ \"very late\",\n      arr_delay > 0 ~ \"late\",\n      TRUE          ~ \"on time\"\n    )\n  )"
+    "text": "3.3 Conditional transformation\nLast week, we showed how mutate() can be used to create new variables or to transform new variables. For example, we converted knots into miles per hour. We also learned about if_else last week which works great for one condition. But what if you have multiple conditions? case_when() allows for a sequence of conditional logic that can be used to transform or create variables with mutate(). For example, here I create a variable called lateness that creates a character variable with the levels \"very late\", \"late\", and \"on time\". The logic is evaluated from top to bottom and TRUE is used to refer to all other remaining cases.\nThe syntax may look a little weird at first but it’s easy to pick up! The logical condition goes to the left of ~ and the output results goes to the right. & and | can be used to combine logical statements.\n\nflights %&gt;%\n  mutate(\n    lateness = case_when(\n      arr_delay &gt; 30 ~ \"very late\",\n      arr_delay &gt; 0 ~ \"late\",\n      TRUE          ~ \"on time\"\n    )\n  )"
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     "title": "3  Data Munging (II)",
     "section": "3.7 left_join()",
-    "text": "3.7 left_join()\nJoins are the main method for combining two datasets with a commmon key column together. There are many types of joins, and we highly recommend you read this chapter on joins in R4DS if you want more info. Below is a quick visual summary of the types of joins you can perform in R. \nFor now we will focus on the “left” join, which merges observations from the right data set to the left data set. This is the join type I use 90% of the time in R. Below is an example of how the left_join() function works.\n\npeople <- tribble(\n  ~name, ~team, \n  \"Aaron\", \"Pacers\",\n  \"Kyle\", \"Wizards\",\n  \"Ajjit\", \"Warriors\",\n  \"Fay\", \"Wizards\"\n)\n\nteam_locations <- tribble(\n  ~team, ~city,\n  \"Warriors\", \"Oakland\",\n  \"Pacers\", \"Indianoplis\",\n  \"Wizards\", \"Washington DC\"\n)\n\nleft_join(x = people, y = team_locations, by = \"team\")\n\n# A tibble: 4 × 3\n  name  team     city         \n  <chr> <chr>    <chr>        \n1 Aaron Pacers   Indianoplis  \n2 Kyle  Wizards  Washington DC\n3 Ajjit Warriors Oakland      \n4 Fay   Wizards  Washington DC\n\n\n\n3.7.1 Exercise 4\nflights contains information about flights and the unit of observation is airplane flights. planes contains information about the airplanes and the unit of observation is the airplane. The common column between these tables is the tailnum column.\nWe want to add information about planes to the flights data set. This is a left join because for every flight in the dataset, we want to append flight information. This is also called a many-to-one join because we are joining many rows from the flights data to one row from the planes data.\nStep 1: Use left_join() to join planes to flights. The common key is tailnum.\nStep 2: Use anti_join() to see observations from flights that don’t have a match in planes and call the output object unmatched_flights. The common key is tailnum.\nStep 2: Use slice() to extract the tailnum value in the first row of unmatched_flights. Call this variable first_unmatched_tailnum\nStep 3: Use filter() to see if first_unmatched_tailnum is in planes. Hint: it shouldn’t be!"
+    "text": "3.7 left_join()\nJoins are the main method for combining two datasets with a commmon key column together. There are many types of joins, and we highly recommend you read this chapter on joins in R4DS if you want more info. Below is a quick visual summary of the types of joins you can perform in R. \nFor now we will focus on the “left” join, which merges observations from the right data set to the left data set. This is the join type I use 90% of the time in R. Below is an example of how the left_join() function works.\n\npeople &lt;- tribble(\n  ~name, ~team, \n  \"Aaron\", \"Pacers\",\n  \"Kyle\", \"Wizards\",\n  \"Ajjit\", \"Warriors\",\n  \"Fay\", \"Wizards\"\n)\n\nteam_locations &lt;- tribble(\n  ~team, ~city,\n  \"Warriors\", \"Oakland\",\n  \"Pacers\", \"Indianoplis\",\n  \"Wizards\", \"Washington DC\"\n)\n\nleft_join(x = people, y = team_locations, by = \"team\")\n\n# A tibble: 4 × 3\n  name  team     city         \n  &lt;chr&gt; &lt;chr&gt;    &lt;chr&gt;        \n1 Aaron Pacers   Indianoplis  \n2 Kyle  Wizards  Washington DC\n3 Ajjit Warriors Oakland      \n4 Fay   Wizards  Washington DC\n\n\n\n3.7.1 Exercise 4\nflights contains information about flights and the unit of observation is airplane flights. planes contains information about the airplanes and the unit of observation is the airplane. The common column between these tables is the tailnum column.\nWe want to add information about planes to the flights data set. This is a left join because for every flight in the dataset, we want to append flight information. This is also called a many-to-one join because we are joining many rows from the flights data to one row from the planes data.\nStep 1: Use left_join() to join planes to flights. The common key is tailnum.\nStep 2: Use anti_join() to see observations from flights that don’t have a match in planes and call the output object unmatched_flights. The common key is tailnum.\nStep 2: Use slice() to extract the tailnum value in the first row of unmatched_flights. Call this variable first_unmatched_tailnum\nStep 3: Use filter() to see if first_unmatched_tailnum is in planes. Hint: it shouldn’t be!"
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-    "text": "3.8 Custom functions\nSometimes functions don’t exist for desired calculations or we want to combine many calculations into one function to reduce copying-and-pasting.\n“You should consider writing a function whenever you’ve copied and pasted a block of code more than twice (i.e. you now have three copies of the same code).” ~ R4DS\nR has a flexible function system that makes it very easy to define custom functions!\nfunction_name <- function(arg1, arg2 = default) {\n  # function body\n}\nThree ingredients\n\nFunction name - usually verbs\nFunction arguments - inputs to the function (optional)\nFunction body\n\n\nsquare <- function(x = 2) {\n  x ^ 2\n}\n\nsquare()\n\n[1] 4\n\nsquare(x = 4)\n\n[1] 16\n\n\nNote: Using tidyverse functions inside of custom functions often requires non-standard evaluation. Please reach out for help when this is your goal.\n\n3.8.1 Exercise 5\nStep 1: Write a function called multiply_xy() that takes arguments x and y and multiplies them together.\nStep 2: Add your favorite number as the default for x and your least favorite number as the default for y.\nStep 3: Call the function and overwrite the default for y with your favorite number."
+    "text": "3.8 Custom functions\nSometimes functions don’t exist for desired calculations or we want to combine many calculations into one function to reduce copying-and-pasting.\n“You should consider writing a function whenever you’ve copied and pasted a block of code more than twice (i.e. you now have three copies of the same code).” ~ R4DS\nR has a flexible function system that makes it very easy to define custom functions!\nfunction_name &lt;- function(arg1, arg2 = default) {\n  # function body\n}\nThree ingredients\n\nFunction name - usually verbs\nFunction arguments - inputs to the function (optional)\nFunction body\n\n\nsquare &lt;- function(x = 2) {\n  x ^ 2\n}\n\nsquare()\n\n[1] 4\n\nsquare(x = 4)\n\n[1] 16\n\n\nNote: Using tidyverse functions inside of custom functions often requires non-standard evaluation. Please reach out for help when this is your goal.\n\n3.8.1 Exercise 5\nStep 1: Write a function called multiply_xy() that takes arguments x and y and multiplies them together.\nStep 2: Add your favorite number as the default for x and your least favorite number as the default for y.\nStep 3: Call the function and overwrite the default for y with your favorite number."
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-    "text": "3.10 Conditional logic\nConditional logic is an important concept in computer programming. We already used ifelse() and case_when() to create indicator variables and conditional variables. Sometimes it’s also useful to run entire chunks of code conditionally.\nif () {\n\n} else if () {\n\n} else {\n\n}\nThree ingredients\n\nConditional predicate (if, else if, else)\nConditional statement (==, >, <, %in%)\nCode body\n\n\n3.10.1 Example\n\nx <- 1000\n\nif (x > 999) {\n  print(\"x is big\")\n} else {\n  print(\"x is small\")\n}\n\n[1] \"x is big\""
+    "text": "3.10 Conditional logic\nConditional logic is an important concept in computer programming. We already used ifelse() and case_when() to create indicator variables and conditional variables. Sometimes it’s also useful to run entire chunks of code conditionally.\nif () {\n\n} else if () {\n\n} else {\n\n}\nThree ingredients\n\nConditional predicate (if, else if, else)\nConditional statement (==, &gt;, &lt;, %in%)\nCode body\n\n\n3.10.1 Example\n\nx &lt;- 1000\n\nif (x &gt; 999) {\n  print(\"x is big\")\n} else {\n  print(\"x is small\")\n}\n\n[1] \"x is big\""
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-    "section": "1.1 Review",
-    "text": "1.1 Review\nWhy Use R instead of Excel?\n\nReproducibility\nScalability\nFlexibility\nIterative\nOpen Source\n\nRemember\n\nEnvironment/scripts/console\nComment your code as you go\nRead your error messages\nWhere possible, type, don’t copy and paste - you will remember better!\nThe assignment operator"
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-    "section": "1.2 Exercise 0 (Set up a project and load packages)",
-    "text": "1.2 Exercise 0 (Set up a project and load packages)\nStep 1: Open RStudio. File > New Project > New Directory > Select the location where you would like to create a new folder that houses your R Project. Call it urbn101\nStep 2: Open an .R script with the button in the top left (sheet with a plus sign icon). Save the script as 01_data-visualization.R.\nStep 3: Type install.packages(\"tidyverse\") and hit enter (submit) to the Console.\nStep 4: Write library(tidyverse) and at the top of 01_data-visualization.R. With the cursor on the line of text, hit Control-Enter (at the same time).\nStep 5: Repeat steps 3 & 4 with install.packages(\"ggplot2\") and library(ggplot2), respectively.\n\ntidyverse - a collection of libraries that use the same syntax/grammar (more on this on Monday)\nggplot2 - for making plots/graphs"
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-    "section": "1.3 Exercise 1 (Make a plot)",
-    "text": "1.3 Exercise 1 (Make a plot)\nStep 1: Submit data() to the console. We will use the airquality dataset.\nStep 2: Type the following in your script:\nggplot(data=airquality)+\n  geom_point(mapping=aes(x=Temp, y=Ozone))\n\nData frames are the only appropriate input for library(ggplot2).\n\nStep 3: Add a comment above the ggplot2 code that describes the plot we created.\nStep 4: Add comments below the data visualization code that describes the argument or function that corresponds to each of the first three components of the grammar of graphics.\nData are the values represented in the visualization.\nAesthetic mappings are directions for how data are mapped in a plot in a way that we can perceive. Aesthetic mappings include linking variables to the x-position, y-position, color, fill, shape, transparency, and size.\nGeometric objects are representations of the data, including points, lines, and polygons."
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-    "section": "1.4 Exercise 2 (Change some of the plot settings)",
-    "text": "1.4 Exercise 2 (Change some of the plot settings)\nStep 1: Duplicate the code from your first chart. Inside aes(), add color = \"red\" (separated by a comma)\nStep 2: Move color = \"red\" from aes() to geom_point(). What changed?\nStep 3: Remove color = \"red\" and add color = Month inside aes().\nStep 4: This is a little cluttered. Add alpha = 0.2 inside geom_point().\nStep 5: Add a plus sign to the end of the geom_point line, type labs(title=\"Air Quality Temperature and Ozone Readings\")\nAesthetic mappings like x and y almost always vary with the data. Aesthetic mappings like color, fill, shape, transparency, and size can vary with the data. But those arguments can also be added as styles that don’t vary with the data. If you include those arguments in aes(), they will show up in the legend (which can be annoying!)."
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-    "section": "1.5 Exercise 3 (Add a regression line/confidence interval)",
-    "text": "1.5 Exercise 3 (Add a regression line/confidence interval)\nStep 1: Reconfigure the data so that the geompoint parentheses are empty (move mapping=aes(x=Temp, y=Ozone) to the ggplot line. Add + to the labs line and add geom_smooth()"
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-    "text": "1.6 Exercise 4 (Scale the axes)\nStep 1: Create a new scatter plot using the msleep data set. Use bodywt on the x-axis and sleep_total on the y-axis.\nStep 2: The y-axis doesn’t contain zero. Below geom_point(), add scale_y_continuous(lim = c(0, NA)). Hint: add + after geom_point().\nStep 3: The x-axis is clustered near zero. Add scale_x_log10() above scale_y_continuous(lim = c(0, NA)).\nScales Turn data values, which are quantitative or categorical, into aesthetic values. This includes not only the x-axis and y-axis, but the ranges of sizes, shapes, and colors of aesthetics."
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-    "section": "1.7 Exercise 5 (Make it look nice!!)",
-    "text": "1.7 Exercise 5 (Make it look nice!!)\nStep 1: Add the following code to your script. Submit it!\nggplot(storms)+ \ngeom_bar(mapping=aes(category))\nStep 2: Run install.packages(\"remotes\") and remotes::install_github(\"UrbanInstitute/urbnthemes\") in the console.\nStep 3: In the lines preceding the chart add and run the following code:\nlibrary(urbnthemes)\nset_urbn_defaults(style = \"print\")\nStep 4: Run the code to make the chart.\nStep 5: Add scale_y_continuous(expand = expand_scale(mult = c(0, 0.1))) and rerun the code.\nTheme controls the visual style of plot with font types, font sizes, background colors, margins, and positioning."
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-    "section": "1.8 Excercise 6 (Multiple little graphs/Faceting)",
-    "text": "1.8 Excercise 6 (Multiple little graphs/Faceting)\nStep 1: Read in Zillow Observed Rent Index (ZORI) data using read_csv('https://raw.githubusercontent.com/UI-Research/urbn101-intro-r/master/homework/zillow_clean.csv')\nStep 2: Clean the zillow data to just the most expensive ciites using code below:\nzillow_clean <- zillow %>%\n  arrange(desc(Avg_price)) %>%\n  slice(1:10) %>%\n  ggplot()+\n  geom_point(mapping=aes(x=Year, y=Avg_price))\nStep 3: In ggplot, plot the zillow_clean data as a scatter plot with year on the x axis and avg_price on the y axis.\nStep 4: Add facet_wrap(~RegionName) after the geom_point(mapping=aes(x=Year, y=Avg_price)) line."
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-    "section": "1.9 Exercise 7 (Mapping)",
-    "text": "1.9 Exercise 7 (Mapping)\nStep 1: Read in UFO sighting data (source: National UFO Reporting Center), link: https://raw.githubusercontent.com/UI-Research/urbn101-intro-r/master/homework/ufo_state.csv\nStep 1: Install urbnmapr devtools::install_github(\"UrbanInstitute/urbnmapr\"), load the urbnmapr library, and update the urbnthemes style to map set_urbn_defaults(style = \"map\").\nStep 2:: Pull a shapefile of US States using urbnmapr and join the UFO counts by state to the shapefile using the code below:\nstates_sf <- get_urbn_map(\"states\", sf = TRUE)\n\nstates_ufo <- states_sf %>% \n  left_join(ufo, by=c(\"state_abbv\"=\"state\"))\nStep 3:: In ggplot, plot the states_ufo dataset, fill it in using the “count” column, add labels, change the outline (colour). Instead of using geom_point or geom_bar using geom_sf.\n Mapping Resources \n\nHow to Create State and County Maps Easily in R\nlibrary(urbnmapr)\nUrban Institute R Users Group website: mapping"
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+    "text": "4.1 Review\n\nWhat is tidy data?\n\nArtwork by @allison_horst\nWhat is a file path?\nWhat is an R Studio Project?\n\n\n\nArtwork by @allison_horst\n\n\nStay DRY: if you find yourself copying and pasting chunks of code, write a function instead.\nWriting functions that use capabilities of the tidyverse can be tricky due to something called Nonstandard Evaluation - feel free to ask any of us for help!"
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+    "text": "4.4 Primary Functions\nIn general, readr, readxl, and haven will come with some variant of a read function for data import, and a variant of write (except for Excel and SAS!) for data export.\n\nreadr::read_csv(), readr::write_csv()\nreadr::read_delim(), readr::write_delim()\nreadxl::read_excel()\nhaven::read_sas()\nhaven::read_dta(), haven::write_dta\n\nFor the simplest use cases, the various read functions will need just one argument: the path to the file you want to load.\n\ndf &lt;- read_csv(\"path/to/your/original/data.csv\")\n\nThe various write functions will need two arguments: the data frame you want to export, and the file path to save it to.\n\nwrite_csv(df, \"path/to/your/modified/data.csv\")"
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-    "text": "2.3 Primary functions\n\nselect()\nrename()\nfilter()\narrange()\nmutate()\ngroup_by()\nsummarize()\n\nAll of these functions have the same structure.\n\nThe first argument is the dataframe\nThe subsequent arguments describe what to do with the data frame, using the variable names (without quotes because you’re already refering to that df)\nSeparate functions by %>%\nThe result is a new data frame.\n\n\n\nIf you have hit an error, trying running it line by line (function by function)\n\nSource: R for data science"
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+    "text": "4.5 Setup\n\n4.5.1 Exercise\nStep 1: Open up your RStudio project, open a R script, save it with a meaningful name, and load the tidyverse, readxl, haven, and arrow.\nStep 2: Use the dir.create() function to create subfolders called data and modified in your R Project.\nStep 3: We’ll be downloading a couple different datasets for today’s examples. Staying DRY, let’s rework the example code from Wednesday into a function we can reuse. Copy and paste the following into your file. Make sure to run the code so the function becomes a part of your environment.\n\ndownload_data &lt;- function(url, path) {\n  if (!file.exists(path)) {\n    download.file(url, path, mode = \"wb\")\n  }\n}"
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-    "text": "2.4 Exercise 0: Creating a Project and Loading Packages\nIf you are using a different computer or didn’t attend sessions 0 or 1, follow steps 1 and 2. If not- skip to step 3.\nStep 1: Open RStudio. File > New Project > New Directory > Select the location where you would like to create a new folder that houses your R Project. Call it urbn101.\nStep 2: Open an .R script with the button in the top left (sheet with a plus sign icon). Save the script as 02_data-munging1.R.\nStep 3: If you have not previously installed library(tidyverse): submit install.packages(\"tidyverse\") to the Console (type and hit enter)\nStep 4: Write library(tidyverse) at the top of 02_data-munging1.R. With the cursor on the line of text, click Control-Enter."
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+    "text": "4.6 The readr Package\nreadr provides functions to read plain text rectangular data - think csv, txt or tsv files.\n\n4.6.1 CSV Files\n\n4.6.1.1 Example\nFirst we’ll download an example csv file from the Urban Institute’s Education data portal:\n\n# Download ipeds data into data/ folder\ndownload_data(\"https://educationdata.urban.org/csv/ipeds/colleges_ipeds_completers.csv\",\n              \"data/colleges_ipeds_completers.csv\")\n\nAnd then use read_csv from the readr package to load the data in.\n\nipeds &lt;- read_csv(\"data/colleges_ipeds_completers.csv\")\n\nWe now have a tibble that can be used with all the ggplot2 and dplyr functions you’ve learned so far. Let’s filter the data to only 2011 and write it out as a separate file:\n\nipeds_2011 &lt;- ipeds %&gt;%\n  filter(year == 2011)\n\nwrite_csv(ipeds_2011, \"modified/colleges_ipeds_completers_2011.csv\")\n\n\n\n4.6.1.2 Exercise\nStep 1: Filter the ipeds data frame to years 2014-2015 for the state of California (HINT: fips code of 6). Be sure to use &lt;- to save it to a new object!\nStep 2: Write the filtered data frame to a file called “ipeds_completers_ca.csv” in your modified folder.\n\n\n\n4.6.2 Other Delimiters\nMost plain text files will use a comma to separate values. Sometimes you’ll see other delimiters used - the most common are tabs \\t and vertical bar | (which is often called a pipe, not to be confused with the tidyverse pipe %&gt;%).\nTo read in a plain text file that uses another delimiter, use the read_delim function. This function will take two arguments: the path to the file you want to load and the character to use as a delimiter.\n\n4.6.2.1 Example\nFirst we’ll download a tab delimited file from the North Carolina Board of Elections:\n\ndownload_data(\"https://s3.amazonaws.com/dl.ncsbe.gov/data/ncvhis30.zip\",\n              \"data/ncvhis30.zip\")\n\nThen load in the file with the correct delimiter set. It may be helpful to open delimtied file with a program like Notepad so you can see the plain text in the file and decide what delimiter to use. In this case we know its a tab:\n\nncvhis &lt;- read_delim(\"data/ncvhis30.zip\", delim = \"\\t\")\n\nNote that readr was able to read the data directly from the zipfile!\nSimilarly, the write_delim() function can be used to write out a plain text file with a specified delim argument.\n\n\n4.6.2.2 Exercise\nStep 1: Use write_delim() to write out ncvhis to a pipe | separated file.\n\n\n\n4.6.3 Common Issues\nNot all data import will be quick and easy. Often times you will run into issues - these can be daunting at first but are often not as bad as they seem.\n\n4.6.3.1 Example\nThe readr package provides a challenge.csv file that will throw numerous parsing issues.\n\nchallenge &lt;- read_csv(readr_example(\"challenge.csv\"))\n\nYou can use the problems function to get a tibble of these issues.\n\nissues &lt;- problems(challenge)\n\nA thousand parsing failures sounds like a lot! But it’s really just one error a thousand times, and not a thousand unique errors to work through.\nThe issue is that readr will read the first thousand lines of a file and use that as the column specification. This file changes data types at line 1001. This is a very common issue when reading text files into R. We can use the guess_max argument to increase the number of lines readr will use to set the column types.\n\nchallenge &lt;- read_csv(readr_example(\"challenge.csv\"),\n                      guess_max = Inf)\n\nYou will run into other problems when importing data like:\n\nthe columns not being in the first row\nweird NA values\nblank rows at the top of files\n\nLuckily read_csv() and its friends have a lot of built in arguments to help deal with those issues. Look at the arguments in the docs using ?read_csv or go to the R4DS data import page for examples of how to deal with all the above problems.\n\n\n\n4.6.4 Exercise\nStep 1: Pull up the documentation for read_csv and take a look of some of the function arguments. There are a lot! You don’t need to know how everything works, but it’s good to have an idea of some of the options available to you."
