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@ -41,7 +41,7 @@
</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">0.7.1.9105</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.8.0</span>
</span>
</div>
@ -187,7 +187,7 @@
<h1>How to conduct AMR analysis</h1>
<h4 class="author">Matthijs S. Berends</h4>
<h4 class="date">12 October 2019</h4>
<h4 class="date">13 October 2019</h4>
<div class="hidden name"><code>AMR.Rmd</code></div>
@ -196,7 +196,7 @@
<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 12 October 2019.</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 13 October 2019.</p>
<div id="introduction" class="section level1">
<h1 class="hasAnchor">
<a href="#introduction" class="anchor"></a>Introduction</h1>
@ -212,21 +212,21 @@
</tr></thead>
<tbody>
<tr class="odd">
<td align="center">2019-10-12</td>
<td align="center">2019-10-13</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">2019-10-12</td>
<td align="center">2019-10-13</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">2019-10-12</td>
<td align="center">2019-10-13</td>
<td align="center">efgh</td>
<td align="center">Escherichia coli</td>
<td align="center">R</td>
@ -321,68 +321,68 @@
</tr></thead>
<tbody>
<tr class="odd">
<td align="center">2017-03-23</td>
<td align="center">O1</td>
<td align="center">2011-07-28</td>
<td align="center">N6</td>
<td align="center">Hospital C</td>
<td align="center">Escherichia 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">F</td>
</tr>
<tr class="even">
<td align="center">2012-08-16</td>
<td align="center">Q6</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>
</tr>
<tr class="odd">
<td align="center">2017-05-07</td>
<td align="center">G5</td>
<td align="center">Hospital A</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>
</tr>
<tr class="even">
<td align="center">2016-10-13</td>
<td align="center">G10</td>
<td align="center">Hospital D</td>
<td align="center">Escherichia coli</td>
<td align="center">I</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">M</td>
</tr>
<tr class="odd">
<td align="center">2017-11-09</td>
<td align="center">M7</td>
<td align="center">Hospital D</td>
<td align="center">Escherichia coli</td>
<td align="center">I</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">M</td>
</tr>
<tr class="even">
<td align="center">2016-03-31</td>
<td align="center">P9</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">R</td>
<td align="center">S</td>
<td align="center">F</td>
</tr>
<tr class="odd">
<td align="center">2015-07-16</td>
<td align="center">U2</td>
<td align="center">Hospital B</td>
<td align="center">Streptococcus pneumoniae</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">F</td>
</tr>
<tr class="even">
<td align="center">2014-10-22</td>
<td align="center">B8</td>
<td align="center">Hospital B</td>
<td align="center">Staphylococcus aureus</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>
</tr>
<tr class="even">
<td align="center">2015-10-29</td>
<td align="center">V10</td>
<td align="center">Hospital D</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>
</tr>
<tr class="odd">
<td align="center">2013-08-21</td>
<td align="center">U1</td>
<td align="center">Hospital A</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">F</td>
</tr>
<tr class="even">
<td align="center">2017-06-24</td>
<td align="center">K6</td>
<td align="center">Hospital A</td>
<td align="center">Staphylococcus aureus</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">M</td>
</tr>
@ -407,8 +407,8 @@
#
# Item Count Percent Cum. Count Cum. Percent
# --- ----- ------- -------- ----------- -------------
# 1 M 10,324 51.62% 10,324 51.62%
# 2 F 9,676 48.38% 20,000 100.00%</code></pre>
# 1 M 10,399 52.00% 10,399 52.00%
# 2 F 9,601 48.01% 20,000 100.00%</code></pre>
<p>So, we can draw at least two conclusions immediately. From a data scientists perspective, the data looks clean: only values <code>M</code> and <code>F</code>. From a researchers perspective: there are slightly more men. Nothing we didn’t already know.</p>
<p>The data is already quite clean, but we still need to transform some variables. The <code>bacteria</code> column now consists of text, and we want to add more variables based on microbial IDs later on. So, we will transform this column to valid IDs. The <code><a href="https://dplyr.tidyverse.org/reference/mutate.html">mutate()</a></code> function of the <code>dplyr</code> package makes this really easy:</p>
<div class="sourceCode" id="cb13"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb13-1" data-line-number="1">data &lt;-<span class="st"> </span>data <span class="op">%&gt;%</span></a>
@ -438,14 +438,14 @@
<a class="sourceLine" id="cb15-18" data-line-number="18"><span class="co"># Pasteurella multocida (no changes)</span></a>
<a class="sourceLine" id="cb15-19" data-line-number="19"><span class="co"># Staphylococcus (no changes)</span></a>
<a class="sourceLine" id="cb15-20" data-line-number="20"><span class="co"># Streptococcus groups A, B, C, G (no changes)</span></a>
<a class="sourceLine" id="cb15-21" data-line-number="21"><span class="co"># Streptococcus pneumoniae (1,451 values changed)</span></a>
<a class="sourceLine" id="cb15-21" data-line-number="21"><span class="co"># Streptococcus pneumoniae (1,461 values changed)</span></a>
<a class="sourceLine" id="cb15-22" data-line-number="22"><span class="co"># Viridans group streptococci (no changes)</span></a>
<a class="sourceLine" id="cb15-23" data-line-number="23"><span class="co"># </span></a>
<a class="sourceLine" id="cb15-24" data-line-number="24"><span class="co"># EUCAST Expert Rules, Intrinsic Resistance and Exceptional Phenotypes (v3.1, 2016)</span></a>
<a class="sourceLine" id="cb15-25" data-line-number="25"><span class="co"># Table 01: Intrinsic resistance in Enterobacteriaceae (1,325 values changed)</span></a>
<a class="sourceLine" id="cb15-25" data-line-number="25"><span class="co"># Table 01: Intrinsic resistance in Enterobacteriaceae (1,290 values changed)</span></a>
<a class="sourceLine" id="cb15-26" data-line-number="26"><span class="co"># Table 02: Intrinsic resistance in non-fermentative Gram-negative bacteria (no changes)</span></a>
<a class="sourceLine" id="cb15-27" data-line-number="27"><span class="co"># Table 03: Intrinsic resistance in other Gram-negative bacteria (no changes)</span></a>
<a class="sourceLine" id="cb15-28" data-line-number="28"><span class="co"># Table 04: Intrinsic resistance in Gram-positive bacteria (2,722 values changed)</span></a>
<a class="sourceLine" id="cb15-28" data-line-number="28"><span class="co"># Table 04: Intrinsic resistance in Gram-positive bacteria (2,724 values changed)</span></a>
<a class="sourceLine" id="cb15-29" data-line-number="29"><span class="co"># Table 08: Interpretive rules for B-lactam agents and Gram-positive cocci (no changes)</span></a>
<a class="sourceLine" id="cb15-30" data-line-number="30"><span class="co"># Table 09: Interpretive rules for B-lactam agents and Gram-negative rods (no changes)</span></a>
<a class="sourceLine" id="cb15-31" data-line-number="31"><span class="co"># Table 11: Interpretive rules for macrolides, lincosamides, and streptogramins (no changes)</span></a>
@ -453,24 +453,24 @@
<a class="sourceLine" id="cb15-33" data-line-number="33"><span class="co"># Table 13: Interpretive rules for quinolones (no changes)</span></a>
<a class="sourceLine" id="cb15-34" data-line-number="34"><span class="co"># </span></a>
<a class="sourceLine" id="cb15-35" data-line-number="35"><span class="co"># Other rules</span></a>
<a class="sourceLine" id="cb15-36" data-line-number="36"><span class="co"># Non-EUCAST: amoxicillin/clav acid = S where ampicillin = S (2,240 values changed)</span></a>
<a class="sourceLine" id="cb15-37" data-line-number="37"><span class="co"># Non-EUCAST: ampicillin = R where amoxicillin/clav acid = R (134 values changed)</span></a>
<a class="sourceLine" id="cb15-36" data-line-number="36"><span class="co"># Non-EUCAST: amoxicillin/clav acid = S where ampicillin = S (2,293 values changed)</span></a>
<a class="sourceLine" id="cb15-37" data-line-number="37"><span class="co"># Non-EUCAST: ampicillin = R where amoxicillin/clav acid = R (126 values changed)</span></a>
<a class="sourceLine" id="cb15-38" data-line-number="38"><span class="co"># Non-EUCAST: piperacillin = R where piperacillin/tazobactam = R (no changes)</span></a>
<a class="sourceLine" id="cb15-39" data-line-number="39"><span class="co"># Non-EUCAST: piperacillin/tazobactam = S where piperacillin = S (no changes)</span></a>
<a class="sourceLine" id="cb15-40" data-line-number="40"><span class="co"># Non-EUCAST: trimethoprim = R where trimethoprim/sulfa = R (no changes)</span></a>
<a class="sourceLine" id="cb15-41" data-line-number="41"><span class="co"># Non-EUCAST: trimethoprim/sulfa = S where trimethoprim = S (no changes)</span></a>
<a class="sourceLine" id="cb15-42" data-line-number="42"><span class="co"># </span></a>
<a class="sourceLine" id="cb15-43" data-line-number="43"><span class="co"># --------------------------------------------------------------------------</span></a>
<a class="sourceLine" id="cb15-44" data-line-number="44"><span class="co"># EUCAST rules affected 6,530 out of 20,000 rows, making a total of 7,872 edits</span></a>
<a class="sourceLine" id="cb15-44" data-line-number="44"><span class="co"># EUCAST rules affected 6,534 out of 20,000 rows, making a total of 7,894 edits</span></a>
<a class="sourceLine" id="cb15-45" data-line-number="45"><span class="co"># =&gt; added 0 test results</span></a>
<a class="sourceLine" id="cb15-46" data-line-number="46"><span class="co"># </span></a>
<a class="sourceLine" id="cb15-47" data-line-number="47"><span class="co"># =&gt; changed 7,872 test results</span></a>
<a class="sourceLine" id="cb15-48" data-line-number="48"><span class="co"># - 104 test results changed from S to I</span></a>
<a class="sourceLine" id="cb15-49" data-line-number="49"><span class="co"># - 4,728 test results changed from S to R</span></a>
<a class="sourceLine" id="cb15-50" data-line-number="50"><span class="co"># - 1,044 test results changed from I to S</span></a>
<a class="sourceLine" id="cb15-51" data-line-number="51"><span class="co"># - 344 test results changed from I to R</span></a>
<a class="sourceLine" id="cb15-52" data-line-number="52"><span class="co"># - 1,620 test results changed from R to S</span></a>
<a class="sourceLine" id="cb15-53" data-line-number="53"><span class="co"># - 32 test results changed from R to I</span></a>
<a class="sourceLine" id="cb15-47" data-line-number="47"><span class="co"># =&gt; changed 7,894 test results</span></a>
<a class="sourceLine" id="cb15-48" data-line-number="48"><span class="co"># - 126 test results changed from S to I</span></a>
<a class="sourceLine" id="cb15-49" data-line-number="49"><span class="co"># - 4,707 test results changed from S to R</span></a>
<a class="sourceLine" id="cb15-50" data-line-number="50"><span class="co"># - 1,073 test results changed from I to S</span></a>
<a class="sourceLine" id="cb15-51" data-line-number="51"><span class="co"># - 317 test results changed from I to R</span></a>
<a class="sourceLine" id="cb15-52" data-line-number="52"><span class="co"># - 1,653 test results changed from R to S</span></a>
<a class="sourceLine" id="cb15-53" data-line-number="53"><span class="co"># - 18 test results changed from R to I</span></a>
<a class="sourceLine" id="cb15-54" data-line-number="54"><span class="co"># --------------------------------------------------------------------------</span></a>
<a class="sourceLine" id="cb15-55" data-line-number="55"><span class="co"># </span></a>
<a class="sourceLine" id="cb15-56" data-line-number="56"><span class="co"># Use eucast_rules(..., verbose = TRUE) (on your original data) to get a data.frame with all specified edits instead.</span></a></code></pre></div>
@ -498,7 +498,7 @@
<a class="sourceLine" id="cb17-3" data-line-number="3"><span class="co"># </span><span class="al">NOTE</span><span class="co">: Using column `bacteria` as input for `col_mo`.</span></a>
<a class="sourceLine" id="cb17-4" data-line-number="4"><span class="co"># </span><span class="al">NOTE</span><span class="co">: Using column `date` as input for `col_date`.</span></a>
<a class="sourceLine" id="cb17-5" data-line-number="5"><span class="co"># </span><span class="al">NOTE</span><span class="co">: Using column `patient_id` as input for `col_patient_id`.</span></a>
<a class="sourceLine" id="cb17-6" data-line-number="6"><span class="co"># =&gt; Found 5,669 first isolates (28.3% of total)</span></a></code></pre></div>
<a class="sourceLine" id="cb17-6" data-line-number="6"><span class="co"># =&gt; Found 5,663 first isolates (28.3% of total)</span></a></code></pre></div>
<p>So only 28.3% 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="cb18"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb18-1" data-line-number="1">data_1st &lt;-<span class="st"> </span>data <span class="op">%&gt;%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb18-2" data-line-number="2"><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>)</a></code></pre></div>
@ -509,7 +509,7 @@
<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 H5, 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 X9, sorted on date:</p>
<table class="table">
<thead><tr class="header">
<th align="center">isolate</th>
@ -525,10 +525,10 @@
<tbody>
<tr class="odd">
<td align="center">1</td>
<td align="center">2010-04-02</td>
<td align="center">H5</td>
<td align="center">2010-01-31</td>
<td align="center">X9</td>
<td align="center">B_ESCHR_COLI</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>
@ -536,10 +536,10 @@
</tr>
<tr class="even">
<td align="center">2</td>
<td align="center">2010-05-11</td>
<td align="center">H5</td>
<td align="center">2010-04-03</td>
<td align="center">X9</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>
@ -547,21 +547,21 @@
</tr>
<tr class="odd">
<td align="center">3</td>
<td align="center">2010-08-11</td>
<td align="center">H5</td>
<td align="center">2010-05-12</td>
<td align="center">X9</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">S</td>
<td align="center">S</td>
<td align="center">FALSE</td>
</tr>
<tr class="even">
<td align="center">4</td>
<td align="center">2010-08-26</td>
<td align="center">H5</td>
<td align="center">2010-08-29</td>
<td align="center">X9</td>
<td align="center">B_ESCHR_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">S</td>
@ -569,10 +569,10 @@
</tr>
<tr class="odd">
<td align="center">5</td>
<td align="center">2010-11-17</td>
<td align="center">H5</td>
<td align="center">2011-01-07</td>
<td align="center">X9</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>
@ -580,30 +580,8 @@
</tr>
<tr class="even">
<td align="center">6</td>
<td align="center">2010-12-20</td>
<td align="center">H5</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>
</tr>
<tr class="odd">
<td align="center">7</td>
<td align="center">2011-03-21</td>
<td align="center">H5</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>
</tr>
<tr class="even">
<td align="center">8</td>
<td align="center">2011-04-16</td>
<td align="center">H5</td>
<td align="center">2011-04-20</td>
<td align="center">X9</td>
<td align="center">B_ESCHR_COLI</td>
<td align="center">S</td>
<td align="center">S</td>
@ -612,24 +590,46 @@
<td align="center">TRUE</td>
</tr>
<tr class="odd">
<td align="center">9</td>
<td align="center">2011-07-03</td>
<td align="center">H5</td>
<td align="center">7</td>
<td align="center">2011-06-24</td>
<td align="center">X9</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">R</td>
<td align="center">FALSE</td>
</tr>
<tr class="even">
<td align="center">8</td>
<td align="center">2011-07-10</td>
<td align="center">X9</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>
</tr>
<tr class="even">
<td align="center">10</td>
<td align="center">2011-07-07</td>
<td align="center">H5</td>
<tr class="odd">
<td align="center">9</td>
<td align="center">2011-11-07</td>
<td align="center">X9</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">FALSE</td>
</tr>
<tr class="even">
<td align="center">10</td>
<td align="center">2011-12-20</td>
<td align="center">X9</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">FALSE</td>
</tr>
@ -646,7 +646,7 @@
<a class="sourceLine" id="cb20-7" data-line-number="7"><span class="co"># </span><span class="al">NOTE</span><span class="co">: Using column `patient_id` as input for `col_patient_id`.</span></a>
<a class="sourceLine" id="cb20-8" data-line-number="8"><span class="co"># </span><span class="al">NOTE</span><span class="co">: Using column `keyab` as input for `col_keyantibiotics`. Use col_keyantibiotics = FALSE to prevent this.</span></a>
<a class="sourceLine" id="cb20-9" data-line-number="9"><span class="co"># [Criterion] Inclusion based on key antibiotics, ignoring I</span></a>
<a class="sourceLine" id="cb20-10" data-line-number="10"><span class="co"># =&gt; Found 15,117 first weighted isolates (75.6% of total)</span></a></code></pre></div>
<a class="sourceLine" id="cb20-10" data-line-number="10"><span class="co"># =&gt; Found 15,141 first weighted isolates (75.7% of total)</span></a></code></pre></div>
<table class="table">
<thead><tr class="header">
<th align="center">isolate</th>
@ -663,10 +663,10 @@
<tbody>
<tr class="odd">
<td align="center">1</td>
<td align="center">2010-04-02</td>
<td align="center">H5</td>
<td align="center">2010-01-31</td>
<td align="center">X9</td>
<td align="center">B_ESCHR_COLI</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>
@ -675,10 +675,10 @@
</tr>
<tr class="even">
<td align="center">2</td>
<td align="center">2010-05-11</td>
<td align="center">H5</td>
<td align="center">2010-04-03</td>
<td align="center">X9</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>
@ -687,22 +687,22 @@
</tr>
<tr class="odd">
<td align="center">3</td>
<td align="center">2010-08-11</td>
<td align="center">H5</td>
<td align="center">2010-05-12</td>
<td align="center">X9</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">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">4</td>
<td align="center">2010-08-26</td>
<td align="center">H5</td>
<td align="center">2010-08-29</td>
<td align="center">X9</td>
<td align="center">B_ESCHR_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">S</td>
@ -711,10 +711,10 @@
</tr>
<tr class="odd">
<td align="center">5</td>
<td align="center">2010-11-17</td>
<td align="center">H5</td>
<td align="center">2011-01-07</td>
<td align="center">X9</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>
@ -723,44 +723,56 @@
</tr>
<tr class="even">
<td align="center">6</td>
<td align="center">2010-12-20</td>
<td align="center">H5</td>
<td align="center">2011-04-20</td>
<td align="center">X9</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">TRUE</td>
<td align="center">TRUE</td>
</tr>
<tr class="odd">
<td align="center">7</td>
<td align="center">2011-03-21</td>
<td align="center">H5</td>
<td align="center">2011-06-24</td>
<td align="center">X9</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">R</td>
<td align="center">R</td>
<td align="center">FALSE</td>
<td align="center">TRUE</td>
</tr>
<tr class="even">
<td align="center">8</td>
<td align="center">2011-04-16</td>
<td align="center">H5</td>
<td align="center">2011-07-10</td>
<td align="center">X9</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">TRUE</td>
<td align="center">FALSE</td>
<td align="center">TRUE</td>
</tr>
<tr class="odd">
<td align="center">9</td>
<td align="center">2011-07-03</td>
<td align="center">H5</td>
<td align="center">2011-11-07</td>
<td align="center">X9</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">FALSE</td>
<td align="center">TRUE</td>
</tr>
<tr class="even">
<td align="center">10</td>
<td align="center">2011-12-20</td>
<td align="center">X9</td>
<td align="center">B_ESCHR_COLI</td>
<td align="center">R</td>
<td align="center">S</td>
@ -769,25 +781,13 @@
<td align="center">FALSE</td>
<td align="center">TRUE</td>
</tr>
<tr class="even">
<td align="center">10</td>
<td align="center">2011-07-07</td>
<td align="center">H5</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">TRUE</td>
</tr>
</tbody>
</table>
<p>Instead of 2, now 8 isolates are flagged. In total, 75.6% of all isolates are marked ‘first weighted’ - 47.2% 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 8 isolates are flagged. In total, 75.7% of all isolates are marked ‘first weighted’ - 47.4% 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>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="cb21"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb21-1" data-line-number="1">data_1st &lt;-<span class="st"> </span>data <span class="op">%&gt;%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb21-2" data-line-number="2"><span class="st"> </span><span class="kw"><a href="../reference/first_isolate.html">filter_first_weighted_isolate</a></span>()</a></code></pre></div>
