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(v1.0.1.9005) as.mo() improvements

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@ -39,7 +38,7 @@
</button>
<span class="navbar-brand">
<a class="navbar-link" href="../index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.0.1</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.0.1.9004</span>
</span>
</div>
@ -75,6 +74,13 @@
Predict antimicrobial resistance
</a>
</li>
<li>
<a href="../articles/PCA.html">
<span class="fa fa-compress"></span>
Conduct principal component analysis for AMR
</a>
</li>
<li>
<a href="../articles/MDR.html">
<span class="fa fa-skull-crossbones"></span>
@ -179,7 +185,7 @@
<h1>How to conduct AMR analysis</h1>
<h4 class="author">Matthijs S. Berends</h4>
<h4 class="date">23 February 2020</h4>
<h4 class="date">17 March 2020</h4>
<div class="hidden name"><code>AMR.Rmd</code></div>
@ -188,7 +194,7 @@
<p><strong>Note:</strong> values on this page will change with every website update since they are based on randomly created values and the page was written in <a href="https://rmarkdown.rstudio.com/">R Markdown</a>. However, the methodology remains unchanged. This page was generated on 23 February 2020.</p>
<p><strong>Note:</strong> values on this page will change with every website update since they are based on randomly created values and the page was written in <a href="https://rmarkdown.rstudio.com/">R Markdown</a>. However, the methodology remains unchanged. This page was generated on 17 March 2020.</p>
<div id="introduction" class="section level1">
<h1 class="hasAnchor">
<a href="#introduction" class="anchor"></a>Introduction</h1>
@ -219,21 +225,21 @@
</tr></thead>
<tbody>
<tr class="odd">
<td align="center">2020-02-23</td>
<td align="center">2020-03-17</td>
<td align="center">abcd</td>
<td align="center">Escherichia coli</td>
<td align="center">S</td>
<td align="center">S</td>
</tr>
<tr class="even">
<td align="center">2020-02-23</td>
<td align="center">2020-03-17</td>
<td align="center">abcd</td>
<td align="center">Escherichia coli</td>
<td align="center">S</td>
<td align="center">R</td>
</tr>
<tr class="odd">
<td align="center">2020-02-23</td>
<td align="center">2020-03-17</td>
<td align="center">efgh</td>
<td align="center">Escherichia coli</td>
<td align="center">R</td>
@ -294,24 +300,24 @@
<div id="put-everything-together" class="section level2">
<h2 class="hasAnchor">
<a href="#put-everything-together" class="anchor"></a>Put everything together</h2>
<p>Using the <code><a href="https://dplyr.tidyverse.org/reference/sample.html">sample()</a></code> function, we can randomly select items from all objects we defined earlier. To let our fake data reflect reality a bit, we will also approximately define the probabilities of bacteria and the antibiotic results with the <code>prob</code> parameter.</p>
<p>Using the <code><a href="https://rdrr.io/r/base/sample.html">sample()</a></code> function, we can randomly select items from all objects we defined earlier. To let our fake data reflect reality a bit, we will also approximately define the probabilities of bacteria and the antibiotic results with the <code>prob</code> parameter.</p>
<div class="sourceCode" id="cb7"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb7-1"><a href="#cb7-1"></a>sample_size &lt;-<span class="st"> </span><span class="dv">20000</span></span>
<span id="cb7-2"><a href="#cb7-2"></a>data &lt;-<span class="st"> </span><span class="kw"><a href="https://rdrr.io/r/base/data.frame.html">data.frame</a></span>(<span class="dt">date =</span> <span class="kw"><a href="https://dplyr.tidyverse.org/reference/sample.html">sample</a></span>(dates, <span class="dt">size =</span> sample_size, <span class="dt">replace =</span> <span class="ot">TRUE</span>),</span>
<span id="cb7-3"><a href="#cb7-3"></a> <span class="dt">patient_id =</span> <span class="kw"><a href="https://dplyr.tidyverse.org/reference/sample.html">sample</a></span>(patients, <span class="dt">size =</span> sample_size, <span class="dt">replace =</span> <span class="ot">TRUE</span>),</span>
<span id="cb7-4"><a href="#cb7-4"></a> <span class="dt">hospital =</span> <span class="kw"><a href="https://dplyr.tidyverse.org/reference/sample.html">sample</a></span>(hospitals, <span class="dt">size =</span> sample_size, <span class="dt">replace =</span> <span class="ot">TRUE</span>,</span>
<span id="cb7-2"><a href="#cb7-2"></a>data &lt;-<span class="st"> </span><span class="kw"><a href="https://rdrr.io/r/base/data.frame.html">data.frame</a></span>(<span class="dt">date =</span> <span class="kw"><a href="https://rdrr.io/r/base/sample.html">sample</a></span>(dates, <span class="dt">size =</span> sample_size, <span class="dt">replace =</span> <span class="ot">TRUE</span>),</span>
<span id="cb7-3"><a href="#cb7-3"></a> <span class="dt">patient_id =</span> <span class="kw"><a href="https://rdrr.io/r/base/sample.html">sample</a></span>(patients, <span class="dt">size =</span> sample_size, <span class="dt">replace =</span> <span class="ot">TRUE</span>),</span>
<span id="cb7-4"><a href="#cb7-4"></a> <span class="dt">hospital =</span> <span class="kw"><a href="https://rdrr.io/r/base/sample.html">sample</a></span>(hospitals, <span class="dt">size =</span> sample_size, <span class="dt">replace =</span> <span class="ot">TRUE</span>,</span>
<span id="cb7-5"><a href="#cb7-5"></a> <span class="dt">prob =</span> <span class="kw"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">0.30</span>, <span class="fl">0.35</span>, <span class="fl">0.15</span>, <span class="fl">0.20</span>)),</span>
<span id="cb7-6"><a href="#cb7-6"></a> <span class="dt">bacteria =</span> <span class="kw"><a href="https://dplyr.tidyverse.org/reference/sample.html">sample</a></span>(bacteria, <span class="dt">size =</span> sample_size, <span class="dt">replace =</span> <span class="ot">TRUE</span>,</span>
<span id="cb7-6"><a href="#cb7-6"></a> <span class="dt">bacteria =</span> <span class="kw"><a href="https://rdrr.io/r/base/sample.html">sample</a></span>(bacteria, <span class="dt">size =</span> sample_size, <span class="dt">replace =</span> <span class="ot">TRUE</span>,</span>
<span id="cb7-7"><a href="#cb7-7"></a> <span class="dt">prob =</span> <span class="kw"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">0.50</span>, <span class="fl">0.25</span>, <span class="fl">0.15</span>, <span class="fl">0.10</span>)),</span>
<span id="cb7-8"><a href="#cb7-8"></a> <span class="dt">AMX =</span> <span class="kw"><a href="https://dplyr.tidyverse.org/reference/sample.html">sample</a></span>(ab_interpretations, <span class="dt">size =</span> sample_size, <span class="dt">replace =</span> <span class="ot">TRUE</span>,</span>
<span id="cb7-8"><a href="#cb7-8"></a> <span class="dt">AMX =</span> <span class="kw"><a href="https://rdrr.io/r/base/sample.html">sample</a></span>(ab_interpretations, <span class="dt">size =</span> sample_size, <span class="dt">replace =</span> <span class="ot">TRUE</span>,</span>
<span id="cb7-9"><a href="#cb7-9"></a> <span class="dt">prob =</span> <span class="kw"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">0.60</span>, <span class="fl">0.05</span>, <span class="fl">0.35</span>)),</span>
<span id="cb7-10"><a href="#cb7-10"></a> <span class="dt">AMC =</span> <span class="kw"><a href="https://dplyr.tidyverse.org/reference/sample.html">sample</a></span>(ab_interpretations, <span class="dt">size =</span> sample_size, <span class="dt">replace =</span> <span class="ot">TRUE</span>,</span>
<span id="cb7-10"><a href="#cb7-10"></a> <span class="dt">AMC =</span> <span class="kw"><a href="https://rdrr.io/r/base/sample.html">sample</a></span>(ab_interpretations, <span class="dt">size =</span> sample_size, <span class="dt">replace =</span> <span class="ot">TRUE</span>,</span>
<span id="cb7-11"><a href="#cb7-11"></a> <span class="dt">prob =</span> <span class="kw"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">0.75</span>, <span class="fl">0.10</span>, <span class="fl">0.15</span>)),</span>
<span id="cb7-12"><a href="#cb7-12"></a> <span class="dt">CIP =</span> <span class="kw"><a href="https://dplyr.tidyverse.org/reference/sample.html">sample</a></span>(ab_interpretations, <span class="dt">size =</span> sample_size, <span class="dt">replace =</span> <span class="ot">TRUE</span>,</span>
<span id="cb7-12"><a href="#cb7-12"></a> <span class="dt">CIP =</span> <span class="kw"><a href="https://rdrr.io/r/base/sample.html">sample</a></span>(ab_interpretations, <span class="dt">size =</span> sample_size, <span class="dt">replace =</span> <span class="ot">TRUE</span>,</span>
<span id="cb7-13"><a href="#cb7-13"></a> <span class="dt">prob =</span> <span class="kw"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">0.80</span>, <span class="fl">0.00</span>, <span class="fl">0.20</span>)),</span>
<span id="cb7-14"><a href="#cb7-14"></a> <span class="dt">GEN =</span> <span class="kw"><a href="https://dplyr.tidyverse.org/reference/sample.html">sample</a></span>(ab_interpretations, <span class="dt">size =</span> sample_size, <span class="dt">replace =</span> <span class="ot">TRUE</span>,</span>
<span id="cb7-14"><a href="#cb7-14"></a> <span class="dt">GEN =</span> <span class="kw"><a href="https://rdrr.io/r/base/sample.html">sample</a></span>(ab_interpretations, <span class="dt">size =</span> sample_size, <span class="dt">replace =</span> <span class="ot">TRUE</span>,</span>
<span id="cb7-15"><a href="#cb7-15"></a> <span class="dt">prob =</span> <span class="kw"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">0.92</span>, <span class="fl">0.00</span>, <span class="fl">0.08</span>)))</span></code></pre></div>
<p>Using the <code><a href="https://dplyr.tidyverse.org/reference/join.html">left_join()</a></code> function from the <code>dplyr</code> package, we can ‘map’ the gender to the patient ID using the <code>patients_table</code> object we created earlier:</p>
<div class="sourceCode" id="cb8"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb8-1"><a href="#cb8-1"></a>data &lt;-<span class="st"> </span>data <span class="op">%&gt;%</span><span class="st"> </span><span class="kw"><a href="https://dplyr.tidyverse.org/reference/join.html">left_join</a></span>(patients_table)</span></code></pre></div>
<p>Using the <code><a href="https://dplyr.tidyverse.org/reference/mutate-joins.html">left_join()</a></code> function from the <code>dplyr</code> package, we can ‘map’ the gender to the patient ID using the <code>patients_table</code> object we created earlier:</p>
<div class="sourceCode" id="cb8"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb8-1"><a href="#cb8-1"></a>data &lt;-<span class="st"> </span>data <span class="op">%&gt;%</span><span class="st"> </span><span class="kw"><a href="https://dplyr.tidyverse.org/reference/mutate-joins.html">left_join</a></span>(patients_table)</span></code></pre></div>