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-    "text": "2.5 Exercise 1: Tidyverse Function Overview\nStep 1: Assign the storms dataset to be called storms. Submit View(storms) in the console.\nYou can also deselect specific columns in your data by using the - operator\nSelect\nselect() is used when you want to limit the number of columns in your dataframe. The arguments after the dataframe should be names of columns, without quotes. For example:\ndataset %>%\n  select(col1, col2)\nStep 2: Create a new dataset with all the variables except for ts_diameter and hu_diameter, by using select and -.\nStep 3: Create a new dataset that only category 5 storms using filter().\nFilter\nfilter() is used when you want to filter to specific rows in your dataframe, based on certain conditions. Filter works with the following comparison operators:\n\n<, >, <=, >=\n==, !=\n\nYou can also chain together multiple condtions with & (and) or | (or). The arguments after the dataframe should be conditions you want to filter your dataset on. For example\ndataset %>%\nfilter(col1>0)\nStep 4: Chain all of these commands together with the %>% operator. Then rename the column pressure to air_pressure.\nRename\nThe syntax for rename is:\ndataset %>% \n  rename(new_name = current_name)\nThis same syntax can be used inside select(), and you can rename and select variables simultanously.\nStep 5: wind is measured in knots. Convert wind into miles per hour. Each knot is equal to 1.15078 miles per hour.\nMutate\nmutate() can make new variables and edit existing variables. New variables are always added to the end of the dataframe. You can use arithmetic arguments, such as +, -, *, /, and ^. The syntax for mutate is:\ndataset %>% \n  mutate(new_column = 100 * other_column)\nStep 6: Make a dataset with storms that occured in the summer (June, July, August).\nThe %in% operator\nThe %in% operator is useful for filtering based on more than one option within one variable.\nfilter(dataset, var1 == \"a\" | var1 == \"b\" | var1 == \"c\")\nis equivalent to\ndataset %>%\nfilter(var1 %in% c(\"a\", \"b\", \"c\"))"
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+    "text": "4.7 The readxl Package\nFor data that is saved as .xls or .xlsx, use the readxl package.\n\n4.7.1 Example\nFor this example we’ll download data from the HUD FHA Single Family Portfolio Snap Shot.\n\ndownload_data(\"https://www.hud.gov/sites/dfiles/Housing/documents/FHA_SFSnapshot_Apr2019.xlsx\",\n              \"data/sfsnap.xlsx\")\n\nThis Excel file contains a number of tables on different sheets of the workbook. We can see a listing of the sheets using the excel_sheets function. As usual, the first argument is the path to the data file.\n\nexcel_sheets(\"data/sfsnap.xlsx\")\n\nWe can then use the read_excel function to load our data into our R session. Again, the first argument is the path to the .xlsx file. We also use the sheet argument to specify which sheet of the workbook we want.\n\npurchases &lt;- read_excel(\"data/sfsnap.xlsx\", sheet = \"Purchase Data April 2019\")\n\nNote that the readxl package only provides functionality to, as the name implies, read Excel files. If you need to write your data out to .xls or .xlsx formats - don’t! We highly recommend saving data files as .csv files wherever possible. But if you you really need to, look into the writexl or openxlsx packages.\n\n\n4.7.2 Exercise\nStep 1: Use read_excel() to load in the table on the “Refinance Data April 2019” sheet into a data frame called refinances."
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-    "text": "2.6 Exercise 2: If/Then and Summarizing\nStep 1: Use the txhousing dataset. Assign the dataset to an object in your global environment named tx_housing.\nStep 2: Compute a new variable, average_price by dividing volume by sales.\nStep 3: Use if_else() to create a variable called flag that is 1 if the average sales price is larger than the median sales price.\nIf/then logic\nif_else() is an important tool in combination with mutate(). It employs if-then logic: the first argument is a binary statement. The second statement says what to do if the first argument is evaluated to TRUE- the third is what to do if it is FALSE This is particularly useful for creating dummy or indicator variables. When you have more than two possible outcomes, it is more efficient to use case_when().\nStep 3a: Type ?case_when into the console.\nStep 4: Type the following code into your script.\ntx_housing %>% \n  group_by(year) %>% \n  summarize(yearly_sales = sum(sales))\nGroup By\ngroup_by groups your dataframe into discrete categories. It can be very powerful when used with summarize.\nSummarize\nsummarize computes summary statistics for as set of observations in your data. The syntax is:\ndataset %>% \n  mutate(max = max(other_column))\nor\ndataset %>%\n  group_by(col1) %>%\n  summarize(count=n())\nStep 5: Add the argument na.rm = TRUE to the sum() function and rerun the code. Add a comment to explain the code.\nStep 6: Sort the results of the previous code in order from most to least sales by adding %>% arrange(desc(yearly_sales)) to the end of the code.\nStep 7a: Which city had the most home sales in January 2008? (Hint: use filter() and arrange()).\nStep 7b: Which city had the least home sales in 2006? (Hint: group by the city and the year- then use summarize()).\nBONUS: Plot the number of annual home sales over time!"
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+    "text": "4.8 The haven Package\nFinally, your SAS, SPSS, or Stata data can find safe haven in R.\n\n4.8.1 Stata Data (.dta)\n\n4.8.1.1 Example\nFirst we’ll download and unzip the 2018 General Social Survey, which comes as a zipped .dta file.\n\ndownload_data(\"http://gss.norc.org/Documents/stata/2018_stata.zip\",\n              \"data/gss.zip\")\n\nunzip(\"data/gss.zip\", exdir = \"data/\")\n\nTo read a .dta file, we’ll use the aptly named read_dta function. As usual, the first argument to the function is the path to the data we want to import.\n\ngss &lt;- read_dta(\"data/GSS2018.dta\")\n\n# We can also read in the zip file directly!\ngss &lt;- read_dta(\"data/gss.zip\")\n\n\n\n\n4.8.2 SAS Data (.sas7bdat)\nWe use read_sas to read in SAS files. This works mostly identical to read_stata, and you can read in .sas7bdat + .sas7bcat, or zipped folders with sas files. Note that while the write_sas() function exists, it doesn’t appear to actually work at the current version of haven.\n\n4.8.2.1 Example\nDownload and read in the 2017 1-year Public Use Microsample for Wyoming from the Census:\n\ndownload_data(\"https://www2.census.gov/programs-surveys/acs/data/pums/2017/1-Year/unix_pwy.zip\",\n              \"data/pums_wy.zip\")\n\n# Unzip the files\nunzip(\"data/pums_wy.zip\", exdir = \"data/\")\n\n# Read in sas file\npums_data = read_sas(\"data/pums_wy.zip\")\n\n# Write out as dta file\npums_data %&gt;% write_dta(\"modified/pums_wy.dta\")"
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-    "text": "2.7 Exercise 3: Put it all together\nStep 1: Use the starwars dataframe. Type count(starwars, homeworld) into your script.\nStep 2: Filter out any characters that have a missing homeworld.\nStep 3: Find the average and median height and mass for humans vs. droids. (Hint: use group_by() and summarize()). Make sure that you are correcting for observations with missing values!\nStep 4: Make a dummy variable called droid that is 1 for droids and 0 for humans. Make a variable called tall that is 1 if the characters are more than 200 cm tall. How many tall humans are there?\nStep 5: What is the most common skin color for droids?"
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+    "text": "4.9 The arrow Package\nSometimes when you save text files, the column types (ie numeric, date, time, text, etc.) get lost and won’t be read back in upon reimporting In this case you may want to save your files as a binary file. One binary file format we suggest is .feather files, which you can read more about. The main benefits of storing data in a binary format is:\n\nColumn types will be recorded and preserved\nImporting and exporting will be faster than other methods\nFile sizes will be smaller as binary files often use compression methods\n\nThe cons are that:\n\n.feather files can only easily be read into R and python currently\nThere are no nice GUI viewers (like Excel/Notepad for CSV data) to actually “see” the data files\n\nSo think before exercising this option! If you mainly will be working with your data in R and have large file sizes or complicated column types, .feather files may be a good option.\nYou can read and write these files with read_feather() and write_feather respectively.\n\n# Write out a feather file to `modified/folder`\nwrite_feather(ipeds_2011, \"modified/colleges_ipeds_completers_2011.feather\")\n\n# Read in a feather file\nipeds_2011 = read_feather(\"modified/colleges_ipeds_completers_2011.feather\")"
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-    "text": "5.3 Vectorization\nThere is less need to write code for control flow in R. First, everything is vectorized so there is rarely a need to iterate to manipulate a vector.\n\n1:10 * 2\n\n [1]  2  4  6  8 10 12 14 16 18 20\n\n\n\ndiamonds %>%\n  select(carat, price) %>%\n  mutate(price_canadian = price * 1.31)\n\n# A tibble: 53,940 × 3\n   carat price price_canadian\n   <dbl> <int>          <dbl>\n 1  0.23   326           427.\n 2  0.21   326           427.\n 3  0.23   327           428.\n 4  0.29   334           438.\n 5  0.31   335           439.\n 6  0.24   336           440.\n 7  0.24   336           440.\n 8  0.26   337           441.\n 9  0.22   337           441.\n10  0.23   338           443.\n# ℹ 53,930 more rows"
+    "text": "5.3 Vectorization\nThere is less need to write code for control flow in R. First, everything is vectorized so there is rarely a need to iterate to manipulate a vector.\n\n1:10 * 2\n\n [1]  2  4  6  8 10 12 14 16 18 20\n\n\n\ndiamonds %&gt;%\n  select(carat, price) %&gt;%\n  mutate(price_canadian = price * 1.31)\n\n# A tibble: 53,940 × 3\n   carat price price_canadian\n   &lt;dbl&gt; &lt;int&gt;          &lt;dbl&gt;\n 1  0.23   326           427.\n 2  0.21   326           427.\n 3  0.23   327           428.\n 4  0.29   334           438.\n 5  0.31   335           439.\n 6  0.24   336           440.\n 7  0.24   336           440.\n 8  0.26   337           441.\n 9  0.22   337           441.\n10  0.23   338           443.\n# ℹ 53,930 more rows"
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-    "text": "5.4 across()\nOn Day 2 we introduced a number of dplyr functions:\n\nmutate(): to transform a variable\nselect(): to subset the columns of a data frame\nfilter(): to subset the rows of a data frame\nsummarise(): to calculate summary statistics\ngroup_by(): to perform an operation by group\n\nSometimes we will want to apply these functions many times. For example, what if we want to manipulate many variables. For this, we will use the new across() function. To demonstrate across(), we’ll use the built-in mtcars data frame:\n\nlibrary(tidyverse)\n\nmtcars <- as_tibble(mtcars)\nhead(mtcars)\n\n# A tibble: 6 × 11\n    mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb\n  <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>\n1  21       6   160   110  3.9   2.62  16.5     0     1     4     4\n2  21       6   160   110  3.9   2.88  17.0     0     1     4     4\n3  22.8     4   108    93  3.85  2.32  18.6     1     1     4     1\n4  21.4     6   258   110  3.08  3.22  19.4     1     0     3     1\n5  18.7     8   360   175  3.15  3.44  17.0     0     0     3     2\n6  18.1     6   225   105  2.76  3.46  20.2     1     0     3     1\n\n\n\n5.4.1 mutate()\nSay we wanted to round all the variables of this data frame. We could write some very repetitive code:\n\nmtcars %>%\n  mutate(\n    mpg = round(mpg),\n    cyl = round(cyl),\n    disp = round(disp),\n    hp = round(hp),\n    drat = round(drat),\n    wt = round(wt),\n    qsec = round(qsec),\n    vs = round(vs),\n    am = round(am),\n    gear = round(gear),\n    carb = round(carb)\n  )\n\n# A tibble: 32 × 11\n     mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb\n   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>\n 1    21     6   160   110     4     3    16     0     1     4     4\n 2    21     6   160   110     4     3    17     0     1     4     4\n 3    23     4   108    93     4     2    19     1     1     4     1\n 4    21     6   258   110     3     3    19     1     0     3     1\n 5    19     8   360   175     3     3    17     0     0     3     2\n 6    18     6   225   105     3     3    20     1     0     3     1\n 7    14     8   360   245     3     4    16     0     0     3     4\n 8    24     4   147    62     4     3    20     1     0     4     2\n 9    23     4   141    95     4     3    23     1     0     4     2\n10    19     6   168   123     4     3    18     1     0     4     4\n# ℹ 22 more rows\n\n\nOr we can let across() do the work for us. All we have to do is pass the function we want applied to all columns of our data frame:\n\nmtcars %>%\n  mutate(across(.cols = everything(), .fns = round))\n\n# A tibble: 32 × 11\n     mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb\n   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>\n 1    21     6   160   110     4     3    16     0     1     4     4\n 2    21     6   160   110     4     3    17     0     1     4     4\n 3    23     4   108    93     4     2    19     1     1     4     1\n 4    21     6   258   110     3     3    19     1     0     3     1\n 5    19     8   360   175     3     3    17     0     0     3     2\n 6    18     6   225   105     3     3    20     1     0     3     1\n 7    14     8   360   245     3     4    16     0     0     3     4\n 8    24     4   147    62     4     3    20     1     0     4     2\n 9    23     4   141    95     4     3    23     1     0     4     2\n10    19     6   168   123     4     3    18     1     0     4     4\n# ℹ 22 more rows\n\n\nTo supply your own expression, we can write our own function and pass it through to across():\n\ndivide_by_ten <- function(x) {\n  new_x <- x / 10\n\n  return(new_x)\n}\n\nmtcars %>%\n  mutate(across(.cols = everything(), .fns = divide_by_ten))\n\n# A tibble: 32 × 11\n     mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb\n   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>\n 1  2.1    0.6  16    11   0.39  0.262  1.65   0     0.1   0.4   0.4\n 2  2.1    0.6  16    11   0.39  0.288  1.70   0     0.1   0.4   0.4\n 3  2.28   0.4  10.8   9.3 0.385 0.232  1.86   0.1   0.1   0.4   0.1\n 4  2.14   0.6  25.8  11   0.308 0.322  1.94   0.1   0     0.3   0.1\n 5  1.87   0.8  36    17.5 0.315 0.344  1.70   0     0     0.3   0.2\n 6  1.81   0.6  22.5  10.5 0.276 0.346  2.02   0.1   0     0.3   0.1\n 7  1.43   0.8  36    24.5 0.321 0.357  1.58   0     0     0.3   0.4\n 8  2.44   0.4  14.7   6.2 0.369 0.319  2      0.1   0     0.4   0.2\n 9  2.28   0.4  14.1   9.5 0.392 0.315  2.29   0.1   0     0.4   0.2\n10  1.92   0.6  16.8  12.3 0.392 0.344  1.83   0.1   0     0.4   0.4\n# ℹ 22 more rows\n\n\nOr we can write it inline using the following syntax where ~ lets dplyr know to expect an inline function, and . is used as a placeholder for the variable.\n\nmtcars %>%\n  mutate(across(.cols = everything(), .fns = ~ . / 10))\n\n# A tibble: 32 × 11\n     mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb\n   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>\n 1  2.1    0.6  16    11   0.39  0.262  1.65   0     0.1   0.4   0.4\n 2  2.1    0.6  16    11   0.39  0.288  1.70   0     0.1   0.4   0.4\n 3  2.28   0.4  10.8   9.3 0.385 0.232  1.86   0.1   0.1   0.4   0.1\n 4  2.14   0.6  25.8  11   0.308 0.322  1.94   0.1   0     0.3   0.1\n 5  1.87   0.8  36    17.5 0.315 0.344  1.70   0     0     0.3   0.2\n 6  1.81   0.6  22.5  10.5 0.276 0.346  2.02   0.1   0     0.3   0.1\n 7  1.43   0.8  36    24.5 0.321 0.357  1.58   0     0     0.3   0.4\n 8  2.44   0.4  14.7   6.2 0.369 0.319  2      0.1   0     0.4   0.2\n 9  2.28   0.4  14.1   9.5 0.392 0.315  2.29   0.1   0     0.4   0.2\n10  1.92   0.6  16.8  12.3 0.392 0.344  1.83   0.1   0     0.4   0.4\n# ℹ 22 more rows\n\n\n\n\n5.4.2 summarize()\nSay we wanted to find the mean of every variable in the mtcars data. In this case we can still use across()!\n\nmtcars %>%\n  summarize(across(.cols = everything(), .fns = mean))\n\n# A tibble: 1 × 11\n    mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb\n  <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>\n1  20.1  6.19  231.  147.  3.60  3.22  17.8 0.438 0.406  3.69  2.81\n\n\nWe can calculate multiple summary statistics by passing in a list() of the statistics we want:\n\nmtcars %>%\n  summarize(\n    n = n(),\n    across(\n      .cols = everything(),\n      .fns = list(mean = mean, median = median)\n    )\n  )\n\n# A tibble: 1 × 25\n      n mpg_mean mpg_median cyl_mean cyl_median disp_mean disp_median hp_mean\n  <int>    <dbl>      <dbl>    <dbl>      <dbl>     <dbl>       <dbl>   <dbl>\n1    32     20.1       19.2     6.19          6      231.        196.    147.\n# ℹ 17 more variables: hp_median <dbl>, drat_mean <dbl>, drat_median <dbl>,\n#   wt_mean <dbl>, wt_median <dbl>, qsec_mean <dbl>, qsec_median <dbl>,\n#   vs_mean <dbl>, vs_median <dbl>, am_mean <dbl>, am_median <dbl>,\n#   gear_mean <dbl>, gear_median <dbl>, carb_mean <dbl>, carb_median <dbl>,\n#   n_mean <dbl>, n_median <int>\n\n\n\n\n5.4.3 Exercise 1\nStep 1: Open up a script, save it with a meaningful name, and load the tidyverse.\nStep 2: Use the mutate() and across() to take the log of each variable in mtcars.\nStep 3: Use the summarise() and across() to find the standard deviation (sd) of each variable in mtcars."
