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cpsievert · cpsievert · commit cd95b45f2890 · 2019-04-05T11:22:27.000-05:00
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 <div id="header">
 <h1 class="title">Interactive web-based data visualization with R, plotly, and shiny</h1>
 <p class="author"><em>Carson Sievert</em></p>
-<p class="date"><em>2019-04-04</em></p>
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 </div>
 <div id="welcome" class="section level1 unnumbered">
 <h1>Welcome</h1>
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 <h1><span class="header-section-number">1</span> Introduction</h1>
 <div id="why-interactive-web-graphics-from-r" class="section level2">
 <h2><span class="header-section-number">1.1</span> Why interactive web graphics <em>from R</em>?</h2>
-<p>As <span class="citation">Wickham and Grolemund (<a href="#ref-r4ds" role="doc-biblioref">2018</a>)</span> argue, the exploratory phase of a data science workflow (Figure <a href="introduction.html#fig:workflow">1.1</a>) requires lots of iteration between data manipulation, visualization, and modeling. Acheiving these tasks through a programming language like <code>R</code> offers the opportunity to scale and automate tasks, document and track them, and reliably reproduce their output. That power, however, typically comes at the cost of increasing the amount of cognitive load involved relative to a GUI-based system.<a href="#fn1" class="footnote-ref" id="fnref1"><sup>1</sup></a> <code>R</code> packages like the <strong>tidyverse</strong> have been incredibly successful due to their ability to limit cognitive load without removing the benefits of performing analysis via code. Moreover, the <strong>tidyverse</strong>’s unifying principles of designing for humans, consistency, and composabilty makes iteration within and between these stages seamless – an important but often overlooked challenge in exploratory data analysis (EDA) <span class="citation">(Tidyverse team <a href="#ref-tidy-principles" role="doc-biblioref">2018</a>)</span>.</p>
+<p>As <span class="citation">Wickham and Grolemund (<a href="#ref-r4ds" role="doc-biblioref">2018</a>)</span> argue, the exploratory phase of a data science workflow (Figure <a href="introduction.html#fig:workflow">1.1</a>) requires lots of iteration between data manipulation, visualization, and modeling. Achieving these tasks through a programming language like <code>R</code> offers the opportunity to scale and automate tasks, document and track them, and reliably reproduce their output. That power, however, typically comes at the cost of increasing the amount of cognitive load involved relative to a GUI-based system.<a href="#fn1" class="footnote-ref" id="fnref1"><sup>1</sup></a> <code>R</code> packages like the <strong>tidyverse</strong> have been incredibly successful due to their ability to limit cognitive load without removing the benefits of performing analysis via code. Moreover, the <strong>tidyverse</strong>’s unifying principles of designing for humans, consistency, and composabilty makes iteration within and between these stages seamless – an important but often overlooked challenge in exploratory data analysis (EDA) <span class="citation">(Tidyverse team <a href="#ref-tidy-principles" role="doc-biblioref">2018</a>)</span>.</p>
 <div class="figure" style="text-align: center"><span id="fig:workflow"></span>
 <img src="images/workflow.svg" alt="The stages of a data science workflow from Wickham and Grolemund (2018)." width="100%" />
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@@ -422,7 +422,7 @@ <h2><span class="header-section-number">1.2</span> What you will learn</h2>
 FIGURE 1.7: An example of what you’ll learn: Figure <a href="linking-views-with-shiny.html#fig:shiny-crossfilter">17.25</a>.
