Deploy commit: Merge pull request #689 from Robinlovelace/Robinlovela…

…ce-patch-1

Update 02-spatial-data.Rmd 83a5f27

02-spatial-data.md

@@ -768,11 +768,13 @@ Consequently, the discrete borders of these features become blurred, and dependi
 <p class="caption">(\#fig:raster-intro-plot2)Examples of continuous and categorical rasters.</p>
 </div>
 
-### R packages for raster data handling
+### R packages for working with raster data
 
-R has several packages able to read and process spatial raster data; see \Wref(the-history-of-r-spatial) for more context.
-However, currently, two main packages with this purpose exist -- **terra** and **stars**.^[We are not mentioning the **raster** package here as it is now being replaced with **terra**.]
-We are focusing on the **terra** package in this book; however, it may be worth knowing the basic similarities and differences between the packages before deciding which one to use.
+Over the last two decades, several packages packages for reading and processing raster datasets have been developed.
+As outlined in Section \@ref(the-history-of-r-spatial), chief among them was **raster**, which led to a step change in R's raster capabilities when it was launched in 2010 and the premier package in the space until the development of **terra** and **stars**.
+Both more recently developed package provide powerful and performant functions for working with raster datasets and there is substantial overlap between their possibly use cases.
+In this book we focus on **terra**, which replaces the older and (in most cases) slower **raster**.
+Before learning about the how **terra**'s class system works, this section describes similarities and differences between **terra** and **raster**; this knowledge will help decide which is most appropriate in different situations.
 
 First, **terra** focuses on the most common raster data model (regular grids), while **stars** also allows storing less popular models (including regular, rotated, sheared, rectilinear, and curvilinear grids).
 While **terra** usually handle one or multi-layered rasters^[It also has an additional class `SpatRasterDataset` for storing many collections of datasets.], the **stars** package provides ways to store raster data cubes -- a raster object with many layers (e.g., bands), for many moments in time (e.g., months), and many attributes (e.g., sensor type A and sensor type B).

adv-map.html

@@ -261,7 +261,7 @@ <h3>
 <p>There are two main types of map aesthetics: those that change with the data and those that are constant.
 Unlike <strong>ggplot2</strong>, which uses the helper function <code><a href="https://ggplot2.tidyverse.org/reference/aes.html">aes()</a></code> to represent variable aesthetics, <strong>tmap</strong> accepts aesthetic arguments directly.
 To map a variable to an aesthetic, pass its column name to the corresponding argument, and to set a fixed aesthetic, pass the desired value instead.<a class="footnote-ref" tabindex="0" data-toggle="popover" data-content="&lt;p&gt;
-If there is a clash between a fixed value and a column name, the column name takes precedence. This can be verified by running the next code chunk after running &lt;code&gt;nz$red = 1:nrow(nz)&lt;/code&gt;.&lt;/p&gt;"><sup>40</sup></a>
+If there is a clash between a fixed value and a column name, the column name takes precedence. This can be verified by running the next code chunk after running &lt;code&gt;nz$red = 1:nrow(nz)&lt;/code&gt;.&lt;/p&gt;"><sup>39</sup></a>
 The most commonly used aesthetics for fill and border layers include color, transparency, line width and line type, set with <code>col</code>, <code>alpha</code>, <code>lwd</code>, and <code>lty</code> arguments, respectively.
 The impact of setting these with fixed values is illustrated in Figure <a href="adv-map.html#fig:tmstatic">8.3</a>.</p>
 <div class="sourceCode" id="cb268"><pre class="downlit sourceCode r">
@@ -368,7 +368,7 @@ <h3>
 Categorical palettes consist of easily distinguishable colors and are most appropriate for categorical data without any particular order such as state names or land cover classes.
 Colors should be intuitive: rivers should be blue, for example, and pastures green.
 Avoid too many categories: maps with large legends and many colors can be uninterpretable.<a class="footnote-ref" tabindex="0" data-toggle="popover" data-content='&lt;p&gt;
-&lt;code&gt;col = "MAP_COLORS"&lt;/code&gt; can be used in maps with a large number of individual polygons (for example, a map of individual countries) to create unique colors for adjacent polygons.&lt;/p&gt;'><sup>41</sup></a></p>
+&lt;code&gt;col = "MAP_COLORS"&lt;/code&gt; can be used in maps with a large number of individual polygons (for example, a map of individual countries) to create unique colors for adjacent polygons.&lt;/p&gt;'><sup>40</sup></a></p>
 <p>The second group is sequential palettes.
 These follow a gradient, for example from light to dark colors (light colors tend to represent lower values), and are appropriate for continuous (numeric) variables.
 Sequential palettes can be single (<code>Blues</code> go from light to dark blue, for example) or multi-color/hue (<code>YlOrBr</code> is gradient from light yellow to brown via orange, for example), as demonstrated in the code chunk below — output not shown, run the code yourself to see the results!</p>
@@ -393,7 +393,7 @@ <h3>
 This property is not preserved in the rainbow color palette; therefore, we suggest avoiding it in geographic data visualization <span class="citation">(<a href="references.html#ref-borland_rainbow_2007" role="doc-biblioref">Borland and Taylor II 2007</a>)</span>.
 Instead, <a href="https://cran.r-project.org/web/packages/viridis/">the viridis color palettes</a>, also available in <strong>tmap</strong>, can be used.
 Secondly, changes in colors should be accessible to the largest number of people.
-Therefore, it is important to use colorblind friendly palettes as often as possible.<a class="footnote-ref" tabindex="0" data-toggle="popover" data-content="&lt;p&gt;See the “Color blindness simulator” options in &lt;code&gt;tmaptools::palette_explorer()&lt;/code&gt;.&lt;/p&gt;"><sup>42</sup></a></p>
+Therefore, it is important to use colorblind friendly palettes as often as possible.<a class="footnote-ref" tabindex="0" data-toggle="popover" data-content="&lt;p&gt;See the “Color blindness simulator” options in &lt;code&gt;tmaptools::palette_explorer()&lt;/code&gt;.&lt;/p&gt;"><sup>41</sup></a></p>
 </div>
 <div id="layouts" class="section level3" number="8.2.5">
 <h3>
@@ -801,7 +801,7 @@ <h2>
 Learn more at: <a href="https://github.com/rstudio/shiny-server" class="uri">https://github.com/rstudio/shiny-server</a>.
 </div>
 <p>Before considering large apps, it is worth seeing a minimal example, named ‘lifeApp,’ in action.<a class="footnote-ref" tabindex="0" data-toggle="popover" data-content="&lt;p&gt;
-The word ‘app’ in this context refers to ‘web application’ and should not be confused with smartphone apps, the more common meaning of the word.&lt;/p&gt;"><sup>43</sup></a>
+The word ‘app’ in this context refers to ‘web application’ and should not be confused with smartphone apps, the more common meaning of the word.&lt;/p&gt;"><sup>42</sup></a>
 The code below defines and launches — with the command <code><a href="https://rdrr.io/pkg/shiny/man/shinyApp.html">shinyApp()</a></code> — a lifeApp, which provides an interactive slider allowing users to make countries appear with progressively lower levels of life expectancy (see Figure <a href="adv-map.html#fig:lifeApp">8.24</a>):</p>
 <div class="sourceCode" id="cb295"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://shiny.rstudio.com/">shiny</a></span><span class="op">)</span>    <span class="co"># for shiny apps</span>