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<h1>PCA biplot with <code>ggplot2</code></h1>
<small class="dont-index">Source: <a href='https://gitlab.com/msberends/AMR/blob/master/R/ggplot_pca.R'><code>R/ggplot_pca.R</code></a></small>
<div class="hidden name"><code>ggplot_pca.Rd</code></div>
</div>
<div class="ref-description">
<p>Produces a <code>ggplot2</code> variant of a so-called <a href='https://en.wikipedia.org/wiki/Biplot'>biplot</a> for PCA (principal component analysis), but is more flexible and more appealing than the base <span style="R">R</span> <code><a href='https://rdrr.io/r/stats/biplot.html'>biplot()</a></code> function.</p>
</div>
<pre class="usage"><span class='fu'>ggplot_pca</span>(
<span class='no'>x</span>,
<span class='kw'>choices</span> <span class='kw'>=</span> <span class='fl'>1</span>:<span class='fl'>2</span>,
<span class='kw'>scale</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>,
<span class='kw'>pc.biplot</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>,
<span class='kw'>labels</span> <span class='kw'>=</span> <span class='kw'>NULL</span>,
<span class='kw'>labels_textsize</span> <span class='kw'>=</span> <span class='fl'>3</span>,
<span class='kw'>labels_text_placement</span> <span class='kw'>=</span> <span class='fl'>1.5</span>,
<span class='kw'>groups</span> <span class='kw'>=</span> <span class='kw'>NULL</span>,
<span class='kw'>ellipse</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>,
<span class='kw'>ellipse_prob</span> <span class='kw'>=</span> <span class='fl'>0.68</span>,
<span class='kw'>ellipse_size</span> <span class='kw'>=</span> <span class='fl'>0.5</span>,
<span class='kw'>ellipse_alpha</span> <span class='kw'>=</span> <span class='fl'>0.5</span>,
<span class='kw'>points_size</span> <span class='kw'>=</span> <span class='fl'>2</span>,
<span class='kw'>points_alpha</span> <span class='kw'>=</span> <span class='fl'>0.25</span>,
<span class='kw'>arrows</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>,
<span class='kw'>arrows_colour</span> <span class='kw'>=</span> <span class='st'>"darkblue"</span>,
<span class='kw'>arrows_size</span> <span class='kw'>=</span> <span class='fl'>0.5</span>,
<span class='kw'>arrows_textsize</span> <span class='kw'>=</span> <span class='fl'>3</span>,
<span class='kw'>arrows_alpha</span> <span class='kw'>=</span> <span class='fl'>0.75</span>,
<span class='kw'>base_textsize</span> <span class='kw'>=</span> <span class='fl'>10</span>,
<span class='no'>...</span>
)</pre>
<h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
<table class="ref-arguments">
<colgroup><col class="name" /><col class="desc" /></colgroup>
<tr>
<th>x</th>
<td><p>an object returned by <code><a href='pca.html'>pca()</a></code>, <code><a href='https://rdrr.io/r/stats/prcomp.html'>prcomp()</a></code> or <code><a href='https://rdrr.io/r/stats/princomp.html'>princomp()</a></code></p></td>
</tr>
<tr>
<th>choices</th>
<td><p>length 2 vector specifying the components to plot. Only the default
is a biplot in the strict sense.</p></td>
</tr>
<tr>
<th>scale</th>
<td><p>The variables are scaled by <code>lambda ^ scale</code> and the
observations are scaled by <code>lambda ^ (1-scale)</code> where
<code>lambda</code> are the singular values as computed by
<code><a href='https://rdrr.io/r/stats/princomp.html'>princomp</a></code>. Normally <code>0 &lt;= scale &lt;= 1</code>, and a warning
will be issued if the specified <code>scale</code> is outside this range.</p></td>
</tr>
<tr>
<th>pc.biplot</th>
<td><p>If true, use what Gabriel (1971) refers to as a "principal component
biplot", with <code>lambda = 1</code> and observations scaled up by sqrt(n) and
variables scaled down by sqrt(n). Then inner products between
variables approximate covariances and distances between observations
approximate Mahalanobis distance.</p></td>
</tr>
<tr>
<th>labels</th>
<td><p>an optional vector of labels for the observations. If set, the labels will be placed below their respective points. When using the <code><a href='pca.html'>pca()</a></code> function as input for <code>x</code>, this will be determined automatically based on the attribute <code>non_numeric_cols</code>, see <code><a href='pca.html'>pca()</a></code>.</p></td>
</tr>
<tr>
<th>labels_textsize</th>
<td><p>the size of the text used for the labels</p></td>
</tr>
<tr>
<th>labels_text_placement</th>
<td><p>adjustment factor the placement of the variable names (<code>&gt;=1</code> means further away from the arrow head)</p></td>
</tr>
<tr>
<th>groups</th>
<td><p>an optional vector of groups for the labels, with the same length as <code>labels</code>. If set, the points and labels will be coloured according to these groups. When using the <code><a href='pca.html'>pca()</a></code> function as input for <code>x</code>, this will be determined automatically based on the attribute <code>non_numeric_cols</code>, see <code><a href='pca.html'>pca()</a></code>.</p></td>
</tr>
<tr>
<th>ellipse</th>
<td><p>a logical to indicate whether a normal data ellipse should be drawn for each group (set with <code>groups</code>)</p></td>
</tr>
<tr>
