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@ -39,7 +56,7 @@
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@ -47,14 +64,14 @@
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@ -63,77 +80,77 @@
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@ -142,21 +159,21 @@
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@ -165,14 +182,14 @@
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@ -187,7 +204,7 @@
</header><script src="EUCAST_files/header-attrs-2.4/header-attrs.js"></script><script src="EUCAST_files/accessible-code-block-0.0.1/empty-anchor.js"></script><div class="row">
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<h1 data-toc-skip>How to apply EUCAST rules</h1>
@ -215,8 +232,8 @@
<a href="#examples" class="anchor"></a>Examples</h2>
<p>These rules can be used to discard impossible bug-drug combinations in your data. For example, <em>Klebsiella</em> produces beta-lactamase that prevents ampicillin (or amoxicillin) from working against it. In other words, practically every strain of <em>Klebsiella</em> is resistant to ampicillin.</p>
<p>Sometimes, laboratory data can still contain such strains with ampicillin being susceptible to ampicillin. This could be because an antibiogram is available before an identification is available, and the antibiogram is then not re-interpreted based on the identification (namely, <em>Klebsiella</em>). EUCAST expert rules solve this, that can be applied using <code><a href="../reference/eucast_rules.html">eucast_rules()</a></code>:</p>
<div class="sourceCode" id="cb1"><pre class="downlit">
<span class="va">oops</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html">data.frame</a></span><span class="op">(</span>mo <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="st">"Klebsiella"</span>,
<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="va">oops</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html">data.frame</a></span><span class="op">(</span>mo <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="st">"Klebsiella"</span>,
<span class="st">"Escherichia"</span><span class="op">)</span>,
ampicillin <span class="op">=</span> <span class="st">"S"</span><span class="op">)</span>
<span class="va">oops</span>
@ -227,19 +244,19 @@
<span class="fu"><a href="../reference/eucast_rules.html">eucast_rules</a></span><span class="op">(</span><span class="va">oops</span>, info <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>
<span class="co"># mo ampicillin</span>
<span class="co"># 1 Klebsiella R</span>
<span class="co"># 2 Escherichia S</span></pre></div>
<span class="co"># 2 Escherichia S</span></code></pre></div>
<p>A more convenient function is <code><a href="../reference/mo_property.html">mo_is_intrinsic_resistant()</a></code> that uses the same guideline, but allows to check for one or more specific microorganisms or antibiotics:</p>
<div class="sourceCode" id="cb2"><pre class="downlit">
<span class="fu"><a href="../reference/mo_property.html">mo_is_intrinsic_resistant</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="st">"Klebsiella"</span>, <span class="st">"Escherichia"</span><span class="op">)</span>,
<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="fu"><a href="../reference/mo_property.html">mo_is_intrinsic_resistant</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="st">"Klebsiella"</span>, <span class="st">"Escherichia"</span><span class="op">)</span>,
<span class="st">"ampicillin"</span><span class="op">)</span>
<span class="co"># [1] TRUE FALSE</span>
<span class="fu"><a href="../reference/mo_property.html">mo_is_intrinsic_resistant</a></span><span class="op">(</span><span class="st">"Klebsiella"</span>,
<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="st">"ampicillin"</span>, <span class="st">"kanamycin"</span><span class="op">)</span><span class="op">)</span>
<span class="co"># [1] TRUE FALSE</span></pre></div>
<span class="co"># [1] TRUE FALSE</span></code></pre></div>
<p>EUCAST rules can not only be used for correction, they can also be used for filling in known resistance and susceptibility based on results of other antimicrobials drugs. This process is called <em>interpretive reading</em>, is basically a form of imputation, and is part of the <code><a href="../reference/eucast_rules.html">eucast_rules()</a></code> function as well:</p>
<div class="sourceCode" id="cb3"><pre class="downlit">
<span class="va">data</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html">data.frame</a></span><span class="op">(</span>mo <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="st">"Staphylococcus aureus"</span>,
<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="va">data</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html">data.frame</a></span><span class="op">(</span>mo <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="st">"Staphylococcus aureus"</span>,
<span class="st">"Enterococcus faecalis"</span>,
<span class="st">"Escherichia coli"</span>,
<span class="st">"Klebsiella pneumoniae"</span>,
@ -251,9 +268,9 @@
CXM <span class="op">=</span> <span class="st">"-"</span>, <span class="co"># Cefuroxime</span>
PEN <span class="op">=</span> <span class="st">"S"</span>, <span class="co"># Benzylenicillin</span>
FOX <span class="op">=</span> <span class="st">"S"</span>, <span class="co"># Cefoxitin</span>
stringsAsFactors <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></pre></div>
<div class="sourceCode" id="cb4"><pre class="downlit">
<span class="va">data</span></pre></div>
stringsAsFactors <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></code></pre></div>
<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="va">data</span></code></pre></div>
<table class="table">
<thead><tr class="header">
<th align="left">mo</th>
@ -318,8 +335,8 @@
</tr>
</tbody>
</table>
<div class="sourceCode" id="cb5"><pre class="downlit">
<span class="fu"><a href="../reference/eucast_rules.html">eucast_rules</a></span><span class="op">(</span><span class="va">data</span><span class="op">)</span></pre></div>
<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="fu"><a href="../reference/eucast_rules.html">eucast_rules</a></span><span class="op">(</span><span class="va">data</span><span class="op">)</span></code></pre></div>
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<th align="left">mo</th>
@ -402,7 +419,7 @@
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@ -47,14 +64,14 @@
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@ -63,77 +80,77 @@
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Conduct AMR analysis
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@ -142,21 +159,21 @@
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@ -165,14 +182,14 @@
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@ -187,7 +204,7 @@
</header><script src="MDR_files/header-attrs-2.4/header-attrs.js"></script><script src="MDR_files/accessible-code-block-0.0.1/empty-anchor.js"></script><div class="row">
</header><script src="MDR_files/header-attrs-2.6/header-attrs.js"></script><div class="row">
<div class="col-md-9 contents">
<div class="page-header toc-ignore">
<h1 data-toc-skip>How to determine multi-drug resistance (MDR)</h1>
@ -244,15 +261,15 @@
<a href="#examples" class="anchor"></a>Examples</h4>
<p>The <code><a href="../reference/mdro.html">mdro()</a></code> function always returns an ordered <code>factor</code>. For example, the output of the default guideline by Magiorakos <em>et al.</em> returns a <code>factor</code> with levels Negative, MDR, XDR or PDR in that order.</p>
<p>The next example uses the <code>example_isolates</code> data set. This is a data set included with this package and contains 2,000 microbial isolates with their full antibiograms. It reflects reality and can be used to practice AMR analysis. If we test the MDR/XDR/PDR guideline on this data set, we get:</p>
<div class="sourceCode" id="cb1"><pre class="downlit">
<span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://dplyr.tidyverse.org">dplyr</a></span><span class="op">)</span> <span class="co"># to support pipes: %&gt;%</span>
<span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://github.com/msberends/cleaner">cleaner</a></span><span class="op">)</span> <span class="co"># to create frequency tables</span></pre></div>
<div class="sourceCode" id="cb2"><pre class="downlit">
<span class="va">example_isolates</span> <span class="op">%&gt;%</span>
<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://dplyr.tidyverse.org">dplyr</a></span><span class="op">)</span> <span class="co"># to support pipes: %&gt;%</span>
<span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://github.com/msberends/cleaner">cleaner</a></span><span class="op">)</span> <span class="co"># to create frequency tables</span></code></pre></div>
<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="va">example_isolates</span> <span class="op">%&gt;%</span>
<span class="fu"><a href="../reference/mdro.html">mdro</a></span><span class="op">(</span><span class="op">)</span> <span class="op">%&gt;%</span>
<span class="fu"><a href="https://rdrr.io/pkg/cleaner/man/freq.html">freq</a></span><span class="op">(</span><span class="op">)</span> <span class="co"># show frequency table of the result</span>
<span class="co"># Warning in warning_("NA introduced for isolates where the available percentage of antimicrobial classes was below ", : NA introduced for isolates where the available percentage of antimicrobial</span>
<span class="co"># classes was below 50% (set with `pct_required_classes`)</span></pre></div>
<span class="co"># classes was below 50% (set with `pct_required_classes`)</span></code></pre></div>
<p><strong>Frequency table</strong></p>
<p>Class: factor &gt; ordered (numeric)<br>
Length: 2,000<br>
@ -288,8 +305,8 @@ Unique: 2</p>
</tbody>
</table>
<p>For another example, I will create a data set to determine multi-drug resistant TB:</p>
<div class="sourceCode" id="cb3"><pre class="downlit">
<span class="co"># a helper function to get a random vector with values S, I and R</span>
<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="co"># a helper function to get a random vector with values S, I and R</span>
<span class="co"># with the probabilities 50% - 10% - 40%</span>
<span class="va">sample_rsi</span> <span class="op">&lt;-</span> <span class="kw">function</span><span class="op">(</span><span class="op">)</span> <span class="op">{</span>