<p>The resulting data set contains 20,000 blood culture isolates. With the <code><a href="https://rdrr.io/r/utils/head.html">head()</a></code> function we can preview the first 6 rows of this data set:</p>
<div class="sourceCode" id="cb9"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb9-1"><a href="#cb9-1"></a><span class="kw"><a href="https://rdrr.io/r/utils/head.html">head</a></span>(data)</span></code></pre></div>
<table class="table">
@ -328,9 +334,20 @@
</tr></thead>
<tbody>
<tr class="odd">
<td align="center">2014-10-31</td>
<td align="center">B4</td>
<td align="center">2013-12-05</td>
<td align="center">H6</td>
<td align="center">Hospital B</td>
<td align="center">Escherichia coli</td>
<td align="center">S</td>
<td align="center">R</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">M</td>
</tr>
<tr class="even">
<td align="center">2015-04-13</td>
<td align="center">I3</td>
<td align="center">Hospital A</td>
<td align="center">Staphylococcus aureus</td>
<td align="center">S</td>
<td align="center">S</td>
@ -338,60 +355,49 @@
<td align="center">S</td>
<td align="center">M</td>
</tr>
<tr class="even">
<td align="center">2012-02-24</td>
<td align="center">E10</td>
<td align="center">Hospital B</td>
<td align="center">Escherichia coli</td>
<td align="center">R</td>
<td align="center">I</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">M</td>
</tr>
<tr class="odd">
<td align="center">2017-11-04</td>
<td align="center">G1</td>
<td align="center">Hospital B</td>
<td align="center">Escherichia coli</td>
<td align="center">S</td>
<td align="center">R</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">M</td>
</tr>
<tr class="even">
<td align="center">2011-10-08</td>
<td align="center">V4</td>
<td align="center">Hospital B</td>
<td align="center">Escherichia coli</td>
<td align="center">2011-10-12</td>
<td align="center">Z1</td>
<td align="center">Hospital C</td>
<td align="center">Klebsiella pneumoniae</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">F</td>
</tr>
<tr class="odd">
<td align="center">2016-02-16</td>
<td align="center">K6</td>
<td align="center">Hospital D</td>
<td align="center">Staphylococcus aureus</td>
<td align="center">S</td>
<td align="center">S</td>
<tr class="even">
<td align="center">2011-11-05</td>
<td align="center">D1</td>
<td align="center">Hospital B</td>
<td align="center">Escherichia coli</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">M</td>
</tr>
<tr class="even">
<td align="center">2014-05-28</td>
<td align="center">N6</td>
<tr class="odd">
<td align="center">2017-12-28</td>
<td align="center">B7</td>
<td align="center">Hospital C</td>
<td align="center">Escherichia coli</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">F</td>
<td align="center">M</td>
</tr>
<tr class="even">
<td align="center">2012-07-25</td>
<td align="center">F8</td>
<td align="center">Hospital D</td>
<td align="center">Escherichia coli</td>
<td align="center">S</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">R</td>
<td align="center">M</td>
</tr>
</tbody>
</table>
@ -423,16 +429,16 @@ Unique: 2</p>
<tr class="odd">
<td align="left">1</td>
<td align="left">M</td>
<td align="right">10,361</td>
<td align="right">51.81%</td>
<td align="right">10,361</td>
<td align="right">51.81%</td>
<td align="right">10,489</td>
<td align="right">52.45%</td>
<td align="right">10,489</td>
<td align="right">52.45%</td>
</tr>
<tr class="even">
<td align="left">2</td>
<td align="left">F</td>
<td align="right">9,639</td>
<td align="right">48.20%</td>
<td align="right">9,511</td>
<td align="right">47.56%</td>
<td align="right">20,000</td>
<td align="right">100.00%</td>
</tr>
@ -472,7 +478,7 @@ Unique: 2</p>
<span id="cb15-3"><a href="#cb15-3"></a><span class="co"># </span><span class="al">NOTE</span><span class="co">: Using column `bacteria` as input for `col_mo`.</span></span>
<span id="cb15-4"><a href="#cb15-4"></a><span class="co"># </span><span class="al">NOTE</span><span class="co">: Using column `date` as input for `col_date`.</span></span>
<span id="cb15-5"><a href="#cb15-5"></a><span class="co"># </span><span class="al">NOTE</span><span class="co">: Using column `patient_id` as input for `col_patient_id`.</span></span></code></pre></div>
<p>So only 28.4% is suitable for resistance analysis! We can now filter on it with the <code><a href="https://dplyr.tidyverse.org/reference/filter.html">filter()</a></code> function, also from the <code>dplyr</code> package:</p>
<p>So only 28.5% is suitable for resistance analysis! We can now filter on it with the <code><a href="https://dplyr.tidyverse.org/reference/filter.html">filter()</a></code> function, also from the <code>dplyr</code> package:</p>
<div class="sourceCode" id="cb16"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb16-1"><a href="#cb16-1"></a>data_1st &lt;-<span class="st"> </span>data <span class="op">%&gt;%</span><span class="st"> </span></span>
<span id="cb16-2"><a href="#cb16-2"></a><span class="st"> </span><span class="kw"><a href="https://dplyr.tidyverse.org/reference/filter.html">filter</a></span>(first <span class="op">==</span><span class="st"> </span><span class="ot">TRUE</span>)</span></code></pre></div>
<p>For future use, the above two syntaxes can be shortened with the <code><a href="../reference/first_isolate.html">filter_first_isolate()</a></code> function:</p>
@ -482,7 +488,7 @@ Unique: 2</p>
<div id="first-weighted-isolates" class="section level2">
<h2 class="hasAnchor">
<a href="#first-weighted-isolates" class="anchor"></a>First <em>weighted</em> isolates</h2>
<p>We made a slight twist to the CLSI algorithm, to take into account the antimicrobial susceptibility profile. Have a look at all isolates of patient Q2, sorted on date:</p>
<p>We made a slight twist to the CLSI algorithm, to take into account the antimicrobial susceptibility profile. Have a look at all isolates of patient A2, sorted on date:</p>
<table class="table">
<thead><tr class="header">
<th align="center">isolate</th>
@ -498,10 +504,10 @@ Unique: 2</p>
<tbody>
<tr class="odd">
<td align="center">1</td>
<td align="center">2010-02-26</td>
<td align="center">Q2</td>
<td align="center">2010-01-14</td>
<td align="center">A2</td>
<td align="center">B_ESCHR_COLI</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
@ -509,32 +515,32 @@ Unique: 2</p>
</tr>
<tr class="even">
<td align="center">2</td>
<td align="center">2010-03-25</td>
<td align="center">Q2</td>
<td align="center">2010-06-29</td>
<td align="center">A2</td>
<td align="center">B_ESCHR_COLI</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">FALSE</td>
</tr>
<tr class="odd">
<td align="center">3</td>
<td align="center">2010-05-23</td>
<td align="center">Q2</td>
<td align="center">2010-07-21</td>
<td align="center">A2</td>
<td align="center">B_ESCHR_COLI</td>
<td align="center">R</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">FALSE</td>
</tr>
<tr class="even">
<td align="center">4</td>
<td align="center">2010-05-24</td>
<td align="center">Q2</td>
<td align="center">2010-08-18</td>
<td align="center">A2</td>
<td align="center">B_ESCHR_COLI</td>
<td align="center">S</td>
<td align="center">I</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
@ -542,10 +548,10 @@ Unique: 2</p>
</tr>
<tr class="odd">
<td align="center">5</td>
<td align="center">2010-09-08</td>
<td align="center">Q2</td>
<td align="center">2010-11-16</td>
<td align="center">A2</td>
<td align="center">B_ESCHR_COLI</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
@ -553,32 +559,32 @@ Unique: 2</p>
</tr>
<tr class="even">
<td align="center">6</td>
<td align="center">2011-03-19</td>
<td align="center">Q2</td>
<td align="center">2011-01-29</td>
<td align="center">A2</td>
<td align="center">B_ESCHR_COLI</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">TRUE</td>
</tr>
<tr class="odd">
<td align="center">7</td>
<td align="center">2011-04-02</td>
<td align="center">Q2</td>
<td align="center">2011-03-30</td>
<td align="center">A2</td>
<td align="center">B_ESCHR_COLI</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">R</td>
<td align="center">R</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">FALSE</td>
</tr>
<tr class="even">
<td align="center">8</td>
<td align="center">2011-04-05</td>
<td align="center">Q2</td>
<td align="center">2011-04-08</td>
<td align="center">A2</td>
<td align="center">B_ESCHR_COLI</td>
<td align="center">I</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">R</td>
<td align="center">S</td>
@ -586,19 +592,19 @@ Unique: 2</p>
</tr>
<tr class="odd">
<td align="center">9</td>
<td align="center">2011-11-13</td>
<td align="center">Q2</td>
<td align="center">2011-05-09</td>
<td align="center">A2</td>
<td align="center">B_ESCHR_COLI</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">FALSE</td>
</tr>
<tr class="even">
<td align="center">10</td>
<td align="center">2011-11-28</td>
<td align="center">Q2</td>
<td align="center">2011-07-16</td>
<td align="center">A2</td>
<td align="center">B_ESCHR_COLI</td>
<td align="center">S</td>
<td align="center">S</td>
@ -634,10 +640,10 @@ Unique: 2</p>
<tbody>
<tr class="odd">
<td align="center">1</td>
<td align="center">2010-02-26</td>
<td align="center">Q2</td>
<td align="center">2010-01-14</td>
<td align="center">A2</td>
<td align="center">B_ESCHR_COLI</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
@ -646,23 +652,23 @@ Unique: 2</p>
</tr>
<tr class="even">
<td align="center">2</td>
<td align="center">2010-03-25</td>
<td align="center">Q2</td>
<td align="center">2010-06-29</td>
<td align="center">A2</td>
<td align="center">B_ESCHR_COLI</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">FALSE</td>
<td align="center">TRUE</td>
<td align="center">FALSE</td>
</tr>
<tr class="odd">
<td align="center">3</td>
<td align="center">2010-05-23</td>
<td align="center">Q2</td>
<td align="center">2010-07-21</td>
<td align="center">A2</td>
<td align="center">B_ESCHR_COLI</td>
<td align="center">R</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">FALSE</td>
@ -670,44 +676,68 @@ Unique: 2</p>
</tr>
<tr class="even">
<td align="center">4</td>
<td align="center">2010-05-24</td>
<td align="center">Q2</td>
<td align="center">2010-08-18</td>
<td align="center">A2</td>
<td align="center">B_ESCHR_COLI</td>
<td align="center">S</td>
<td align="center">I</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">FALSE</td>
<td align="center">TRUE</td>
<td align="center">FALSE</td>
</tr>
<tr class="odd">
<td align="center">5</td>
<td align="center">2010-09-08</td>
<td align="center">Q2</td>