 </p>
 </div>
-<p>By going through the code behind these examples, you’ll that many of them leverage other <code>R</code> packages in their implementation. To highlight a few of the R packages that you’ll see:</p>
+<p>By going through the code behind these examples, you’ll see that many of them leverage other <code>R</code> packages in their implementation. To highlight a few of the R packages that you’ll see:</p>
 <ul>
 <li><strong>dplyr</strong> and <strong>tidyr</strong>
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@@ -455,7 +455,7 @@ <h2><span class="header-section-number">1.2</span> What you will learn</h2>
 <li><p><em>Publishing views:</em> discusses various techniques for exporting (as well as embedding) <strong>plotly</strong> graphs to various file formats (e.g., HTML, svg, pdf, png, etc). Also, Chapter <a href="editing-views.html#editing-views">12</a> demonstrates how one could leverage editable layout components HTML to touch-up a graph, then export to a static file format of interest before publication. Indeed, this book was created using the techniques from this section.</p></li>
 <li><p><em>Combining multiple views:</em> demonstrates how to combine multiple data views into a single web page (arranging) or graphic (animation). Most of these techniques are shown using <strong>plotly</strong> graphs, but techniques from Section <a href="arranging-views.html#arranging-htmlwidgets">13.2</a> extend to any HTML content generated via <strong>htmltools</strong> (which includes <strong>htmlwidgets</strong>).</p></li>
 <li><p><em>Linking multiple views:</em> provides an overview of the two models for linking <strong>plotly</strong> graph(s) to other data views. The first model, covered in Chapter <a href="client-side-linking.html#graphical-queries">16.1</a>, outlines <strong>plotly</strong>’s support for linking views purely client-side, meaning the resulting graphs render in any web browser on any machine without requiring external software. The second model, covered in Chapter <a href="linking-views-with-shiny.html#linking-views-with-shiny">17</a>, demonstrates how to link <strong>plotly</strong> with other views via <strong>shiny</strong>, a reactive web application framework for <code>R</code>. Relatively speaking, the second model grants the <code>R</code> user way more power and flexbility, but comes at the cost of requiring more computational infrastructure. That being said, RStudio provides accessible resources for deploying <strong>shiny</strong> apps <a href="https://shiny.rstudio.com/articles/#deployment" class="uri">https://shiny.rstudio.com/articles/#deployment</a>.</p></li>
-<li><p><em>Custom behavior with JavaScript:</em> demonstrates various ways to customize <strong>plotly</strong> graphs by writing custom JavaScript to handle certain user events. This part of the book is designed to be approachable for <code>R</code> users that want learn just enough JavaScript to <strong>plotly</strong> to do something it doesn’t “natively” support.</p></li>
+<li><p><em>Custom behavior with JavaScript:</em> demonstrates various ways to customize <strong>plotly</strong> graphs by writing custom JavaScript to handle certain user events. This part of the book is designed to be approachable for <code>R</code> users that want to learn just enough JavaScript to <strong>plotly</strong> to do something it doesn’t “natively” support.</p></li>
 <li><p><em>Various special topics</em>: offers a grab-bag of topics that address common questions, mostly related to the customization of <strong>plotly</strong> graphs in <code>R</code>.</p></li>
 </ol>
 <p>You might already notice that this book often uses the term ‘view’ or ‘data view’, so here we take a moment to frame its use in a wider context. As <span class="citation">Wills (<a href="#ref-Wills2008" role="doc-biblioref">2008</a>)</span> puts it: “a ‘data view’ is anything that gives the user a way of examining data so as to gain insight and understanding. A data view is usually thought of as a barchart, scatterplot, or other traditional statistical graphic, but we use the term more generally, including ‘views’ such as the results of a regression analysis, a neural net prediction, or a set of descriptive statistics”. In this book, more often than not, the term ‘view’ typically refers to a <strong>plotly</strong> graph or other <strong>htmlwidgets</strong> (e.g., <strong>DT</strong>, <strong>leaflet</strong>, etc). In particular, Chapter <a href="client-side-linking.html#graphical-queries">16.1</a> is all about linking multiple <strong>htmlwidgets</strong> together through a graphical database querying framework. However, the term ‘view’ takes on a more general interpretation in Chapter <a href="linking-views-with-shiny.html#linking-views-with-shiny">17</a> since the reactive programming framework that <strong>shiny</strong> provides allows us to have a more general conversation surrounding linked data views.</p>
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