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<a href="index.html"><h1 class="title">R for reproducible scientific analysis</h1></a>
<h2 class="subtitle">Creating publication quality graphics</h2>
<section class="objectives panel panel-warning">
<div class="panel-heading">
<h2><span class="glyphicon glyphicon-certificate"></span>Learning objectives</h2>
</div>
<div class="panel-body">
<ul>
<li>To be able to use ggplot2 to generate publication quality graphics</li>
<li>To understand the basics of the grammar of graphics:</li>
<li>The aesthetics layer</li>
<li>The geometry layer</li>
<li>Adding statistics</li>
<li>Transforming scales</li>
<li>Coloring or paneling by groups.</li>
</ul>
</div>
</section>
<p>Plotting our data is one of the best ways to quickly explore it and the various relationships between variables.</p>
<p>There are three main plotting systems in R, the <a href="http://www.statmethods.net/graphs/">base plotting system</a>, the <a href="http://www.statmethods.net/advgraphs/trellis.html">lattice</a> package, and the <a href="http://www.statmethods.net/advgraphs/ggplot2.html">ggplot2</a> package.</p>
<p>Today we’ll be learning about the ggplot2 package, because it is the most effective for creating publication quality graphics.</p>
<p>ggplot2 is built on the grammar of graphics, the idea that any plot can be expressed from the same set of components: a <strong>data</strong> set, a <strong>coordinate system</strong>, and a set of <strong>geoms</strong>–the visual representation of data points.</p>
<p>The key to understanding ggplot2 is thinking about a figure in layers: just like you might do in an image editing program like Photoshop, Illustrator, or Inkscape.</p>
<p>Let’s start off with an example:</p>
<pre class="sourceCode r"><code class="sourceCode r"><span class="kw">library</span>(ggplot2)
<span class="kw">ggplot</span>(<span class="dt">data =</span> gapminder, <span class="kw">aes</span>(<span class="dt">x =</span> lifeExp, <span class="dt">y =</span> gdpPercap)) +
<span class="st"> </span><span class="kw">geom_point</span>()</code></pre>
<p><img src="fig/08-plot-ggplot2-lifeExp-vs-gdpPercap-scatter-1.png" title="plot of chunk lifeExp-vs-gdpPercap-scatter" alt="plot of chunk lifeExp-vs-gdpPercap-scatter" style="display: block; margin: auto;" /></p>
<p>So the first thing we do is call the <code>ggplot</code> function. This function lets R know that we’re creating a new plot, and any of the arguments we give the <code>ggplot</code> function are the <em>global</em> options for the plot: they apply to all layers on the plot.</p>
<p>We’ve passed in two arguments to <code>ggplot</code>. First, we tell <code>ggplot</code> what data we want to show on our figure, in this example the gapminder data we read in earlier. For the second argument we passed in the <code>aes</code> function, which tells <code>ggplot</code> how variables in the <strong>data</strong> map to <em>aesthetic</em> properties of the figure, in this case the <strong>x</strong> and <strong>y</strong> locations. Here we told <code>ggplot</code> we want to plot the “lifeExp” column of the gapminder data frame on the x-axis, and the “gdpPercap” column on the y-axis. Notice that we didn’t need to explicitly pass <code>aes</code> these columns (e.g. <code>x = gapminder[, "lifeExp"]</code>), this is because <code>ggplot</code> is smart enough to know to look in the <strong>data</strong> for that column!</p>
<p>By itself, the call to <code>ggplot</code> isn’t enough to draw a figure:</p>
<pre class="sourceCode r"><code class="sourceCode r"><span class="kw">ggplot</span>(<span class="dt">data =</span> gapminder, <span class="kw">aes</span>(<span class="dt">x =</span> lifeExp, <span class="dt">y =</span> gdpPercap))</code></pre>