<td align="center">2010-11-16</td>
<td align="center">A2</td>
<td align="center">B_ESCHR_COLI</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">FALSE</td>
<td align="center">TRUE</td>
<td align="center">FALSE</td>
</tr>
<tr class="even">
<td align="center">6</td>
<td align="center">2011-03-19</td>
<td align="center">Q2</td>
<td align="center">2011-01-29</td>
<td align="center">A2</td>
<td align="center">B_ESCHR_COLI</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">TRUE</td>
<td align="center">TRUE</td>
</tr>
<tr class="odd">
<td align="center">7</td>
<td align="center">2011-04-02</td>
<td align="center">Q2</td>
<td align="center">2011-03-30</td>
<td align="center">A2</td>
<td align="center">B_ESCHR_COLI</td>
<td align="center">R</td>
<td align="center">R</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">FALSE</td>
<td align="center">TRUE</td>
</tr>
<tr class="even">
<td align="center">8</td>
<td align="center">2011-04-08</td>
<td align="center">A2</td>
<td align="center">B_ESCHR_COLI</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">FALSE</td>
<td align="center">TRUE</td>
</tr>
<tr class="odd">
<td align="center">9</td>
<td align="center">2011-05-09</td>
<td align="center">A2</td>
<td align="center">B_ESCHR_COLI</td>
<td align="center">S</td>
<td align="center">S</td>
@ -717,21 +747,9 @@ Unique: 2</p>
<td align="center">FALSE</td>
</tr>
<tr class="even">
<td align="center">8</td>
<td align="center">2011-04-05</td>
<td align="center">Q2</td>
<td align="center">B_ESCHR_COLI</td>
<td align="center">I</td>
<td align="center">S</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">FALSE</td>
<td align="center">FALSE</td>
</tr>
<tr class="odd">
<td align="center">9</td>
<td align="center">2011-11-13</td>
<td align="center">Q2</td>
<td align="center">10</td>
<td align="center">2011-07-16</td>
<td align="center">A2</td>
<td align="center">B_ESCHR_COLI</td>
<td align="center">S</td>
<td align="center">S</td>
@ -740,25 +758,13 @@ Unique: 2</p>
<td align="center">FALSE</td>
<td align="center">TRUE</td>
</tr>
<tr class="even">
<td align="center">10</td>
<td align="center">2011-11-28</td>
<td align="center">Q2</td>
<td align="center">B_ESCHR_COLI</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">FALSE</td>
<td align="center">FALSE</td>
</tr>
</tbody>
</table>
<p>Instead of 2, now 7 isolates are flagged. In total, 74.8% of all isolates are marked ‘first weighted’ - 46.4% more than when using the CLSI guideline. In real life, this novel algorithm will yield 5-10% more isolates than the classic CLSI guideline.</p>
<p>Instead of 2, now 6 isolates are flagged. In total, 75.8% of all isolates are marked ‘first weighted’ - 47.3% more than when using the CLSI guideline. In real life, this novel algorithm will yield 5-10% more isolates than the classic CLSI guideline.</p>
<p>As with <code><a href="../reference/first_isolate.html">filter_first_isolate()</a></code>, there’s a shortcut for this new algorithm too:</p>
<div class="sourceCode" id="cb19"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb19-1"><a href="#cb19-1"></a>data_1st &lt;-<span class="st"> </span>data <span class="op">%&gt;%</span><span class="st"> </span></span>
<span id="cb19-2"><a href="#cb19-2"></a><span class="st"> </span><span class="kw"><a href="../reference/first_isolate.html">filter_first_weighted_isolate</a></span>()</span></code></pre></div>
<p>So we end up with 14,960 isolates for analysis.</p>
<p>So we end up with 15,159 isolates for analysis.</p>
<p>We can remove unneeded columns:</p>
<div class="sourceCode" id="cb20"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb20-1"><a href="#cb20-1"></a>data_1st &lt;-<span class="st"> </span>data_1st <span class="op">%&gt;%</span><span class="st"> </span></span>
<span id="cb20-2"><a href="#cb20-2"></a><span class="st"> </span><span class="kw"><a href="https://dplyr.tidyverse.org/reference/select.html">select</a></span>(<span class="op">-</span><span class="kw"><a href="https://rdrr.io/r/base/c.html">c</a></span>(first, keyab))</span></code></pre></div>
@ -782,9 +788,24 @@ Unique: 2</p>
</tr></thead>
<tbody>
<tr class="odd">
<td align="center">2014-10-31</td>
<td align="center">B4</td>
<td align="center">2013-12-05</td>
<td align="center">H6</td>
<td align="center">Hospital B</td>
<td align="center">B_ESCHR_COLI</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">M</td>
<td align="center">Gram-negative</td>
<td align="center">Escherichia</td>
<td align="center">coli</td>
<td align="center">TRUE</td>
</tr>
<tr class="even">
<td align="center">2015-04-13</td>
<td align="center">I3</td>
<td align="center">Hospital A</td>
<td align="center">B_STPHY_AURS</td>
<td align="center">S</td>
<td align="center">S</td>
@ -796,76 +817,61 @@ Unique: 2</p>
<td align="center">aureus</td>
<td align="center">TRUE</td>
</tr>
<tr class="even">
<td align="center">2012-02-24</td>
<td align="center">E10</td>
<td align="center">Hospital B</td>
<td align="center">B_ESCHR_COLI</td>
<td align="center">R</td>
<td align="center">I</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">M</td>
<td align="center">Gram-negative</td>
<td align="center">Escherichia</td>
<td align="center">coli</td>
<td align="center">TRUE</td>
</tr>
<tr class="odd">
<td align="center">2017-11-04</td>
<td align="center">G1</td>
<td align="center">Hospital B</td>
<td align="center">B_ESCHR_COLI</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">M</td>
<td align="center">Gram-negative</td>
<td align="center">Escherichia</td>
<td align="center">coli</td>
<td align="center">TRUE</td>
</tr>
<tr class="even">
<td align="center">2011-10-08</td>
<td align="center">V4</td>
<td align="center">Hospital B</td>
<td align="center">B_ESCHR_COLI</td>
<td align="center">2011-10-12</td>
<td align="center">Z1</td>
<td align="center">Hospital C</td>
<td align="center">B_KLBSL_PNMN</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">F</td>
<td align="center">Gram-negative</td>
<td align="center">Klebsiella</td>
<td align="center">pneumoniae</td>
<td align="center">TRUE</td>
</tr>
<tr class="even">
<td align="center">2011-11-05</td>
<td align="center">D1</td>
<td align="center">Hospital B</td>
<td align="center">B_ESCHR_COLI</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">M</td>
<td align="center">Gram-negative</td>
<td align="center">Escherichia</td>
<td align="center">coli</td>
<td align="center">TRUE</td>
</tr>
<tr class="odd">
<td align="center">2016-02-16</td>
<td align="center">K6</td>
<td align="center">Hospital D</td>
<td align="center">B_STPHY_AURS</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">R</td>
<td align="center">M</td>
<td align="center">Gram-positive</td>
<td align="center">Staphylococcus</td>
<td align="center">aureus</td>
<td align="center">TRUE</td>
</tr>
<tr class="even">
<td align="center">2014-05-28</td>
<td align="center">N6</td>
<td align="center">2017-12-28</td>
<td align="center">B7</td>
<td align="center">Hospital C</td>
<td align="center">B_ESCHR_COLI</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">F</td>
<td align="center">M</td>
<td align="center">Gram-negative</td>
<td align="center">Escherichia</td>
<td align="center">coli</td>
<td align="center">TRUE</td>
</tr>
<tr class="even">
<td align="center">2012-07-25</td>
<td align="center">F8</td>
<td align="center">Hospital D</td>
<td align="center">B_ESCHR_COLI</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">R</td>
<td align="center">M</td>
<td align="center">Gram-negative</td>
<td align="center">Escherichia</td>
<td align="center">coli</td>
@ -890,8 +896,8 @@ Unique: 2</p>
<div class="sourceCode" id="cb23"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb23-1"><a href="#cb23-1"></a>data_1st <span class="op">%&gt;%</span><span class="st"> </span><span class="kw"><a href="https://rdrr.io/pkg/cleaner/man/freq.html">freq</a></span>(genus, species)</span></code></pre></div>
<p><strong>Frequency table</strong></p>
<p>Class: character<br>
Length: 14,960<br>
Available: 14,960 (100%, NA: 0 = 0%)<br>
Length: 15,159<br>
Available: 15,159 (100%, NA: 0 = 0%)<br>
Unique: 4</p>
<p>Shortest: 16<br>
Longest: 24</p>
@ -908,33 +914,33 @@ Longest: 24</p>
<tr class="odd">
<td align="left">1</td>
<td align="left">Escherichia coli</td>
<td align="right">7,397</td>
<td align="right">49.45%</td>
<td align="right">7,397</td>
<td align="right">49.45%</td>
<td align="right">7,543</td>
<td align="right">49.76%</td>
<td align="right">7,543</td>
<td align="right">49.76%</td>
</tr>
<tr class="even">
<td align="left">2</td>
<td align="left">Staphylococcus aureus</td>
<td align="right">3,685</td>
<td align="right">24.63%</td>
<td align="right">11,082</td>
<td align="right">74.08%</td>
<td align="right">3,783</td>
<td align="right">24.96%</td>
<td align="right">11,326</td>
<td align="right">74.71%</td>
</tr>
<tr class="odd">
<td align="left">3</td>
<td align="left">Streptococcus pneumoniae</td>
<td align="right">2,361</td>
<td align="right">15.78%</td>
<td align="right">13,443</td>
<td align="right">89.86%</td>
<td align="right">2,316</td>
<td align="right">15.28%</td>
<td align="right">13,642</td>
<td align="right">89.99%</td>
</tr>
<tr class="even">
<td align="left">4</td>
<td align="left">Klebsiella pneumoniae</td>
<td align="right">1,517</td>
<td align="right">10.14%</td>
<td align="right">14,960</td>
<td align="right">10.01%</td>
<td align="right">15,159</td>
<td align="right">100.00%</td>
</tr>
</tbody>
@ -946,7 +952,7 @@ Longest: 24</p>
<p>The functions <code><a href="../reference/proportion.html">resistance()</a></code> and <code><a href="../reference/proportion.html">susceptibility()</a></code> can be used to calculate antimicrobial resistance or susceptibility. For more specific analyses, the functions <code><a href="../reference/proportion.html">proportion_S()</a></code>, <code><a href="../reference/proportion.html">proportion_SI()</a></code>, <code><a href="../reference/proportion.html">proportion_I()</a></code>, <code><a href="../reference/proportion.html">proportion_IR()</a></code> and <code><a href="../reference/proportion.html">proportion_R()</a></code> can be used to determine the proportion of a specific antimicrobial outcome.</p>
<p>As per the EUCAST guideline of 2019, we calculate resistance as the proportion of R (<code><a href="../reference/proportion.html">proportion_R()</a></code>, equal to <code><a href="../reference/proportion.html">resistance()</a></code>) and susceptibility as the proportion of S and I (<code><a href="../reference/proportion.html">proportion_SI()</a></code>, equal to <code><a href="../reference/proportion.html">susceptibility()</a></code>). These functions can be used on their own:</p>
