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@ -40,7 +40,7 @@
</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">0.7.1.9067</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.7.1.9076</span>
</span>
</div>
@ -185,7 +185,7 @@
<h1>How to conduct AMR analysis</h1>
<h4 class="author">Matthijs S. Berends</h4>
<h4 class="date">28 August 2019</h4>
<h4 class="date">20 September 2019</h4>
<div class="hidden name"><code>AMR.Rmd</code></div>
@ -194,7 +194,7 @@
<p><strong>Note:</strong> values on this page will change with every website update since they are based on randomly created values and the page was written in <a href="https://rmarkdown.rstudio.com/">R Markdown</a>. However, the methodology remains unchanged. This page was generated on 28 August 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 20 September 2019.</p>
<div id="introduction" class="section level1">
<h1 class="hasAnchor">
<a href="#introduction" class="anchor"></a>Introduction</h1>
@ -210,21 +210,21 @@
</tr></thead>
<tbody>
<tr class="odd">
<td align="center">2019-08-28</td>
<td align="center">2019-09-20</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">2019-08-28</td>
<td align="center">2019-09-20</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">2019-08-28</td>
<td align="center">2019-09-20</td>
<td align="center">efgh</td>
<td align="center">Escherichia coli</td>
<td align="center">R</td>
@ -300,8 +300,7 @@
<a class="sourceLine" id="cb7-12" data-line-number="12"> <span class="dt">CIP =</span> <span class="kw"><a href="https://dplyr.tidyverse.org/reference/sample.html">sample</a></span>(ab_interpretations, <span class="dt">size =</span> sample_size, <span class="dt">replace =</span> <span class="ot">TRUE</span>,</a>
<a class="sourceLine" id="cb7-13" data-line-number="13"> <span class="dt">prob =</span> <span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/c">c</a></span>(<span class="fl">0.80</span>, <span class="fl">0.00</span>, <span class="fl">0.20</span>)),</a>
<a class="sourceLine" id="cb7-14" data-line-number="14"> <span class="dt">GEN =</span> <span class="kw"><a href="https://dplyr.tidyverse.org/reference/sample.html">sample</a></span>(ab_interpretations, <span class="dt">size =</span> sample_size, <span class="dt">replace =</span> <span class="ot">TRUE</span>,</a>
<a class="sourceLine" id="cb7-15" data-line-number="15"> <span class="dt">prob =</span> <span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/c">c</a></span>(<span class="fl">0.92</span>, <span class="fl">0.00</span>, <span class="fl">0.08</span>))</a>
<a class="sourceLine" id="cb7-16" data-line-number="16"> )</a></code></pre></div>
<a class="sourceLine" id="cb7-15" data-line-number="15"> <span class="dt">prob =</span> <span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/c">c</a></span>(<span class="fl">0.92</span>, <span class="fl">0.00</span>, <span class="fl">0.08</span>)))</a></code></pre></div>
<p>Using the <code><a href="https://dplyr.tidyverse.org/reference/join.html">left_join()</a></code> function from the <code>dplyr</code> package, we can ‘map’ the gender to the patient ID using the <code>patients_table</code> object we created earlier:</p>
<div class="sourceCode" id="cb8"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb8-1" data-line-number="1">data &lt;-<span class="st"> </span>data <span class="op">%&gt;%</span><span class="st"> </span><span class="kw"><a href="https://dplyr.tidyverse.org/reference/join.html">left_join</a></span>(patients_table)</a></code></pre></div>
<p>The resulting data set contains 20,000 blood culture isolates. With the <code><a href="https://www.rdocumentation.org/packages/utils/topics/head">head()</a></code> function we can preview the first 6 values of this data set:</p>
@ -320,67 +319,67 @@
</tr></thead>
<tbody>
<tr class="odd">
<td align="center">2017-06-03</td>
<td align="center">E4</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">R</td>
<td align="center">S</td>
<td align="center">M</td>
</tr>
<tr class="even">
<td align="center">2015-02-25</td>
<td align="center">J4</td>
<td align="center">Hospital A</td>
<td align="center">Streptococcus pneumoniae</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">M</td>