<span class="fu"><a href="https://rdrr.io/r/base/sample.html">sample</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="st">"S"</span>, <span class="st">"I"</span>, <span class="st">"R"</span><span class="op">)</span>,
@ -304,44 +321,44 @@ Unique: 2</p>
ethambutol <span class="op">=</span> <span class="fu">sample_rsi</span><span class="op">(</span><span class="op">)</span>,
pyrazinamide <span class="op">=</span> <span class="fu">sample_rsi</span><span class="op">(</span><span class="op">)</span>,
moxifloxacin <span class="op">=</span> <span class="fu">sample_rsi</span><span class="op">(</span><span class="op">)</span>,
kanamycin <span class="op">=</span> <span class="fu">sample_rsi</span><span class="op">(</span><span class="op">)</span><span class="op">)</span></pre></div>
kanamycin <span class="op">=</span> <span class="fu">sample_rsi</span><span class="op">(</span><span class="op">)</span><span class="op">)</span></code></pre></div>
<p>Because all column names are automatically verified for valid drug names or codes, this would have worked exactly the same:</p>
<div class="sourceCode" id="cb4"><pre class="downlit">
<span class="va">my_TB_data</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html">data.frame</a></span><span class="op">(</span>RIF <span class="op">=</span> <span class="fu">sample_rsi</span><span class="op">(</span><span class="op">)</span>,
<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="va">my_TB_data</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html">data.frame</a></span><span class="op">(</span>RIF <span class="op">=</span> <span class="fu">sample_rsi</span><span class="op">(</span><span class="op">)</span>,
INH <span class="op">=</span> <span class="fu">sample_rsi</span><span class="op">(</span><span class="op">)</span>,
GAT <span class="op">=</span> <span class="fu">sample_rsi</span><span class="op">(</span><span class="op">)</span>,
ETH <span class="op">=</span> <span class="fu">sample_rsi</span><span class="op">(</span><span class="op">)</span>,
PZA <span class="op">=</span> <span class="fu">sample_rsi</span><span class="op">(</span><span class="op">)</span>,
MFX <span class="op">=</span> <span class="fu">sample_rsi</span><span class="op">(</span><span class="op">)</span>,
KAN <span class="op">=</span> <span class="fu">sample_rsi</span><span class="op">(</span><span class="op">)</span><span class="op">)</span></pre></div>
KAN <span class="op">=</span> <span class="fu">sample_rsi</span><span class="op">(</span><span class="op">)</span><span class="op">)</span></code></pre></div>
<p>The data set now looks like this:</p>
<div class="sourceCode" id="cb5"><pre class="downlit">
<span class="fu"><a href="https://rdrr.io/r/utils/head.html">head</a></span><span class="op">(</span><span class="va">my_TB_data</span><span class="op">)</span>
<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/utils/head.html">head</a></span><span class="op">(</span><span class="va">my_TB_data</span><span class="op">)</span>
<span class="co"># rifampicin isoniazid gatifloxacin ethambutol pyrazinamide moxifloxacin</span>
<span class="co"># 1 R I R S S S</span>
<span class="co"># 2 S R I R R S</span>
<span class="co"># 3 R S R R R R</span>
<span class="co"># 4 R S I R I S</span>
<span class="co"># 5 S R S R S R</span>
<span class="co"># 6 R R S R S S</span>
<span class="co"># 1 S R S R R S</span>
<span class="co"># 2 S R S S S S</span>
<span class="co"># 3 S S S S R S</span>
<span class="co"># 4 S R S R S S</span>
<span class="co"># 5 R S R S R S</span>
<span class="co"># 6 S R R R S S</span>
<span class="co"># kanamycin</span>
<span class="co"># 1 R</span>
<span class="co"># 1 S</span>
<span class="co"># 2 S</span>
<span class="co"># 3 S</span>
<span class="co"># 4 S</span>
<span class="co"># 5 R</span>
<span class="co"># 6 R</span></pre></div>
<span class="co"># 6 S</span></code></pre></div>
<p>We can now add the interpretation of MDR-TB to our data set. You can use:</p>
<div class="sourceCode" id="cb6"><pre class="downlit">
<span class="fu"><a href="../reference/mdro.html">mdro</a></span><span class="op">(</span><span class="va">my_TB_data</span>, guideline <span class="op">=</span> <span class="st">"TB"</span><span class="op">)</span></pre></div>
<div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="fu"><a href="../reference/mdro.html">mdro</a></span><span class="op">(</span><span class="va">my_TB_data</span>, guideline <span class="op">=</span> <span class="st">"TB"</span><span class="op">)</span></code></pre></div>
<p>or its shortcut <code><a href="../reference/mdro.html">mdr_tb()</a></code>:</p>
<div class="sourceCode" id="cb7"><pre class="downlit">
<span class="va">my_TB_data</span><span class="op">$</span><span class="va">mdr</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/mdro.html">mdr_tb</a></span><span class="op">(</span><span class="va">my_TB_data</span><span class="op">)</span>
<div class="sourceCode" id="cb7"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="va">my_TB_data</span><span class="op">$</span><span class="va">mdr</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/mdro.html">mdr_tb</a></span><span class="op">(</span><span class="va">my_TB_data</span><span class="op">)</span>
<span class="co"># NOTE: No column found as input for `col_mo`, assuming all records contain</span>
<span class="co"># Mycobacterium tuberculosis.</span></pre></div>
<span class="co"># Mycobacterium tuberculosis.</span></code></pre></div>
<p>Create a frequency table of the results:</p>
<div class="sourceCode" id="cb8"><pre class="downlit">
<span class="fu"><a href="https://rdrr.io/pkg/cleaner/man/freq.html">freq</a></span><span class="op">(</span><span class="va">my_TB_data</span><span class="op">$</span><span class="va">mdr</span><span class="op">)</span></pre></div>
<div class="sourceCode" id="cb8"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/pkg/cleaner/man/freq.html">freq</a></span><span class="op">(</span><span class="va">my_TB_data</span><span class="op">$</span><span class="va">mdr</span><span class="op">)</span></code></pre></div>
<p><strong>Frequency table</strong></p>
<p>Class: factor &gt; ordered (numeric)<br>
Length: 5,000<br>
@ -361,40 +378,40 @@ Unique: 5</p>
<tr class="odd">
<td align="left">1</td>
<td align="left">Mono-resistant</td>
<td align="right">3244</td>
<td align="right">64.88%</td>
<td align="right">3244</td>
<td align="right">64.88%</td>
<td align="right">3314</td>
<td align="right">66.28%</td>
<td align="right">3314</td>
<td align="right">66.28%</td>
</tr>
<tr class="even">
<td align="left">2</td>
<td align="left">Negative</td>
<td align="right">644</td>
<td align="right">12.88%</td>
<td align="right">3888</td>
<td align="right">77.76%</td>
<td align="right">637</td>
<td align="right">12.74%</td>
<td align="right">3951</td>
<td align="right">79.02%</td>
</tr>
<tr class="odd">
<td align="left">3</td>
<td align="left">Multi-drug-resistant</td>
<td align="right">613</td>
<td align="right">12.26%</td>
<td align="right">4501</td>
<td align="right">90.02%</td>
<td align="right">565</td>
<td align="right">11.30%</td>
<td align="right">4516</td>
<td align="right">90.32%</td>
</tr>
<tr class="even">
<td align="left">4</td>
<td align="left">Poly-resistant</td>
<td align="right">304</td>
<td align="right">6.08%</td>
<td align="right">4805</td>
<td align="right">96.10%</td>
<td align="right">298</td>
<td align="right">5.96%</td>
<td align="right">4814</td>
<td align="right">96.28%</td>
</tr>
<tr class="odd">
<td align="left">5</td>
<td align="left">Extensively drug-resistant</td>
<td align="right">195</td>
<td align="right">3.90%</td>
<td align="right">186</td>
<td align="right">3.72%</td>
<td align="right">5000</td>
<td align="right">100.00%</td>
</tr>
@ -416,7 +433,7 @@ Unique: 5</p>
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@ -63,77 +80,77 @@
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</header><script src="PCA_files/header-attrs-2.4/header-attrs.js"></script><script src="PCA_files/accessible-code-block-0.0.1/empty-anchor.js"></script><div class="row">
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<h1 data-toc-skip>How to conduct principal component analysis (PCA) for AMR</h1>
@ -209,8 +226,8 @@
<h1 class="hasAnchor">
<a href="#transforming" class="anchor"></a>Transforming</h1>
<p>For PCA, we need to transform our AMR data first. This is what the <code>example_isolates</code> data set in this package looks like:</p>
<div class="sourceCode" id="cb1"><pre class="downlit">
<span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://msberends.github.io/AMR/">AMR</a></span><span class="op">)</span>
<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://msberends.github.io/AMR/">AMR</a></span><span class="op">)</span>
<span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://dplyr.tidyverse.org">dplyr</a></span><span class="op">)</span>
<span class="fu"><a href="https://tibble.tidyverse.org/reference/glimpse.html">glimpse</a></span><span class="op">(</span><span class="va">example_isolates</span><span class="op">)</span>
<span class="co"># Rows: 2,000</span>
@ -263,10 +280,10 @@
<span class="co"># $ CHL &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span>
<span class="co"># $ COL &lt;rsi&gt; NA, NA, R, R, R, R, R, R, R, R, R, R, NA, NA, NA, R, …</span>
<span class="co"># $ MUP &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span>
<span class="co"># $ RIF &lt;rsi&gt; R, R, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, R, R…</span></pre></div>
<span class="co"># $ RIF &lt;rsi&gt; R, R, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, R, R…</span></code></pre></div>
<p>Now to transform this to a data set with only resistance percentages per taxonomic order and genus:</p>
<div class="sourceCode" id="cb2"><pre class="downlit">
<span class="va">resistance_data</span> <span class="op">&lt;-</span> <span class="va">example_isolates</span> <span class="op">%&gt;%</span>
<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="va">resistance_data</span> <span class="op">&lt;-</span> <span class="va">example_isolates</span> <span class="op">%&gt;%</span>
<span class="fu"><a href="https://dplyr.tidyverse.org/reference/group_by.html">group_by</a></span><span class="op">(</span>order <span class="op">=</span> <span class="fu"><a href="../reference/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>