<p>So we end up with 15,117 isolates for analysis.</p>
<p>So we end up with 15,141 isolates for analysis.</p>
<p>We can remove unneeded columns:</p>
<div class="sourceCode" id="cb22"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb22-1" data-line-number="1">data_1st &lt;-<span class="st"> </span>data_1st <span class="op">%&gt;%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb22-2" data-line-number="2"><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))</a></code></pre></div>
@ -811,88 +811,88 @@
</tr></thead>
<tbody>
<tr class="odd">
<td align="center">2017-03-23</td>
<td align="center">O1</td>
<td align="center">2011-07-28</td>
<td align="center">N6</td>
<td align="center">Hospital C</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">F</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">2012-08-16</td>
<td align="center">Q6</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">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">2017-05-07</td>
<td align="center">G5</td>
<td align="center">Hospital A</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">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">2016-10-13</td>
<td align="center">G10</td>
<td align="center">Hospital D</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">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">2017-11-09</td>
<td align="center">M7</td>
<td align="center">Hospital D</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">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">2016-03-31</td>
<td align="center">P9</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">R</td>
<td align="center">S</td>
<td align="center">F</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">2015-07-16</td>
<td align="center">U2</td>
<td align="center">Hospital B</td>
<td align="center">B_STRPT_PNMN</td>
<td align="center">R</td>
<td align="center">R</td>
<td align="center">R</td>
<td align="center">R</td>
<td align="center">F</td>
<td align="center">Gram-positive</td>
<td align="center">Streptococcus</td>
<td align="center">pneumoniae</td>
<td align="center">TRUE</td>
</tr>
<tr class="even">
<td align="center">2014-10-22</td>
<td align="center">B8</td>
<td align="center">Hospital B</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">2015-10-29</td>
<td align="center">V10</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">S</td>
<td align="center">F</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">2013-08-21</td>
<td align="center">U1</td>
<td align="center">Hospital A</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">F</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">2017-06-24</td>
<td align="center">K6</td>
<td align="center">Hospital A</td>
<td align="center">B_STPHY_AURS</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">M</td>
<td align="center">Gram-positive</td>
@ -919,7 +919,7 @@
<div class="sourceCode" id="cb25"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb25-1" data-line-number="1">data_1st <span class="op">%&gt;%</span><span class="st"> </span><span class="kw"><a href="https://rdrr.io/pkg/clean/man/freq.html">freq</a></span>(genus, species)</a></code></pre></div>
<p><strong>Frequency table</strong></p>
<p>Class: character<br>
Length: 15,117 (of which NA: 0 = 0%)<br>
Length: 15,141 (of which NA: 0 = 0%)<br>
Unique: 4</p>
<p>Shortest: 16<br>
Longest: 24</p>
@ -936,33 +936,33 @@ Longest: 24</p>
<tr class="odd">
<td align="left">1</td>
<td align="left">Escherichia coli</td>
<td align="right">7,403</td>
<td align="right">48.97%</td>
<td align="right">7,403</td>
<td align="right">48.97%</td>
<td align="right">7,470</td>
<td align="right">49.34%</td>
<td align="right">7,470</td>
<td align="right">49.34%</td>
</tr>
<tr class="even">
<td align="left">2</td>
<td align="left">Staphylococcus aureus</td>
<td align="right">3,757</td>
<td align="right">24.85%</td>
<td align="right">11,160</td>
<td align="right">73.82%</td>
<td align="right">3,803</td>
<td align="right">25.12%</td>
<td align="right">11,273</td>
<td align="right">74.45%</td>
</tr>
<tr class="odd">
<td align="left">3</td>
<td align="left">Streptococcus pneumoniae</td>
<td align="right">2,307</td>
<td align="right">15.26%</td>
<td align="right">13,467</td>
<td align="right">89.09%</td>
<td align="right">2,290</td>
<td align="right">15.12%</td>
<td align="right">13,563</td>
<td align="right">89.58%</td>
</tr>
<tr class="even">
<td align="left">4</td>
<td align="left">Klebsiella pneumoniae</td>
<td align="right">1,650</td>
<td align="right">10.91%</td>
<td align="right">15,117</td>
<td align="right">1,578</td>
<td align="right">10.42%</td>
<td align="right">15,141</td>
<td align="right">100.00%</td>
</tr>
</tbody>
@ -973,7 +973,7 @@ Longest: 24</p>
<a href="#resistance-percentages" class="anchor"></a>Resistance percentages</h2>
<p>The functions <code><a href="../reference/portion.html">portion_S()</a></code>, <code><a href="../reference/portion.html">portion_SI()</a></code>, <code><a href="../reference/portion.html">portion_I()</a></code>, <code><a href="../reference/portion.html">portion_IR()</a></code> and <code><a href="../reference/portion.html">portion_R()</a></code> can be used to determine the portion of a specific antimicrobial outcome. As per the EUCAST guideline of 2019, we calculate resistance as the portion of R (<code><a href="../reference/portion.html">portion_R()</a></code>) and susceptibility as the portion of S and I (<code><a href="../reference/portion.html">portion_SI()</a></code>). These functions can be used on their own:</p>
<div class="sourceCode" id="cb26"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb26-1" data-line-number="1">data_1st <span class="op">%&gt;%</span><span class="st"> </span><span class="kw"><a href="../reference/portion.html">portion_R</a></span>(AMX)</a>
<a class="sourceLine" id="cb26-2" data-line-number="2"><span class="co"># [1] 0.470133</span></a></code></pre></div>
<a class="sourceLine" id="cb26-2" data-line-number="2"><span class="co"># [1] 0.4656231</span></a></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="cb27"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb27-1" data-line-number="1">data_1st <span class="op">%&gt;%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb27-2" data-line-number="2"><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></a>
@ -986,19 +986,19 @@ Longest: 24</p>
<tbody>
<tr class="odd">
<td align="center">Hospital A</td>
<td align="center">0.4604743</td>
<td align="center">0.4748959</td>
</tr>
<tr class="even">
<td align="center">Hospital B</td>
<td align="center">0.4653298</td>
<td align="center">0.4543224</td>
</tr>
<tr class="odd">
<td align="center">Hospital C</td>
<td align="center">0.4791855</td>
<td align="center">0.4659041</td>
</tr>
<tr class="even">
<td align="center">Hospital D</td>
<td align="center">0.4865761</td>
<td align="center">0.4712529</td>
</tr>
</tbody>
</table>
@ -1016,23 +1016,23 @@ Longest: 24</p>
<tbody>
<tr class="odd">
<td align="center">Hospital A</td>
<td align="center">0.4604743</td>
<td align="center">4554</td>
<td align="center">0.4748959</td>
<td align="center">4561</td>
</tr>
<tr class="even">
<td align="center">Hospital B</td>
<td align="center">0.4653298</td>
<td align="center">5336</td>
<td align="center">0.4543224</td>
<td align="center">5298</td>
</tr>
<tr class="odd">
<td align="center">Hospital C</td>
<td align="center">0.4791855</td>
<td align="center">2210</td>
<td align="center">0.4659041</td>
<td align="center">2273</td>
</tr>
<tr class="even">
<td align="center">Hospital D</td>
<td align="center">0.4865761</td>
<td align="center">3017</td>
<td align="center">0.4712529</td>
<td align="center">3009</td>
</tr>
</tbody>
</table>
@ -1052,27 +1052,27 @@ Longest: 24</p>
<tbody>
<tr class="odd">
<td align="center">Escherichia</td>
<td align="center">0.9227340</td>
<td align="center">0.8974740</td>
<td align="center">0.9940565</td>
<td align="center">0.9285141</td>
<td align="center">0.8966533</td>
<td align="center">0.9929050</td>
</tr>
<tr class="even">
<td align="center">Klebsiella</td>
<td align="center">0.8127273</td>
<td align="center">0.9024242</td>
<td align="center">0.9872727</td>
<td align="center">0.8225602</td>
<td align="center">0.9017744</td>
<td align="center">0.9866920</td>
</tr>
<tr class="odd">
<td align="center">Staphylococcus</td>
<td align="center">0.9291988</td>
<td align="center">0.9124301</td>
<td align="center">0.9933458</td>
<td align="center">0.9250592</td>
<td align="center">0.9261110</td>
<td align="center">0.9968446</td>
</tr>
<tr class="even">
<td align="center">Streptococcus</td>
<td align="center">0.6146511</td>
<td align="center">0.6200873</td>
<td align="center">0.0000000</td>
<td align="center">0.6146511</td>
<td align="center">0.6200873</td>
</tr>
</tbody>
</table>

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@ -13,8 +13,9 @@
<link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png">
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<h1>How to apply EUCAST rules</h1>
<h4 class="author">Matthijs S. Berends</h4>
<h4 class="date">13 August 2019</h4>
<h4 class="date">13 October 2019</h4>
<div class="hidden name"><code>EUCAST.Rmd</code></div>
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<h1>How to use the <em>G</em>-test</h1>
<h4 class="author">Matthijs S. Berends</h4>
<h4 class="date">02 June 2019</h4>
<div class="hidden name"><code>G_test.Rmd</code></div>
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<p><em>(will be available soon - in the meanwhile, please read about <a href="./../reference/g.test.html">this <em>G</em>-test in the manual</a>)</em></p>
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@ -41,7 +41,7 @@
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@ -187,7 +187,7 @@
<h1>How to determine multi-drug resistance (MDR)</h1>
<h4 class="author">Matthijs S. Berends</h4>
<h4 class="date">30 September 2019</h4>
<h4 class="date">13 October 2019</h4>
<div class="hidden name"><code>MDR.Rmd</code></div>
@ -230,18 +230,18 @@
<p>The data set looks like this now:</p>
<div class="sourceCode" id="cb3"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb3-1" data-line-number="1"><span class="kw"><a href="https://rdrr.io/r/utils/head.html">head</a></span>(my_TB_data)</a>
<a class="sourceLine" id="cb3-2" data-line-number="2"><span class="co"># rifampicin isoniazid gatifloxacin ethambutol pyrazinamide moxifloxacin</span></a>
<a class="sourceLine" id="cb3-3" data-line-number="3"><span class="co"># 1 R S R R S R</span></a>
<a class="sourceLine" id="cb3-4" data-line-number="4"><span class="co"># 2 I R S R R R</span></a>
<a class="sourceLine" id="cb3-5" data-line-number="5"><span class="co"># 3 R R R S R R</span></a>
<a class="sourceLine" id="cb3-6" data-line-number="6"><span class="co"># 4 R S I I R S</span></a>
<a class="sourceLine" id="cb3-7" data-line-number="7"><span class="co"># 5 S S I I R S</span></a>
<a class="sourceLine" id="cb3-8" data-line-number="8"><span class="co"># 6 S S S R S S</span></a>
<a class="sourceLine" id="cb3-3" data-line-number="3"><span class="co"># 1 R S S I S S</span></a>
<a class="sourceLine" id="cb3-4" data-line-number="4"><span class="co"># 2 I R S R S R</span></a>
<a class="sourceLine" id="cb3-5" data-line-number="5"><span class="co"># 3 S S R R R R</span></a>
<a class="sourceLine" id="cb3-6" data-line-number="6"><span class="co"># 4 S R R S S S</span></a>
<a class="sourceLine" id="cb3-7" data-line-number="7"><span class="co"># 5 S R R S R R</span></a>
<a class="sourceLine" id="cb3-8" data-line-number="8"><span class="co"># 6 R S S R R S</span></a>
<a class="sourceLine" id="cb3-9" data-line-number="9"><span class="co"># kanamycin</span></a>
<a class="sourceLine" id="cb3-10" data-line-number="10"><span class="co"># 1 R</span></a>
<a class="sourceLine" id="cb3-11" data-line-number="11"><span class="co"># 2 S</span></a>
<a class="sourceLine" id="cb3-12" data-line-number="12"><span class="co"># 3 S</span></a>
<a class="sourceLine" id="cb3-13" data-line-number="13"><span class="co"># 4 R</span></a>
<a class="sourceLine" id="cb3-14" data-line-number="14"><span class="co"># 5 R</span></a>
<a class="sourceLine" id="cb3-11" data-line-number="11"><span class="co"># 2 R</span></a>
<a class="sourceLine" id="cb3-12" data-line-number="12"><span class="co"># 3 I</span></a>
<a class="sourceLine" id="cb3-13" data-line-number="13"><span class="co"># 4 S</span></a>
<a class="sourceLine" id="cb3-14" data-line-number="14"><span class="co"># 5 S</span></a>
<a class="sourceLine" id="cb3-15" data-line-number="15"><span class="co"># 6 S</span></a></code></pre></div>
<p>We can now add the interpretation of MDR-TB to our data set:</p>
<div class="sourceCode" id="cb4"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb4-1" data-line-number="1">my_TB_data<span class="op">$</span>mdr &lt;-<span class="st"> </span><span class="kw"><a href="../reference/mdro.html">mdr_tb</a></span>(my_TB_data)</a>
@ -251,7 +251,8 @@
<a class="sourceLine" id="cb4-5" data-line-number="5"><span class="co"># Version: WHO/HTM/TB/2014.11</span></a>
<a class="sourceLine" id="cb4-6" data-line-number="6"><span class="co"># Author: WHO (World Health Organization)</span></a>
<a class="sourceLine" id="cb4-7" data-line-number="7"><span class="co"># Source: https://www.who.int/tb/publications/pmdt_companionhandbook/en/</span></a>
<a class="sourceLine" id="cb4-8" data-line-number="8"><span class="co"># </span><span class="al">NOTE</span><span class="co">: Reliability might be improved if these antimicrobial results would be available too: CAP (capreomycin), RIB (rifabutin), RFP (rifapentine)</span></a></code></pre></div>
<a class="sourceLine" id="cb4-8" data-line-number="8"><span class="co"># </span><span class="al">NOTE</span><span class="co">: Auto-guessing columns suitable for analysis...</span></a>
<a class="sourceLine" id="cb4-9" data-line-number="9"><span class="co"># </span><span class="al">NOTE</span><span class="co">: Reliability might be improved if these antimicrobial results would be available too: CAP (capreomycin), RIB (rifabutin), RFP (rifapentine)</span></a></code></pre></div>
<p>We also created a package dedicated to data cleaning and checking, called the <code>clean</code> package. It gets automatically installed with the <code>AMR</code> package, so we only have to load it:</p>
<div class="sourceCode" id="cb5"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb5-1" data-line-number="1"><span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span>(clean)</a></code></pre></div>
<p>It contains the <code><a href="https://rdrr.io/pkg/clean/man/freq.html">freq()</a></code> function, to create a frequency table:</p>
@ -274,40 +275,40 @@ Unique: 5</p>
<tr class="odd">
<td align="left">1</td>
<td align="left">Mono-resistant</td>
<td align="right">3250</td>
<td align="right">65.00%</td>
<td align="right">3250</td>
<td align="right">65.00%</td>
<td align="right">3246</td>
<td align="right">64.92%</td>
<td align="right">3246</td>
<td align="right">64.92%</td>
</tr>
<tr class="even">
<td align="left">2</td>
<td align="left">Negative</td>
<td align="right">634</td>
<td align="right">12.68%</td>
<td align="right">3884</td>
<td align="right">77.68%</td>
<td align="right">681</td>
<td align="right">13.62%</td>
<td align="right">3927</td>
<td align="right">78.54%</td>
</tr>
<tr class="odd">
<td align="left">3</td>
<td align="left">Multi-drug-resistant</td>
<td align="right">623</td>
<td align="right">12.46%</td>
<td align="right">4507</td>
<td align="right">90.14%</td>
<td align="right">593</td>
<td align="right">11.86%</td>
<td align="right">4520</td>
<td align="right">90.40%</td>
</tr>
<tr class="even">
<td align="left">4</td>
<td align="left">Poly-resistant</td>
<td align="right">282</td>
<td align="right">5.64%</td>
<td align="right">4789</td>
<td align="right">95.78%</td>
<td align="right">276</td>
<td align="right">5.52%</td>
<td align="right">4796</td>
<td align="right">95.92%</td>
</tr>
<tr class="odd">
<td align="left">5</td>
<td align="left">Extensive drug-resistant</td>
<td align="right">211</td>
<td align="right">4.22%</td>
<td align="right">204</td>
<td align="right">4.08%</td>
<td align="right">5000</td>
<td align="right">100.00%</td>
</tr>

389
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<h1>How to predict antimicrobial resistance</h1>
<h4 class="author">Matthijs S. Berends</h4>
<h4 class="date">09 February 2019</h4>
<div class="hidden name"><code>Predict.Rmd</code></div>
</div>
<div id="needed-r-packages" class="section level2">
<h2 class="hasAnchor">
<a href="#needed-r-packages" class="anchor"></a>Needed R packages</h2>
<p>As with many uses in R, we need some additional packages for AMR analysis. Our package works closely together with the <a href="https://www.tidyverse.org">tidyverse packages</a> <a href="https://dplyr.tidyverse.org/"><code>dplyr</code></a> and <a href="https://ggplot2.tidyverse.org"><code>ggplot2</code></a> by <a href="https://www.linkedin.com/in/hadleywickham/">Dr Hadley Wickham</a>. The tidyverse tremendously improves the way we conduct data science - it allows for a very natural way of writing syntaxes and creating beautiful plots in R.</p>
<p>Our <code>AMR</code> package depends on these packages and even extends their use and functions.</p>
<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb1-1" data-line-number="1"><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/library">library</a></span>(dplyr)</a>
<a class="sourceLine" id="cb1-2" data-line-number="2"><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/library">library</a></span>(ggplot2)</a>
<a class="sourceLine" id="cb1-3" data-line-number="3"><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/library">library</a></span>(AMR)</a>
<a class="sourceLine" id="cb1-4" data-line-number="4"></a>
<a class="sourceLine" id="cb1-5" data-line-number="5"><span class="co"># (if not yet installed, install with:)</span></a>
<a class="sourceLine" id="cb1-6" data-line-number="6"><span class="co"># install.packages(c("tidyverse", "AMR"))</span></a></code></pre></div>
</div>
<div id="prediction-analysis" class="section level2">
<h2 class="hasAnchor">
<a href="#prediction-analysis" class="anchor"></a>Prediction analysis</h2>
<p>Our package contains a function <code><a href="../reference/resistance_predict.html">resistance_predict()</a></code>, which takes the same input as functions for <a href="./articles/AMR.html">other AMR analysis</a>. Based on a date column, it calculates cases per year and uses a regression model to predict antimicrobial resistance.</p>
<p>It is basically as easy as:</p>
<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb2-1" data-line-number="1"><span class="co"># resistance prediction of piperacillin/tazobactam (pita):</span></a>
<a class="sourceLine" id="cb2-2" data-line-number="2"><span class="kw"><a href="../reference/resistance_predict.html">resistance_predict</a></span>(<span class="dt">tbl =</span> septic_patients, <span class="dt">col_date =</span> <span class="st">"date"</span>, <span class="dt">col_ab =</span> <span class="st">"pita"</span>)</a>
<a class="sourceLine" id="cb2-3" data-line-number="3"></a>
<a class="sourceLine" id="cb2-4" data-line-number="4"><span class="co"># or:</span></a>
<a class="sourceLine" id="cb2-5" data-line-number="5">septic_patients <span class="op">%&gt;%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb2-6" data-line-number="6"><span class="st"> </span><span class="kw"><a href="../reference/resistance_predict.html">resistance_predict</a></span>(<span class="dt">col_ab =</span> <span class="st">"pita"</span>)</a>
<a class="sourceLine" id="cb2-7" data-line-number="7"></a>
<a class="sourceLine" id="cb2-8" data-line-number="8"><span class="co"># to bind it to object 'predict_pita' for example:</span></a>
<a class="sourceLine" id="cb2-9" data-line-number="9">predict_pita &lt;-<span class="st"> </span>septic_patients <span class="op">%&gt;%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb2-10" data-line-number="10"><span class="st"> </span><span class="kw"><a href="../reference/resistance_predict.html">resistance_predict</a></span>(<span class="dt">col_ab =</span> <span class="st">"pita"</span>)</a></code></pre></div>
<pre><code># NOTE: Using column `date` as input for `col_date`.
