diff --git a/04_basic_data_processing.qmd b/04_basic_data_processing.qmd
index 2461753..bdbc271 100644
--- a/04_basic_data_processing.qmd
+++ b/04_basic_data_processing.qmd
@@ -226,14 +226,15 @@ Now that our data is clean, we can get more complete summaries to understand it
 summary(flower_clean_df)
 ```
 
-Now let's imagine we want to study the distribution of values for weight. We can use a histogram to check the shape.
+Now let's imagine we want to study the distribution of values for weight. We can use a histogram to check the shape of this distribution.
 
 ```{r histogram for weight}
 hist(
     flower_clean_df$weight, 
     breaks = 15,
+    xlim = c(5, 25),
     xlab = "Weight",
-    main = "Histogram for weight"
+    main = "Few weights are above 20"
 )
 ```
 
@@ -241,9 +242,9 @@ Or we can get a simpler description using a box plot.
 ```{r boxplot for weight}
 boxplot(
     flower_clean_df$weight, 
-    xlab = "height", 
+    ylab = "Weight", 
     col = "darkgreen",
-    main = "Boxplot for weight"
+    main = "Most weights are between 9 and 15"
 )
 ```
 
@@ -279,7 +280,7 @@ legend(
 )
 ```
 
-Now let's see how frequently the values of nitrogen and treat combine with each other, but only for flowers with a leaf area greater than 13.
+Now let's see how frequently the values of nitrogen and treat combine with each other, but only for flowers with a leaf area greater than 13. We can use a mosaic plot for this.
 
 ```{r mosaic plot for nitrogen vs treat}
 nitrogen_by_treat_table = xtabs(
diff --git a/docs/01_the_r_environment.html b/docs/01_the_r_environment.html
index 70bd2c5..111e84f 100644
--- a/docs/01_the_r_environment.html
+++ b/docs/01_the_r_environment.html
@@ -2,7 +2,7 @@
 <html xmlns="http://www.w3.org/1999/xhtml" lang="en" xml:lang="en"><head>
 
 <meta charset="utf-8">
-<meta name="generator" content="quarto-1.4.553">
+<meta name="generator" content="quarto-1.3.450">
 
 <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes">
 
@@ -49,13 +49,7 @@
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-  "keyboard-shortcut": [
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-  "show-item-context": false,
+  "limit": 20,
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@@ -64,7 +58,6 @@
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-      <button type="button" class="quarto-btn-toggle btn" data-bs-toggle="collapse" data-bs-target=".quarto-sidebar-collapse-item" aria-controls="quarto-sidebar" aria-expanded="false" aria-label="Toggle sidebar navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">
+      <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(); }">
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-        <nav class="quarto-page-breadcrumbs" aria-label="breadcrumb"><ol class="breadcrumb"><li class="breadcrumb-item"><a href="./01_the_r_environment.html"><span class="chapter-number">1</span>&nbsp; <span class="chapter-title">The R <em>Environment</em></span></a></li></ol></nav>
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-        </a>
+      <nav class="quarto-page-breadcrumbs" aria-label="breadcrumb"><ol class="breadcrumb"><li class="breadcrumb-item"><a href="./01_the_r_environment.html"><span class="chapter-number">1</span>&nbsp; <span class="chapter-title">The R *Environment*</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>
@@ -95,7 +88,7 @@
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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 collapse-horizontal quarto-sidebar-collapse-item 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="./">Introduction to R</a> 
@@ -144,7 +137,7 @@
     </ul>
     </div>
 </nav>
-<div id="quarto-sidebar-glass" class="quarto-sidebar-collapse-item" data-bs-toggle="collapse" data-bs-target=".quarto-sidebar-collapse-item"></div>
+<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">
@@ -179,10 +172,8 @@ <h1 class="title"><span class="chapter-number">1</span>&nbsp; <span class="chapt
   </div>
   
 
-
 </header>
 
-
 <p><strong>R</strong> is an environment for statistical modeling and graphics. That it is an <em>environment</em> instead of a <em>package</em> is one of the main reasons why <strong>R</strong> is distinct from other statistical programs. A package only gives us a fixed set of tools. An environment allows us to modify, combine and even create tools to suit our specific needs.</p>
 <section id="why-becoming-a-user" class="level2" data-number="1.1">
 <h2 data-number="1.1" class="anchored" data-anchor-id="why-becoming-a-user"><span class="header-section-number">1.1</span> Why becoming a useR?</h2>
@@ -200,11 +191,7 @@ <h2 data-number="1.1" class="anchored" data-anchor-id="why-becoming-a-user"><spa
 <p>and, in 8 lines of code or less, make a plot like this:</p>
 <div class="cell">
 <div class="cell-output-display">
-<div>
-<figure class="figure">
-<p><img src="01_the_r_environment_files/figure-html/height by nitrogen boxplots-1.png" class="img-fluid figure-img" width="672"></p>
-</figure>
-</div>
+<p><img src="01_the_r_environment_files/figure-html/height by nitrogen boxplots-1.png" class="img-fluid" width="672"></p>
 </div>
 </div>
 <p>You can also find many powerful tools to fit statistical models (both bayesian and frequentist), including:</p>
@@ -336,33 +323,6 @@ <h3 data-number="1.4.1" class="anchored" data-anchor-id="how-to-get-rstudio"><sp
       }
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@@ -429,15 +389,13 @@ <h3 data-number="1.4.1" class="anchored" data-anchor-id="how-to-get-rstudio"><sp
       return localAlternateSentinel;
     }
   }
-  const darkModeDefault = false;
-  let localAlternateSentinel = darkModeDefault ? 'alternate' : 'default';
+  let localAlternateSentinel = 'default';
   // Dark / light mode switch
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@@ -516,27 +474,10 @@ <h3 data-number="1.4.1" class="anchored" data-anchor-id="how-to-get-rstudio"><sp
     // clear code selection
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   });
-    var localhostRegex = new RegExp(/^(?:http|https):\/\/localhost\:?[0-9]*\//);
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+  function tippyHover(el, contentFn) {
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+      content: contentFn,
       maxWidth: 500,
       delay: 100,
       arrow: false,
@@ -546,17 +487,8 @@ <h3 data-number="1.4.1" class="anchored" data-anchor-id="how-to-get-rstudio"><sp
       interactive: true,
       interactiveBorder: 10,
       theme: 'quarto',
-      placement: 'bottom-start',
+      placement: 'bottom-start'
     };
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-    }
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@@ -568,130 +500,7 @@ <h3 data-number="1.4.1" class="anchored" data-anchor-id="how-to-get-rstudio"><sp
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-            const note = htmlDoc.getElementById(id);
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-        // This is a special case and we should probably do some content thinning / targeting