genus <span class="op">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_genus</a></span><span class="op">(</span><span class="va">mo</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="co"># and genus as we do here</span>
<span class="fu"><a href="https://dplyr.tidyverse.org/reference/summarise_all.html">summarise_if</a></span><span class="op">(</span><span class="va">is.rsi</span>, <span class="va">resistance</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="co"># then get resistance of all drugs</span>
@ -283,40 +300,40 @@
<span class="co"># 3 Actinomycetales Cutibacterium NA NA NA NA NA NA NA NA</span>
<span class="co"># 4 Actinomycetales Dermabacter NA NA NA NA NA NA NA NA</span>
<span class="co"># 5 Actinomycetales Micrococcus NA NA NA NA NA NA NA NA</span>
<span class="co"># 6 Actinomycetales Rothia NA NA NA NA NA NA NA NA</span></pre></div>
<span class="co"># 6 Actinomycetales Rothia NA NA NA NA NA NA NA NA</span></code></pre></div>
</div>
<div id="perform-principal-component-analysis" class="section level1">
<h1 class="hasAnchor">
<a href="#perform-principal-component-analysis" class="anchor"></a>Perform principal component analysis</h1>
<p>The new <code><a href="../reference/pca.html">pca()</a></code> function will automatically filter on rows that contain numeric values in all selected variables, so we now only need to do:</p>
<div class="sourceCode" id="cb3"><pre class="downlit">
<span class="va">pca_result</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/pca.html">pca</a></span><span class="op">(</span><span class="va">resistance_data</span><span class="op">)</span>
<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="va">pca_result</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/pca.html">pca</a></span><span class="op">(</span><span class="va">resistance_data</span><span class="op">)</span>
<span class="co"># NOTE: Columns selected for PCA: AMC CXM CTX CAZ GEN TOB TMP SXT. Total</span>
<span class="co"># observations available: 7.</span></pre></div>
<span class="co"># observations available: 7.</span></code></pre></div>
<p>The result can be reviewed with the good old <code><a href="https://rdrr.io/r/base/summary.html">summary()</a></code> function:</p>
<div class="sourceCode" id="cb4"><pre class="downlit">
<span class="fu"><a href="https://rdrr.io/r/base/summary.html">summary</a></span><span class="op">(</span><span class="va">pca_result</span><span class="op">)</span>
<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/base/summary.html">summary</a></span><span class="op">(</span><span class="va">pca_result</span><span class="op">)</span>
<span class="co"># Importance of components:</span>
<span class="co"># PC1 PC2 PC3 PC4 PC5 PC6 PC7</span>
<span class="co"># Standard deviation 2.154 1.6807 0.61365 0.33902 0.20757 0.03136 1.733e-16</span>
<span class="co"># Proportion of Variance 0.580 0.3531 0.04707 0.01437 0.00539 0.00012 0.000e+00</span>
<span class="co"># Cumulative Proportion 0.580 0.9331 0.98012 0.99449 0.99988 1.00000 1.000e+00</span></pre></div>
<span class="co"># Cumulative Proportion 0.580 0.9331 0.98012 0.99449 0.99988 1.00000 1.000e+00</span></code></pre></div>
<p>Good news. The first two components explain a total of 93.3% of the variance (see the PC1 and PC2 values of the <em>Proportion of Variance</em>. We can create a so-called biplot with the base R <code><a href="https://rdrr.io/r/stats/biplot.html">biplot()</a></code> function, to see which antimicrobial resistance per drug explain the difference per microorganism.</p>
</div>
<div id="plotting-the-results" class="section level1">
<h1 class="hasAnchor">
<a href="#plotting-the-results" class="anchor"></a>Plotting the results</h1>
<div class="sourceCode" id="cb5"><pre class="downlit">
<span class="fu"><a href="https://rdrr.io/r/stats/biplot.html">biplot</a></span><span class="op">(</span><span class="va">pca_result</span><span class="op">)</span></pre></div>
<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/stats/biplot.html">biplot</a></span><span class="op">(</span><span class="va">pca_result</span><span class="op">)</span></code></pre></div>
<p><img src="PCA_files/figure-html/unnamed-chunk-5-1.png" width="750"></p>
<p>But we cant see the explanation of the points. Perhaps this works better with our new <code><a href="../reference/ggplot_pca.html">ggplot_pca()</a></code> function, that automatically adds the right labels and even groups:</p>
<div class="sourceCode" id="cb6"><pre class="downlit">
<span class="fu"><a href="../reference/ggplot_pca.html">ggplot_pca</a></span><span class="op">(</span><span class="va">pca_result</span><span class="op">)</span></pre></div>
<div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="fu"><a href="../reference/ggplot_pca.html">ggplot_pca</a></span><span class="op">(</span><span class="va">pca_result</span><span class="op">)</span></code></pre></div>
<p><img src="PCA_files/figure-html/unnamed-chunk-6-1.png" width="750"></p>
<p>You can also print an ellipse per group, and edit the appearance:</p>
<div class="sourceCode" id="cb7"><pre class="downlit">
<span class="fu"><a href="../reference/ggplot_pca.html">ggplot_pca</a></span><span class="op">(</span><span class="va">pca_result</span>, ellipse <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span> <span class="op">+</span>
<span class="fu">ggplot2</span><span class="fu">::</span><span class="fu"><a href="https://ggplot2.tidyverse.org/reference/labs.html">labs</a></span><span class="op">(</span>title <span class="op">=</span> <span class="st">"An AMR/PCA biplot!"</span><span class="op">)</span></pre></div>
<div class="sourceCode" id="cb7"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="fu"><a href="../reference/ggplot_pca.html">ggplot_pca</a></span><span class="op">(</span><span class="va">pca_result</span>, ellipse <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span> <span class="op">+</span>
<span class="fu">ggplot2</span><span class="fu">::</span><span class="fu"><a href="https://ggplot2.tidyverse.org/reference/labs.html">labs</a></span><span class="op">(</span>title <span class="op">=</span> <span class="st">"An AMR/PCA biplot!"</span><span class="op">)</span></code></pre></div>
<p><img src="PCA_files/figure-html/unnamed-chunk-7-1.png" width="750"></p>
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@ -336,7 +353,7 @@
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@ -187,13 +204,13 @@
</header><script src="SPSS_files/header-attrs-2.4/header-attrs.js"></script><script src="SPSS_files/accessible-code-block-0.0.1/empty-anchor.js"></script><div class="row">
</header><script src="SPSS_files/header-attrs-2.6/header-attrs.js"></script><div class="row">
<div class="col-md-9 contents">
<div class="page-header toc-ignore">
<h1 data-toc-skip>How to import data from SPSS / SAS / Stata</h1>
<h4 class="author">Matthijs S. Berends</h4>
<h4 class="date">08 December 2020</h4>
<h4 class="date">21 December 2020</h4>
<small class="dont-index">Source: <a href="https://github.com/msberends/AMR/blob/master/vignettes/SPSS.Rmd"><code>vignettes/SPSS.Rmd</code></a></small>
<div class="hidden name"><code>SPSS.Rmd</code></div>
@ -247,8 +264,8 @@
</li>
</ul>
<p>To demonstrate the first point:</p>
<div class="sourceCode" id="cb1"><pre class="downlit">
<span class="co"># not all values are valid MIC values:</span>
<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="co"># not all values are valid MIC values:</span>
<span class="fu"><a href="../reference/as.mic.html">as.mic</a></span><span class="op">(</span><span class="fl">0.125</span><span class="op">)</span>
<span class="co"># Class &lt;mic&gt;</span>
<span class="co"># [1] 0.125</span>
@ -279,7 +296,7 @@
<span class="co"># [4] "fluclox" "flucloxacilina" "flucloxacillin" </span>
<span class="co"># [7] "flucloxacilline" "flucloxacillinum" "fluorochloroxacillin"</span>
<span class="fu"><a href="../reference/ab_property.html">ab_atc</a></span><span class="op">(</span><span class="st">"floxapen"</span><span class="op">)</span>
<span class="co"># [1] "J01CF05"</span></pre></div>
<span class="co"># [1] "J01CF05"</span></code></pre></div>
</div>
<div id="import-data-from-spsssasstata" class="section level2">
<h2 class="hasAnchor">
@ -295,8 +312,8 @@
<p><img src="https://github.com/msberends/AMR/raw/master/docs/import2.png"></p>
<p>If you want named variables to be imported as factors so it resembles SPSS more, use <code><a href="https://haven.tidyverse.org/reference/as_factor.html">as_factor()</a></code>.</p>
<p>The difference is this:</p>
<div class="sourceCode" id="cb2"><pre class="downlit">
<span class="va">SPSS_data</span>
<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="va">SPSS_data</span>
<span class="co"># # A tibble: 4,203 x 4</span>
<span class="co"># v001 sex status statusage</span>
<span class="co"># &lt;dbl&gt; &lt;dbl+lbl&gt; &lt;dbl+lbl&gt; &lt;dbl&gt;</span>
@ -326,74 +343,74 @@
<span class="co"># 8 10011 Male alive 73.1</span>
<span class="co"># 9 10017 Male alive 56.7</span>
<span class="co"># 10 10018 Female alive 66.6</span>
<span class="co"># # … with 4,193 more rows</span></pre></div>
<span class="co"># # … with 4,193 more rows</span></code></pre></div>
</div>
<div id="base-r" class="section level3">
<h3 class="hasAnchor">
<a href="#base-r" class="anchor"></a>Base R</h3>
<p>To import data from SPSS, SAS or Stata, you can use the <a href="https://haven.tidyverse.org/">great <code>haven</code> package</a> yourself:</p>
<div class="sourceCode" id="cb3"><pre class="downlit">
<span class="co"># download and install the latest version:</span>
<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="co"># download and install the latest version:</span>
<span class="fu"><a href="https://rdrr.io/r/utils/install.packages.html">install.packages</a></span><span class="op">(</span><span class="st">"haven"</span><span class="op">)</span>
<span class="co"># load the package you just installed:</span>
<span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="http://haven.tidyverse.org">haven</a></span><span class="op">)</span> </pre></div>
<span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="http://haven.tidyverse.org">haven</a></span><span class="op">)</span> </code></pre></div>
<p>You can now import files as follows:</p>