#
# Logistic regression model (logit) with binomial distribution
# ------------------------------------------------------------
#
# Call:
# glm(formula = df_matrix ~ year, family = binomial)
#
# Deviance Residuals:
# Min 1Q Median 3Q Max
# -2.9224 -1.3120 0.0170 0.7586 3.1932
#
# Coefficients:
# Estimate Std. Error z value Pr(&gt;|z|)
# (Intercept) -222.92857 45.93922 -4.853 1.22e-06 ***
# year 0.10994 0.02284 4.814 1.48e-06 ***
# ---
# Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#
# (Dispersion parameter for binomial family taken to be 1)
#
# Null deviance: 59.794 on 14 degrees of freedom
# Residual deviance: 35.191 on 13 degrees of freedom
# AIC: 93.464
#
# Number of Fisher Scoring iterations: 4</code></pre>
<p>The function will look for a data column itself if <code>col_date</code> is not set. The result is nothing more than a <code>data.frame</code>, containing the years, number of observations, actual observed resistance, the estimated resistance and the standard error below and above the estimation:</p>
<div class="sourceCode" id="cb4"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb4-1" data-line-number="1">predict_pita</a>
<a class="sourceLine" id="cb4-2" data-line-number="2"><span class="co"># year value se_min se_max observations observed estimated</span></a>
<a class="sourceLine" id="cb4-3" data-line-number="3"><span class="co"># 1 2003 0.06250000 NA NA 32 0.06250000 0.06177594</span></a>
<a class="sourceLine" id="cb4-4" data-line-number="4"><span class="co"># 2 2004 0.08536585 NA NA 82 0.08536585 0.06846343</span></a>
<a class="sourceLine" id="cb4-5" data-line-number="5"><span class="co"># 3 2005 0.10000000 NA NA 60 0.10000000 0.07581637</span></a>
<a class="sourceLine" id="cb4-6" data-line-number="6"><span class="co"># 4 2006 0.05084746 NA NA 59 0.05084746 0.08388789</span></a>
<a class="sourceLine" id="cb4-7" data-line-number="7"><span class="co"># 5 2007 0.12121212 NA NA 66 0.12121212 0.09273250</span></a>
<a class="sourceLine" id="cb4-8" data-line-number="8"><span class="co"># 6 2008 0.04166667 NA NA 72 0.04166667 0.10240539</span></a>
<a class="sourceLine" id="cb4-9" data-line-number="9"><span class="co"># 7 2009 0.01639344 NA NA 61 0.01639344 0.11296163</span></a>
<a class="sourceLine" id="cb4-10" data-line-number="10"><span class="co"># 8 2010 0.09433962 NA NA 53 0.09433962 0.12445516</span></a>
<a class="sourceLine" id="cb4-11" data-line-number="11"><span class="co"># 9 2011 0.18279570 NA NA 93 0.18279570 0.13693759</span></a>
<a class="sourceLine" id="cb4-12" data-line-number="12"><span class="co"># 10 2012 0.30769231 NA NA 65 0.30769231 0.15045682</span></a>
<a class="sourceLine" id="cb4-13" data-line-number="13"><span class="co"># 11 2013 0.08620690 NA NA 58 0.08620690 0.16505550</span></a>
<a class="sourceLine" id="cb4-14" data-line-number="14"><span class="co"># 12 2014 0.15254237 NA NA 59 0.15254237 0.18076926</span></a>
<a class="sourceLine" id="cb4-15" data-line-number="15"><span class="co"># 13 2015 0.27272727 NA NA 55 0.27272727 0.19762493</span></a>
<a class="sourceLine" id="cb4-16" data-line-number="16"><span class="co"># 14 2016 0.25000000 NA NA 84 0.25000000 0.21563859</span></a>
<a class="sourceLine" id="cb4-17" data-line-number="17"><span class="co"># 15 2017 0.16279070 NA NA 86 0.16279070 0.23481370</span></a>
<a class="sourceLine" id="cb4-18" data-line-number="18"><span class="co"># 16 2018 0.25513926 0.2228376 0.2874409 NA NA 0.25513926</span></a>
<a class="sourceLine" id="cb4-19" data-line-number="19"><span class="co"># 17 2019 0.27658825 0.2386811 0.3144954 NA NA 0.27658825</span></a>
<a class="sourceLine" id="cb4-20" data-line-number="20"><span class="co"># 18 2020 0.29911630 0.2551715 0.3430611 NA NA 0.29911630</span></a>
<a class="sourceLine" id="cb4-21" data-line-number="21"><span class="co"># 19 2021 0.32266085 0.2723340 0.3729877 NA NA 0.32266085</span></a>
<a class="sourceLine" id="cb4-22" data-line-number="22"><span class="co"># 20 2022 0.34714076 0.2901847 0.4040968 NA NA 0.34714076</span></a>
<a class="sourceLine" id="cb4-23" data-line-number="23"><span class="co"># 21 2023 0.37245666 0.3087318 0.4361815 NA NA 0.37245666</span></a>
<a class="sourceLine" id="cb4-24" data-line-number="24"><span class="co"># 22 2024 0.39849187 0.3279750 0.4690088 NA NA 0.39849187</span></a>
<a class="sourceLine" id="cb4-25" data-line-number="25"><span class="co"># 23 2025 0.42511415 0.3479042 0.5023241 NA NA 0.42511415</span></a>
<a class="sourceLine" id="cb4-26" data-line-number="26"><span class="co"># 24 2026 0.45217796 0.3684992 0.5358568 NA NA 0.45217796</span></a>
<a class="sourceLine" id="cb4-27" data-line-number="27"><span class="co"># 25 2027 0.47952757 0.3897276 0.5693275 NA NA 0.47952757</span></a>
<a class="sourceLine" id="cb4-28" data-line-number="28"><span class="co"># 26 2028 0.50700045 0.4115444 0.6024565 NA NA 0.50700045</span></a>
<a class="sourceLine" id="cb4-29" data-line-number="29"><span class="co"># 27 2029 0.53443111 0.4338908 0.6349714 NA NA 0.53443111</span></a></code></pre></div>
<p>The function <code>plot</code> is available in base R, and can be extended by other packages to depend the output based on the type of input. We extended its function to cope with resistance predictions:</p>
<div class="sourceCode" id="cb5"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb5-1" data-line-number="1"><span class="kw"><a href="https://www.rdocumentation.org/packages/graphics/topics/plot">plot</a></span>(predict_pita)</a></code></pre></div>
<p><img src="Predict_files/figure-html/unnamed-chunk-4-1.png" width="720"></p>
<p>We also support the <code>ggplot2</code> package with the function <code><a href="../reference/resistance_predict.html">ggplot_rsi_predict()</a></code>:</p>
<div class="sourceCode" id="cb6"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb6-1" data-line-number="1"><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/library">library</a></span>(ggplot2)</a>
<a class="sourceLine" id="cb6-2" data-line-number="2"><span class="kw"><a href="../reference/resistance_predict.html">ggplot_rsi_predict</a></span>(predict_pita)</a>
<a class="sourceLine" id="cb6-3" data-line-number="3"><span class="co"># Warning: Removed 15 rows containing missing values (geom_errorbar).</span></a></code></pre></div>
<p><img src="Predict_files/figure-html/unnamed-chunk-5-1.png" width="720"></p>
<div id="choosing-the-right-model" class="section level3">
<h3 class="hasAnchor">
<a href="#choosing-the-right-model" class="anchor"></a>Choosing the right model</h3>
<p>Resistance is not easily predicted; if we look at vancomycin resistance in Gram positives, the spread (i.e. standard error) is enormous:</p>
<div class="sourceCode" id="cb7"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb7-1" data-line-number="1">septic_patients <span class="op">%&gt;%</span></a>
<a class="sourceLine" id="cb7-2" data-line-number="2"><span class="st"> </span><span class="kw"><a href="https://dplyr.tidyverse.org/reference/filter.html">filter</a></span>(<span class="kw"><a href="../reference/mo_property.html">mo_gramstain</a></span>(mo) <span class="op">==</span><span class="st"> "Gram positive"</span>) <span class="op">%&gt;%</span></a>
<a class="sourceLine" id="cb7-3" data-line-number="3"><span class="st"> </span><span class="kw"><a href="../reference/resistance_predict.html">resistance_predict</a></span>(<span class="dt">col_ab =</span> <span class="st">"vanc"</span>, <span class="dt">year_min =</span> <span class="dv">2010</span>, <span class="dt">info =</span> <span class="ot">FALSE</span>) <span class="op">%&gt;%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb7-4" data-line-number="4"><span class="st"> </span><span class="kw"><a href="https://www.rdocumentation.org/packages/graphics/topics/plot">plot</a></span>()</a>
<a class="sourceLine" id="cb7-5" data-line-number="5"><span class="co"># </span><span class="al">NOTE</span><span class="co">: Using column `date` as input for `col_date`.</span></a></code></pre></div>
<p><img src="Predict_files/figure-html/unnamed-chunk-6-1.png" width="720"></p>
<p>Vancomycin resistance could be 100% in ten years, but might also stay around 0%.</p>
<p>You can define the model with the <code>model</code> parameter. The default model is a generalised linear regression model using a binomial distribution, assuming that a period of zero resistance was followed by a period of increasing resistance leading slowly to more and more resistance.</p>
<p>Valid values are:</p>
<table class="table">
<colgroup>
<col width="32%">
<col width="25%">
<col width="42%">
</colgroup>
<thead><tr class="header">
<th>Input values</th>
<th>Function used by R</th>
<th>Type of model</th>
</tr></thead>
<tbody>
<tr class="odd">
<td>
<code>"binomial"</code> or <code>"binom"</code> or <code>"logit"</code>
</td>
<td><code><a href="https://www.rdocumentation.org/packages/stats/topics/glm">glm(..., family = binomial)</a></code></td>
<td>Generalised linear model with binomial distribution</td>
</tr>
<tr class="even">
<td>
<code>"loglin"</code> or <code>"poisson"</code>
</td>
<td><code><a href="https://www.rdocumentation.org/packages/stats/topics/glm">glm(..., family = poisson)</a></code></td>
<td>Generalised linear model with poisson distribution</td>
</tr>
<tr class="odd">
<td>
<code>"lin"</code> or <code>"linear"</code>
</td>
<td><code><a href="https://www.rdocumentation.org/packages/stats/topics/lm">lm()</a></code></td>
<td>Linear model</td>
</tr>
</tbody>
</table>
<p>For the vancomycin resistance in Gram positive bacteria, a linear model might be more appropriate since no (left half of a) binomial distribution is to be expected based on observed years:</p>
<div class="sourceCode" id="cb8"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb8-1" data-line-number="1">septic_patients <span class="op">%&gt;%</span></a>
<a class="sourceLine" id="cb8-2" data-line-number="2"><span class="st"> </span><span class="kw"><a href="https://dplyr.tidyverse.org/reference/filter.html">filter</a></span>(<span class="kw"><a href="../reference/mo_property.html">mo_gramstain</a></span>(mo) <span class="op">==</span><span class="st"> "Gram positive"</span>) <span class="op">%&gt;%</span></a>
<a class="sourceLine" id="cb8-3" data-line-number="3"><span class="st"> </span><span class="kw"><a href="../reference/resistance_predict.html">resistance_predict</a></span>(<span class="dt">col_ab =</span> <span class="st">"vanc"</span>, <span class="dt">year_min =</span> <span class="dv">2010</span>, <span class="dt">info =</span> <span class="ot">FALSE</span>, <span class="dt">model =</span> <span class="st">"linear"</span>) <span class="op">%&gt;%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb8-4" data-line-number="4"><span class="st"> </span><span class="kw"><a href="https://www.rdocumentation.org/packages/graphics/topics/plot">plot</a></span>()</a>
<a class="sourceLine" id="cb8-5" data-line-number="5"><span class="co"># </span><span class="al">NOTE</span><span class="co">: Using column `date` as input for `col_date`.</span></a></code></pre></div>
<p><img src="Predict_files/figure-html/unnamed-chunk-7-1.png" width="720"></p>
<p>This seems more likely, doesn’t it?</p>
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<h1>How to import data from SPSS / SAS / Stata</h1>
<h4 class="author">Matthijs S. Berends</h4>
<h4 class="date">13 August 2019</h4>
<h4 class="date">13 October 2019</h4>
<div class="hidden name"><code>SPSS.Rmd</code></div>
@ -252,7 +254,7 @@
<a class="sourceLine" id="cb1-11" data-line-number="11"><span class="co"># [1] "Gram-negative"</span></a>
<a class="sourceLine" id="cb1-12" data-line-number="12"></a>
<a class="sourceLine" id="cb1-13" data-line-number="13"><span class="co"># Klebsiella is intrinsic resistant to amoxicllin, according to EUCAST:</span></a>
<a class="sourceLine" id="cb1-14" data-line-number="14">klebsiella_test &lt;-<span class="st"> </span><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/data.frame">data.frame</a></span>(<span class="dt">mo =</span> <span class="st">"klebsiella"</span>, </a>
<a class="sourceLine" id="cb1-14" data-line-number="14">klebsiella_test &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">mo =</span> <span class="st">"klebsiella"</span>, </a>
<a class="sourceLine" id="cb1-15" data-line-number="15"> <span class="dt">amox =</span> <span class="st">"S"</span>,</a>
<a class="sourceLine" id="cb1-16" data-line-number="16"> <span class="dt">stringsAsFactors =</span> <span class="ot">FALSE</span>)</a>
<a class="sourceLine" id="cb1-17" data-line-number="17">klebsiella_test <span class="co"># (our original data)</span></a>
@ -324,9 +326,9 @@
<a href="#base-r" class="anchor"></a>Base R</h3>
<p>To import data from SPSS, SAS or Stata, you can use the <a href="https://haven.tidyverse.org/">great <code>haven</code> package</a> yourself:</p>
<div class="sourceCode" id="cb3"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb3-1" data-line-number="1"><span class="co"># download and install the latest version:</span></a>
<a class="sourceLine" id="cb3-2" data-line-number="2"><span class="kw"><a href="https://www.rdocumentation.org/packages/utils/topics/install.packages">install.packages</a></span>(<span class="st">"haven"</span>)</a>
<a class="sourceLine" id="cb3-2" data-line-number="2"><span class="kw"><a href="https://rdrr.io/r/utils/install.packages.html">install.packages</a></span>(<span class="st">"haven"</span>)</a>
<a class="sourceLine" id="cb3-3" data-line-number="3"><span class="co"># load the package you just installed:</span></a>
<a class="sourceLine" id="cb3-4" data-line-number="4"><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/library">library</a></span>(haven) </a></code></pre></div>
<a class="sourceLine" id="cb3-4" data-line-number="4"><span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span>(haven) </a></code></pre></div>
<p>You can now import files as follows:</p>
<div id="spss" class="section level4">
<h4 class="hasAnchor">
@ -384,6 +386,7 @@
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<p>Developed by <a href="https://www.rug.nl/staff/m.s.berends/">Matthijs S. Berends</a>, <a href="https://www.rug.nl/staff/c.f.luz/">Christian F. Luz</a>, <a href="https://www.rug.nl/staff/a.w.friedrich/">Alex W. Friedrich</a>, <a href="https://www.rug.nl/staff/b.sinha/">Bhanu N. M. Sinha</a>, <a href="https://www.rug.nl/staff/c.j.albers/">Casper J. Albers</a>, <a href="https://www.rug.nl/staff/c.glasner/">Corinna Glasner</a>.</p>
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<h1>How to work with WHONET data</h1>
<h4 class="author">Matthijs S. Berends</h4>
<h4 class="date">13 August 2019</h4>
<h4 class="date">13 October 2019</h4>
<div class="hidden name"><code>WHONET.Rmd</code></div>
@ -199,7 +201,7 @@
<a href="#import-of-data" class="anchor"></a>Import of data</h1>
<p>This tutorial assumes you already imported the WHONET data with e.g. the <a href="https://readxl.tidyverse.org/"><code>readxl</code> package</a>. In RStudio, this can be done using the menu button ‘Import Dataset’ in the tab ‘Environment’. Choose the option ‘From Excel’ and select your exported file. Make sure date fields are imported correctly.</p>
<p>An example syntax could look like this:</p>
<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb1-1" data-line-number="1"><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/library">library</a></span>(readxl)</a>
<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb1-1" data-line-number="1"><span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span>(readxl)</a>
<a class="sourceLine" id="cb1-2" data-line-number="2">data &lt;-<span class="st"> </span><span class="kw"><a href="https://readxl.tidyverse.org/reference/read_excel.html">read_excel</a></span>(<span class="dt">path =</span> <span class="st">"path/to/your/file.xlsx"</span>)</a></code></pre></div>
<p>This package comes with an <a href="https://msberends.gitlab.io/AMR/reference/WHONET.html">example data set <code>WHONET</code></a>. We will use it for this analysis.</p>
</div>
@ -207,12 +209,12 @@
<h1 class="hasAnchor">
<a href="#preparation" class="anchor"></a>Preparation</h1>
<p>First, load the relevant packages if you did not yet did this. I use the tidyverse for all of my analyses. All of them. If you don’t know it yet, I suggest you read about it on their website: <a href="https://www.tidyverse.org/" class="uri">https://www.tidyverse.org/</a>.</p>
<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb2-1" data-line-number="1"><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/library">library</a></span>(dplyr) <span class="co"># part of tidyverse</span></a>
<a class="sourceLine" id="cb2-2" data-line-number="2"><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/library">library</a></span>(ggplot2) <span class="co"># part of tidyverse</span></a>
<a class="sourceLine" id="cb2-3" data-line-number="3"><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/library">library</a></span>(AMR) <span class="co"># this package</span></a></code></pre></div>
<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb2-1" data-line-number="1"><span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span>(dplyr) <span class="co"># part of tidyverse</span></a>
<a class="sourceLine" id="cb2-2" data-line-number="2"><span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span>(ggplot2) <span class="co"># part of tidyverse</span></a>
<a class="sourceLine" id="cb2-3" data-line-number="3"><span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span>(AMR) <span class="co"># this package</span></a></code></pre></div>
<p>We will have to transform some variables to simplify and automate the analysis:</p>
<ul>
<li>Microorganisms should be transformed to our own microorganism IDs (called an <code>mo</code>) using <a href="https://msberends.gitlab.io/AMR/reference/ITIS.html">the ITIS reference data set</a>, which contains all ~20,000 microorganisms from the taxonomic kingdoms Bacteria, Fungi and Protozoa. We do the tranformation with <code><a href="../reference/as.mo.html">as.mo()</a></code>. This function also recognises almost all WHONET abbreviations of microorganisms.</li>
