More minor page count tweaks & fixes

And re-convert with latest htmlbook

oreilly/communication.html

@@ -1,28 +1,18 @@
 <section data-type="chapter" id="chp-communication">
 <h1><span id="sec-communication" class="quarto-section-identifier d-none d-lg-block"><span class="chapter-title">Communication</span></span></h1>
-<section id="introduction" data-type="sect1">
+<section id="communication-introduction" data-type="sect1">
 <h1>
 Introduction</h1>
 <p>In <a href="#chp-EDA" data-type="xref">#chp-EDA</a>, you learned how to use plots as tools for <em>exploration</em>. When you make exploratory plots, you know—even before looking—which variables the plot will display. You made each plot for a purpose, could quickly look at it, and then move on to the next plot. In the course of most analyses, you’ll produce tens or hundreds of plots, most of which are immediately thrown away.</p>
 <p>Now that you understand your data, you need to <em>communicate</em> your understanding to others. Your audience will likely not share your background knowledge and will not be deeply invested in the data. To help others quickly build up a good mental model of the data, you will need to invest considerable effort in making your plots as self-explanatory as possible. In this chapter, you’ll learn some of the tools that ggplot2 provides to do so.</p>
 <p>This chapter focuses on the tools you need to create good graphics. We assume that you know what you want, and just need to know how to do it. For that reason, we highly recommend pairing this chapter with a good general visualization book. We particularly like <a href="https://www.amazon.com/gp/product/0321934075/">The Truthful Art</a>, by Albert Cairo. It doesn’t teach the mechanics of creating visualizations, but instead focuses on what you need to think about in order to create effective graphics.</p>
 
-<section id="prerequisites" data-type="sect2">
+<section id="communication-prerequisites" data-type="sect2">
 <h2>
 Prerequisites</h2>
 <p>In this chapter, we’ll focus once again on ggplot2. We’ll also use a little dplyr for data manipulation, <strong>scales</strong> to override the default breaks, labels, transformations and palettes, and a few ggplot2 extension packages, including <strong>ggrepel</strong> (<a href="https://ggrepel.slowkow.com/">https://ggrepel.slowkow.com</a>) by Kamil Slowikowski and <strong>patchwork</strong> (<a href="https://patchwork.data-imaginist.com/">https://patchwork.data-imaginist.com</a>) by Thomas Lin Pedersen. Don’t forget that you’ll need to install those packages with <code><a href="https://rdrr.io/r/utils/install.packages.html">install.packages()</a></code> if you don’t already have them.</p>
 <div class="cell">
 <pre data-type="programlisting" data-code-language="r">library(tidyverse)
-#&gt; ── Attaching core tidyverse packages ──────────────── tidyverse 1.3.2.9000 ──
-#&gt; ✔ dplyr     1.0.99.9000     ✔ readr     2.1.3      
-#&gt; ✔ forcats   0.5.2.9000      ✔ stringr   1.5.0.9000 
-#&gt; ✔ ggplot2   3.4.0.9000      ✔ tibble    3.1.8      
-#&gt; ✔ lubridate 1.9.0           ✔ tidyr     1.2.1.9001 
-#&gt; ✔ purrr     1.0.1           
-#&gt; ── Conflicts ─────────────────────────────────────── tidyverse_conflicts() ──
-#&gt; ✖ dplyr::filter() masks stats::filter()
-#&gt; ✖ dplyr::lag()    masks stats::lag()
-#&gt; ℹ Use the ]8;;http://conflicted.r-lib.org/conflicted package]8;; to force all conflicts to become errors
 library(ggrepel)
 library(patchwork)</pre>
 </div>
@@ -91,7 +81,7 @@ ggplot(df, aes(x, y)) +
 </div>
 </div>
 
-<section id="exercises" data-type="sect2">
+<section id="communication-exercises" data-type="sect2">
 <h2>
 Exercises</h2>
 <ol type="1"><li><p>Create one plot on the fuel economy data with customized <code>title</code>, <code>subtitle</code>, <code>caption</code>, <code>x</code>, <code>y</code>, and <code>color</code> labels.</p></li>
@@ -280,12 +270,12 @@ ggplot(mpg, aes(x = displ, y = hwy)) +
 #&gt; decreasing fuel economy.</pre>
 </div>
 <p>Remember, in addition to <code><a href="https://ggplot2.tidyverse.org/reference/geom_text.html">geom_text()</a></code>, you have many other geoms in ggplot2 available to help annotate your plot. A couple ideas:</p>
-<ul><li><p>Use <code><a href="https://ggplot2.tidyverse.org/reference/geom_abline.html">geom_hline()</a></code> and <code><a href="https://ggplot2.tidyverse.org/reference/geom_abline.html">geom_vline()</a></code> to add reference lines. We often make them thick (<code>size = 2</code>) and white (<code>color = white</code>), and draw them underneath the primary data layer. That makes them easy to see, without drawing attention away from the data.</p></li>
+<ul><li><p>Use <code><a href="https://ggplot2.tidyverse.org/reference/geom_abline.html">geom_hline()</a></code> and <code><a href="https://ggplot2.tidyverse.org/reference/geom_abline.html">geom_vline()</a></code> to add reference lines. We often make them thick (<code>linewidth = 2</code>) and white (<code>color = white</code>), and draw them underneath the primary data layer. That makes them easy to see, without drawing attention away from the data.</p></li>
 <li><p>Use <code><a href="https://ggplot2.tidyverse.org/reference/geom_tile.html">geom_rect()</a></code> to draw a rectangle around points of interest. The boundaries of the rectangle are defined by aesthetics <code>xmin</code>, <code>xmax</code>, <code>ymin</code>, <code>ymax</code>.</p></li>
 <li><p>Use <code><a href="https://ggplot2.tidyverse.org/reference/geom_segment.html">geom_segment()</a></code> with the <code>arrow</code> argument to draw attention to a point with an arrow. Use aesthetics <code>x</code> and <code>y</code> to define the starting location, and <code>xend</code> and <code>yend</code> to define the end location.</p></li>
 </ul><p>The only limit is your imagination (and your patience with positioning annotations to be aesthetically pleasing)!</p>
 
