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  <main id="main" class="col-md-9"><div class="page-header">
      <img src="../logo.svg" class="logo" alt=""><h1>PCA Biplot with <code>ggplot2</code></h1>
      <small class="dont-index">Source: <a href="https://github.com/msberends/AMR/blob/main/R/ggplot_pca.R" class="external-link"><code>R/ggplot_pca.R</code></a></small>
      <div class="d-none name"><code>ggplot_pca.Rd</code></div>
    </div>

    <div class="ref-description section level2">
    <p>Produces a <code>ggplot2</code> variant of a so-called <a href="https://en.wikipedia.org/wiki/Biplot" class="external-link">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" class="external-link">biplot()</a></code> function.</p>
    </div>

    <div class="section level2">
    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">ggplot_pca</span><span class="op">(</span><span class="va">x</span>, choices <span class="op">=</span> <span class="fl">1</span><span class="op">:</span><span class="fl">2</span>, scale <span class="op">=</span> <span class="fl">1</span>, pc.biplot <span class="op">=</span> <span class="cn">TRUE</span>,</span>
<span>  labels <span class="op">=</span> <span class="cn">NULL</span>, labels_textsize <span class="op">=</span> <span class="fl">3</span>, labels_text_placement <span class="op">=</span> <span class="fl">1.5</span>,</span>
<span>  groups <span class="op">=</span> <span class="cn">NULL</span>, ellipse <span class="op">=</span> <span class="cn">TRUE</span>, ellipse_prob <span class="op">=</span> <span class="fl">0.68</span>,</span>
<span>  ellipse_size <span class="op">=</span> <span class="fl">0.5</span>, ellipse_alpha <span class="op">=</span> <span class="fl">0.5</span>, points_size <span class="op">=</span> <span class="fl">2</span>,</span>
<span>  points_alpha <span class="op">=</span> <span class="fl">0.25</span>, arrows <span class="op">=</span> <span class="cn">TRUE</span>, arrows_colour <span class="op">=</span> <span class="st">"darkblue"</span>,</span>
<span>  arrows_size <span class="op">=</span> <span class="fl">0.5</span>, arrows_textsize <span class="op">=</span> <span class="fl">3</span>, arrows_textangled <span class="op">=</span> <span class="cn">TRUE</span>,</span>
<span>  arrows_alpha <span class="op">=</span> <span class="fl">0.75</span>, base_textsize <span class="op">=</span> <span class="fl">10</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
    </div>

    <div class="section level2">
    <h2 id="source">Source<a class="anchor" aria-label="anchor" href="#source"></a></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" class="external-link">https://github.com/vqv/ggbiplot</a> (retrieved: 2 March 2020, their latest commit: <a href="https://github.com/vqv/ggbiplot/commit/7325e880485bea4c07465a0304c470608fffb5d9" class="external-link"><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>Hardened all input possibilities by defining the exact type of user input for every argument</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, fixed grammatical errors and added integrity checks</p></li>
<li><p>Updated documentation</p></li>
</ol></div>
    <div class="section level2">
    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>


<dl><dt id="arg-x">x<a class="anchor" aria-label="anchor" href="#arg-x"></a></dt>
<dd><p>an object returned by <code><a href="pca.html">pca()</a></code>, <code><a href="https://rdrr.io/r/stats/prcomp.html" class="external-link">prcomp()</a></code> or <code><a href="https://rdrr.io/r/stats/princomp.html" class="external-link">princomp()</a></code></p></dd>


<dt id="arg-choices">choices<a class="anchor" aria-label="anchor" href="#arg-choices"></a></dt>
<dd><p>length 2 vector specifying the components to plot. Only the default
    is a biplot in the strict sense.</p></dd>


<dt id="arg-scale">scale<a class="anchor" aria-label="anchor" href="#arg-scale"></a></dt>
<dd><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" class="external-link">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></dd>


<dt id="arg-pc-biplot">pc.biplot<a class="anchor" aria-label="anchor" href="#arg-pc-biplot"></a></dt>
<dd><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></dd>