+    "text": "5.4 across()\nOn Day 2 we introduced a number of dplyr functions:\n\nmutate(): to transform a variable\nselect(): to subset the columns of a data frame\nfilter(): to subset the rows of a data frame\nsummarise(): to calculate summary statistics\ngroup_by(): to perform an operation by group\n\nSometimes we will want to apply these functions many times. For example, what if we want to manipulate many variables. For this, we will use the new across() function. To demonstrate across(), we’ll use the built-in mtcars data frame:\n\nlibrary(tidyverse)\n\nmtcars &lt;- as_tibble(mtcars)\nhead(mtcars)\n\n# A tibble: 6 × 11\n    mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb\n  &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; &lt;dbl&gt; &lt;dbl&gt;\n1  21       6   160   110  3.9   2.62  16.5     0     1     4     4\n2  21       6   160   110  3.9   2.88  17.0     0     1     4     4\n3  22.8     4   108    93  3.85  2.32  18.6     1     1     4     1\n4  21.4     6   258   110  3.08  3.22  19.4     1     0     3     1\n5  18.7     8   360   175  3.15  3.44  17.0     0     0     3     2\n6  18.1     6   225   105  2.76  3.46  20.2     1     0     3     1\n\n\n\n5.4.1 mutate()\nSay we wanted to round all the variables of this data frame. We could write some very repetitive code:\n\nmtcars %&gt;%\n  mutate(\n    mpg = round(mpg),\n    cyl = round(cyl),\n    disp = round(disp),\n    hp = round(hp),\n    drat = round(drat),\n    wt = round(wt),\n    qsec = round(qsec),\n    vs = round(vs),\n    am = round(am),\n    gear = round(gear),\n    carb = round(carb)\n  )\n\n# A tibble: 32 × 11\n     mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb\n   &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; &lt;dbl&gt; &lt;dbl&gt;\n 1    21     6   160   110     4     3    16     0     1     4     4\n 2    21     6   160   110     4     3    17     0     1     4     4\n 3    23     4   108    93     4     2    19     1     1     4     1\n 4    21     6   258   110     3     3    19     1     0     3     1\n 5    19     8   360   175     3     3    17     0     0     3     2\n 6    18     6   225   105     3     3    20     1     0     3     1\n 7    14     8   360   245     3     4    16     0     0     3     4\n 8    24     4   147    62     4     3    20     1     0     4     2\n 9    23     4   141    95     4     3    23     1     0     4     2\n10    19     6   168   123     4     3    18     1     0     4     4\n# ℹ 22 more rows\n\n\nOr we can let across() do the work for us. All we have to do is pass the function we want applied to all columns of our data frame:\n\nmtcars %&gt;%\n  mutate(across(.cols = everything(), .fns = round))\n\n# A tibble: 32 × 11\n     mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb\n   &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; &lt;dbl&gt; &lt;dbl&gt;\n 1    21     6   160   110     4     3    16     0     1     4     4\n 2    21     6   160   110     4     3    17     0     1     4     4\n 3    23     4   108    93     4     2    19     1     1     4     1\n 4    21     6   258   110     3     3    19     1     0     3     1\n 5    19     8   360   175     3     3    17     0     0     3     2\n 6    18     6   225   105     3     3    20     1     0     3     1\n 7    14     8   360   245     3     4    16     0     0     3     4\n 8    24     4   147    62     4     3    20     1     0     4     2\n 9    23     4   141    95     4     3    23     1     0     4     2\n10    19     6   168   123     4     3    18     1     0     4     4\n# ℹ 22 more rows\n\n\nTo supply your own expression, we can write our own function and pass it through to across():\n\ndivide_by_ten &lt;- function(x) {\n  new_x &lt;- x / 10\n\n  return(new_x)\n}\n\nmtcars %&gt;%\n  mutate(across(.cols = everything(), .fns = divide_by_ten))\n\n# A tibble: 32 × 11\n     mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb\n   &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; &lt;dbl&gt; &lt;dbl&gt;\n 1  2.1    0.6  16    11   0.39  0.262  1.65   0     0.1   0.4   0.4\n 2  2.1    0.6  16    11   0.39  0.288  1.70   0     0.1   0.4   0.4\n 3  2.28   0.4  10.8   9.3 0.385 0.232  1.86   0.1   0.1   0.4   0.1\n 4  2.14   0.6  25.8  11   0.308 0.322  1.94   0.1   0     0.3   0.1\n 5  1.87   0.8  36    17.5 0.315 0.344  1.70   0     0     0.3   0.2\n 6  1.81   0.6  22.5  10.5 0.276 0.346  2.02   0.1   0     0.3   0.1\n 7  1.43   0.8  36    24.5 0.321 0.357  1.58   0     0     0.3   0.4\n 8  2.44   0.4  14.7   6.2 0.369 0.319  2      0.1   0     0.4   0.2\n 9  2.28   0.4  14.1   9.5 0.392 0.315  2.29   0.1   0     0.4   0.2\n10  1.92   0.6  16.8  12.3 0.392 0.344  1.83   0.1   0     0.4   0.4\n# ℹ 22 more rows\n\n\nOr we can write it inline using the following syntax where ~ lets dplyr know to expect an inline function, and . is used as a placeholder for the variable.\n\nmtcars %&gt;%\n  mutate(across(.cols = everything(), .fns = ~ . / 10))\n\n# A tibble: 32 × 11\n     mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb\n   &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; &lt;dbl&gt; &lt;dbl&gt;\n 1  2.1    0.6  16    11   0.39  0.262  1.65   0     0.1   0.4   0.4\n 2  2.1    0.6  16    11   0.39  0.288  1.70   0     0.1   0.4   0.4\n 3  2.28   0.4  10.8   9.3 0.385 0.232  1.86   0.1   0.1   0.4   0.1\n 4  2.14   0.6  25.8  11   0.308 0.322  1.94   0.1   0     0.3   0.1\n 5  1.87   0.8  36    17.5 0.315 0.344  1.70   0     0     0.3   0.2\n 6  1.81   0.6  22.5  10.5 0.276 0.346  2.02   0.1   0     0.3   0.1\n 7  1.43   0.8  36    24.5 0.321 0.357  1.58   0     0     0.3   0.4\n 8  2.44   0.4  14.7   6.2 0.369 0.319  2      0.1   0     0.4   0.2\n 9  2.28   0.4  14.1   9.5 0.392 0.315  2.29   0.1   0     0.4   0.2\n10  1.92   0.6  16.8  12.3 0.392 0.344  1.83   0.1   0     0.4   0.4\n# ℹ 22 more rows\n\n\n\n\n5.4.2 summarize()\nSay we wanted to find the mean of every variable in the mtcars data. In this case we can still use across()!\n\nmtcars %&gt;%\n  summarize(across(.cols = everything(), .fns = mean))\n\n# A tibble: 1 × 11\n    mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb\n  &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; &lt;dbl&gt; &lt;dbl&gt;\n1  20.1  6.19  231.  147.  3.60  3.22  17.8 0.438 0.406  3.69  2.81\n\n\nWe can calculate multiple summary statistics by passing in a list() of the statistics we want:\n\nmtcars %&gt;%\n  summarize(\n    n = n(),\n    across(\n      .cols = everything(),\n      .fns = list(mean = mean, median = median)\n    )\n  )\n\n# A tibble: 1 × 25\n      n mpg_mean mpg_median cyl_mean cyl_median disp_mean disp_median hp_mean\n  &lt;int&gt;    &lt;dbl&gt;      &lt;dbl&gt;    &lt;dbl&gt;      &lt;dbl&gt;     &lt;dbl&gt;       &lt;dbl&gt;   &lt;dbl&gt;\n1    32     20.1       19.2     6.19          6      231.        196.    147.\n# ℹ 17 more variables: hp_median &lt;dbl&gt;, drat_mean &lt;dbl&gt;, drat_median &lt;dbl&gt;,\n#   wt_mean &lt;dbl&gt;, wt_median &lt;dbl&gt;, qsec_mean &lt;dbl&gt;, qsec_median &lt;dbl&gt;,\n#   vs_mean &lt;dbl&gt;, vs_median &lt;dbl&gt;, am_mean &lt;dbl&gt;, am_median &lt;dbl&gt;,\n#   gear_mean &lt;dbl&gt;, gear_median &lt;dbl&gt;, carb_mean &lt;dbl&gt;, carb_median &lt;dbl&gt;,\n#   n_mean &lt;dbl&gt;, n_median &lt;int&gt;\n\n\n\n\n5.4.3 Exercise 1\nStep 1: Open up a script, save it with a meaningful name, and load the tidyverse.\nStep 2: Use the mutate() and across() to take the log of each variable in mtcars.\nStep 3: Use the summarise() and across() to find the standard deviation (sd) of each variable in mtcars."
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     "title": "5  Iteration (or becoming an R Superhero)",
     "section": "5.5 where()",
-    "text": "5.5 where()\n\n5.5.1 Example\nTo demonstrate where() we’ll use the diamonds data frame from ggplot2:\n\nhead(diamonds)\n\n# A tibble: 6 × 10\n  carat cut       color clarity depth table price     x     y     z\n  <dbl> <ord>     <ord> <ord>   <dbl> <dbl> <int> <dbl> <dbl> <dbl>\n1  0.23 Ideal     E     SI2      61.5    55   326  3.95  3.98  2.43\n2  0.21 Premium   E     SI1      59.8    61   326  3.89  3.84  2.31\n3  0.23 Good      E     VS1      56.9    65   327  4.05  4.07  2.31\n4  0.29 Premium   I     VS2      62.4    58   334  4.2   4.23  2.63\n5  0.31 Good      J     SI2      63.3    58   335  4.34  4.35  2.75\n6  0.24 Very Good J     VVS2     62.8    57   336  3.94  3.96  2.48\n\n\nWhat happens if we try to use mutate(acoss(.cols = everything(), .fns = log)) to log transform each variable in this data frame?\n\ndiamonds %>%\n  mutate(across(.cols = everything(), .fns = log))\n\nError in `mutate()`:\nℹ In argument: `across(.cols = everything(), .fns = log)`.\nCaused by error in `across()`:\n! Can't compute column `cut`.\nCaused by error in `Math.factor()`:\n! 'log' not meaningful for factors\n\n\nIt doesn’t work because diamonds has some non-numeric columns. We could find all the columns that are numeric:\n\nglimpse(diamonds)\n\nRows: 53,940\nColumns: 10\n$ carat   <dbl> 0.23, 0.21, 0.23, 0.29, 0.31, 0.24, 0.24, 0.26, 0.22, 0.23, 0.…\n$ cut     <ord> Ideal, Premium, Good, Premium, Good, Very Good, Very Good, Ver…\n$ color   <ord> E, E, E, I, J, J, I, H, E, H, J, J, F, J, E, E, I, J, J, J, I,…\n$ clarity <ord> SI2, SI1, VS1, VS2, SI2, VVS2, VVS1, SI1, VS2, VS1, SI1, VS1, …\n$ depth   <dbl> 61.5, 59.8, 56.9, 62.4, 63.3, 62.8, 62.3, 61.9, 65.1, 59.4, 64…\n$ table   <dbl> 55, 61, 65, 58, 58, 57, 57, 55, 61, 61, 55, 56, 61, 54, 62, 58…\n$ price   <int> 326, 326, 327, 334, 335, 336, 336, 337, 337, 338, 339, 340, 34…\n$ x       <dbl> 3.95, 3.89, 4.05, 4.20, 4.34, 3.94, 3.95, 4.07, 3.87, 4.00, 4.…\n$ y       <dbl> 3.98, 3.84, 4.07, 4.23, 4.35, 3.96, 3.98, 4.11, 3.78, 4.05, 4.…\n$ z       <dbl> 2.43, 2.31, 2.31, 2.63, 2.75, 2.48, 2.47, 2.53, 2.49, 2.39, 2.…\n\n\nAnd then write a lot of repetitive mutate() statements again. Instead let’s use where() as follows:\n\ndiamonds %>%\n  mutate(across(.cols = where(is.numeric), .fns = log))\n\n# A tibble: 53,940 × 10\n   carat cut       color clarity depth table price     x     y     z\n   <dbl> <ord>     <ord> <ord>   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>\n 1 -1.47 Ideal     E     SI2      4.12  4.01  5.79  1.37  1.38 0.888\n 2 -1.56 Premium   E     SI1      4.09  4.11  5.79  1.36  1.35 0.837\n 3 -1.47 Good      E     VS1      4.04  4.17  5.79  1.40  1.40 0.837\n 4 -1.24 Premium   I     VS2      4.13  4.06  5.81  1.44  1.44 0.967\n 5 -1.17 Good      J     SI2      4.15  4.06  5.81  1.47  1.47 1.01 \n 6 -1.43 Very Good J     VVS2     4.14  4.04  5.82  1.37  1.38 0.908\n 7 -1.43 Very Good I     VVS1     4.13  4.04  5.82  1.37  1.38 0.904\n 8 -1.35 Very Good H     SI1      4.13  4.01  5.82  1.40  1.41 0.928\n 9 -1.51 Fair      E     VS2      4.18  4.11  5.82  1.35  1.33 0.912\n10 -1.47 Very Good H     VS1      4.08  4.11  5.82  1.39  1.40 0.871\n# ℹ 53,930 more rows\n\n\nWhen using the where() with across(), we first supply the condition that must be met, and then the function to apply to the columns that meet that condition. So in the above example, we first test if a column is.numeric, and then apply log to those that are.\nWe can use select() and where() to subset only those columns that are numeric:\n\ndiamonds %>%\n  select(where(is.numeric))\n\n# A tibble: 53,940 × 7\n   carat depth table price     x     y     z\n   <dbl> <dbl> <dbl> <int> <dbl> <dbl> <dbl>\n 1  0.23  61.5    55   326  3.95  3.98  2.43\n 2  0.21  59.8    61   326  3.89  3.84  2.31\n 3  0.23  56.9    65   327  4.05  4.07  2.31\n 4  0.29  62.4    58   334  4.2   4.23  2.63\n 5  0.31  63.3    58   335  4.34  4.35  2.75\n 6  0.24  62.8    57   336  3.94  3.96  2.48\n 7  0.24  62.3    57   336  3.95  3.98  2.47\n 8  0.26  61.9    55   337  4.07  4.11  2.53\n 9  0.22  65.1    61   337  3.87  3.78  2.49\n10  0.23  59.4    61   338  4     4.05  2.39\n# ℹ 53,930 more rows\n\n\n\n\n5.5.2 Exercise 2\nNote: Each step is a separate task. They need not be piped together.\nStep 1: Use select() and where() to subset all the columns from diamonds that are factor variables.\nStep 2: Use the mutate(), across(), and where() to take the sqrt of each numeric variable in diamonds.\nStep 3: Use the summarise, across(), and where() to find the mean and standard deviation of each numeric variable in diamonds.\nStep 4: Use summarise() and across() to find the mean and standard deviation of each numeric variable in diamonds, first using group_by(), across(), and where() to group by all factor variables."
+    "text": "5.5 where()\n\n5.5.1 Example\nTo demonstrate where() we’ll use the diamonds data frame from ggplot2:\n\nhead(diamonds)\n\n# A tibble: 6 × 10\n  carat cut       color clarity depth table price     x     y     z\n  &lt;dbl&gt; &lt;ord&gt;     &lt;ord&gt; &lt;ord&gt;   &lt;dbl&gt; &lt;dbl&gt; &lt;int&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt;\n1  0.23 Ideal     E     SI2      61.5    55   326  3.95  3.98  2.43\n2  0.21 Premium   E     SI1      59.8    61   326  3.89  3.84  2.31\n3  0.23 Good      E     VS1      56.9    65   327  4.05  4.07  2.31\n4  0.29 Premium   I     VS2      62.4    58   334  4.2   4.23  2.63\n5  0.31 Good      J     SI2      63.3    58   335  4.34  4.35  2.75\n6  0.24 Very Good J     VVS2     62.8    57   336  3.94  3.96  2.48\n\n\nWhat happens if we try to use mutate(acoss(.cols = everything(), .fns = log)) to log transform each variable in this data frame?\n\ndiamonds %&gt;%\n  mutate(across(.cols = everything(), .fns = log))\n\nError in `mutate()`:\nℹ In argument: `across(.cols = everything(), .fns = log)`.\nCaused by error in `across()`:\n! Can't compute column `cut`.\nCaused by error in `Math.factor()`:\n! 'log' not meaningful for factors\n\n\nIt doesn’t work because diamonds has some non-numeric columns. We could find all the columns that are numeric:\n\nglimpse(diamonds)\n\nRows: 53,940\nColumns: 10\n$ carat   &lt;dbl&gt; 0.23, 0.21, 0.23, 0.29, 0.31, 0.24, 0.24, 0.26, 0.22, 0.23, 0.…\n$ cut     &lt;ord&gt; Ideal, Premium, Good, Premium, Good, Very Good, Very Good, Ver…\n$ color   &lt;ord&gt; E, E, E, I, J, J, I, H, E, H, J, J, F, J, E, E, I, J, J, J, I,…\n$ clarity &lt;ord&gt; SI2, SI1, VS1, VS2, SI2, VVS2, VVS1, SI1, VS2, VS1, SI1, VS1, …\n$ depth   &lt;dbl&gt; 61.5, 59.8, 56.9, 62.4, 63.3, 62.8, 62.3, 61.9, 65.1, 59.4, 64…\n$ table   &lt;dbl&gt; 55, 61, 65, 58, 58, 57, 57, 55, 61, 61, 55, 56, 61, 54, 62, 58…\n$ price   &lt;int&gt; 326, 326, 327, 334, 335, 336, 336, 337, 337, 338, 339, 340, 34…\n$ x       &lt;dbl&gt; 3.95, 3.89, 4.05, 4.20, 4.34, 3.94, 3.95, 4.07, 3.87, 4.00, 4.…\n$ y       &lt;dbl&gt; 3.98, 3.84, 4.07, 4.23, 4.35, 3.96, 3.98, 4.11, 3.78, 4.05, 4.…\n$ z       &lt;dbl&gt; 2.43, 2.31, 2.31, 2.63, 2.75, 2.48, 2.47, 2.53, 2.49, 2.39, 2.…\n\n\nAnd then write a lot of repetitive mutate() statements again. Instead let’s use where() as follows:\n\ndiamonds %&gt;%\n  mutate(across(.cols = where(is.numeric), .fns = log))\n\n# A tibble: 53,940 × 10\n   carat cut       color clarity depth table price     x     y     z\n   &lt;dbl&gt; &lt;ord&gt;     &lt;ord&gt; &lt;ord&gt;   &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt;\n 1 -1.47 Ideal     E     SI2      4.12  4.01  5.79  1.37  1.38 0.888\n 2 -1.56 Premium   E     SI1      4.09  4.11  5.79  1.36  1.35 0.837\n 3 -1.47 Good      E     VS1      4.04  4.17  5.79  1.40  1.40 0.837\n 4 -1.24 Premium   I     VS2      4.13  4.06  5.81  1.44  1.44 0.967\n 5 -1.17 Good      J     SI2      4.15  4.06  5.81  1.47  1.47 1.01 \n 6 -1.43 Very Good J     VVS2     4.14  4.04  5.82  1.37  1.38 0.908\n 7 -1.43 Very Good I     VVS1     4.13  4.04  5.82  1.37  1.38 0.904\n 8 -1.35 Very Good H     SI1      4.13  4.01  5.82  1.40  1.41 0.928\n 9 -1.51 Fair      E     VS2      4.18  4.11  5.82  1.35  1.33 0.912\n10 -1.47 Very Good H     VS1      4.08  4.11  5.82  1.39  1.40 0.871\n# ℹ 53,930 more rows\n\n\nWhen using the where() with across(), we first supply the condition that must be met, and then the function to apply to the columns that meet that condition. So in the above example, we first test if a column is.numeric, and then apply log to those that are.\nWe can use select() and where() to subset only those columns that are numeric:\n\ndiamonds %&gt;%\n  select(where(is.numeric))\n\n# A tibble: 53,940 × 7\n   carat depth table price     x     y     z\n   &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;int&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt;\n 1  0.23  61.5    55   326  3.95  3.98  2.43\n 2  0.21  59.8    61   326  3.89  3.84  2.31\n 3  0.23  56.9    65   327  4.05  4.07  2.31\n 4  0.29  62.4    58   334  4.2   4.23  2.63\n 5  0.31  63.3    58   335  4.34  4.35  2.75\n 6  0.24  62.8    57   336  3.94  3.96  2.48\n 7  0.24  62.3    57   336  3.95  3.98  2.47\n 8  0.26  61.9    55   337  4.07  4.11  2.53\n 9  0.22  65.1    61   337  3.87  3.78  2.49\n10  0.23  59.4    61   338  4     4.05  2.39\n# ℹ 53,930 more rows\n\n\n\n\n5.5.2 Exercise 2\nNote: Each step is a separate task. They need not be piped together.\nStep 1: Use select() and where() to subset all the columns from diamonds that are factor variables.\nStep 2: Use the mutate(), across(), and where() to take the sqrt of each numeric variable in diamonds.\nStep 3: Use the summarise, across(), and where() to find the mean and standard deviation of each numeric variable in diamonds.\nStep 4: Use summarise() and across() to find the mean and standard deviation of each numeric variable in diamonds, first using group_by(), across(), and where() to group by all factor variables."
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-    "text": "5.6 Selector Helpers\nSometimes we want to use across() with many variables but not all variables or not all variables of a certain type. We can use a character vector to pick a subset of columns.\n\ndiamonds %>%\n  mutate(across(.cols = c(\"x\", \"y\", \"z\"), .fns = log))\n\n# A tibble: 53,940 × 10\n   carat cut       color clarity depth table price     x     y     z\n   <dbl> <ord>     <ord> <ord>   <dbl> <dbl> <int> <dbl> <dbl> <dbl>\n 1  0.23 Ideal     E     SI2      61.5    55   326  1.37  1.38 0.888\n 2  0.21 Premium   E     SI1      59.8    61   326  1.36  1.35 0.837\n 3  0.23 Good      E     VS1      56.9    65   327  1.40  1.40 0.837\n 4  0.29 Premium   I     VS2      62.4    58   334  1.44  1.44 0.967\n 5  0.31 Good      J     SI2      63.3    58   335  1.47  1.47 1.01 \n 6  0.24 Very Good J     VVS2     62.8    57   336  1.37  1.38 0.908\n 7  0.24 Very Good I     VVS1     62.3    57   336  1.37  1.38 0.904\n 8  0.26 Very Good H     SI1      61.9    55   337  1.40  1.41 0.928\n 9  0.22 Fair      E     VS2      65.1    61   337  1.35  1.33 0.912\n10  0.23 Very Good H     VS1      59.4    61   338  1.39  1.40 0.871\n# ℹ 53,930 more rows\n\n\nSometimes this is cumbersome. Fortunately, library(dplyr) has helpers that can create character vectors for use. For instance, if all of the variables we care about start with “e”, then we can\n\ndata %>%\n  mutate(across(.cols = starts_with(\"e\"), .fns = log))\n\n\nThere are several selector helpers:\n\ncontains()\nends_with()\nmatches()\nnum_range()\none_of()\nstarts_with()"
+    "text": "5.6 Selector Helpers\nSometimes we want to use across() with many variables but not all variables or not all variables of a certain type. We can use a character vector to pick a subset of columns.\n\ndiamonds %&gt;%\n  mutate(across(.cols = c(\"x\", \"y\", \"z\"), .fns = log))\n\n# A tibble: 53,940 × 10\n   carat cut       color clarity depth table price     x     y     z\n   &lt;dbl&gt; &lt;ord&gt;     &lt;ord&gt; &lt;ord&gt;   &lt;dbl&gt; &lt;dbl&gt; &lt;int&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt;\n 1  0.23 Ideal     E     SI2      61.5    55   326  1.37  1.38 0.888\n 2  0.21 Premium   E     SI1      59.8    61   326  1.36  1.35 0.837\n 3  0.23 Good      E     VS1      56.9    65   327  1.40  1.40 0.837\n 4  0.29 Premium   I     VS2      62.4    58   334  1.44  1.44 0.967\n 5  0.31 Good      J     SI2      63.3    58   335  1.47  1.47 1.01 \n 6  0.24 Very Good J     VVS2     62.8    57   336  1.37  1.38 0.908\n 7  0.24 Very Good I     VVS1     62.3    57   336  1.37  1.38 0.904\n 8  0.26 Very Good H     SI1      61.9    55   337  1.40  1.41 0.928\n 9  0.22 Fair      E     VS2      65.1    61   337  1.35  1.33 0.912\n10  0.23 Very Good H     VS1      59.4    61   338  1.39  1.40 0.871\n# ℹ 53,930 more rows\n\n\nSometimes this is cumbersome. Fortunately, library(dplyr) has helpers that can create character vectors for use. For instance, if all of the variables we care about start with “e”, then we can\n\ndata %&gt;%\n  mutate(across(.cols = starts_with(\"e\"), .fns = log))\n\n\nThere are several selector helpers:\n\ncontains()\nends_with()\nmatches()\nnum_range()\none_of()\nstarts_with()"
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-    "text": "5.7 pivot_*\nWarning: across() is not an excuse to avoid tidy data! For example, the following is bad. Why does this violate tidy data? How much pain would we experience if we wanted to plot this data?\n\ndata <- tribble(\n  ~state, ~`1999`, ~`2000`, ~`2001`,\n  \"Maryland\", 100, 110, 121,\n  \"Virginia\", 205, 204, 203\n)\n\nWe use pivot_longer() to transform data from wide to long and pivot_wider() to transform data from long to wide.\nWe can fix the above data with pivot_longer()\n\ndata_long <- data %>%\n  pivot_longer(\n    cols = -state,\n    names_to = \"year\",\n    values_to = \"widget_factories\"\n  )\n\ndata_long\n\n# A tibble: 6 × 3\n  state    year  widget_factories\n  <chr>    <chr>            <dbl>\n1 Maryland 1999               100\n2 Maryland 2000               110\n3 Maryland 2001               121\n4 Virginia 1999               205\n5 Virginia 2000               204\n6 Virginia 2001               203\n\n\nWe can unfix the data with pivot_wider()\n\ndata_long %>%\n  pivot_wider(\n    id_cols = state,\n    names_from = year,\n    values_from = widget_factories\n  )\n\n# A tibble: 2 × 4\n  state    `1999` `2000` `2001`\n  <chr>     <dbl>  <dbl>  <dbl>\n1 Maryland    100    110    121\n2 Virginia    205    204    203\n\n\n\n5.7.1 Tidy data\n\nHappy families are all alike; every unhappy family is unhappy in its own way. ~ Leo Tolstoy\n\n\ntidy datasets are all alike but every messy dataset is messy in its own way ~ Hadley Wickham\n\nTools optimized for one data format are going to be more powerful than tools optimized for any data format. If we can standardize our data, then we will have really powerful tools.\nUnfortunately, most people do not pay enough attention to the structure, storage, and dissemination of data."
+    "text": "5.7 pivot_*\nWarning: across() is not an excuse to avoid tidy data! For example, the following is bad. Why does this violate tidy data? How much pain would we experience if we wanted to plot this data?\n\ndata &lt;- tribble(\n  ~state, ~`1999`, ~`2000`, ~`2001`,\n  \"Maryland\", 100, 110, 121,\n  \"Virginia\", 205, 204, 203\n)\n\nWe use pivot_longer() to transform data from wide to long and pivot_wider() to transform data from long to wide.\nWe can fix the above data with pivot_longer()\n\ndata_long &lt;- data %&gt;%\n  pivot_longer(\n    cols = -state,\n    names_to = \"year\",\n    values_to = \"widget_factories\"\n  )\n\ndata_long\n\n# A tibble: 6 × 3\n  state    year  widget_factories\n  &lt;chr&gt;    &lt;chr&gt;            &lt;dbl&gt;\n1 Maryland 1999               100\n2 Maryland 2000               110\n3 Maryland 2001               121\n4 Virginia 1999               205\n5 Virginia 2000               204\n6 Virginia 2001               203\n\n\nWe can unfix the data with pivot_wider()\n\ndata_long %&gt;%\n  pivot_wider(\n    id_cols = state,\n    names_from = year,\n    values_from = widget_factories\n  )\n\n# A tibble: 2 × 4\n  state    `1999` `2000` `2001`\n  &lt;chr&gt;     &lt;dbl&gt;  &lt;dbl&gt;  &lt;dbl&gt;\n1 Maryland    100    110    121\n2 Virginia    205    204    203\n\n\n\n5.7.1 Tidy data\n\nHappy families are all alike; every unhappy family is unhappy in its own way. ~ Leo Tolstoy\n\n\ntidy datasets are all alike but every messy dataset is messy in its own way ~ Hadley Wickham\n\nTools optimized for one data format are going to be more powerful than tools optimized for any data format. If we can standardize our data, then we will have really powerful tools.\nUnfortunately, most people do not pay enough attention to the structure, storage, and dissemination of data."
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     "section": "5.8 library(purrr)",
-    "text": "5.8 library(purrr)\nSometimes, we’re going to want to iterate on a data structure other than a tibble or iterate a function that isn’t vectorized.\n\n5.8.1 Many files\nOne approach is to use for loops. For loops are useful and should be understood. This code writes nine .csv files with 1000 random draws from a standard normal distribution.\n\ndir.create(\"data\")\n\nfor (i in 1:9) {\n  tibble(\n    id = 1:1000,\n    value = rnorm(1000)\n  ) %>%\n    write_csv(path = paste0(\"data/data\", i, \".csv\"))\n}\n\nlibrary(purrr) is a powerful alternative to for loops that is designed for functional programming. This code reads in the nine .csv files!\n\nfiles <- list.files(path = \"data\", full.names = TRUE)\n\nmap(.x = files, .f = read_csv)\n\nmap() always returns a list! map_*() functions can be used to return different data structures. For instance, map_chr() will always return a character vector. map_df() is like the code above but returns one data frame instead of a list of smaller data frames.\n\nmap_df(.x = files, .f = read_csv)\n\n\n\n5.8.2 Many models\n\nlibrary(broom)\n\n# estimate a linear model for each of seven colors\nmany_models <- diamonds %>%\n  split(diamonds$color) %>%\n  map(~ lm(formula = price ~ carat + cut, data = .))\n\n# extract model diagnostics from each model\nmany_models_results <- bind_cols(\n  color = names(many_models),\n  map_df(many_models, glance)\n)\n\n# plot\nmany_models_results %>%\n  ggplot(aes(color, r.squared)) +\n  geom_col() +\n  scale_y_continuous(\n    expand = c(0, 0),\n    limits = c(0, 1)\n  ) +\n  labs(title = \"R-Squared for linear models estimated on subsets by color\") +\n  theme_minimal()\n\n\n\n\nThese examples are beyond the scope of an introductory class but I wanted to demonstrate the power of iteration in R. These tools can be used iterate data visualizations, R Markdown reports, statistical models, computer operations like file creation and transfer, and more."
