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<h1 class="title toc-ignore">Data visualization with ggplot2 (notes)</h1>
<h4 class="author"><em>Data Carpentry contributors</em></h4>
</div>
<div id="TOC">
<ul>
<li><a href="#load-libraries">load libraries</a><ul>
<li><a href="#challenge">Challenge</a></li>
</ul></li>
<li><a href="#other-aesthetics">Other aesthetics</a><ul>
<li><a href="#challenge-1">Challenge</a></li>
</ul></li>
<li><a href="#layers">Layers</a><ul>
<li><a href="#challenge-2">Challenge</a></li>
</ul></li>
<li><a href="#groups">Groups</a></li>
<li><a href="#univariate-geoms">Univariate geoms</a><ul>
<li><a href="#challenge-3">Challenge</a></li>
<li><a href="#boxplot">Boxplot</a></li>
<li><a href="#challenge-4">Challenge</a></li>
</ul></li>
<li><a href="#faceting">Faceting</a><ul>
<li><a href="#challenge-5">Challenge</a></li>
<li><a href="#facet_grid">facet_grid</a></li>
</ul></li>
<li><a href="#saving-plots-to-a-file">Saving plots to a file</a></li>
<li><a href="#customizing-plots">Customizing plots</a><ul>
<li><a href="#axis-limits">Axis limits</a></li>
<li><a href="#color-choices">Color choices</a></li>
</ul></li>
<li><a href="#themes">Themes</a></li>
</ul>
</div>
<p>Load the cleaned/reduced data:</p>
<pre class="sourceCode r"><code class="sourceCode r">reduced <-<span class="st"> </span><span class="kw">read.csv</span>(<span class="st">"http://kbroman.org/datacarp/portal_data_reduced.csv"</span>)</code></pre>
<p>Or:</p>
<pre class="sourceCode r"><code class="sourceCode r"><span class="kw">download.file</span>(<span class="st">"http://kbroman.org/datacarp/portal_data_reduced.csv"</span>,
<span class="st">"CleanData/portal_data_reduced.csv"</span>)
reduced <-<span class="st"> </span><span class="kw">read.csv</span>(<span class="st">"CleanData/portal_data_reduced.csv"</span>)</code></pre>
<div id="load-libraries" class="section level2">
<h2>load libraries</h2>
<pre class="sourceCode r"><code class="sourceCode r"><span class="kw">library</span>(ggplot2)
<span class="kw">library</span>(dplyr)</code></pre>
<p><strong>Mention ggplot vs base graphics</strong></p>
<p>First plot:</p>
<pre class="sourceCode r"><code class="sourceCode r"><span class="kw">ggplot</span>(reduced, <span class="kw">aes</span>(<span class="dt">x =</span> weight, <span class="dt">y =</span> hindfoot_length)) +<span class="st"> </span><span class="kw">geom_point</span>()</code></pre>
<p>Key concepts of “grammar of graphics”: - <em>aesthetics</em> map features of the data to features of the visualization (for example, the <code>weight</code> variable is mapped to the y-axis coordinate) - <em>geoms</em> concern what actually gets plotted (here, each row in the data becomes a point in the plot)</p>
<ul>
<li><code>ggplot()</code> creates a graphics objects</li>
<li>additional controls added with the <code>+</code> operator</li>
<li>actual plot made when object is printed</li>
</ul>
<pre class="sourceCode r"><code class="sourceCode r">p1 <-<span class="st"> </span><span class="kw">ggplot</span>(reduced, <span class="kw">aes</span>(<span class="dt">x=</span>weight, <span class="dt">y=</span>hindfoot_length))
p2 <-<span class="st"> </span>p1 +<span class="st"> </span><span class="kw">geom_point</span>()
<span class="kw">print</span>(p2)</code></pre>
<p>Can be convenient to have saved the results. For example, to make that last plot have <code>weight</code> on the log scale:</p>
<pre class="sourceCode r"><code class="sourceCode r">p2 +<span class="st"> </span><span class="kw">scale_x_log10</span>()</code></pre>
<p>and on square-root scale:</p>
<pre class="sourceCode r"><code class="sourceCode r">p2 +<span class="st"> </span><span class="kw">scale_x_sqrt</span>()</code></pre>
<div id="challenge" class="section level3">
<h3>Challenge</h3>
<p>Make a scatterplot of <code>hindfoot_length</code> vs <code>weight</code>, but only for the <code>species_id</code>, <code>"DM"</code>.</p>
</div>
</div>
<div id="other-aesthetics" class="section level2">
<h2>Other aesthetics</h2>
<p>For scatterplot, <code>shape</code>, <code>size</code>, <code>color</code>, and <code>alpha</code>.</p>