<li>Microorganisms should be transformed to our own microorganism IDs (called an <code>mo</code>) using <a href="https://msberends.gitlab.io/AMR/reference/catalogue_of_life">our Catalogue of Life reference data set</a>, which contains all ~70,000 microorganisms from the taxonomic kingdoms Bacteria, Fungi and Protozoa. We do the tranformation with <code><a href="../reference/as.mo.html">as.mo()</a></code>. This function also recognises almost all WHONET abbreviations of microorganisms.</li>
<li>Antimicrobial results or interpretations have to be clean and valid. In other words, they should only contain values <code>"S"</code>, <code>"I"</code> or <code>"R"</code>. That is exactly where the <code><a href="../reference/as.rsi.html">as.rsi()</a></code> function is for.</li>
</ul>
<div class="sourceCode" id="cb3"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb3-1" data-line-number="1"><span class="co"># transform variables</span></a>
@ -223,18 +225,19 @@
<a class="sourceLine" id="cb3-6" data-line-number="6"><span class="st"> </span><span class="kw"><a href="https://dplyr.tidyverse.org/reference/mutate_all.html">mutate_at</a></span>(<span class="kw"><a href="https://dplyr.tidyverse.org/reference/vars.html">vars</a></span>(AMP_ND10<span class="op">:</span>CIP_EE), as.rsi)</a></code></pre></div>
<p>No errors or warnings, so all values are transformed succesfully.</p>
<p>We created a package dedicated to data cleaning and checking, called the <code>clean</code> package. It gets automatically installed with the <code>AMR</code> package, so we only have to load it:</p>
<div class="sourceCode" id="cb4"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb4-1" data-line-number="1"><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/library">library</a></span>(clean)</a></code></pre></div>
<p>It contains the <code><a href="https://www.rdocumentation.org/packages/clean/topics/freq">freq()</a></code> function, to create frequency tables.</p>
<div class="sourceCode" id="cb4"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb4-1" data-line-number="1"><span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span>(clean)</a></code></pre></div>
<p>It contains the <code><a href="https://rdrr.io/pkg/clean/man/freq.html">freq()</a></code> function, to create frequency tables.</p>
<p>So let’s check our data, with a couple of frequency tables:</p>
<div class="sourceCode" id="cb5"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb5-1" data-line-number="1"><span class="co"># our newly created `mo` variable</span></a>
<a class="sourceLine" id="cb5-2" data-line-number="2">data <span class="op">%&gt;%</span><span class="st"> </span><span class="kw"><a href="https://www.rdocumentation.org/packages/clean/topics/freq">freq</a></span>(mo, <span class="dt">nmax =</span> <span class="dv">10</span>)</a></code></pre></div>
<a class="sourceLine" id="cb5-2" data-line-number="2">data <span class="op">%&gt;%</span><span class="st"> </span><span class="kw"><a href="https://rdrr.io/pkg/clean/man/freq.html">freq</a></span>(mo, <span class="dt">nmax =</span> <span class="dv">10</span>)</a></code></pre></div>
<p><strong>Frequency table</strong></p>
<p>Class: mo (character)<br>
Length: 500 (of which NA: 0 = 0.00%)<br>
Length: 500 (of which NA: 0 = 0%)<br>
Unique: 39</p>
<p>Families: 10<br>
Genera: 17<br>
Species: 39</p>
<p>Gram-negative: 281 (56.20%)<br>
Gram-positive: 219 (43.80%)<br>
Nr of genera: 17<br>
Nr of species: 39</p>
<table class="table">
<thead><tr class="header">
<th align="left"></th>
@ -247,7 +250,7 @@ Species: 39</p>
<tbody>
<tr class="odd">
<td align="left">1</td>
<td align="left">B_ESCHR_COL</td>
<td align="left">B_ESCHR_COLI</td>
<td align="right">245</td>
<td align="right">49.0%</td>
<td align="right">245</td>
@ -255,7 +258,7 @@ Species: 39</p>
</tr>
<tr class="even">
<td align="left">2</td>
<td align="left">B_STPHY_CNS</td>
<td align="left">B_STPHY_CONS</td>
<td align="right">74</td>
<td align="right">14.8%</td>
<td align="right">319</td>
@ -263,7 +266,7 @@ Species: 39</p>
</tr>
<tr class="odd">
<td align="left">3</td>
<td align="left">B_STPHY_EPI</td>
<td align="left">B_STPHY_EPDR</td>
<td align="right">38</td>
<td align="right">7.6%</td>
<td align="right">357</td>
@ -271,7 +274,7 @@ Species: 39</p>
</tr>
<tr class="even">
<td align="left">4</td>
<td align="left">B_STRPT_PNE</td>
<td align="left">B_STRPT_PNMN</td>
<td align="right">31</td>
<td align="right">6.2%</td>
<td align="right">388</td>
@ -279,7 +282,7 @@ Species: 39</p>
</tr>
<tr class="odd">
<td align="left">5</td>
<td align="left">B_STPHY_HOM</td>
<td align="left">B_STPHY_HMNS</td>
<td align="right">21</td>
<td align="right">4.2%</td>
<td align="right">409</td>
@ -287,7 +290,7 @@ Species: 39</p>
</tr>
<tr class="even">
<td align="left">6</td>
<td align="left">B_PROTS_MIR</td>
<td align="left">B_PROTS_MRBL</td>
<td align="right">9</td>
<td align="right">1.8%</td>
<td align="right">418</td>
@ -295,7 +298,7 @@ Species: 39</p>
</tr>
<tr class="odd">
<td align="left">7</td>
<td align="left">B_ENTRC_IUM</td>
<td align="left">B_ENTRC_FACM</td>
<td align="right">8</td>
<td align="right">1.6%</td>
<td align="right">426</td>
@ -303,7 +306,7 @@ Species: 39</p>
</tr>
<tr class="even">
<td align="left">8</td>
<td align="left">B_STPHY_CAP</td>
<td align="left">B_STPHY_CPTS</td>
<td align="right">8</td>
<td align="right">1.6%</td>
<td align="right">434</td>
@ -311,7 +314,7 @@ Species: 39</p>
</tr>
<tr class="odd">
<td align="left">9</td>
<td align="left">B_ENTRB_CLO</td>
<td align="left">B_ENTRB_CLOC</td>
<td align="right">5</td>
<td align="right">1.0%</td>
<td align="right">439</td>
@ -319,7 +322,7 @@ Species: 39</p>
</tr>
<tr class="even">
<td align="left">10</td>
<td align="left">B_ENTRC_COL</td>
<td align="left">B_ENTRC_CLMB</td>
<td align="right">4</td>
<td align="right">0.8%</td>
<td align="right">443</td>
@ -327,14 +330,14 @@ Species: 39</p>
</tr>
</tbody>
</table>
<p>(omitted 29 entries, n = 57 [11.4%])</p>
<p>(omitted 29 entries, n = 57 [11.40%])</p>
<div class="sourceCode" id="cb6"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb6-1" data-line-number="1"></a>
<a class="sourceLine" id="cb6-2" data-line-number="2"><span class="co"># our transformed antibiotic columns</span></a>
<a class="sourceLine" id="cb6-3" data-line-number="3"><span class="co"># amoxicillin/clavulanic acid (J01CR02) as an example</span></a>
<a class="sourceLine" id="cb6-4" data-line-number="4">data <span class="op">%&gt;%</span><span class="st"> </span><span class="kw"><a href="https://www.rdocumentation.org/packages/clean/topics/freq">freq</a></span>(AMC_ND2)</a></code></pre></div>
<a class="sourceLine" id="cb6-4" data-line-number="4">data <span class="op">%&gt;%</span><span class="st"> </span><span class="kw"><a href="https://rdrr.io/pkg/clean/man/freq.html">freq</a></span>(AMC_ND2)</a></code></pre></div>
<p><strong>Frequency table</strong></p>
<p>Class: factor &gt; ordered &gt; rsi (numeric)<br>
Length: 500 (of which NA: 19 = 3.80%)<br>
Length: 500 (of which NA: 19 = 3.8%)<br>
Levels: 3: S &lt; I &lt; R<br>
Unique: 3</p>
<p>%SI: 78.6%</p>
@ -352,25 +355,25 @@ Unique: 3</p>
<td align="left">1</td>
<td align="left">S</td>
<td align="right">356</td>
<td align="right">74.0%</td>
<td align="right">74.01%</td>
<td align="right">356</td>
<td align="right">74.0%</td>
<td align="right">74.01%</td>
</tr>
<tr class="even">
<td align="left">2</td>
<td align="left">R</td>
<td align="right">103</td>
<td align="right">21.4%</td>
<td align="right">21.41%</td>
<td align="right">459</td>
<td align="right">95.4%</td>
<td align="right">95.43%</td>
</tr>
<tr class="odd">
<td align="left">3</td>
<td align="left">I</td>
<td align="right">22</td>
<td align="right">4.6%</td>
<td align="right">4.57%</td>
<td align="right">481</td>
<td align="right">100.0%</td>
<td align="right">100.00%</td>
</tr>
</tbody>
</table>
@ -383,6 +386,7 @@ Unique: 3</p>
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@ -397,13 +401,15 @@ Unique: 3</p>
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<h1>How to get properties of an antibiotic</h1>
<h4 class="author">Matthijs S. Berends</h4>
<h4 class="date">22 June 2019</h4>
<div class="hidden name"><code>ab_property.Rmd</code></div>
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<h1>How to get properties of an antibiotic</h1>
<h4 class="author">Matthijs S. Berends</h4>
<h4 class="date">27 March 2019</h4>
<div class="hidden name"><code>atc_property.Rmd</code></div>
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<h1>Benchmarks</h1>
<h4 class="author">Matthijs S. Berends</h4>
<h4 class="date">23 September 2019</h4>
<h4 class="date">13 October 2019</h4>
<div class="hidden name"><code>benchmarks.Rmd</code></div>
@ -237,45 +197,45 @@
<p><small>Source: <a href="https://gitlab.com/msberends/AMR/blob/master/vignettes/benchmarks.Rmd" class="uri">https://gitlab.com/msberends/AMR/blob/master/vignettes/benchmarks.Rmd</a></small></p>
<p>One of the most important features of this package is the complete microbial taxonomic database, supplied by the <a href="http://catalogueoflife.org">Catalogue of Life</a>. We created a function <code>as.mo()</code> that transforms any user input value to a valid microbial ID by using intelligent rules combined with the taxonomic tree of Catalogue of Life.</p>
<p>Using the <code>microbenchmark</code> package, we can review the calculation performance of this function. Its function <code>microbenchmark()</code> runs different input expressions independently of each other and measures their time-to-result.</p>
<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb1-1" data-line-number="1"><span class="kw">library</span>(microbenchmark)</a>
<a class="sourceLine" id="cb1-2" data-line-number="2"><span class="kw">library</span>(AMR)</a></code></pre></div>
<p>One of the most important features of this package is the complete microbial taxonomic database, supplied by the <a href="http://catalogueoflife.org">Catalogue of Life</a>. We created a function <code><a href="../reference/as.mo.html">as.mo()</a></code> that transforms any user input value to a valid microbial ID by using intelligent rules combined with the taxonomic tree of Catalogue of Life.</p>
<p>Using the <code>microbenchmark</code> package, we can review the calculation performance of this function. Its function <code><a href="https://rdrr.io/pkg/microbenchmark/man/microbenchmark.html">microbenchmark()</a></code> runs different input expressions independently of each other and measures their time-to-result.</p>
<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb1-1" data-line-number="1"><span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span>(microbenchmark)</a>
<a class="sourceLine" id="cb1-2" data-line-number="2"><span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span>(AMR)</a></code></pre></div>
<p>In the next test, we try to ‘coerce’ different input values for <em>Staphylococcus aureus</em>. The actual result is the same every time: it returns its microorganism code <code>B_STPHY_AURS</code> (<em>B</em> stands for <em>Bacteria</em>, the taxonomic kingdom).</p>
<p>But the calculation time differs a lot:</p>
<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb2-1" data-line-number="1">S.aureus &lt;-<span class="st"> </span><span class="kw">microbenchmark</span>(</a>
<a class="sourceLine" id="cb2-2" data-line-number="2"> <span class="kw">as.mo</span>(<span class="st">&quot;sau&quot;</span>), <span class="co"># WHONET code</span></a>
<a class="sourceLine" id="cb2-3" data-line-number="3"> <span class="kw">as.mo</span>(<span class="st">&quot;stau&quot;</span>),</a>
<a class="sourceLine" id="cb2-4" data-line-number="4"> <span class="kw">as.mo</span>(<span class="st">&quot;STAU&quot;</span>),</a>
<a class="sourceLine" id="cb2-5" data-line-number="5"> <span class="kw">as.mo</span>(<span class="st">&quot;staaur&quot;</span>),</a>
<a class="sourceLine" id="cb2-6" data-line-number="6"> <span class="kw">as.mo</span>(<span class="st">&quot;STAAUR&quot;</span>),</a>
<a class="sourceLine" id="cb2-7" data-line-number="7"> <span class="kw">as.mo</span>(<span class="st">&quot;S. aureus&quot;</span>),</a>
<a class="sourceLine" id="cb2-8" data-line-number="8"> <span class="kw">as.mo</span>(<span class="st">&quot;S aureus&quot;</span>),</a>
<a class="sourceLine" id="cb2-9" data-line-number="9"> <span class="kw">as.mo</span>(<span class="st">&quot;Staphylococcus aureus&quot;</span>), <span class="co"># official taxonomic name</span></a>
<a class="sourceLine" id="cb2-10" data-line-number="10"> <span class="kw">as.mo</span>(<span class="st">&quot;Staphylococcus aureus (MRSA)&quot;</span>), <span class="co"># additional text</span></a>
<a class="sourceLine" id="cb2-11" data-line-number="11"> <span class="kw">as.mo</span>(<span class="st">&quot;Sthafilokkockus aaureuz&quot;</span>), <span class="co"># incorrect spelling</span></a>
<a class="sourceLine" id="cb2-12" data-line-number="12"> <span class="kw">as.mo</span>(<span class="st">&quot;MRSA&quot;</span>), <span class="co"># Methicillin Resistant S. aureus</span></a>
<a class="sourceLine" id="cb2-13" data-line-number="13"> <span class="kw">as.mo</span>(<span class="st">&quot;VISA&quot;</span>), <span class="co"># Vancomycin Intermediate S. aureus</span></a>
<a class="sourceLine" id="cb2-14" data-line-number="14"> <span class="kw">as.mo</span>(<span class="st">&quot;VRSA&quot;</span>), <span class="co"># Vancomycin Resistant S. aureus</span></a>
<a class="sourceLine" id="cb2-15" data-line-number="15"> <span class="kw">as.mo</span>(<span class="dv">22242419</span>), <span class="co"># Catalogue of Life ID</span></a>
<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb2-1" data-line-number="1">S.aureus &lt;-<span class="st"> </span><span class="kw"><a href="https://rdrr.io/pkg/microbenchmark/man/microbenchmark.html">microbenchmark</a></span>(</a>
<a class="sourceLine" id="cb2-2" data-line-number="2"> <span class="kw"><a href="../reference/as.mo.html">as.mo</a></span>(<span class="st">"sau"</span>), <span class="co"># WHONET code</span></a>
<a class="sourceLine" id="cb2-3" data-line-number="3"> <span class="kw"><a href="../reference/as.mo.html">as.mo</a></span>(<span class="st">"stau"</span>),</a>
<a class="sourceLine" id="cb2-4" data-line-number="4"> <span class="kw"><a href="../reference/as.mo.html">as.mo</a></span>(<span class="st">"STAU"</span>),</a>
<a class="sourceLine" id="cb2-5" data-line-number="5"> <span class="kw"><a href="../reference/as.mo.html">as.mo</a></span>(<span class="st">"staaur"</span>),</a>
<a class="sourceLine" id="cb2-6" data-line-number="6"> <span class="kw"><a href="../reference/as.mo.html">as.mo</a></span>(<span class="st">"STAAUR"</span>),</a>
<a class="sourceLine" id="cb2-7" data-line-number="7"> <span class="kw"><a href="../reference/as.mo.html">as.mo</a></span>(<span class="st">"S. aureus"</span>),</a>
<a class="sourceLine" id="cb2-8" data-line-number="8"> <span class="kw"><a href="../reference/as.mo.html">as.mo</a></span>(<span class="st">"S aureus"</span>),</a>
<a class="sourceLine" id="cb2-9" data-line-number="9"> <span class="kw"><a href="../reference/as.mo.html">as.mo</a></span>(<span class="st">"Staphylococcus aureus"</span>), <span class="co"># official taxonomic name</span></a>
<a class="sourceLine" id="cb2-10" data-line-number="10"> <span class="kw"><a href="../reference/as.mo.html">as.mo</a></span>(<span class="st">"Staphylococcus aureus (MRSA)"</span>), <span class="co"># additional text</span></a>
<a class="sourceLine" id="cb2-11" data-line-number="11"> <span class="kw"><a href="../reference/as.mo.html">as.mo</a></span>(<span class="st">"Sthafilokkockus aaureuz"</span>), <span class="co"># incorrect spelling</span></a>
<a class="sourceLine" id="cb2-12" data-line-number="12"> <span class="kw"><a href="../reference/as.mo.html">as.mo</a></span>(<span class="st">"MRSA"</span>), <span class="co"># Methicillin Resistant S. aureus</span></a>
<a class="sourceLine" id="cb2-13" data-line-number="13"> <span class="kw"><a href="../reference/as.mo.html">as.mo</a></span>(<span class="st">"VISA"</span>), <span class="co"># Vancomycin Intermediate S. aureus</span></a>
<a class="sourceLine" id="cb2-14" data-line-number="14"> <span class="kw"><a href="../reference/as.mo.html">as.mo</a></span>(<span class="st">"VRSA"</span>), <span class="co"># Vancomycin Resistant S. aureus</span></a>
<a class="sourceLine" id="cb2-15" data-line-number="15"> <span class="kw"><a href="../reference/as.mo.html">as.mo</a></span>(<span class="dv">22242419</span>), <span class="co"># Catalogue of Life ID</span></a>
<a class="sourceLine" id="cb2-16" data-line-number="16"> <span class="dt">times =</span> <span class="dv">10</span>)</a>
<a class="sourceLine" id="cb2-17" data-line-number="17"><span class="kw">print</span>(S.aureus, <span class="dt">unit =</span> <span class="st">&quot;ms&quot;</span>, <span class="dt">signif =</span> <span class="dv">2</span>)</a>
<a class="sourceLine" id="cb2-17" data-line-number="17"><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>)</a>
<a class="sourceLine" id="cb2-18" data-line-number="18"><span class="co"># Unit: milliseconds</span></a>
<a class="sourceLine" id="cb2-19" data-line-number="19"><span class="co"># expr min lq mean median uq max</span></a>
<a class="sourceLine" id="cb2-20" data-line-number="20"><span class="co"># as.mo(&quot;sau&quot;) 9.1 9.1 12.0 9.5 10.0 35</span></a>
<a class="sourceLine" id="cb2-21" data-line-number="21"><span class="co"># as.mo(&quot;stau&quot;) 31.0 32.0 37.0 33.0 34.0 58</span></a>
<a class="sourceLine" id="cb2-22" data-line-number="22"><span class="co"># as.mo(&quot;STAU&quot;) 31.0 32.0 34.0 34.0 35.0 37</span></a>
<a class="sourceLine" id="cb2-23" data-line-number="23"><span class="co"># as.mo(&quot;staaur&quot;) 8.6 9.1 9.7 9.8 10.0 11</span></a>
<a class="sourceLine" id="cb2-24" data-line-number="24"><span class="co"># as.mo(&quot;STAAUR&quot;) 8.7 8.9 17.0 9.4 12.0 57</span></a>
<a class="sourceLine" id="cb2-25" data-line-number="25"><span class="co"># as.mo(&quot;S. aureus&quot;) 23.0 24.0 34.0 26.0 46.0 54</span></a>
<a class="sourceLine" id="cb2-26" data-line-number="26"><span class="co"># as.mo(&quot;S aureus&quot;) 23.0 24.0 28.0 25.0 28.0 53</span></a>
<a class="sourceLine" id="cb2-27" data-line-number="27"><span class="co"># as.mo(&quot;Staphylococcus aureus&quot;) 29.0 29.0 31.0 30.0 32.0 34</span></a>
<a class="sourceLine" id="cb2-28" data-line-number="28"><span class="co"># as.mo(&quot;Staphylococcus aureus (MRSA)&quot;) 570.0 590.0 620.0 620.0 650.0 690</span></a>
<a class="sourceLine" id="cb2-29" data-line-number="29"><span class="co"># as.mo(&quot;Sthafilokkockus aaureuz&quot;) 310.0 320.0 350.0 330.0 340.0 480</span></a>