<div class="sourceCode" id="cb24"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb24-1"><a href="#cb24-1"></a>data_1st <span class="op">%&gt;%</span><span class="st"> </span><span class="kw"><a href="../reference/proportion.html">resistance</a></span>(AMX)</span>
<span id="cb24-2"><a href="#cb24-2"></a><span class="co"># [1] 0.4635695</span></span></code></pre></div>
<span id="cb24-2"><a href="#cb24-2"></a><span class="co"># [1] 0.4593311</span></span></code></pre></div>
<p>Or can be used in conjuction with <code><a href="https://dplyr.tidyverse.org/reference/group_by.html">group_by()</a></code> and <code><a href="https://dplyr.tidyverse.org/reference/summarise.html">summarise()</a></code>, both from the <code>dplyr</code> package:</p>
<div class="sourceCode" id="cb25"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb25-1"><a href="#cb25-1"></a>data_1st <span class="op">%&gt;%</span><span class="st"> </span></span>
<span id="cb25-2"><a href="#cb25-2"></a><span class="st"> </span><span class="kw"><a href="https://dplyr.tidyverse.org/reference/group_by.html">group_by</a></span>(hospital) <span class="op">%&gt;%</span><span class="st"> </span></span>
@ -959,19 +965,19 @@ Longest: 24</p>
<tbody>
<tr class="odd">
<td align="center">Hospital A</td>
<td align="center">0.4490022</td>
<td align="center">0.4554126</td>
</tr>
<tr class="even">
<td align="center">Hospital B</td>
<td align="center">0.4746786</td>
<td align="center">0.4619586</td>
</tr>
<tr class="odd">
<td align="center">Hospital C</td>
<td align="center">0.4505104</td>
<td align="center">0.4566170</td>
</tr>
<tr class="even">
<td align="center">Hospital D</td>
<td align="center">0.4760039</td>
<td align="center">0.4625695</td>
</tr>
</tbody>
</table>
@ -989,23 +995,23 @@ Longest: 24</p>
<tbody>
<tr class="odd">
<td align="center">Hospital A</td>
<td align="center">0.4490022</td>
<td align="center">4510</td>
<td align="center">0.4554126</td>
<td align="center">4508</td>
</tr>
<tr class="even">
<td align="center">Hospital B</td>
<td align="center">0.4746786</td>
<td align="center">5134</td>
<td align="center">0.4619586</td>
<td align="center">5310</td>
</tr>
<tr class="odd">
<td align="center">Hospital C</td>
<td align="center">0.4505104</td>
<td align="center">2253</td>
<td align="center">0.4566170</td>
<td align="center">2282</td>
</tr>
<tr class="even">
<td align="center">Hospital D</td>
<td align="center">0.4760039</td>
<td align="center">3063</td>
<td align="center">0.4625695</td>
<td align="center">3059</td>
</tr>
</tbody>
</table>
@ -1025,27 +1031,27 @@ Longest: 24</p>
<tbody>
<tr class="odd">
<td align="center">Escherichia</td>
<td align="center">0.9251048</td>
<td align="center">0.8952278</td>
<td align="center">0.9928349</td>
<td align="center">0.9241681</td>
<td align="center">0.8932785</td>
<td align="center">0.9935039</td>
</tr>
<tr class="even">
<td align="center">Klebsiella</td>
<td align="center">0.9169413</td>
<td align="center">0.9083718</td>
<td align="center">0.9947264</td>
<td align="center">0.9215557</td>
<td align="center">0.9004614</td>
<td align="center">0.9953856</td>
</tr>
<tr class="odd">
<td align="center">Staphylococcus</td>
<td align="center">0.9305292</td>
<td align="center">0.9158752</td>
<td align="center">0.9937585</td>
<td align="center">0.9188475</td>
<td align="center">0.9254560</td>
<td align="center">0.9939202</td>
</tr>
<tr class="even">
<td align="center">Streptococcus</td>
<td align="center">0.6027107</td>
<td align="center">0.6178756</td>
<td align="center">0.0000000</td>
<td align="center">0.6027107</td>
<td align="center">0.6178756</td>
</tr>
</tbody>
</table>
@ -1158,7 +1164,7 @@ Longest: 24</p>
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@ -1206,7 +1212,7 @@ Longest: 24</p>
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<script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet">
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<meta property="og:description" content="">
<meta property="og:image" content="https://msberends.gitlab.io/AMR/logo.png">
<meta name="twitter:card" content="summary">
<meta property="og:description" content="AMR">
<meta property="og:image" content="/logo.svg">
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@ -186,7 +185,7 @@
<h1>How to apply EUCAST rules</h1>
<h4 class="author">Matthijs S. Berends</h4>
<h4 class="date">14 March 2020</h4>
<h4 class="date">17 March 2020</h4>
<div class="hidden name"><code>EUCAST.Rmd</code></div>
@ -368,7 +367,7 @@
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@ -389,7 +388,7 @@
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@ -12,15 +12,14 @@
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@ -39,7 +38,7 @@
</button>
<span class="navbar-brand">
<a class="navbar-link" href="../index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.0.1</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.0.1.9004</span>
</span>
</div>
@ -75,6 +74,13 @@
Predict antimicrobial resistance
</a>
</li>
<li>
<a href="../articles/PCA.html">
<span class="fa fa-compress"></span>
Conduct principal component analysis for AMR
</a>
</li>
<li>
<a href="../articles/MDR.html">
<span class="fa fa-skull-crossbones"></span>
@ -179,7 +185,7 @@
<h1>How to determine multi-drug resistance (MDR)</h1>
<h4 class="author">Matthijs S. Berends</h4>
<h4 class="date">23 February 2020</h4>
<h4 class="date">17 March 2020</h4>
<div class="hidden name"><code>MDR.Rmd</code></div>
@ -274,7 +280,7 @@ Unique: 2</p>
<div class="sourceCode" id="cb3"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb3-1"><a href="#cb3-1"></a><span class="co"># a helper function to get a random vector with values S, I and R</span></span>
<span id="cb3-2"><a href="#cb3-2"></a><span class="co"># with the probabilities 50% - 10% - 40%</span></span>
<span id="cb3-3"><a href="#cb3-3"></a>sample_rsi &lt;-<span class="st"> </span><span class="cf">function</span>() {</span>
<span id="cb3-4"><a href="#cb3-4"></a> <span class="kw"><a href="https://dplyr.tidyverse.org/reference/sample.html">sample</a></span>(<span class="kw"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="st">"S"</span>, <span class="st">"I"</span>, <span class="st">"R"</span>),</span>
<span id="cb3-4"><a href="#cb3-4"></a> <span class="kw"><a href="https://rdrr.io/r/base/sample.html">sample</a></span>(<span class="kw"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="st">"S"</span>, <span class="st">"I"</span>, <span class="st">"R"</span>),</span>
<span id="cb3-5"><a href="#cb3-5"></a> <span class="dt">size =</span> <span class="dv">5000</span>,</span>
<span id="cb3-6"><a href="#cb3-6"></a> <span class="dt">prob =</span> <span class="kw"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">0.5</span>, <span class="fl">0.1</span>, <span class="fl">0.4</span>),</span>
<span id="cb3-7"><a href="#cb3-7"></a> <span class="dt">replace =</span> <span class="ot">TRUE</span>)</span>
@ -298,19 +304,19 @@ Unique: 2</p>
<p>The data set now looks like this:</p>
<div class="sourceCode" id="cb5"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb5-1"><a href="#cb5-1"></a><span class="kw"><a href="https://rdrr.io/r/utils/head.html">head</a></span>(my_TB_data)</span>
<span id="cb5-2"><a href="#cb5-2"></a><span class="co"># rifampicin isoniazid gatifloxacin ethambutol pyrazinamide moxifloxacin</span></span>
<span id="cb5-3"><a href="#cb5-3"></a><span class="co"># 1 S R S R R I</span></span>
<span id="cb5-4"><a href="#cb5-4"></a><span class="co"># 2 S R S S R S</span></span>
<span id="cb5-5"><a href="#cb5-5"></a><span class="co"># 3 I S R R S R</span></span>
<span id="cb5-6"><a href="#cb5-6"></a><span class="co"># 4 S S S S S R</span></span>
<span id="cb5-7"><a href="#cb5-7"></a><span class="co"># 5 S S S I R R</span></span>
<span id="cb5-8"><a href="#cb5-8"></a><span class="co"># 6 I I R R S S</span></span>
<span id="cb5-3"><a href="#cb5-3"></a><span class="co"># 1 S R R I R R</span></span>
<span id="cb5-4"><a href="#cb5-4"></a><span class="co"># 2 I R R R S R</span></span>
<span id="cb5-5"><a href="#cb5-5"></a><span class="co"># 3 R R R S R S</span></span>
<span id="cb5-6"><a href="#cb5-6"></a><span class="co"># 4 S S R R R S</span></span>
<span id="cb5-7"><a href="#cb5-7"></a><span class="co"># 5 S R S R S S</span></span>
<span id="cb5-8"><a href="#cb5-8"></a><span class="co"># 6 R S S S R S</span></span>
<span id="cb5-9"><a href="#cb5-9"></a><span class="co"># kanamycin</span></span>
<span id="cb5-10"><a href="#cb5-10"></a><span class="co"># 1 S</span></span>
<span id="cb5-11"><a href="#cb5-11"></a><span class="co"># 2 I</span></span>
<span id="cb5-11"><a href="#cb5-11"></a><span class="co"># 2 S</span></span>
<span id="cb5-12"><a href="#cb5-12"></a><span class="co"># 3 R</span></span>
<span id="cb5-13"><a href="#cb5-13"></a><span class="co"># 4 R</span></span>
<span id="cb5-14"><a href="#cb5-14"></a><span class="co"># 5 R</span></span>
<span id="cb5-15"><a href="#cb5-15"></a><span class="co"># 6 I</span></span></code></pre></div>
<span id="cb5-15"><a href="#cb5-15"></a><span class="co"># 6 R</span></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="sourceCode r"><code class="sourceCode r"><span id="cb6-1"><a href="#cb6-1"></a><span class="kw"><a href="../reference/mdro.html">mdro</a></span>(my_TB_data, <span class="dt">guideline =</span> <span class="st">"TB"</span>)</span></code></pre></div>
<p>or its shortcut <code><a href="../reference/mdro.html">mdr_tb()</a></code>:</p>
@ -339,40 +345,40 @@ Unique: 5</p>
<tr class="odd">
<td align="left">1</td>
<td align="left">Mono-resistant</td>
<td align="right">3274</td>
<td align="right">65.48%</td>
<td align="right">3274</td>
<td align="right">65.48%</td>
<td align="right">3312</td>
<td align="right">66.24%</td>
<td align="right">3312</td>
<td align="right">66.24%</td>
</tr>
<tr class="even">
<td align="left">2</td>
<td align="left">Negative</td>
<td align="right">651</td>
<td align="right">13.02%</td>
<td align="right">3925</td>
<td align="right">78.50%</td>
<td align="right">634</td>
<td align="right">12.68%</td>
<td align="right">3946</td>
<td align="right">78.92%</td>
</tr>
<tr class="odd">
<td align="left">3</td>
<td align="left">Multi-drug-resistant</td>
<td align="right">612</td>
<td align="right">12.24%</td>
<td align="right">4537</td>
<td align="right">90.74%</td>
<td align="right">554</td>
<td align="right">11.08%</td>
<td align="right">4500</td>
<td align="right">90.00%</td>
</tr>
<tr class="even">
<td align="left">4</td>
<td align="left">Poly-resistant</td>
<td align="right">288</td>
<td align="right">5.76%</td>
<td align="right">4825</td>
<td align="right">96.50%</td>
<td align="right">291</td>
<td align="right">5.82%</td>
<td align="right">4791</td>
<td align="right">95.82%</td>