+    "text": "5.8 library(purrr)\nSometimes, we’re going to want to iterate on a data structure other than a tibble or iterate a function that isn’t vectorized.\n\n5.8.1 Many files\nOne approach is to use for loops. For loops are useful and should be understood. This code writes nine .csv files with 1000 random draws from a standard normal distribution.\n\ndir.create(\"data\")\n\nfor (i in 1:9) {\n  tibble(\n    id = 1:1000,\n    value = rnorm(1000)\n  ) %&gt;%\n    write_csv(path = paste0(\"data/data\", i, \".csv\"))\n}\n\nlibrary(purrr) is a powerful alternative to for loops that is designed for functional programming. This code reads in the nine .csv files!\n\nfiles &lt;- list.files(path = \"data\", full.names = TRUE)\n\nmap(.x = files, .f = read_csv)\n\nmap() always returns a list! map_*() functions can be used to return different data structures. For instance, map_chr() will always return a character vector. map_df() is like the code above but returns one data frame instead of a list of smaller data frames.\n\nmap_df(.x = files, .f = read_csv)\n\n\n\n5.8.2 Many models\n\nlibrary(broom)\n\n# estimate a linear model for each of seven colors\nmany_models &lt;- diamonds %&gt;%\n  split(diamonds$color) %&gt;%\n  map(~ lm(formula = price ~ carat + cut, data = .))\n\n# extract model diagnostics from each model\nmany_models_results &lt;- bind_cols(\n  color = names(many_models),\n  map_df(many_models, glance)\n)\n\n# plot\nmany_models_results %&gt;%\n  ggplot(aes(color, r.squared)) +\n  geom_col() +\n  scale_y_continuous(\n    expand = c(0, 0),\n    limits = c(0, 1)\n  ) +\n  labs(title = \"R-Squared for linear models estimated on subsets by color\") +\n  theme_minimal()\n\n\n\n\nThese examples are beyond the scope of an introductory class but I wanted to demonstrate the power of iteration in R. These tools can be used iterate data visualizations, R Markdown reports, statistical models, computer operations like file creation and transfer, and more."
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     "title": "6  Putting it All Together (and into Markdown)",
     "section": "6.2 Helpful Packages",
-    "text": "6.2 Helpful Packages\n\nImport Data\n\nreadr - for plain text files\nreadxl - for Excel files\nhaven - for SAS, SPSS, and Stata data formats\nhere - set directory\nremotes or devtools - install packages from Github\narrow - read/save binary data\n\nManipulate data\n\ntidyverse - format tidy data (includes the following packages)\ndplyr - wrangle data using %>%\nlubridate - clean dates\nstringr - clean strings\npurrr - iterate\n\nVisualize data\n\nggplot2 - plots!\nurbnthemes - format and style according to Urban Style\nurbnmapr - create Urban themed maps"
+    "text": "6.2 Helpful Packages\n\nImport Data\n\nreadr - for plain text files\nreadxl - for Excel files\nhaven - for SAS, SPSS, and Stata data formats\nhere - set directory\nremotes or devtools - install packages from Github\narrow - read/save binary data\n\nManipulate data\n\ntidyverse - format tidy data (includes the following packages)\ndplyr - wrangle data using %&gt;%\nlubridate - clean dates\nstringr - clean strings\npurrr - iterate\n\nVisualize data\n\nggplot2 - plots!\nurbnthemes - format and style according to Urban Style\nurbnmapr - create Urban themed maps"
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@@ -551,75 +432,5 @@
     "title": "6  Putting it All Together (and into Markdown)",
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     "text": "6.5 Resources\n\nRStudio R Markdown guide\nR Markdown: The Definitive Guide\nR4DS"
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-    "section": "4.1 Review",
-    "text": "4.1 Review\n\nWhat is tidy data?\n\nArtwork by @allison_horst\nWhat is a file path?\nWhat is an R Studio Project?\n\n\n\nArtwork by @allison_horst\n\n\nStay DRY: if you find yourself copying and pasting chunks of code, write a function instead.\nWriting functions that use capabilities of the tidyverse can be tricky due to something called Nonstandard Evaluation - feel free to ask any of us for help!"
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-    "section": "4.2 Motivation",
-    "text": "4.2 Motivation\n\ndiamonds, storms, or starwars are nice examples, but how do I load data for an actual research project?\nData import is a crucial yet often overlooked step of truly replicable and reproducible research."
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-    "section": "4.3 Packages",
-    "text": "4.3 Packages\nWe’ll be using three packages from the tidyverse for data import and export. These three should cover just about all of your use cases at Urban.\n\nreadr - for plain text files\nreadxl - for Excel files (.xlsx, .xls)\nhaven - for SAS (.sas7bdat + .sas7bcat files), SPSS (.sav files), and Stata (.dta files up to Stata version 17) formats\n\nOne note - while all three packages are installed with the tidyverse, only readr is loaded when you run library(tidyverse) in your R session. The other packages must be loaded separately."
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-    "section": "4.4 Primary Functions",
-    "text": "4.4 Primary Functions\nIn general, readr, readxl, and haven will come with some variant of a read function for data import, and a variant of write (except for Excel and SAS!) for data export.\n\nreadr::read_csv(), readr::write_csv()\nreadr::read_delim(), readr::write_delim()\nreadxl::read_excel()\nhaven::read_sas()\nhaven::read_dta(), haven::write_dta\n\nFor the simplest use cases, the various read functions will need just one argument: the path to the file you want to load.\n\ndf <- read_csv(\"path/to/your/original/data.csv\")\n\nThe various write functions will need two arguments: the data frame you want to export, and the file path to save it to.\n\nwrite_csv(df, \"path/to/your/modified/data.csv\")"
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-    "section": "4.5 Setup",
-    "text": "4.5 Setup\n\n4.5.1 Exercise\nStep 1: Open up your RStudio project, open a R script, save it with a meaningful name, and load the tidyverse, readxl, haven, and arrow.\nStep 2: Use the dir.create() function to create subfolders called data and modified in your R Project.\nStep 3: We’ll be downloading a couple different datasets for today’s examples. Staying DRY, let’s rework the example code from Wednesday into a function we can reuse. Copy and paste the following into your file. Make sure to run the code so the function becomes a part of your environment.\n\ndownload_data <- function(url, path) {\n  if (!file.exists(path)) {\n    download.file(url, path, mode = \"wb\")\n  }\n}"
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-    "section": "4.6 The readr Package",
-    "text": "4.6 The readr Package\nreadr provides functions to read plain text rectangular data - think csv, txt or tsv files.\n\n4.6.1 CSV Files\n\n4.6.1.1 Example\nFirst we’ll download an example csv file from the Urban Institute’s Education data portal:\n\n# Download ipeds data into data/ folder\ndownload_data(\"https://educationdata.urban.org/csv/ipeds/colleges_ipeds_completers.csv\",\n              \"data/colleges_ipeds_completers.csv\")\n\nAnd then use read_csv from the readr package to load the data in.\n\nipeds <- read_csv(\"data/colleges_ipeds_completers.csv\")\n\nWe now have a tibble that can be used with all the ggplot2 and dplyr functions you’ve learned so far. Let’s filter the data to only 2011 and write it out as a separate file:\n\nipeds_2011 <- ipeds %>%\n  filter(year == 2011)\n\nwrite_csv(ipeds_2011, \"modified/colleges_ipeds_completers_2011.csv\")\n\n\n\n4.6.1.2 Exercise\nStep 1: Filter the ipeds data frame to years 2014-2015 for the state of California (HINT: fips code of 6). Be sure to use <- to save it to a new object!\nStep 2: Write the filtered data frame to a file called “ipeds_completers_ca.csv” in your modified folder.\n\n\n\n4.6.2 Other Delimiters\nMost plain text files will use a comma to separate values. Sometimes you’ll see other delimiters used - the most common are tabs \\t and vertical bar | (which is often called a pipe, not to be confused with the tidyverse pipe %>%).\nTo read in a plain text file that uses another delimiter, use the read_delim function. This function will take two arguments: the path to the file you want to load and the character to use as a delimiter.\n\n4.6.2.1 Example\nFirst we’ll download a tab delimited file from the North Carolina Board of Elections:\n\ndownload_data(\"https://s3.amazonaws.com/dl.ncsbe.gov/data/ncvhis30.zip\",\n              \"data/ncvhis30.zip\")\n\nThen load in the file with the correct delimiter set. It may be helpful to open delimtied file with a program like Notepad so you can see the plain text in the file and decide what delimiter to use. In this case we know its a tab:\n\nncvhis <- read_delim(\"data/ncvhis30.zip\", delim = \"\\t\")\n\nNote that readr was able to read the data directly from the zipfile!\nSimilarly, the write_delim() function can be used to write out a plain text file with a specified delim argument.\n\n\n4.6.2.2 Exercise\nStep 1: Use write_delim() to write out ncvhis to a pipe | separated file.\n\n\n\n4.6.3 Common Issues\nNot all data import will be quick and easy. Often times you will run into issues - these can be daunting at first but are often not as bad as they seem.\n\n4.6.3.1 Example\nThe readr package provides a challenge.csv file that will throw numerous parsing issues.\n\nchallenge <- read_csv(readr_example(\"challenge.csv\"))\n\nYou can use the problems function to get a tibble of these issues.\n\nissues <- problems(challenge)\n\nA thousand parsing failures sounds like a lot! But it’s really just one error a thousand times, and not a thousand unique errors to work through.\nThe issue is that readr will read the first thousand lines of a file and use that as the column specification. This file changes data types at line 1001. This is a very common issue when reading text files into R. We can use the guess_max argument to increase the number of lines readr will use to set the column types.\n\nchallenge <- read_csv(readr_example(\"challenge.csv\"),\n                      guess_max = Inf)\n\nYou will run into other problems when importing data like:\n\nthe columns not being in the first row\nweird NA values\nblank rows at the top of files\n\nLuckily read_csv() and its friends have a lot of built in arguments to help deal with those issues. Look at the arguments in the docs using ?read_csv or go to the R4DS data import page for examples of how to deal with all the above problems.\n\n\n\n4.6.4 Exercise\nStep 1: Pull up the documentation for read_csv and take a look of some of the function arguments. There are a lot! You don’t need to know how everything works, but it’s good to have an idea of some of the options available to you."
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-    "title": "4  Data Import",
-    "section": "4.7 The readxl Package",
-    "text": "4.7 The readxl Package\nFor data that is saved as .xls or .xlsx, use the readxl package.\n\n4.7.1 Example\nFor this example we’ll download data from the HUD FHA Single Family Portfolio Snap Shot.\n\ndownload_data(\"https://www.hud.gov/sites/dfiles/Housing/documents/FHA_SFSnapshot_Apr2019.xlsx\",\n              \"data/sfsnap.xlsx\")\n\nThis Excel file contains a number of tables on different sheets of the workbook. We can see a listing of the sheets using the excel_sheets function. As usual, the first argument is the path to the data file.\n\nexcel_sheets(\"data/sfsnap.xlsx\")\n\nWe can then use the read_excel function to load our data into our R session. Again, the first argument is the path to the .xlsx file. We also use the sheet argument to specify which sheet of the workbook we want.\n\npurchases <- read_excel(\"data/sfsnap.xlsx\", sheet = \"Purchase Data April 2019\")\n\nNote that the readxl package only provides functionality to, as the name implies, read Excel files. If you need to write your data out to .xls or .xlsx formats - don’t! We highly recommend saving data files as .csv files wherever possible. But if you you really need to, look into the writexl or openxlsx packages.\n\n\n4.7.2 Exercise\nStep 1: Use read_excel() to load in the table on the “Refinance Data April 2019” sheet into a data frame called refinances."
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-    "section": "4.8 The haven Package",
-    "text": "4.8 The haven Package\nFinally, your SAS, SPSS, or Stata data can find safe haven in R.\n\n4.8.1 Stata Data (.dta)\n\n4.8.1.1 Example\nFirst we’ll download and unzip the 2018 General Social Survey, which comes as a zipped .dta file.\n\ndownload_data(\"http://gss.norc.org/Documents/stata/2018_stata.zip\",\n              \"data/gss.zip\")\n\nunzip(\"data/gss.zip\", exdir = \"data/\")\n\nTo read a .dta file, we’ll use the aptly named read_dta function. As usual, the first argument to the function is the path to the data we want to import.\n\ngss <- read_dta(\"data/GSS2018.dta\")\n\n# We can also read in the zip file directly!\ngss <- read_dta(\"data/gss.zip\")\n\n\n\n\n4.8.2 SAS Data (.sas7bdat)\nWe use read_sas to read in SAS files. This works mostly identical to read_stata, and you can read in .sas7bdat + .sas7bcat, or zipped folders with sas files. Note that while the write_sas() function exists, it doesn’t appear to actually work at the current version of haven.\n\n4.8.2.1 Example\nDownload and read in the 2017 1-year Public Use Microsample for Wyoming from the Census:\n\ndownload_data(\"https://www2.census.gov/programs-surveys/acs/data/pums/2017/1-Year/unix_pwy.zip\",\n              \"data/pums_wy.zip\")\n\n# Unzip the files\nunzip(\"data/pums_wy.zip\", exdir = \"data/\")\n\n# Read in sas file\npums_data = read_sas(\"data/pums_wy.zip\")\n\n# Write out as dta file\npums_data %>% write_dta(\"modified/pums_wy.dta\")"
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-    "section": "4.9 The arrow Package",
-    "text": "4.9 The arrow Package\nSometimes when you save text files, the column types (ie numeric, date, time, text, etc.) get lost and won’t be read back in upon reimporting In this case you may want to save your files as a binary file. One binary file format we suggest is .feather files, which you can read more about. The main benefits of storing data in a binary format is:\n\nColumn types will be recorded and preserved\nImporting and exporting will be faster than other methods\nFile sizes will be smaller as binary files often use compression methods\n\nThe cons are that:\n\n.feather files can only easily be read into R and python currently\nThere are no nice GUI viewers (like Excel/Notepad for CSV data) to actually “see” the data files\n\nSo think before exercising this option! If you mainly will be working with your data in R and have large file sizes or complicated column types, .feather files may be a good option.\nYou can read and write these files with read_feather() and write_feather respectively.\n\n# Write out a feather file to `modified/folder`\nwrite_feather(ipeds_2011, \"modified/colleges_ipeds_completers_2011.feather\")\n\n# Read in a feather file\nipeds_2011 = read_feather(\"modified/colleges_ipeds_completers_2011.feather\")"
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\ No newline at end of file
diff --git a/docs/site_libs/bootstrap/bootstrap-icons.css b/docs/site_libs/bootstrap/bootstrap-icons.css
index f51d04b..94f1940 100644
--- a/docs/site_libs/bootstrap/bootstrap-icons.css
+++ b/docs/site_libs/bootstrap/bootstrap-icons.css
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+.bi-h-circle::before { content: "\f7fb"; }
+.bi-h-square-fill::before { content: "\f7fc"; }
+.bi-h-square::before { content: "\f7fd"; }
+.bi-indent::before { content: "\f7fe"; }
+.bi-lungs-fill::before { content: "\f7ff"; }
+.bi-lungs::before { content: "\f800"; }
+.bi-microsoft-teams::before { content: "\f801"; }
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+.bi-p-circle::before { content: "\f805"; }
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+.bi-p-square::before { content: "\f807"; }
+.bi-pass-fill::before { content: "\f808"; }
+.bi-pass::before { content: "\f809"; }
+.bi-prescription::before { content: "\f80a"; }
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+.bi-r-circle-fill::before { content: "\f80e"; }
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+.bi-r-square::before { content: "\f811"; }
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+.bi-rewind-btn-fill::before { content: "\f814"; }
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+.bi-rewind-circle::before { content: "\f817"; }
+.bi-rewind-fill::before { content: "\f818"; }
+.bi-rewind::before { content: "\f819"; }
+.bi-train-freight-front-fill::before { content: "\f81a"; }
+.bi-train-freight-front::before { content: "\f81b"; }
+.bi-train-front-fill::before { content: "\f81c"; }
+.bi-train-front::before { content: "\f81d"; }
+.bi-train-lightrail-front-fill::before { content: "\f81e"; }
+.bi-train-lightrail-front::before { content: "\f81f"; }
+.bi-truck-front-fill::before { content: "\f820"; }
+.bi-truck-front::before { content: "\f821"; }
+.bi-ubuntu::before { content: "\f822"; }
+.bi-unindent::before { content: "\f823"; }
+.bi-unity::before { content: "\f824"; }
+.bi-universal-access-circle::before { content: "\f825"; }
+.bi-universal-access::before { content: "\f826"; }
+.bi-virus::before { content: "\f827"; }
+.bi-virus2::before { content: "\f828"; }
+.bi-wechat::before { content: "\f829"; }
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+.bi-sign-stop-fill::before { content: "\f82b"; }
+.bi-sign-stop-lights-fill::before { content: "\f82c"; }
+.bi-sign-stop-lights::before { content: "\f82d"; }
+.bi-sign-stop::before { content: "\f82e"; }
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+.bi-sign-turn-right-fill::before { content: "\f831"; }
+.bi-sign-turn-right::before { content: "\f832"; }
+.bi-sign-turn-slight-left-fill::before { content: "\f833"; }
+.bi-sign-turn-slight-left::before { content: "\f834"; }
+.bi-sign-turn-slight-right-fill::before { content: "\f835"; }
+.bi-sign-turn-slight-right::before { content: "\f836"; }
+.bi-sign-yield-fill::before { content: "\f837"; }
+.bi-sign-yield::before { content: "\f838"; }
+.bi-ev-station-fill::before { content: "\f839"; }
+.bi-ev-station::before { content: "\f83a"; }
+.bi-fuel-pump-diesel-fill::before { content: "\f83b"; }
+.bi-fuel-pump-diesel::before { content: "\f83c"; }
+.bi-fuel-pump-fill::before { content: "\f83d"; }
+.bi-fuel-pump::before { content: "\f83e"; }
+.bi-0-circle-fill::before { content: "\f83f"; }
+.bi-0-circle::before { content: "\f840"; }
+.bi-0-square-fill::before { content: "\f841"; }
+.bi-0-square::before { content: "\f842"; }
+.bi-rocket-fill::before { content: "\f843"; }
+.bi-rocket-takeoff-fill::before { content: "\f844"; }
+.bi-rocket-takeoff::before { content: "\f845"; }
+.bi-rocket::before { content: "\f846"; }
+.bi-stripe::before { content: "\f847"; }
+.bi-subscript::before { content: "\f848"; }
+.bi-superscript::before { content: "\f849"; }
+.bi-trello::before { content: "\f84a"; }
+.bi-envelope-at-fill::before { content: "\f84b"; }
+.bi-envelope-at::before { content: "\f84c"; }
+.bi-regex::before { content: "\f84d"; }
+.bi-text-wrap::before { content: "\f84e"; }
+.bi-sign-dead-end-fill::before { content: "\f84f"; }
+.bi-sign-dead-end::before { content: "\f850"; }
+.bi-sign-do-not-enter-fill::before { content: "\f851"; }
+.bi-sign-do-not-enter::before { content: "\f852"; }
+.bi-sign-intersection-fill::before { content: "\f853"; }
+.bi-sign-intersection-side-fill::before { content: "\f854"; }
+.bi-sign-intersection-side::before { content: "\f855"; }
+.bi-sign-intersection-t-fill::before { content: "\f856"; }
+.bi-sign-intersection-t::before { content: "\f857"; }
+.bi-sign-intersection-y-fill::before { content: "\f858"; }
+.bi-sign-intersection-y::before { content: "\f859"; }
+.bi-sign-intersection::before { content: "\f85a"; }
+.bi-sign-merge-left-fill::before { content: "\f85b"; }
+.bi-sign-merge-left::before { content: "\f85c"; }
+.bi-sign-merge-right-fill::before { content: "\f85d"; }
+.bi-sign-merge-right::before { content: "\f85e"; }
+.bi-sign-no-left-turn-fill::before { content: "\f85f"; }
+.bi-sign-no-left-turn::before { content: "\f860"; }
+.bi-sign-no-parking-fill::before { content: "\f861"; }
+.bi-sign-no-parking::before { content: "\f862"; }
+.bi-sign-no-right-turn-fill::before { content: "\f863"; }
+.bi-sign-no-right-turn::before { content: "\f864"; }
+.bi-sign-railroad-fill::before { content: "\f865"; }
+.bi-sign-railroad::before { content: "\f866"; }
+.bi-building-add::before { content: "\f867"; }
+.bi-building-check::before { content: "\f868"; }
+.bi-building-dash::before { content: "\f869"; }
+.bi-building-down::before { content: "\f86a"; }
+.bi-building-exclamation::before { content: "\f86b"; }
+.bi-building-fill-add::before { content: "\f86c"; }
+.bi-building-fill-check::before { content: "\f86d"; }
+.bi-building-fill-dash::before { content: "\f86e"; }
+.bi-building-fill-down::before { content: "\f86f"; }
+.bi-building-fill-exclamation::before { content: "\f870"; }
+.bi-building-fill-gear::before { content: "\f871"; }
+.bi-building-fill-lock::before { content: "\f872"; }
+.bi-building-fill-slash::before { content: "\f873"; }
+.bi-building-fill-up::before { content: "\f874"; }
+.bi-building-fill-x::before { content: "\f875"; }
+.bi-building-fill::before { content: "\f876"; }
+.bi-building-gear::before { content: "\f877"; }
+.bi-building-lock::before { content: "\f878"; }