<a class="sourceLine" id="cb2-30" data-line-number="30"><span class="co"># as.mo(&quot;MRSA&quot;) 8.7 9.0 12.0 9.5 9.7 32</span></a>
<a class="sourceLine" id="cb2-31" data-line-number="31"><span class="co"># as.mo(&quot;VISA&quot;) 19.0 20.0 22.0 22.0 24.0 26</span></a>
<a class="sourceLine" id="cb2-32" data-line-number="32"><span class="co"># as.mo(&quot;VRSA&quot;) 19.0 20.0 28.0 22.0 43.0 48</span></a>
<a class="sourceLine" id="cb2-33" data-line-number="33"><span class="co"># as.mo(22242419) 18.0 19.0 25.0 22.0 23.0 41</span></a>
<a class="sourceLine" id="cb2-19" data-line-number="19"><span class="co"># expr min lq mean median uq max</span></a>
<a class="sourceLine" id="cb2-20" data-line-number="20"><span class="co"># as.mo("sau") 9.5 9.7 15 10 11 34</span></a>
<a class="sourceLine" id="cb2-21" data-line-number="21"><span class="co"># as.mo("stau") 31.0 32.0 38 33 34 62</span></a>
<a class="sourceLine" id="cb2-22" data-line-number="22"><span class="co"># as.mo("STAU") 31.0 32.0 39 35 39 56</span></a>
<a class="sourceLine" id="cb2-23" data-line-number="23"><span class="co"># as.mo("staaur") 9.6 10.0 17 11 31 31</span></a>
<a class="sourceLine" id="cb2-24" data-line-number="24"><span class="co"># as.mo("STAAUR") 9.6 9.8 13 10 10 34</span></a>
<a class="sourceLine" id="cb2-25" data-line-number="25"><span class="co"># as.mo("S. aureus") 24.0 25.0 27 25 28 33</span></a>
<a class="sourceLine" id="cb2-26" data-line-number="26"><span class="co"># as.mo("S aureus") 24.0 25.0 31 25 43 51</span></a>
<a class="sourceLine" id="cb2-27" data-line-number="27"><span class="co"># as.mo("Staphylococcus aureus") 29.0 30.0 32 31 34 39</span></a>
<a class="sourceLine" id="cb2-28" data-line-number="28"><span class="co"># as.mo("Staphylococcus aureus (MRSA)") 550.0 580.0 610 600 620 680</span></a>
<a class="sourceLine" id="cb2-29" data-line-number="29"><span class="co"># as.mo("Sthafilokkockus aaureuz") 290.0 310.0 360 340 380 530</span></a>
<a class="sourceLine" id="cb2-30" data-line-number="30"><span class="co"># as.mo("MRSA") 9.6 10.0 16 10 30 32</span></a>
<a class="sourceLine" id="cb2-31" data-line-number="31"><span class="co"># as.mo("VISA") 19.0 20.0 21 20 21 22</span></a>
<a class="sourceLine" id="cb2-32" data-line-number="32"><span class="co"># as.mo("VRSA") 20.0 20.0 26 21 24 46</span></a>
<a class="sourceLine" id="cb2-33" data-line-number="33"><span class="co"># as.mo(22242419) 18.0 18.0 19 19 19 22</span></a>
<a class="sourceLine" id="cb2-34" data-line-number="34"><span class="co"># neval</span></a>
<a class="sourceLine" id="cb2-35" data-line-number="35"><span class="co"># 10</span></a>
<a class="sourceLine" id="cb2-36" data-line-number="36"><span class="co"># 10</span></a>
@ -291,158 +251,162 @@
<a class="sourceLine" id="cb2-46" data-line-number="46"><span class="co"># 10</span></a>
<a class="sourceLine" id="cb2-47" data-line-number="47"><span class="co"># 10</span></a>
<a class="sourceLine" id="cb2-48" data-line-number="48"><span class="co"># 10</span></a></code></pre></div>
<p><img src="benchmarks_files/figure-html/unnamed-chunk-5-1.png" width="562.5" /></p>
<p><img src="benchmarks_files/figure-html/unnamed-chunk-5-1.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. The second input is the only one that has to be looked up thoroughly. All the others are known codes (the first one is a WHONET code) or common laboratory codes, or common full organism names like the last one. Full organism names are always preferred.</p>
<p>To achieve this speed, the <code>as.mo</code> function also takes into account the prevalence of human pathogenic microorganisms. The downside 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>
<div class="sourceCode" id="cb3"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb3-1" data-line-number="1">M.semesiae &lt;-<span class="st"> </span><span class="kw">microbenchmark</span>(<span class="kw">as.mo</span>(<span class="st">&quot;metsem&quot;</span>),</a>
<a class="sourceLine" id="cb3-2" data-line-number="2"> <span class="kw">as.mo</span>(<span class="st">&quot;METSEM&quot;</span>),</a>
<a class="sourceLine" id="cb3-3" data-line-number="3"> <span class="kw">as.mo</span>(<span class="st">&quot;M. semesiae&quot;</span>),</a>
<a class="sourceLine" id="cb3-4" data-line-number="4"> <span class="kw">as.mo</span>(<span class="st">&quot;M. semesiae&quot;</span>),</a>
<a class="sourceLine" id="cb3-5" data-line-number="5"> <span class="kw">as.mo</span>(<span class="st">&quot;Methanosarcina semesiae&quot;</span>),</a>
<div class="sourceCode" id="cb3"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb3-1" data-line-number="1">M.semesiae &lt;-<span class="st"> </span><span class="kw"><a href="https://rdrr.io/pkg/microbenchmark/man/microbenchmark.html">microbenchmark</a></span>(<span class="kw"><a href="../reference/as.mo.html">as.mo</a></span>(<span class="st">"metsem"</span>),</a>
<a class="sourceLine" id="cb3-2" data-line-number="2"> <span class="kw"><a href="../reference/as.mo.html">as.mo</a></span>(<span class="st">"METSEM"</span>),</a>
<a class="sourceLine" id="cb3-3" data-line-number="3"> <span class="kw"><a href="../reference/as.mo.html">as.mo</a></span>(<span class="st">"M. semesiae"</span>),</a>
<a class="sourceLine" id="cb3-4" data-line-number="4"> <span class="kw"><a href="../reference/as.mo.html">as.mo</a></span>(<span class="st">"M. semesiae"</span>),</a>
<a class="sourceLine" id="cb3-5" data-line-number="5"> <span class="kw"><a href="../reference/as.mo.html">as.mo</a></span>(<span class="st">"Methanosarcina semesiae"</span>),</a>
<a class="sourceLine" id="cb3-6" data-line-number="6"> <span class="dt">times =</span> <span class="dv">10</span>)</a>
<a class="sourceLine" id="cb3-7" data-line-number="7"><span class="kw">print</span>(M.semesiae, <span class="dt">unit =</span> <span class="st">&quot;ms&quot;</span>, <span class="dt">signif =</span> <span class="dv">4</span>)</a>
<a class="sourceLine" id="cb3-7" data-line-number="7"><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>)</a>
<a class="sourceLine" id="cb3-8" data-line-number="8"><span class="co"># Unit: milliseconds</span></a>
<a class="sourceLine" id="cb3-9" data-line-number="9"><span class="co"># expr min lq mean median uq</span></a>
<a class="sourceLine" id="cb3-10" data-line-number="10"><span class="co"># as.mo(&quot;metsem&quot;) 1343.00 1379.00 1415.00 1404.00 1424.00</span></a>
<a class="sourceLine" id="cb3-11" data-line-number="11"><span class="co"># as.mo(&quot;METSEM&quot;) 1335.00 1356.00 1418.00 1410.00 1451.00</span></a>
<a class="sourceLine" id="cb3-12" data-line-number="12"><span class="co"># as.mo(&quot;M. semesiae&quot;) 1852.00 2045.00 2081.00 2107.00 2154.00</span></a>
<a class="sourceLine" id="cb3-13" data-line-number="13"><span class="co"># as.mo(&quot;M. semesiae&quot;) 1961.00 2037.00 2095.00 2085.00 2123.00</span></a>
<a class="sourceLine" id="cb3-14" data-line-number="14"><span class="co"># as.mo(&quot;Methanosarcina semesiae&quot;) 30.55 31.13 34.35 32.63 33.33</span></a>
<a class="sourceLine" id="cb3-10" data-line-number="10"><span class="co"># as.mo("metsem") 1293.00 1304.00 1394.00 1316.00 1395.00</span></a>
<a class="sourceLine" id="cb3-11" data-line-number="11"><span class="co"># as.mo("METSEM") 1232.00 1278.00 1510.00 1309.00 1518.00</span></a>
<a class="sourceLine" id="cb3-12" data-line-number="12"><span class="co"># as.mo("M. semesiae") 1874.00 1928.00 2063.00 1961.00 2167.00</span></a>
<a class="sourceLine" id="cb3-13" data-line-number="13"><span class="co"># as.mo("M. semesiae") 1883.00 1926.00 2091.00 1978.00 2060.00</span></a>
<a class="sourceLine" id="cb3-14" data-line-number="14"><span class="co"># as.mo("Methanosarcina semesiae") 30.23 31.64 35.26 31.82 38.29</span></a>
<a class="sourceLine" id="cb3-15" data-line-number="15"><span class="co"># max neval</span></a>
<a class="sourceLine" id="cb3-16" data-line-number="16"><span class="co"># 1579.00 10</span></a>
<a class="sourceLine" id="cb3-17" data-line-number="17"><span class="co"># 1557.00 10</span></a>
<a class="sourceLine" id="cb3-18" data-line-number="18"><span class="co"># 2163.00 10</span></a>
<a class="sourceLine" id="cb3-19" data-line-number="19"><span class="co"># 2336.00 10</span></a>
<a class="sourceLine" id="cb3-20" data-line-number="20"><span class="co"># 54.12 10</span></a></code></pre></div>
<p>That takes 15.6 times as much time on average. A value of 100 milliseconds means it can only determine ~10 different input values per second. 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 almost fast - these are the most probable input from most data sets.</p>
<a class="sourceLine" id="cb3-16" data-line-number="16"><span class="co"># 1929.00 10</span></a>
<a class="sourceLine" id="cb3-17" data-line-number="17"><span class="co"># 2721.00 10</span></a>
<a class="sourceLine" id="cb3-18" data-line-number="18"><span class="co"># 2676.00 10</span></a>
<a class="sourceLine" id="cb3-19" data-line-number="19"><span class="co"># 2974.00 10</span></a>
<a class="sourceLine" id="cb3-20" data-line-number="20"><span class="co"># 52.65 10</span></a></code></pre></div>
<p>That takes 15.8 times as much time on average. A value of 100 milliseconds means it can only determine ~10 different input values per second. 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 almost fast - these 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-9-1.png" width="562.5" /></p>
<p>In reality, the <code>as.mo()</code> functions <strong>learns from its own output to speed up determinations for next times</strong>. In above figure, this effect was disabled to show the difference with the boxplot below - when you would use <code>as.mo()</code> yourself:</p>
<p><img src="benchmarks_files/figure-html/unnamed-chunk-11-1.png" width="562.5" /></p>
<p><img src="benchmarks_files/figure-html/unnamed-chunk-9-1.png" width="562.5"></p>
<p>In reality, the <code><a href="../reference/as.mo.html">as.mo()</a></code> functions <strong>learns from its own output to speed up determinations for next times</strong>. In above figure, this effect was disabled to show the difference with the boxplot below - when you would use <code><a href="../reference/as.mo.html">as.mo()</a></code> yourself:</p>
<p><img src="benchmarks_files/figure-html/unnamed-chunk-11-1.png" width="562.5"></p>
<p>The highest outliers are the first times. All next determinations were done in only thousands of seconds.</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>
<div id="repetitive-results" class="section level3">
<h3>Repetitive results</h3>
<p>Repetitive results are unique values that are present more than once. Unique values will only be calculated once by <code>as.mo()</code>. We will use <code>mo_name()</code> for this test - a helper function that returns the full microbial name (genus, species and possibly subspecies) which uses <code>as.mo()</code> internally.</p>
<div class="sourceCode" id="cb4"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb4-1" data-line-number="1"><span class="kw">library</span>(dplyr)</a>
<h3 class="hasAnchor">
<a href="#repetitive-results" class="anchor"></a>Repetitive results</h3>
<p>Repetitive results are unique values that are present more than once. Unique values will only be calculated once by <code><a href="../reference/as.mo.html">as.mo()</a></code>. We will use <code><a href="../reference/mo_property.html">mo_name()</a></code> for this test - a helper function that returns the full microbial name (genus, species and possibly subspecies) which uses <code><a href="../reference/as.mo.html">as.mo()</a></code> internally.</p>
<div class="sourceCode" id="cb4"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb4-1" data-line-number="1"><span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span>(dplyr)</a>
<a class="sourceLine" id="cb4-2" data-line-number="2"><span class="co"># take all MO codes from the example_isolates data set</span></a>
<a class="sourceLine" id="cb4-3" data-line-number="3">x &lt;-<span class="st"> </span>example_isolates<span class="op">$</span>mo <span class="op">%&gt;%</span></a>
<a class="sourceLine" id="cb4-4" data-line-number="4"><span class="st"> </span><span class="co"># keep only the unique ones</span></a>
<a class="sourceLine" id="cb4-5" data-line-number="5"><span class="st"> </span><span class="kw">unique</span>() <span class="op">%&gt;%</span></a>
<a class="sourceLine" id="cb4-5" data-line-number="5"><span class="st"> </span><span class="kw"><a href="https://rdrr.io/r/base/unique.html">unique</a></span>() <span class="op">%&gt;%</span></a>
<a class="sourceLine" id="cb4-6" data-line-number="6"><span class="st"> </span><span class="co"># pick 50 of them at random</span></a>
<a class="sourceLine" id="cb4-7" data-line-number="7"><span class="st"> </span><span class="kw">sample</span>(<span class="dv">50</span>) <span class="op">%&gt;%</span></a>
<a class="sourceLine" id="cb4-7" data-line-number="7"><span class="st"> </span><span class="kw"><a href="https://dplyr.tidyverse.org/reference/sample.html">sample</a></span>(<span class="dv">50</span>) <span class="op">%&gt;%</span></a>
<a class="sourceLine" id="cb4-8" data-line-number="8"><span class="st"> </span><span class="co"># paste that 10,000 times</span></a>
<a class="sourceLine" id="cb4-9" data-line-number="9"><span class="st"> </span><span class="kw">rep</span>(<span class="dv">10000</span>) <span class="op">%&gt;%</span></a>
<a class="sourceLine" id="cb4-9" data-line-number="9"><span class="st"> </span><span class="kw"><a href="https://rdrr.io/r/base/rep.html">rep</a></span>(<span class="dv">10000</span>) <span class="op">%&gt;%</span></a>
<a class="sourceLine" id="cb4-10" data-line-number="10"><span class="st"> </span><span class="co"># scramble it</span></a>
<a class="sourceLine" id="cb4-11" data-line-number="11"><span class="st"> </span><span class="kw">sample</span>()</a>
<a class="sourceLine" id="cb4-11" data-line-number="11"><span class="st"> </span><span class="kw"><a href="https://dplyr.tidyverse.org/reference/sample.html">sample</a></span>()</a>
<a class="sourceLine" id="cb4-12" data-line-number="12"> </a>
<a class="sourceLine" id="cb4-13" data-line-number="13"><span class="co"># got indeed 50 times 10,000 = half a million?</span></a>
<a class="sourceLine" id="cb4-14" data-line-number="14"><span class="kw">length</span>(x)</a>
<a class="sourceLine" id="cb4-14" data-line-number="14"><span class="kw"><a href="https://rdrr.io/r/base/length.html">length</a></span>(x)</a>
<a class="sourceLine" id="cb4-15" data-line-number="15"><span class="co"># [1] 500000</span></a>
<a class="sourceLine" id="cb4-16" data-line-number="16"></a>
<a class="sourceLine" id="cb4-17" data-line-number="17"><span class="co"># and how many unique values do we have?</span></a>
<a class="sourceLine" id="cb4-18" data-line-number="18"><span class="kw">n_distinct</span>(x)</a>
<a class="sourceLine" id="cb4-18" data-line-number="18"><span class="kw"><a href="https://dplyr.tidyverse.org/reference/n_distinct.html">n_distinct</a></span>(x)</a>
<a class="sourceLine" id="cb4-19" data-line-number="19"><span class="co"># [1] 50</span></a>
<a class="sourceLine" id="cb4-20" data-line-number="20"></a>
<a class="sourceLine" id="cb4-21" data-line-number="21"><span class="co"># now let&#39;s see:</span></a>
<a class="sourceLine" id="cb4-22" data-line-number="22">run_it &lt;-<span class="st"> </span><span class="kw">microbenchmark</span>(<span class="kw">mo_name</span>(x),</a>
<a class="sourceLine" id="cb4-21" data-line-number="21"><span class="co"># now let's see:</span></a>
<a class="sourceLine" id="cb4-22" data-line-number="22">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="kw"><a href="../reference/mo_property.html">mo_name</a></span>(x),</a>
<a class="sourceLine" id="cb4-23" data-line-number="23"> <span class="dt">times =</span> <span class="dv">10</span>)</a>
<a class="sourceLine" id="cb4-24" data-line-number="24"><span class="kw">print</span>(run_it, <span class="dt">unit =</span> <span class="st">&quot;ms&quot;</span>, <span class="dt">signif =</span> <span class="dv">3</span>)</a>
<a class="sourceLine" id="cb4-24" data-line-number="24"><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>)</a>
<a class="sourceLine" id="cb4-25" data-line-number="25"><span class="co"># Unit: milliseconds</span></a>
<a class="sourceLine" id="cb4-26" data-line-number="26"><span class="co"># expr min lq mean median uq max neval</span></a>
<a class="sourceLine" id="cb4-27" data-line-number="27"><span class="co"># mo_name(x) 626 639 663 658 682 731 10</span></a></code></pre></div>
<p>So transforming 500,000 values (!!) of 50 unique values only takes 0.66 seconds (657 ms). You only lose time on your unique input values.</p>
<a class="sourceLine" id="cb4-27" data-line-number="27"><span class="co"># mo_name(x) 596 604 651 641 663 813 10</span></a></code></pre></div>
<p>So transforming 500,000 values (!!) of 50 unique values only takes 0.64 seconds (641 ms). You only lose time on your unique input values.</p>
</div>
<div id="precalculated-results" class="section level3">
<h3>Precalculated results</h3>
<p>What about precalculated results? If the input is an already precalculated result of a helper function like <code>mo_name()</code>, it almost doesn’t take any time at all (see ‘C’ below):</p>
<div class="sourceCode" id="cb5"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb5-1" data-line-number="1">run_it &lt;-<span class="st"> </span><span class="kw">microbenchmark</span>(<span class="dt">A =</span> <span class="kw">mo_name</span>(<span class="st">&quot;B_STPHY_AURS&quot;</span>),</a>
<a class="sourceLine" id="cb5-2" data-line-number="2"> <span class="dt">B =</span> <span class="kw">mo_name</span>(<span class="st">&quot;S. aureus&quot;</span>),</a>
<a class="sourceLine" id="cb5-3" data-line-number="3"> <span class="dt">C =</span> <span class="kw">mo_name</span>(<span class="st">&quot;Staphylococcus aureus&quot;</span>),</a>
<h3 class="hasAnchor">
<a href="#precalculated-results" class="anchor"></a>Precalculated results</h3>
<p>What about precalculated results? If the input is an already precalculated result of a helper function like <code><a href="../reference/mo_property.html">mo_name()</a></code>, it almost doesn’t take any time at all (see ‘C’ below):</p>