<pre class="sourceCode r"><code class="sourceCode r">surveys_plot <-<span class="st"> </span><span class="kw">ggplot</span>(reduced, <span class="kw">aes</span>(<span class="dt">x =</span> weight, <span class="dt">y =</span> hindfoot_length))
surveys_plot +<span class="st"> </span><span class="kw">geom_point</span>(<span class="dt">alpha =</span> <span class="fl">0.1</span>)
surveys_plot +<span class="st"> </span><span class="kw">geom_point</span>(<span class="dt">alpha =</span> <span class="fl">0.1</span>, <span class="dt">color =</span> <span class="st">"slateblue"</span>)
surveys_plot +<span class="st"> </span><span class="kw">geom_point</span>(<span class="dt">alpha =</span> <span class="fl">0.1</span>, <span class="dt">color =</span> <span class="st">"slateblue"</span>, <span class="dt">size=</span><span class="fl">0.5</span>)</code></pre>
<p>Things get more interesting when we assign these aesthetics to data.</p>
<pre class="sourceCode r"><code class="sourceCode r">surveys_plot +<span class="st"> </span><span class="kw">geom_point</span>(<span class="kw">aes</span>(<span class="dt">color =</span> species_id))</code></pre>
<div id="challenge-1" class="section level3">
<h3>Challenge</h3>
<p>Use dplyr to calculate the mean <code>weight</code> and <code>hindfoot_length</code> as well as the sample size for each species.</p>
<p>Make a scatterplot of mean <code>hindfoot_length</code> vs <code>mean_weight</code>, with the sizes of the points corresponding to the sample size.</p>
</div>
</div>
<div id="layers" class="section level2">
<h2>Layers</h2>
<p>You can use <code>geom_line()</code> to make a line plot. For example, let’s plot the counts of animals by year.</p>
<pre class="sourceCode r"><code class="sourceCode r">count_by_year <-<span class="st"> </span>reduced %>%
<span class="st"> </span><span class="kw">group_by</span>(year) %>%
<span class="st"> </span>tally
p <-<span class="st"> </span><span class="kw">ggplot</span>(count_by_year, <span class="kw">aes</span>(<span class="dt">x=</span>year, <span class="dt">y=</span>n))
p +<span class="st"> </span><span class="kw">geom_line</span>()</code></pre>
<p>You can use <em>both</em> <code>geom_line</code> and <code>geom_point</code> to make a line plot with points at the data values.</p>
<pre class="sourceCode r"><code class="sourceCode r">p +<span class="st"> </span><span class="kw">geom_line</span>() +<span class="st"> </span><span class="kw">geom_point</span>()</code></pre>
<p><strong>layers</strong>: a given plot can have multiple layers of geometric objects, plotted one on top of another.</p>
<p>If we add colors, we’ll see this better.</p>
<pre class="sourceCode r"><code class="sourceCode r">p +<span class="st"> </span><span class="kw">geom_line</span>(<span class="dt">color=</span><span class="st">"lightblue"</span>) +<span class="st"> </span><span class="kw">geom_point</span>(<span class="dt">color=</span><span class="st">"violetred"</span>)</code></pre>
<p>If we switch the order of <code>geom_point</code> and <code>geom_line</code>, we’ll reverse the layers.</p>
<pre class="sourceCode r"><code class="sourceCode r">p +<span class="st"> </span><span class="kw">geom_point</span>(<span class="dt">color=</span><span class="st">"violetred"</span>) +<span class="st"> </span><span class="kw">geom_line</span>(<span class="dt">color=</span><span class="st">"lightblue"</span>)</code></pre>
<p><strong>global aesthetics</strong>: esthetics included in the call to <code>ggplot()</code> (or completely separately) are made to be the defaults for all layers, but we can separately control the aesthetics for each layer. For example, we could color the points by year:</p>
<pre class="sourceCode r"><code class="sourceCode r">p +<span class="st"> </span><span class="kw">geom_line</span>() +<span class="st"> </span><span class="kw">geom_point</span>(<span class="kw">aes</span>(<span class="dt">color=</span>year))</code></pre>
<p>Compare that to the following:</p>