+.bi-building-slash::before { content: "\f879"; }
+.bi-building-up::before { content: "\f87a"; }
+.bi-building-x::before { content: "\f87b"; }
+.bi-buildings-fill::before { content: "\f87c"; }
+.bi-buildings::before { content: "\f87d"; }
+.bi-bus-front-fill::before { content: "\f87e"; }
+.bi-bus-front::before { content: "\f87f"; }
+.bi-ev-front-fill::before { content: "\f880"; }
+.bi-ev-front::before { content: "\f881"; }
+.bi-globe-americas::before { content: "\f882"; }
+.bi-globe-asia-australia::before { content: "\f883"; }
+.bi-globe-central-south-asia::before { content: "\f884"; }
+.bi-globe-europe-africa::before { content: "\f885"; }
+.bi-house-add-fill::before { content: "\f886"; }
+.bi-house-add::before { content: "\f887"; }
+.bi-house-check-fill::before { content: "\f888"; }
+.bi-house-check::before { content: "\f889"; }
+.bi-house-dash-fill::before { content: "\f88a"; }
+.bi-house-dash::before { content: "\f88b"; }
+.bi-house-down-fill::before { content: "\f88c"; }
+.bi-house-down::before { content: "\f88d"; }
+.bi-house-exclamation-fill::before { content: "\f88e"; }
+.bi-house-exclamation::before { content: "\f88f"; }
+.bi-house-gear-fill::before { content: "\f890"; }
+.bi-house-gear::before { content: "\f891"; }
+.bi-house-lock-fill::before { content: "\f892"; }
+.bi-house-lock::before { content: "\f893"; }
+.bi-house-slash-fill::before { content: "\f894"; }
+.bi-house-slash::before { content: "\f895"; }
+.bi-house-up-fill::before { content: "\f896"; }
+.bi-house-up::before { content: "\f897"; }
+.bi-house-x-fill::before { content: "\f898"; }
+.bi-house-x::before { content: "\f899"; }
+.bi-person-add::before { content: "\f89a"; }
+.bi-person-down::before { content: "\f89b"; }
+.bi-person-exclamation::before { content: "\f89c"; }
+.bi-person-fill-add::before { content: "\f89d"; }
+.bi-person-fill-check::before { content: "\f89e"; }
+.bi-person-fill-dash::before { content: "\f89f"; }
+.bi-person-fill-down::before { content: "\f8a0"; }
+.bi-person-fill-exclamation::before { content: "\f8a1"; }
+.bi-person-fill-gear::before { content: "\f8a2"; }
+.bi-person-fill-lock::before { content: "\f8a3"; }
+.bi-person-fill-slash::before { content: "\f8a4"; }
+.bi-person-fill-up::before { content: "\f8a5"; }
+.bi-person-fill-x::before { content: "\f8a6"; }
+.bi-person-gear::before { content: "\f8a7"; }
+.bi-person-lock::before { content: "\f8a8"; }
+.bi-person-slash::before { content: "\f8a9"; }
+.bi-person-up::before { content: "\f8aa"; }
+.bi-scooter::before { content: "\f8ab"; }
+.bi-taxi-front-fill::before { content: "\f8ac"; }
+.bi-taxi-front::before { content: "\f8ad"; }
+.bi-amd::before { content: "\f8ae"; }
+.bi-database-add::before { content: "\f8af"; }
+.bi-database-check::before { content: "\f8b0"; }
+.bi-database-dash::before { content: "\f8b1"; }
+.bi-database-down::before { content: "\f8b2"; }
+.bi-database-exclamation::before { content: "\f8b3"; }
+.bi-database-fill-add::before { content: "\f8b4"; }
+.bi-database-fill-check::before { content: "\f8b5"; }
+.bi-database-fill-dash::before { content: "\f8b6"; }
+.bi-database-fill-down::before { content: "\f8b7"; }
+.bi-database-fill-exclamation::before { content: "\f8b8"; }
+.bi-database-fill-gear::before { content: "\f8b9"; }
+.bi-database-fill-lock::before { content: "\f8ba"; }
+.bi-database-fill-slash::before { content: "\f8bb"; }
+.bi-database-fill-up::before { content: "\f8bc"; }
+.bi-database-fill-x::before { content: "\f8bd"; }
+.bi-database-fill::before { content: "\f8be"; }
+.bi-database-gear::before { content: "\f8bf"; }
+.bi-database-lock::before { content: "\f8c0"; }
+.bi-database-slash::before { content: "\f8c1"; }
+.bi-database-up::before { content: "\f8c2"; }
+.bi-database-x::before { content: "\f8c3"; }
+.bi-database::before { content: "\f8c4"; }
+.bi-houses-fill::before { content: "\f8c5"; }
+.bi-houses::before { content: "\f8c6"; }
+.bi-nvidia::before { content: "\f8c7"; }
+.bi-person-vcard-fill::before { content: "\f8c8"; }
+.bi-person-vcard::before { content: "\f8c9"; }
+.bi-sina-weibo::before { content: "\f8ca"; }
+.bi-tencent-qq::before { content: "\f8cb"; }
+.bi-wikipedia::before { content: "\f8cc"; }
diff --git a/docs/site_libs/bootstrap/bootstrap-icons.woff b/docs/site_libs/bootstrap/bootstrap-icons.woff
index b26ccd1..18d21d4 100644
Binary files a/docs/site_libs/bootstrap/bootstrap-icons.woff and b/docs/site_libs/bootstrap/bootstrap-icons.woff differ
diff --git a/docs/site_libs/bootstrap/bootstrap.min.css b/docs/site_libs/bootstrap/bootstrap.min.css
index 6d96bb2..b868f7a 100644
--- a/docs/site_libs/bootstrap/bootstrap.min.css
+++ b/docs/site_libs/bootstrap/bootstrap.min.css
@@ -3,8 +3,8 @@
  * Copyright 2011-2021 The Bootstrap Authors
  * Copyright 2011-2021 Twitter, Inc.
  * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)
- */:root{--bs-blue: #0d6efd;--bs-indigo: #6610f2;--bs-purple: #6f42c1;--bs-pink: #d63384;--bs-red: #dc3545;--bs-orange: #fd7e14;--bs-yellow: #ffc107;--bs-green: #198754;--bs-teal: #20c997;--bs-cyan: #0dcaf0;--bs-white: #ffffff;--bs-gray: #6c757d;--bs-gray-dark: #343a40;--bs-gray-100: #f8f9fa;--bs-gray-200: #e9ecef;--bs-gray-300: #dee2e6;--bs-gray-400: #ced4da;--bs-gray-500: #adb5bd;--bs-gray-600: #6c757d;--bs-gray-700: #495057;--bs-gray-800: #343a40;--bs-gray-900: #212529;--bs-default: #dee2e6;--bs-primary: #0d6efd;--bs-secondary: #6c757d;--bs-success: #198754;--bs-info: #0dcaf0;--bs-warning: #ffc107;--bs-danger: #dc3545;--bs-light: #f8f9fa;--bs-dark: #212529;--bs-default-rgb: 222, 226, 230;--bs-primary-rgb: 13, 110, 253;--bs-secondary-rgb: 108, 117, 125;--bs-success-rgb: 25, 135, 84;--bs-info-rgb: 13, 202, 240;--bs-warning-rgb: 255, 193, 7;--bs-danger-rgb: 220, 53, 69;--bs-light-rgb: 248, 249, 250;--bs-dark-rgb: 33, 37, 41;--bs-white-rgb: 255, 255, 255;--bs-black-rgb: 0, 0, 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#ffffff}*,*::before,*::after{box-sizing:border-box}:root{font-size:var(--bs-root-font-size)}body{margin:0;font-family:var(--bs-body-font-family);font-size:var(--bs-body-font-size);font-weight:var(--bs-body-font-weight);line-height:var(--bs-body-line-height);color:var(--bs-body-color);text-align:var(--bs-body-text-align);background-color:var(--bs-body-bg);-webkit-text-size-adjust:100%;-webkit-tap-highlight-color:rgba(0,0,0,0)}hr{margin:1rem 0;color:inherit;background-color:currentColor;border:0;opacity:.25}hr:not([size]){height:1px}h6,.h6,h5,.h5,h4,.h4,h3,.h3,h2,.h2,h1,.h1{margin-top:0;margin-bottom:.5rem;font-weight:500;line-height:1.2}h1,.h1{font-size:calc(1.345rem + 1.14vw)}@media(min-width: 1200px){h1,.h1{font-size:2.2rem}}h2,.h2{font-size:calc(1.3rem + 0.6vw)}@media(min-width: 1200px){h2,.h2{font-size:1.75rem}}h3,.h3{font-size:calc(1.275rem + 0.3vw)}@media(min-width: 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1rem}img,svg{vertical-align:middle}table{caption-side:bottom;border-collapse:collapse}caption{padding-top:.5rem;padding-bottom:.5rem;color:#6c757d;text-align:left}th{text-align:inherit;text-align:-webkit-match-parent}thead,tbody,tfoot,tr,td,th{border-color:inherit;border-style:solid;border-width:0}label{display:inline-block}button{border-radius:0}button:focus:not(:focus-visible){outline:0}input,button,select,optgroup,textarea{margin:0;font-family:inherit;font-size:inherit;line-height:inherit}button,select{text-transform:none}[role=button]{cursor:pointer}select{word-wrap:normal}select:disabled{opacity:1}[list]::-webkit-calendar-picker-indicator{display:none}button,[type=button],[type=reset],[type=submit]{-webkit-appearance:button}button:not(:disabled),[type=button]:not(:disabled),[type=reset]:not(:disabled),[type=submit]:not(:disabled){cursor:pointer}::-moz-focus-inner{padding:0;border-style:none}textarea{resize:vertical}fieldset{min-width:0;padding:0;margin:0;border:0}legend{float:left;width:100%;padding:0;margin-bottom:.5rem;font-size:calc(1.275rem + 0.3vw);line-height:inherit}@media(min-width: 1200px){legend{font-size:1.5rem}}legend+*{clear:left}::-webkit-datetime-edit-fields-wrapper,::-webkit-datetime-edit-text,::-webkit-datetime-edit-minute,::-webkit-datetime-edit-hour-field,::-webkit-datetime-edit-day-field,::-webkit-datetime-edit-month-field,::-webkit-datetime-edit-year-field{padding:0}::-webkit-inner-spin-button{height:auto}[type=search]{outline-offset:-2px;-webkit-appearance:textfield}::-webkit-search-decoration{-webkit-appearance:none}::-webkit-color-swatch-wrapper{padding:0}::file-selector-button{font:inherit}::-webkit-file-upload-button{font:inherit;-webkit-appearance:button}output{display:inline-block}iframe{border:0}summary{display:list-item;cursor:pointer}progress{vertical-align:baseline}[hidden]{display:none !important}.lead{font-size:1.25rem;font-weight:300}.display-1{font-size:calc(1.625rem + 4.5vw);font-weight:300;line-height:1.2}@media(min-width: 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e=t.delegateTarget||t.currentTarget,n=this.action(e)||"copy",t=s({action:n,container:this.container,target:this.target(e),text:this.text(e)});this.emit(t?"success":"error",{action:n,text:t,trigger:e,clearSelection:function(){e&&e.focus(),document.activeElement.blur(),window.getSelection().removeAllRanges()}})}},{key:"defaultAction",value:function(t){return v("action",t)}},{key:"defaultTarget",value:function(t){t=v("target",t);if(t)return document.querySelector(t)}},{key:"defaultText",value:function(t){return v("text",t)}},{key:"destroy",value:function(){this.listener.destroy()}}])&&d(t.prototype,e),n&&d(t,n),r}()},828:function(t){var e;"undefined"==typeof Element||Element.prototype.matches||((e=Element.prototype).matches=e.matchesSelector||e.mozMatchesSelector||e.msMatchesSelector||e.oMatchesSelector||e.webkitMatchesSelector),t.exports=function(t,e){for(;t&&9!==t.nodeType;){if("function"==typeof t.matches&&t.matches(e))return t;t=t.parentNode}}},438:function(t,e,n){var u=n(828);function i(t,e,n,o,r){var i=function(e,n,t,o){return function(t){t.delegateTarget=u(t.target,n),t.delegateTarget&&o.call(e,t)}}.apply(this,arguments);return t.addEventListener(n,i,r),{destroy:function(){t.removeEventListener(n,i,r)}}}t.exports=function(t,e,n,o,r){return"function"==typeof t.addEventListener?i.apply(null,arguments):"function"==typeof n?i.bind(null,document).apply(null,arguments):("string"==typeof t&&(t=document.querySelectorAll(t)),Array.prototype.map.call(t,function(t){return i(t,e,n,o,r)}))}},879:function(t,n){n.node=function(t){return void 0!==t&&t instanceof HTMLElement&&1===t.nodeType},n.nodeList=function(t){var e=Object.prototype.toString.call(t);return void 0!==t&&("[object NodeList]"===e||"[object HTMLCollection]"===e)&&"length"in t&&(0===t.length||n.node(t[0]))},n.string=function(t){return"string"==typeof t||t instanceof String},n.fn=function(t){return"[object Function]"===Object.prototype.toString.call(t)}},370:function(t,e,n){var f=n(879),l=n(438);t.exports=function(t,e,n){if(!t&&!e&&!n)throw new Error("Missing required arguments");if(!f.string(e))throw new TypeError("Second argument must be a String");if(!f.fn(n))throw new TypeError("Third argument must be a Function");if(f.node(t))return c=e,a=n,(u=t).addEventListener(c,a),{destroy:function(){u.removeEventListener(c,a)}};if(f.nodeList(t))return o=t,r=e,i=n,Array.prototype.forEach.call(o,function(t){t.addEventListener(r,i)}),{destroy:function(){Array.prototype.forEach.call(o,function(t){t.removeEventListener(r,i)})}};if(f.string(t))return t=t,e=e,n=n,l(document.body,t,e,n);throw new TypeError("First argument must be a String, HTMLElement, HTMLCollection, or NodeList");var o,r,i,u,c,a}},817:function(t){t.exports=function(t){var e,n="SELECT"===t.nodeName?(t.focus(),t.value):"INPUT"===t.nodeName||"TEXTAREA"===t.nodeName?((e=t.hasAttribute("readonly"))||t.setAttribute("readonly",""),t.select(),t.setSelectionRange(0,t.value.length),e||t.removeAttribute("readonly"),t.value):(t.hasAttribute("contenteditable")&&t.focus(),n=window.getSelection(),(e=document.createRange()).selectNodeContents(t),n.removeAllRanges(),n.addRange(e),n.toString());return n}},279:function(t){function e(){}e.prototype={on:function(t,e,n){var o=this.e||(this.e={});return(o[t]||(o[t]=[])).push({fn:e,ctx:n}),this},once:function(t,e,n){var o=this;function r(){o.off(t,r),e.apply(n,arguments)}return r._=e,this.on(t,r,n)},emit:function(t){for(var e=[].slice.call(arguments,1),n=((this.e||(this.e={}))[t]||[]).slice(),o=0,r=n.length;o<r;o++)n[o].fn.apply(n[o].ctx,e);return this},off:function(t,e){var n=this.e||(this.e={}),o=n[t],r=[];if(o&&e)for(var i=0,u=o.length;i<u;i++)o[i].fn!==e&&o[i].fn._!==e&&r.push(o[i]);return r.length?n[t]=r:delete n[t],this}},t.exports=e,t.exports.TinyEmitter=e}},r={},o.n=function(t){var e=t&&t.__esModule?function(){return t.default}:function(){return t};return o.d(e,{a:e}),e},o.d=function(t,e){for(var n in e)o.o(e,n)&&!o.o(t,n)&&Object.defineProperty(t,n,{enumerable:!0,get:e[n]})},o.o=function(t,e){return Object.prototype.hasOwnProperty.call(t,e)},o(686).default;function o(t){if(r[t])return r[t].exports;var e=r[t]={exports:{}};return n[t](e,e.exports,o),e.exports}var n,r});
\ No newline at end of file
+!function(t,e){"object"==typeof exports&&"object"==typeof module?module.exports=e():"function"==typeof define&&define.amd?define([],e):"object"==typeof exports?exports.ClipboardJS=e():t.ClipboardJS=e()}(this,function(){return n={686:function(t,e,n){"use strict";n.d(e,{default:function(){return b}});var e=n(279),i=n.n(e),e=n(370),u=n.n(e),e=n(817),r=n.n(e);function c(t){try{return document.execCommand(t)}catch(t){return}}var a=function(t){t=r()(t);return c("cut"),t};function o(t,e){var n,o,t=(n=t,o="rtl"===document.documentElement.getAttribute("dir"),(t=document.createElement("textarea")).style.fontSize="12pt",t.style.border="0",t.style.padding="0",t.style.margin="0",t.style.position="absolute",t.style[o?"right":"left"]="-9999px",o=window.pageYOffset||document.documentElement.scrollTop,t.style.top="".concat(o,"px"),t.setAttribute("readonly",""),t.value=n,t);return e.container.appendChild(t),e=r()(t),c("copy"),t.remove(),e}var f=function(t){var e=1<arguments.length&&void 0!==arguments[1]?arguments[1]:{container:document.body},n="";return"string"==typeof t?n=o(t,e):t instanceof HTMLInputElement&&!["text","search","url","tel","password"].includes(null==t?void 0:t.type)?n=o(t.value,e):(n=r()(t),c("copy")),n};function l(t){return(l="function"==typeof Symbol&&"symbol"==typeof Symbol.iterator?function(t){return typeof t}:function(t){return t&&"function"==typeof Symbol&&t.constructor===Symbol&&t!==Symbol.prototype?"symbol":typeof t})(t)}var s=function(){var t=0<arguments.length&&void 0!==arguments[0]?arguments[0]:{},e=t.action,n=void 0===e?"copy":e,o=t.container,e=t.target,t=t.text;if("copy"!==n&&"cut"!==n)throw new Error('Invalid "action" value, use either "copy" or "cut"');if(void 0!==e){if(!e||"object"!==l(e)||1!==e.nodeType)throw new Error('Invalid "target" value, use a valid Element');if("copy"===n&&e.hasAttribute("disabled"))throw new Error('Invalid "target" attribute. Please use "readonly" instead of "disabled" attribute');if("cut"===n&&(e.hasAttribute("readonly")||e.hasAttribute("disabled")))throw new Error('Invalid "target" attribute. You can\'t cut text from elements with "readonly" or "disabled" attributes')}return t?f(t,{container:o}):e?"cut"===n?a(e):f(e,{container:o}):void 0};function p(t){return(p="function"==typeof Symbol&&"symbol"==typeof Symbol.iterator?function(t){return typeof t}:function(t){return t&&"function"==typeof Symbol&&t.constructor===Symbol&&t!==Symbol.prototype?"symbol":typeof t})(t)}function d(t,e){for(var n=0;n<e.length;n++){var o=e[n];o.enumerable=o.enumerable||!1,o.configurable=!0,"value"in o&&(o.writable=!0),Object.defineProperty(t,o.key,o)}}function y(t,e){return(y=Object.setPrototypeOf||function(t,e){return t.__proto__=e,t})(t,e)}function h(n){var o=function(){if("undefined"==typeof Reflect||!Reflect.construct)return!1;if(Reflect.construct.sham)return!1;if("function"==typeof Proxy)return!0;try{return Date.prototype.toString.call(Reflect.construct(Date,[],function(){})),!0}catch(t){return!1}}();return function(){var t,e=v(n);return t=o?(t=v(this).constructor,Reflect.construct(e,arguments,t)):e.apply(this,arguments),e=this,!(t=t)||"object"!==p(t)&&"function"!=typeof t?function(t){if(void 0!==t)return t;throw new ReferenceError("this hasn't been initialised - super() hasn't been called")}(e):t}}function v(t){return(v=Object.setPrototypeOf?Object.getPrototypeOf:function(t){return t.__proto__||Object.getPrototypeOf(t)})(t)}function m(t,e){t="data-clipboard-".concat(t);if(e.hasAttribute(t))return e.getAttribute(t)}var b=function(){!function(t,e){if("function"!=typeof e&&null!==e)throw new TypeError("Super expression must either be null or a function");t.prototype=Object.create(e&&e.prototype,{constructor:{value:t,writable:!0,configurable:!0}}),e&&y(t,e)}(r,i());var t,e,n,o=h(r);function r(t,e){var n;return function(t){if(!(t instanceof r))throw new TypeError("Cannot call a class as a function")}(this),(n=o.call(this)).resolveOptions(e),n.listenClick(t),n}return t=r,n=[{key:"copy",value:function(t){var e=1<arguments.length&&void 0!==arguments[1]?arguments[1]:{container:document.body};return f(t,e)}},{key:"cut",value:function(t){return a(t)}},{key:"isSupported",value:function(){var t=0<arguments.length&&void 0!==arguments[0]?arguments[0]:["copy","cut"],t="string"==typeof t?[t]:t,e=!!document.queryCommandSupported;return t.forEach(function(t){e=e&&!!document.queryCommandSupported(t)}),e}}],(e=[{key:"resolveOptions",value:function(){var t=0<arguments.length&&void 0!==arguments[0]?arguments[0]:{};this.action="function"==typeof t.action?t.action:this.defaultAction,this.target="function"==typeof t.target?t.target:this.defaultTarget,this.text="function"==typeof t.text?t.text:this.defaultText,this.container="object"===p(t.container)?t.container:document.body}},{key:"listenClick",value:function(t){var e=this;this.listener=u()(t,"click",function(t){return e.onClick(t)})}},{key:"onClick",value:function(t){var e=t.delegateTarget||t.currentTarget,n=this.action(e)||"copy",t=s({action:n,container:this.container,target:this.target(e),text:this.text(e)});this.emit(t?"success":"error",{action:n,text:t,trigger:e,clearSelection:function(){e&&e.focus(),window.getSelection().removeAllRanges()}})}},{key:"defaultAction",value:function(t){return m("action",t)}},{key:"defaultTarget",value:function(t){t=m("target",t);if(t)return document.querySelector(t)}},{key:"defaultText",value:function(t){return m("text",t)}},{key:"destroy",value:function(){this.listener.destroy()}}])&&d(t.prototype,e),n&&d(t,n),r}()},828:function(t){var e;"undefined"==typeof Element||Element.prototype.matches||((e=Element.prototype).matches=e.matchesSelector||e.mozMatchesSelector||e.msMatchesSelector||e.oMatchesSelector||e.webkitMatchesSelector),t.exports=function(t,e){for(;t&&9!==t.nodeType;){if("function"==typeof t.matches&&t.matches(e))return t;t=t.parentNode}}},438:function(t,e,n){var u=n(828);function i(t,e,n,o,r){var i=function(e,n,t,o){return function(t){t.delegateTarget=u(t.target,n),t.delegateTarget&&o.call(e,t)}}.apply(this,arguments);return t.addEventListener(n,i,r),{destroy:function(){t.removeEventListener(n,i,r)}}}t.exports=function(t,e,n,o,r){return"function"==typeof t.addEventListener?i.apply(null,arguments):"function"==typeof n?i.bind(null,document).apply(null,arguments):("string"==typeof t&&(t=document.querySelectorAll(t)),Array.prototype.map.call(t,function(t){return i(t,e,n,o,r)}))}},879:function(t,n){n.node=function(t){return void 0!==t&&t instanceof HTMLElement&&1===t.nodeType},n.nodeList=function(t){var e=Object.prototype.toString.call(t);return void 0!==t&&("[object NodeList]"===e||"[object HTMLCollection]"===e)&&"length"in t&&(0===t.length||n.node(t[0]))},n.string=function(t){return"string"==typeof t||t instanceof String},n.fn=function(t){return"[object Function]"===Object.prototype.toString.call(t)}},370:function(t,e,n){var f=n(879),l=n(438);t.exports=function(t,e,n){if(!t&&!e&&!n)throw new Error("Missing required arguments");if(!f.string(e))throw new TypeError("Second argument must be a String");if(!f.fn(n))throw new TypeError("Third argument must be a Function");if(f.node(t))return c=e,a=n,(u=t).addEventListener(c,a),{destroy:function(){u.removeEventListener(c,a)}};if(f.nodeList(t))return o=t,r=e,i=n,Array.prototype.forEach.call(o,function(t){t.addEventListener(r,i)}),{destroy:function(){Array.prototype.forEach.call(o,function(t){t.removeEventListener(r,i)})}};if(f.string(t))return t=t,e=e,n=n,l(document.body,t,e,n);throw new TypeError("First argument must be a String, HTMLElement, HTMLCollection, or NodeList");var o,r,i,u,c,a}},817:function(t){t.exports=function(t){var e,n="SELECT"===t.nodeName?(t.focus(),t.value):"INPUT"===t.nodeName||"TEXTAREA"===t.nodeName?((e=t.hasAttribute("readonly"))||t.setAttribute("readonly",""),t.select(),t.setSelectionRange(0,t.value.length),e||t.removeAttribute("readonly"),t.value):(t.hasAttribute("contenteditable")&&t.focus(),n=window.getSelection(),(e=document.createRange()).selectNodeContents(t),n.removeAllRanges(),n.addRange(e),n.toString());return n}},279:function(t){function e(){}e.prototype={on:function(t,e,n){var o=this.e||(this.e={});return(o[t]||(o[t]=[])).push({fn:e,ctx:n}),this},once:function(t,e,n){var o=this;function r(){o.off(t,r),e.apply(n,arguments)}return r._=e,this.on(t,r,n)},emit:function(t){for(var e=[].slice.call(arguments,1),n=((this.e||(this.e={}))[t]||[]).slice(),o=0,r=n.length;o<r;o++)n[o].fn.apply(n[o].ctx,e);return this},off:function(t,e){var n=this.e||(this.e={}),o=n[t],r=[];if(o&&e)for(var i=0,u=o.length;i<u;i++)o[i].fn!==e&&o[i].fn._!==e&&r.push(o[i]);return r.length?n[t]=r:delete n[t],this}},t.exports=e,t.exports.TinyEmitter=e}},r={},o.n=function(t){var e=t&&t.__esModule?function(){return t.default}:function(){return t};return o.d(e,{a:e}),e},o.d=function(t,e){for(var n in e)o.o(e,n)&&!o.o(t,n)&&Object.defineProperty(t,n,{enumerable:!0,get:e[n]})},o.o=function(t,e){return Object.prototype.hasOwnProperty.call(t,e)},o(686).default;function o(t){if(r[t])return r[t].exports;var e=r[t]={exports:{}};return n[t](e,e.exports,o),e.exports}var n,r});
\ No newline at end of file
diff --git a/docs/site_libs/quarto-html/quarto-syntax-highlighting.css b/docs/site_libs/quarto-html/quarto-syntax-highlighting.css
index 36cb328..d9fd98f 100644
--- a/docs/site_libs/quarto-html/quarto-syntax-highlighting.css
+++ b/docs/site_libs/quarto-html/quarto-syntax-highlighting.css
@@ -55,62 +55,85 @@ div.sourceCode pre.sourceCode {
 