</tr>
<tr class="odd">
<td align="center">2014-08-28</td>
<td align="center">F7</td>
<td align="center">2011-05-18</td>
<td align="center">O8</td>
<td align="center">Hospital B</td>
<td align="center">Staphylococcus aureus</td>
<td align="center">S</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">2011-12-16</td>
<td align="center">U7</td>
<td align="center">Hospital A</td>
<td align="center">Staphylococcus aureus</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>
</tr>
<tr class="odd">
<td align="center">2015-02-09</td>
<td align="center">C8</td>
<td align="center">Hospital A</td>
<td align="center">Escherichia coli</td>
<tr class="even">
<td align="center">2011-03-28</td>
<td align="center">Q8</td>
<td align="center">Hospital B</td>
<td align="center">Streptococcus pneumoniae</td>
<td align="center">R</td>
<td align="center">I</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">M</td>
<td align="center">F</td>
</tr>
<tr class="even">
<td align="center">2014-11-26</td>
<td align="center">Y5</td>
<td align="center">Hospital C</td>
<td align="center">Escherichia coli</td>
<tr class="odd">
<td align="center">2015-12-27</td>
<td align="center">W2</td>
<td align="center">Hospital A</td>
<td align="center">Klebsiella pneumoniae</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="even">
<td align="center">2014-05-30</td>
<td align="center">X6</td>
<td align="center">Hospital B</td>
<td align="center">Escherichia 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">F</td>
</tr>
<tr class="odd">
<td align="center">2015-07-30</td>
<td align="center">Q3</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="even">
<td align="center">2016-11-03</td>
<td align="center">O8</td>
<td align="center">Hospital D</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">S</td>
<td align="center">F</td>
@ -406,8 +405,8 @@
#
# Item Count Percent Cum. Count Cum. Percent
# --- ----- ------- -------- ----------- -------------
# 1 M 10,360 51.8% 10,360 51.8%
# 2 F 9,640 48.2% 20,000 100.0%</code></pre>
# 1 M 10,330 51.6% 10,330 51.6%
# 2 F 9,670 48.4% 20,000 100.0%</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>
@ -437,14 +436,14 @@
<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 (1,477 values changed)</span></a>
<a class="sourceLine" id="cb15-21" data-line-number="21"><span class="co"># Streptococcus pneumoniae (1,405 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 (1,306 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 (2,760 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 (2,639 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>
@ -452,24 +451,24 @@
<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 (2,250 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 (100 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 (2,299 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 (101 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 6,548 out of 20,000 rows, making a total of 7,893 edits</span></a>
<a class="sourceLine" id="cb15-44" data-line-number="44"><span class="co"># EUCAST rules affected 6,427 out of 20,000 rows, making a total of 7,734 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 7,893 test results</span></a>
<a class="sourceLine" id="cb15-48" data-line-number="48"><span class="co"># - 102 test results changed from S to I</span></a>
<a class="sourceLine" id="cb15-49" data-line-number="49"><span class="co"># - 4,732 test results changed from S to R</span></a>
<a class="sourceLine" id="cb15-50" data-line-number="50"><span class="co"># - 1,108 test results changed from I to S</span></a>
<a class="sourceLine" id="cb15-51" data-line-number="51"><span class="co"># - 328 test results changed from I to R</span></a>
<a class="sourceLine" id="cb15-52" data-line-number="52"><span class="co"># - 1,603 test results changed from R to S</span></a>
<a class="sourceLine" id="cb15-53" data-line-number="53"><span class="co"># - 20 test results changed from R to I</span></a>
<a class="sourceLine" id="cb15-47" data-line-number="47"><span class="co"># =&gt; changed 7,734 test results</span></a>
<a class="sourceLine" id="cb15-48" data-line-number="48"><span class="co"># - 99 test results changed from S to I</span></a>