<div id="spss" class="section level4">
<h4 class="hasAnchor">
<a href="#spss" class="anchor"></a>SPSS</h4>
<p>To read files from SPSS into R:</p>
<div class="sourceCode" id="cb4"><pre class="downlit">
<span class="co"># read any SPSS file based on file extension (best way):</span>
<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="co"># read any SPSS file based on file extension (best way):</span>
<span class="fu"><a href="https://haven.tidyverse.org/reference/read_spss.html">read_spss</a></span><span class="op">(</span>file <span class="op">=</span> <span class="st">"path/to/file"</span><span class="op">)</span>
<span class="co"># read .sav or .zsav file:</span>
<span class="fu"><a href="https://haven.tidyverse.org/reference/read_spss.html">read_sav</a></span><span class="op">(</span>file <span class="op">=</span> <span class="st">"path/to/file"</span><span class="op">)</span>
<span class="co"># read .por file:</span>
<span class="fu"><a href="https://haven.tidyverse.org/reference/read_spss.html">read_por</a></span><span class="op">(</span>file <span class="op">=</span> <span class="st">"path/to/file"</span><span class="op">)</span></pre></div>
<span class="fu"><a href="https://haven.tidyverse.org/reference/read_spss.html">read_por</a></span><span class="op">(</span>file <span class="op">=</span> <span class="st">"path/to/file"</span><span class="op">)</span></code></pre></div>
<p>Do not forget about <code><a href="https://haven.tidyverse.org/reference/as_factor.html">as_factor()</a></code>, as mentioned above.</p>
<p>To export your R objects to the SPSS file format:</p>
<div class="sourceCode" id="cb5"><pre class="downlit">
<span class="co"># save as .sav file:</span>
<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="co"># save as .sav file:</span>
<span class="fu"><a href="https://haven.tidyverse.org/reference/read_spss.html">write_sav</a></span><span class="op">(</span>data <span class="op">=</span> <span class="va">yourdata</span>, path <span class="op">=</span> <span class="st">"path/to/file"</span><span class="op">)</span>
<span class="co"># save as compressed .zsav file:</span>
<span class="fu"><a href="https://haven.tidyverse.org/reference/read_spss.html">write_sav</a></span><span class="op">(</span>data <span class="op">=</span> <span class="va">yourdata</span>, path <span class="op">=</span> <span class="st">"path/to/file"</span>, compress <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></pre></div>
<span class="fu"><a href="https://haven.tidyverse.org/reference/read_spss.html">write_sav</a></span><span class="op">(</span>data <span class="op">=</span> <span class="va">yourdata</span>, path <span class="op">=</span> <span class="st">"path/to/file"</span>, compress <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></code></pre></div>
</div>
<div id="sas" class="section level4">
<h4 class="hasAnchor">
<a href="#sas" class="anchor"></a>SAS</h4>
<p>To read files from SAS into R:</p>
<div class="sourceCode" id="cb6"><pre class="downlit">
<span class="co"># read .sas7bdat + .sas7bcat files:</span>
<div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="co"># read .sas7bdat + .sas7bcat files:</span>
<span class="fu"><a href="https://haven.tidyverse.org/reference/read_sas.html">read_sas</a></span><span class="op">(</span>data_file <span class="op">=</span> <span class="st">"path/to/file"</span>, catalog_file <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span>
<span class="co"># read SAS transport files (version 5 and version 8):</span>
<span class="fu"><a href="https://haven.tidyverse.org/reference/read_xpt.html">read_xpt</a></span><span class="op">(</span>file <span class="op">=</span> <span class="st">"path/to/file"</span><span class="op">)</span></pre></div>
<span class="fu"><a href="https://haven.tidyverse.org/reference/read_xpt.html">read_xpt</a></span><span class="op">(</span>file <span class="op">=</span> <span class="st">"path/to/file"</span><span class="op">)</span></code></pre></div>
<p>To export your R objects to the SAS file format:</p>
<div class="sourceCode" id="cb7"><pre class="downlit">
<span class="co"># save as regular SAS file:</span>
<div class="sourceCode" id="cb7"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="co"># save as regular SAS file:</span>
<span class="fu"><a href="https://haven.tidyverse.org/reference/read_sas.html">write_sas</a></span><span class="op">(</span>data <span class="op">=</span> <span class="va">yourdata</span>, path <span class="op">=</span> <span class="st">"path/to/file"</span><span class="op">)</span>
<span class="co"># the SAS transport format is an open format </span>
<span class="co"># (required for submission of the data to the FDA)</span>
<span class="fu"><a href="https://haven.tidyverse.org/reference/read_xpt.html">write_xpt</a></span><span class="op">(</span>data <span class="op">=</span> <span class="va">yourdata</span>, path <span class="op">=</span> <span class="st">"path/to/file"</span>, version <span class="op">=</span> <span class="fl">8</span><span class="op">)</span></pre></div>
<span class="fu"><a href="https://haven.tidyverse.org/reference/read_xpt.html">write_xpt</a></span><span class="op">(</span>data <span class="op">=</span> <span class="va">yourdata</span>, path <span class="op">=</span> <span class="st">"path/to/file"</span>, version <span class="op">=</span> <span class="fl">8</span><span class="op">)</span></code></pre></div>
</div>
<div id="stata" class="section level4">
<h4 class="hasAnchor">
<a href="#stata" class="anchor"></a>Stata</h4>
<p>To read files from Stata into R:</p>
<div class="sourceCode" id="cb8"><pre class="downlit">
<span class="co"># read .dta file:</span>
<div class="sourceCode" id="cb8"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="co"># read .dta file:</span>
<span class="fu"><a href="https://haven.tidyverse.org/reference/read_dta.html">read_stata</a></span><span class="op">(</span>file <span class="op">=</span> <span class="st">"/path/to/file"</span><span class="op">)</span>
<span class="co"># works exactly the same:</span>
<span class="fu"><a href="https://haven.tidyverse.org/reference/read_dta.html">read_dta</a></span><span class="op">(</span>file <span class="op">=</span> <span class="st">"/path/to/file"</span><span class="op">)</span></pre></div>
<span class="fu"><a href="https://haven.tidyverse.org/reference/read_dta.html">read_dta</a></span><span class="op">(</span>file <span class="op">=</span> <span class="st">"/path/to/file"</span><span class="op">)</span></code></pre></div>
<p>To export your R objects to the Stata file format:</p>
<div class="sourceCode" id="cb9"><pre class="downlit">
<span class="co"># save as .dta file, Stata version 14:</span>
<div class="sourceCode" id="cb9"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="co"># save as .dta file, Stata version 14:</span>
<span class="co"># (supports Stata v8 until v15 at the time of writing)</span>
<span class="fu"><a href="https://haven.tidyverse.org/reference/read_dta.html">write_dta</a></span><span class="op">(</span>data <span class="op">=</span> <span class="va">yourdata</span>, path <span class="op">=</span> <span class="st">"/path/to/file"</span>, version <span class="op">=</span> <span class="fl">14</span><span class="op">)</span></pre></div>
<span class="fu"><a href="https://haven.tidyverse.org/reference/read_dta.html">write_dta</a></span><span class="op">(</span>data <span class="op">=</span> <span class="va">yourdata</span>, path <span class="op">=</span> <span class="st">"/path/to/file"</span>, version <span class="op">=</span> <span class="fl">14</span><span class="op">)</span></code></pre></div>
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@ -414,7 +431,7 @@
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@ -187,7 +204,7 @@
</header><script src="WHONET_files/header-attrs-2.4/header-attrs.js"></script><script src="WHONET_files/accessible-code-block-0.0.1/empty-anchor.js"></script><div class="row">
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<h1 data-toc-skip>How to work with WHONET data</h1>
@ -205,38 +222,38 @@
<a href="#import-of-data" class="anchor"></a>Import of data</h3>
<p>This tutorial assumes you already imported the WHONET data with e.g. the <a href="https://readxl.tidyverse.org/"><code>readxl</code> package</a>. In RStudio, this can be done using the menu button Import Dataset in the tab Environment. Choose the option From Excel and select your exported file. Make sure date fields are imported correctly.</p>
<p>An example syntax could look like this:</p>
<div class="sourceCode" id="cb1"><pre class="downlit">
<span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://readxl.tidyverse.org">readxl</a></span><span class="op">)</span>
<span class="va">data</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://readxl.tidyverse.org/reference/read_excel.html">read_excel</a></span><span class="op">(</span>path <span class="op">=</span> <span class="st">"path/to/your/file.xlsx"</span><span class="op">)</span></pre></div>
<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://readxl.tidyverse.org">readxl</a></span><span class="op">)</span>
<span class="va">data</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://readxl.tidyverse.org/reference/read_excel.html">read_excel</a></span><span class="op">(</span>path <span class="op">=</span> <span class="st">"path/to/your/file.xlsx"</span><span class="op">)</span></code></pre></div>
<p>This package comes with an <a href="https://msberends.github.io/AMR/reference/WHONET.html">example data set <code>WHONET</code></a>. We will use it for this analysis.</p>
</div>
<div id="preparation" class="section level3">
<h3 class="hasAnchor">
<a href="#preparation" class="anchor"></a>Preparation</h3>
<p>First, load the relevant packages if you did not yet did this. I use the tidyverse for all of my analyses. All of them. If you dont know it yet, I suggest you read about it on their website: <a href="https://www.tidyverse.org/" class="uri">https://www.tidyverse.org/</a>.</p>
<div class="sourceCode" id="cb2"><pre class="downlit">
<span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://dplyr.tidyverse.org">dplyr</a></span><span class="op">)</span> <span class="co"># part of tidyverse</span>
<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://dplyr.tidyverse.org">dplyr</a></span><span class="op">)</span> <span class="co"># part of tidyverse</span>