 code span.ot {
   color: #003B4F;
+  font-style: inherit;
 }
 
 code span.at {
   color: #657422;
+  font-style: inherit;
 }
 
 code span.ss {
   color: #20794D;
+  font-style: inherit;
 }
 
 code span.an {
   color: #5E5E5E;
+  font-style: inherit;
 }
 
 code span.fu {
   color: #4758AB;
+  font-style: inherit;
 }
 
 code span.st {
   color: #20794D;
+  font-style: inherit;
 }
 
 code span.cf {
   color: #003B4F;
+  font-style: inherit;
 }
 
 code span.op {
   color: #5E5E5E;
+  font-style: inherit;
 }
 
 code span.er {
   color: #AD0000;
+  font-style: inherit;
 }
 
 code span.bn {
   color: #AD0000;
+  font-style: inherit;
 }
 
 code span.al {
   color: #AD0000;
+  font-style: inherit;
 }
 
 code span.va {
   color: #111111;
+  font-style: inherit;
+}
+
+code span.bu {
+  font-style: inherit;
+}
+
+code span.ex {
+  font-style: inherit;
 }
 
 code span.pp {
   color: #AD0000;
+  font-style: inherit;
 }
 
 code span.in {
   color: #5E5E5E;
+  font-style: inherit;
 }
 
 code span.vs {
   color: #20794D;
+  font-style: inherit;
 }
 
 code span.wa {
@@ -125,22 +148,27 @@ code span.do {
 
 code span.im {
   color: #00769E;
+  font-style: inherit;
 }
 
 code span.ch {
   color: #20794D;
+  font-style: inherit;
 }
 
 code span.dt {
   color: #AD0000;
+  font-style: inherit;
 }
 
 code span.fl {
   color: #AD0000;
+  font-style: inherit;
 }
 
 code span.co {
   color: #5E5E5E;
+  font-style: inherit;
 }
 
 code span.cv {
@@ -150,18 +178,22 @@ code span.cv {
 
 code span.cn {
   color: #8f5902;
+  font-style: inherit;
 }
 
 code span.sc {
   color: #5E5E5E;
+  font-style: inherit;
 }
 
 code span.dv {
   color: #AD0000;
+  font-style: inherit;
 }
 
 code span.kw {
   color: #003B4F;
+  font-style: inherit;
 }
 
 .prevent-inlining {
diff --git a/docs/site_libs/quarto-html/quarto.js b/docs/site_libs/quarto-html/quarto.js
index e62a8cb..c3935c7 100644
--- a/docs/site_libs/quarto-html/quarto.js
+++ b/docs/site_libs/quarto-html/quarto.js
@@ -5,7 +5,52 @@ const sectionChanged = new CustomEvent("quarto-sectionChanged", {
   composed: false,
 });
 
+const layoutMarginEls = () => {
+  // Find any conflicting margin elements and add margins to the
+  // top to prevent overlap
+  const marginChildren = window.document.querySelectorAll(
+    ".column-margin.column-container > * "
+  );
+
+  let lastBottom = 0;
+  for (const marginChild of marginChildren) {
+    if (marginChild.offsetParent !== null) {
+      // clear the top margin so we recompute it
+      marginChild.style.marginTop = null;
+      const top = marginChild.getBoundingClientRect().top + window.scrollY;
+      console.log({
+        childtop: marginChild.getBoundingClientRect().top,
+        scroll: window.scrollY,
+        top,
+        lastBottom,
+      });
+      if (top < lastBottom) {
+        const margin = lastBottom - top;
+        marginChild.style.marginTop = `${margin}px`;
+      }
+      const styles = window.getComputedStyle(marginChild);
+      const marginTop = parseFloat(styles["marginTop"]);
+
+      console.log({
+        top,
+        height: marginChild.getBoundingClientRect().height,
+        marginTop,
+        total: top + marginChild.getBoundingClientRect().height + marginTop,
+      });
+      lastBottom = top + marginChild.getBoundingClientRect().height + marginTop;
+    }
+  }
+};
+
 window.document.addEventListener("DOMContentLoaded", function (_event) {
+  // Recompute the position of margin elements anytime the body size changes
+  if (window.ResizeObserver) {
+    const resizeObserver = new window.ResizeObserver(
+      throttle(layoutMarginEls, 50)
+    );
+    resizeObserver.observe(window.document.body);
+  }
+
   const tocEl = window.document.querySelector('nav.toc-active[role="doc-toc"]');
   const sidebarEl = window.document.getElementById("quarto-sidebar");
   const leftTocEl = window.document.getElementById("quarto-sidebar-toc-left");
@@ -259,6 +304,7 @@ window.document.addEventListener("DOMContentLoaded", function (_event) {
 
   const manageSidebarVisiblity = (el, placeholderDescriptor) => {
     let isVisible = true;
+    let elRect;
 
     return (hiddenRegions) => {
       if (el === null) {
@@ -269,11 +315,6 @@ window.document.addEventListener("DOMContentLoaded", function (_event) {
       const lastChildEl = el.lastElementChild;
 
       if (lastChildEl) {
-        // Find the top and bottom o the element that is being managed
-        const elTop = el.offsetTop;
-        const elBottom =
-          elTop + lastChildEl.offsetTop + lastChildEl.offsetHeight;
-
         // Converts the sidebar to a menu
         const convertToMenu = () => {
           for (const child of el.children) {
@@ -281,100 +322,112 @@ window.document.addEventListener("DOMContentLoaded", function (_event) {
             child.style.overflow = "hidden";
           }
 
-          const toggleContainer = window.document.createElement("div");
-          toggleContainer.style.width = "100%";
-          toggleContainer.classList.add("zindex-over-content");
-          toggleContainer.classList.add("quarto-sidebar-toggle");
-          toggleContainer.classList.add("headroom-target"); // Marks this to be managed by headeroom
-          toggleContainer.id = placeholderDescriptor.id;
-          toggleContainer.style.position = "fixed";
-
-          const toggleIcon = window.document.createElement("i");
-          toggleIcon.classList.add("quarto-sidebar-toggle-icon");
-          toggleIcon.classList.add("bi");
-          toggleIcon.classList.add("bi-caret-down-fill");
-
-          const toggleTitle = window.document.createElement("div");
-          const titleEl = window.document.body.querySelector(
-            placeholderDescriptor.titleSelector
-          );
-          if (titleEl) {
-            toggleTitle.append(titleEl.innerText, toggleIcon);
-          }
-          toggleTitle.classList.add("zindex-over-content");
-          toggleTitle.classList.add("quarto-sidebar-toggle-title");
-          toggleContainer.append(toggleTitle);
-
-          const toggleContents = window.document.createElement("div");
-          toggleContents.classList = el.classList;
-          toggleContents.classList.add("zindex-over-content");
-          toggleContents.classList.add("quarto-sidebar-toggle-contents");
-          for (const child of el.children) {
-            if (child.id === "toc-title") {
-              continue;
-            }
-
-            const clone = child.cloneNode(true);
-            clone.style.opacity = 1;
-            clone.style.display = null;
-            toggleContents.append(clone);
-          }
-          toggleContents.style.height = "0px";
-          toggleContainer.append(toggleContents);
-          el.parentElement.prepend(toggleContainer);
-
-          // Process clicks
-          let tocShowing = false;
-          // Allow the caller to control whether this is dismissed
-          // when it is clicked (e.g. sidebar navigation supports
-          // opening and closing the nav tree, so don't dismiss on click)
-          const clickEl = placeholderDescriptor.dismissOnClick
-            ? toggleContainer
-            : toggleTitle;
-
-          const closeToggle = () => {
-            if (tocShowing) {
-              toggleContainer.classList.remove("expanded");
-              toggleContents.style.height = "0px";
-              tocShowing = false;
+          nexttick(() => {
+            const toggleContainer = window.document.createElement("div");
+            toggleContainer.style.width = "100%";
+            toggleContainer.classList.add("zindex-over-content");
+            toggleContainer.classList.add("quarto-sidebar-toggle");
+            toggleContainer.classList.add("headroom-target"); // Marks this to be managed by headeroom
+            toggleContainer.id = placeholderDescriptor.id;
+            toggleContainer.style.position = "fixed";
+
+            const toggleIcon = window.document.createElement("i");
+            toggleIcon.classList.add("quarto-sidebar-toggle-icon");
+            toggleIcon.classList.add("bi");
+            toggleIcon.classList.add("bi-caret-down-fill");
+
+            const toggleTitle = window.document.createElement("div");
+            const titleEl = window.document.body.querySelector(
+              placeholderDescriptor.titleSelector
+            );
+            if (titleEl) {
+              toggleTitle.append(
+                titleEl.textContent || titleEl.innerText,
+                toggleIcon
+              );
             }
-          };
-
-          const positionToggle = () => {
-            // position the element (top left of parent, same width as parent)
-            const elRect = el.getBoundingClientRect();
-            toggleContainer.style.left = `${elRect.left}px`;
-            toggleContainer.style.top = `${elRect.top}px`;
-            toggleContainer.style.width = `${elRect.width}px`;
-          };
-
-          // Get rid of any expanded toggle if the user scrolls
-          window.document.addEventListener(
-            "scroll",
-            throttle(() => {
-              closeToggle();
-            }, 50)
-          );
-
-          // Handle positioning of the toggle
-          window.addEventListener(
-            "resize",
-            throttle(() => {
-              positionToggle();
-            }, 50)
-          );
-          positionToggle();
-
-          // Process the click
-          clickEl.onclick = () => {
-            if (!tocShowing) {
-              toggleContainer.classList.add("expanded");
-              toggleContents.style.height = null;
-              tocShowing = true;
-            } else {
-              closeToggle();
+            toggleTitle.classList.add("zindex-over-content");
+            toggleTitle.classList.add("quarto-sidebar-toggle-title");
+            toggleContainer.append(toggleTitle);
+
+            const toggleContents = window.document.createElement("div");
+            toggleContents.classList = el.classList;
+            toggleContents.classList.add("zindex-over-content");
+            toggleContents.classList.add("quarto-sidebar-toggle-contents");
+            for (const child of el.children) {
+              if (child.id === "toc-title") {
+                continue;
+              }
+
+              const clone = child.cloneNode(true);
+              clone.style.opacity = 1;
+              clone.style.display = null;
+              toggleContents.append(clone);
             }
-          };
+            toggleContents.style.height = "0px";
+            const positionToggle = () => {
+              // position the element (top left of parent, same width as parent)
+              if (!elRect) {
+                elRect = el.getBoundingClientRect();
+              }
+              toggleContainer.style.left = `${elRect.left}px`;
+              toggleContainer.style.top = `${elRect.top}px`;
+              toggleContainer.style.width = `${elRect.width}px`;
+            };
+            positionToggle();
+
+            toggleContainer.append(toggleContents);
+            el.parentElement.prepend(toggleContainer);
+
+            // Process clicks
+            let tocShowing = false;
+            // Allow the caller to control whether this is dismissed
+            // when it is clicked (e.g. sidebar navigation supports
+            // opening and closing the nav tree, so don't dismiss on click)
+            const clickEl = placeholderDescriptor.dismissOnClick
+              ? toggleContainer
+              : toggleTitle;
+
+            const closeToggle = () => {
+              if (tocShowing) {
+                toggleContainer.classList.remove("expanded");
+                toggleContents.style.height = "0px";
+                tocShowing = false;
+              }
+            };
+
+            // Get rid of any expanded toggle if the user scrolls
+            window.document.addEventListener(
+              "scroll",
+              throttle(() => {
+                closeToggle();
+              }, 50)
+            );
+
+            // Handle positioning of the toggle
+            window.addEventListener(
+              "resize",
+              throttle(() => {
+                elRect = undefined;
+                positionToggle();
+              }, 50)
+            );
+
+            window.addEventListener("quarto-hrChanged", () => {
+              elRect = undefined;
+            });
+
+            // Process the click
+            clickEl.onclick = () => {
+              if (!tocShowing) {
+                toggleContainer.classList.add("expanded");
+                toggleContents.style.height = null;
+                tocShowing = true;
+              } else {
+                closeToggle();
+              }
+            };
+          });
         };
 
         // Converts a sidebar from a menu back to a sidebar
@@ -398,6 +451,11 @@ window.document.addEventListener("DOMContentLoaded", function (_event) {
           convertToMenu();
           isVisible = false;
         } else {
+          // Find the top and bottom o the element that is being managed
+          const elTop = el.offsetTop;
+          const elBottom =
+            elTop + lastChildEl.offsetTop + lastChildEl.offsetHeight;
+
           if (!isVisible) {
             // If the element is current not visible reveal if there are
             // no conflicts with overlay regions
@@ -418,26 +476,51 @@ window.document.addEventListener("DOMContentLoaded", function (_event) {
     };
   };
 
-  // Find any conflicting margin elements and add margins to the
-  // top to prevent overlap
-  const marginChildren = window.document.querySelectorAll(
-    ".column-margin.column-container > * "
-  );
+  const tabEls = document.querySelectorAll('a[data-bs-toggle="tab"]');
+  for (const tabEl of tabEls) {
+    const id = tabEl.getAttribute("data-bs-target");
+    if (id) {
+      const columnEl = document.querySelector(
+        `${id} .column-margin, .tabset-margin-content`
+      );
+      if (columnEl)
+        tabEl.addEventListener("shown.bs.tab", function (event) {
+          const el = event.srcElement;
+          if (el) {
+            const visibleCls = `${el.id}-margin-content`;
+            // walk up until we find a parent tabset
+            let panelTabsetEl = el.parentElement;
+            while (panelTabsetEl) {
+              if (panelTabsetEl.classList.contains("panel-tabset")) {
+                break;
+              }
+              panelTabsetEl = panelTabsetEl.parentElement;
+            }
 
-  nexttick(() => {
-    let lastBottom = 0;
-    for (const marginChild of marginChildren) {
-      const top = marginChild.getBoundingClientRect().top + window.scrollY;
-      if (top < lastBottom) {
-        const margin = lastBottom - top;
-        marginChild.style.marginTop = `${margin}px`;
-      }
-      const styles = window.getComputedStyle(marginChild);
-      const marginTop = parseFloat(styles["marginTop"]);
+            if (panelTabsetEl) {
+              const prevSib = panelTabsetEl.previousElementSibling;
+              if (
+                prevSib &&
+                prevSib.classList.contains("tabset-margin-container")
+              ) {
+                const childNodes = prevSib.querySelectorAll(
+                  ".tabset-margin-content"
+                );
+                for (const childEl of childNodes) {
+                  if (childEl.classList.contains(visibleCls)) {
+                    childEl.classList.remove("collapse");
+                  } else {
+                    childEl.classList.add("collapse");
+                  }
+                }
+              }
+            }
+          }
 
-      lastBottom = top + marginChild.getBoundingClientRect().height + marginTop;
+          layoutMarginEls();
+        });
     }
-  });
+  }
 
   // Manage the visibility of the toc and the sidebar
   const marginScrollVisibility = manageSidebarVisiblity(marginSidebarEl, {
@@ -506,8 +589,9 @@ window.document.addEventListener("DOMContentLoaded", function (_event) {
   const kOverlapPaddingSize = 10;
   function toRegions(els) {
     return els.map((el) => {
+      const boundRect = el.getBoundingClientRect();
       const top =
-        el.getBoundingClientRect().top +
+        boundRect.top +
         document.documentElement.scrollTop -
         kOverlapPaddingSize;
       return {
@@ -517,11 +601,51 @@ window.document.addEventListener("DOMContentLoaded", function (_event) {
     });
   }
 
+  let hasObserved = false;
+  const visibleItemObserver = (els) => {
+    let visibleElements = [...els];
+    const intersectionObserver = new IntersectionObserver(
+      (entries, _observer) => {
+        entries.forEach((entry) => {
+          if (entry.isIntersecting) {
+            if (visibleElements.indexOf(entry.target) === -1) {
+              visibleElements.push(entry.target);
+            }
+          } else {
+            visibleElements = visibleElements.filter((visibleEntry) => {
+              return visibleEntry !== entry;
+            });
+          }
+        });
+
+        if (!hasObserved) {
+          hideOverlappedSidebars();
+        }
+        hasObserved = true;
+      },
+      {}
+    );
+    els.forEach((el) => {
+      intersectionObserver.observe(el);
+    });
+
+    return {
+      getVisibleEntries: () => {
+        return visibleElements;
+      },
+    };
+  };
+
+  const rightElementObserver = visibleItemObserver(rightSideConflictEls);
+  const leftElementObserver = visibleItemObserver(leftSideConflictEls);
+
   const hideOverlappedSidebars = () => {
-    marginScrollVisibility(toRegions(rightSideConflictEls));
-    sidebarScrollVisiblity(toRegions(leftSideConflictEls));
+    marginScrollVisibility(toRegions(rightElementObserver.getVisibleEntries()));
+    sidebarScrollVisiblity(toRegions(leftElementObserver.getVisibleEntries()));
     if (tocLeftScrollVisibility) {
-      tocLeftScrollVisibility(toRegions(leftSideConflictEls));
+      tocLeftScrollVisibility(
+        toRegions(leftElementObserver.getVisibleEntries())
+      );
     }
   };
 
@@ -543,7 +667,6 @@ window.document.addEventListener("DOMContentLoaded", function (_event) {
       manageTransition("TOC", slow);
       manageTransition("quarto-sidebar", slow);
     };
-
     const readerMode = !isReaderMode();
     setReaderModeValue(readerMode);
 
@@ -590,6 +713,9 @@ window.document.addEventListener("DOMContentLoaded", function (_event) {
   };
   let localReaderMode = null;
 
+  const tocOpenDepthStr = tocEl?.getAttribute("data-toc-expanded");
+  const tocOpenDepth = tocOpenDepthStr ? Number(tocOpenDepthStr) : 1;
+
   // Walk the TOC and collapse/expand nodes
   // Nodes are expanded if:
   // - they are top level
@@ -615,7 +741,13 @@ window.document.addEventListener("DOMContentLoaded", function (_event) {
 
     // Process the collapse state if this is an UL
     if (el.tagName === "UL") {
-      if (depth === 1 || hasActiveChild || prevSiblingIsActiveLink(el)) {
+      if (tocOpenDepth === -1 && depth > 1) {
+        el.classList.add("collapse");
+      } else if (
+        depth <= tocOpenDepth ||
+        hasActiveChild ||
+        prevSiblingIsActiveLink(el)
+      ) {
         el.classList.remove("collapse");
       } else {
         el.classList.add("collapse");
diff --git a/docs/site_libs/quarto-nav/quarto-nav.js b/docs/site_libs/quarto-nav/quarto-nav.js
index b41b31e..3b21201 100644
--- a/docs/site_libs/quarto-nav/quarto-nav.js
+++ b/docs/site_libs/quarto-nav/quarto-nav.js
@@ -8,6 +8,47 @@ const headroomChanged = new CustomEvent("quarto-hrChanged", {
 window.document.addEventListener("DOMContentLoaded", function () {
   let init = false;
 