</tr>
<tr class="odd">
<td align="left">5</td>
<td align="left">Extensively drug-resistant</td>
<td align="right">175</td>
<td align="right">3.50%</td>
<td align="right">209</td>
<td align="right">4.18%</td>
<td align="right">5000</td>
<td align="right">100.00%</td>
</tr>
@ -381,7 +387,7 @@ Unique: 5</p>
</div>
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@ -394,7 +400,7 @@ Unique: 5</p>
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<h1>How to conduct principal component analysis (PCA) for AMR</h1>
<h1 data-toc-skip>How to conduct principal component analysis (PCA) for AMR</h1>
<h4 class="author">Matthijs S. Berends</h4>
<h4 class="date">14 March 2020</h4>
<h4 class="date">13 April 2020</h4>
<small class="dont-index">Source: <a href="https://gitlab.com/msberends/AMR/blob/master/vignettes/PCA.Rmd"><code>vignettes/PCA.Rmd</code></a></small>
<div class="hidden name"><code>PCA.Rmd</code></div>
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@ -204,124 +204,117 @@
<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="sourceCode r"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1"></a><span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span>(AMR)</span>
<span id="cb1-2"><a href="#cb1-2"></a><span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span>(dplyr)</span>
<span id="cb1-3"><a href="#cb1-3"></a><span class="kw"><a href="https://dplyr.tidyverse.org/reference/reexports.html">glimpse</a></span>(example_isolates)</span>
<span id="cb1-4"><a href="#cb1-4"></a><span class="co"># Observations: 2,000</span></span>
<span id="cb1-5"><a href="#cb1-5"></a><span class="co"># Variables: 49</span></span>
<span id="cb1-6"><a href="#cb1-6"></a><span class="co"># $ date &lt;date&gt; 2002-01-02, 2002-01-03, 2002-01-07, 2002-01-07, 2002…</span></span>
<span id="cb1-7"><a href="#cb1-7"></a><span class="co"># $ hospital_id &lt;fct&gt; D, D, B, B, B, B, D, D, B, B, D, D, D, D, D, B, B, B,…</span></span>
<span id="cb1-8"><a href="#cb1-8"></a><span class="co"># $ ward_icu &lt;lgl&gt; FALSE, FALSE, TRUE, TRUE, TRUE, TRUE, FALSE, FALSE, T…</span></span>
<span id="cb1-9"><a href="#cb1-9"></a><span class="co"># $ ward_clinical &lt;lgl&gt; TRUE, TRUE, FALSE, FALSE, FALSE, FALSE, TRUE, TRUE, F…</span></span>
<span id="cb1-10"><a href="#cb1-10"></a><span class="co"># $ ward_outpatient &lt;lgl&gt; FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALS…</span></span>
<span id="cb1-11"><a href="#cb1-11"></a><span class="co"># $ age &lt;dbl&gt; 65, 65, 45, 45, 45, 45, 78, 78, 45, 79, 67, 67, 71, 7…</span></span>
<span id="cb1-12"><a href="#cb1-12"></a><span class="co"># $ gender &lt;chr&gt; "F", "F", "F", "F", "F", "F", "M", "M", "F", "F", "M"…</span></span>
<span id="cb1-13"><a href="#cb1-13"></a><span class="co"># $ patient_id &lt;chr&gt; "A77334", "A77334", "067927", "067927", "067927", "06…</span></span>
<span id="cb1-14"><a href="#cb1-14"></a><span class="co"># $ mo &lt;mo&gt; B_ESCHR_COLI, B_ESCHR_COLI, B_STPHY_EPDR, B_STPHY_EPDR…</span></span>
<span id="cb1-15"><a href="#cb1-15"></a><span class="co"># $ PEN &lt;rsi&gt; R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R,…</span></span>
<span id="cb1-16"><a href="#cb1-16"></a><span class="co"># $ OXA &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span></span>
<span id="cb1-17"><a href="#cb1-17"></a><span class="co"># $ FLC &lt;rsi&gt; NA, NA, R, R, R, R, S, S, R, S, S, S, NA, NA, NA, NA,…</span></span>
<span id="cb1-18"><a href="#cb1-18"></a><span class="co"># $ AMX &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span></span>
<span id="cb1-19"><a href="#cb1-19"></a><span class="co"># $ AMC &lt;rsi&gt; I, I, NA, NA, NA, NA, S, S, NA, NA, S, S, I, I, R, I,…</span></span>
<span id="cb1-20"><a href="#cb1-20"></a><span class="co"># $ AMP &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span></span>
<span id="cb1-21"><a href="#cb1-21"></a><span class="co"># $ TZP &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span></span>
<span id="cb1-22"><a href="#cb1-22"></a><span class="co"># $ CZO &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span></span>
<span id="cb1-23"><a href="#cb1-23"></a><span class="co"># $ FEP &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span></span>
<span id="cb1-24"><a href="#cb1-24"></a><span class="co"># $ CXM &lt;rsi&gt; I, I, R, R, R, R, S, S, R, S, S, S, S, S, NA, S, S, R…</span></span>
<span id="cb1-25"><a href="#cb1-25"></a><span class="co"># $ FOX &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span></span>
<span id="cb1-26"><a href="#cb1-26"></a><span class="co"># $ CTX &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, S, S,…</span></span>
<span id="cb1-27"><a href="#cb1-27"></a><span class="co"># $ CAZ &lt;rsi&gt; NA, NA, R, R, R, R, R, R, R, R, R, R, NA, NA, NA, S, …</span></span>
<span id="cb1-28"><a href="#cb1-28"></a><span class="co"># $ CRO &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, S, S,…</span></span>
<span id="cb1-29"><a href="#cb1-29"></a><span class="co"># $ GEN &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span></span>
<span id="cb1-30"><a href="#cb1-30"></a><span class="co"># $ TOB &lt;rsi&gt; NA, NA, NA, NA, NA, NA, S, S, NA, NA, NA, NA, S, S, N…</span></span>
<span id="cb1-31"><a href="#cb1-31"></a><span class="co"># $ AMK &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span></span>
<span id="cb1-32"><a href="#cb1-32"></a><span class="co"># $ KAN &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span></span>
<span id="cb1-33"><a href="#cb1-33"></a><span class="co"># $ TMP &lt;rsi&gt; R, R, S, S, R, R, R, R, S, S, NA, NA, S, S, S, S, S, …</span></span>
<span id="cb1-34"><a href="#cb1-34"></a><span class="co"># $ SXT &lt;rsi&gt; R, R, S, S, NA, NA, NA, NA, S, S, NA, NA, S, S, S, S,…</span></span>
<span id="cb1-35"><a href="#cb1-35"></a><span class="co"># $ NIT &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span></span>
<span id="cb1-36"><a href="#cb1-36"></a><span class="co"># $ FOS &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span></span>
<span id="cb1-37"><a href="#cb1-37"></a><span class="co"># $ LNZ &lt;rsi&gt; R, R, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, R, R…</span></span>
<span id="cb1-38"><a href="#cb1-38"></a><span class="co"># $ CIP &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, S, S, NA, NA, NA, NA,…</span></span>
<span id="cb1-39"><a href="#cb1-39"></a><span class="co"># $ MFX &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span></span>
<span id="cb1-40"><a href="#cb1-40"></a><span class="co"># $ VAN &lt;rsi&gt; R, R, S, S, S, S, S, S, S, S, NA, NA, R, R, R, R, R, …</span></span>
<span id="cb1-41"><a href="#cb1-41"></a><span class="co"># $ TEC &lt;rsi&gt; R, R, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, R, R…</span></span>
<span id="cb1-42"><a href="#cb1-42"></a><span class="co"># $ TCY &lt;rsi&gt; R, R, S, S, S, S, S, S, S, I, S, S, NA, NA, I, R, R, …</span></span>
<span id="cb1-43"><a href="#cb1-43"></a><span class="co"># $ TGC &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span></span>
<span id="cb1-44"><a href="#cb1-44"></a><span class="co"># $ DOX &lt;rsi&gt; NA, NA, S, S, S, S, S, S, S, NA, S, S, NA, NA, NA, R,…</span></span>
<span id="cb1-45"><a href="#cb1-45"></a><span class="co"># $ ERY &lt;rsi&gt; R, R, R, R, R, R, S, S, R, S, S, S, R, R, R, R, R, R,…</span></span>
<span id="cb1-46"><a href="#cb1-46"></a><span class="co"># $ CLI &lt;rsi&gt; NA, NA, NA, NA, NA, R, NA, NA, NA, NA, NA, NA, NA, NA…</span></span>
<span id="cb1-47"><a href="#cb1-47"></a><span class="co"># $ AZM &lt;rsi&gt; R, R, R, R, R, R, S, S, R, S, S, S, R, R, R, R, R, R,…</span></span>
<span id="cb1-48"><a href="#cb1-48"></a><span class="co"># $ IPM &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, S, S,…</span></span>
<span id="cb1-49"><a href="#cb1-49"></a><span class="co"># $ MEM &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span></span>
<span id="cb1-50"><a href="#cb1-50"></a><span class="co"># $ MTR &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span></span>
<span id="cb1-51"><a href="#cb1-51"></a><span class="co"># $ CHL &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span></span>
<span id="cb1-52"><a href="#cb1-52"></a><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>
<span id="cb1-53"><a href="#cb1-53"></a><span class="co"># $ MUP &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span></span>
<span id="cb1-54"><a href="#cb1-54"></a><span class="co"># $ RIF &lt;rsi&gt; R, R, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, R, R…</span></span></code></pre></div>
<div class="sourceCode" id="cb1"><html><body><pre class="r"><span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="no">AMR</span>)
<span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="no">dplyr</span>)
<span class="fu"><a href="https://dplyr.tidyverse.org/reference/reexports.html">glimpse</a></span>(<span class="no">example_isolates</span>)
<span class="co"># Rows: 2,000</span>
<span class="co"># Columns: 49</span>
<span class="co"># $ date &lt;date&gt; 2002-01-02, 2002-01-03, 2002-01-07, 2002-01-07, 2002…</span>
<span class="co"># $ hospital_id &lt;fct&gt; D, D, B, B, B, B, D, D, B, B, D, D, D, D, D, B, B, B,…</span>
<span class="co"># $ ward_icu &lt;lgl&gt; FALSE, FALSE, TRUE, TRUE, TRUE, TRUE, FALSE, FALSE, T…</span>
<span class="co"># $ ward_clinical &lt;lgl&gt; TRUE, TRUE, FALSE, FALSE, FALSE, FALSE, TRUE, TRUE, F…</span>
<span class="co"># $ ward_outpatient &lt;lgl&gt; FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALS…</span>
<span class="co"># $ age &lt;dbl&gt; 65, 65, 45, 45, 45, 45, 78, 78, 45, 79, 67, 67, 71, 7…</span>
<span class="co"># $ gender &lt;chr&gt; "F", "F", "F", "F", "F", "F", "M", "M", "F", "F", "M"…</span>
<span class="co"># $ patient_id &lt;chr&gt; "A77334", "A77334", "067927", "067927", "067927", "06…</span>
<span class="co"># $ mo &lt;mo&gt; B_ESCHR_COLI, B_ESCHR_COLI, B_STPHY_EPDR, B_STPHY_EPDR…</span>
<span class="co"># $ PEN &lt;rsi&gt; R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R,…</span>
<span class="co"># $ OXA &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span>
<span class="co"># $ FLC &lt;rsi&gt; NA, NA, R, R, R, R, S, S, R, S, S, S, NA, NA, NA, NA,…</span>
<span class="co"># $ AMX &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span>
<span class="co"># $ AMC &lt;rsi&gt; I, I, NA, NA, NA, NA, S, S, NA, NA, S, S, I, I, R, I,…</span>
<span class="co"># $ AMP &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span>
<span class="co"># $ TZP &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span>
<span class="co"># $ CZO &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span>