<span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="http://ggplot2.tidyverse.org">ggplot2</a></span><span class="op">)</span> <span class="co"># part of tidyverse</span>
<span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://msberends.github.io/AMR/">AMR</a></span><span class="op">)</span> <span class="co"># this package</span>
<span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://github.com/msberends/cleaner">cleaner</a></span><span class="op">)</span> <span class="co"># to create frequency tables</span></pre></div>
<span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://github.com/msberends/cleaner">cleaner</a></span><span class="op">)</span> <span class="co"># to create frequency tables</span></code></pre></div>
<p>We will have to transform some variables to simplify and automate the analysis:</p>
<ul>
<li>Microorganisms should be transformed to our own microorganism IDs (called an <code>mo</code>) using <a href="https://msberends.github.io/AMR/reference/catalogue_of_life">our Catalogue of Life reference data set</a>, which contains all ~70,000 microorganisms from the taxonomic kingdoms Bacteria, Fungi and Protozoa. We do the tranformation with <code><a href="../reference/as.mo.html">as.mo()</a></code>. This function also recognises almost all WHONET abbreviations of microorganisms.</li>
<li>Antimicrobial results or interpretations have to be clean and valid. In other words, they should only contain values <code>"S"</code>, <code>"I"</code> or <code>"R"</code>. That is exactly where the <code><a href="../reference/as.rsi.html">as.rsi()</a></code> function is for.</li>
</ul>
<div class="sourceCode" id="cb3"><pre class="downlit">
<span class="co"># transform variables</span>
<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="co"># transform variables</span>
<span class="va">data</span> <span class="op">&lt;-</span> <span class="va">WHONET</span> <span class="op">%&gt;%</span>
<span class="co"># get microbial ID based on given organism</span>
<span class="fu"><a href="https://dplyr.tidyverse.org/reference/mutate.html">mutate</a></span><span class="op">(</span>mo <span class="op">=</span> <span class="fu"><a href="../reference/as.mo.html">as.mo</a></span><span class="op">(</span><span class="va">Organism</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span>
<span class="co"># transform everything from "AMP_ND10" to "CIP_EE" to the new `rsi` class</span>
<span class="fu"><a href="https://dplyr.tidyverse.org/reference/mutate_all.html">mutate_at</a></span><span class="op">(</span><span class="fu"><a href="https://dplyr.tidyverse.org/reference/vars.html">vars</a></span><span class="op">(</span><span class="va">AMP_ND10</span><span class="op">:</span><span class="va">CIP_EE</span><span class="op">)</span>, <span class="va">as.rsi</span><span class="op">)</span></pre></div>
<span class="fu"><a href="https://dplyr.tidyverse.org/reference/mutate_all.html">mutate_at</a></span><span class="op">(</span><span class="fu"><a href="https://dplyr.tidyverse.org/reference/vars.html">vars</a></span><span class="op">(</span><span class="va">AMP_ND10</span><span class="op">:</span><span class="va">CIP_EE</span><span class="op">)</span>, <span class="va">as.rsi</span><span class="op">)</span></code></pre></div>
<p>No errors or warnings, so all values are transformed succesfully.</p>
<p>We also created a package dedicated to data cleaning and checking, called the <code>cleaner</code> package. Its <code><a href="https://rdrr.io/pkg/cleaner/man/freq.html">freq()</a></code> function can be used to create frequency tables.</p>
<p>So lets check our data, with a couple of frequency tables:</p>
<div class="sourceCode" id="cb4"><pre class="downlit">
<span class="co"># our newly created `mo` variable, put in the mo_name() function</span>
<span class="va">data</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/pkg/cleaner/man/freq.html">freq</a></span><span class="op">(</span><span class="fu"><a href="../reference/mo_property.html">mo_name</a></span><span class="op">(</span><span class="va">mo</span><span class="op">)</span>, nmax <span class="op">=</span> <span class="fl">10</span><span class="op">)</span></pre></div>
<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="co"># our newly created `mo` variable, put in the mo_name() function</span>
<span class="va">data</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/pkg/cleaner/man/freq.html">freq</a></span><span class="op">(</span><span class="fu"><a href="../reference/mo_property.html">mo_name</a></span><span class="op">(</span><span class="va">mo</span><span class="op">)</span>, nmax <span class="op">=</span> <span class="fl">10</span><span class="op">)</span></code></pre></div>
<p><strong>Frequency table</strong></p>
<p>Class: character<br>
Length: 500<br>
@ -337,10 +354,10 @@ Longest: 40</p>
</tbody>
</table>
<p>(omitted 27 entries, n = 56 [11.20%])</p>
<div class="sourceCode" id="cb5"><pre class="downlit">
<span class="co"># our transformed antibiotic columns</span>
<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="co"># our transformed antibiotic columns</span>
<span class="co"># amoxicillin/clavulanic acid (J01CR02) as an example</span>
<span class="va">data</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/pkg/cleaner/man/freq.html">freq</a></span><span class="op">(</span><span class="va">AMC_ND2</span><span class="op">)</span></pre></div>
<span class="va">data</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/pkg/cleaner/man/freq.html">freq</a></span><span class="op">(</span><span class="va">AMC_ND2</span><span class="op">)</span></code></pre></div>
<p><strong>Frequency table</strong></p>
<p>Class: factor &gt; ordered &gt; rsi (numeric)<br>
Length: 500<br>
@ -391,11 +408,11 @@ Drug group: Beta-lactams/penicillins<br>
<h3 class="hasAnchor">
<a href="#a-first-glimpse-at-results" class="anchor"></a>A first glimpse at results</h3>
<p>An easy <code>ggplot</code> will already give a lot of information, using the included <code><a href="../reference/ggplot_rsi.html">ggplot_rsi()</a></code> function:</p>
<div class="sourceCode" id="cb6"><pre class="downlit">
<span class="va">data</span> <span class="op">%&gt;%</span>
<div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="va">data</span> <span class="op">%&gt;%</span>
<span class="fu"><a href="https://dplyr.tidyverse.org/reference/group_by.html">group_by</a></span><span class="op">(</span><span class="va">Country</span><span class="op">)</span> <span class="op">%&gt;%</span>
<span class="fu"><a href="https://dplyr.tidyverse.org/reference/select.html">select</a></span><span class="op">(</span><span class="va">Country</span>, <span class="va">AMP_ND2</span>, <span class="va">AMC_ED20</span>, <span class="va">CAZ_ED10</span>, <span class="va">CIP_ED5</span><span class="op">)</span> <span class="op">%&gt;%</span>
<span class="fu"><a href="../reference/ggplot_rsi.html">ggplot_rsi</a></span><span class="op">(</span>translate_ab <span class="op">=</span> <span class="st">'ab'</span>, facet <span class="op">=</span> <span class="st">"Country"</span>, datalabels <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></pre></div>
<span class="fu"><a href="../reference/ggplot_rsi.html">ggplot_rsi</a></span><span class="op">(</span>translate_ab <span class="op">=</span> <span class="st">'ab'</span>, facet <span class="op">=</span> <span class="st">"Country"</span>, datalabels <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></code></pre></div>
<p><img src="WHONET_files/figure-html/unnamed-chunk-7-1.png" width="720"></p>
</div>
</div>
@ -413,7 +430,7 @@ Drug group: Beta-lactams/penicillins<br>
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@ -39,7 +56,7 @@
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@ -63,77 +80,77 @@
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</header><script src="benchmarks_files/header-attrs-2.4/header-attrs.js"></script><script src="benchmarks_files/accessible-code-block-0.0.1/empty-anchor.js"></script><div class="row">
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<h1 data-toc-skip>Benchmarks</h1>
@ -202,15 +219,15 @@
<p>One of the most important features of this package is the complete microbial taxonomic database, supplied by the <a href="http://catalogueoflife.org">Catalogue of Life</a>. We created a function <code><a href="../reference/as.mo.html">as.mo()</a></code> that transforms any user input value to a valid microbial ID by using intelligent rules combined with the taxonomic tree of Catalogue of Life.</p>
<p>Using the <code>microbenchmark</code> package, we can review the calculation performance of this function. Its function <code>microbenchmark()</code> runs different input expressions independently of each other and measures their time-to-result.</p>
<div class="sourceCode" id="cb1"><pre class="downlit">
<span class="va">microbenchmark</span> <span class="op">&lt;-</span> <span class="fu">microbenchmark</span><span class="fu">::</span><span class="va"><a href="https://rdrr.io/pkg/microbenchmark/man/microbenchmark.html">microbenchmark</a></span>
<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="va">microbenchmark</span> <span class="op">&lt;-</span> <span class="fu">microbenchmark</span><span class="fu">::</span><span class="va"><a href="https://rdrr.io/pkg/microbenchmark/man/microbenchmark.html">microbenchmark</a></span>
<span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://msberends.github.io/AMR/">AMR</a></span><span class="op">)</span>
<span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://dplyr.tidyverse.org">dplyr</a></span><span class="op">)</span></pre></div>
<span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://dplyr.tidyverse.org">dplyr</a></span><span class="op">)</span></code></pre></div>
<p>In the next test, we try to coerce different input values into the microbial code of <em>Staphylococcus aureus</em>. Coercion is a computational process of forcing output based on an input. For microorganism names, coercing user input to taxonomically valid microorganism names is crucial to ensure correct interpretation and to enable grouping based on taxonomic properties.</p>
<p>The actual result is the same every time: it returns its microorganism code <code>B_STPHY_AURS</code> (<em>B</em> stands for <em>Bacteria</em>, the taxonomic kingdom).</p>