-        fetch(url)
-        .then(res => res.text())
-        .then(html => {
-          const parser = new DOMParser();
-          const htmlDoc = parser.parseFromString(html, "text/html");
-          const note = htmlDoc.querySelector('main.content');
-          if (note !== null) {
-            // This should only happen for chapter cross references
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+      return note.innerHTML;
     });
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@@ -735,7 +544,6 @@ <h3 data-number="1.4.1" class="anchored" data-anchor-id="how-to-get-rstudio"><sp
             }
             div.style.top = top - 2 + "px";
             div.style.height = height + 4 + "px";
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@@ -761,32 +569,6 @@ <h3 data-number="1.4.1" class="anchored" data-anchor-id="how-to-get-rstudio"><sp
         });
         selectedAnnoteEl = undefined;
       };
-        // Handle positioning of the toggle
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@@ -848,12 +630,12 @@ <h3 data-number="1.4.1" class="anchored" data-anchor-id="how-to-get-rstudio"><sp
 </script>
 <nav class="page-navigation">
   <div class="nav-page nav-page-previous">
-      <a href="./index.html" class="pagination-link" aria-label="Preface">
+      <a href="./index.html" class="pagination-link">
         <i class="bi bi-arrow-left-short"></i> <span class="nav-page-text">Preface</span>
       </a>          
   </div>
   <div class="nav-page nav-page-next">
-      <a href="./02_getting_started_with_r.html" class="pagination-link" aria-label="Getting Started with R">
+      <a href="./02_getting_started_with_r.html" class="pagination-link">
         <span class="nav-page-text"><span class="chapter-number">2</span>&nbsp; <span class="chapter-title">Getting Started with R</span></span> <i class="bi bi-arrow-right-short"></i>
       </a>
   </div>
@@ -862,5 +644,4 @@ <h3 data-number="1.4.1" class="anchored" data-anchor-id="how-to-get-rstudio"><sp
 
 
 
-
 </body></html>
\ No newline at end of file
diff --git a/docs/04_basic_data_processing.html b/docs/04_basic_data_processing.html
index 2ff3def..9e9c1f2 100644
--- a/docs/04_basic_data_processing.html
+++ b/docs/04_basic_data_processing.html
@@ -464,14 +464,15 @@ <h2 data-number="4.3" class="anchored" data-anchor-id="data-summaries-and-visual
  Max.   :49.20   Max.   :189.60   Max.   :17.000  </code></pre>
 </div>
 </div>
-<p>Now let’s imagine we want to study the distribution of values for weight. We can use a histogram to check the shape.</p>
+<p>Now let’s imagine we want to study the distribution of values for weight. We can use a histogram to check the shape of this distribution.</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><span class="fu">hist</span>(</span>
 <span id="cb36-2"><a href="#cb36-2" aria-hidden="true" tabindex="-1"></a>    flower_clean_df<span class="sc">$</span>weight, </span>
 <span id="cb36-3"><a href="#cb36-3" aria-hidden="true" tabindex="-1"></a>    <span class="at">breaks =</span> <span class="dv">15</span>,</span>
-<span id="cb36-4"><a href="#cb36-4" aria-hidden="true" tabindex="-1"></a>    <span class="at">xlab =</span> <span class="st">"Weight"</span>,</span>
-<span id="cb36-5"><a href="#cb36-5" aria-hidden="true" tabindex="-1"></a>    <span class="at">main =</span> <span class="st">"Histogram for weight"</span></span>
-<span id="cb36-6"><a href="#cb36-6" aria-hidden="true" tabindex="-1"></a>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<span id="cb36-4"><a href="#cb36-4" aria-hidden="true" tabindex="-1"></a>    <span class="at">xlim =</span> <span class="fu">c</span>(<span class="dv">5</span>, <span class="dv">25</span>),</span>
+<span id="cb36-5"><a href="#cb36-5" aria-hidden="true" tabindex="-1"></a>    <span class="at">xlab =</span> <span class="st">"Weight"</span>,</span>
+<span id="cb36-6"><a href="#cb36-6" aria-hidden="true" tabindex="-1"></a>    <span class="at">main =</span> <span class="st">"Few weights are above 20"</span></span>
+<span id="cb36-7"><a href="#cb36-7" aria-hidden="true" tabindex="-1"></a>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output-display">
 <p><img src="04_basic_data_processing_files/figure-html/histogram for weight-1.png" class="img-fluid" width="672"></p>
 </div>
@@ -480,9 +481,9 @@ <h2 data-number="4.3" class="anchored" data-anchor-id="data-summaries-and-visual
 <div class="cell">
 <div class="sourceCode cell-code" id="cb37"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb37-1"><a href="#cb37-1" aria-hidden="true" tabindex="-1"></a><span class="fu">boxplot</span>(</span>
 <span id="cb37-2"><a href="#cb37-2" aria-hidden="true" tabindex="-1"></a>    flower_clean_df<span class="sc">$</span>weight, </span>
-<span id="cb37-3"><a href="#cb37-3" aria-hidden="true" tabindex="-1"></a>    <span class="at">xlab =</span> <span class="st">"height"</span>, </span>
+<span id="cb37-3"><a href="#cb37-3" aria-hidden="true" tabindex="-1"></a>    <span class="at">ylab =</span> <span class="st">"Weight"</span>, </span>
 <span id="cb37-4"><a href="#cb37-4" aria-hidden="true" tabindex="-1"></a>    <span class="at">col =</span> <span class="st">"darkgreen"</span>,</span>
-<span id="cb37-5"><a href="#cb37-5" aria-hidden="true" tabindex="-1"></a>    <span class="at">main =</span> <span class="st">"Boxplot for weight"</span></span>
+<span id="cb37-5"><a href="#cb37-5" aria-hidden="true" tabindex="-1"></a>    <span class="at">main =</span> <span class="st">"Most weights are between 9 and 15"</span></span>
 <span id="cb37-6"><a href="#cb37-6" 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-display">
 <p><img src="04_basic_data_processing_files/figure-html/boxplot for weight-1.png" class="img-fluid" width="672"></p>
@@ -521,7 +522,7 @@ <h2 data-number="4.3" class="anchored" data-anchor-id="data-summaries-and-visual
 <p><img src="04_basic_data_processing_files/figure-html/leaf area vs shoot area by treat-1.png" class="img-fluid" width="672"></p>
 </div>
 </div>
-<p>Now let’s see how frequently the values of nitrogen and treat combine with each other, but only for flowers with a leaf area greater than 13.</p>
+<p>Now let’s see how frequently the values of nitrogen and treat combine with each other, but only for flowers with a leaf area greater than 13. We can use a mosaic plot for this.</p>
 <div class="cell">
 <div class="sourceCode cell-code" id="cb40"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb40-1"><a href="#cb40-1" aria-hidden="true" tabindex="-1"></a>nitrogen_by_treat_table <span class="ot">=</span> <span class="fu">xtabs</span>(</span>
 <span id="cb40-2"><a href="#cb40-2" aria-hidden="true" tabindex="-1"></a>    <span class="at">formula =</span> <span class="sc">~</span> nitrogen <span class="sc">+</span> treat,</span>