<pre class="output"><code>Error: No layers in plot
</code></pre>
<p>We need to tell <code>ggplot</code> how we want to visually represent the data, which we do by adding a new <strong>geom</strong> layer. In our example, we used <code>geom_point</code>, which tells <code>ggplot</code> we want to visually represent the relationship between <strong>x</strong> and <strong>y</strong> as a scatterplot of points:</p>
<pre class="sourceCode r"><code class="sourceCode r"><span class="kw">ggplot</span>(<span class="dt">data =</span> gapminder, <span class="kw">aes</span>(<span class="dt">x =</span> lifeExp, <span class="dt">y =</span> gdpPercap)) +
<span class="st"> </span><span class="kw">geom_point</span>()</code></pre>
<p><img src="fig/08-plot-ggplot2-lifeExp-vs-gdpPercap-scatter2-1.png" title="plot of chunk lifeExp-vs-gdpPercap-scatter2" alt="plot of chunk lifeExp-vs-gdpPercap-scatter2" style="display: block; margin: auto;" /></p>
<section class="challenge panel panel-success">
<div class="panel-heading">
<h4><span class="glyphicon glyphicon-pencil"></span>Challenge 1</h4>
</div>
<div class="panel-body">
<p>Modify the example so that the figure visualise how life expectancy has changed over time:</p>
<pre class="sourceCode r"><code class="sourceCode r"><span class="kw">ggplot</span>(<span class="dt">data =</span> gapminder, <span class="kw">aes</span>(<span class="dt">x =</span> lifeExp, <span class="dt">y =</span> gdpPercap)) +<span class="st"> </span><span class="kw">geom_point</span>()</code></pre>
<p>Hint: the gapminder dataset has a column called “year”, which should appear on the x-axis.</p>
</div>
</section>
<section class="challenge panel panel-success">
<div class="panel-heading">
<h4><span class="glyphicon glyphicon-pencil"></span>Challenge 2</h4>
</div>
<div class="panel-body">
<p>In the previous examples and challenge we’ve used the <code>aes</code> function to tell the scatterplot <strong>geom</strong> about the <strong>x</strong> and <strong>y</strong> locations of each point. Another <em>aesthetic</em> property we can modify is the point <em>color</em>. Modify the code from the previous challenge to <strong>color</strong> the points by the “continent” column. What trends do you see in the data? Are they what you expected?</p>
</div>
</section>
<h3 id="layers">Layers</h3>
<p>Using a scatterplot probably isn’t the best for visualising change over time. Instead, let’s tell <code>ggplot</code> to visualise the data as a line plot:</p>
<pre class="sourceCode r"><code class="sourceCode r"><span class="kw">ggplot</span>(<span class="dt">data =</span> gapminder, <span class="kw">aes</span>(<span class="dt">x=</span>year, <span class="dt">y=</span>lifeExp, <span class="dt">by=</span>country, <span class="dt">color=</span>continent)) +
<span class="st"> </span><span class="kw">geom_line</span>()</code></pre>
<p><img src="fig/08-plot-ggplot2-lifeExp-line-1.png" title="plot of chunk lifeExp-line" alt="plot of chunk lifeExp-line" style="display: block; margin: auto;" /></p>
<p>Instead of adding a <code>geom_point</code> layer, we’ve added a <code>geom_line</code> layer. We’ve added the <strong>by</strong> <em>aesthetic</em>, which tells <code>ggplot</code> to draw a line for each country.</p>
<p>But what if we want to visualise both lines and points on the plot? We can simply add another layer to the plot:</p>
<pre class="sourceCode r"><code class="sourceCode r"><span class="kw">ggplot</span>(<span class="dt">data =</span> gapminder, <span class="kw">aes</span>(<span class="dt">x=</span>year, <span class="dt">y=</span>lifeExp, <span class="dt">by=</span>country, <span class="dt">color=</span>continent)) +
<span class="st"> </span><span class="kw">geom_line</span>() +<span class="st"> </span><span class="kw">geom_point</span>()</code></pre>