<p>But the calculation time differs a lot:</p>
<div class="sourceCode" id="cb2"><pre class="downlit">
<span class="va">S.aureus</span> <span class="op">&lt;-</span> <span class="fu">microbenchmark</span><span class="op">(</span>
<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="va">S.aureus</span> <span class="op">&lt;-</span> <span class="fu">microbenchmark</span><span class="op">(</span>
<span class="fu"><a href="../reference/as.mo.html">as.mo</a></span><span class="op">(</span><span class="st">"sau"</span><span class="op">)</span>, <span class="co"># WHONET code</span>
<span class="fu"><a href="../reference/as.mo.html">as.mo</a></span><span class="op">(</span><span class="st">"stau"</span><span class="op">)</span>,
<span class="fu"><a href="../reference/as.mo.html">as.mo</a></span><span class="op">(</span><span class="st">"STAU"</span><span class="op">)</span>,
@ -227,20 +244,20 @@
times <span class="op">=</span> <span class="fl">10</span><span class="op">)</span>
<span class="fu"><a href="https://docs.ropensci.org/skimr/reference/print.html">print</a></span><span class="op">(</span><span class="va">S.aureus</span>, unit <span class="op">=</span> <span class="st">"ms"</span>, signif <span class="op">=</span> <span class="fl">2</span><span class="op">)</span>
<span class="co"># Unit: milliseconds</span>
<span class="co"># expr min lq mean median uq max</span>
<span class="co"># as.mo("sau") 13.0 13.0 17.0 15.0 16.0 43.0</span>
<span class="co"># as.mo("stau") 120.0 120.0 140.0 150.0 150.0 160.0</span>
<span class="co"># as.mo("STAU") 120.0 150.0 150.0 150.0 160.0 160.0</span>
<span class="co"># as.mo("staaur") 13.0 13.0 17.0 14.0 16.0 44.0</span>
<span class="co"># as.mo("STAAUR") 13.0 14.0 19.0 16.0 16.0 48.0</span>
<span class="co"># as.mo("S. aureus") 34.0 58.0 61.0 63.0 68.0 86.0</span>
<span class="co"># as.mo("S aureus") 33.0 34.0 46.0 36.0 63.0 63.0</span>
<span class="co"># as.mo("Staphylococcus aureus") 2.3 2.5 2.5 2.5 2.6 2.7</span>
<span class="co"># as.mo("Staphylococcus aureus (MRSA)") 980.0 980.0 1000.0 1000.0 1000.0 1100.0</span>
<span class="co"># as.mo("Sthafilokkockus aaureuz") 430.0 430.0 440.0 440.0 440.0 460.0</span>
<span class="co"># as.mo("MRSA") 13.0 14.0 18.0 16.0 17.0 43.0</span>
<span class="co"># as.mo("VISA") 21.0 22.0 23.0 22.0 24.0 25.0</span>
<span class="co"># as.mo("VRSA") 22.0 23.0 35.0 27.0 52.0 55.0</span>
<span class="co"># expr min lq mean median uq max</span>
<span class="co"># as.mo("sau") 10.0 11.0 20.0 13.0 38.0 41.0</span>
<span class="co"># as.mo("stau") 100.0 110.0 120.0 110.0 140.0 140.0</span>
<span class="co"># as.mo("STAU") 100.0 110.0 130.0 140.0 140.0 160.0</span>
<span class="co"># as.mo("staaur") 10.0 12.0 23.0 14.0 38.0 58.0</span>
<span class="co"># as.mo("STAAUR") 10.0 11.0 18.0 13.0 14.0 42.0</span>
<span class="co"># as.mo("S. aureus") 26.0 30.0 46.0 44.0 58.0 78.0</span>
<span class="co"># as.mo("S aureus") 26.0 27.0 32.0 31.0 31.0 56.0</span>
<span class="co"># as.mo("Staphylococcus aureus") 2.0 2.4 2.6 2.7 2.8 3.1</span>
<span class="co"># as.mo("Staphylococcus aureus (MRSA)") 890.0 910.0 920.0 920.0 930.0 960.0</span>
<span class="co"># as.mo("Sthafilokkockus aaureuz") 380.0 390.0 400.0 390.0 400.0 430.0</span>
<span class="co"># as.mo("MRSA") 9.9 11.0 16.0 14.0 14.0 41.0</span>
<span class="co"># as.mo("VISA") 17.0 19.0 28.0 21.0 45.0 52.0</span>
<span class="co"># as.mo("VRSA") 17.0 19.0 26.0 21.0 22.0 53.0</span>
<span class="co"># neval</span>
<span class="co"># 10</span>
<span class="co"># 10</span>
@ -254,7 +271,7 @@
<span class="co"># 10</span>
<span class="co"># 10</span>
<span class="co"># 10</span>
<span class="co"># 10</span></pre></div>
<span class="co"># 10</span></code></pre></div>
<p><img src="benchmarks_files/figure-html/unnamed-chunk-4-1.png" width="562.5"></p>
<p>In the table above, all measurements are shown in milliseconds (thousands of seconds). A value of 5 milliseconds means it can determine 200 input values per second. It case of 100 milliseconds, this is only 10 input values per second. It is clear that accepted taxonomic names are extremely fast, but some variations can take up to 500-1000 times as much time.</p>
<p>To improve performance, two important calculations take almost no time at all: <strong>repetitive results</strong> and <strong>already precalculated results</strong>.</p>
@ -262,8 +279,8 @@
<h3 class="hasAnchor">
<a href="#repetitive-results" class="anchor"></a>Repetitive results</h3>
<p>Repetitive results are unique values that are present more than once. Unique values will only be calculated once by <code><a href="../reference/as.mo.html">as.mo()</a></code>. We will use <code><a href="../reference/mo_property.html">mo_name()</a></code> for this test - a helper function that returns the full microbial name (genus, species and possibly subspecies) which uses <code><a href="../reference/as.mo.html">as.mo()</a></code> internally.</p>
<div class="sourceCode" id="cb3"><pre class="downlit">
<span class="co"># take all MO codes from the example_isolates data set</span>
<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="co"># take all MO codes from the example_isolates data set</span>
<span class="va">x</span> <span class="op">&lt;-</span> <span class="va">example_isolates</span><span class="op">$</span><span class="va">mo</span> <span class="op">%&gt;%</span>
<span class="co"># and copy them a thousand times</span>
<span class="fu"><a href="https://rdrr.io/r/base/rep.html">rep</a></span><span class="op">(</span><span class="fl">1000</span><span class="op">)</span> <span class="op">%&gt;%</span>
@ -284,27 +301,27 @@
<span class="fu"><a href="https://docs.ropensci.org/skimr/reference/print.html">print</a></span><span class="op">(</span><span class="va">run_it</span>, unit <span class="op">=</span> <span class="st">"ms"</span>, signif <span class="op">=</span> <span class="fl">3</span><span class="op">)</span>
<span class="co"># Unit: milliseconds</span>
<span class="co"># expr min lq mean median uq max neval</span>
<span class="co"># mo_name(x) 145 190 221 213 248 313 10</span></pre></div>
<p>So getting official taxonomic names of 2,000,000 (!!) items consisting of 90 unique values only takes 0.213 seconds. You only lose time on your unique input values.</p>
<span class="co"># mo_name(x) 128 167 205 195 232 298 10</span></code></pre></div>
<p>So getting official taxonomic names of 2,000,000 (!!) items consisting of 90 unique values only takes 0.195 seconds. You only lose time on your unique input values.</p>
</div>
<div id="precalculated-results" class="section level3">
<h3 class="hasAnchor">
<a href="#precalculated-results" class="anchor"></a>Precalculated results</h3>
<p>What about precalculated results? If the input is an already precalculated result of a helper function like <code><a href="../reference/mo_property.html">mo_name()</a></code>, it almost doesnt take any time at all (see C below):</p>
<div class="sourceCode" id="cb4"><pre class="downlit">
<span class="va">run_it</span> <span class="op">&lt;-</span> <span class="fu">microbenchmark</span><span class="op">(</span>A <span class="op">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_name</a></span><span class="op">(</span><span class="st">"STAAUR"</span><span class="op">)</span>,
<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="va">run_it</span> <span class="op">&lt;-</span> <span class="fu">microbenchmark</span><span class="op">(</span>A <span class="op">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_name</a></span><span class="op">(</span><span class="st">"STAAUR"</span><span class="op">)</span>,
B <span class="op">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_name</a></span><span class="op">(</span><span class="st">"S. aureus"</span><span class="op">)</span>,
C <span class="op">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_name</a></span><span class="op">(</span><span class="st">"Staphylococcus aureus"</span><span class="op">)</span>,
times <span class="op">=</span> <span class="fl">10</span><span class="op">)</span>
<span class="fu"><a href="https://docs.ropensci.org/skimr/reference/print.html">print</a></span><span class="op">(</span><span class="va">run_it</span>, unit <span class="op">=</span> <span class="st">"ms"</span>, signif <span class="op">=</span> <span class="fl">3</span><span class="op">)</span>
<span class="co"># Unit: milliseconds</span>
<span class="co"># expr min lq mean median uq max neval</span>
<span class="co"># A 8.36 8.59 8.99 9.07 9.29 9.62 10</span>
<span class="co"># B 26.80 27.50 28.10 28.00 29.10 29.80 10</span>
<span class="co"># C 2.06 2.15 6.68 2.50 2.69 45.20 10</span></pre></div>
<p>So going from <code><a href="../reference/mo_property.html">mo_name("Staphylococcus aureus")</a></code> to <code>"Staphylococcus aureus"</code> takes 0.0025 seconds - it doesnt even start calculating <em>if the result would be the same as the expected resulting value</em>. That goes for all helper functions:</p>
<div class="sourceCode" id="cb5"><pre class="downlit">
<span class="va">run_it</span> <span class="op">&lt;-</span> <span class="fu">microbenchmark</span><span class="op">(</span>A <span class="op">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_species</a></span><span class="op">(</span><span class="st">"aureus"</span><span class="op">)</span>,
<span class="co"># A 7.35 7.70 8.15 8.11 8.68 8.96 10</span>
<span class="co"># B 22.50 22.80 28.10 24.30 25.70 62.60 10</span>
<span class="co"># C 1.84 1.98 2.13 2.13 2.17 2.43 10</span></code></pre></div>
<p>So going from <code><a href="../reference/mo_property.html">mo_name("Staphylococcus aureus")</a></code> to <code>"Staphylococcus aureus"</code> takes 0.0021 seconds - it doesnt even start calculating <em>if the result would be the same as the expected resulting value</em>. That goes for all helper functions:</p>
<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="va">run_it</span> <span class="op">&lt;-</span> <span class="fu">microbenchmark</span><span class="op">(</span>A <span class="op">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_species</a></span><span class="op">(</span><span class="st">"aureus"</span><span class="op">)</span>,
B <span class="op">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_genus</a></span><span class="op">(</span><span class="st">"Staphylococcus"</span><span class="op">)</span>,