<a class="sourceLine" id="cb15-49" data-line-number="49"><span class="co"># - 4,591 test results changed from S to R</span></a>
<a class="sourceLine" id="cb15-50" data-line-number="50"><span class="co"># - 1,111 test results changed from I to S</span></a>
<a class="sourceLine" id="cb15-51" data-line-number="51"><span class="co"># - 289 test results changed from I to R</span></a>
<a class="sourceLine" id="cb15-52" data-line-number="52"><span class="co"># - 1,622 test results changed from R to S</span></a>
<a class="sourceLine" id="cb15-53" data-line-number="53"><span class="co"># - 22 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>
@ -497,8 +496,8 @@
<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 5,693 first isolates (28.5% of total)</span></a></code></pre></div>
<p>So only 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>
<a class="sourceLine" id="cb17-6" data-line-number="6"><span class="co"># =&gt; Found 5,635 first isolates (28.2% of total)</span></a></code></pre></div>
<p>So only 28.2% 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>
<p>For future use, the above two syntaxes can be shortened with the <code><a href="../reference/first_isolate.html">filter_first_isolate()</a></code> function:</p>
@ -508,7 +507,7 @@
<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 Q3, 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 O6, sorted on date:</p>
<table class="table">
<thead><tr class="header">
<th align="center">isolate</th>
@ -524,32 +523,32 @@
<tbody>
<tr class="odd">
<td align="center">1</td>
<td align="center">2010-01-10</td>
<td align="center">Q3</td>
<td align="center">B_ESCHR_COL</td>
<td align="center">2010-01-29</td>
<td align="center">O6</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">R</td>
<td align="center">S</td>
<td align="center">TRUE</td>
</tr>
<tr class="even">
<td align="center">2</td>
<td align="center">2010-03-10</td>
<td align="center">Q3</td>
<td align="center">B_ESCHR_COL</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">2010-04-29</td>
<td align="center">O6</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>
<tr class="odd">
<td align="center">3</td>
<td align="center">2010-04-30</td>
<td align="center">Q3</td>
<td align="center">B_ESCHR_COL</td>
<td align="center">R</td>
<td align="center">2010-08-31</td>
<td align="center">O6</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>
@ -557,10 +556,10 @@
</tr>
<tr class="even">
<td align="center">4</td>
<td align="center">2010-07-02</td>
<td align="center">Q3</td>
<td align="center">B_ESCHR_COL</td>
<td align="center">R</td>
<td align="center">2010-09-18</td>
<td align="center">O6</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>
@ -568,21 +567,21 @@
</tr>
<tr class="odd">
<td align="center">5</td>
<td align="center">2010-10-10</td>
<td align="center">Q3</td>
<td align="center">B_ESCHR_COL</td>
<td align="center">2011-03-23</td>
<td align="center">O6</td>
<td align="center">B_ESCHR_COLI</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">FALSE</td>
<td align="center">S</td>
<td align="center">TRUE</td>
</tr>
<tr class="even">
<td align="center">6</td>
<td align="center">2010-11-26</td>
<td align="center">Q3</td>
<td align="center">B_ESCHR_COL</td>
<td align="center">R</td>
<td align="center">2011-03-24</td>
<td align="center">O6</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>
@ -590,9 +589,9 @@
</tr>
<tr class="odd">
<td align="center">7</td>
<td align="center">2011-01-09</td>
<td align="center">Q3</td>
<td align="center">B_ESCHR_COL</td>
<td align="center">2011-07-19</td>
<td align="center">O6</td>
<td align="center">B_ESCHR_COLI</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
@ -601,20 +600,20 @@
</tr>
<tr class="even">
<td align="center">8</td>
<td align="center">2011-02-01</td>
<td align="center">Q3</td>
<td align="center">B_ESCHR_COL</td>
<td align="center">2011-08-04</td>
<td align="center">O6</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">R</td>
<td align="center">S</td>
<td align="center">TRUE</td>
<td align="center">FALSE</td>
</tr>
<tr class="odd">
<td align="center">9</td>
<td align="center">2011-03-20</td>
<td align="center">Q3</td>
<td align="center">B_ESCHR_COL</td>