<p><img src="fig/08-plot-ggplot2-lifeExp-line-point-1.png" title="plot of chunk lifeExp-line-point" alt="plot of chunk lifeExp-line-point" style="display: block; margin: auto;" /></p>
<p>It’s important to note that each layer is drawn on top of the previous layer. In this example, the points have been drawn <em>on top of</em> the lines. Here’s a demonstration:</p>
<pre class="sourceCode r"><code class="sourceCode r"><span class="kw">ggplot</span>(<span class="dt">data =</span> gapminder, <span class="kw">aes</span>(<span class="dt">x=</span>year, <span class="dt">y=</span>lifeExp, <span class="dt">by=</span>country)) +
<span class="st"> </span><span class="kw">geom_line</span>(<span class="kw">aes</span>(<span class="dt">color=</span>continent)) +<span class="st"> </span><span class="kw">geom_point</span>()</code></pre>
<p><img src="fig/08-plot-ggplot2-lifeExp-layer-example-1-1.png" title="plot of chunk lifeExp-layer-example-1" alt="plot of chunk lifeExp-layer-example-1" style="display: block; margin: auto;" /></p>
<p>In this example, the <em>aesthetic</em> mapping of <strong>color</strong> has been moved from the global plot options in <code>ggplot</code> to the <code>geom_line</code> layer so it no longer applies to the points. Now we can clearly see that the points are drawn on top of the lines.</p>
<section class="challenge panel panel-success">
<div class="panel-heading">
<h4><span class="glyphicon glyphicon-pencil"></span>Challenge 3</h4>
</div>
<div class="panel-body">
<p>Switch the order of the point and line layers from the previous example. What happened?</p>
</div>
</section>
<h3 id="transformations-and-statistics">Transformations and statistics</h3>
<p>Ggplot also makes it easy to overlay statistical models over the data. To demonstrate we’ll go back to our first example:</p>
<pre class="sourceCode r"><code class="sourceCode r"><span class="kw">ggplot</span>(<span class="dt">data =</span> gapminder, <span class="kw">aes</span>(<span class="dt">x =</span> lifeExp, <span class="dt">y =</span> gdpPercap, <span class="dt">color=</span>continent)) +
<span class="st"> </span><span class="kw">geom_point</span>()</code></pre>
<p><img src="fig/08-plot-ggplot2-lifeExp-vs-gdpPercap-scatter3-1.png" title="plot of chunk lifeExp-vs-gdpPercap-scatter3" alt="plot of chunk lifeExp-vs-gdpPercap-scatter3" style="display: block; margin: auto;" /></p>
<p>Currently it’s hard to see the relationship between the points due to some strong outliers in GDP per capita. We can change the scale of units on the y axis using the <em>scale</em> functions. These control the mapping between the data values and visual values of an aesthetic.</p>
<pre class="sourceCode r"><code class="sourceCode r"><span class="kw">ggplot</span>(<span class="dt">data =</span> gapminder, <span class="kw">aes</span>(<span class="dt">x =</span> lifeExp, <span class="dt">y =</span> gdpPercap)) +
<span class="st"> </span><span class="kw">geom_point</span>() +<span class="st"> </span><span class="kw">scale_y_log10</span>()</code></pre>
<p><img src="fig/08-plot-ggplot2-axis-scale-1.png" title="plot of chunk axis-scale" alt="plot of chunk axis-scale" style="display: block; margin: auto;" /></p>
<p>The <code>log10</code> function applied a transformation to the values of the gdpPercap column before rendering them on the plot, so that each multiple of 10 now only corresponds to an increase in 1 on the transformed scale, e.g. a GDP per capita of 1,000 is now 3 on the y axis, a value of 10,000 corresponds to 4 on the y axis and so on. This makes it easier to visualise the spread of data on the y-axis.</p>
<p>We can fit a simple relationship to the data by adding another layer, <code>geom_smooth</code>:</p>