<pre class="sourceCode r"><code class="sourceCode r">p +<span class="st"> </span><span class="kw">geom_line</span>() +<span class="st"> </span><span class="kw">geom_point</span>() +<span class="st"> </span><span class="kw">aes</span>(<span class="dt">color=</span>year)</code></pre>
<div id="challenge-2" class="section level3">
<h3>Challenge</h3>
<p>Make a plot of counts of <code>species_id</code> <code>"DM"</code> and <code>"DS"</code> by year.</p>
</div>
</div>
<div id="groups" class="section level2">
<h2>Groups</h2>
<p>Suppose, in that last challenge, we’d wanted to have black lines but the points colored by species. We might have done this:</p>
<pre class="sourceCode r"><code class="sourceCode r">counts_dm_ds <-<span class="st"> </span>reduced %>%<span class="st"> </span><span class="kw">filter</span>(species_id %in%<span class="st"> </span><span class="kw">c</span>(<span class="st">"DM"</span>, <span class="st">"DS"</span>)) %>%
<span class="st"> </span><span class="kw">group_by</span>(species_id, year) %>%<span class="st"> </span>tally
p <-<span class="st"> </span><span class="kw">ggplot</span>(counts_dm_ds, <span class="kw">aes</span>(<span class="dt">x=</span>year, <span class="dt">y=</span>n))
p +<span class="st"> </span><span class="kw">geom_line</span>() +<span class="st"> </span><span class="kw">geom_point</span>(<span class="kw">aes</span>(<span class="dt">color=</span>species_id))</code></pre>
<p>The points get connected left-to-right, which is not what we want.</p>
<p>If we make the <code>color=species_id</code> aesthetic <em>global</em>, we don’t have this problem.</p>
<pre class="sourceCode r"><code class="sourceCode r">p +<span class="st"> </span><span class="kw">geom_line</span>() +<span class="st"> </span><span class="kw">geom_point</span>() +<span class="st"> </span><span class="kw">aes</span>(<span class="dt">color=</span>species_id)</code></pre>
<p>Alternatively, we can use the <code>group</code> aesthetic, which indicates that certain data points go together. This way the lines can be a constant color.</p>
<pre class="sourceCode r"><code class="sourceCode r">p +<span class="st"> </span><span class="kw">geom_line</span>(<span class="kw">aes</span>(<span class="dt">group=</span>species_id)) +<span class="st"> </span><span class="kw">geom_point</span>(<span class="kw">aes</span>(<span class="dt">color=</span>species_id))</code></pre>
<p>We could also make the group aesthetic global</p>
<pre class="sourceCode r"><code class="sourceCode r">p +<span class="st"> </span><span class="kw">aes</span>(<span class="dt">group=</span>species_id) +<span class="st"> </span><span class="kw">geom_line</span>() +<span class="st"> </span><span class="kw">geom_point</span>(<span class="kw">aes</span>(<span class="dt">color=</span>species_id))</code></pre>
</div>
<div id="univariate-geoms" class="section level2">
<h2>Univariate geoms</h2>
<p>We’ve focused so far on scatterplots, but one can also create one-dimensional summaries, such as histograms or boxplots.</p>
<div id="challenge-3" class="section level3">
<h3>Challenge</h3>
<p>Try using <code>geom_histogram()</code> to make a histogram visualization of the distribution of <code>weight</code>.</p>
<p>Hint: You want <code>weight</code> as the x-axis aesthetic. Try specifying <code>bins</code> in <code>geom_histogram()</code>.</p>
</div>
<div id="boxplot" class="section level3">
<h3>Boxplot</h3>
<p>Visualising the distribution of weight within each species.</p>
<pre class="sourceCode r"><code class="sourceCode r"><span class="kw">ggplot</span>(reduced, <span class="kw">aes</span>(<span class="dt">x =</span> species_id, <span class="dt">y =</span> hindfoot_length)) +
<span class="st"> </span><span class="kw">geom_boxplot</span>()</code></pre>
<p>By adding points to boxplot, we can have a better idea of the number of measurements and of their distribution:</p>
<pre class="sourceCode r"><code class="sourceCode r"><span class="kw">ggplot</span>(reduced, <span class="kw">aes</span>(<span class="dt">x =</span> species_id, <span class="dt">y =</span> hindfoot_length)) +
<span class="st"> </span><span class="kw">geom_boxplot</span>(<span class="dt">alpha =</span> <span class="dv">0</span>) +