<td align="center">2011-08-15</td>
<td align="center">O6</td>
<td align="center">B_ESCHR_COLI</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
@ -623,9 +622,9 @@
</tr>
<tr class="even">
<td align="center">10</td>
<td align="center">2011-08-04</td>
<td align="center">Q3</td>
<td align="center">B_ESCHR_COL</td>
<td align="center">2011-08-22</td>
<td align="center">O6</td>
<td align="center">B_ESCHR_COLI</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
@ -645,7 +644,7 @@
<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 15,134 first weighted isolates (75.7% of total)</span></a></code></pre></div>
<a class="sourceLine" id="cb20-10" data-line-number="10"><span class="co"># =&gt; Found 15,041 first weighted isolates (75.2% of total)</span></a></code></pre></div>
<table class="table">
<thead><tr class="header">
<th align="center">isolate</th>
@ -662,34 +661,34 @@
<tbody>
<tr class="odd">
<td align="center">1</td>
<td align="center">2010-01-10</td>
<td align="center">Q3</td>
<td align="center">B_ESCHR_COL</td>
<td align="center">2010-01-29</td>
<td align="center">O6</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">R</td>
<td align="center">S</td>
<td align="center">TRUE</td>
<td align="center">TRUE</td>
</tr>
<tr class="even">
<td align="center">2</td>
<td align="center">2010-03-10</td>
<td align="center">Q3</td>
<td align="center">B_ESCHR_COL</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">2010-04-29</td>
<td align="center">O6</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>
<td align="center">TRUE</td>
</tr>
<tr class="odd">
<td align="center">3</td>
<td align="center">2010-04-30</td>
<td align="center">Q3</td>
<td align="center">B_ESCHR_COL</td>
<td align="center">R</td>
<td align="center">2010-08-31</td>
<td align="center">O6</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>
@ -698,10 +697,10 @@
</tr>
<tr class="even">
<td align="center">4</td>
<td align="center">2010-07-02</td>
<td align="center">Q3</td>
<td align="center">B_ESCHR_COL</td>
<td align="center">R</td>
<td align="center">2010-09-18</td>
<td align="center">O6</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>
@ -710,57 +709,57 @@
</tr>
<tr class="odd">
<td align="center">5</td>
<td align="center">2010-10-10</td>
<td align="center">Q3</td>
<td align="center">B_ESCHR_COL</td>
<td align="center">2011-03-23</td>
<td align="center">O6</td>
<td align="center">B_ESCHR_COLI</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">FALSE</td>
<td align="center">S</td>
<td align="center">TRUE</td>
<td align="center">TRUE</td>
</tr>
<tr class="even">
<td align="center">6</td>
<td align="center">2010-11-26</td>
<td align="center">Q3</td>
<td align="center">B_ESCHR_COL</td>
<td align="center">R</td>
<td align="center">2011-03-24</td>
<td align="center">O6</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">FALSE</td>
</tr>
<tr class="odd">
<td align="center">7</td>
<td align="center">2011-01-09</td>
<td align="center">Q3</td>
<td align="center">B_ESCHR_COL</td>
<td align="center">2011-07-19</td>
<td align="center">O6</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">FALSE</td>
</tr>
<tr class="even">
<td align="center">8</td>
<td align="center">2011-02-01</td>
<td align="center">Q3</td>
<td align="center">B_ESCHR_COL</td>
<td align="center">2011-08-04</td>
<td align="center">O6</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">R</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">2011-03-20</td>
<td align="center">Q3</td>
<td align="center">B_ESCHR_COL</td>
<td align="center">2011-08-15</td>
<td align="center">O6</td>
<td align="center">B_ESCHR_COLI</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
@ -770,9 +769,9 @@
</tr>
<tr class="even">
<td align="center">10</td>
<td align="center">2011-08-04</td>
<td align="center">Q3</td>
<td align="center">B_ESCHR_COL</td>
<td align="center">2011-08-22</td>
<td align="center">O6</td>
<td align="center">B_ESCHR_COLI</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
@ -782,11 +781,11 @@
</tr>
</tbody>
</table>
<p>Instead of 2, now 8 isolates are flagged. In total, of all isolates are marked ‘first weighted’ - more than when using the CLSI guideline. In real life, this novel algorithm will yield 5-10% more isolates than the classic CLSI guideline.</p>