+  // Manage the back to top button, if one is present.
+  let lastScrollTop = window.pageYOffset || document.documentElement.scrollTop;
+  const scrollDownBuffer = 5;
+  const scrollUpBuffer = 35;
+  const btn = document.getElementById("quarto-back-to-top");
+  const hideBackToTop = () => {
+    btn.style.display = "none";
+  };
+  const showBackToTop = () => {
+    btn.style.display = "inline-block";
+  };
+  if (btn) {
+    window.document.addEventListener(
+      "scroll",
+      function () {
+        const currentScrollTop =
+          window.pageYOffset || document.documentElement.scrollTop;
+
+        // Shows and hides the button 'intelligently' as the user scrolls
+        if (currentScrollTop - scrollDownBuffer > lastScrollTop) {
+          hideBackToTop();
+          lastScrollTop = currentScrollTop <= 0 ? 0 : currentScrollTop;
+        } else if (currentScrollTop < lastScrollTop - scrollUpBuffer) {
+          showBackToTop();
+          lastScrollTop = currentScrollTop <= 0 ? 0 : currentScrollTop;
+        }
+
+        // Show the button at the bottom, hides it at the top
+        if (currentScrollTop <= 0) {
+          hideBackToTop();
+        } else if (
+          window.innerHeight + currentScrollTop >=
+          document.body.offsetHeight
+        ) {
+          showBackToTop();
+        }
+      },
+      false
+    );
+  }
+
   function throttle(func, wait) {
     var timeout;
     return function () {
@@ -149,6 +190,18 @@ window.document.addEventListener("DOMContentLoaded", function () {
     };
   }
 
+  window.addEventListener(
+    "hashchange",
+    function (e) {
+      if (
+        getComputedStyle(document.documentElement).scrollBehavior !== "smooth"
+      ) {
+        window.scrollTo(0, window.pageYOffset - headerOffset());
+      }
+    },
+    false
+  );
+
   // Observe size changed for the header
   const headerEl = window.document.querySelector("header.fixed-top");
   if (headerEl && window.ResizeObserver) {
@@ -172,7 +225,9 @@ window.document.addEventListener("DOMContentLoaded", function () {
   if (window.location.protocol !== "file:") {
     const links = window.document.querySelectorAll("a");
     for (let i = 0; i < links.length; i++) {
-      links[i].href = links[i].href.replace(/\/index\.html/, "/");
+      if (links[i].href) {
+        links[i].href = links[i].href.replace(/\/index\.html/, "/");
+      }
     }
 
     // Fixup any sharing links that require urls
diff --git a/docs/site_libs/quarto-search/autocomplete.umd.js b/docs/site_libs/quarto-search/autocomplete.umd.js
index 3f2dcf0..619c57c 100644
--- a/docs/site_libs/quarto-search/autocomplete.umd.js
+++ b/docs/site_libs/quarto-search/autocomplete.umd.js
@@ -1,3 +1,3 @@
-/*! @algolia/autocomplete-js 1.5.3 | MIT License | © Algolia, Inc. and contributors | https://github.com/algolia/autocomplete */
-!function(e,t){"object"==typeof exports&&"undefined"!=typeof module?t(exports):"function"==typeof define&&define.amd?define(["exports"],t):t((e="undefined"!=typeof globalThis?globalThis:e||self)["@algolia/autocomplete-js"]={})}(this,(function(e){"use strict";function t(e,t){var n=Object.keys(e);if(Object.getOwnPropertySymbols){var r=Object.getOwnPropertySymbols(e);t&&(r=r.filter((function(t){return Object.getOwnPropertyDescriptor(e,t).enumerable}))),n.push.apply(n,r)}return n}function n(e){for(var n=1;n<arguments.length;n++){var r=null!=arguments[n]?arguments[n]:{};n%2?t(Object(r),!0).forEach((function(t){o(e,t,r[t])})):Object.getOwnPropertyDescriptors?Object.defineProperties(e,Object.getOwnPropertyDescriptors(r)):t(Object(r)).forEach((function(t){Object.defineProperty(e,t,Object.getOwnPropertyDescriptor(r,t))}))}return e}function r(e){return r="function"==typeof Symbol&&"symbol"==typeof Symbol.iterator?function(e){return typeof e}:function(e){return e&&"function"==typeof Symbol&&e.constructor===Symbol&&e!==Symbol.prototype?"symbol":typeof e},r(e)}function o(e,t,n){return t in e?Object.defineProperty(e,t,{value:n,enumerable:!0,configurable:!0,writable:!0}):e[t]=n,e}function i(){return i=Object.assign||function(e){for(var t=1;t<arguments.length;t++){var n=arguments[t];for(var r in n)Object.prototype.hasOwnProperty.call(n,r)&&(e[r]=n[r])}return e},i.apply(this,arguments)}function u(e,t){if(null==e)return{};var n,r,o=function(e,t){if(null==e)return{};var n,r,o={},i=Object.keys(e);for(r=0;r<i.length;r++)n=i[r],t.indexOf(n)>=0||(o[n]=e[n]);return o}(e,t);if(Object.getOwnPropertySymbols){var i=Object.getOwnPropertySymbols(e);for(r=0;r<i.length;r++)n=i[r],t.indexOf(n)>=0||Object.prototype.propertyIsEnumerable.call(e,n)&&(o[n]=e[n])}return o}function a(e){return function(e){if(Array.isArray(e))return c(e)}(e)||function(e){if("undefined"!=typeof Symbol&&null!=e[Symbol.iterator]||null!=e["@@iterator"])return Array.from(e)}(e)||function(e,t){if(!e)return;if("string"==typeof e)return c(e,t);var n=Object.prototype.toString.call(e).slice(8,-1);"Object"===n&&e.constructor&&(n=e.constructor.name);if("Map"===n||"Set"===n)return Array.from(e);if("Arguments"===n||/^(?:Ui|I)nt(?:8|16|32)(?:Clamped)?Array$/.test(n))return c(e,t)}(e)||function(){throw new TypeError("Invalid attempt to spread non-iterable instance.\nIn order to be iterable, non-array objects must have a [Symbol.iterator]() method.")}()}function c(e,t){(null==t||t>e.length)&&(t=e.length);for(var n=0,r=new Array(t);n<t;n++)r[n]=e[n];return r}function l(e){return{current:e}}function s(e,t){var n=void 0;return function(){for(var r=arguments.length,o=new Array(r),i=0;i<r;i++)o[i]=arguments[i];n&&clearTimeout(n),n=setTimeout((function(){return e.apply(void 0,o)}),t)}}function p(e){return e.reduce((function(e,t){return e.concat(t)}),[])}var f=0;function d(){return"autocomplete-".concat(f++)}function v(e,t){return t.reduce((function(e,t){return e&&e[t]}),e)}function m(e){return 0===e.collections.length?0:e.collections.reduce((function(e,t){return e+t.items.length}),0)}var h=function(){},g="1.5.3",y=[{segment:"autocomplete-core",version:g}];function b(e,t){var n=t;return{then:function(t,r){return b(e.then(_(t,n,e),_(r,n,e)),n)},catch:function(t){return b(e.catch(_(t,n,e)),n)},finally:function(t){return t&&n.onCancelList.push(t),b(e.finally(_(t&&function(){return n.onCancelList=[],t()},n,e)),n)},cancel:function(){n.isCanceled=!0;var e=n.onCancelList;n.onCancelList=[],e.forEach((function(e){e()}))},isCanceled:function(){return!0===n.isCanceled}}}function O(e){return b(e,{isCanceled:!1,onCancelList:[]})}function _(e,t,n){return e?function(n){return t.isCanceled?n:e(n)}:n}function P(e,t,n,r){if(!n)return null;if(e<0&&(null===t||null!==r&&0===t))return n+e;var o=(null===t?-1:t)+e;return o<=-1||o>=n?null===r?null:0:o}function j(e,t){var n=Object.keys(e);if(Object.getOwnPropertySymbols){var r=Object.getOwnPropertySymbols(e);t&&(r=r.filter((function(t){return Object.getOwnPropertyDescriptor(e,t).enumerable}))),n.push.apply(n,r)}return n}function w(e,t,n){return t in e?Object.defineProperty(e,t,{value:n,enumerable:!0,configurable:!0,writable:!0}):e[t]=n,e}function S(e,t){var n=[];return Promise.resolve(e(t)).then((function(e){return Promise.all(e.filter((function(e){return Boolean(e)})).map((function(e){if(e.sourceId,n.includes(e.sourceId))throw new Error("[Autocomplete] The `sourceId` ".concat(JSON.stringify(e.sourceId)," is not unique."));n.push(e.sourceId);var t=function(e){for(var t=1;t<arguments.length;t++){var n=null!=arguments[t]?arguments[t]:{};t%2?j(Object(n),!0).forEach((function(t){w(e,t,n[t])})):Object.getOwnPropertyDescriptors?Object.defineProperties(e,Object.getOwnPropertyDescriptors(n)):j(Object(n)).forEach((function(t){Object.defineProperty(e,t,Object.getOwnPropertyDescriptor(n,t))}))}return e}({getItemInputValue:function(e){return 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diff --git a/docs/site_libs/quarto-search/fuse.min.js b/docs/site_libs/quarto-search/fuse.min.js
index ca37378..adc2835 100644
--- a/docs/site_libs/quarto-search/fuse.min.js
+++ b/docs/site_libs/quarto-search/fuse.min.js
@@ -1,9 +1,9 @@
 /**
- * Fuse.js v6.5.3 - Lightweight fuzzy-search (http://fusejs.io)
+ * Fuse.js v6.6.2 - Lightweight fuzzy-search (http://fusejs.io)
  *
- * Copyright (c) 2021 Kiro Risk (http://kiro.me)
+ * Copyright (c) 2022 Kiro Risk (http://kiro.me)
  * All Rights Reserved. Apache Software License 2.0
  *
  * http://www.apache.org/licenses/LICENSE-2.0
  */
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o=!!r.length;return{isMatch:o,score:o?0:1,indices:r}}}],[{key:"type",get:function(){return"include"}},{key:"multiRegex",get:function(){return/^'"(.*)"$/}},{key:"singleRegex",get:function(){return/^'(.*)$/}}]),n}(z),Q=[K,H,B,J,V,U,q,G],X=Q.length,Y=/ +(?=(?:[^\"]*\"[^\"]*\")*[^\"]*$)/;function Z(e){var t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};return e.split("|").map((function(e){for(var n=e.trim().split(Y).filter((function(e){return e&&!!e.trim()})),r=[],i=0,o=n.length;i<o;i+=1){for(var c=n[i],a=!1,s=-1;!a&&++s<X;){var u=Q[s],h=u.isMultiMatch(c);h&&(r.push(new u(h,t)),a=!0)}if(!a)for(s=-1;++s<X;){var l=Q[s],f=l.isSingleMatch(c);if(f){r.push(new l(f,t));break}}}return r}))}var ee=new Set([G.type,H.type]),te=function(){function e(t){var n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{},i=n.isCaseSensitive,o=void 0===i?I.isCaseSensitive:i,c=n.includeMatches,a=void 0===c?I.includeMatches:c,s=n.minMatchCharLength,u=void 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r&&(k.indices=o),k}}return{isMatch:!1,score:1}}}],[{key:"condition",value:function(e,t){return t.useExtendedSearch}}]),e}(),ne=[];function re(e,t){for(var n=0,r=ne.length;n<r;n+=1){var i=ne[n];if(i.condition(e,t))return new i(e,t)}return new T(e,t)}var ie="$and",oe="$or",ce="$path",ae="$val",se=function(e){return!(!e[ie]&&!e[oe])},ue=function(e){return!!e[ce]},he=function(e){return!v(e)&&m(e)&&!se(e)},le=function(e){return c({},ie,Object.keys(e).map((function(t){return c({},t,e[t])})))};function fe(e,t){var n=t.ignoreFieldNorm,r=void 0===n?I.ignoreFieldNorm:n;e.forEach((function(e){var t=1;e.matches.forEach((function(e){var n=e.key,i=e.norm,o=e.score,c=n?n.weight:null;t*=Math.pow(0===o&&c?Number.EPSILON:o,(c||1)*(r?1:i))})),e.score=t}))}function de(e,t){var n=e.matches;t.matches=[],k(n)&&n.forEach((function(e){if(k(e.indices)&&e.indices.length){var n={indices:e.indices,value:e.value};e.key&&(n.key=e.key.src),e.idx>-1&&(n.refIndex=e.idx),t.matches.push(n)}}))}function ve(e,t){t.score=e.score}function ge(e,t){var n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{},r=n.includeMatches,i=void 0===r?I.includeMatches:r,o=n.includeScore,c=void 0===o?I.includeScore:o,a=[];return i&&a.push(de),c&&a.push(ve),e.map((function(e){var n=e.idx,r={item:t[n],refIndex:n};return a.length&&a.forEach((function(t){t(e,r)})),r}))}var ye=function(){function e(n){var i=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{},o=arguments.length>2?arguments[2]:void 0;r(this,e),this.options=t(t({},I),i),this.options.useExtendedSearch,this._keyStore=new S(this.options.keys),this.setCollection(n,o)}return o(e,[{key:"setCollection",value:function(e,t){if(this._docs=e,t&&!(t instanceof $))throw new Error("Incorrect 'index' type");this._myIndex=t||F(this.options.keys,this._docs,{getFn:this.options.getFn,fieldNormWeight:this.options.fieldNormWeight})}},{key:"add",value:function(e){k(e)&&(this._docs.push(e),this._myIndex.add(e))}},{key:"remove",value:function(){for(var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:function(){return!1},t=[],n=0,r=this._docs.length;n<r;n+=1){var i=this._docs[n];e(i,n)&&(this.removeAt(n),n-=1,r-=1,t.push(i))}return t}},{key:"removeAt",value:function(e){this._docs.splice(e,1),this._myIndex.removeAt(e)}},{key:"getIndex",value:function(){return this._myIndex}},{key:"search",value:function(e){var t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{},n=t.limit,r=void 0===n?-1:n,i=this.options,o=i.includeMatches,c=i.includeScore,a=i.shouldSort,s=i.sortFn,u=i.ignoreFieldNorm,h=g(e)?g(this._docs[0])?this._searchStringList(e):this._searchObjectList(e):this._searchLogical(e);return fe(h,{ignoreFieldNorm:u}),a&&h.sort(s),y(r)&&r>-1&&(h=h.slice(0,r)),ge(h,this._docs,{includeMatches:o,includeScore:c})}},{key:"_searchStringList",value:function(e){var t=re(e,this.options),n=this._myIndex.records,r=[];return n.forEach((function(e){var n=e.v,i=e.i,o=e.n;if(k(n)){var c=t.searchIn(n),a=c.isMatch,s=c.score,u=c.indices;a&&r.push({item:n,idx:i,matches:[{score:s,value:n,norm:o,indices:u}]})}})),r}},{key:"_searchLogical",value:function(e){var t=this,n=function(e,t){var n=(arguments.length>2&&void 0!==arguments[2]?arguments[2]:{}).auto,r=void 0===n||n,i=function e(n){var i=Object.keys(n),o=ue(n);if(!o&&i.length>1&&!se(n))return e(le(n));if(he(n)){var c=o?n[ce]:i[0],a=o?n[ae]:n[c];if(!g(a))throw new Error(x(c));var s={keyId:j(c),pattern:a};return r&&(s.searcher=re(a,t)),s}var u={children:[],operator:i[0]};return i.forEach((function(t){var r=n[t];v(r)&&r.forEach((function(t){u.children.push(e(t))}))})),u};return se(e)||(e=le(e)),i(e)}(e,this.options),r=function e(n,r,i){if(!n.children){var o=n.keyId,c=n.searcher,a=t._findMatches({key:t._keyStore.get(o),value:t._myIndex.getValueForItemAtKeyId(r,o),searcher:c});return a&&a.length?[{idx:i,item:r,matches:a}]:[]}for(var s=[],u=0,h=n.children.length;u<h;u+=1){var l=e(n.children[u],r,i);if(l.length)s.push.apply(s,f(l));else if(n.operator===ie)return[]}return s},i=this._myIndex.records,o={},c=[];return i.forEach((function(e){var t=e.$,i=e.i;if(k(t)){var a=r(n,t,i);a.length&&(o[i]||(o[i]={idx:i,item:t,matches:[]},c.push(o[i])),a.forEach((function(e){var t,n=e.matches;(t=o[i].matches).push.apply(t,f(n))})))}})),c}},{key:"_searchObjectList",value:function(e){var t=this,n=re(e,this.options),r=this._myIndex,i=r.keys,o=r.records,c=[];return o.forEach((function(e){var r=e.$,o=e.i;if(k(r)){var a=[];i.forEach((function(e,i){a.push.apply(a,f(t._findMatches({key:e,value:r[i],searcher:n})))})),a.length&&c.push({idx:o,item:r,matches:a})}})),c}},{key:"_findMatches",value:function(e){var t=e.key,n=e.value,r=e.searcher;if(!k(n))return[];var i=[];if(v(n))n.forEach((function(e){var n=e.v,o=e.i,c=e.n;if(k(n)){var a=r.searchIn(n),s=a.isMatch,u=a.score,h=a.indices;s&&i.push({score:u,key:t,value:n,idx:o,norm:c,indices:h})}}));else{var o=n.v,c=n.n,a=r.searchIn(o),s=a.isMatch,u=a.score,h=a.indices;s&&i.push({score:u,key:t,value:o,norm:c,indices:h})}return i}}]),e}();return ye.version="6.6.2",ye.createIndex=F,ye.parseIndex=function(e){var t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{},n=t.getFn,r=void 0===n?I.getFn:n,i=t.fieldNormWeight,o=void 0===i?I.fieldNormWeight:i,c=e.keys,a=e.records,s=new $({getFn:r,fieldNormWeight:o});return s.setKeys(c),s.setIndexRecords(a),s},ye.config=I,function(){ne.push.apply(ne,arguments)}(te),ye},"object"==typeof exports&&"undefined"!=typeof module?module.exports=t():"function"==typeof define&&define.amd?define(t):(e="undefined"!=typeof globalThis?globalThis:e||self).Fuse=t();
\ No newline at end of file
diff --git a/docs/site_libs/quarto-search/quarto-search.js b/docs/site_libs/quarto-search/quarto-search.js
index 6fd4b5b..f5d852d 100644
--- a/docs/site_libs/quarto-search/quarto-search.js
+++ b/docs/site_libs/quarto-search/quarto-search.js
@@ -45,7 +45,7 @@ window.document.addEventListener("DOMContentLoaded", function (_event) {
   // highlight matches on the page
   if (query !== null && mainEl) {
     // perform any highlighting
-    highlight(query, mainEl);
+    highlight(escapeRegExp(query), mainEl);
 
     // fix up the URL to remove the q query param
     const replacementUrl = new URL(window.location);
@@ -80,23 +80,20 @@ window.document.addEventListener("DOMContentLoaded", function (_event) {
   // the media query since we generate different HTML for sidebar overlays than we do
   // for sidebar input UI)
   const detachedMediaQuery =
-    quartoSearchOptions.type === "overlay"
-      ? "all"
-      : quartoSearchOptions.location === "navbar"
-      ? "(max-width: 991px)"
-      : "none";
+    quartoSearchOptions.type === "overlay" ? "all" : "(max-width: 991px)";
 
   // If configured, include the analytics client to send insights
   const plugins = configurePlugins(quartoSearchOptions);
 
   let lastState = null;
-  const { setIsOpen } = autocomplete({
+  const { setIsOpen, setQuery, setCollections } = autocomplete({
     container: searchEl,
     detachedMediaQuery: detachedMediaQuery,
     defaultActiveItemId: 0,
     panelContainer: "#quarto-search-results",
     panelPlacement: quartoSearchOptions["panel-placement"],
     debug: false,
+    openOnFocus: true,
     plugins,
     classNames: {
       form: "d-flex",
@@ -280,6 +277,10 @@ window.document.addEventListener("DOMContentLoaded", function (_event) {
             }
           },
           getItems({ query }) {
+            if (query === null || query === "") {
+              return [];
+            }
+
             const limit = quartoSearchOptions.limit;
             if (quartoSearchOptions.algolia) {
               return algoliaSearch(query, limit, quartoSearchOptions.algolia);
@@ -299,9 +300,15 @@ window.document.addEventListener("DOMContentLoaded", function (_event) {
           },
           templates: {
             noResults({ createElement }) {
+              const hasQuery = lastState.query;
+
               return createElement(
                 "div",
-                { class: "quarto-search-no-results" },
+                {
+                  class: `quarto-search-no-results${
+                    hasQuery ? "" : " no-query"
+                  }`,
+                },
                 language["search-no-results-text"]
               );
             },
@@ -361,6 +368,12 @@ window.document.addEventListener("DOMContentLoaded", function (_event) {
     },
   });
 
+  window.quartoOpenSearch = () => {
+    setIsOpen(false);
+    setIsOpen(true);
+    focusSearchInput();
+  };
+
   // Remove the labeleledby attribute since it is pointing
   // to a non-existent label
   if (quartoSearchOptions.type === "overlay") {
@@ -976,6 +989,10 @@ function clearHighlight(searchterm, el) {
   }
 }
 
+function escapeRegExp(string) {
+  return string.replace(/[.*+?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string
+}
+
 // highlight matches
 function highlight(term, el) {
   const termRegex = new RegExp(term, "ig");
diff --git a/docs/www/images/tidy-data.png b/docs/www/images/tidy-data.png
new file mode 100644
index 0000000..4287d74
Binary files /dev/null and b/docs/www/images/tidy-data.png differ
diff --git a/intro_series_book/02_lesson.qmd b/intro_series_book/02_lesson.qmd
index f8dd9fd..92aeaa1 100644
--- a/intro_series_book/02_lesson.qmd
+++ b/intro_series_book/02_lesson.qmd
@@ -1,60 +1,70 @@
 ---
 title: "Data Munging 1"
-author: "Fay Walker"
-date: "`r format(Sys.time(), '%B %d, %Y %H:%M')`"
+author: "Aaron R. Williams"
+date: today
 output:
-  html-document:
-    number-sections: false
+  html:
+    code-line-numbers: true
+    number-sections: true
+    number-depth: 2
     embed-resources: true
-    code-folding: hide
+    code-fold: show
     toc: true
-    toc-float: true
     css: style.scss
-    editor-options:
-      chunk-output-type: console
+editor_options: 
+  chunk_output_type: console
 ---
 
-```{=html}
-<style>
-@import url('https://fonts.googleapis.com/css?family=Lato&display=swap');
-</style>
-```
-<link href="https://fonts.googleapis.com/css?family=Lato&display=swap" rel="stylesheet">
-
 ## Review
 
--   Assignment Operator
 -   Console/Environment/Script
--   Comment your code and read your error messages
+-   Comment your code with `#` and read your error messages
+
+## Assignment
+
+`<-` is the assignment operator. An object created on the right side of an assignment operator is assigned to a name on the left side of an assignment operator. Assignment operators are important for saving the consequences of operations and functions. Without assignment, the result of a calculation is not saved for use in future calculations. Operations without assignment operators will typically be printed to the console but not saved for future use.
+
+```{r}
+a <- 1
+b <- 2
+
+c <- a + b
+
+c
+
+```
+
+## Tidy Data
 
-## The tidyverse package
+> The [tidyverse](https://www.tidyverse.org/) is an opinionated collection of R packages designed for data science. All packages share an underlying design philosophy, grammar, and data structures. \~ tidyverse.org
 
--   Collection of packages using same syntax and grammar
--   Works with "tidy" data
--   [Uses tibbles](https://r4ds.had.co.nz/tibbles.html)
--   %\>%
+`library(tidyverse)` contains:
 
-## Primary functions
+-   [ggplot2](https://ggplot2.tidyverse.org), for data visualization.
+-   [dplyr](https://dplyr.tidyverse.org), for data manipulation.
+-   [tidyr](https://tidyr.tidyverse.org), for data tidying.
+-   [readr](https://readr.tidyverse.org), for data import.
+-   [purrr](https://purrr.tidyverse.org), for functional programming.
+-   [tibble](https://tibble.tidyverse.org), for tibbles, a modern re-imagining of data frames.
+-   [stringr](https://github.com/tidyverse/stringr), for strings.
+-   [forcats](https://github.com/hadley/forcats), for factors.
 