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+    "text": "1.1 Why becoming a useR?\nR is gratis and it runs on Windows, MacOS, and several Unix platforms. With R you can start with a data set like this:\n\n\n  treat nitrogen block height weight leafarea shootarea flowers\n1   tip   medium     1    7.5   7.62     11.7      31.9       1\n2   tip   medium     1   10.7  12.14     14.1      46.0      10\n3   tip   medium     1   11.2  12.76      7.1      66.7      10\n4   tip   medium     1   10.4   8.78     11.9      20.3       1\n5   tip   medium     1   10.4  13.58     14.5      26.9       4\n\n\nand, in 8 lines of code or less, make a plot like this:\n\n\n\n\n\nYou can also find many powerful tools to fit statistical models (both bayesian and frequentist), including:\n\nGeneralized linear models (including linear regression)\nSurvival analysis\nTime series analysis\nRandom and Mixed effects models (aka hierarchical models, aka multilevel models)\nClassification and clustering\nSample size and power calculations\nMultivariable analysis (e.g., factor analysis, principal component analysis, and structural equations modeling)\n\nEven better, new tools become available in R all the time. As with other open source programming languages, everyone can examine and contribute to R’s code. Users constantly publish their own code “packages” (ready-to-use tools), which expand R’s base capabilities. As of March 2019, users have contributed over 13,700 packages to Comprehensive R Archive Network (CRAN), many of which perform complex statistical routines that are not (and may never be) available in other statistical software systems.\nYou can choose your own way of using R. In Windows, there are several ways to use R. The standard R graphical unit interface (GUI) allows you to point and click to do many basic tasks. Another GUI is R Commander, developed by John Fox at McMaster University. R Commander displays the underlying R code for each analysis to help the user learn the programming language. Tinn-R is another GUI from Jose Claudio Faria. These GUIs are friendly and easy to grasp if you’re a beginner. But to use all of R’s capabilities you will need to do more than point and click.\nA more complete way of using R is through an integrated development environment (IDE), which, in short, helps you code. The most popular IDE for R is RStudio, which organizes the user’s screen into panes that display scripts, objects, graphics, and the R console.\nIn these notes, we will use RStudio a lot. The goal is for you to start taking full advantage of R’s capabilities."
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-    "text": "1.2 Why Isn’t Everyone a UseR?\nMaybe because many users of statistics only learn how to use one statistical software, often the one taught in their first statistics course. In the past, R was almost never this first language (although nowadays more schools are teaching how to use it).\nBesides, even if people have used R, they can struggle to get comfortable and productive with it, especially if they have little coding experience. Typing commands explicitly is more difficult than pointing and clicking. Also, each package has its own rules to learn. You can find a lot of good help for popular packages written by professional developers, but not so much for smaller packages written by other common users. Worst of all, some of the messages R displays if you make a mistake are uninformative, so fixing the problem can be difficult.\nDon’t get frustrated! You don’t have to be an expert programmer to use R. The benefits are worth spending some time up front.",
-    "crumbs": [
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+    "text": "1.2 Why Isn’t Everyone a UseR?\nMaybe because many users of statistics only learn how to use one statistical software, often the one taught in their first statistics course. In the past, R was almost never this first language (although nowadays more schools are teaching how to use it).\nBesides, even if people have used R, they can struggle to get comfortable and productive with it, especially if they have little coding experience. Typing commands explicitly is more difficult than pointing and clicking. Also, each package has its own rules to learn. You can find a lot of good help for popular packages written by professional developers, but not so much for smaller packages written by other common users. Worst of all, some of the messages R displays if you make a mistake are uninformative, so fixing the problem can be difficult.\nDon’t get frustrated! You don’t have to be an expert programmer to use R. The benefits are worth spending some time up front."
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-    "text": "1.3 Suggestions for Learning R\n\nLearn interactively! Retype and experiment with lots of sample code; you won’t break it. These notes contain several code examples and you can find many more online.\nDon’t worry about getting errors. Even experienced R users make errors all the time. Besides, you can learn a lot from error messages.\nAsk other R users for help.\nSome useful links are:\n\nhttps://www.r-project.org: The R Home page, the central webpage for the R project. Here you will find links for downloading R, downloading additional packages for R, and almost everything else that you would like to know about the software or the people behind it.\nhttps://cran.r-project.org/web/views/: Task views summarize the most important packages involved in a subject field or analysis type.\nhttps://journal.r-project.org: The R Journal\nhttps://stats.stackexchange.com: Cross-Validated\nhttps://www.r-bloggers.com\nhttps://stats.idre.ucla.edu/r/: Institute for Digital Research and Education at UCLA\nhttps://socialsciences.mcmaster.ca/jfox/: John Fox’s home page\nhttps://sas-and-r.blogspot.com/: Examples of code to perform same task in SAS and R.",
-    "crumbs": [
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+    "text": "1.3 Suggestions for Learning R\n\nLearn interactively! Retype and experiment with lots of sample code; you won’t break it. These notes contain several code examples and you can find many more online.\nDon’t worry about getting errors. Even experienced R users make errors all the time. Besides, you can learn a lot from error messages.\nAsk other R users for help.\nSome useful links are:\n\nhttps://www.r-project.org: The R Home page, the central webpage for the R project. Here you will find links for downloading R, downloading additional packages for R, and almost everything else that you would like to know about the software or the people behind it.\nhttps://cran.r-project.org/web/views/: Task views summarize the most important packages involved in a subject field or analysis type.\nhttps://journal.r-project.org: The R Journal\nhttps://stats.stackexchange.com: Cross-Validated\nhttps://www.r-bloggers.com\nhttps://stats.idre.ucla.edu/r/: Institute for Digital Research and Education at UCLA\nhttps://socialsciences.mcmaster.ca/jfox/: John Fox’s home page\nhttps://sas-and-r.blogspot.com/: Examples of code to perform same task in SAS and R."