C <span class="op">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_name</a></span><span class="op">(</span><span class="st">"Staphylococcus aureus"</span><span class="op">)</span>,
D <span class="op">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_family</a></span><span class="op">(</span><span class="st">"Staphylococcaceae"</span><span class="op">)</span>,
@ -316,22 +333,22 @@
<span class="fu"><a href="https://docs.ropensci.org/skimr/reference/print.html">print</a></span><span class="op">(</span><span class="va">run_it</span>, unit <span class="op">=</span> <span class="st">"ms"</span>, signif <span class="op">=</span> <span class="fl">3</span><span class="op">)</span>
<span class="co"># Unit: milliseconds</span>
<span class="co"># expr min lq mean median uq max neval</span>
<span class="co"># A 1.68 1.91 2.13 2.08 2.43 2.62 10</span>
<span class="co"># B 1.89 1.99 2.16 2.05 2.30 2.69 10</span>
<span class="co"># C 1.87 1.91 2.09 2.00 2.28 2.46 10</span>
<span class="co"># D 1.65 1.71 1.92 1.87 1.98 2.58 10</span>
<span class="co"># E 1.64 1.74 1.90 1.87 1.98 2.34 10</span>
<span class="co"># F 1.86 1.92 2.05 2.05 2.16 2.27 10</span>
<span class="co"># G 1.73 1.80 1.99 1.97 2.14 2.29 10</span>
<span class="co"># H 1.85 1.93 2.08 1.98 2.15 2.93 10</span></pre></div>
<span class="co"># A 1.60 1.63 1.74 1.70 1.83 2.02 10</span>
<span class="co"># B 1.37 1.40 1.62 1.54 1.74 2.07 10</span>
<span class="co"># C 1.39 1.45 1.63 1.65 1.77 1.86 10</span>
<span class="co"># D 1.37 1.40 1.58 1.51 1.62 2.27 10</span>
<span class="co"># E 1.44 1.64 1.70 1.66 1.81 2.13 10</span>
<span class="co"># F 1.38 1.42 1.57 1.58 1.67 1.81 10</span>
<span class="co"># G 1.38 1.62 1.64 1.63 1.70 1.82 10</span>
<span class="co"># H 1.44 1.65 1.84 1.82 1.99 2.60 10</span></code></pre></div>
<p>Of course, when running <code><a href="../reference/mo_property.html">mo_phylum("Firmicutes")</a></code> the function has zero knowledge about the actual microorganism, namely <em>S. aureus</em>. But since the result would be <code>"Firmicutes"</code> anyway, there is no point in calculating the result. And because this package knows all phyla of all known bacteria (according to the Catalogue of Life), it can just return the initial value immediately.</p>
</div>
<div id="results-in-other-languages" class="section level3">
<h3 class="hasAnchor">
<a href="#results-in-other-languages" class="anchor"></a>Results in other languages</h3>
<p>When the system language is non-English and supported by this <code>AMR</code> package, some functions will have a translated result. This almost doest take extra time:</p>
<div class="sourceCode" id="cb6"><pre class="downlit">
<span class="fu"><a href="../reference/mo_property.html">mo_name</a></span><span class="op">(</span><span class="st">"CoNS"</span>, language <span class="op">=</span> <span class="st">"en"</span><span class="op">)</span> <span class="co"># or just mo_name("CoNS") on an English system</span>
<div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="fu"><a href="../reference/mo_property.html">mo_name</a></span><span class="op">(</span><span class="st">"CoNS"</span>, language <span class="op">=</span> <span class="st">"en"</span><span class="op">)</span> <span class="co"># or just mo_name("CoNS") on an English system</span>
<span class="co"># [1] "Coagulase-negative Staphylococcus (CoNS)"</span>
<span class="fu"><a href="../reference/mo_property.html">mo_name</a></span><span class="op">(</span><span class="st">"CoNS"</span>, language <span class="op">=</span> <span class="st">"es"</span><span class="op">)</span> <span class="co"># or just mo_name("CoNS") on a Spanish system</span>
@ -351,13 +368,13 @@
<span class="fu"><a href="https://docs.ropensci.org/skimr/reference/print.html">print</a></span><span class="op">(</span><span class="va">run_it</span>, unit <span class="op">=</span> <span class="st">"ms"</span>, signif <span class="op">=</span> <span class="fl">4</span><span class="op">)</span>
<span class="co"># Unit: milliseconds</span>
<span class="co"># expr min lq mean median uq max neval</span>
<span class="co"># en 16.16 17.85 22.42 18.54 19.61 68.28 100</span>
<span class="co"># de 19.55 21.38 24.37 21.96 22.95 61.55 100</span>
<span class="co"># nl 31.64 35.08 40.58 36.34 37.88 79.00 100</span>
<span class="co"># es 18.03 21.46 25.61 22.09 23.30 64.97 100</span>
<span class="co"># it 17.82 21.01 23.29 21.80 22.82 58.47 100</span>
<span class="co"># fr 18.19 21.29 25.63 21.93 23.20 60.48 100</span>
<span class="co"># pt 17.76 21.07 24.81 21.83 23.39 65.00 100</span></pre></div>
<span class="co"># en 15.77 16.36 18.40 16.85 17.50 53.58 100</span>
<span class="co"># de 19.03 19.70 24.60 20.14 21.06 59.83 100</span>
<span class="co"># nl 30.93 32.03 36.58 32.80 33.78 85.59 100</span>
<span class="co"># es 18.78 19.62 24.49 19.98 20.60 58.73 100</span>
<span class="co"># it 18.80 19.50 22.68 19.92 20.89 63.99 100</span>
<span class="co"># fr 18.78 19.59 21.99 19.90 20.75 58.87 100</span>
<span class="co"># pt 18.79 19.65 23.04 20.08 20.82 59.30 100</span></code></pre></div>
<p>Currently supported are German, Dutch, Spanish, Italian, French and Portuguese.</p>
</div>
</div>
@ -375,7 +392,7 @@
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<h1 data-toc-skip>How to predict antimicrobial resistance</h1>
@ -205,37 +222,37 @@
<a href="#needed-r-packages" class="anchor"></a>Needed R packages</h2>
<p>As with many uses in R, we need some additional packages for AMR analysis. Our package works closely together with the <a href="https://www.tidyverse.org">tidyverse packages</a> <a href="https://dplyr.tidyverse.org/"><code>dplyr</code></a> and <a href="https://ggplot2.tidyverse.org"><code>ggplot2</code></a> by Dr Hadley Wickham. The tidyverse tremendously improves the way we conduct data science - it allows for a very natural way of writing syntaxes and creating beautiful plots in R.</p>
<p>Our <code>AMR</code> package depends on these packages and even extends their use and functions.</p>
<div class="sourceCode" id="cb1"><pre class="downlit">
<span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://dplyr.tidyverse.org">dplyr</a></span><span class="op">)</span>
<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://dplyr.tidyverse.org">dplyr</a></span><span class="op">)</span>
<span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="http://ggplot2.tidyverse.org">ggplot2</a></span><span class="op">)</span>
<span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://msberends.github.io/AMR/">AMR</a></span><span class="op">)</span>
<span class="co"># (if not yet installed, install with:)</span>
<span class="co"># install.packages(c("tidyverse", "AMR"))</span></pre></div>
<span class="co"># install.packages(c("tidyverse", "AMR"))</span></code></pre></div>
</div>
<div id="prediction-analysis" class="section level2">
<h2 class="hasAnchor">
<a href="#prediction-analysis" class="anchor"></a>Prediction analysis</h2>
<p>Our package contains a function <code><a href="../reference/resistance_predict.html">resistance_predict()</a></code>, which takes the same input as functions for <a href="./AMR.html">other AMR analysis</a>. Based on a date column, it calculates cases per year and uses a regression model to predict antimicrobial resistance.</p>
<p>It is basically as easy as:</p>
<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1"></a><span class="co"># resistance prediction of piperacillin/tazobactam (TZP):</span></span>
<span id="cb2-2"><a href="#cb2-2"></a><span class="kw">resistance_predict</span>(<span class="dt">tbl =</span> example_isolates, <span class="dt">col_date =</span> <span class="st">"date"</span>, <span class="dt">col_ab =</span> <span class="st">"TZP"</span>, <span class="dt">model =</span> <span class="st">"binomial"</span>)</span>
<span id="cb2-3"><a href="#cb2-3"></a></span>
<span id="cb2-4"><a href="#cb2-4"></a><span class="co"># or:</span></span>
<span id="cb2-5"><a href="#cb2-5"></a>example_isolates <span class="op">%&gt;%</span><span class="st"> </span></span>
<span id="cb2-6"><a href="#cb2-6"></a><span class="st"> </span><span class="kw">resistance_predict</span>(<span class="dt">col_ab =</span> <span class="st">"TZP"</span>,</span>
<span id="cb2-7"><a href="#cb2-7"></a> model <span class="st">"binomial"</span>)</span>
<span id="cb2-8"><a href="#cb2-8"></a></span>
<span id="cb2-9"><a href="#cb2-9"></a><span class="co"># to bind it to object 'predict_TZP' for example:</span></span>
<span id="cb2-10"><a href="#cb2-10"></a>predict_TZP &lt;-<span class="st"> </span>example_isolates <span class="op">%&gt;%</span><span class="st"> </span></span>
<span id="cb2-11"><a href="#cb2-11"></a><span class="st"> </span><span class="kw">resistance_predict</span>(<span class="dt">col_ab =</span> <span class="st">"TZP"</span>,</span>
<span id="cb2-12"><a href="#cb2-12"></a> <span class="dt">model =</span> <span class="st">"binomial"</span>)</span></code></pre></div>
<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a><span class="co"># resistance prediction of piperacillin/tazobactam (TZP):</span></span>
<span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a><span class="fu">resistance_predict</span>(<span class="at">tbl =</span> example_isolates, <span class="at">col_date =</span> <span class="st">"date"</span>, <span class="at">col_ab =</span> <span class="st">"TZP"</span>, <span class="at">model =</span> <span class="st">"binomial"</span>)</span>
<span id="cb2-3"><a href="#cb2-3" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb2-4"><a href="#cb2-4" aria-hidden="true" tabindex="-1"></a><span class="co"># or:</span></span>
<span id="cb2-5"><a href="#cb2-5" aria-hidden="true" tabindex="-1"></a>example_isolates <span class="sc">%&gt;%</span> </span>
<span id="cb2-6"><a href="#cb2-6" aria-hidden="true" tabindex="-1"></a> <span class="fu">resistance_predict</span>(<span class="at">col_ab =</span> <span class="st">"TZP"</span>,</span>
<span id="cb2-7"><a href="#cb2-7" aria-hidden="true" tabindex="-1"></a> model <span class="st">"binomial"</span>)</span>
<span id="cb2-8"><a href="#cb2-8" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb2-9"><a href="#cb2-9" aria-hidden="true" tabindex="-1"></a><span class="co"># to bind it to object 'predict_TZP' for example:</span></span>