<dt id="arg-labels">labels<a class="anchor" aria-label="anchor" href="#arg-labels"></a></dt>
<dd><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></dd>


<dt id="arg-labels-textsize">labels_textsize<a class="anchor" aria-label="anchor" href="#arg-labels-textsize"></a></dt>
<dd><p>the size of the text used for the labels</p></dd>


<dt id="arg-labels-text-placement">labels_text_placement<a class="anchor" aria-label="anchor" href="#arg-labels-text-placement"></a></dt>
<dd><p>adjustment factor the placement of the variable names (<code>&gt;=1</code> means further away from the arrow head)</p></dd>


<dt id="arg-groups">groups<a class="anchor" aria-label="anchor" href="#arg-groups"></a></dt>
<dd><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></dd>


<dt id="arg-ellipse">ellipse<a class="anchor" aria-label="anchor" href="#arg-ellipse"></a></dt>
<dd><p>a <a href="https://rdrr.io/r/base/logical.html" class="external-link">logical</a> to indicate whether a normal data ellipse should be drawn for each group (set with <code>groups</code>)</p></dd>


<dt id="arg-ellipse-prob">ellipse_prob<a class="anchor" aria-label="anchor" href="#arg-ellipse-prob"></a></dt>
<dd><p>statistical size of the ellipse in normal probability</p></dd>


<dt id="arg-ellipse-size">ellipse_size<a class="anchor" aria-label="anchor" href="#arg-ellipse-size"></a></dt>
<dd><p>the size of the ellipse line</p></dd>


<dt id="arg-ellipse-alpha">ellipse_alpha<a class="anchor" aria-label="anchor" href="#arg-ellipse-alpha"></a></dt>
<dd><p>the alpha (transparency) of the ellipse line</p></dd>


<dt id="arg-points-size">points_size<a class="anchor" aria-label="anchor" href="#arg-points-size"></a></dt>
<dd><p>the size of the points</p></dd>


<dt id="arg-points-alpha">points_alpha<a class="anchor" aria-label="anchor" href="#arg-points-alpha"></a></dt>
<dd><p>the alpha (transparency) of the points</p></dd>


<dt id="arg-arrows">arrows<a class="anchor" aria-label="anchor" href="#arg-arrows"></a></dt>
<dd><p>a <a href="https://rdrr.io/r/base/logical.html" class="external-link">logical</a> to indicate whether arrows should be drawn</p></dd>


<dt id="arg-arrows-colour">arrows_colour<a class="anchor" aria-label="anchor" href="#arg-arrows-colour"></a></dt>
<dd><p>the colour of the arrow and their text</p></dd>


<dt id="arg-arrows-size">arrows_size<a class="anchor" aria-label="anchor" href="#arg-arrows-size"></a></dt>
<dd><p>the size (thickness) of the arrow lines</p></dd>


<dt id="arg-arrows-textsize">arrows_textsize<a class="anchor" aria-label="anchor" href="#arg-arrows-textsize"></a></dt>
<dd><p>the size of the text at the end of the arrows</p></dd>


<dt id="arg-arrows-textangled">arrows_textangled<a class="anchor" aria-label="anchor" href="#arg-arrows-textangled"></a></dt>
<dd><p>a <a href="https://rdrr.io/r/base/logical.html" class="external-link">logical</a> whether the text at the end of the arrows should be angled</p></dd>


<dt id="arg-arrows-alpha">arrows_alpha<a class="anchor" aria-label="anchor" href="#arg-arrows-alpha"></a></dt>
<dd><p>the alpha (transparency) of the arrows and their text</p></dd>


<dt id="arg-base-textsize">base_textsize<a class="anchor" aria-label="anchor" href="#arg-base-textsize"></a></dt>
<dd><p>the text size for all plot elements except the labels and arrows</p></dd>


<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
<dd><p>arguments passed on to functions</p></dd>

</dl></div>
    <div class="section level2">
    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
    <p>The colours for labels and points can be changed by adding another scale layer for colour, such as <code><a href="https://ggplot2.tidyverse.org/reference/scale_viridis.html" class="external-link">scale_colour_viridis_d()</a></code> and <code><a href="https://ggplot2.tidyverse.org/reference/scale_brewer.html" class="external-link">scale_colour_brewer()</a></code>.</p>
    </div>