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-    "text": "1.4 How to get R\nR is already installed on the lab workstations. The following information is useful if you need to install R on another machine.\nAt the R Project Web Page the most important link is at the left hand side of the screen, under the “Download” heading. Click on the CRAN link (Comprehensive R Archive Network), and, after you choose one of the U.S. mirrors, you will be taken to the page that you will use to download everything R-related.\nOnce you find the CRAN web page, take the following steps to obtain R:\n\nClick on “Download R for X” that best describes your operating system (Linux, OS X, Windows).\nWhen using Windows, click on the “base” subdirectory. This will allow you to download the base R packages.\nClick the “Download R 4.X.X for Windows” link. R is updated quite frequently. At the time of this printing, version 4.4.0 is available. Save the .exe file somewhere on your computer.\nDouble-click on the .exe file once it is downloaded. An installation window will appear to guide you through the setup of R in your machine.\nOnce you finish, you should have an R icon on your desktop that gives you a shortcut to the R system.\n\n\n1.4.1 How to get RStudio\nRStudio is already installed on the lab workstations. The following information is useful if you need to install RStudio on another machine. You must install R before you install RStudio. Otherwise, RStudio will not work.\nVisit https://posit.co/downloads/ and click on “Download RStudio”. Choose the version for your operating system (Linux, OS X, Windows) and download the installer. Then double click on the installer .exe file and follow the instructions on the screen to install RStudio.",
-    "crumbs": [
-      "<span class='chapter-number'>1</span>  <span class='chapter-title'>The R *Environment*</span>"
-    ]
+    "text": "1.4 How to get R\nR is already installed on the lab workstations. The following information is useful if you need to install R on another machine.\nAt the R Project Web Page the most important link is at the left hand side of the screen, under the “Download” heading. Click on the CRAN link (Comprehensive R Archive Network), and, after you choose one of the U.S. mirrors, you will be taken to the page that you will use to download everything R-related.\nOnce you find the CRAN web page, take the following steps to obtain R:\n\nClick on “Download R for X” that best describes your operating system (Linux, OS X, Windows).\nWhen using Windows, click on the “base” subdirectory. This will allow you to download the base R packages.\nClick the “Download R 4.X.X for Windows” link. R is updated quite frequently. At the time of this printing, version 4.4.0 is available. Save the .exe file somewhere on your computer.\nDouble-click on the .exe file once it is downloaded. An installation window will appear to guide you through the setup of R in your machine.\nOnce you finish, you should have an R icon on your desktop that gives you a shortcut to the R system.\n\n\n1.4.1 How to get RStudio\nRStudio is already installed on the lab workstations. The following information is useful if you need to install RStudio on another machine. You must install R before you install RStudio. Otherwise, RStudio will not work.\nVisit https://posit.co/downloads/ and click on “Download RStudio”. Choose the version for your operating system (Linux, OS X, Windows) and download the installer. Then double click on the installer .exe file and follow the instructions on the screen to install RStudio."
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@@ -213,7 +186,7 @@
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     "title": "4  Basic data processing",
     "section": "4.3 Data summaries and visualizations",
-    "text": "4.3 Data summaries and visualizations\nNow that our data is clean, we can get more complete summaries to understand it better. Function summary() recognizes the type of each column and displays an intuitively appropriate summary:\n\nsummary(flower_clean_df)\n\n   treat      nitrogen      block         height           weight      \n notip:48   high  :32   Min.   :1.0   Min.   : 1.200   Min.   : 5.790  \n tip  :48   low   :32   1st Qu.:1.0   1st Qu.: 4.475   1st Qu.: 9.027  \n            medium:32   Median :1.5   Median : 6.450   Median :11.395  \n                        Mean   :1.5   Mean   : 6.840   Mean   :12.155  \n                        3rd Qu.:2.0   3rd Qu.: 9.025   3rd Qu.:14.537  \n                        Max.   :2.0   Max.   :17.200   Max.   :23.890  \n   leaf_area       shoot_area        flowers      \n Min.   : 5.80   Min.   :  5.80   Min.   : 1.000  \n 1st Qu.:11.07   1st Qu.: 39.05   1st Qu.: 4.000  \n Median :13.45   Median : 70.05   Median : 6.000  \n Mean   :14.05   Mean   : 79.78   Mean   : 7.062  \n 3rd Qu.:16.45   3rd Qu.:113.28   3rd Qu.: 9.000  \n Max.   :49.20   Max.   :189.60   Max.   :17.000  \n\n\nNow let’s imagine we want to study the distribution of values for weight. We can use a histogram to check the shape.\n\nhist(\n    flower_clean_df$weight, \n    breaks = 15,\n    xlab = \"Weight\",\n    main = \"Histogram for weight\"\n)\n\n\n\n\nOr we can get a simpler description using a box plot.\n\nboxplot(\n    flower_clean_df$weight, \n    xlab = \"height\", \n    col = \"darkgreen\",\n    main = \"Boxplot for weight\"\n)\n\n\n\n\nA single box plot has less information than a histogram. But it is easier to compare box plots to look for “big” differences between distributions. Let’s compare the distributions of height by nitrogen level:\n\nboxplot(\n    height ~ nitrogen,\n    data = flower_clean_df, \n    col = c(\"yellow\", \"blue\", \"pink\"),\n    main = \"No clear association between height and nitrogen\"\n)\n\n\n\n\nNow let’s investigate the relationship between shoot area and leaf area. And let’s check whether that relationship changes depending on the value of treat. We can use a scatter plot with shoot area and leaf area, and we can color each point by their treat value.\n\nplot(\n    x = flower_clean_df$leaf_area,\n    y = flower_clean_df$shoot_area, \n    col = flower_clean_df$treat,\n    main = \"Shoot area seems proportional to leaf area\",\n    xlab = \"Leaf area\",\n    ylab = \"Shoot area\"\n)\n# Add a legend to the plot\nlegend(\n    x = \"bottomright\", \n    legend = levels(flower_clean_df$treat), \n    col = 1:length(levels(flower_clean_df$treat)), \n    pch = 16\n)\n\n\n\n\nNow let’s see how frequently the values of nitrogen and treat combine with each other, but only for flowers with a leaf area greater than 13.\n\nnitrogen_by_treat_table = xtabs(\n    formula = ~ nitrogen + treat,\n    data = flower_clean_df[which(flower_clean_df$leaf_area &gt; 13),]\n)\nnitrogen_by_treat_table\n\n        treat\nnitrogen notip tip\n  high      14  12\n  low        7   3\n  medium    10   7\n\nmosaicplot(nitrogen_by_treat_table, main = \"Nitrogen by treat table\")"