<span class="st"> </span><span class="kw">geom_jitter</span>(<span class="dt">alpha =</span> <span class="fl">0.3</span>, <span class="dt">color =</span> <span class="st">"tomato"</span>)</code></pre>
<p>Notice how the boxplot layer is behind the jitter layer? What do you need to change in the code to put the boxplot in front of the points such that it’s not hidden.</p>
</div>
<div id="challenge-4" class="section level3">
<h3>Challenge</h3>
<p>A variant on the box plot is the violin plot. Use <code>geom_violin()</code> to make violin plots of <code>hindfoot_length</code> by <code>species_id</code>.</p>
</div>
</div>
<div id="faceting" class="section level2">
<h2>Faceting</h2>
<p>ggplot has a special technique called <em>faceting</em> that allows to split one plot into multiple plots based on a factor included in the dataset. We will use it to make one plot for a time series for each species.</p>
<pre class="sourceCode r"><code class="sourceCode r">yearly_counts <-<span class="st"> </span>reduced %>%<span class="st"> </span><span class="kw">group_by</span>(year, species_id) %>%<span class="st"> </span>tally
<span class="kw">ggplot</span>(yearly_counts, <span class="kw">aes</span>(<span class="dt">x =</span> year, <span class="dt">y =</span> n, <span class="dt">group =</span> species_id, <span class="dt">colour =</span> species_id)) +
<span class="st"> </span><span class="kw">geom_line</span>() +
<span class="st"> </span><span class="kw">facet_wrap</span>(~<span class="st"> </span>species_id)</code></pre>
<p>Now we would like to split line in each plot by sex of each individual measured. To do that we need to make counts in data frame grouped by sex.</p>
<div id="challenge-5" class="section level3">
<h3>Challenge</h3>
<ul>
<li><p>Calculate counts grouped by year, species_id, and sex</p></li>
<li><p>make the faceted plot splitting further by sex (within each panel)</p></li>
<li><p>color by sex rather than species</p></li>
</ul>
<p>Suppose I make a similar plot of average weight by species:</p>
<pre class="sourceCode r"><code class="sourceCode r">yearly_weight <-<span class="st"> </span>reduced %>%
<span class="st"> </span><span class="kw">group_by</span>(year, species_id, sex) %>%
<span class="st"> </span><span class="kw">summarise</span>(<span class="dt">avg_weight =</span> <span class="kw">mean</span>(weight, <span class="dt">na.rm =</span> <span class="ot">TRUE</span>))
<span class="kw">ggplot</span>(yearly_weight, <span class="kw">aes</span>(<span class="dt">x=</span>year, <span class="dt">y=</span>avg_weight, <span class="dt">color =</span> species_id, <span class="dt">group =</span> species_id)) +
<span class="st"> </span><span class="kw">geom_line</span>() +
<span class="st"> </span><span class="kw">facet_wrap</span>(~<span class="st"> </span>species_id)</code></pre>
<p>Why do we see those steps in the plot?</p>
<p><strong>Oops</strong> need to group by sex</p>
<pre class="sourceCode r"><code class="sourceCode r">yearly_weight <-<span class="st"> </span>reduced %>%
<span class="st"> </span><span class="kw">group_by</span>(year, species_id, sex) %>%
<span class="st"> </span><span class="kw">summarise</span>(<span class="dt">avg_weight =</span> <span class="kw">mean</span>(weight, <span class="dt">na.rm =</span> <span class="ot">TRUE</span>))
<span class="kw">ggplot</span>(yearly_weight, <span class="kw">aes</span>(<span class="dt">x=</span>year, <span class="dt">y=</span>avg_weight, <span class="dt">color =</span> sex, <span class="dt">group =</span> sex)) +
<span class="st"> </span><span class="kw">geom_line</span>() +
<span class="st"> </span><span class="kw">facet_wrap</span>(~<span class="st"> </span>species_id)</code></pre>
</div>
<div id="facet_grid" class="section level3">
<h3>facet_grid</h3>
<p>The <code>facet_wrap</code> geometry extracts plots into an arbitrary number of dimensions to allow them to cleanly fit on one page. On the other hand, the <code>facet_grid</code> geometry allows you to explicitly specify how you want your plots to be arranged via formula notation (<code>rows ~ columns</code>; a <code>.</code> can be used as a placeholder that indicates only one row or column).</p>