    <div class="section level2">
    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="co"># `example_isolates` is a data set available in the AMR package.</span></span></span>
<span class="r-in"><span><span class="co"># See ?example_isolates.</span></span></span>
<span class="r-in"><span></span></span>
<span class="r-in"><span><span class="co"># \donttest{</span></span></span>
<span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">require</a></span><span class="op">(</span><span class="st"><a href="https://dplyr.tidyverse.org" class="external-link">"dplyr"</a></span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span></span>
<span class="r-in"><span>  <span class="co"># calculate the resistance per group first</span></span></span>
<span class="r-in"><span>  <span class="va">resistance_data</span> <span class="op">&lt;-</span> <span class="va">example_isolates</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span></span>
<span class="r-in"><span>    <span class="fu"><a href="https://dplyr.tidyverse.org/reference/group_by.html" class="external-link">group_by</a></span><span class="op">(</span></span></span>
<span class="r-in"><span>      order <span class="op">=</span> <span class="fu"><a href="mo_property.html">mo_order</a></span><span class="op">(</span><span class="va">mo</span><span class="op">)</span>, <span class="co"># group on anything, like order</span></span></span>
<span class="r-in"><span>      genus <span class="op">=</span> <span class="fu"><a href="mo_property.html">mo_genus</a></span><span class="op">(</span><span class="va">mo</span><span class="op">)</span></span></span>
<span class="r-in"><span>    <span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span> <span class="co">#   and genus as we do here;</span></span></span>
<span class="r-in"><span>    <span class="fu"><a href="https://dplyr.tidyverse.org/reference/filter.html" class="external-link">filter</a></span><span class="op">(</span><span class="fu"><a href="https://dplyr.tidyverse.org/reference/context.html" class="external-link">n</a></span><span class="op">(</span><span class="op">)</span> <span class="op">&gt;=</span> <span class="fl">30</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span> <span class="co"># filter on only 30 results per group</span></span></span>
<span class="r-in"><span>    <span class="fu"><a href="https://dplyr.tidyverse.org/reference/summarise_all.html" class="external-link">summarise_if</a></span><span class="op">(</span><span class="va">is.sir</span>, <span class="va">resistance</span><span class="op">)</span> <span class="co"># then get resistance of all drugs</span></span></span>
<span class="r-in"><span></span></span>
<span class="r-in"><span>  <span class="co"># now conduct PCA for certain antimicrobial drugs</span></span></span>
<span class="r-in"><span>  <span class="va">pca_result</span> <span class="op">&lt;-</span> <span class="va">resistance_data</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span></span>
<span class="r-in"><span>    <span class="fu"><a href="pca.html">pca</a></span><span class="op">(</span><span class="va">AMC</span>, <span class="va">CXM</span>, <span class="va">CTX</span>, <span class="va">CAZ</span>, <span class="va">GEN</span>, <span class="va">TOB</span>, <span class="va">TMP</span>, <span class="va">SXT</span><span class="op">)</span></span></span>
<span class="r-in"><span></span></span>
<span class="r-in"><span>  <span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">pca_result</span><span class="op">)</span></span></span>
<span class="r-in"><span></span></span>
<span class="r-in"><span>  <span class="co"># old base R plotting method:</span></span></span>
<span class="r-in"><span>  <span class="fu"><a href="https://rdrr.io/r/stats/biplot.html" class="external-link">biplot</a></span><span class="op">(</span><span class="va">pca_result</span>, main <span class="op">=</span> <span class="st">"Base R biplot"</span><span class="op">)</span></span></span>
<span class="r-in"><span></span></span>
<span class="r-in"><span>  <span class="co"># new ggplot2 plotting method using this package:</span></span></span>
<span class="r-in"><span>  <span class="kw">if</span> <span class="op">(</span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">require</a></span><span class="op">(</span><span class="st"><a href="https://ggplot2.tidyverse.org" class="external-link">"ggplot2"</a></span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span></span>