+    "text": "4.3 Data summaries and visualizations\nNow that our data is clean, we can get more complete summaries to understand it better. Function summary() recognizes the type of each column and displays an intuitively appropriate summary:\n\nsummary(flower_clean_df)\n\n   treat      nitrogen      block         height           weight      \n notip:48   high  :32   Min.   :1.0   Min.   : 1.200   Min.   : 5.790  \n tip  :48   low   :32   1st Qu.:1.0   1st Qu.: 4.475   1st Qu.: 9.027  \n            medium:32   Median :1.5   Median : 6.450   Median :11.395  \n                        Mean   :1.5   Mean   : 6.840   Mean   :12.155  \n                        3rd Qu.:2.0   3rd Qu.: 9.025   3rd Qu.:14.537  \n                        Max.   :2.0   Max.   :17.200   Max.   :23.890  \n   leaf_area       shoot_area        flowers      \n Min.   : 5.80   Min.   :  5.80   Min.   : 1.000  \n 1st Qu.:11.07   1st Qu.: 39.05   1st Qu.: 4.000  \n Median :13.45   Median : 70.05   Median : 6.000  \n Mean   :14.05   Mean   : 79.78   Mean   : 7.062  \n 3rd Qu.:16.45   3rd Qu.:113.28   3rd Qu.: 9.000  \n Max.   :49.20   Max.   :189.60   Max.   :17.000  \n\n\nNow let’s imagine we want to study the distribution of values for weight. We can use a histogram to check the shape of this distribution.\n\nhist(\n    flower_clean_df$weight, \n    breaks = 15,\n    xlim = c(5, 25),\n    xlab = \"Weight\",\n    main = \"Few weights are above 20\"\n)\n\n\n\n\nOr we can get a simpler description using a box plot.\n\nboxplot(\n    flower_clean_df$weight, \n    ylab = \"Weight\", \n    col = \"darkgreen\",\n    main = \"Most weights are between 9 and 15\"\n)\n\n\n\n\nA single box plot has less information than a histogram. But it is easier to compare box plots to look for “big” differences between distributions. Let’s compare the distributions of height by nitrogen level:\n\nboxplot(\n    height ~ nitrogen,\n    data = flower_clean_df, \n    col = c(\"yellow\", \"blue\", \"pink\"),\n    main = \"No clear association between height and nitrogen\"\n)\n\n\n\n\nNow let’s investigate the relationship between shoot area and leaf area. And let’s check whether that relationship changes depending on the value of treat. We can use a scatter plot with shoot area and leaf area, and we can color each point by their treat value.\n\nplot(\n    x = flower_clean_df$leaf_area,\n    y = flower_clean_df$shoot_area, \n    col = flower_clean_df$treat,\n    main = \"Shoot area seems proportional to leaf area\",\n    xlab = \"Leaf area\",\n    ylab = \"Shoot area\"\n)\n# Add a legend to the plot\nlegend(\n    x = \"bottomright\", \n    legend = levels(flower_clean_df$treat), \n    col = 1:length(levels(flower_clean_df$treat)), \n    pch = 16\n)\n\n\n\n\nNow let’s see how frequently the values of nitrogen and treat combine with each other, but only for flowers with a leaf area greater than 13. We can use a mosaic plot for this.\n\nnitrogen_by_treat_table = xtabs(\n    formula = ~ nitrogen + treat,\n    data = flower_clean_df[which(flower_clean_df$leaf_area &gt; 13),]\n)\nnitrogen_by_treat_table\n\n        treat\nnitrogen notip tip\n  high      14  12\n  low        7   3\n  medium    10   7\n\nmosaicplot(nitrogen_by_treat_table, main = \"Nitrogen by treat table\")"
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diff --git a/load_clean_flower_df.R b/load_clean_flower_df.R
new file mode 100644
index 0000000..c863ce6
--- /dev/null
+++ b/load_clean_flower_df.R
@@ -0,0 +1,7 @@
+flower_df <- read.table("data_files/flower.csv", header = TRUE, sep = ",")
+colnames(flower_df) <- c(
+    "treat", "nitrogen", "block", "height", "weight", "leaf_area", 
+    "shoot_area", "flowers"
+)
+flower_df["treat"] <- factor(flower_df$treat)
+flower_df["nitrogen"] <- factor(flower_df$nitrogen)
\ No newline at end of file
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index e31771d..b9d6307 100644
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@@ -357,19 +422,24 @@ <h2>What is CSCAR?</h2>
 <li class="fragment">A unit of the Office of the Vice President of Research.</li>
 <li class="fragment">Guides and trains researchers in data collection, management, and analysis.</li>
 <li class="fragment">Also helps researchers to use technical software and advanced computing.</li>
-<li class="fragment">Address: The University of Michigan, 3560 Rackham, 915 E. Washington St., Ann Arbor, MI 48109-1070.</li>
 </ul>
 </section>
 <section id="cscar-is-here-to-help-you" class="slide level2">
 <h2>CSCAR is here to help you</h2>
 <ul>
 <li class="fragment">Free, one-hour consultations with graduate-level statisticians.</li>
-<li class="fragment">To request a consultation: email <a href="mailto:deskpeople@umich.edu" class="email">deskpeople@umich.edu</a>, or fill <a href="https://docs.google.com/forms/d/e/1FAIpQLSei-twcjFkoobUrVwSQTmSxdKKEc1Ub8w5LHmeIZUmTV1wmIg/viewform?pli=1">this form</a>. Or visit <a href="cscar.research.umich.edu">cscar.research.umich.edu</a>.</li>
-<li class="fragment">Walk-in consultations with GSRAs. Self-schedule using <a href="https://calendar.google.com/calendar/u/0/selfsched?sstoken=UUMyTFpCR1RXbmhYfGRlZmF1bHR8ZWNjNGJlMWZlYTA4ZWE5NzYzNmNkNzgyZjUyZDYxNDg">this link</a>.</li>
 <li class="fragment">GSRAs are available Monday through Friday, between 9am and 5pm (we close on Tuesdays between noon and 1pm).</li>
 <li class="fragment">All of our scheduled appointments can be either remote or in-person.</li>
 </ul>
 </section>
+<section id="contact-cscar" class="slide level2">
+<h2>Contact CSCAR</h2>
+<ul>
+<li class="fragment">To request a consultation: email <a href="mailto:deskpeople@umich.edu" class="email">deskpeople@umich.edu</a>, or fill <a href="https://docs.google.com/forms/d/e/1FAIpQLSei-twcjFkoobUrVwSQTmSxdKKEc1Ub8w5LHmeIZUmTV1wmIg/viewform?pli=1">this form</a>. Or visit <a href="cscar.research.umich.edu">cscar.research.umich.edu</a>.</li>
+<li class="fragment">Self-schedule a consultation with a GSRA using <a href="https://calendar.google.com/calendar/u/0/selfsched?sstoken=UUMyTFpCR1RXbmhYfGRlZmF1bHR8ZWNjNGJlMWZlYTA4ZWE5NzYzNmNkNzgyZjUyZDYxNDg">this link</a>.</li>
+<li class="fragment">Address: The University of Michigan, 3560 Rackham, 915 E. Washington St., Ann Arbor, MI 48109-1070.</li>
+</ul>
+</section>
 <section id="who-am-i" class="slide level2">
 <h2>Who am I?</h2>
 <ul>
@@ -394,12 +464,12 @@ <h2>R is cool for statistics and graphics</h2>
 
 <div class="cell">
 <div class="cell-output cell-output-stdout">
-<pre><code>  treat nitrogen block height weight leafarea shootarea flowers
-1   tip   medium     1    7.5   7.62     11.7      31.9       1