<div class="sourceCode" id="cb5"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb5-1" data-line-number="1">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_name</a></span>(<span class="st">"B_STPHY_AURS"</span>),</a>
<a class="sourceLine" id="cb5-2" data-line-number="2"> <span class="dt">B =</span> <span class="kw"><a href="../reference/mo_property.html">mo_name</a></span>(<span class="st">"S. aureus"</span>),</a>
<a class="sourceLine" id="cb5-3" data-line-number="3"> <span class="dt">C =</span> <span class="kw"><a href="../reference/mo_property.html">mo_name</a></span>(<span class="st">"Staphylococcus aureus"</span>),</a>
<a class="sourceLine" id="cb5-4" data-line-number="4"> <span class="dt">times =</span> <span class="dv">10</span>)</a>
<a class="sourceLine" id="cb5-5" data-line-number="5"><span class="kw">print</span>(run_it, <span class="dt">unit =</span> <span class="st">&quot;ms&quot;</span>, <span class="dt">signif =</span> <span class="dv">3</span>)</a>
<a class="sourceLine" id="cb5-5" data-line-number="5"><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>)</a>
<a class="sourceLine" id="cb5-6" data-line-number="6"><span class="co"># Unit: milliseconds</span></a>
<a class="sourceLine" id="cb5-7" data-line-number="7"><span class="co"># expr min lq mean median uq max neval</span></a>
<a class="sourceLine" id="cb5-8" data-line-number="8"><span class="co"># A 6.260 6.320 9.050 6.360 6.810 32.40 10</span></a>
<a class="sourceLine" id="cb5-9" data-line-number="9"><span class="co"># B 22.800 23.000 23.800 23.200 23.900 28.20 10</span></a>
<a class="sourceLine" id="cb5-10" data-line-number="10"><span class="co"># C 0.709 0.813 0.836 0.843 0.854 0.96 10</span></a></code></pre></div>
<p>So going from <code>mo_name(&quot;Staphylococcus aureus&quot;)</code> to <code>&quot;Staphylococcus aureus&quot;</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"><a class="sourceLine" id="cb6-1" data-line-number="1">run_it &lt;-<span class="st"> </span><span class="kw">microbenchmark</span>(<span class="dt">A =</span> <span class="kw">mo_species</span>(<span class="st">&quot;aureus&quot;</span>),</a>
<a class="sourceLine" id="cb6-2" data-line-number="2"> <span class="dt">B =</span> <span class="kw">mo_genus</span>(<span class="st">&quot;Staphylococcus&quot;</span>),</a>
<a class="sourceLine" id="cb6-3" data-line-number="3"> <span class="dt">C =</span> <span class="kw">mo_name</span>(<span class="st">&quot;Staphylococcus aureus&quot;</span>),</a>
<a class="sourceLine" id="cb6-4" data-line-number="4"> <span class="dt">D =</span> <span class="kw">mo_family</span>(<span class="st">&quot;Staphylococcaceae&quot;</span>),</a>
<a class="sourceLine" id="cb6-5" data-line-number="5"> <span class="dt">E =</span> <span class="kw">mo_order</span>(<span class="st">&quot;Bacillales&quot;</span>),</a>
<a class="sourceLine" id="cb6-6" data-line-number="6"> <span class="dt">F =</span> <span class="kw">mo_class</span>(<span class="st">&quot;Bacilli&quot;</span>),</a>
<a class="sourceLine" id="cb6-7" data-line-number="7"> <span class="dt">G =</span> <span class="kw">mo_phylum</span>(<span class="st">&quot;Firmicutes&quot;</span>),</a>
<a class="sourceLine" id="cb6-8" data-line-number="8"> <span class="dt">H =</span> <span class="kw">mo_kingdom</span>(<span class="st">&quot;Bacteria&quot;</span>),</a>
<a class="sourceLine" id="cb5-7" data-line-number="7"><span class="co"># expr min lq mean median uq max neval</span></a>
<a class="sourceLine" id="cb5-8" data-line-number="8"><span class="co"># A 6.300 6.460 6.79 6.500 6.810 8.32 10</span></a>
<a class="sourceLine" id="cb5-9" data-line-number="9"><span class="co"># B 24.600 24.700 28.70 25.600 26.600 51.30 10</span></a>
<a class="sourceLine" id="cb5-10" data-line-number="10"><span class="co"># C 0.778 0.828 0.93 0.854 0.872 1.63 10</span></a></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 0.0009 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"><a class="sourceLine" id="cb6-1" data-line-number="1">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>),</a>
<a class="sourceLine" id="cb6-2" data-line-number="2"> <span class="dt">B =</span> <span class="kw"><a href="../reference/mo_property.html">mo_genus</a></span>(<span class="st">"Staphylococcus"</span>),</a>
<a class="sourceLine" id="cb6-3" data-line-number="3"> <span class="dt">C =</span> <span class="kw"><a href="../reference/mo_property.html">mo_name</a></span>(<span class="st">"Staphylococcus aureus"</span>),</a>
<a class="sourceLine" id="cb6-4" data-line-number="4"> <span class="dt">D =</span> <span class="kw"><a href="../reference/mo_property.html">mo_family</a></span>(<span class="st">"Staphylococcaceae"</span>),</a>
<a class="sourceLine" id="cb6-5" data-line-number="5"> <span class="dt">E =</span> <span class="kw"><a href="../reference/mo_property.html">mo_order</a></span>(<span class="st">"Bacillales"</span>),</a>
<a class="sourceLine" id="cb6-6" data-line-number="6"> <span class="dt">F =</span> <span class="kw"><a href="../reference/mo_property.html">mo_class</a></span>(<span class="st">"Bacilli"</span>),</a>
<a class="sourceLine" id="cb6-7" data-line-number="7"> <span class="dt">G =</span> <span class="kw"><a href="../reference/mo_property.html">mo_phylum</a></span>(<span class="st">"Firmicutes"</span>),</a>
<a class="sourceLine" id="cb6-8" data-line-number="8"> <span class="dt">H =</span> <span class="kw"><a href="../reference/mo_property.html">mo_kingdom</a></span>(<span class="st">"Bacteria"</span>),</a>
<a class="sourceLine" id="cb6-9" data-line-number="9"> <span class="dt">times =</span> <span class="dv">10</span>)</a>
<a class="sourceLine" id="cb6-10" data-line-number="10"><span class="kw">print</span>(run_it, <span class="dt">unit =</span> <span class="st">&quot;ms&quot;</span>, <span class="dt">signif =</span> <span class="dv">3</span>)</a>
<a class="sourceLine" id="cb6-10" data-line-number="10"><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>)</a>
<a class="sourceLine" id="cb6-11" data-line-number="11"><span class="co"># Unit: milliseconds</span></a>
<a class="sourceLine" id="cb6-12" data-line-number="12"><span class="co"># expr min lq mean median uq max neval</span></a>
<a class="sourceLine" id="cb6-13" data-line-number="13"><span class="co"># A 0.466 0.466 0.495 0.475 0.510 0.622 10</span></a>
<a class="sourceLine" id="cb6-14" data-line-number="14"><span class="co"># B 0.497 0.511 0.558 0.517 0.575 0.844 10</span></a>
<a class="sourceLine" id="cb6-15" data-line-number="15"><span class="co"># C 0.709 0.783 0.876 0.857 0.956 1.110 10</span></a>
<a class="sourceLine" id="cb6-16" data-line-number="16"><span class="co"># D 0.477 0.486 0.547 0.514 0.639 0.669 10</span></a>
<a class="sourceLine" id="cb6-17" data-line-number="17"><span class="co"># E 0.468 0.476 0.504 0.481 0.520 0.630 10</span></a>
<a class="sourceLine" id="cb6-18" data-line-number="18"><span class="co"># F 0.454 0.461 0.509 0.475 0.522 0.687 10</span></a>
<a class="sourceLine" id="cb6-19" data-line-number="19"><span class="co"># G 0.459 0.465 0.522 0.475 0.587 0.637 10</span></a>
<a class="sourceLine" id="cb6-20" data-line-number="20"><span class="co"># H 0.432 0.460 0.502 0.469 0.535 0.623 10</span></a></code></pre></div>
<p>Of course, when running <code>mo_phylum(&quot;Firmicutes&quot;)</code> the function has zero knowledge about the actual microorganism, namely <em>S. aureus</em>. But since the result would be <code>&quot;Firmicutes&quot;</code> too, 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>
<a class="sourceLine" id="cb6-13" data-line-number="13"><span class="co"># A 0.457 0.463 0.477 0.476 0.483 0.511 10</span></a>
<a class="sourceLine" id="cb6-14" data-line-number="14"><span class="co"># B 0.486 0.496 0.508 0.499 0.512 0.569 10</span></a>
<a class="sourceLine" id="cb6-15" data-line-number="15"><span class="co"># C 0.781 0.803 0.825 0.829 0.847 0.854 10</span></a>
<a class="sourceLine" id="cb6-16" data-line-number="16"><span class="co"># D 0.479 0.495 0.528 0.510 0.517 0.749 10</span></a>
<a class="sourceLine" id="cb6-17" data-line-number="17"><span class="co"># E 0.457 0.464 0.484 0.475 0.498 0.547 10</span></a>
<a class="sourceLine" id="cb6-18" data-line-number="18"><span class="co"># F 0.458 0.464 0.472 0.468 0.477 0.496 10</span></a>
<a class="sourceLine" id="cb6-19" data-line-number="19"><span class="co"># G 0.446 0.453 0.462 0.459 0.467 0.499 10</span></a>
<a class="sourceLine" id="cb6-20" data-line-number="20"><span class="co"># H 0.426 0.458 0.467 0.467 0.472 0.507 10</span></a></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> too, there is no point in calculating the result. And because this package ‘knows’ all phyla of all known bacteria (according to the Catalogue of Life), it can just return the initial value immediately.</p>
</div>
<div id="results-in-other-languages" class="section level3">
<h3>Results in other languages</h3>
<h3 class="hasAnchor">
<a href="#results-in-other-languages" class="anchor"></a>Results in other languages</h3>
<p>When the system language is non-English and supported by this <code>AMR</code> package, some functions will have a translated result. This almost does’t take extra time:</p>
<div class="sourceCode" id="cb7"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb7-1" data-line-number="1"><span class="kw">mo_name</span>(<span class="st">&quot;CoNS&quot;</span>, <span class="dt">language =</span> <span class="st">&quot;en&quot;</span>) <span class="co"># or just mo_name(&quot;CoNS&quot;) on an English system</span></a>
<a class="sourceLine" id="cb7-2" data-line-number="2"><span class="co"># [1] &quot;Coagulase-negative Staphylococcus (CoNS)&quot;</span></a>
<div class="sourceCode" id="cb7"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb7-1" data-line-number="1"><span class="kw"><a href="../reference/mo_property.html">mo_name</a></span>(<span class="st">"CoNS"</span>, <span class="dt">language =</span> <span class="st">"en"</span>) <span class="co"># or just mo_name("CoNS") on an English system</span></a>
<a class="sourceLine" id="cb7-2" data-line-number="2"><span class="co"># [1] "Coagulase-negative Staphylococcus (CoNS)"</span></a>
<a class="sourceLine" id="cb7-3" data-line-number="3"></a>
<a class="sourceLine" id="cb7-4" data-line-number="4"><span class="kw">mo_name</span>(<span class="st">&quot;CoNS&quot;</span>, <span class="dt">language =</span> <span class="st">&quot;es&quot;</span>) <span class="co"># or just mo_name(&quot;CoNS&quot;) on a Spanish system</span></a>
<a class="sourceLine" id="cb7-5" data-line-number="5"><span class="co"># [1] &quot;Staphylococcus coagulasa negativo (SCN)&quot;</span></a>
<a class="sourceLine" id="cb7-4" data-line-number="4"><span class="kw"><a href="../reference/mo_property.html">mo_name</a></span>(<span class="st">"CoNS"</span>, <span class="dt">language =</span> <span class="st">"es"</span>) <span class="co"># or just mo_name("CoNS") on a Spanish system</span></a>
<a class="sourceLine" id="cb7-5" data-line-number="5"><span class="co"># [1] "Staphylococcus coagulasa negativo (SCN)"</span></a>
<a class="sourceLine" id="cb7-6" data-line-number="6"></a>
<a class="sourceLine" id="cb7-7" data-line-number="7"><span class="kw">mo_name</span>(<span class="st">&quot;CoNS&quot;</span>, <span class="dt">language =</span> <span class="st">&quot;nl&quot;</span>) <span class="co"># or just mo_name(&quot;CoNS&quot;) on a Dutch system</span></a>
<a class="sourceLine" id="cb7-8" data-line-number="8"><span class="co"># [1] &quot;Coagulase-negatieve Staphylococcus (CNS)&quot;</span></a>
<a class="sourceLine" id="cb7-7" data-line-number="7"><span class="kw"><a href="../reference/mo_property.html">mo_name</a></span>(<span class="st">"CoNS"</span>, <span class="dt">language =</span> <span class="st">"nl"</span>) <span class="co"># or just mo_name("CoNS") on a Dutch system</span></a>
<a class="sourceLine" id="cb7-8" data-line-number="8"><span class="co"># [1] "Coagulase-negatieve Staphylococcus (CNS)"</span></a>
<a class="sourceLine" id="cb7-9" data-line-number="9"></a>
<a class="sourceLine" id="cb7-10" data-line-number="10">run_it &lt;-<span class="st"> </span><span class="kw">microbenchmark</span>(<span class="dt">en =</span> <span class="kw">mo_name</span>(<span class="st">&quot;CoNS&quot;</span>, <span class="dt">language =</span> <span class="st">&quot;en&quot;</span>),</a>
<a class="sourceLine" id="cb7-11" data-line-number="11"> <span class="dt">de =</span> <span class="kw">mo_name</span>(<span class="st">&quot;CoNS&quot;</span>, <span class="dt">language =</span> <span class="st">&quot;de&quot;</span>),</a>
<a class="sourceLine" id="cb7-12" data-line-number="12"> <span class="dt">nl =</span> <span class="kw">mo_name</span>(<span class="st">&quot;CoNS&quot;</span>, <span class="dt">language =</span> <span class="st">&quot;nl&quot;</span>),</a>
<a class="sourceLine" id="cb7-13" data-line-number="13"> <span class="dt">es =</span> <span class="kw">mo_name</span>(<span class="st">&quot;CoNS&quot;</span>, <span class="dt">language =</span> <span class="st">&quot;es&quot;</span>),</a>
<a class="sourceLine" id="cb7-14" data-line-number="14"> <span class="dt">it =</span> <span class="kw">mo_name</span>(<span class="st">&quot;CoNS&quot;</span>, <span class="dt">language =</span> <span class="st">&quot;it&quot;</span>),</a>
<a class="sourceLine" id="cb7-15" data-line-number="15"> <span class="dt">fr =</span> <span class="kw">mo_name</span>(<span class="st">&quot;CoNS&quot;</span>, <span class="dt">language =</span> <span class="st">&quot;fr&quot;</span>),</a>
<a class="sourceLine" id="cb7-16" data-line-number="16"> <span class="dt">pt =</span> <span class="kw">mo_name</span>(<span class="st">&quot;CoNS&quot;</span>, <span class="dt">language =</span> <span class="st">&quot;pt&quot;</span>),</a>
<a class="sourceLine" id="cb7-10" data-line-number="10">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">en =</span> <span class="kw"><a href="../reference/mo_property.html">mo_name</a></span>(<span class="st">"CoNS"</span>, <span class="dt">language =</span> <span class="st">"en"</span>),</a>
<a class="sourceLine" id="cb7-11" data-line-number="11"> <span class="dt">de =</span> <span class="kw"><a href="../reference/mo_property.html">mo_name</a></span>(<span class="st">"CoNS"</span>, <span class="dt">language =</span> <span class="st">"de"</span>),</a>
<a class="sourceLine" id="cb7-12" data-line-number="12"> <span class="dt">nl =</span> <span class="kw"><a href="../reference/mo_property.html">mo_name</a></span>(<span class="st">"CoNS"</span>, <span class="dt">language =</span> <span class="st">"nl"</span>),</a>
<a class="sourceLine" id="cb7-13" data-line-number="13"> <span class="dt">es =</span> <span class="kw"><a href="../reference/mo_property.html">mo_name</a></span>(<span class="st">"CoNS"</span>, <span class="dt">language =</span> <span class="st">"es"</span>),</a>
<a class="sourceLine" id="cb7-14" data-line-number="14"> <span class="dt">it =</span> <span class="kw"><a href="../reference/mo_property.html">mo_name</a></span>(<span class="st">"CoNS"</span>, <span class="dt">language =</span> <span class="st">"it"</span>),</a>
<a class="sourceLine" id="cb7-15" data-line-number="15"> <span class="dt">fr =</span> <span class="kw"><a href="../reference/mo_property.html">mo_name</a></span>(<span class="st">"CoNS"</span>, <span class="dt">language =</span> <span class="st">"fr"</span>),</a>
<a class="sourceLine" id="cb7-16" data-line-number="16"> <span class="dt">pt =</span> <span class="kw"><a href="../reference/mo_property.html">mo_name</a></span>(<span class="st">"CoNS"</span>, <span class="dt">language =</span> <span class="st">"pt"</span>),</a>
<a class="sourceLine" id="cb7-17" data-line-number="17"> <span class="dt">times =</span> <span class="dv">10</span>)</a>
<a class="sourceLine" id="cb7-18" data-line-number="18"><span class="kw">print</span>(run_it, <span class="dt">unit =</span> <span class="st">&quot;ms&quot;</span>, <span class="dt">signif =</span> <span class="dv">4</span>)</a>
<a class="sourceLine" id="cb7-18" data-line-number="18"><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>)</a>
<a class="sourceLine" id="cb7-19" data-line-number="19"><span class="co"># Unit: milliseconds</span></a>
<a class="sourceLine" id="cb7-20" data-line-number="20"><span class="co"># expr min lq mean median uq max neval</span></a>
<a class="sourceLine" id="cb7-21" data-line-number="21"><span class="co"># en 18.35 18.55 21.13 18.70 18.79 43.22 10</span></a>
<a class="sourceLine" id="cb7-22" data-line-number="22"><span class="co"># de 19.69 19.94 20.81 20.24 20.74 25.64 10</span></a>
<a class="sourceLine" id="cb7-23" data-line-number="23"><span class="co"># nl 25.28 25.42 28.05 25.55 26.59 48.83 10</span></a>
<a class="sourceLine" id="cb7-24" data-line-number="24"><span class="co"># es 19.77 19.95 22.83 20.29 20.76 46.03 10</span></a>
<a class="sourceLine" id="cb7-25" data-line-number="25"><span class="co"># it 19.81 19.88 20.19 20.13 20.55 20.85 10</span></a>
<a class="sourceLine" id="cb7-26" data-line-number="26"><span class="co"># fr 19.62 20.02 22.79 20.26 21.23 44.33 10</span></a>
<a class="sourceLine" id="cb7-27" data-line-number="27"><span class="co"># pt 20.05 20.37 23.19 20.67 21.46 44.96 10</span></a></code></pre></div>
<a class="sourceLine" id="cb7-21" data-line-number="21"><span class="co"># en 19.99 20.17 25.15 20.21 22.19 43.86 10</span></a>
<a class="sourceLine" id="cb7-22" data-line-number="22"><span class="co"># de 21.17 21.47 26.00 21.51 21.85 44.12 10</span></a>
<a class="sourceLine" id="cb7-23" data-line-number="23"><span class="co"># nl 26.69 26.71 27.24 27.10 27.80 28.04 10</span></a>
<a class="sourceLine" id="cb7-24" data-line-number="24"><span class="co"># es 21.20 21.36 24.26 21.51 21.85 43.76 10</span></a>
<a class="sourceLine" id="cb7-25" data-line-number="25"><span class="co"># it 21.09 21.24 26.27 21.59 22.46 46.14 10</span></a>
<a class="sourceLine" id="cb7-26" data-line-number="26"><span class="co"># fr 21.16 21.35 23.98 21.51 22.11 44.93 10</span></a>
<a class="sourceLine" id="cb7-27" data-line-number="27"><span class="co"># pt 21.10 21.13 21.35 21.28 21.37 22.24 10</span></a></code></pre></div>
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<h1>How to create frequency tables</h1>
<h4 class="author">Matthijs S. Berends</h4>
<h4 class="date">10 July 2019</h4>
<div class="hidden name"><code>freq.Rmd</code></div>
</div>