<pre class="sourceCode r"><code class="sourceCode r"><span class="kw">ggplot</span>(<span class="dt">data =</span> gapminder, <span class="kw">aes</span>(<span class="dt">x =</span> lifeExp, <span class="dt">y =</span> gdpPercap)) +
<span class="st"> </span><span class="kw">geom_point</span>() +<span class="st"> </span><span class="kw">scale_y_log10</span>() +<span class="st"> </span><span class="kw">geom_smooth</span>(<span class="dt">method=</span><span class="st">"lm"</span>)</code></pre>
<p><img src="fig/08-plot-ggplot2-lm-fit-1.png" title="plot of chunk lm-fit" alt="plot of chunk lm-fit" style="display: block; margin: auto;" /></p>
<p>We can make the line thicker by <em>setting</em> the <strong>size</strong> aesthetic in the <code>geom_smooth</code> layer:</p>
<pre class="sourceCode r"><code class="sourceCode r"><span class="kw">ggplot</span>(<span class="dt">data =</span> gapminder, <span class="kw">aes</span>(<span class="dt">x =</span> lifeExp, <span class="dt">y =</span> gdpPercap)) +
<span class="st"> </span><span class="kw">geom_point</span>() +<span class="st"> </span><span class="kw">scale_y_log10</span>() +<span class="st"> </span><span class="kw">geom_smooth</span>(<span class="dt">method=</span><span class="st">"lm"</span>, <span class="dt">size=</span><span class="fl">1.5</span>)</code></pre>
<p><img src="fig/08-plot-ggplot2-lm-fit2-1.png" title="plot of chunk lm-fit2" alt="plot of chunk lm-fit2" style="display: block; margin: auto;" /></p>
<p>There are two ways an <em>aesthetic</em> can be specified. Here we <em>set</em> the <strong>size</strong> aesthetic by passing it as an argument to <code>geom_smooth</code>. Previously in the lesson we’ve used the <code>aes</code> function to define a <em>mapping</em> between data variables and their visual representation.</p>
<section class="challenge panel panel-success">
<div class="panel-heading">
<h4><span class="glyphicon glyphicon-pencil"></span>Challenge 4</h4>
</div>
<div class="panel-body">
<p>Modify the color and size of the points on the point layer in the previous example.</p>
<p>Hint: do not use the <code>aes</code> function.</p>
</div>
</section>
<h3 id="multi-panel-figures">Multi-panel figures</h3>
<p>Earlier we visualised the change in life expectancy over time across all countries in one plot. Alternatively, we can split this out over multiple panels by adding a layer of <strong>facet</strong> panels:</p>
<pre class="sourceCode r"><code class="sourceCode r"><span class="kw">ggplot</span>(<span class="dt">data =</span> gapminder, <span class="kw">aes</span>(<span class="dt">x =</span> year, <span class="dt">y =</span> lifeExp, <span class="dt">color=</span>continent)) +
<span class="st"> </span><span class="kw">geom_line</span>() +<span class="st"> </span><span class="kw">facet_wrap</span>( ~<span class="st"> </span>country)</code></pre>
<p><img src="fig/08-plot-ggplot2-facet-1.png" title="plot of chunk facet" alt="plot of chunk facet" style="display: block; margin: auto;" /></p>
<p>The <code>facet_wrap</code> layer took a “formula” as its argument, denoted by the tilde (~). This tells R to draw a panel for each unique value in the country column of the gapminder dataset.</p>
<h3 id="modifying-text">Modifying text</h3>
<p>To clean this figure up for a publication we need to change some of the text elements. The x-axis is way too cluttered, and the y axis should read “Life expectancy”, rather than the column name in the data frame.</p>
<p>We can do this by adding a couple of different layers. The <strong>theme</strong> layer controls the axis text, and overall text size, and there are special layers for changing the axis labels. To change the legend title, we need to use the <strong>scales</strong> layer.</p>