<p>Instead of 2, now 6 isolates are flagged. In total, 75.2% of all isolates are marked ‘first weighted’ - 47% 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 15,134 isolates for analysis.</p>
<p>So we end up with 15,041 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://www.rdocumentation.org/packages/base/topics/c">c</a></span>(first, keyab))</a></code></pre></div>
@ -812,58 +811,58 @@
<tbody>
<tr class="odd">
<td>1</td>
<td align="center">2017-06-03</td>
<td align="center">E4</td>
<td align="center">Hospital D</td>
<td align="center">B_ESCHR_COL</td>
<td align="center">2011-05-18</td>
<td align="center">O8</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">R</td>
<td align="center">S</td>
<td align="center">M</td>
<td align="center">Gram-negative</td>
<td align="center">Escherichia</td>
<td align="center">coli</td>
<td align="center">S</td>
<td align="center">F</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>2</td>
<td align="center">2015-02-25</td>
<td align="center">J4</td>
<td align="center">Hospital A</td>
<td align="center">B_STRPT_PNE</td>
<td align="center">2011-03-28</td>
<td align="center">Q8</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">S</td>
<td align="center">R</td>
<td align="center">M</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="odd">
<td>4</td>
<td align="center">2011-12-16</td>
<td align="center">U7</td>
<td>3</td>
<td align="center">2015-12-27</td>
<td align="center">W2</td>
<td align="center">Hospital A</td>
<td align="center">B_STPHY_AUR</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">B_KLBSL_PNMN</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-positive</td>
<td align="center">Staphylococcus</td>
<td align="center">aureus</td>
<td align="center">Gram-negative</td>
<td align="center">Klebsiella</td>
<td align="center">pneumoniae</td>
<td align="center">TRUE</td>
</tr>
<tr class="even">
<td>6</td>
<td align="center">2014-11-26</td>
<td align="center">Y5</td>
<td align="center">Hospital C</td>
<td align="center">B_ESCHR_COL</td>
<td>5</td>
<td align="center">2015-07-30</td>
<td align="center">Q3</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>
@ -875,30 +874,14 @@
<td align="center">TRUE</td>
</tr>
<tr class="odd">
<td>7</td>
<td align="center">2010-08-09</td>
<td align="center">C5</td>
<td align="center">Hospital B</td>
<td align="center">B_ESCHR_COL</td>
<td>6</td>
<td align="center">2016-11-03</td>
<td align="center">O8</td>
<td align="center">Hospital D</td>
<td align="center">B_STPHY_AURS</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">R</td>
<td align="center">M</td>
<td align="center">Gram-negative</td>
<td align="center">Escherichia</td>
<td align="center">coli</td>
<td align="center">TRUE</td>
</tr>
<tr class="even">
<td>8</td>
<td align="center">2011-07-24</td>
<td align="center">W6</td>
<td align="center">Hospital B</td>
<td align="center">B_STPHY_AUR</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-positive</td>
@ -906,6 +889,22 @@
<td align="center">aureus</td>
<td align="center">TRUE</td>
</tr>
<tr class="even">
<td>7</td>
<td align="center">2013-04-03</td>
<td align="center">F8</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">M</td>
<td align="center">Gram-negative</td>
<td align="center">Escherichia</td>
<td align="center">coli</td>
<td align="center">TRUE</td>
</tr>
</tbody>
</table>
<p>Time for the analysis!</p>
@ -925,7 +924,7 @@
<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://www.rdocumentation.org/packages/clean/topics/freq">freq</a></span>(genus, species)</a></code></pre></div>
<p><strong>Frequency table</strong></p>
<p>Class: character<br>
Length: 15,134 (of which NA: 0 = 0.00%)<br>
Length: 15,041 (of which NA: 0 = 0.00%)<br>
Unique: 4</p>
<p>Shortest: 16<br>
Longest: 24</p>
@ -942,33 +941,33 @@ Longest: 24</p>
<tr class="odd">
<td align="left">1</td>
<td align="left">Escherichia coli</td>
<td align="right">7,330</td>
<td align="right">48.4%</td>
<td align="right">7,330</td>
<td align="right">48.4%</td>
<td align="right">7,491</td>
<td align="right">49.8%</td>
<td align="right">7,491</td>
<td align="right">49.8%</td>