-2   tip   medium     1   10.7  12.14     14.1      46.0      10
-3   tip   medium     1   11.2  12.76      7.1      66.7      10
-4   tip   medium     1   10.4   8.78     11.9      20.3       1
-5   tip   medium     1   10.4  13.58     14.5      26.9       4</code></pre>
+<pre><code>  treat nitrogen block height weight leaf_area shoot_area flowers
+1   tip   medium     1    7.5   7.62      11.7       31.9       1
+2   tip   medium     1   10.7  12.14      14.1       46.0      10
+3   tip   medium     1   11.2  12.76       7.1       66.7      10
+4   tip   medium     1   10.4   8.78      11.9       20.3       1
+5   tip   medium     1   10.4  13.58      14.5       26.9       4</code></pre>
 </div>
 </div>
 </section>
@@ -421,11 +491,10 @@ <h2>R has plenty of statistical tools and models</h2>
 <li class="fragment">Generalized linear models (including linear regression).</li>
 <li class="fragment">Survival analysis.</li>
 <li class="fragment">Time series analysis.</li>
-<li class="fragment">Random and Mixed effects models (aka hierarchical models, aka multilevel models).</li>
+<li class="fragment">Random and Mixed effects models.</li>
 <li class="fragment">Classification and clustering.</li>
 <li class="fragment">Sample size and power calculations.</li>
-<li class="fragment">Multivariable analysis (e.g., factor analysis, principal component analysis, and structural equations modeling).</li>
-<li class="fragment">And more.</li>
+<li class="fragment">Multivariable analysis (e.g., factor analysis, PCA, and SEM).</li>
 </ul>
 </section>
 <section id="even-more-tools-and-models" class="slide level2">
@@ -439,9 +508,9 @@ <h2>Even more tools and models</h2>
 <h2>Why Isn’t Everyone a Use<strong>R</strong>?</h2>
 <ul>
 <li class="fragment">Some people only use whatever they learned first, which is not always <strong>R</strong>.</li>
+<li class="fragment">Other software seems friendlier.</li>
 <li class="fragment">Need to learn rules of packages you use.</li>
 <li class="fragment">Help pages and error messages may be hard to understand.</li>
-<li class="fragment">Other software seems friendlier.</li>
 </ul>
 </section>
 <section id="suggestions-for-learning-r" class="slide level2">
@@ -474,12 +543,135 @@ <h2>More useful links</h2>
 </ul>
 </div>
 </section></section>
+<section>
 <section id="lets-start-coding" class="title-slide slide level1 center">
 <h1>Let’s start coding</h1>
-<div class="footer footer-default">
 
+</section>
+<section id="practice-your-arithmetic" class="slide level2">
+<h2>Practice your arithmetic</h2>
+<p>Think of an integer, double it, add six, divide it in half, subtract the number you started with, and then square it. If your code is correct, the answer should be nine.</p>
+</section>
+<section id="object-names-have-rules" class="slide level2">
+<h2>Object names have rules</h2>
+<ul>
+<li class="fragment">Names can be a combination of letters, digits, periods <code>.</code> and underscores <code>_</code>.</li>
+<li class="fragment">Names can <em>not</em> include white spaces.</li>
+<li class="fragment">If a name starts with a period <code>.</code>, it can <em>not</em> be followed by a digit.</li>
+<li class="fragment">Names can <em>not</em> start with a number or an underscore <code>_</code>.</li>
+<li class="fragment">Names are case-sensitive (<code>age</code>, <code>Age</code> and <code>AGE</code> are three different objects).</li>
+<li class="fragment">Reserved words can <em>not</em> be used as names (<code>TRUE</code>, <code>FALSE</code>, <code>NULL</code>, <code>if</code>, …).</li>
+</ul>
+</section>
+<section id="tips-for-naming-objects" class="slide level2">
+<h2>Tips for naming objects</h2>
+<ul>
+<li class="fragment">Avoid giving your object the same name as a built-in function.</li>
+<li class="fragment">To separate words, use an underscore (<code>my_object</code>) or a dot (<code>my.object</code>), or capitalize the different words (<code>MyObject</code>). Choose your favorite way, but <em>be consistent</em> with it.</li>
+<li class="fragment">Use names that illustrate what you want to do with the objects.</li>
+</ul>
+</section>
+<section id="exercise" class="slide level2">
+<h2>Exercise</h2>
+<p>Write a function that can simulate the roll of a pair of six-sided dice an arbitrary number of times. This function should return a vector with the values of the red die that were strictly larger than the corresponding values of the blue die. Hint: to simulate rolling a die, you can use the function <code>sample()</code>.</p>
+</section>
+<section id="coercion" class="slide level2">
+<h2>Coercion</h2>
+<p>When adding different data types to the same atomic vector, <strong>R</strong> follows specific rules to coerce everything to be of the same type.</p>
+<ul>
+<li class="fragment">If a character string is present in an atomic vector, <strong>R</strong> will convert all other values to character strings.</li>
+<li class="fragment">If a vector only contains logicals and numbers, <strong>R</strong> will convert the logicals to numbers; every <code>TRUE</code> becomes a <code>1</code>, and every <code>FALSE</code> becomes a <code>0</code>.</li>
+<li class="fragment"><code>NA</code>s are never coerced automatically.</li>
+</ul>
+</section>
+<section id="make-a-histogram" class="slide level2">
+<h2>Make a histogram</h2>
+<p>Use the example I showed to you to make a histogram for variable <code>height</code>. Bonus: can you color the bars?</p>
+<p>Your histogram result should resemble this one:</p>
+
+<img data-src="slideshow_files/figure-revealjs/histogram%20of%20height-1.png" width="624" class="r-stretch quarto-figure-center"></section>
+<section class="slide level2">
+
+<p>Here is the code for the histogram:</p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb2"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1"></a><span class="fu">hist</span>(</span>
+<span id="cb2-2"><a href="#cb2-2"></a>    flower_df<span class="sc">$</span>height,</span>
+<span id="cb2-3"><a href="#cb2-3"></a>    <span class="at">breaks =</span> <span class="dv">12</span>,</span>
+<span id="cb2-4"><a href="#cb2-4"></a>    <span class="at">xlim =</span> <span class="fu">c</span>(<span class="dv">0</span>, <span class="dv">20</span>),</span>
+<span id="cb2-5"><a href="#cb2-5"></a>    <span class="at">xlab =</span> <span class="st">"Height"</span>,</span>
+<span id="cb2-6"><a href="#cb2-6"></a>    <span class="at">main =</span> <span class="st">"Few weights are above 20"</span>,</span>
+<span id="cb2-7"><a href="#cb2-7"></a>    <span class="at">col =</span> <span class="st">"green"</span></span>
+<span id="cb2-8"><a href="#cb2-8"></a>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</div>
+</section>
+<section id="make-a-boxplot" class="slide level2">