<span class="co"># $ FEP &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span>
<span class="co"># $ CXM &lt;rsi&gt; I, I, R, R, R, R, S, S, R, S, S, S, S, S, NA, S, S, R…</span>
<span class="co"># $ FOX &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span>
<span class="co"># $ CTX &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, S, S,…</span>
<span class="co"># $ CAZ &lt;rsi&gt; NA, NA, R, R, R, R, R, R, R, R, R, R, NA, NA, NA, S, …</span>
<span class="co"># $ CRO &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, S, S,…</span>
<span class="co"># $ GEN &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span>
<span class="co"># $ TOB &lt;rsi&gt; NA, NA, NA, NA, NA, NA, S, S, NA, NA, NA, NA, S, S, N…</span>
<span class="co"># $ AMK &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span>
<span class="co"># $ KAN &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span>
<span class="co"># $ TMP &lt;rsi&gt; R, R, S, S, R, R, R, R, S, S, NA, NA, S, S, S, S, S, …</span>
<span class="co"># $ SXT &lt;rsi&gt; R, R, S, S, NA, NA, NA, NA, S, S, NA, NA, S, S, S, S,…</span>
<span class="co"># $ NIT &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span>
<span class="co"># $ FOS &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span>
<span class="co"># $ LNZ &lt;rsi&gt; R, R, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, R, R…</span>
<span class="co"># $ CIP &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, S, S, NA, NA, NA, NA,…</span>
<span class="co"># $ MFX &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span>
<span class="co"># $ VAN &lt;rsi&gt; R, R, S, S, S, S, S, S, S, S, NA, NA, R, R, R, R, R, …</span>
<span class="co"># $ TEC &lt;rsi&gt; R, R, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, R, R…</span>
<span class="co"># $ TCY &lt;rsi&gt; R, R, S, S, S, S, S, S, S, I, S, S, NA, NA, I, R, R, …</span>
<span class="co"># $ TGC &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span>
<span class="co"># $ DOX &lt;rsi&gt; NA, NA, S, S, S, S, S, S, S, NA, S, S, NA, NA, NA, R,…</span>
<span class="co"># $ ERY &lt;rsi&gt; R, R, R, R, R, R, S, S, R, S, S, S, R, R, R, R, R, R,…</span>
<span class="co"># $ CLI &lt;rsi&gt; NA, NA, NA, NA, NA, R, NA, NA, NA, NA, NA, NA, NA, NA…</span>
<span class="co"># $ AZM &lt;rsi&gt; R, R, R, R, R, R, S, S, R, S, S, S, R, R, R, R, R, R,…</span>
<span class="co"># $ IPM &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, S, S,…</span>
<span class="co"># $ MEM &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span>
<span class="co"># $ MTR &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span>
<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></body></html></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="sourceCode r"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1"></a>resistance_data &lt;-<span class="st"> </span>example_isolates <span class="op">%&gt;%</span><span class="st"> </span></span>
<span id="cb2-2"><a href="#cb2-2"></a><span class="st"> </span><span class="kw"><a href="https://dplyr.tidyverse.org/reference/group_by.html">group_by</a></span>(<span class="dt">order =</span> <span class="kw"><a href="../reference/mo_property.html">mo_order</a></span>(mo), <span class="co"># group on anything, like order</span></span>
<span id="cb2-3"><a href="#cb2-3"></a> <span class="dt">genus =</span> <span class="kw"><a href="../reference/mo_property.html">mo_genus</a></span>(mo)) <span class="op">%&gt;%</span><span class="st"> </span><span class="co"># and genus as we do here</span></span>
<span id="cb2-4"><a href="#cb2-4"></a><span class="st"> </span><span class="kw"><a href="https://dplyr.tidyverse.org/reference/summarise_all.html">summarise_if</a></span>(is.rsi, resistance) <span class="op">%&gt;%</span><span class="st"> </span><span class="co"># then get resistance of all drugs</span></span>
<span id="cb2-5"><a href="#cb2-5"></a><span class="st"> </span><span class="kw"><a href="https://dplyr.tidyverse.org/reference/select.html">select</a></span>(order, genus, AMC, CXM, CTX, </span>
<span id="cb2-6"><a href="#cb2-6"></a> CAZ, GEN, TOB, TMP, SXT) <span class="co"># and select only relevant columns</span></span>
<span id="cb2-7"><a href="#cb2-7"></a></span>
<span id="cb2-8"><a href="#cb2-8"></a><span class="kw"><a href="https://rdrr.io/r/utils/head.html">head</a></span>(resistance_data)</span>
<span id="cb2-9"><a href="#cb2-9"></a><span class="co"># # A tibble: 6 x 10</span></span>
<span id="cb2-10"><a href="#cb2-10"></a><span class="co"># # Groups: order [2]</span></span>
<span id="cb2-11"><a href="#cb2-11"></a><span class="co"># order genus AMC CXM CTX CAZ GEN TOB TMP SXT</span></span>
<span id="cb2-12"><a href="#cb2-12"></a><span class="co"># &lt;chr&gt; &lt;chr&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt;</span></span>
<span id="cb2-13"><a href="#cb2-13"></a><span class="co"># 1 (unknown orde… Micrococcoides NA NA NA NA NA NA NA NA</span></span>
<span id="cb2-14"><a href="#cb2-14"></a><span class="co"># 2 Actinomycetal… Actinomyces NA NA NA NA NA NA NA NA</span></span>
<span id="cb2-15"><a href="#cb2-15"></a><span class="co"># 3 Actinomycetal… Corynebacterium NA NA NA NA NA NA NA NA</span></span>
<span id="cb2-16"><a href="#cb2-16"></a><span class="co"># 4 Actinomycetal… Dermabacter NA NA NA NA NA NA NA NA</span></span>
<span id="cb2-17"><a href="#cb2-17"></a><span class="co"># 5 Actinomycetal… Micrococcus NA NA NA NA NA NA NA NA</span></span>
<span id="cb2-18"><a href="#cb2-18"></a><span class="co"># 6 Actinomycetal… Propionibacter… NA NA NA NA NA NA NA NA</span></span></code></pre></div>
<div class="sourceCode" id="cb2"><html><body><pre class="r"><span class="no">resistance_data</span> <span class="kw">&lt;-</span> <span class="no">example_isolates</span> <span class="kw">%&gt;%</span>
<span class="fu"><a href="https://dplyr.tidyverse.org/reference/group_by.html">group_by</a></span>(<span class="kw">order</span> <span class="kw">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_order</a></span>(<span class="no">mo</span>), <span class="co"># group on anything, like order</span>
<span class="kw">genus</span> <span class="kw">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_genus</a></span>(<span class="no">mo</span>)) <span class="kw">%&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="no">is.rsi</span>, <span class="no">resistance</span>) <span class="kw">%&gt;%</span> <span class="co"># then get resistance of all drugs</span>
<span class="fu"><a href="https://dplyr.tidyverse.org/reference/select.html">select</a></span>(<span class="no">order</span>, <span class="no">genus</span>, <span class="no">AMC</span>, <span class="no">CXM</span>, <span class="no">CTX</span>,
<span class="no">CAZ</span>, <span class="no">GEN</span>, <span class="no">TOB</span>, <span class="no">TMP</span>, <span class="no">SXT</span>) <span class="co"># and select only relevant columns</span>
<span class="fu"><a href="https://rdrr.io/r/utils/head.html">head</a></span>(<span class="no">resistance_data</span>)
<span class="co"># # A tibble: 6 x 10</span>
<span class="co"># # Groups: order [2]</span>
<span class="co"># order genus AMC CXM CTX CAZ GEN TOB TMP SXT</span>
<span class="co"># &lt;chr&gt; &lt;chr&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt;</span>
<span class="co"># 1 (unknown orde… Micrococcoides NA NA NA NA NA NA NA NA</span>
<span class="co"># 2 Actinomycetal… Actinomyces NA NA NA NA NA NA NA NA</span>
<span class="co"># 3 Actinomycetal… Corynebacterium NA NA NA NA NA NA NA NA</span>
<span class="co"># 4 Actinomycetal… Dermabacter NA NA NA NA NA NA NA NA</span>
<span class="co"># 5 Actinomycetal… Micrococcus NA NA NA NA NA NA NA NA</span>
<span class="co"># 6 Actinomycetal… Propionibacter… NA NA NA NA NA NA NA NA</span></pre></body></html></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="sourceCode r"><code class="sourceCode r"><span id="cb3-1"><a href="#cb3-1"></a>pca_result &lt;-<span class="st"> </span><span class="kw"><a href="../reference/pca.html">pca</a></span>(resistance_data)</span>
<span id="cb3-2"><a href="#cb3-2"></a><span class="co"># </span><span class="al">NOTE</span><span class="co">: Columns selected for PCA: AMC/CXM/CTX/CAZ/GEN/TOB/TMP/SXT.</span></span>
<span id="cb3-3"><a href="#cb3-3"></a><span class="co"># Total observations available: 7.</span></span></code></pre></div>
<div class="sourceCode" id="cb3"><html><body><pre class="r"><span class="no">pca_result</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../reference/pca.html">pca</a></span>(<span class="no">resistance_data</span>)
<span class="co"># NOTE: Columns selected for PCA: AMC/CXM/CTX/CAZ/GEN/TOB/TMP/SXT.</span>
<span class="co"># Total observations available: 7.</span></pre></body></html></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="sourceCode r"><code class="sourceCode r"><span id="cb4-1"><a href="#cb4-1"></a><span class="kw"><a href="https://rdrr.io/r/base/summary.html">summary</a></span>(pca_result)</span>
<span id="cb4-2"><a href="#cb4-2"></a><span class="co"># Importance of components:</span></span>
<span id="cb4-3"><a href="#cb4-3"></a><span class="co"># PC1 PC2 PC3 PC4 PC5 PC6 PC7</span></span>
<span id="cb4-4"><a href="#cb4-4"></a><span class="co"># Standard deviation 2.1580 1.6783 0.61282 0.33017 0.20150 0.03190 2.123e-16</span></span>
<span id="cb4-5"><a href="#cb4-5"></a><span class="co"># Proportion of Variance 0.5821 0.3521 0.04694 0.01363 0.00508 0.00013 0.000e+00</span></span>
<span id="cb4-6"><a href="#cb4-6"></a><span class="co"># Cumulative Proportion 0.5821 0.9342 0.98117 0.99480 0.99987 1.00000 1.000e+00</span></span></code></pre></div>
<div class="sourceCode" id="cb4"><html><body><pre class="r"><span class="fu"><a href="https://rdrr.io/r/base/summary.html">summary</a></span>(<span class="no">pca_result</span>)
<span class="co"># Importance of components:</span>
<span class="co"># PC1 PC2 PC3 PC4 PC5 PC6 PC7</span>
<span class="co"># Standard deviation 2.1580 1.6783 0.61282 0.33017 0.20150 0.03190 2.123e-16</span>
<span class="co"># Proportion of Variance 0.5821 0.3521 0.04694 0.01363 0.00508 0.00013 0.000e+00</span>
<span class="co"># Cumulative Proportion 0.5821 0.9342 0.98117 0.99480 0.99987 1.00000 1.000e+00</span></pre></body></html></div>
<p>Good news. The first two components explain a total of 93.4% 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="sourceCode r"><code class="sourceCode r"><span id="cb5-1"><a href="#cb5-1"></a><span class="kw"><a href="https://rdrr.io/r/stats/biplot.html">biplot</a></span>(pca_result)</span></code></pre></div>
<div class="sourceCode" id="cb5"><html><body><pre class="r"><span class="fu"><a href="https://rdrr.io/r/stats/biplot.html">biplot</a></span>(<span class="no">pca_result</span>)</pre></body></html></div>