<pre class="sourceCode r"><code class="sourceCode r"><span class="kw">ggplot</span>(<span class="dt">data =</span> gapminder, <span class="kw">aes</span>(<span class="dt">x =</span> year, <span class="dt">y =</span> lifeExp, <span class="dt">color=</span>continent)) +
<span class="st"> </span><span class="kw">geom_line</span>() +<span class="st"> </span><span class="kw">facet_wrap</span>( ~<span class="st"> </span>country) +
<span class="st"> </span><span class="kw">xlab</span>(<span class="st">"Year"</span>) +<span class="st"> </span><span class="kw">ylab</span>(<span class="st">"Life expectancy"</span>) +<span class="st"> </span><span class="kw">ggtitle</span>(<span class="st">"Figure 1"</span>) +
<span class="st"> </span><span class="kw">scale_fill_discrete</span>(<span class="dt">name=</span><span class="st">"Continent"</span>) +
<span class="st"> </span><span class="kw">theme</span>(<span class="dt">axis.text.x=</span><span class="kw">element_blank</span>(), <span class="dt">axis.ticks.x=</span><span class="kw">element_blank</span>())</code></pre>
<p><img src="fig/08-plot-ggplot2-theme-1.png" title="plot of chunk theme" alt="plot of chunk theme" style="display: block; margin: auto;" /></p>
<p>This is just a taste of what you can do with <code>ggplot2</code>. RStudio provides a really useful <a href="http://www.rstudio.com/wp-content/uploads/2015/03/ggplot2-cheatsheet.pdf">cheat sheet</a> of the different layers available, and more extensive documentation is available on the <a href="http://docs.ggplot2.org/current/">ggplot2 website</a>. Finally, if you have no idea how to change something, a quick google search will usually send you to a relevant question and answer on Stack Overflow with reusable code to modify!</p>
<section class="challenge panel panel-success">
<div class="panel-heading">
<h4><span class="glyphicon glyphicon-pencil"></span>Challenge 5</h4>
</div>
<div class="panel-body">
<p>Create a density plot of GDP per capita, filled by continent.</p>
<p>Advanced: - Transform the x axis to better visualise the data spread. - Add a facet layer to panel the density plots by year.</p>
</div>
</section>
<h2 id="challenge-solutions">Challenge solutions</h2>
<section class="challenge panel panel-success">
<div class="panel-heading">
<h4><span class="glyphicon glyphicon-pencil"></span>Solution to challenge 1</h4>
</div>
<div class="panel-body">
<p>Modify the example so that the figure visualise how life expectancy has changed over time:</p>
<pre class="sourceCode r"><code class="sourceCode r"><span class="kw">ggplot</span>(<span class="dt">data =</span> gapminder, <span class="kw">aes</span>(<span class="dt">x =</span> year, <span class="dt">y =</span> lifeExp)) +<span class="st"> </span><span class="kw">geom_point</span>()</code></pre>
<p><img src="fig/08-plot-ggplot2-ch1-sol-1.png" title="plot of chunk ch1-sol" alt="plot of chunk ch1-sol" style="display: block; margin: auto;" /></p>
</div>
</section>
<section class="challenge panel panel-success">
<div class="panel-heading">
<h4><span class="glyphicon glyphicon-pencil"></span>Solution to challenge 2</h4>
</div>
<div class="panel-body">
<p>In the previous examples and challenge we’ve used the <code>aes</code> function to tell the scatterplot <strong>geom</strong> about the <strong>x</strong> and <strong>y</strong> locations of each point. Another <em>aesthetic</em> property we can modify is the point <em>color</em>. Modify the code from the previous challenge to <strong>color</strong> the points by the “continent” column. What trends do you see in the data? Are they what you expected?</p>