+<h2>Make a boxplot</h2>
+<p>Use the example I showed to you to make a histogram for variable <code>leaf_area</code>. Bonus: can you color the box?</p>
+
+<img data-src="slideshow_files/figure-revealjs/boxplot%20for%20leaf_area-1.png" width="384" class="r-stretch quarto-figure-center"></section>
+<section class="slide level2">
+
+<p>Here is the code for the boxplot.</p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb3"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb3-1"><a href="#cb3-1"></a><span class="fu">boxplot</span>(</span>
+<span id="cb3-2"><a href="#cb3-2"></a>    flower_df<span class="sc">$</span>leaf_area, </span>
+<span id="cb3-3"><a href="#cb3-3"></a>    <span class="at">ylab =</span> <span class="st">"Leaf area"</span>, </span>
+<span id="cb3-4"><a href="#cb3-4"></a>    <span class="at">col =</span> <span class="st">"blue"</span>,</span>
+<span id="cb3-5"><a href="#cb3-5"></a>    <span class="at">main =</span> <span class="st">"Most leaf areas are between 11 and 18"</span></span>
+<span id="cb3-6"><a href="#cb3-6"></a>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 </section>
+<section id="make-a-scatterplot" class="slide level2">
+<h2>Make a scatterplot</h2>
+<p>Use the example I showed to you to make a scatter plot with <code>height</code> and <code>weight</code>, coloring by nitrogen level. Remember to add a legend to the plot. Your scatter plot should resemble this one:</p>
+</section>
+<section class="slide level2">
+
+
+<img data-src="slideshow_files/figure-revealjs/unnamed-chunk-4-1.png" width="576" class="r-stretch quarto-figure-center"></section>
+<section class="slide level2">
+
+<p>Here is the code for the scatter plot.</p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb4"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb4-1"><a href="#cb4-1"></a><span class="fu">plot</span>(</span>
+<span id="cb4-2"><a href="#cb4-2"></a>    <span class="at">x =</span> flower_df<span class="sc">$</span>weight,</span>
+<span id="cb4-3"><a href="#cb4-3"></a>    <span class="at">y =</span> flower_df<span class="sc">$</span>height, </span>
+<span id="cb4-4"><a href="#cb4-4"></a>    <span class="at">col =</span> flower_df<span class="sc">$</span>nitrogen,</span>
+<span id="cb4-5"><a href="#cb4-5"></a>    <span class="at">main =</span> <span class="st">"No clear association between height and weight"</span>,</span>
+<span id="cb4-6"><a href="#cb4-6"></a>    <span class="at">xlab =</span> <span class="st">"Weight"</span>,</span>
+<span id="cb4-7"><a href="#cb4-7"></a>    <span class="at">ylab =</span> <span class="st">"Height"</span></span>
+<span id="cb4-8"><a href="#cb4-8"></a>)</span>
+<span id="cb4-9"><a href="#cb4-9"></a><span class="co"># Add a legend to the plot</span></span>
+<span id="cb4-10"><a href="#cb4-10"></a><span class="fu">legend</span>(</span>
+<span id="cb4-11"><a href="#cb4-11"></a>    <span class="at">x =</span> <span class="st">"bottomright"</span>, </span>
+<span id="cb4-12"><a href="#cb4-12"></a>    <span class="at">legend =</span> <span class="fu">levels</span>(flower_df<span class="sc">$</span>nitrogen), </span>
+<span id="cb4-13"><a href="#cb4-13"></a>    <span class="at">col =</span> <span class="dv">1</span><span class="sc">:</span><span class="fu">length</span>(<span class="fu">levels</span>(flower_df<span class="sc">$</span>nitrogen)), </span>
+<span id="cb4-14"><a href="#cb4-14"></a>    <span class="at">pch =</span> <span class="dv">16</span></span>
+<span id="cb4-15"><a href="#cb4-15"></a>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</div>
+</section>
+<section id="make-a-mosaic-plot" class="slide level2">
+<h2>Make a mosaic plot</h2>
+<p>Use the example I showed to you to make a mosaic plot to visualize how frequently the values of nitrogen and treat combine with each other, but only for flowers with a weight below 10. Your plot should resemble this:</p>
+</section>
+<section class="slide level2">
+
+
+<img data-src="slideshow_files/figure-revealjs/mosaic%20plot%20for%20nitrogen%20vs%20treat-1.png" width="480" class="r-stretch quarto-figure-center"></section>
+<section class="slide level2">
+
+<p>Here is the code for the mosaic plot</p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb5"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb5-1"><a href="#cb5-1"></a>nitrogen_by_treat_table <span class="ot">=</span> <span class="fu">xtabs</span>(</span>
+<span id="cb5-2"><a href="#cb5-2"></a>    <span class="at">formula =</span> <span class="sc">~</span> nitrogen <span class="sc">+</span> treat,</span>
+<span id="cb5-3"><a href="#cb5-3"></a>    <span class="at">data =</span> flower_df[<span class="fu">which</span>(flower_df<span class="sc">$</span>weight <span class="sc">&lt;</span> <span class="dv">10</span>),]</span>
+<span id="cb5-4"><a href="#cb5-4"></a>)</span>
+<span id="cb5-5"><a href="#cb5-5"></a><span class="fu">mosaicplot</span>(nitrogen_by_treat_table, <span class="at">main =</span> <span class="st">"Nitrogen by treat, weight below 10"</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</div>
+<div class="footer footer-default">
+
+</div>
+</section></section>
     </div>
   </div>
 
diff --git a/slideshow.qmd b/slideshow.qmd
index 9d9eab6..abaf7c0 100644
--- a/slideshow.qmd
+++ b/slideshow.qmd
@@ -6,6 +6,11 @@ format:
     incremental: true
 ---
 
+```{r}
+source("load_clean_flower_df.R")
+```
+
+
 # Welcome
 
 ## About this workshop
@@ -21,16 +26,20 @@ format:
 + A unit of the Office of the Vice President of Research.
 + Guides and trains researchers in data collection, management, and analysis.
 + Also helps researchers to use technical software and advanced computing.
-+ Address: The University of Michigan, 3560 Rackham, 915 E. Washington St., Ann Arbor, MI 48109-1070.
 
 ## CSCAR is here to help you
 
 + Free, one-hour consultations with graduate-level statisticians.
-+ To request a consultation: email <deskpeople@umich.edu>, or fill [this form](https://docs.google.com/forms/d/e/1FAIpQLSei-twcjFkoobUrVwSQTmSxdKKEc1Ub8w5LHmeIZUmTV1wmIg/viewform?pli=1). Or visit [cscar.research.umich.edu](cscar.research.umich.edu).
-+ Walk-in consultations with GSRAs. Self-schedule using [this link](https://calendar.google.com/calendar/u/0/selfsched?sstoken=UUMyTFpCR1RXbmhYfGRlZmF1bHR8ZWNjNGJlMWZlYTA4ZWE5NzYzNmNkNzgyZjUyZDYxNDg). 
 + GSRAs are available Monday through Friday, between 9am and 5pm (we close on Tuesdays between noon and 1pm). 
 + All of our scheduled appointments can be either remote or in-person.
 
+## Contact CSCAR
+
++ To request a consultation: email <deskpeople@umich.edu>, or fill [this form](https://docs.google.com/forms/d/e/1FAIpQLSei-twcjFkoobUrVwSQTmSxdKKEc1Ub8w5LHmeIZUmTV1wmIg/viewform?pli=1). Or visit [cscar.research.umich.edu](cscar.research.umich.edu).