<p><img src="PCA_files/figure-html/unnamed-chunk-5-1.png" width="750"></p>
<p>But we can’t see the explanation of the points. Perhaps this works better with the 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="sourceCode r"><code class="sourceCode r"><span id="cb6-1"><a href="#cb6-1"></a><span class="kw"><a href="../reference/ggplot_pca.html">ggplot_pca</a></span>(pca_result)</span></code></pre></div>
<p>But we can’t 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"><html><body><pre class="r"><span class="fu"><a href="../reference/ggplot_pca.html">ggplot_pca</a></span>(<span class="no">pca_result</span>)</pre></body></html></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="sourceCode r"><code class="sourceCode r"><span id="cb7-1"><a href="#cb7-1"></a><span class="kw"><a href="../reference/ggplot_pca.html">ggplot_pca</a></span>(pca_result, <span class="dt">ellipse =</span> <span class="ot">TRUE</span>) <span class="op">+</span></span>
<span id="cb7-2"><a href="#cb7-2"></a><span class="st"> </span>ggplot2<span class="op">::</span><span class="kw"><a href="https://ggplot2.tidyverse.org/reference/labs.html">labs</a></span>(<span class="dt">title =</span> <span class="st">"An AMR/PCA biplot!"</span>)</span></code></pre></div>
<div class="sourceCode" id="cb7"><html><body><pre class="r">
<span class="fu"><a href="../reference/ggplot_pca.html">ggplot_pca</a></span>(<span class="no">pca_result</span>, <span class="kw">ellipse</span> <span class="kw">=</span> <span class="fl">TRUE</span>) +
<span class="kw pkg">ggplot2</span><span class="kw ns">::</span><span class="fu"><a href="https://ggplot2.tidyverse.org/reference/labs.html">labs</a></span>(<span class="kw">title</span> <span class="kw">=</span> <span class="st">"An AMR/PCA biplot!"</span>)</pre></body></html></div>
<p><img src="PCA_files/figure-html/unnamed-chunk-7-1.png" width="750"></p>
</div>
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<li><a href="#perform-principal-component-analysis">Perform principal component analysis</a></li>
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Predict antimicrobial resistance
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Conduct principal component analysis for AMR
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<h1>How to import data from SPSS / SAS / Stata</h1>
<h4 class="author">Matthijs S. Berends</h4>
<h4 class="date">23 February 2020</h4>
<h4 class="date">17 March 2020</h4>
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@ -75,6 +74,13 @@
Predict antimicrobial resistance
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Conduct principal component analysis for AMR
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@ -179,7 +185,7 @@
<h1>How to work with WHONET data</h1>
<h4 class="author">Matthijs S. Berends</h4>
<h4 class="date">23 February 2020</h4>
<h4 class="date">17 March 2020</h4>
<div class="hidden name"><code>WHONET.Rmd</code></div>
@ -379,7 +385,7 @@ Unique: 3</p>
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@ -186,7 +185,7 @@
<h1>Benchmarks</h1>
<h4 class="author">Matthijs S. Berends</h4>
<h4 class="date">14 March 2020</h4>
<h4 class="date">17 March 2020</h4>
<div class="hidden name"><code>benchmarks.Rmd</code></div>
@ -220,21 +219,36 @@
<span id="cb2-16"><a href="#cb2-16"></a> <span class="dt">times =</span> <span class="dv">10</span>)</span>
<span id="cb2-17"><a href="#cb2-17"></a><span class="kw"><a href="https://rdrr.io/r/base/print.html">print</a></span>(S.aureus, <span class="dt">unit =</span> <span class="st">"ms"</span>, <span class="dt">signif =</span> <span class="dv">2</span>)</span>
<span id="cb2-18"><a href="#cb2-18"></a><span class="co"># Unit: milliseconds</span></span>
<span id="cb2-19"><a href="#cb2-19"></a><span class="co"># expr min lq mean median uq max neval</span></span>
<span id="cb2-20"><a href="#cb2-20"></a><span class="co"># as.mo("sau") 8.9 9.3 9.6 9.6 9.9 10 10</span></span>
<span id="cb2-21"><a href="#cb2-21"></a><span class="co"># as.mo("stau") 41.0 41.0 51.0 43.0 67.0 74 10</span></span>
<span id="cb2-22"><a href="#cb2-22"></a><span class="co"># as.mo("STAU") 39.0 41.0 49.0 42.0 56.0 72 10</span></span>
<span id="cb2-23"><a href="#cb2-23"></a><span class="co"># as.mo("staaur") 9.0 9.2 9.7 9.5 9.9 11 10</span></span>
<span id="cb2-24"><a href="#cb2-24"></a><span class="co"># as.mo("STAAUR") 9.5 9.8 24.0 21.0 38.0 45 10</span></span>
<span id="cb2-25"><a href="#cb2-25"></a><span class="co"># as.mo("S. aureus") 15.0 16.0 26.0 18.0 38.0 61 10</span></span>
<span id="cb2-26"><a href="#cb2-26"></a><span class="co"># as.mo("S aureus") 15.0 15.0 17.0 16.0 17.0 21 10</span></span>
<span id="cb2-27"><a href="#cb2-27"></a><span class="co"># as.mo("Staphylococcus aureus") 5.2 5.6 8.4 6.0 6.5 30 10</span></span>
<span id="cb2-28"><a href="#cb2-28"></a><span class="co"># as.mo("Staphylococcus aureus (MRSA)") 640.0 690.0 710.0 710.0 720.0 760 10</span></span>
<span id="cb2-29"><a href="#cb2-29"></a><span class="co"># as.mo("Sthafilokkockus aaureuz") 350.0 360.0 420.0 400.0 490.0 510 10</span></span>
<span id="cb2-30"><a href="#cb2-30"></a><span class="co"># as.mo("MRSA") 9.2 9.3 16.0 10.0 10.0 49 10</span></span>
<span id="cb2-31"><a href="#cb2-31"></a><span class="co"># as.mo("VISA") 25.0 27.0 46.0 56.0 57.0 60 10</span></span>
<span id="cb2-32"><a href="#cb2-32"></a><span class="co"># as.mo("VRSA") 26.0 27.0 39.0 28.0 32.0 120 10</span></span>
<span id="cb2-33"><a href="#cb2-33"></a><span class="co"># as.mo(22242419) 120.0 140.0 170.0 140.0 150.0 410 10</span></span></code></pre></div>
<span id="cb2-19"><a href="#cb2-19"></a><span class="co"># expr min lq mean median uq max</span></span>
<span id="cb2-20"><a href="#cb2-20"></a><span class="co"># as.mo("sau") 9.1 9.5 12.0 9.8 10.0 34.0</span></span>
<span id="cb2-21"><a href="#cb2-21"></a><span class="co"># as.mo("stau") 38.0 41.0 55.0 41.0 42.0 160.0</span></span>
<span id="cb2-22"><a href="#cb2-22"></a><span class="co"># as.mo("STAU") 41.0 41.0 50.0 44.0 63.0 67.0</span></span>
<span id="cb2-23"><a href="#cb2-23"></a><span class="co"># as.mo("staaur") 8.6 8.8 9.4 9.4 9.6 10.0</span></span>
<span id="cb2-24"><a href="#cb2-24"></a><span class="co"># as.mo("STAAUR") 8.6 8.8 12.0 9.3 9.8 37.0</span></span>
<span id="cb2-25"><a href="#cb2-25"></a><span class="co"># as.mo("S. aureus") 14.0 15.0 51.0 27.0 43.0 250.0</span></span>
<span id="cb2-26"><a href="#cb2-26"></a><span class="co"># as.mo("S aureus") 14.0 15.0 16.0 15.0 16.0 18.0</span></span>
<span id="cb2-27"><a href="#cb2-27"></a><span class="co"># as.mo("Staphylococcus aureus") 5.1 5.3 5.7 5.5 6.0 6.6</span></span>
<span id="cb2-28"><a href="#cb2-28"></a><span class="co"># as.mo("Staphylococcus aureus (MRSA)") 640.0 670.0 700.0 690.0 730.0 760.0</span></span>
<span id="cb2-29"><a href="#cb2-29"></a><span class="co"># as.mo("Sthafilokkockus aaureuz") 370.0 380.0 420.0 400.0 440.0 510.0</span></span>
<span id="cb2-30"><a href="#cb2-30"></a><span class="co"># as.mo("MRSA") 8.6 8.7 11.0 9.1 9.6 32.0</span></span>
<span id="cb2-31"><a href="#cb2-31"></a><span class="co"># as.mo("VISA") 24.0 25.0 38.0 38.0 50.0 53.0</span></span>
<span id="cb2-32"><a href="#cb2-32"></a><span class="co"># as.mo("VRSA") 24.0 25.0 49.0 37.0 52.0 150.0</span></span>
<span id="cb2-33"><a href="#cb2-33"></a><span class="co"># as.mo(22242419) 130.0 140.0 150.0 140.0 160.0 160.0</span></span>
<span id="cb2-34"><a href="#cb2-34"></a><span class="co"># neval</span></span>
<span id="cb2-35"><a href="#cb2-35"></a><span class="co"># 10</span></span>
<span id="cb2-36"><a href="#cb2-36"></a><span class="co"># 10</span></span>
<span id="cb2-37"><a href="#cb2-37"></a><span class="co"># 10</span></span>
<span id="cb2-38"><a href="#cb2-38"></a><span class="co"># 10</span></span>
<span id="cb2-39"><a href="#cb2-39"></a><span class="co"># 10</span></span>
<span id="cb2-40"><a href="#cb2-40"></a><span class="co"># 10</span></span>
<span id="cb2-41"><a href="#cb2-41"></a><span class="co"># 10</span></span>
<span id="cb2-42"><a href="#cb2-42"></a><span class="co"># 10</span></span>
<span id="cb2-43"><a href="#cb2-43"></a><span class="co"># 10</span></span>
<span id="cb2-44"><a href="#cb2-44"></a><span class="co"># 10</span></span>
<span id="cb2-45"><a href="#cb2-45"></a><span class="co"># 10</span></span>
<span id="cb2-46"><a href="#cb2-46"></a><span class="co"># 10</span></span>
<span id="cb2-47"><a href="#cb2-47"></a><span class="co"># 10</span></span>
<span id="cb2-48"><a href="#cb2-48"></a><span class="co"># 10</span></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.</p>
<p>To achieve this speed, the <code>as.mo</code> function also takes into account the prevalence of human pathogenic microorganisms. The downside of this is of course that less prevalent microorganisms will be determined less fast. See this example for the ID of <em>Methanosarcina semesiae</em> (<code>B_MTHNSR_SEMS</code>), a bug probably never found before in humans:</p>
@ -246,19 +260,19 @@
<span id="cb3-6"><a href="#cb3-6"></a> <span class="dt">times =</span> <span class="dv">10</span>)</span>
<span id="cb3-7"><a href="#cb3-7"></a><span class="kw"><a href="https://rdrr.io/r/base/print.html">print</a></span>(M.semesiae, <span class="dt">unit =</span> <span class="st">"ms"</span>, <span class="dt">signif =</span> <span class="dv">4</span>)</span>
<span id="cb3-8"><a href="#cb3-8"></a><span class="co"># Unit: milliseconds</span></span>
<span id="cb3-9"><a href="#cb3-9"></a><span class="co"># expr min lq mean median uq</span></span>
<span id="cb3-10"><a href="#cb3-10"></a><span class="co"># as.mo("metsem") 1485.000 1507.000 1524.00 1519.000 1538.000</span></span>
<span id="cb3-11"><a href="#cb3-11"></a><span class="co"># as.mo("METSEM") 1371.000 1495.000 1557.00 1567.000 1633.000</span></span>
<span id="cb3-12"><a href="#cb3-12"></a><span class="co"># as.mo("M. semesiae") 16.010 16.310 25.38 16.480 42.840</span></span>
<span id="cb3-13"><a href="#cb3-13"></a><span class="co"># as.mo("M. semesiae") 15.700 15.900 16.74 16.370 17.480</span></span>
<span id="cb3-14"><a href="#cb3-14"></a><span class="co"># as.mo("Methanosarcina semesiae") 5.885 6.116 11.79 6.347 8.155</span></span>
<span id="cb3-9"><a href="#cb3-9"></a><span class="co"># expr min lq mean median uq</span></span>
<span id="cb3-10"><a href="#cb3-10"></a><span class="co"># as.mo("metsem") 1530.00 1561.000 1618.00 1620.000 1665.000</span></span>
<span id="cb3-11"><a href="#cb3-11"></a><span class="co"># as.mo("METSEM") 1482.00 1523.000 1544.00 1555.000 1567.000</span></span>
<span id="cb3-12"><a href="#cb3-12"></a><span class="co"># as.mo("M. semesiae") 15.01 16.140 27.85 17.080 43.620</span></span>