</tr>
<tr class="even">
<td align="left">2</td>
<td align="left">Staphylococcus aureus</td>
<td align="right">3,816</td>
<td align="right">25.2%</td>
<td align="right">11,146</td>
<td align="right">73.6%</td>
<td align="right">3,732</td>
<td align="right">24.8%</td>
<td align="right">11,223</td>
<td align="right">74.6%</td>
</tr>
<tr class="odd">
<td align="left">3</td>
<td align="left">Streptococcus pneumoniae</td>
<td align="right">2,386</td>
<td align="right">15.8%</td>
<td align="right">13,532</td>
<td align="right">2,223</td>
<td align="right">14.8%</td>
<td align="right">13,446</td>
<td align="right">89.4%</td>
</tr>
<tr class="even">
<td align="left">4</td>
<td align="left">Klebsiella pneumoniae</td>
<td align="right">1,602</td>
<td align="right">1,595</td>
<td align="right">10.6%</td>
<td align="right">15,134</td>
<td align="right">15,041</td>
<td align="right">100.0%</td>
</tr>
</tbody>
@ -979,7 +978,7 @@ 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] 0.4649795</span></a></code></pre></div>
<a class="sourceLine" id="cb26-2" data-line-number="2"><span class="co"># [1] 0.4677216</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>
@ -992,19 +991,19 @@ Longest: 24</p>
<tbody>
<tr class="odd">
<td align="center">Hospital A</td>
<td align="center">0.4684865</td>
<td align="center">0.4646398</td>
</tr>
<tr class="even">
<td align="center">Hospital B</td>
<td align="center">0.4595616</td>
<td align="center">0.4578497</td>
</tr>
<tr class="odd">
<td align="center">Hospital C</td>
<td align="center">0.4685616</td>
<td align="center">0.4776586</td>
</tr>
<tr class="even">
<td align="center">Hospital D</td>
<td align="center">0.4664897</td>
<td align="center">0.4822200</td>
</tr>
</tbody>
</table>
@ -1022,23 +1021,23 @@ Longest: 24</p>
<tbody>
<tr class="odd">
<td align="center">Hospital A</td>
<td align="center">0.4684865</td>
<td align="center">4506</td>
<td align="center">0.4646398</td>
<td align="center">4539</td>
</tr>
<tr class="even">
<td align="center">Hospital B</td>
<td align="center">0.4595616</td>
<td align="center">5292</td>
<td align="center">0.4578497</td>
<td align="center">5255</td>
</tr>
<tr class="odd">
<td align="center">Hospital C</td>
<td align="center">0.4685616</td>
<td align="center">2322</td>
<td align="center">0.4776586</td>
<td align="center">2238</td>
</tr>
<tr class="even">
<td align="center">Hospital D</td>
<td align="center">0.4664897</td>
<td align="center">3014</td>
<td align="center">0.4822200</td>
<td align="center">3009</td>
</tr>
</tbody>
</table>
@ -1058,27 +1057,27 @@ Longest: 24</p>
<tbody>
<tr class="odd">
<td align="center">Escherichia</td>
<td align="center">0.9289222</td>
<td align="center">0.8896317</td>
<td align="center">0.9924966</td>
<td align="center">0.9236417</td>
<td align="center">0.8994794</td>
<td align="center">0.9942598</td>
</tr>
<tr class="even">
<td align="center">Klebsiella</td>
<td align="center">0.8021223</td>
<td align="center">0.9082397</td>
<td align="center">0.9812734</td>
<td align="center">0.8206897</td>
<td align="center">0.9028213</td>
<td align="center">0.9868339</td>
</tr>
<tr class="odd">
<td align="center">Staphylococcus</td>
<td align="center">0.9263627</td>
<td align="center">0.9129979</td>
<td align="center">0.9900419</td>
<td align="center">0.9228296</td>
<td align="center">0.9265809</td>
<td align="center">0.9951768</td>
</tr>
<tr class="even">
<td align="center">Streptococcus</td>
<td align="center">0.6240570</td>
<td align="center">0.6171840</td>
<td align="center">0.0000000</td>
<td align="center">0.6240570</td>
<td align="center">0.6171840</td>
</tr>
</tbody>
</table>

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@ -40,7 +40,7 @@
</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">0.7.1.9075</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.7.1.9076</span>
</span>
</div>
@ -185,7 +185,7 @@
<h1>Benchmarks</h1>
<h4 class="author">Matthijs S. Berends</h4>
<h4 class="date">18 September 2019</h4>
<h4 class="date">20 September 2019</h4>
<div class="hidden name"><code>benchmarks.Rmd</code></div>
@ -219,36 +219,36 @@
<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"><a href="https://www.rdocumentation.org/packages/base/topics/print">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</span></a>