<th>ellipse_prob</th>
<td><p>statistical size of the ellipse in normal probability</p></td>
</tr>
<tr>
<th>ellipse_size</th>
<td><p>the size of the ellipse line</p></td>
</tr>
<tr>
<th>ellipse_alpha</th>
<td><p>the alpha (transparency) of the ellipse line</p></td>
</tr>
<tr>
<th>points_size</th>
<td><p>the size of the points</p></td>
</tr>
<tr>
<th>points_alpha</th>
<td><p>the alpha (transparency) of the points</p></td>
</tr>
<tr>
<th>arrows</th>
<td><p>a logical to indicate whether arrows should be drawn</p></td>
</tr>
<tr>
<th>arrows_colour</th>
<td><p>the colour of the arrow and their text</p></td>
</tr>
<tr>
<th>arrows_size</th>
<td><p>the size (thickness) of the arrow lines</p></td>
</tr>
<tr>
<th>arrows_textsize</th>
<td><p>the size of the text at the end of the arrows</p></td>
</tr>
<tr>
<th>arrows_alpha</th>
<td><p>the alpha (transparency) of the arrows and their text</p></td>
</tr>
<tr>
<th>base_textsize</th>
<td><p>the text size for all plot elements except the labels and arrows</p></td>
</tr>
<tr>
<th>...</th>
<td><p>Parameters passed on to functions</p></td>
</tr>
</table>
<h2 class="hasAnchor" id="source"><a class="anchor" href="#source"></a>Source</h2>
<p>The <code>ggplot_pca()</code> function is based on the <code>ggbiplot()</code> function from the <code>ggbiplot</code> package by Vince Vu, as found on GitHub: <a href='https://github.com/vqv/ggbiplot'>https://github.com/vqv/ggbiplot</a> (retrieved: 2 March 2020, their latest commit: <a href='https://github.com/vqv/ggbiplot/commit/7325e880485bea4c07465a0304c470608fffb5d9'><code>7325e88</code></a>; 12 February 2015).</p>
<p>As per their GPL-2 licence that demands documentation of code changes, the changes made based on the source code were:</p><ol>
<li><p>Rewritten code to remove the dependency on packages <code>plyr</code>, <code>scales</code> and <code>grid</code></p></li>
<li><p>Parametrised more options, like arrow and ellipse settings</p></li>
<li><p>Added total amount of explained variance as a caption in the plot</p></li>
<li><p>Cleaned all syntax based on the <code>lintr</code> package and added integrity checks</p></li>
<li><p>Updated documentation</p></li>
</ol>
<h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
<p>The colours for labels and points can be changed by adding another scale layer for colour, like <code>scale_colour_viridis_d()</code> or <code>scale_colour_brewer()</code>.</p>
<h2 class="hasAnchor" id="maturing-lifecycle"><a class="anchor" href="#maturing-lifecycle"></a>Maturing lifecycle</h2>
<p><img src='figures/lifecycle_maturing.svg' style=margin-bottom:5px /> <br />
The <a href='lifecycle.html'>lifecycle</a> of this function is <strong>maturing</strong>. The unlying code of a maturing function has been roughed out, but finer details might still change. Since this function needs wider usage and more extensive testing, you are very welcome <a href='https://gitlab.com/msberends/AMR/-/issues'>to suggest changes at our repository</a> or <a href='AMR.html'>write us an email (see section 'Contact Us')</a>.</p>
<h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
<pre class="examples"><span class='co'># `example_isolates` is a dataset available in the AMR package.</span>
<span class='co'># See ?example_isolates.</span>
<span class='kw'>if</span> (<span class='fl'>FALSE</span>) {
<span class='co'># See ?pca for more info about Principal Component Analysis (PCA).</span>
<span class='fu'><a href='https://rdrr.io/r/base/library.html'>library</a></span>(<span class='no'>dplyr</span>)
<span class='no'>pca_model</span> <span class='kw'>&lt;-</span> <span class='no'>example_isolates</span> <span class='kw'>%&gt;%</span>
<span class='fu'><a href='https://dplyr.tidyverse.org/reference/filter.html'>filter</a></span>(<span class='fu'><a href='mo_property.html'>mo_genus</a></span>(<span class='no'>mo</span>) <span class='kw'>==</span> <span class='st'>"Staphylococcus"</span>) <span class='kw'>%&gt;%</span>
<span class='fu'><a href='https://dplyr.tidyverse.org/reference/group_by.html'>group_by</a></span>(<span class='kw'>species</span> <span class='kw'>=</span> <span class='fu'><a href='mo_property.html'>mo_shortname</a></span>(<span class='no'>mo</span>)) <span class='kw'>%&gt;%</span>
<span class='fu'><a href='https://dplyr.tidyverse.org/reference/summarise_all.html'>summarise_if</a></span> (<span class='no'>is.rsi</span>, <span class='no'>resistance</span>) <span class='kw'>%&gt;%</span>
<span class='fu'><a href='pca.html'>pca</a></span>(<span class='no'>FLC</span>, <span class='no'>AMC</span>, <span class='no'>CXM</span>, <span class='no'>GEN</span>, <span class='no'>TOB</span>, <span class='no'>TMP</span>, <span class='no'>SXT</span>, <span class='no'>CIP</span>, <span class='no'>TEC</span>, <span class='no'>TCY</span>, <span class='no'>ERY</span>)
<span class='co'># old</span>
<span class='fu'><a href='https://rdrr.io/r/stats/biplot.html'>biplot</a></span>(<span class='no'>pca_model</span>)
<span class='co'># new </span>
<span class='fu'>ggplot_pca</span>(<span class='no'>pca_model</span>)
}</pre>
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