<span id="cb3-13"><a href="#cb3-13"></a><span class="co"># as.mo("M. semesiae") 15.54 16.200 19.30 16.570 17.040</span></span>
<span id="cb3-14"><a href="#cb3-14"></a><span class="co"># as.mo("Methanosarcina semesiae") 6.05 6.307 9.35 6.437 7.649</span></span>
<span id="cb3-15"><a href="#cb3-15"></a><span class="co"># max neval</span></span>
<span id="cb3-16"><a href="#cb3-16"></a><span class="co"># 1577.00 10</span></span>
<span id="cb3-17"><a href="#cb3-17"></a><span class="co"># 1663.00 10</span></span>
<span id="cb3-18"><a href="#cb3-18"></a><span class="co"># 48.53 10</span></span>
<span id="cb3-19"><a href="#cb3-19"></a><span class="co"># 18.55 10</span></span>
<span id="cb3-20"><a href="#cb3-20"></a><span class="co"># 32.92 10</span></span></code></pre></div>
<p>That takes 5.5 times as much time on average. We can conclude that looking up arbitrary codes of less prevalent microorganisms is the worst way to go, in terms of calculation performance. Full names (like <em>Methanosarcina semesiae</em>) are always very fast and only take some thousands of seconds to coerce - they are the most probable input from most data sets.</p>
<span id="cb3-16"><a href="#cb3-16"></a><span class="co"># 1710.00 10</span></span>
<span id="cb3-17"><a href="#cb3-17"></a><span class="co"># 1586.00 10</span></span>
<span id="cb3-18"><a href="#cb3-18"></a><span class="co"># 50.39 10</span></span>
<span id="cb3-19"><a href="#cb3-19"></a><span class="co"># 42.95 10</span></span>
<span id="cb3-20"><a href="#cb3-20"></a><span class="co"># 33.42 10</span></span></code></pre></div>
<p>That takes 5.7 times as much time on average. We can conclude that looking up arbitrary codes of less prevalent microorganisms is the worst way to go, in terms of calculation performance. Full names (like <em>Methanosarcina semesiae</em>) are always very fast and only take some thousands of seconds to coerce - they are the most probable input from most data sets.</p>
<p>In the figure below, we compare <em>Escherichia coli</em> (which is very common) with <em>Prevotella brevis</em> (which is moderately common) and with <em>Methanosarcina semesiae</em> (which is uncommon):</p>
<p><img src="benchmarks_files/figure-html/unnamed-chunk-6-1.png" width="900"></p>
<p>Uncommon microorganisms take a lot more time than common microorganisms. To relieve this pitfall and further improve performance, two important calculations take almost no time at all: <strong>repetitive results</strong> and <strong>already precalculated results</strong>.</p>
@ -292,8 +306,8 @@
<span id="cb4-24"><a href="#cb4-24"></a><span class="kw"><a href="https://rdrr.io/r/base/print.html">print</a></span>(run_it, <span class="dt">unit =</span> <span class="st">"ms"</span>, <span class="dt">signif =</span> <span class="dv">3</span>)</span>
<span id="cb4-25"><a href="#cb4-25"></a><span class="co"># Unit: milliseconds</span></span>
<span id="cb4-26"><a href="#cb4-26"></a><span class="co"># expr min lq mean median uq max neval</span></span>
<span id="cb4-27"><a href="#cb4-27"></a><span class="co"># mo_name(x) 542 585 605 601 614 738 100</span></span></code></pre></div>
<p>So transforming 500,000 values (!!) of 50 unique values only takes 0.6 seconds (600 ms). You only lose time on your unique input values.</p>
<span id="cb4-27"><a href="#cb4-27"></a><span class="co"># mo_name(x) 572 626 648 645 666 792 100</span></span></code></pre></div>
<p>So transforming 500,000 values (!!) of 50 unique values only takes 0.64 seconds (644 ms). You only lose time on your unique input values.</p>
</div>
<div id="precalculated-results" class="section level3">
<h3 class="hasAnchor">
@ -305,10 +319,10 @@
<span id="cb5-4"><a href="#cb5-4"></a> <span class="dt">times =</span> <span class="dv">10</span>)</span>
<span id="cb5-5"><a href="#cb5-5"></a><span class="kw"><a href="https://rdrr.io/r/base/print.html">print</a></span>(run_it, <span class="dt">unit =</span> <span class="st">"ms"</span>, <span class="dt">signif =</span> <span class="dv">3</span>)</span>
<span id="cb5-6"><a href="#cb5-6"></a><span class="co"># Unit: milliseconds</span></span>
<span id="cb5-7"><a href="#cb5-7"></a><span class="co"># expr min lq mean median uq max neval</span></span>
<span id="cb5-8"><a href="#cb5-8"></a><span class="co"># A 6.760 6.900 7.43 7.070 7.540 9.290 10</span></span>
<span id="cb5-9"><a href="#cb5-9"></a><span class="co"># B 14.200 14.400 18.80 14.900 16.000 51.500 10</span></span>
<span id="cb5-10"><a href="#cb5-10"></a><span class="co"># C 0.586 0.726 0.74 0.757 0.763 0.804 10</span></span></code></pre></div>
<span id="cb5-7"><a href="#cb5-7"></a><span class="co"># expr min lq mean median uq max neval</span></span>
<span id="cb5-8"><a href="#cb5-8"></a><span class="co"># A 6.730 6.860 7.110 6.950 7.170 8.47 10</span></span>
<span id="cb5-9"><a href="#cb5-9"></a><span class="co"># B 14.500 14.800 18.100 15.500 15.900 43.10 10</span></span>
<span id="cb5-10"><a href="#cb5-10"></a><span class="co"># C 0.726 0.753 0.821 0.791 0.882 1.04 10</span></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 0.0008 seconds - it doesn’t 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="cb6"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb6-1"><a href="#cb6-1"></a>run_it &lt;-<span class="st"> </span><span class="kw"><a href="https://rdrr.io/pkg/microbenchmark/man/microbenchmark.html">microbenchmark</a></span>(<span class="dt">A =</span> <span class="kw"><a href="../reference/mo_property.html">mo_species</a></span>(<span class="st">"aureus"</span>),</span>
<span id="cb6-2"><a href="#cb6-2"></a> <span class="dt">B =</span> <span class="kw"><a href="../reference/mo_property.html">mo_genus</a></span>(<span class="st">"Staphylococcus"</span>),</span>
@ -322,14 +336,14 @@
<span id="cb6-10"><a href="#cb6-10"></a><span class="kw"><a href="https://rdrr.io/r/base/print.html">print</a></span>(run_it, <span class="dt">unit =</span> <span class="st">"ms"</span>, <span class="dt">signif =</span> <span class="dv">3</span>)</span>
<span id="cb6-11"><a href="#cb6-11"></a><span class="co"># Unit: milliseconds</span></span>
<span id="cb6-12"><a href="#cb6-12"></a><span class="co"># expr min lq mean median uq max neval</span></span>
<span id="cb6-13"><a href="#cb6-13"></a><span class="co"># A 0.374 0.381 0.389 0.389 0.395 0.416 10</span></span>
<span id="cb6-14"><a href="#cb6-14"></a><span class="co"># B 0.404 0.411 0.422 0.421 0.425 0.452 10</span></span>
<span id="cb6-15"><a href="#cb6-15"></a><span class="co"># C 0.615 0.711 0.726 0.730 0.751 0.861 10</span></span>
<span id="cb6-16"><a href="#cb6-16"></a><span class="co"># D 0.405 0.409 0.429 0.428 0.435 0.485 10</span></span>
<span id="cb6-17"><a href="#cb6-17"></a><span class="co"># E 0.381 0.384 0.392 0.390 0.394 0.429 10</span></span>
<span id="cb6-18"><a href="#cb6-18"></a><span class="co"># F 0.365 0.366 0.379 0.375 0.383 0.419 10</span></span>
<span id="cb6-19"><a href="#cb6-19"></a><span class="co"># G 0.362 0.372 0.378 0.380 0.388 0.391 10</span></span>
<span id="cb6-20"><a href="#cb6-20"></a><span class="co"># H 0.378 0.381 0.403 0.387 0.393 0.556 10</span></span></code></pre></div>
<span id="cb6-13"><a href="#cb6-13"></a><span class="co"># A 0.348 0.398 0.405 0.404 0.419 0.438 10</span></span>
<span id="cb6-14"><a href="#cb6-14"></a><span class="co"># B 0.407 0.416 0.461 0.426 0.490 0.667 10</span></span>
<span id="cb6-15"><a href="#cb6-15"></a><span class="co"># C 0.701 0.745 0.757 0.754 0.769 0.814 10</span></span>
<span id="cb6-16"><a href="#cb6-16"></a><span class="co"># D 0.435 0.440 0.497 0.464 0.569 0.588 10</span></span>
<span id="cb6-17"><a href="#cb6-17"></a><span class="co"># E 0.351 0.397 0.435 0.419 0.489 0.520 10</span></span>
<span id="cb6-18"><a href="#cb6-18"></a><span class="co"># F 0.358 0.385 0.396 0.393 0.420 0.430 10</span></span>
<span id="cb6-19"><a href="#cb6-19"></a><span class="co"># G 0.322 0.363 0.409 0.401 0.450 0.508 10</span></span>
<span id="cb6-20"><a href="#cb6-20"></a><span class="co"># H 0.340 0.372 0.384 0.385 0.403 0.432 10</span></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">
@ -356,18 +370,18 @@
<span id="cb7-18"><a href="#cb7-18"></a><span class="kw"><a href="https://rdrr.io/r/base/print.html">print</a></span>(run_it, <span class="dt">unit =</span> <span class="st">"ms"</span>, <span class="dt">signif =</span> <span class="dv">4</span>)</span>
<span id="cb7-19"><a href="#cb7-19"></a><span class="co"># Unit: milliseconds</span></span>
<span id="cb7-20"><a href="#cb7-20"></a><span class="co"># expr min lq mean median uq max neval</span></span>
<span id="cb7-21"><a href="#cb7-21"></a><span class="co"># en 24.76 26.92 35.44 27.70 31.93 143.10 100</span></span>
<span id="cb7-22"><a href="#cb7-22"></a><span class="co"># de 26.46 28.18 33.90 29.51 30.51 64.85 100</span></span>
<span id="cb7-23"><a href="#cb7-23"></a><span class="co"># nl 32.40 34.89 39.79 35.94 37.28 75.95 100</span></span>
<span id="cb7-24"><a href="#cb7-24"></a><span class="co"># es 26.41 28.80 34.46 29.56 31.58 67.56 100</span></span>
<span id="cb7-25"><a href="#cb7-25"></a><span class="co"># it 26.44 28.52 35.22 29.30 30.37 156.00 100</span></span>
<span id="cb7-26"><a href="#cb7-26"></a><span class="co"># fr 26.24 28.09 34.78 29.52 31.23 65.88 100</span></span>
<span id="cb7-27"><a href="#cb7-27"></a><span class="co"># pt 26.28 28.32 36.00 29.49 32.22 66.76 100</span></span></code></pre></div>
<span id="cb7-21"><a href="#cb7-21"></a><span class="co"># en 25.28 26.99 33.53 27.88 29.51 61.96 100</span></span>
<span id="cb7-22"><a href="#cb7-22"></a><span class="co"># de 26.99 29.27 36.92 30.02 33.09 160.30 100</span></span>
<span id="cb7-23"><a href="#cb7-23"></a><span class="co"># nl 32.37 34.58 40.59 35.88 37.10 67.43 100</span></span>
<span id="cb7-24"><a href="#cb7-24"></a><span class="co"># es 27.54 29.14 34.40 29.88 31.45 61.89 100</span></span>
<span id="cb7-25"><a href="#cb7-25"></a><span class="co"># it 26.77 28.79 34.28 29.73 31.30 69.41 100</span></span>
<span id="cb7-26"><a href="#cb7-26"></a><span class="co"># fr 27.01 28.81 34.96 29.39 30.79 161.10 100</span></span>
<span id="cb7-27"><a href="#cb7-27"></a><span class="co"># pt 26.76 28.85 34.13 29.69 31.74 63.08 100</span></span></code></pre></div>
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Predict antimicrobial resistance
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<h1>How to predict antimicrobial resistance</h1>
<h4 class="author">Matthijs S. Berends</h4>
<h4 class="date">23 February 2020</h4>
<h4 class="date">17 March 2020</h4>
<div class="hidden name"><code>resistance_predict.Rmd</code></div>
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