++ Self-schedule a consultation with a GSRA using [this link](https://calendar.google.com/calendar/u/0/selfsched?sstoken=UUMyTFpCR1RXbmhYfGRlZmF1bHR8ZWNjNGJlMWZlYTA4ZWE5NzYzNmNkNzgyZjUyZDYxNDg).
++ Address: The University of Michigan, 3560 Rackham, 915 E. Washington St., Ann Arbor, MI 48109-1070.
+
+
 ## Who am I?
 
 + Abner Heredia Bustos, a data science consultant at CSCAR.
@@ -46,9 +55,8 @@ format:
 
 ---
 
-```{r loading messy flower data}
+```{r loading flower data}
 #| echo: false
-flower_df <- read.table("data_files/flower.csv", header = TRUE, sep = ",")
 head(flower_df, 5)
 ```
 
@@ -77,12 +85,10 @@ boxplot(
 + Generalized linear models (including linear regression).
 + Survival analysis.
 + Time series analysis.
-+ Random and Mixed effects models (aka hierarchical models, aka multilevel
-    models).
++ Random and Mixed effects models.
 + Classification and clustering.
 + Sample size and power calculations.
-+ Multivariable analysis (e.g., factor analysis, principal component analysis, and structural equations modeling).
-+ And more.
++ Multivariable analysis (e.g., factor analysis, PCA, and SEM).
 
 ## Even more tools and models
 
@@ -129,7 +135,191 @@ boxplot(
 
 # Let's start coding
 
+## Practice your arithmetic
+
+Think of an integer, double it, add six, divide it in half, subtract the number you started with, and then square it. If your code is correct, the answer should be nine.
+
+## Object names have rules
+
++ Names can be a combination of letters, digits, periods `.` and underscores
+  `_`.
++ Names can *not* include white spaces.
++ If a name starts with a period `.`, it can *not* be followed by a digit.
++ Names can *not* start with a number or an underscore `_`.
++ Names are case-sensitive (`age`, `Age` and `AGE` are three different objects).
++ Reserved words can *not* be used as names (`TRUE`, `FALSE`, `NULL`, `if`, ...).
+
+## Tips for naming objects
+
++ Avoid giving your object the same name as a built-in function.
++ To separate words, use an underscore (`my_object`) or a dot (`my.object`), or capitalize the different words (`MyObject`). Choose your favorite way, but *be consistent* with it.
++ Use names that illustrate what you want to do with the objects.
+
+## Exercise
+
+Write a function that can simulate the roll of a pair of six-sided dice an arbitrary number of times. This function should return a vector with the values of the red die that were strictly larger than the corresponding values of the blue die. Hint: to simulate rolling a die, you can use the function `sample()`.
+
+## Coercion
+
+When adding different data types to the same atomic vector, **R** follows specific rules to coerce everything to be of the same type.
+
++ If a character string is present in an atomic vector, **R** will convert all other values to character strings. 
++ If a vector only contains logicals and numbers, **R** will convert the logicals to numbers; every `TRUE` becomes a `1`, and every `FALSE` becomes a `0`. 
++ `NA`s are never coerced automatically.
+
+## Make a histogram
+
+Use the example I showed to you to make a histogram for variable `height`. Bonus: can you color the bars?
+
+Your histogram result should resemble this one:
+
+```{r histogram of height}
+#| echo: false
+#| fig-width: 6.5
+#| fig-height: 5
+#| fig-align: center
+hist(
+    flower_df$height,
+    breaks = 12,
+    xlim = c(0, 20),
+    xlab = "Height",
+    main = "Few weights are above 20",
+    col = "green"
+)
+```
+
+---
+
+Here is the code for the histogram:
+
+```{r}
+#| echo: true
+#| eval: false
+hist(
+    flower_df$height,
+    breaks = 12,
+    xlim = c(0, 20),
+    xlab = "Height",
+    main = "Few weights are above 20",
+    col = "green"
+)
+```
+
+## Make a boxplot
+
+Use the example I showed to you to make a histogram for variable `leaf_area`. Bonus: can you color the box?
+
+```{r boxplot for leaf_area}
+#| echo: false
+#| fig-width: 4
+#| fig-height: 5
+#| fig-align: center
+boxplot(
+    flower_df$leaf_area, 
+    ylab = "Leaf area", 
+    col = "blue",
+    main = "Most leaf areas are between 11 and 18"
+)
+```
+
+---
+
+Here is the code for the boxplot.
+
+```{r}
+#| echo: true
+#| eval: false
+boxplot(
+    flower_df$leaf_area, 
+    ylab = "Leaf area", 
+    col = "blue",
+    main = "Most leaf areas are between 11 and 18"
+)
+```
+
+
+## Make a scatterplot
+
+Use the example I showed to you to make a scatter plot with `height` and `weight`, coloring by nitrogen level. Remember to add a legend to the plot. Your scatter plot should resemble this one:
+
+---
+
+```{r}
+#| echo: false
+#| fig-width: 6
+#| fig-height: 4.5
+#| fig-align: center
+plot(
+    x = flower_df$weight,
+    y = flower_df$height, 
+    col = flower_df$nitrogen,
+    main = "No clear association between height and weight",
+    xlab = "Weight",
+    ylab = "Height"
+)
+# Add a legend to the plot
+legend(
+    x = "bottomright", 
+    legend = levels(flower_df$nitrogen), 
+    col = 1:length(levels(flower_df$nitrogen)), 
+    pch = 16
+)
+```
+
+---
+
+Here is the code for the scatter plot.
+
+```{r}
+#| echo: true
+#| eval: false
+plot(
+    x = flower_df$weight,
+    y = flower_df$height, 
+    col = flower_df$nitrogen,
+    main = "No clear association between height and weight",
+    xlab = "Weight",
+    ylab = "Height"
+)
+# Add a legend to the plot
+legend(
+    x = "bottomright", 
+    legend = levels(flower_df$nitrogen), 
+    col = 1:length(levels(flower_df$nitrogen)), 
+    pch = 16
+)
+```
+
 
+## Make a mosaic plot
 
+Use the example I showed to you to make a mosaic plot to visualize how frequently the values of nitrogen and treat combine with each other, but only for flowers with a weight below 10. Your plot should resemble this:
+
+---
 
+```{r mosaic plot for nitrogen vs treat}
+#| echo: false
+#| fig-width: 5
+#| fig-height: 4
+#| fig-align: center
+nitrogen_by_treat_table = xtabs(
+    formula = ~ nitrogen + treat,
+    data = flower_df[which(flower_df$weight < 10),]
+)
+mosaicplot(nitrogen_by_treat_table, main = "Nitrogen by treat, weight below 10")
+```
+
+---
+
+Here is the code for the mosaic plot
+
+```{r}
+#| echo: true
+#| eval: false
+nitrogen_by_treat_table = xtabs(
+    formula = ~ nitrogen + treat,
+    data = flower_df[which(flower_df$weight < 10),]
+)
+mosaicplot(nitrogen_by_treat_table, main = "Nitrogen by treat, weight below 10")
+```
 
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