--   `select()`
--   `rename()`
--   `filter()`
--   `arrange()`
--   `mutate()`
--   `group_by()`
--   `summarize()`
+### Tidy data
 
-All of these functions have the same structure.
+The defining opinion of the tidyverse is its wholehearted adoption of tidy data. Tidy data has three features:
 
-1.  The first argument is the dataframe
-2.  The subsequent arguments describe what to do with the data frame, using the variable names (without quotes because you're already refering to that df)
-3.  Separate functions by %\>%
-4.  The result is a new data frame.
+1.  Each variable forms a column.
+2.  Each observation forms a row.
+3.  Each type of observational unit forms a dataframe. (This is from the paper, not the book)
 
-If you have hit an error, trying running it line by line (function by function)
+![](www/images/tidy-data.png)
 
 Source: [R for data science](https://r4ds.had.co.nz/transform.html)
 
+> Tidy datasets are all alike, but every messy dataset is messy in its own way. \~ [Hadley Wickham](https://r4ds.had.co.nz/tidy-data.html)
+
+The tidy approach to data science is powerful because it breaks data work into two distinct parts. First, get the data into a tidy format. Second, use tools optimized for tidy data. By standardizing the data structure for most community-created tools, the framework oriented diffuse development and reduced the friction of data work.
+
 ## Exercise 0: Creating a Project and Loading Packages
 
 If you are using a different computer or didn't attend sessions 0 or 1, follow steps 1 and 2. If not- skip to step 3.
@@ -65,130 +75,446 @@ If you are using a different computer or didn't attend sessions 0 or 1, follow s
 
 <font color="#55b748">**Step 3:**</font> If you have not previously installed `library(tidyverse)`: submit `install.packages("tidyverse")` to the Console (type and hit enter)
 
-<font color="#55b748">**Step 4:**</font> Write `library(tidyverse)` at the top of `02_data-munging1.R`. With the cursor on the line of text, click Control-Enter.
+We'll focus on the key dplyr syntax using the March 2020 Annual Social and Economic Supplement (ASEC) to the Current Population Survey (CPS). Run the following code to load the data.
+
+<font color="#55b748">**Step 4:**</font> Add and run the following code to load ASEC data.
+
+```{r}
+#| label: load-asec
+#| message: false
+
+library(tidyverse)
+
+asec <- read_csv(
+  paste0(
+    "https://raw.githubusercontent.com/awunderground/awunderground-data/",
+    "main/cps/cps-asec.csv"
+  )
+)
+
+```
+
+We can use `glimpse(asec)` to quickly view the data. We can also use `View(asec)` to open up `asec` in RStudio.
+
+<br>
+
+```{r glimpse-asec}
+glimpse(x = asec)
+
+```
+
+<br>
+
+We're going to learn seven functions and one new piece of syntax from `library(dplyr)` that will be our main tools for manipulating tidy frames. These functions and a few extensions outlined in the [Data Transformation Cheat Sheet](https://rstudio.com/resources/cheatsheets/) are the core of data analysis in the Tidyverse.
+
+## 1. `select()`
+
+`select()` drops columns from a dataframe and/or reorders the columns in a dataframe. The arguments after the name of the dataframe should be the names of columns you wish to keep, without quotes. All other columns not listed are dropped.
+
+<br>
+
+```{r select-example}
+select(.data = asec, year, month, serial)
+
+```
+
+<br>
+
+This works great until the goal is to select 99 of 100 variables. Fortunately, `-` can be used to remove variables. You can also select all but multiple variables by listing them with the `-` symbol separated by commas.
+
+<br>
+
+```{r select-substract-example}
+select(.data = asec, -asecflag)
+```
+
+<br>
 
-## Exercise 1: Tidyverse Function Overview
+### `r kableExtra::text_spec("Exercise 1", color = "#1696d2")`
 
-<font color="#55b748">**Step 1:**</font> Assign the `storms` dataset to be called storms. Submit `View(storms)` in the console.
+::: {.panel-tabset}
 
-You can also deselect specific columns in your data by using the `-` operator
+#### Prompt
 
-<font color="#ec008b">**Select**</font>
+1.  Select `pernum` and `inctot` from `asec`.
 
-`select()` is used when you want to limit the number of **columns** in your dataframe. The arguments after the dataframe should be names of columns, without quotes. For example:
+#### Solution
 
-    dataset %>%
-      select(col1, col2)
+1.  Select `pernum` and `inctot` from `asec`.
 
-<font color="#55b748">**Step 2:**</font> Create a new dataset with all the variables except for `ts_diameter` and `hu_diameter`, by using `select` and `-`.
+```{r}
+#| eval: false
 
-<font color="#55b748">**Step 3:**</font> Create a new dataset that only category 5 storms using `filter()`.
+select(.data = asec, inctot, asec)
 
-<font color="#ec008b">**Filter**</font>
+```
+
+:::
+
+$$\cdots$$
+
+## 2. `rename()`
+
+`rename()` renames columns in a data frame. The pattern is `new_name = old_name`.
 
-`filter()` is used when you want to filter to specific rows in your dataframe, based on certain conditions. Filter works with the following comparison operators:
+<br>
 
--   `<`, `>`, `<=`, `>=`
--   `==`, `!=`
+```{r rename-example}
+rename(.data = asec, serial_number = serial)
 
-You can also chain together multiple condtions with `&` (and) or `|` (or). The arguments after the dataframe should be conditions you want to filter your dataset on. For example
+```
 
-    dataset %>%
-    filter(col1>0)
+<br>
 
-<font color="#55b748">**Step 4:**</font> Chain all of these commands together with the `%>%` operator. Then rename the column `pressure` to `air_pressure`.
+You can also rename a selection of variables using `rename_with()`. The `.cols` argument is used to select the columns to rename and takes a `tidyselect` statement like those we introduced above. Here, we're using the `where()` selection helper which selects all columns where a given condition is TRUE. The default value for the `.cols` argument is `everything()` which selects all columns in the dataset.
 
-<font color="#ec008b">**Rename**</font>
+<br>
 
-The syntax for rename is:
+```{r rename_all-example}
+rename_with(.data = asec, .fn = toupper, .cols = where(is.numeric))
 
-    dataset %>% 
-      rename(new_name = current_name)
+```
 
-This same syntax can be used inside `select()`, and you can rename and select variables simultanously.
+<br>
 
-<font color="#55b748">**Step 5:**</font> `wind` is measured in knots. Convert `wind` into miles per hour. Each knot is equal to 1.15078 miles per hour.
+Most `dplyr` functions can rename columns simply by prefacing the operation with `new_name =`. For example, this can be done with `select()`:
 
-<font color="#ec008b">**Mutate**</font>
+<br>
 
-`mutate()` can make new variables and edit existing variables. New variables are always added to the end of the dataframe. You can use arithmetic arguments, such as `+`, `-`, `*`, `/`, and `^`. The syntax for mutate is:
+```{r select-with-rename}
+select(.data = asec, year, month, serial_number = serial)
 
-    dataset %>% 
-      mutate(new_column = 100 * other_column)
+```
 
-<font color="#55b748">**Step 6:**</font> Make a dataset with storms that occured in the summer (June, July, August).
+<br>
 
-<font color="#ec008b">**The `%in%` operator**</font>
+## 3. `filter()`
 
-The `%in%` operator is useful for filtering based on more than one option within one variable.
+`filter()` reduces the number of observations in a dataframe. Every column in a dataframe has a name. Rows do not necessarily have names in a dataframe, so rows need to be filtered based on logical conditions.
 
-    filter(dataset, var1 == "a" | var1 == "b" | var1 == "c")
+`==`, `<`, `>`, `<=`, `>=`, `!=`, `%in%`, and `is.na()` are all operators that can be used for logical conditions. `!` can be used to negate a condition and `&` and `|` can be used to combine conditions. `|` means or.
 
-is equivalent to
+<br>
 
-    dataset %>%
-    filter(var1 %in% c("a", "b", "c"))
+```{r filter-example}
+# return rows with pernum of 1 and incwage > $100,000
+filter(.data = asec, pernum == 1 & incwage > 100000)
+
+```
 
-## Exercise 2: If/Then and Summarizing
+[IPUMS CPS](https://cps.ipums.org/cps-action/variables/group) contains full documentation with information about `pernum` and `incwage`.
 
-<font color="#55b748">**Step 1:**</font> Use the `txhousing` dataset. Assign the dataset to an object in your global environment named `tx_housing`.
+<br>
 
-<font color="#55b748">**Step 2:**</font> Compute a new variable, `average_price` by dividing `volume` by `sales`.
+### `r kableExtra::text_spec("Exercise 2", color = "#1696d2")`
 
-<font color="#55b748">**Step 3:**</font> Use `if_else()` to create a variable called `flag` that is 1 if the average sales price is larger than the median sales price.
+::: {.panel-tabset}
 
-<font color="#ec008b">**If/then logic**</font>
+#### Prompt
 
-`if_else()` is an important tool in combination with `mutate()`. It employs if-then logic: the first argument is a binary statement. The second statement says what to do if the first argument is evaluated to `TRUE`- the third is what to do if it is `FALSE` This is particularly useful for creating dummy or indicator variables. When you have more than two possible outcomes, it is more efficient to use `case_when()`.
+1.  Filter `asec` to rows with `month` equal to `"March"`.
+2.  Filter `asec` to rows with `inctot` less than `999999999`.
+3.  Filter `asec` to rows with `pernum` equal to `3` and `inctot` less than `999999999`.
 
-<font color="#55b748">**Step 3a:**</font> Type `?case_when` into the console.
+#### Solution
 
-<font color="#55b748">**Step 4:**</font> Type the following code into your script.
+1.  Filter `asec` to rows with `month` equal to `"March"`.
+2.  Filter `asec` to rows with `inctot` less than `999999999`.
+3.  Filter `asec` to rows with `pernum` equal to `3` and `inctot` less than `999999999`.
 
-    tx_housing %>% 
-      group_by(year) %>% 
-      summarize(yearly_sales = sum(sales))
+```{r}
+#| eval: false
 
-<font color="#ec008b">**Group By**</font>
+filter(asec, month == "March")
 
-`group_by` groups your dataframe into discrete categories. It can be very powerful when used with `summarize`.
+filter(asec, inctot < 999999999)
 
-<font color="#ec008b">**Summarize**</font>
+filter(asec, pernum == 3, inctot < 999999999)
 
-`summarize` computes summary statistics for as set of observations in your data. The syntax is:
 
-    dataset %>% 
-      mutate(max = max(other_column))
+```
+
+:::
+
+## 4. `arrange()`
+
+`arrange()` sorts the rows of a data frame in alpha-numeric order based on the values of a variable or variables. The dataframe is sorted by the first variable first and each subsequent variable is used to break ties. `desc()` is used to reverse the sort order for a given variable.
+
+<br>
+
+```{r arrange-example}
+# sort pernum is descending order because high pernums are interesting
+arrange(.data = asec, desc(pernum))
+
+```
+
+<br>
 
-or
+### `r kableExtra::text_spec("Exercise 3", color = "#1696d2")`
 
-    dataset %>%
-      group_by(col1) %>%
-      summarize(count=n())
+::: {.panel-tabset}
 
-<font color="#55b748">**Step 5:**</font> Add the argument `na.rm = TRUE` to the `sum()` function and rerun the code. Add a comment to explain the code.
+#### Prompt
 
-<font color="#55b748">**Step 6:**</font> Sort the results of the previous code in order from most to least sales by adding `%>% arrange(desc(yearly_sales))` to the end of the code.
+1.  Sort `asec` in descending order by `pernum` and ascending order by `inctot`.
 
-<font color="#55b748">**Step 7a:**</font> Which city had the most home sales in January 2008? (Hint: use `filter()` and `arrange()`).
+#### Solution
+
+1.  Sort `asec` in descending order by `pernum` and ascending order by `inctot`.
+
+```{r}
+#| eval: false
+
+arrange(asec, desc(pernum), inctot)
+
+```
+
+:::
+
+## 5. `mutate()`
+
+`mutate()` creates new variables or edits existing variables. We can use arithmetic arguments, such as `+`, `-`, `*`, `/`, and `^`. We can also custom functions and functions from packages. For example, we can use `library(stringr)` for string manipulation and `library(lubridate)` for date manipulation.
+
+<br>
+
+Variables are created by adding a new column name, like `inctot_adjusted`, to the left of `=` in `mutate()`.
+
+```{r mutate-example-new-var}
+# adjust inctot for underreporting
+mutate(.data = asec, inctot_adjusted = inctot * 1.1)
+
+```
+
+<br>
+
+Variables are edited by including an existing column name, like `inctot`, to the left of `=` in `mutate()`.
+
+```{r mutate-example-overwrite}
+# adjust income because of underreporting
+mutate(.data = asec, inctot = inctot * 1.1)
+
+```
 
-<font color="#55b748">**Step 7b:**</font> Which city had the least home sales in 2006? (Hint: group by the city and the year- then use `summarize()`).
+<br>
 
-<font color="#55b748">**BONUS:**</font> Plot the number of annual home sales over time!
+Conditional logic inside of `mutate()` with functions like `if_else()` and `case_when()` is key to mastering data munging in R.
 
-## Exercise 3: Put it all together
+### `r kableExtra::text_spec("Exercise 4", color = "#1696d2")`
 
-<font color="#55b748">**Step 1:**</font> Use the `starwars` dataframe. Type `count(starwars, homeworld)` into your script.
+::: {.panel-tabset}
+
+#### Prompt
+
+1.  Create a new variable called `in_poverty`. If `offtotval` is less than `offcutoff` then use `"Below Poverty Line"`. Otherwise, use `"Above Poverty Line"`. *Hint:* `if_else()` is useful and works like the IF command in Microsoft Excel.
+
+#### Solution
+
+1.  Create a new variable called `in_poverty`. If `offtotval` is less than `offcutoff` then use `"Below Poverty Line"`. Otherwise, use `"Above Poverty Line"`. *Hint:* `if_else()` is useful and works like the IF command in Microsoft Excel.
+
+```{r}
+#| eval: false
+mutate(
+  asec,
+  in_poverty = if_else(
+    condition = offtotval < offcutoff, 
+    true = "Below Poverty Line", 
+    false = "Above Poverty Line"
+  )
+)
+
+```
+
+:::
+
+## `%>%`
+
+Data munging is tiring when each operation needs to be assigned to a name with `<-`. The pipe, `%>%`, allows lines of code to be chained together so the assignment operator only needs to be used once.
+
+Consider this fake code [example from Hadley Wickham](https://twitter.com/hadleywickham/status/1359852563726819332?lang=en):
+
+```{r}
+#| eval: false
+I %>% 
+  tumble(out_of = "bed") %>% 
+  stumble(to = "the kitchen") %>% 
+  pour(who = "myself", unit = "cup", what = "ambition") %>% 
+  yawn() %>% 
+  stretch() %>% 
+  try(come_to_life())
+
+```
+
+<br>
+
+`%>%` passes the output from function as the first argument in a subsequent function. For example, this line can be rewritten:
+
+<br>
+
+```{r pipe-example, eval = FALSE}
+# old way
+mutate(.data = asec, inctot_adjusted = inctot * 1.1)
+
+# new way
+asec %>%
+  mutate(inctot_adjusted = inctot * 1.1)
+
+```
+
+<br>
+
+See the power:
+
+<br>
+
+```{r pipe-data-cleaning}
+new_asec <- asec %>%
+  filter(pernum == 1) %>%
+  select(year, month, pernum, inctot) %>%
+  mutate(inctot_adjusted = inctot * 1.1) %>%
+  select(-inctot)
+
+new_asec
+
+```
+
+<br>
+
+## 6. `summarize()`
+
+`summarize()` collapses many rows in a dataframe into fewer rows with summary statistics of the many rows. `n()`, `mean()`, and `sum()` are common summary statistics. Renaming is useful with `summarize()`!
+
+<br>
+
+```{r summarize-example}
+# summarize without renaming the statistics
+asec %>%
+  summarize(mean(ftotval), mean(inctot))
+
+# summarize and rename the statistics
+asec %>%
+  summarize(
+    mean_ftotval = mean(ftotval), 
+    mean_inctot = mean(inctot)
+  )
+
+```
+
+<br>
+
+`summarize()` returns a data frame. This means all dplyr functions can be used on the output of `summarize()`. This is powerful! Manipulating summary statistics in Stata and SAS can be a chore. Here, it's just another dataframe that can be manipulated with a tool set optimized for dataframes: dplyr.
+
+## 7. `group_by()`
+
+`group_by()` groups a dataframe based on specified variables. `summarize()` with grouped dataframes creates subgroup summary statistics. `mutate()` with `group_by()` calculates grouped summaries for each row.
+
+<br>
+
+```{r group_by-example}
+asec %>%
+  group_by(pernum) %>%
+  summarize(
+    n = n(),
+    mean_ftotval = mean(ftotval), 
+    mean_inctot = mean(inctot)
+  )
+
+```
+
+<br>
+
+Dataframes can be grouped by multiple variables.
+
+Grouped tibbles include metadata about groups. For example, `Groups:   pernum, offpov [40]`. One grouping is dropped each time `summarize()` is used. It is easy to forget if a dataframe is grouped, so it is safe to include `ungroup()` at the end of a section of functions.
+
+<br>
+
+```{r group_by-example-many-groups}
+asec %>%
+  group_by(pernum, offpov) %>%
+  summarize(
+    n = n(),
+    mean_ftotval = mean(ftotval), 
+    mean_inctot = mean(inctot)
+  ) %>%
+  arrange(offpov) %>%
+  ungroup()
+
+```
+
+<br>
+
+### `r kableExtra::text_spec("Exercise 5", color = "#1696d2")`
+
+::: {.panel-tabset}
+
+#### Prompt
+
+1.  `filter()` to only include observations with `"In Poverty Universe"` in `offpovuniv`.
+2.  `group_by()` `offpov`.
+3.  Use `summarize()` and `n()` to count the number of observations in poverty.
+
+#### Solution
+
+1.  `filter()` to only include observations with `"In Poverty Universe"` in `offpovuniv`.
+2.  `group_by()` `offpov`.
+3.  Use `summarize()` and `n()` to count the number of observations in poverty.
+
+```{r}
+#| eval: false
+
+asec %>%
+  filter(offpovuniv == "In Poverty Universe") %>%
+  group_by(offpov) %>%
+  summarize(n())
+
+```
+
+:::
+
+### `r kableExtra::text_spec("Exercise 6", color = "#1696d2")`
+
+::: {.panel-tabset}
+
+#### Prompt
+
+1.  `filter()` to only include observations with `"In Poverty Universe"`.
+2.  `group_by()` `cpsid`.
+3.  Use `mutate(family_size = n())` to calculate the family size for each observation in `asec`.
+4.  `ungroup()`
+5.  `group_by()` `family_size`, and `offpov`.
+6.  Use `summarize()` and `n()` to see how many families of each size are experiencing poverty. 
+
+#### Solution
+
+1.  `filter()` to only include observations with `"In Poverty Universe"`.
+2.  `group_by()` `cpsid`.
+3.  Use `mutate(family_size = n())` to calculate the family size for each observation in `asec`.
+4.  `ungroup()`
+5.  `group_by()` `family_size`, and `offpov`.
+6.  Use `summarize()` and `n()` to see how many families of each size are experiencing poverty. 
+
+```{r}
+#| eval: false
+
+asec %>%
+  filter(offpovuniv == "In Poverty Universe") %>%
+  group_by(cpsid) %>%
+  mutate(family_size = n()) %>%
+  ungroup() %>%
+  group_by(family_size, offpov) %>%
+  summarize(n())
+
+```
 
-<font color="#55b748">**Step 2:**</font> Filter out any characters that have a missing `homeworld`.
+:::
 
-<font color="#55b748">**Step 3:**</font> Find the average and median height and mass for humans vs. droids. (Hint: use `group_by()` and `summarize()`). Make sure that you are correcting for observations with missing values!
+**Are the estimates from the previous two exercises correct?**
 
-<font color="#55b748">**Step 4:**</font> Make a dummy variable called `droid` that is `1` for droids and `0` for humans. Make a variable called `tall` that is `1` if the characters are more than 200 cm tall. How many tall humans are there?
+Let's look at a [Census Report](https://www.census.gov/library/publications/2020/demo/p60-270.html) to see how many people were in poverty in 2019. We estimated about 16,500 people. The Census Bureau says 34.0 million people.
 
-<font color="#55b748">**Step 5:**</font> What is the most common skin color for droids?
+No! We did not account for sampling weights, so our estimates are incorrect. [`library(srvyr)`](https://cran.r-project.org/web/packages/srvyr/index.html) has tools for weighted estimation with complex surveys.
 
 ## Resources
 
--   [R for Data Science: data transformation](https://r4ds.had.co.nz/transform.html)
+-   [R for Data Science: data transformation](https://r4ds.hadley.nz/data-transform)
 -   [Data wrangling cheat sheet](https://www.rstudio.com/wp-content/uploads/2015/02/data-wrangling-cheatsheet.pdf)
diff --git a/intro_series_book/_quarto.yml b/intro_series_book/_quarto.yml
index 1df02ed..e5eb1d4 100644
--- a/intro_series_book/_quarto.yml
+++ b/intro_series_book/_quarto.yml
@@ -4,8 +4,8 @@ project:
 
 book:
   title: "Intro to R"
-  author: "Amy Rogin and Aaron Williams"
-  date: "8/11/2023"
+  author: "Amy Rogin and Aaron R. Williams"
+  date: today
   chapters:
     - index.qmd
     - 01_lesson.qmd
@@ -20,8 +20,3 @@ bibliography: references.bib
 format:
   html:
     css: style.scss
-  pdf:
-    documentclass: scrreprt
-
-editor: visual
-
diff --git a/intro_series_book/www/images/tidy-data.png b/intro_series_book/www/images/tidy-data.png
new file mode 100644
index 0000000..4287d74
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