Missing OS level dependencies added for units, rgdal V8, jqr and protolite packages, missing R dependencies concaveman and devtools added, typo corrected: "flexbility" to "flexibility" in three files

.travis.yml

@@ -1,3 +1,10 @@
+before_install:
+  - sudo add-apt-repository ppa:ubuntugis/ppa -y
+  - sudo apt-get update -q
+  - sudo apt-get install -y libudunits2-dev proj-bin gdal-bin libgdal-dev libproj-dev 
+    libv8-dev libjq-dev libprotobuf-dev protobuf-compiler
+
+
 language: R
 cache:
   packages: true

DESCRIPTION

@@ -28,6 +28,8 @@ Depends:
   trelliscopejs,
   ggstat,
   ggforce,
+  concaveman,
+  devtools,
   ggmosaic,
   ggalt,
   GGally,

docs/introduction.html

@@ -453,7 +453,7 @@ <h2><span class="header-section-number">1.2</span> What you will learn</h2>
 <li><p><em>Creating views:</em> introduces the process of transforming data into graphics via <strong>plotly</strong>’s programmatic interface. It focuses mostly on <code>plot_ly()</code>, which can interface directly with the underlying plotly.js graphing library, but emphasis is put on features unique to the <code>R</code> package that make it easier to transform data into graphics. Another way to create graphs with <strong>plotly</strong> is to use the <code>ggplotly()</code> function to transform <strong>ggplot2</strong> graphs into <strong>plotly</strong> graphs. Section <a href="overview.html#intro-ggplotly">2.3</a> discusses when and why <code>ggplotly()</code> might be desirable to <code>plot_ly()</code>. It’s also worth mentioning that this part (nor the book as a whole) does not intend to cover every possible chart type and option available in <strong>plotly</strong> – it’s more of a presentation of the most generally useful techniques with the greater <code>R</code> ecosystem in mind. For a more exhaustive gallery of examples of what <strong>plotly</strong> itself is capable of, see <a href="https://plot.ly/r/" class="uri">https://plot.ly/r/</a>.</p></li>
 <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 Section <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>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 Section <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 flexibility, 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 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>

images/empty

@@ -0,0 +1 @@
+

introduction.Rmd

@@ -109,7 +109,7 @@ This book contains six parts and each part contains numerous chapters. A summary
 
 3. _Combining multiple views:_ demonstrates how to combine multiple data views into a single web page (arranging) or graphic (animation). Most of these techniques are shown using **plotly** graphs, but techniques from Section \@ref(arranging-htmlwidgets) extend to any HTML content generated via **htmltools** (which includes **htmlwidgets**).
 
-4. _Linking multiple views:_ provides an overview of the two models for linking **plotly** graph(s) to other data views. The first model, covered in Section \@ref(graphical-queries), outlines **plotly**'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 \@ref(linking-views-with-shiny), demonstrates how to link **plotly** with other views via **shiny**, a reactive web application framework for `R`. Relatively speaking, the second model grants the `R` 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 **shiny** apps <https://shiny.rstudio.com/articles/#deployment>.
+4. _Linking multiple views:_ provides an overview of the two models for linking **plotly** graph(s) to other data views. The first model, covered in Section \@ref(graphical-queries), outlines **plotly**'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 \@ref(linking-views-with-shiny), demonstrates how to link **plotly** with other views via **shiny**, a reactive web application framework for `R`. Relatively speaking, the second model grants the `R` user way more power and flexibility, but comes at the cost of requiring more computational infrastructure. That being said, RStudio provides accessible resources for deploying **shiny** apps <https://shiny.rstudio.com/articles/#deployment>.
 
 5. _Custom behavior with JavaScript:_ demonstrates various ways to customize **plotly** graphs by writing custom JavaScript to handle certain user events. This part of the book is designed to be approachable for `R` users that want to learn just enough JavaScript to **plotly** to do something it doesn't "natively" support.