<pre class="sourceCode r"><code class="sourceCode r">## One column, facet by rows
yearly_weight %>%<span class="st"> </span><span class="kw">filter</span>(species_id %in%<span class="st"> </span><span class="kw">c</span>(<span class="st">"DM"</span>, <span class="st">"DO"</span>, <span class="st">"DS"</span>)) %>%
<span class="st"> </span><span class="kw">ggplot</span>(<span class="kw">aes</span>(<span class="dt">x=</span>year, <span class="dt">y=</span>avg_weight, <span class="dt">color =</span> species_id, <span class="dt">group =</span> species_id)) +
<span class="st"> </span><span class="kw">geom_line</span>() +
<span class="st"> </span><span class="kw">facet_grid</span>(sex ~<span class="st"> </span>.)</code></pre>
<pre class="sourceCode r"><code class="sourceCode r"><span class="co"># One row, facet by column</span>
yearly_weight %>%<span class="st"> </span><span class="kw">filter</span>(species_id %in%<span class="st"> </span><span class="kw">c</span>(<span class="st">"DM"</span>, <span class="st">"DO"</span>, <span class="st">"DS"</span>)) %>%
<span class="st"> </span><span class="kw">ggplot</span>(<span class="kw">aes</span>(<span class="dt">x=</span>year, <span class="dt">y=</span>avg_weight, <span class="dt">color =</span> species_id, <span class="dt">group =</span> species_id)) +
<span class="st"> </span><span class="kw">geom_line</span>() +
<span class="st"> </span><span class="kw">facet_grid</span>( ~<span class="st"> </span>sex)</code></pre>
<pre class="sourceCode r"><code class="sourceCode r"><span class="co"># separate panel for each sex and species</span>
yearly_weight %>%<span class="st"> </span><span class="kw">filter</span>(species_id %in%<span class="st"> </span><span class="kw">c</span>(<span class="st">"DM"</span>, <span class="st">"DO"</span>, <span class="st">"DS"</span>)) %>%
<span class="st"> </span><span class="kw">ggplot</span>(<span class="kw">aes</span>(<span class="dt">x=</span>year, <span class="dt">y=</span>avg_weight, <span class="dt">color =</span> species_id, <span class="dt">group =</span> species_id)) +
<span class="st"> </span><span class="kw">geom_line</span>() +
<span class="st"> </span><span class="kw">facet_grid</span>(species_id ~<span class="st"> </span>sex)</code></pre>
</div>
</div>
<div id="saving-plots-to-a-file" class="section level2">
<h2>Saving plots to a file</h2>
<p>If you want to save a plot, to share with others, use the <code>ggsave</code> function.</p>
<p>The default is to save the last plot that you created, but I think it’s safer to first save the plot as an object and pass that to <code>ggsave</code>. Also give the height and width in inches.</p>
<pre class="sourceCode r"><code class="sourceCode r">p <-<span class="st"> </span><span class="kw">ggplot</span>(reduced, <span class="kw">aes</span>(<span class="dt">x=</span>weight, <span class="dt">y=</span>hindfoot_length)) +<span class="st"> </span><span class="kw">geom_point</span>()
<span class="kw">ggsave</span>(<span class="st">"scatter.png"</span>, p, <span class="dt">height=</span><span class="dv">6</span>, <span class="dt">width=</span><span class="dv">8</span>)</code></pre>
<p>The image file type is taken from the file name extension. To make a PDF instead:</p>
<pre class="sourceCode r"><code class="sourceCode r"><span class="kw">ggsave</span>(<span class="st">"scatter.pdf"</span>, p, <span class="dt">height=</span><span class="dv">6</span>, <span class="dt">width=</span><span class="dv">8</span>)</code></pre>
<p>Use <code>scale</code> to adjust the sizes of things, for example for a talk/poster versus a paper/report. Use <code>scale < 1</code> to make the various elements bigger relative to the plotting area.</p>
<pre class="sourceCode r"><code class="sourceCode r"><span class="kw">ggsave</span>(<span class="st">"scatter_2.png"</span>, p, <span class="dt">height=</span><span class="dv">6</span>, <span class="dt">width=</span><span class="dv">8</span>, <span class="dt">scale=</span><span class="fl">0.8</span>)</code></pre>