<div id="introduction" class="section level2">
<h2 class="hasAnchor">
<a href="#introduction" class="anchor"></a>Introduction</h2>
<p>Frequency tables (or frequency distributions) are summaries of the distribution of values in a sample. With the <code><a href="../reference/freq.html">freq()</a></code> function, you can create univariate frequency tables. Multiple variables will be pasted into one variable, so it forces a univariate distribution. We take the <code>septic_patients</code> dataset (included in this AMR package) as example.</p>
</div>
<div id="frequencies-of-one-variable" class="section level2">
<h2 class="hasAnchor">
<a href="#frequencies-of-one-variable" class="anchor"></a>Frequencies of one variable</h2>
<p>To only show and quickly review the content of one variable, you can just select this variable in various ways. Let’s say we want to get the frequencies of the <code>gender</code> variable of the <code>septic_patients</code> dataset:</p>
<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb1-1" data-line-number="1"><span class="co"># Any of these will work:</span></a>
<a class="sourceLine" id="cb1-2" data-line-number="2"><span class="co"># freq(septic_patients$gender)</span></a>
<a class="sourceLine" id="cb1-3" data-line-number="3"><span class="co"># freq(septic_patients[, "gender"])</span></a>
<a class="sourceLine" id="cb1-4" data-line-number="4"></a>
<a class="sourceLine" id="cb1-5" data-line-number="5"><span class="co"># Using tidyverse:</span></a>
<a class="sourceLine" id="cb1-6" data-line-number="6"><span class="co"># septic_patients$gender %&gt;% freq()</span></a>
<a class="sourceLine" id="cb1-7" data-line-number="7"><span class="co"># septic_patients[, "gender"] %&gt;% freq()</span></a>
<a class="sourceLine" id="cb1-8" data-line-number="8"><span class="co"># septic_patients %&gt;% freq("gender")</span></a>
<a class="sourceLine" id="cb1-9" data-line-number="9"></a>
<a class="sourceLine" id="cb1-10" data-line-number="10"><span class="co"># Probably the fastest and easiest:</span></a>
<a class="sourceLine" id="cb1-11" data-line-number="11">septic_patients <span class="op">%&gt;%</span><span class="st"> </span><span class="kw"><a href="../reference/freq.html">freq</a></span>(gender) </a></code></pre></div>
<p><strong>Frequency table of <code>gender</code> from <code>septic_patients</code> (2,000 x 49)</strong></p>
<p>Class: character<br>
Length: 2,000 (of which NA: 0 = 0.00%)<br>
Unique: 2</p>
<p>Shortest: 1<br>
Longest: 1</p>
<table class="table">
<thead><tr class="header">
<th align="left"></th>
<th align="left">Item</th>
<th align="right">Count</th>
<th align="right">Percent</th>
<th align="right">Cum. Count</th>
<th align="right">Cum. Percent</th>
</tr></thead>
<tbody>
<tr class="odd">
<td align="left">1</td>
<td align="left">M</td>
<td align="right">1,031</td>
<td align="right">51.6%</td>
<td align="right">1,031</td>
<td align="right">51.6%</td>
</tr>
<tr class="even">
<td align="left">2</td>
<td align="left">F</td>
<td align="right">969</td>
<td align="right">48.4%</td>
<td align="right">2,000</td>
<td align="right">100.0%</td>
</tr>
</tbody>
</table>
<p>This immediately shows the class of the variable, its length and availability (i.e. the amount of <code>NA</code>), the amount of unique values and (most importantly) that among septic patients men are more prevalent than women.</p>
</div>
<div id="frequencies-of-more-than-one-variable" class="section level2">
<h2 class="hasAnchor">
<a href="#frequencies-of-more-than-one-variable" class="anchor"></a>Frequencies of more than one variable</h2>
<p>Multiple variables will be pasted into one variable to review individual cases, keeping a univariate frequency table.</p>
<p>For illustration, we could add some more variables to the <code>septic_patients</code> dataset to learn about bacterial properties:</p>
<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb2-1" data-line-number="1">my_patients &lt;-<span class="st"> </span>septic_patients <span class="op">%&gt;%</span><span class="st"> </span><span class="kw"><a href="../reference/join.html">left_join_microorganisms</a></span>()</a>
<a class="sourceLine" id="cb2-2" data-line-number="2"><span class="co"># Joining, by = "mo"</span></a></code></pre></div>
<p>Now all variables of the <code>microorganisms</code> dataset have been joined to the <code>septic_patients</code> dataset. The <code>microorganisms</code> dataset consists of the following variables:</p>
<div class="sourceCode" id="cb3"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb3-1" data-line-number="1"><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/colnames">colnames</a></span>(microorganisms)</a>
<a class="sourceLine" id="cb3-2" data-line-number="2"><span class="co"># [1] "mo" "col_id" "fullname" "kingdom" "phylum" </span></a>
<a class="sourceLine" id="cb3-3" data-line-number="3"><span class="co"># [6] "class" "order" "family" "genus" "species" </span></a>
<a class="sourceLine" id="cb3-4" data-line-number="4"><span class="co"># [11] "subspecies" "rank" "ref" "species_id" "source" </span></a>
<a class="sourceLine" id="cb3-5" data-line-number="5"><span class="co"># [16] "prevalence"</span></a></code></pre></div>
<p>If we compare the dimensions between the old and new dataset, we can see that these 15 variables were added:</p>
<div class="sourceCode" id="cb4"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb4-1" data-line-number="1"><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/dim">dim</a></span>(septic_patients)</a>
<a class="sourceLine" id="cb4-2" data-line-number="2"><span class="co"># [1] 2000 49</span></a>
<a class="sourceLine" id="cb4-3" data-line-number="3"><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/dim">dim</a></span>(my_patients)</a>
<a class="sourceLine" id="cb4-4" data-line-number="4"><span class="co"># [1] 2000 64</span></a></code></pre></div>
<p>So now the <code>genus</code> and <code>species</code> variables are available. A frequency table of these combined variables can be created like this:</p>
<div class="sourceCode" id="cb5"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb5-1" data-line-number="1">my_patients <span class="op">%&gt;%</span></a>
<a class="sourceLine" id="cb5-2" data-line-number="2"><span class="st"> </span><span class="kw"><a href="../reference/freq.html">freq</a></span>(genus, species, <span class="dt">nmax =</span> <span class="dv">15</span>)</a></code></pre></div>
<p><strong>Frequency table of <code>genus</code> and <code>species</code> from <code>my_patients</code> (2,000 x 64)</strong></p>
<p>Columns: 2<br>
Length: 2,000 (of which NA: 0 = 0.00%)<br>
Unique: 95</p>
<p>Shortest: 8<br>
Longest: 34</p>
<table class="table">
<thead><tr class="header">
<th align="left"></th>
<th align="left">Item</th>
<th align="right">Count</th>
<th align="right">Percent</th>
<th align="right">Cum. Count</th>
<th align="right">Cum. Percent</th>
</tr></thead>
<tbody>
<tr class="odd">
<td align="left">1</td>
<td align="left">Escherichia coli</td>
<td align="right">467</td>
<td align="right">23.4%</td>
<td align="right">467</td>
<td align="right">23.4%</td>
</tr>
<tr class="even">
<td align="left">2</td>
<td align="left">Staphylococcus coagulase-negative</td>
<td align="right">313</td>
<td align="right">15.6%</td>
<td align="right">780</td>
<td align="right">39.0%</td>
</tr>
<tr class="odd">
<td align="left">3</td>
<td align="left">Staphylococcus aureus</td>
<td align="right">235</td>
<td align="right">11.7%</td>
<td align="right">1,015</td>
<td align="right">50.7%</td>
</tr>
<tr class="even">
<td align="left">4</td>
<td align="left">Staphylococcus epidermidis</td>
<td align="right">174</td>
<td align="right">8.7%</td>
<td align="right">1,189</td>
<td align="right">59.4%</td>
</tr>
<tr class="odd">
<td align="left">5</td>
<td align="left">Streptococcus pneumoniae</td>
<td align="right">117</td>
<td align="right">5.8%</td>
<td align="right">1,306</td>
<td align="right">65.3%</td>
</tr>
<tr class="even">
<td align="left">6</td>
<td align="left">Staphylococcus hominis</td>
<td align="right">81</td>
<td align="right">4.0%</td>
<td align="right">1,387</td>
<td align="right">69.4%</td>
</tr>
<tr class="odd">
<td align="left">7</td>
<td align="left">Klebsiella pneumoniae</td>
<td align="right">58</td>
<td align="right">2.9%</td>
<td align="right">1,445</td>
<td align="right">72.2%</td>
</tr>
<tr class="even">
<td align="left">8</td>
<td align="left">Enterococcus faecalis</td>
<td align="right">39</td>
<td align="right">2.0%</td>
<td align="right">1,484</td>
<td align="right">74.2%</td>
</tr>
<tr class="odd">
<td align="left">9</td>
<td align="left">Proteus mirabilis</td>
<td align="right">36</td>
<td align="right">1.8%</td>
<td align="right">1,520</td>
<td align="right">76.0%</td>
</tr>
<tr class="even">
<td align="left">10</td>
<td align="left">Pseudomonas aeruginosa</td>
<td align="right">30</td>
<td align="right">1.5%</td>
<td align="right">1,550</td>
<td align="right">77.5%</td>
</tr>
<tr class="odd">
<td align="left">11</td>
<td align="left">Serratia marcescens</td>
<td align="right">25</td>
<td align="right">1.2%</td>
<td align="right">1,575</td>
<td align="right">78.8%</td>
</tr>
<tr class="even">
<td align="left">12</td>
<td align="left">Enterobacter cloacae</td>
<td align="right">23</td>
<td align="right">1.2%</td>
<td align="right">1,598</td>
<td align="right">79.9%</td>
</tr>
<tr class="odd">
<td align="left">13</td>
<td align="left">Enterococcus faecium</td>
<td align="right">21</td>
<td align="right">1.0%</td>
<td align="right">1,619</td>
<td align="right">81.0%</td>
</tr>
<tr class="even">
<td align="left">14</td>
<td align="left">Staphylococcus capitis</td>
<td align="right">21</td>
<td align="right">1.0%</td>
<td align="right">1,640</td>
<td align="right">82.0%</td>
</tr>
<tr class="odd">
<td align="left">15</td>
<td align="left">Bacteroides fragilis</td>
<td align="right">20</td>
<td align="right">1.0%</td>
<td align="right">1,660</td>
<td align="right">83.0%</td>
</tr>
</tbody>
</table>
<p>(omitted 80 entries, n = 340 [17.0%])</p>
</div>
<div id="frequencies-of-numeric-values" class="section level2">
<h2 class="hasAnchor">
<a href="#frequencies-of-numeric-values" class="anchor"></a>Frequencies of numeric values</h2>
<p>Frequency tables can be created of any input.</p>
<p>In case of numeric values (like integers, doubles, etc.) additional descriptive statistics will be calculated and shown into the header:</p>
<div class="sourceCode" id="cb6"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb6-1" data-line-number="1"><span class="co"># # get age distribution of unique patients</span></a>
<a class="sourceLine" id="cb6-2" data-line-number="2">septic_patients <span class="op">%&gt;%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb6-3" data-line-number="3"><span class="st"> </span><span class="kw">distinct</span>(patient_id, <span class="dt">.keep_all =</span> <span class="ot">TRUE</span>) <span class="op">%&gt;%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb6-4" data-line-number="4"><span class="st"> </span><span class="kw"><a href="../reference/freq.html">freq</a></span>(age, <span class="dt">nmax =</span> <span class="dv">5</span>, <span class="dt">header =</span> <span class="ot">TRUE</span>)</a></code></pre></div>
<p><strong>Frequency table of <code>age</code> from a <code>data.frame</code> (981 x 49)</strong></p>
<p>Class: numeric<br>
Length: 981 (of which NA: 0 = 0.00%)<br>
Unique: 73</p>
<p>Mean: 71.08<br>
SD: 14.05 (CV: 0.20, MAD: 13.34)<br>
Five-Num: 14 | 63 | 74 | 82 | 97 (IQR: 19, CQV: 0.13)<br>
Outliers: 15 (unique count: 12)</p>
<table class="table">
<thead><tr class="header">
<th align="left"></th>
<th align="right">Item</th>
<th align="right">Count</th>
<th align="right">Percent</th>
<th align="right">Cum. Count</th>
<th align="right">Cum. Percent</th>
</tr></thead>
<tbody>
<tr class="odd">
<td align="left">1</td>
<td align="right">83</td>
<td align="right">44</td>
<td align="right">4.5%</td>
<td align="right">44</td>
<td align="right">4.5%</td>
</tr>
<tr class="even">
<td align="left">2</td>
<td align="right">76</td>
<td align="right">43</td>
<td align="right">4.4%</td>
<td align="right">87</td>
<td align="right">8.9%</td>
</tr>
<tr class="odd">
<td align="left">3</td>
<td align="right">75</td>
<td align="right">37</td>
<td align="right">3.8%</td>
<td align="right">124</td>
<td align="right">12.6%</td>
</tr>
<tr class="even">
<td align="left">4</td>
<td align="right">82</td>
<td align="right">33</td>
<td align="right">3.4%</td>
<td align="right">157</td>
<td align="right">16.0%</td>
</tr>
<tr class="odd">
<td align="left">5</td>
<td align="right">78</td>
<td align="right">32</td>
<td align="right">3.3%</td>
<td align="right">189</td>
<td align="right">19.3%</td>
</tr>
</tbody>
</table>
<p>(omitted 68 entries, n = 792 [80.7%])</p>
<p>So the following properties are determined, where <code>NA</code> values are always ignored:</p>
<ul>
<li><p><strong>Mean</strong></p></li>
<li><p><strong>Standard deviation</strong></p></li>
<li><p><strong>Coefficient of variation</strong> (CV), the standard deviation divided by the mean</p></li>
<li><p><strong>Mean absolute deviation</strong> (MAD), the median of the absolute deviations from the median - a more robust statistic than the standard deviation</p></li>
<li><p><strong>Five numbers of Tukey</strong>, namely: the minimum, Q1, median, Q3 and maximum</p></li>
<li><p><strong>Interquartile range</strong> (IQR), the distance between Q1 and Q3</p></li>
<li><p><strong>Coefficient of quartile variation</strong> (CQV, sometimes called <em>coefficient of dispersion</em>), calculated as (Q3 - Q1) / (Q3 + Q1) using <code><a href="https://www.rdocumentation.org/packages/stats/topics/quantile">quantile()</a></code> with <code>type = 6</code> as quantile algorithm to comply with SPSS standards</p></li>
<li><p><strong>Outliers</strong> (total count and unique count)</p></li>
</ul>
<p>So for example, the above frequency table quickly shows the median age of patients being 74.</p>
</div>
<div id="frequencies-of-factors" class="section level2">
<h2 class="hasAnchor">
<a href="#frequencies-of-factors" class="anchor"></a>Frequencies of factors</h2>
<p>To sort frequencies of factors on their levels instead of item count, use the <code>sort.count</code> parameter.</p>
<p><code>sort.count</code> is <code>TRUE</code> by default. Compare this default behaviour…</p>
<div class="sourceCode" id="cb7"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb7-1" data-line-number="1">septic_patients <span class="op">%&gt;%</span></a>
<a class="sourceLine" id="cb7-2" data-line-number="2"><span class="st"> </span><span class="kw"><a href="../reference/freq.html">freq</a></span>(hospital_id)</a></code></pre></div>
<p><strong>Frequency table of <code>hospital_id</code> from <code>septic_patients</code> (2,000 x 49)</strong></p>
<p>Class: factor (numeric)<br>
Length: 2,000 (of which NA: 0 = 0.00%)<br>
Levels: 4: A, B, C, D<br>
Unique: 4</p>
<table class="table">
<thead><tr class="header">
<th align="left"></th>
<th align="left">Item</th>
<th align="right">Count</th>
<th align="right">Percent</th>
<th align="right">Cum. Count</th>
<th align="right">Cum. Percent</th>
</tr></thead>
<tbody>
<tr class="odd">
<td align="left">1</td>
<td align="left">D</td>
<td align="right">762</td>
<td align="right">38.1%</td>
<td align="right">762</td>
<td align="right">38.1%</td>
</tr>
<tr class="even">
<td align="left">2</td>
<td align="left">B</td>
<td align="right">663</td>
<td align="right">33.2%</td>
<td align="right">1,425</td>
<td align="right">71.2%</td>
</tr>
<tr class="odd">
<td align="left">3</td>
<td align="left">A</td>
<td align="right">321</td>
<td align="right">16.0%</td>
<td align="right">1,746</td>
<td align="right">87.3%</td>
</tr>
<tr class="even">
<td align="left">4</td>
<td align="left">C</td>
<td align="right">254</td>
<td align="right">12.7%</td>
<td align="right">2,000</td>
<td align="right">100.0%</td>
</tr>
</tbody>
</table>
<p>… to this, where items are now sorted on factor levels:</p>
<div class="sourceCode" id="cb8"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb8-1" data-line-number="1">septic_patients <span class="op">%&gt;%</span></a>
<a class="sourceLine" id="cb8-2" data-line-number="2"><span class="st"> </span><span class="kw"><a href="../reference/freq.html">freq</a></span>(hospital_id, <span class="dt">sort.count =</span> <span class="ot">FALSE</span>)</a></code></pre></div>
<p><strong>Frequency table of <code>hospital_id</code> from <code>septic_patients</code> (2,000 x 49)</strong></p>
<p>Class: factor (numeric)<br>
Length: 2,000 (of which NA: 0 = 0.00%)<br>
Levels: 4: A, B, C, D<br>
Unique: 4</p>
<table class="table">
<thead><tr class="header">
<th align="left"></th>
<th align="left">Item</th>
<th align="right">Count</th>
<th align="right">Percent</th>
<th align="right">Cum. Count</th>
<th align="right">Cum. Percent</th>
</tr></thead>
<tbody>
<tr class="odd">
<td align="left">1</td>
<td align="left">A</td>
<td align="right">321</td>
<td align="right">16.0%</td>
<td align="right">321</td>
<td align="right">16.0%</td>
</tr>
<tr class="even">
<td align="left">2</td>
<td align="left">B</td>
<td align="right">663</td>
<td align="right">33.2%</td>
<td align="right">984</td>
<td align="right">49.2%</td>
</tr>
<tr class="odd">
<td align="left">3</td>
<td align="left">C</td>
<td align="right">254</td>
<td align="right">12.7%</td>
<td align="right">1,238</td>
<td align="right">61.9%</td>
</tr>
<tr class="even">
<td align="left">4</td>
<td align="left">D</td>
<td align="right">762</td>
<td align="right">38.1%</td>
<td align="right">2,000</td>
<td align="right">100.0%</td>
</tr>
</tbody>
</table>
<p>All classes will be printed into the header. Variables with the new <code>rsi</code> class of this AMR package are actually ordered factors and have three classes (look at <code>Class</code> in the header):</p>
<div class="sourceCode" id="cb9"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb9-1" data-line-number="1">septic_patients <span class="op">%&gt;%</span></a>
<a class="sourceLine" id="cb9-2" data-line-number="2"><span class="st"> </span><span class="kw"><a href="../reference/freq.html">freq</a></span>(AMX, <span class="dt">header =</span> <span class="ot">TRUE</span>)</a></code></pre></div>
<p><strong>Frequency table of <code>AMX</code> from <code>septic_patients</code> (2,000 x 49)</strong></p>