<pre class="sourceCode r"><code class="sourceCode r"><span class="kw">ggplot</span>(<span class="dt">data =</span> gapminder, <span class="kw">aes</span>(<span class="dt">x =</span> year, <span class="dt">y =</span> lifeExp, <span class="dt">color=</span>continent)) +
<span class="st"> </span><span class="kw">geom_point</span>()</code></pre>
<p><img src="fig/08-plot-ggplot2-ch2-sol-1.png" title="plot of chunk ch2-sol" alt="plot of chunk ch2-sol" style="display: block; margin: auto;" /></p>
</div>
</section>
<section class="challenge panel panel-success">
<div class="panel-heading">
<h4><span class="glyphicon glyphicon-pencil"></span>Solution to challenge 3</h4>
</div>
<div class="panel-body">
<p>Switch the order of the point and line layers from the previous example. What happened?</p>
<pre class="sourceCode r"><code class="sourceCode r"><span class="kw">ggplot</span>(<span class="dt">data =</span> gapminder, <span class="kw">aes</span>(<span class="dt">x=</span>year, <span class="dt">y=</span>lifeExp, <span class="dt">by=</span>country)) +
<span class="st"> </span><span class="kw">geom_point</span>() +<span class="st"> </span><span class="kw">geom_line</span>(<span class="kw">aes</span>(<span class="dt">color=</span>continent))</code></pre>
<p><img src="fig/08-plot-ggplot2-ch3-sol-1.png" title="plot of chunk ch3-sol" alt="plot of chunk ch3-sol" style="display: block; margin: auto;" /></p>
<p>The lines now get drawn over the points!</p>
</div>
</section>
<section class="challenge panel panel-success">
<div class="panel-heading">
<h4><span class="glyphicon glyphicon-pencil"></span>Solution to challenge 4</h4>
</div>
<div class="panel-body">
<p>Modify the color and size of the points on the point layer in the previous example.</p>
<p>Hint: do not use the <code>aes</code> function.</p>
<pre class="sourceCode r"><code class="sourceCode r"><span class="kw">ggplot</span>(<span class="dt">data =</span> gapminder, <span class="kw">aes</span>(<span class="dt">x =</span> lifeExp, <span class="dt">y =</span> gdpPercap)) +
<span class="st"> </span><span class="kw">geom_point</span>(<span class="dt">size=</span><span class="dv">3</span>, <span class="dt">color=</span><span class="st">"orange"</span>) +<span class="st"> </span><span class="kw">scale_y_log10</span>() +
<span class="st"> </span><span class="kw">geom_smooth</span>(<span class="dt">method=</span><span class="st">"lm"</span>, <span class="dt">size=</span><span class="fl">1.5</span>)</code></pre>
<p><img src="fig/08-plot-ggplot2-ch4-sol-1.png" title="plot of chunk ch4-sol" alt="plot of chunk ch4-sol" style="display: block; margin: auto;" /></p>
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</section>
<section class="challenge panel panel-success">
<div class="panel-heading">
<h4><span class="glyphicon glyphicon-pencil"></span>Solution to challenge 5</h4>
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<div class="panel-body">
<p>Create a density plot of GDP per capita, filled by continent.</p>
<p>Advanced: - Transform the x axis to better visualise the data spread. - Add a facet layer to panel the density plots by year.</p>
<pre class="sourceCode r"><code class="sourceCode r"><span class="kw">ggplot</span>(<span class="dt">data =</span> gapminder, <span class="kw">aes</span>(<span class="dt">x =</span> gdpPercap, <span class="dt">fill=</span>continent)) +
<span class="st"> </span><span class="kw">geom_density</span>(<span class="dt">alpha=</span><span class="fl">0.6</span>) +<span class="st"> </span><span class="kw">facet_wrap</span>( ~<span class="st"> </span>year) +<span class="st"> </span><span class="kw">scale_x_log10</span>()</code></pre>
<p><img src="fig/08-plot-ggplot2-ch5-sol-1.png" title="plot of chunk ch5-sol" alt="plot of chunk ch5-sol" style="display: block; margin: auto;" /></p>
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