</div>
<div id="customizing-plots" class="section level2">
<h2>Customizing plots</h2>
<div id="axis-limits" class="section level3">
<h3>Axis limits</h3>
<p>When faceting, the different panels are given common x- and y-axis limits. If we were to create separate plots (say one for each country), we would need to do a bit extra to ensure that common axis limits are used.</p>
<p>Recall the <code>scale_x_log10()</code> function that we had used to create the log scale for the x axis. This can take an argument <code>limits</code> (a vector of length 2) defining the minimum and maximum values plotted.</p>
<p>There is also a <code>scale_y_log10()</code> function, but if you want to change the y-axis limits without going to a log scale, you would use <code>scale_y_continuous()</code>. (Similarly, there’s a <code>scale_x_continuous</code>.)</p>
<p>For example, to plot the data for China, using axis limits defined by the full data, we’d do the following:</p>
<pre class="sourceCode r"><code class="sourceCode r">xrange <-<span class="st"> </span><span class="kw">range</span>(reduced$weight)
yrange <-<span class="st"> </span><span class="kw">range</span>(reduced$hindfoot_length)
p <-<span class="st"> </span>reduced %>%<span class="st"> </span><span class="kw">filter</span>(species_id==<span class="st">"DM"</span>) %>%
<span class="st"> </span><span class="kw">ggplot</span>(<span class="kw">aes</span>(<span class="dt">x=</span>weight, <span class="dt">y=</span>hindfoot_length)) +
<span class="st"> </span><span class="kw">geom_point</span>()
p +<span class="st"> </span><span class="kw">scale_x_log10</span>(<span class="dt">limits=</span>xrange) +
<span class="st"> </span><span class="kw">scale_y_continuous</span>(<span class="dt">limits=</span>yrange)</code></pre>
</div>
<div id="color-choices" class="section level3">
<h3>Color choices</h3>
<p>If you don’t like the choices for point colors, you can customize them in a number of ways. First, you can use <code>scale_color_manual()</code> with a vector of your preferred choices. (If it’s <code>fill</code> rather than <code>color</code> that you want to change, you’ll need to use <code>scale_fill_manual()</code>.)</p>
<pre class="sourceCode r"><code class="sourceCode r">p <-<span class="st"> </span>reduced %>%<span class="st"> </span><span class="kw">filter</span>(species_id %in%<span class="st"> </span><span class="kw">c</span>(<span class="st">"DM"</span>, <span class="st">"DS"</span>, <span class="st">"DO"</span>)) %>%
<span class="st"> </span><span class="kw">ggplot</span>(<span class="kw">aes</span>(<span class="dt">x=</span>weight, <span class="dt">y=</span>hindfoot_length)) +
<span class="st"> </span><span class="kw">geom_point</span>(<span class="kw">aes</span>(<span class="dt">color=</span>species_id))
colors <-<span class="st"> </span><span class="kw">c</span>(<span class="st">"blue"</span>, <span class="st">"green"</span>, <span class="st">"orange"</span>)
p +<span class="st"> </span><span class="kw">scale_color_manual</span>(<span class="dt">values=</span>colors)</code></pre>
<p>You can also use RGB hex values.</p>
<pre class="sourceCode r"><code class="sourceCode r">hexcolors <-<span class="st"> </span><span class="kw">c</span>(<span class="st">"#001F3F"</span>, <span class="st">"#0074D9"</span>, <span class="st">"#01FF70"</span>)
p +<span class="st"> </span><span class="kw">scale_color_manual</span>(<span class="dt">values=</span>hexcolors)</code></pre>
</div>
</div>
<div id="themes" class="section level2">
<h2>Themes</h2>
<p>Not everyone gray background and such in the default ggplot plots.</p>
<p>But you can apply one of a variety of “themes” to control the overall appearance of plots.</p>
<p>One that a lot of people like is <code>theme_bw()</code>. Add it to a plot, and the overall appearance changes.</p>
<pre class="sourceCode r"><code class="sourceCode r">p +<span class="st"> </span><span class="kw">theme_bw</span>()</code></pre>
<p><br/> <br/> <br/> <br/> <br/> <br/> <br/> <br/> <br/> <br/></p>
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