<span id="cb2-10"><a href="#cb2-10" aria-hidden="true" tabindex="-1"></a>predict_TZP <span class="ot">&lt;-</span> example_isolates <span class="sc">%&gt;%</span> </span>
<span id="cb2-11"><a href="#cb2-11" aria-hidden="true" tabindex="-1"></a> <span class="fu">resistance_predict</span>(<span class="at">col_ab =</span> <span class="st">"TZP"</span>,</span>
<span id="cb2-12"><a href="#cb2-12" aria-hidden="true" tabindex="-1"></a> <span class="at">model =</span> <span class="st">"binomial"</span>)</span></code></pre></div>
<p>The function will look for a date column itself if <code>col_date</code> is not set.</p>
<p>When running any of these commands, a summary of the regression model will be printed unless using <code><a href="../reference/resistance_predict.html">resistance_predict(..., info = FALSE)</a></code>.</p>
<pre><code># NOTE: Using column 'date' as input for `col_date`.</code></pre>
<p>This text is only a printed summary - the actual result (output) of the function is a <code>data.frame</code> containing for each year: the number of observations, the actual observed resistance, the estimated resistance and the standard error below and above the estimation:</p>
<div class="sourceCode" id="cb4"><pre class="downlit">
<span class="va">predict_TZP</span>
<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="va">predict_TZP</span>
<span class="co"># year value se_min se_max observations observed estimated</span>
<span class="co"># 1 2002 0.20000000 NA NA 15 0.20000000 0.05616378</span>
<span class="co"># 2 2003 0.06250000 NA NA 32 0.06250000 0.06163839</span>
@ -265,31 +282,31 @@
<span class="co"># 26 2027 0.41315710 0.3244399 0.5018743 NA NA 0.41315710</span>
<span class="co"># 27 2028 0.43730688 0.3418075 0.5328063 NA NA 0.43730688</span>
<span class="co"># 28 2029 0.46175755 0.3597639 0.5637512 NA NA 0.46175755</span>
<span class="co"># 29 2030 0.48639359 0.3782932 0.5944939 NA NA 0.48639359</span></pre></div>
<span class="co"># 29 2030 0.48639359 0.3782932 0.5944939 NA NA 0.48639359</span></code></pre></div>
<p>The function <code>plot</code> is available in base R, and can be extended by other packages to depend the output based on the type of input. We extended its function to cope with resistance predictions:</p>
<div class="sourceCode" id="cb5"><pre class="downlit">
<span class="fu"><a href="../reference/plot.html">plot</a></span><span class="op">(</span><span class="va">predict_TZP</span><span class="op">)</span></pre></div>
<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="fu"><a href="../reference/plot.html">plot</a></span><span class="op">(</span><span class="va">predict_TZP</span><span class="op">)</span></code></pre></div>
<p><img src="resistance_predict_files/figure-html/unnamed-chunk-4-1.png" width="720"></p>
<p>This is the fastest way to plot the result. It automatically adds the right axes, error bars, titles, number of available observations and type of model.</p>
<p>We also support the <code>ggplot2</code> package with our custom function <code><a href="../reference/resistance_predict.html">ggplot_rsi_predict()</a></code> to create more appealing plots:</p>
<div class="sourceCode" id="cb6"><pre class="downlit">
<span class="fu"><a href="../reference/resistance_predict.html">ggplot_rsi_predict</a></span><span class="op">(</span><span class="va">predict_TZP</span><span class="op">)</span></pre></div>
<div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="fu"><a href="../reference/resistance_predict.html">ggplot_rsi_predict</a></span><span class="op">(</span><span class="va">predict_TZP</span><span class="op">)</span></code></pre></div>
<p><img src="resistance_predict_files/figure-html/unnamed-chunk-5-1.png" width="720"></p>
<div class="sourceCode" id="cb7"><pre class="downlit">
<div class="sourceCode" id="cb7"><pre class="downlit sourceCode r">
<code class="sourceCode R">
<span class="co"># choose for error bars instead of a ribbon</span>
<span class="fu"><a href="../reference/resistance_predict.html">ggplot_rsi_predict</a></span><span class="op">(</span><span class="va">predict_TZP</span>, ribbon <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></pre></div>
<span class="fu"><a href="../reference/resistance_predict.html">ggplot_rsi_predict</a></span><span class="op">(</span><span class="va">predict_TZP</span>, ribbon <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></code></pre></div>
<p><img src="resistance_predict_files/figure-html/unnamed-chunk-5-2.png" width="720"></p>
<div id="choosing-the-right-model" class="section level3">
<h3 class="hasAnchor">
<a href="#choosing-the-right-model" class="anchor"></a>Choosing the right model</h3>
<p>Resistance is not easily predicted; if we look at vancomycin resistance in Gram-positive bacteria, the spread (i.e. standard error) is enormous:</p>
<div class="sourceCode" id="cb8"><pre class="downlit">
<span class="va">example_isolates</span> <span class="op">%&gt;%</span>
<div class="sourceCode" id="cb8"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="va">example_isolates</span> <span class="op">%&gt;%</span>
<span class="fu"><a href="https://dplyr.tidyverse.org/reference/filter.html">filter</a></span><span class="op">(</span><span class="fu"><a href="../reference/mo_property.html">mo_gramstain</a></span><span class="op">(</span><span class="va">mo</span>, language <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span> <span class="op">==</span> <span class="st">"Gram-positive"</span><span class="op">)</span> <span class="op">%&gt;%</span>
<span class="fu"><a href="../reference/resistance_predict.html">resistance_predict</a></span><span class="op">(</span>col_ab <span class="op">=</span> <span class="st">"VAN"</span>, year_min <span class="op">=</span> <span class="fl">2010</span>, info <span class="op">=</span> <span class="cn">FALSE</span>, model <span class="op">=</span> <span class="st">"binomial"</span><span class="op">)</span> <span class="op">%&gt;%</span>
<span class="fu"><a href="../reference/resistance_predict.html">ggplot_rsi_predict</a></span><span class="op">(</span><span class="op">)</span>
<span class="co"># NOTE: Using column 'date' as input for `col_date`.</span></pre></div>
<span class="co"># NOTE: Using column 'date' as input for `col_date`.</span></code></pre></div>
<p><img src="resistance_predict_files/figure-html/unnamed-chunk-6-1.png" width="720"></p>
<p>Vancomycin resistance could be 100% in ten years, but might also stay around 0%.</p>
<p>You can define the model with the <code>model</code> parameter. The model chosen above is a generalised linear regression model using a binomial distribution, assuming that a period of zero resistance was followed by a period of increasing resistance leading slowly to more and more resistance.</p>
@ -330,17 +347,17 @@
</tbody>
</table>
<p>For the vancomycin resistance in Gram-positive bacteria, a linear model might be more appropriate since no binomial distribution is to be expected based on the observed years:</p>
<div class="sourceCode" id="cb9"><pre class="downlit">
<span class="va">example_isolates</span> <span class="op">%&gt;%</span>
<div class="sourceCode" id="cb9"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="va">example_isolates</span> <span class="op">%&gt;%</span>
<span class="fu"><a href="https://dplyr.tidyverse.org/reference/filter.html">filter</a></span><span class="op">(</span><span class="fu"><a href="../reference/mo_property.html">mo_gramstain</a></span><span class="op">(</span><span class="va">mo</span>, language <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span> <span class="op">==</span> <span class="st">"Gram-positive"</span><span class="op">)</span> <span class="op">%&gt;%</span>
<span class="fu"><a href="../reference/resistance_predict.html">resistance_predict</a></span><span class="op">(</span>col_ab <span class="op">=</span> <span class="st">"VAN"</span>, year_min <span class="op">=</span> <span class="fl">2010</span>, info <span class="op">=</span> <span class="cn">FALSE</span>, model <span class="op">=</span> <span class="st">"linear"</span><span class="op">)</span> <span class="op">%&gt;%</span>
<span class="fu"><a href="../reference/resistance_predict.html">ggplot_rsi_predict</a></span><span class="op">(</span><span class="op">)</span>
<span class="co"># NOTE: Using column 'date' as input for `col_date`.</span></pre></div>
<span class="co"># NOTE: Using column 'date' as input for `col_date`.</span></code></pre></div>
<p><img src="resistance_predict_files/figure-html/unnamed-chunk-7-1.png" width="720"></p>
<p>This seems more likely, doesnt it?</p>
<p>The model itself is also available from the object, as an <code>attribute</code>:</p>
<div class="sourceCode" id="cb10"><pre class="downlit">
<span class="va">model</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/attributes.html">attributes</a></span><span class="op">(</span><span class="va">predict_TZP</span><span class="op">)</span><span class="op">$</span><span class="va">model</span>
<div class="sourceCode" id="cb10"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="va">model</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/attributes.html">attributes</a></span><span class="op">(</span><span class="va">predict_TZP</span><span class="op">)</span><span class="op">$</span><span class="va">model</span>
<span class="fu"><a href="https://rdrr.io/r/base/summary.html">summary</a></span><span class="op">(</span><span class="va">model</span><span class="op">)</span><span class="op">$</span><span class="va">family</span>
<span class="co"># </span>
@ -350,7 +367,7 @@
<span class="fu"><a href="https://rdrr.io/r/base/summary.html">summary</a></span><span class="op">(</span><span class="va">model</span><span class="op">)</span><span class="op">$</span><span class="va">coefficients</span>
<span class="co"># Estimate Std. Error z value Pr(&gt;|z|)</span>
<span class="co"># (Intercept) -200.67944891 46.17315349 -4.346237 1.384932e-05</span>
<span class="co"># year 0.09883005 0.02295317 4.305725 1.664395e-05</span></pre></div>
<span class="co"># year 0.09883005 0.02295317 4.305725 1.664395e-05</span></code></pre></div>
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@ -370,7 +387,7 @@
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@ -39,7 +56,7 @@
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@ -63,77 +80,77 @@
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