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<center><h1>Correlation matrix with ggally</h1></center>
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<center><p style="max-width: 600px; margin-top: 40px">This post explains how to build a <a href="correlogram.html">correlogram</a> with the <code>ggally</code> R package. It provides several reproducible examples with explanation and <code>R</code> code.</p></center>
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<a class="btn btn-secondary btn-xl text-uppercase js-scroll-trigger" href='correlogram.html'>Correlogram section</a>
<a class="btn btn-secondary btn-xl text-uppercase js-scroll-trigger" href='https://www.data-to-viz.com/graph/correlogram.html'>Data to Viz</a>
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<h1 id="scatterplot-matrix-with-ggpairs">Scatterplot matrix with <code>ggpairs()</code></h1>
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<p>The <code>ggpairs()</code> function of the <code>GGally</code> package allows to build a great <a href="correlogram.html">scatterplot matrix</a>.</p>
<p><a href="scatterplot.html">Scatterplots</a> of each pair of numeric variable are drawn on the left part of the figure. Pearson correlation is displayed on the right. Variable distribution is available on the diagonal.</p>
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<p><img src="199-correlation-matrix-with-ggally_files/figure-html/thecode-1.png" width="100%" /></p>
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<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="co"># Quick display of two cabapilities of GGally, to assess the distribution and correlation of variables </span>
<span class="kw">library</span>(GGally)
<span class="co"># Create data </span>
data <-<span class="st"> </span><span class="kw">data.frame</span>( <span class="dt">var1 =</span> <span class="dv">1</span><span class="op">:</span><span class="dv">100</span> <span class="op">+</span><span class="st"> </span><span class="kw">rnorm</span>(<span class="dv">100</span>,<span class="dt">sd=</span><span class="dv">20</span>), <span class="dt">v2 =</span> <span class="dv">1</span><span class="op">:</span><span class="dv">100</span> <span class="op">+</span><span class="st"> </span><span class="kw">rnorm</span>(<span class="dv">100</span>,<span class="dt">sd=</span><span class="dv">27</span>), <span class="dt">v3 =</span> <span class="kw">rep</span>(<span class="dv">1</span>, <span class="dv">100</span>) <span class="op">+</span><span class="st"> </span><span class="kw">rnorm</span>(<span class="dv">100</span>, <span class="dt">sd =</span> <span class="dv">1</span>))
data<span class="op">$</span>v4 =<span class="st"> </span>data<span class="op">$</span>var1 <span class="op">**</span><span class="st"> </span><span class="dv">2</span>
data<span class="op">$</span>v5 =<span class="st"> </span><span class="op">-</span>(data<span class="op">$</span>var1 <span class="op">**</span><span class="st"> </span><span class="dv">2</span>)
<span class="co"># Check correlations (as scatterplots), distribution and print corrleation coefficient </span>
<span class="kw">ggpairs</span>(data, <span class="dt">title=</span><span class="st">"correlogram with ggpairs()"</span>) </code></pre></div>
<h1 id="visualize-correlation-with-ggcorr">Visualize correlation with <code>ggcorr()</code></h1>
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<p>The <code>ggcorr()</code> function allows to visualize the correlation of each pair of variable as a square. Note that the <code>method</code> argument allows to pick the correlation type you desire.</p>
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<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="co"># Quick display of two cabapilities of GGally, to assess the distribution and correlation of variables </span>
<span class="kw">library</span>(GGally)
<span class="co"># Create data </span>
data <-<span class="st"> </span><span class="kw">data.frame</span>( <span class="dt">var1 =</span> <span class="dv">1</span><span class="op">:</span><span class="dv">100</span> <span class="op">+</span><span class="st"> </span><span class="kw">rnorm</span>(<span class="dv">100</span>,<span class="dt">sd=</span><span class="dv">20</span>), <span class="dt">v2 =</span> <span class="dv">1</span><span class="op">:</span><span class="dv">100</span> <span class="op">+</span><span class="st"> </span><span class="kw">rnorm</span>(<span class="dv">100</span>,<span class="dt">sd=</span><span class="dv">27</span>), <span class="dt">v3 =</span> <span class="kw">rep</span>(<span class="dv">1</span>, <span class="dv">100</span>) <span class="op">+</span><span class="st"> </span><span class="kw">rnorm</span>(<span class="dv">100</span>, <span class="dt">sd =</span> <span class="dv">1</span>))
data<span class="op">$</span>v4 =<span class="st"> </span>data<span class="op">$</span>var1 <span class="op">**</span><span class="st"> </span><span class="dv">2</span>
data<span class="op">$</span>v5 =<span class="st"> </span><span class="op">-</span>(data<span class="op">$</span>var1 <span class="op">**</span><span class="st"> </span><span class="dv">2</span>)
<span class="co"># Check correlation between variables</span>
<span class="co">#cor(data) </span>
<span class="co"># Nice visualization of correlations</span>
<span class="kw">ggcorr</span>(data, <span class="dt">method =</span> <span class="kw">c</span>(<span class="st">"everything"</span>, <span class="st">"pearson"</span>)) </code></pre></div>
<h1 id="category">Split by group</h1>
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<p>It is possible to use <a href="ggplot2.html">ggplot2</a> aesthetics on the chart, for instance to color each category.</p>
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<p><img src="199-correlation-matrix-with-ggally_files/figure-html/thecode3-1.png" width="100%" /></p>
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<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="co"># Quick display of two cabapilities of GGally, to assess the distribution and correlation of variables </span>
<span class="kw">library</span>(GGally)
<span class="co"># From the help page:</span>
<span class="kw">data</span>(flea)
<span class="kw">ggpairs</span>(flea, <span class="dt">columns =</span> <span class="dv">2</span><span class="op">:</span><span class="dv">4</span>, ggplot2<span class="op">::</span><span class="kw">aes</span>(<span class="dt">colour=</span>species)) </code></pre></div>
<h1 id="change-plot-types">Change plot types</h1>
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Change the type of plot used on each part of the <a href="correlogram.html">correlogram</a>. This is done with the <code>upper</code> and <code>lower</code> argument.
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<p><img src="199-correlation-matrix-with-ggally_files/figure-html/thecode4-1.png" width="100%" /></p>
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<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="co"># Quick display of two cabapilities of GGally, to assess the distribution and correlation of variables </span>
<span class="kw">library</span>(GGally)
<span class="co"># From the help page:</span>
<span class="kw">data</span>(tips, <span class="dt">package =</span> <span class="st">"reshape"</span>)
<span class="kw">ggpairs</span>(
tips[, <span class="kw">c</span>(<span class="dv">1</span>, <span class="dv">3</span>, <span class="dv">4</span>, <span class="dv">2</span>)],
<span class="dt">upper =</span> <span class="kw">list</span>(<span class="dt">continuous =</span> <span class="st">"density"</span>, <span class="dt">combo =</span> <span class="st">"box_no_facet"</span>),
<span class="dt">lower =</span> <span class="kw">list</span>(<span class="dt">continuous =</span> <span class="st">"points"</span>, <span class="dt">combo =</span> <span class="st">"dot_no_facet"</span>)
)</code></pre></div>
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<p>This document is a work by <a href="https://www.yan-holtz.com">Yan Holtz</a>. Any feedback is highly encouraged. You can fill an issue on <a href="https://github.com/holtzy/D3-graph-gallery/issues">Github</a>, drop me a message on <a href="https://twitter.com/R_Graph_Gallery">Twitter</a>, or send an email pasting <a href="">yan.holtz.data</a> with <a href="">gmail.com</a>.</p>
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