-<section id="exercises-1" data-type="sect2">
+<section id="communication-exercises-1" data-type="sect2">
 <h2>
 Exercises</h2>
 <ol type="1"><li><p>Use <code><a href="https://ggplot2.tidyverse.org/reference/geom_text.html">geom_text()</a></code> with infinite positions to place text at the four corners of the plot.</p></li>
@@ -603,7 +593,7 @@ mpg |&gt;
 </div>
 </div>
 </div>
-<p>You can also set the <code>limits</code> on individual scales. Reducing the limits is basically equivalent to subsetting the data. It is generally more useful if you want <em>expand</em> the limits, for example, to match scales across different plots. For example, if we extract two classes of cars and plot them separately, it’s difficult to compare the plots because all three scales (the x-axis, the y-axis, and the color aesthetic) have different ranges.</p>
+<p>You can also set the <code>limits</code> on individual scales. Reducing the limits is basically equivalent to subsetting the data. It is generally more useful if you want to <em>expand</em> the limits, for example, to match scales across different plots. For example, if we extract two classes of cars and plot them separately, it’s difficult to compare the plots because all three scales (the x-axis, the y-axis, and the color aesthetic) have different ranges.</p>
 <div>
 <pre data-type="programlisting" data-code-language="r">suv &lt;- mpg |&gt; filter(class == "suv")
 compact &lt;- mpg |&gt; filter(class == "compact")
@@ -655,7 +645,7 @@ ggplot(compact, aes(x = displ, y = hwy, color = drv)) +
 <p>In this particular case, you could have simply used faceting, but this technique is useful more generally, if for instance, you want to spread plots over multiple pages of a report.</p>
 </section>
 
-<section id="exercises-2" data-type="sect2">
+<section id="communication-exercises-2" data-type="sect2">
 <h2>
 Exercises</h2>
 <ol type="1"><li>
@@ -740,7 +730,7 @@ Themes</h1>
 </div>
 <p>For an overview of all <code><a href="https://ggplot2.tidyverse.org/reference/theme.html">theme()</a></code> components, see help with <code><a href="https://ggplot2.tidyverse.org/reference/theme.html">?theme</a></code>. The <a href="https://ggplot2-book.org/">ggplot2 book</a> is also a great place to go for the full details on theming.</p>
 
-<section id="exercises-3" data-type="sect2">
+<section id="communication-exercises-3" data-type="sect2">
 <h2>
 Exercises</h2>
 <ol type="1"><li>Pick a theme offered by the ggthemes package and apply it to the last plot you made.</li>
@@ -808,14 +798,14 @@ p5 &lt;- ggplot(mpg, aes(x = cty, y = hwy, color = drv)) +
     guides = "collect",
     heights = c(1, 3, 2, 4)
     ) &amp;
-  theme(legend.position = "bottom")</pre>
+  theme(legend.position = "top")</pre>
 <div class="cell-output-display">
 <p><img src="communication_files/figure-html/unnamed-chunk-45-1.png" alt="Five plots laid out such that first two plots are next to each other. Plots three and four are underneath them. And the fifth plot stretches under them. The patchworked plot is titled &quot;City and highway mileage for cars with different drive trains&quot; and captioned &quot;Source: Source: https://fueleconomy.gov&quot;. The first two plots are side-by-side box plots. Plots 3 and 4 are density plots. And the fifth plot is a faceted scatterplot. Each of these plots show geoms colored by drive train, but the patchworked plot has only one legend that applies to all of them, above the plots and beneath the title." width="576"/></p>
 </div>
 </div>
 <p>If you’d like to learn more about combining and layout out multiple plots with patchwork, we recommend looking through the guides on the package website: <a href="https://patchwork.data-imaginist.com" class="uri">https://patchwork.data-imaginist.com</a>.</p>
 
-<section id="exercises-4" data-type="sect2">
+<section id="communication-exercises-4" data-type="sect2">
 <h2>
 Exercises</h2>
 <ol type="1"><li>
@@ -848,7 +838,7 @@ p3 &lt;- ggplot(mpg, aes(x = cty, y = hwy)) +
 </ol></section>
 </section>
 
-<section id="summary" data-type="sect1">
+<section id="communication-summary" data-type="sect1">
 <h1>
 Summary</h1>
 <p>In this chapter you’ve learned about adding plot labels such as title, subtitle, caption as well as modifying default axis labels, using annotation to add informational text to your plot or to highlight specific data points, customizing the axis scales, and changing the theme of your plot. You’ve also learned about combining multiple plots in a single graph using both simple and complex plot layouts.</p>