<span class="r-in"><span>    <span class="fu">ggplot_pca</span><span class="op">(</span><span class="va">pca_result</span><span class="op">)</span> <span class="op">+</span></span></span>
<span class="r-in"><span>      <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/labs.html" class="external-link">labs</a></span><span class="op">(</span>title <span class="op">=</span> <span class="st">"ggplot2 biplot"</span><span class="op">)</span></span></span>
<span class="r-in"><span>  <span class="op">}</span></span></span>
<span class="r-in"><span>  <span class="kw">if</span> <span class="op">(</span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">require</a></span><span class="op">(</span><span class="st"><a href="https://ggplot2.tidyverse.org" class="external-link">"ggplot2"</a></span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span></span>
<span class="r-in"><span>    <span class="co"># still extendible with any ggplot2 function</span></span></span>
<span class="r-in"><span>    <span class="fu">ggplot_pca</span><span class="op">(</span><span class="va">pca_result</span><span class="op">)</span> <span class="op">+</span></span></span>
<span class="r-in"><span>      <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/scale_viridis.html" class="external-link">scale_colour_viridis_d</a></span><span class="op">(</span><span class="op">)</span> <span class="op">+</span></span></span>
<span class="r-in"><span>      <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/labs.html" class="external-link">labs</a></span><span class="op">(</span>title <span class="op">=</span> <span class="st">"ggplot2 biplot"</span><span class="op">)</span></span></span>
<span class="r-in"><span>  <span class="op">}</span></span></span>
<span class="r-in"><span><span class="op">}</span></span></span>
<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>There were 73 warnings in `summarise()`.</span>
<span class="r-wrn co"><span class="r-pr">#&gt;</span> The first warning was:</span>
<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> In argument: `PEN = (function (..., minimum = 30, as_percent = FALSE,</span>
<span class="r-wrn co"><span class="r-pr">#&gt;</span>   only_all_tested = FALSE) ...`.</span>
<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> In group 5: `order = "Lactobacillales"` `genus = "Enterococcus"`.</span>
<span class="r-wrn co"><span class="r-pr">#&gt;</span> Caused by warning:</span>
<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span style="color: #BBBB00;">!</span> Introducing NA: only 14 results available for PEN in group: order =</span>
<span class="r-wrn co"><span class="r-pr">#&gt;</span> "Lactobacillales", genus = "Enterococcus" (<span style="background-color: #EEEEEE;">minimum</span> = 30).</span>
<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> Run `dplyr::last_dplyr_warnings()` to see the 72 remaining warnings.</span>
<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #0000BB;">ℹ Columns selected for PCA: "</span><span style="color: #0000BB; font-weight: bold;">AMC</span><span style="color: #0000BB;">", "</span><span style="color: #0000BB; font-weight: bold;">CAZ</span><span style="color: #0000BB;">", "</span><span style="color: #0000BB; font-weight: bold;">CTX</span><span style="color: #0000BB;">", "</span><span style="color: #0000BB; font-weight: bold;">CXM</span><span style="color: #0000BB;">", "</span><span style="color: #0000BB; font-weight: bold;">GEN</span><span style="color: #0000BB;">", "</span><span style="color: #0000BB; font-weight: bold;">SXT</span><span style="color: #0000BB;">",</span></span>
<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #0000BB;">  "</span><span style="color: #0000BB; font-weight: bold;">TMP</span><span style="color: #0000BB;">", and "</span><span style="color: #0000BB; font-weight: bold;">TOB</span><span style="color: #0000BB;">". Total observations available: 7.</span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Groups (n=4, named as 'order'):</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> [1] "Caryophanales"    "Enterobacterales" "Lactobacillales"  "Pseudomonadales" </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
<span class="r-plt img"><img src="ggplot_pca-1.png" alt="" width="700" height="433"></span>
<span class="r-plt img"><img src="ggplot_pca-2.png" alt="" width="700" height="433"></span>
<span class="r-in"><span><span class="co"># }</span></span></span>
</code></pre></div>
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