<p>Class: factor &gt; ordered &gt; rsi (numeric)<br>
Length: 2,000 (of which NA: 771 = 38.55%)<br>
Levels: 3: S &lt; I &lt; R<br>
Unique: 3</p>
<p>Drug: Amoxicillin (AMX, J01CA04)<br>
Group: Beta-lactams/penicillins<br>
%SI: 44.43%</p>
<table class="table">
<thead><tr class="header">
<th align="left"></th>
<th align="left">Item</th>
<th align="right">Count</th>
<th align="right">Percent</th>
<th align="right">Cum. Count</th>
<th align="right">Cum. Percent</th>
</tr></thead>
<tbody>
<tr class="odd">
<td align="left">1</td>
<td align="left">R</td>
<td align="right">683</td>
<td align="right">55.6%</td>
<td align="right">683</td>
<td align="right">55.6%</td>
</tr>
<tr class="even">
<td align="left">2</td>
<td align="left">S</td>
<td align="right">543</td>
<td align="right">44.2%</td>
<td align="right">1,226</td>
<td align="right">99.8%</td>
</tr>
<tr class="odd">
<td align="left">3</td>
<td align="left">I</td>
<td align="right">3</td>
<td align="right">0.2%</td>
<td align="right">1,229</td>
<td align="right">100.0%</td>
</tr>
</tbody>
</table>
</div>
<div id="frequencies-of-dates" class="section level2">
<h2 class="hasAnchor">
<a href="#frequencies-of-dates" class="anchor"></a>Frequencies of dates</h2>
<p>Frequencies of dates will show the oldest and newest date in the data, and the amount of days between them:</p>
<div class="sourceCode" id="cb10"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb10-1" data-line-number="1">septic_patients <span class="op">%&gt;%</span></a>
<a class="sourceLine" id="cb10-2" data-line-number="2"><span class="st"> </span><span class="kw"><a href="../reference/freq.html">freq</a></span>(date, <span class="dt">nmax =</span> <span class="dv">5</span>, <span class="dt">header =</span> <span class="ot">TRUE</span>)</a></code></pre></div>
<p><strong>Frequency table of <code>date</code> from <code>septic_patients</code> (2,000 x 49)</strong></p>
<p>Class: Date (numeric)<br>
Length: 2,000 (of which NA: 0 = 0.00%)<br>
Unique: 1,140</p>
<p>Oldest: 2 January 2002<br>
Newest: 28 December 2017 (+5,839)<br>
Median: 31 July 2009 (47.39%)</p>
<table class="table">
<thead><tr class="header">
<th align="left"></th>
<th align="left">Item</th>
<th align="right">Count</th>
<th align="right">Percent</th>
<th align="right">Cum. Count</th>
<th align="right">Cum. Percent</th>
</tr></thead>
<tbody>
<tr class="odd">
<td align="left">1</td>
<td align="left">2016-05-21</td>
<td align="right">10</td>
<td align="right">0.5%</td>
<td align="right">10</td>
<td align="right">0.5%</td>
</tr>
<tr class="even">
<td align="left">2</td>
<td align="left">2004-11-15</td>
<td align="right">8</td>
<td align="right">0.4%</td>
<td align="right">18</td>
<td align="right">0.9%</td>
</tr>
<tr class="odd">
<td align="left">3</td>
<td align="left">2013-07-29</td>
<td align="right">8</td>
<td align="right">0.4%</td>
<td align="right">26</td>
<td align="right">1.3%</td>
</tr>
<tr class="even">
<td align="left">4</td>
<td align="left">2017-06-12</td>
<td align="right">8</td>
<td align="right">0.4%</td>
<td align="right">34</td>
<td align="right">1.7%</td>
</tr>
<tr class="odd">
<td align="left">5</td>
<td align="left">2015-11-19</td>
<td align="right">7</td>
<td align="right">0.4%</td>
<td align="right">41</td>
<td align="right">2.0%</td>
</tr>
</tbody>
</table>
<p>(omitted 1,135 entries, n = 1,959 [98.0%])</p>
</div>
<div id="assigning-a-frequency-table-to-an-object" class="section level2">
<h2 class="hasAnchor">
<a href="#assigning-a-frequency-table-to-an-object" class="anchor"></a>Assigning a frequency table to an object</h2>
<p>A frequency table is actually a regular <code>data.frame</code>, with the exception that it contains an additional class.</p>
<div class="sourceCode" id="cb11"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb11-1" data-line-number="1">my_df &lt;-<span class="st"> </span>septic_patients <span class="op">%&gt;%</span><span class="st"> </span><span class="kw"><a href="../reference/freq.html">freq</a></span>(age)</a>
<a class="sourceLine" id="cb11-2" data-line-number="2"><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/class">class</a></span>(my_df)</a></code></pre></div>
<p>[1] “freq” “data.frame”</p>
<p>Because of this additional class, a frequency table prints like the examples above. But the object itself contains the complete table without a row limitation:</p>
<div class="sourceCode" id="cb12"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb12-1" data-line-number="1"><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/dim">dim</a></span>(my_df)</a></code></pre></div>
<p>[1] 74 5</p>
</div>
<div id="additional-parameters" class="section level2">
<h2 class="hasAnchor">
<a href="#additional-parameters" class="anchor"></a>Additional parameters</h2>
<div id="parameter-na-rm" class="section level3">
<h3 class="hasAnchor">
<a href="#parameter-na-rm" class="anchor"></a>Parameter <code>na.rm</code>
</h3>
<p>With the <code>na.rm</code> parameter you can remove <code>NA</code> values from the frequency table (defaults to <code>TRUE</code>, but the number of <code>NA</code> values will always be shown into the header):</p>
<div class="sourceCode" id="cb13"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb13-1" data-line-number="1">septic_patients <span class="op">%&gt;%</span></a>
<a class="sourceLine" id="cb13-2" data-line-number="2"><span class="st"> </span><span class="kw"><a href="../reference/freq.html">freq</a></span>(AMX, <span class="dt">na.rm =</span> <span class="ot">FALSE</span>)</a></code></pre></div>
<p><strong>Frequency table of <code>AMX</code> from <code>septic_patients</code> (2,000 x 49)</strong></p>
<p>Class: factor &gt; ordered &gt; rsi (numeric)<br>
Length: 2,000 (of which NA: 771 = 38.55%)<br>
Levels: 3: S &lt; I &lt; R<br>
Unique: 4</p>
<p>Drug: Amoxicillin (AMX, J01CA04)<br>
Group: Beta-lactams/penicillins<br>
%SI: 44.43%</p>
<table class="table">
<thead><tr class="header">
<th align="left"></th>
<th align="left">Item</th>
<th align="right">Count</th>
<th align="right">Percent</th>
<th align="right">Cum. Count</th>
<th align="right">Cum. Percent</th>
</tr></thead>
<tbody>
<tr class="odd">
<td align="left">1</td>
<td align="left">(NA)</td>
<td align="right">771</td>
<td align="right">38.6%</td>
<td align="right">771</td>
<td align="right">38.6%</td>
</tr>
<tr class="even">
<td align="left">2</td>
<td align="left">R</td>
<td align="right">683</td>
<td align="right">34.2%</td>
<td align="right">1,454</td>
<td align="right">72.7%</td>
</tr>
<tr class="odd">
<td align="left">3</td>
<td align="left">S</td>
<td align="right">543</td>
<td align="right">27.2%</td>
<td align="right">1,997</td>
<td align="right">99.8%</td>
</tr>
<tr class="even">
<td align="left">4</td>
<td align="left">I</td>
<td align="right">3</td>
<td align="right">0.2%</td>
<td align="right">2,000</td>
<td align="right">100.0%</td>
</tr>
</tbody>
</table>
</div>
<div id="parameter-row-names" class="section level3">
<h3 class="hasAnchor">
<a href="#parameter-row-names" class="anchor"></a>Parameter <code>row.names</code>
</h3>
<p>A frequency table shows row indices. To remove them, use <code>row.names = FALSE</code>:</p>
<div class="sourceCode" id="cb14"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb14-1" data-line-number="1">septic_patients <span class="op">%&gt;%</span></a>
<a class="sourceLine" id="cb14-2" data-line-number="2"><span class="st"> </span><span class="kw"><a href="../reference/freq.html">freq</a></span>(hospital_id, <span class="dt">row.names =</span> <span class="ot">FALSE</span>)</a></code></pre></div>
<p><strong>Frequency table of <code>hospital_id</code> from <code>septic_patients</code> (2,000 x 49)</strong></p>
<p>Class: factor (numeric)<br>
Length: 2,000 (of which NA: 0 = 0.00%)<br>
Levels: 4: A, B, C, D<br>
Unique: 4</p>
<table class="table">
<thead><tr class="header">
<th align="left">Item</th>
<th align="right">Count</th>
<th align="right">Percent</th>
<th align="right">Cum. Count</th>
<th align="right">Cum. Percent</th>
</tr></thead>
<tbody>
<tr class="odd">
<td align="left">D</td>
<td align="right">762</td>
<td align="right">38.1%</td>
<td align="right">762</td>
<td align="right">38.1%</td>
</tr>
<tr class="even">
<td align="left">B</td>
<td align="right">663</td>
<td align="right">33.2%</td>
<td align="right">1,425</td>
<td align="right">71.2%</td>
</tr>
<tr class="odd">
<td align="left">A</td>
<td align="right">321</td>
<td align="right">16.0%</td>
<td align="right">1,746</td>
<td align="right">87.3%</td>
</tr>
<tr class="even">
<td align="left">C</td>
<td align="right">254</td>
<td align="right">12.7%</td>
<td align="right">2,000</td>
<td align="right">100.0%</td>
</tr>
</tbody>
</table>
</div>
<div id="parameter-markdown" class="section level3">
<h3 class="hasAnchor">
<a href="#parameter-markdown" class="anchor"></a>Parameter <code>markdown</code>
</h3>
<p>The <code>markdown</code> parameter is <code>TRUE</code> at default in non-interactive sessions, like in reports created with R Markdown. This will always print all rows, unless <code>nmax</code> is set. Without markdown (like in regular R), a frequency table would print like:</p>
<div class="sourceCode" id="cb15"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb15-1" data-line-number="1">septic_patients <span class="op">%&gt;%</span></a>
<a class="sourceLine" id="cb15-2" data-line-number="2"><span class="st"> </span><span class="kw"><a href="../reference/freq.html">freq</a></span>(hospital_id, <span class="dt">markdown =</span> <span class="ot">FALSE</span>)</a>
<a class="sourceLine" id="cb15-3" data-line-number="3"><span class="co"># Frequency table of `hospital_id` from `septic_patients` (2,000 x 49) </span></a>
<a class="sourceLine" id="cb15-4" data-line-number="4"><span class="co"># </span></a>
<a class="sourceLine" id="cb15-5" data-line-number="5"><span class="co"># Class: factor (numeric)</span></a>
<a class="sourceLine" id="cb15-6" data-line-number="6"><span class="co"># Length: 2,000 (of which NA: 0 = 0.00%)</span></a>
<a class="sourceLine" id="cb15-7" data-line-number="7"><span class="co"># Levels: 4: A, B, C, D</span></a>
<a class="sourceLine" id="cb15-8" data-line-number="8"><span class="co"># Unique: 4</span></a>
<a class="sourceLine" id="cb15-9" data-line-number="9"><span class="co"># </span></a>
<a class="sourceLine" id="cb15-10" data-line-number="10"><span class="co"># Item Count Percent Cum. Count Cum. Percent</span></a>
<a class="sourceLine" id="cb15-11" data-line-number="11"><span class="co"># --- ----- ------ -------- ----------- -------------</span></a>
<a class="sourceLine" id="cb15-12" data-line-number="12"><span class="co"># 1 D 762 38.1% 762 38.1%</span></a>
<a class="sourceLine" id="cb15-13" data-line-number="13"><span class="co"># 2 B 663 33.2% 1,425 71.2%</span></a>
<a class="sourceLine" id="cb15-14" data-line-number="14"><span class="co"># 3 A 321 16.0% 1,746 87.3%</span></a>
<a class="sourceLine" id="cb15-15" data-line-number="15"><span class="co"># 4 C 254 12.7% 2,000 100.0%</span></a></code></pre></div>
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<h1>How to get properties of a microorganism</h1>
<h4 class="author">Matthijs S. Berends</h4>
<h4 class="date">07 June 2019</h4>
<div class="hidden name"><code>mo_property.Rmd</code></div>
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<h1>How to predict antimicrobial resistance</h1>
<h4 class="author">Matthijs S. Berends</h4>
<h4 class="date">28 August 2019</h4>
<h4 class="date">13 October 2019</h4>
<div class="hidden name"><code>resistance_predict.Rmd</code></div>
@ -199,9 +201,9 @@
<a href="#needed-r-packages" class="anchor"></a>Needed R packages</h2>
<p>As with many uses in R, we need some additional packages for AMR analysis. Our package works closely together with the <a href="https://www.tidyverse.org">tidyverse packages</a> <a href="https://dplyr.tidyverse.org/"><code>dplyr</code></a> and <a href="https://ggplot2.tidyverse.org"><code>ggplot2</code></a> by Dr Hadley Wickham. The tidyverse tremendously improves the way we conduct data science - it allows for a very natural way of writing syntaxes and creating beautiful plots in R.</p>
<p>Our <code>AMR</code> package depends on these packages and even extends their use and functions.</p>
<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb1-1" data-line-number="1"><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/library">library</a></span>(dplyr)</a>
<a class="sourceLine" id="cb1-2" data-line-number="2"><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/library">library</a></span>(ggplot2)</a>
<a class="sourceLine" id="cb1-3" data-line-number="3"><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/library">library</a></span>(AMR)</a>
<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb1-1" data-line-number="1"><span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span>(dplyr)</a>
<a class="sourceLine" id="cb1-2" data-line-number="2"><span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span>(ggplot2)</a>
<a class="sourceLine" id="cb1-3" data-line-number="3"><span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span>(AMR)</a>
<a class="sourceLine" id="cb1-4" data-line-number="4"></a>
<a class="sourceLine" id="cb1-5" data-line-number="5"><span class="co"># (if not yet installed, install with:)</span></a>
<a class="sourceLine" id="cb1-6" data-line-number="6"><span class="co"># install.packages(c("tidyverse", "AMR"))</span></a></code></pre></div>
@ -282,7 +284,7 @@
<a class="sourceLine" id="cb4-28" data-line-number="28"><span class="co"># 26 2028 0.47970658 0.3804212 0.5789920 NA NA 0.47970658</span></a>
<a class="sourceLine" id="cb4-29" data-line-number="29"><span class="co"># 27 2029 0.50734745 0.4021241 0.6125708 NA NA 0.50734745</span></a></code></pre></div>
<p>The function <code>plot</code> is available in base R, and can be extended by other packages to depend the output based on the type of input. We extended its function to cope with resistance predictions:</p>
<div class="sourceCode" id="cb5"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb5-1" data-line-number="1"><span class="kw"><a href="https://www.rdocumentation.org/packages/graphics/topics/plot">plot</a></span>(predict_TZP)</a></code></pre></div>
<div class="sourceCode" id="cb5"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb5-1" data-line-number="1"><span class="kw"><a href="https://rdrr.io/r/graphics/plot.html">plot</a></span>(predict_TZP)</a></code></pre></div>
<p><img src="resistance_predict_files/figure-html/unnamed-chunk-4-1.png" width="720"></p>
<p>This is the fastest way to plot the result. It automatically adds the right axes, error bars, titles, number of available observations and type of model.</p>
<p>We also support the <code>ggplot2</code> package with our custom function <code><a href="../reference/resistance_predict.html">ggplot_rsi_predict()</a></code> to create more appealing plots:</p>
@ -321,21 +323,21 @@
<td>
<code>"binomial"</code> or <code>"binom"</code> or <code>"logit"</code>
</td>
<td><code><a href="https://www.rdocumentation.org/packages/stats/topics/glm">glm(..., family = binomial)</a></code></td>
<td><code><a href="https://rdrr.io/r/stats/glm.html">glm(..., family = binomial)</a></code></td>
<td>Generalised linear model with binomial distribution</td>
</tr>
<tr class="even">
<td>
<code>"loglin"</code> or <code>"poisson"</code>
</td>
<td><code><a href="https://www.rdocumentation.org/packages/stats/topics/glm">glm(..., family = poisson)</a></code></td>
<td><code><a href="https://rdrr.io/r/stats/glm.html">glm(..., family = poisson)</a></code></td>
<td>Generalised linear model with poisson distribution</td>
</tr>
<tr class="odd">
<td>
<code>"lin"</code> or <code>"linear"</code>
</td>
<td><code><a href="https://www.rdocumentation.org/packages/stats/topics/lm">lm()</a></code></td>
<td><code><a href="https://rdrr.io/r/stats/lm.html">lm()</a></code></td>
<td>Linear model</td>
</tr>
</tbody>
@ -349,14 +351,14 @@
<p><img src="resistance_predict_files/figure-html/unnamed-chunk-7-1.png" width="720"></p>
<p>This seems more likely, doesn’t it?</p>
<p>The model itself is also available from the object, as an <code>attribute</code>:</p>
<div class="sourceCode" id="cb10"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb10-1" data-line-number="1">model &lt;-<span class="st"> </span><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/attributes">attributes</a></span>(predict_TZP)<span class="op">$</span>model</a>
<div class="sourceCode" id="cb10"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb10-1" data-line-number="1">model &lt;-<span class="st"> </span><span class="kw"><a href="https://rdrr.io/r/base/attributes.html">attributes</a></span>(predict_TZP)<span class="op">$</span>model</a>
<a class="sourceLine" id="cb10-2" data-line-number="2"></a>
<a class="sourceLine" id="cb10-3" data-line-number="3"><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/summary">summary</a></span>(model)<span class="op">$</span>family</a>
<a class="sourceLine" id="cb10-3" data-line-number="3"><span class="kw"><a href="https://rdrr.io/r/base/summary.html">summary</a></span>(model)<span class="op">$</span>family</a>
<a class="sourceLine" id="cb10-4" data-line-number="4"><span class="co"># </span></a>
<a class="sourceLine" id="cb10-5" data-line-number="5"><span class="co"># Family: binomial </span></a>
<a class="sourceLine" id="cb10-6" data-line-number="6"><span class="co"># Link function: logit</span></a>
<a class="sourceLine" id="cb10-7" data-line-number="7"></a>
<a class="sourceLine" id="cb10-8" data-line-number="8"><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/summary">summary</a></span>(model)<span class="op">$</span>coefficients</a>
<a class="sourceLine" id="cb10-8" data-line-number="8"><span class="kw"><a href="https://rdrr.io/r/base/summary.html">summary</a></span>(model)<span class="op">$</span>coefficients</a>
<a class="sourceLine" id="cb10-9" data-line-number="9"><span class="co"># Estimate Std. Error z value Pr(&gt;|z|)</span></a>
<a class="sourceLine" id="cb10-10" data-line-number="10"><span class="co"># (Intercept) -224.3987194 48.0335384 -4.671709 2.987038e-06</span></a>
<a class="sourceLine" id="cb10-11" data-line-number="11"><span class="co"># year 0.1106102 0.0238753 4.632831 3.606990e-06</span></a></code></pre></div>
@ -365,6 +367,7 @@
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