<a class="sourceLine" id="cb2-20" data-line-number="20"><span class="co"># as.mo("sau") 8.5 8.9 12.0 9.1 9.7</span></a>
<a class="sourceLine" id="cb2-21" data-line-number="21"><span class="co"># as.mo("stau") 31.0 32.0 48.0 34.0 55.0</span></a>
<a class="sourceLine" id="cb2-22" data-line-number="22"><span class="co"># as.mo("STAU") 31.0 31.0 35.0 32.0 33.0</span></a>
<a class="sourceLine" id="cb2-23" data-line-number="23"><span class="co"># as.mo("staaur") 8.6 8.8 12.0 9.0 9.3</span></a>
<a class="sourceLine" id="cb2-24" data-line-number="24"><span class="co"># as.mo("STAAUR") 8.6 8.9 9.0 9.0 9.1</span></a>
<a class="sourceLine" id="cb2-25" data-line-number="25"><span class="co"># as.mo("S. aureus") 23.0 23.0 26.0 24.0 24.0</span></a>
<a class="sourceLine" id="cb2-26" data-line-number="26"><span class="co"># as.mo("S aureus") 23.0 23.0 31.0 24.0 44.0</span></a>
<a class="sourceLine" id="cb2-27" data-line-number="27"><span class="co"># as.mo("Staphylococcus aureus") 27.0 28.0 29.0 29.0 29.0</span></a>
<a class="sourceLine" id="cb2-28" data-line-number="28"><span class="co"># as.mo("Staphylococcus aureus (MRSA)") 550.0 560.0 590.0 580.0 590.0</span></a>
<a class="sourceLine" id="cb2-29" data-line-number="29"><span class="co"># as.mo("Sthafilokkockus aaureuz") 270.0 290.0 340.0 300.0 330.0</span></a>
<a class="sourceLine" id="cb2-30" data-line-number="30"><span class="co"># as.mo("MRSA") 8.7 8.8 9.1 9.1 9.4</span></a>
<a class="sourceLine" id="cb2-31" data-line-number="31"><span class="co"># as.mo("VISA") 18.0 19.0 20.0 19.0 20.0</span></a>
<a class="sourceLine" id="cb2-32" data-line-number="32"><span class="co"># as.mo("VRSA") 19.0 19.0 23.0 19.0 22.0</span></a>
<a class="sourceLine" id="cb2-33" data-line-number="33"><span class="co"># as.mo(22242419) 18.0 18.0 37.0 30.0 42.0</span></a>
<a class="sourceLine" id="cb2-34" data-line-number="34"><span class="co"># max neval</span></a>
<a class="sourceLine" id="cb2-35" data-line-number="35"><span class="co"># 31.0 10</span></a>
<a class="sourceLine" id="cb2-36" data-line-number="36"><span class="co"># 120.0 10</span></a>
<a class="sourceLine" id="cb2-37" data-line-number="37"><span class="co"># 59.0 10</span></a>
<a class="sourceLine" id="cb2-38" data-line-number="38"><span class="co"># 34.0 10</span></a>
<a class="sourceLine" id="cb2-39" data-line-number="39"><span class="co"># 9.2 10</span></a>
<a class="sourceLine" id="cb2-40" data-line-number="40"><span class="co"># 49.0 10</span></a>
<a class="sourceLine" id="cb2-41" data-line-number="41"><span class="co"># 53.0 10</span></a>
<a class="sourceLine" id="cb2-42" data-line-number="42"><span class="co"># 30.0 10</span></a>
<a class="sourceLine" id="cb2-43" data-line-number="43"><span class="co"># 670.0 10</span></a>
<a class="sourceLine" id="cb2-44" data-line-number="44"><span class="co"># 620.0 10</span></a>
<a class="sourceLine" id="cb2-45" data-line-number="45"><span class="co"># 9.5 10</span></a>
<a class="sourceLine" id="cb2-46" data-line-number="46"><span class="co"># 28.0 10</span></a>
<a class="sourceLine" id="cb2-47" data-line-number="47"><span class="co"># 45.0 10</span></a>
<a class="sourceLine" id="cb2-48" data-line-number="48"><span class="co"># 110.0 10</span></a></code></pre></div>
<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") 8.4 8.8 23.0 8.8 30.0 100</span></a>
<a class="sourceLine" id="cb2-21" data-line-number="21"><span class="co"># as.mo("stau") 31.0 31.0 42.0 35.0 54.0 60</span></a>
<a class="sourceLine" id="cb2-22" data-line-number="22"><span class="co"># as.mo("STAU") 31.0 32.0 39.0 33.0 53.0 56</span></a>
<a class="sourceLine" id="cb2-23" data-line-number="23"><span class="co"># as.mo("staaur") 8.5 8.9 11.0 9.0 9.2 33</span></a>
<a class="sourceLine" id="cb2-24" data-line-number="24"><span class="co"># as.mo("STAAUR") 8.6 8.8 9.4 9.3 9.4 11</span></a>
<a class="sourceLine" id="cb2-25" data-line-number="25"><span class="co"># as.mo("S. aureus") 23.0 24.0 28.0 26.0 28.0 51</span></a>
<a class="sourceLine" id="cb2-26" data-line-number="26"><span class="co"># as.mo("S aureus") 23.0 24.0 29.0 25.0 25.0 51</span></a>
<a class="sourceLine" id="cb2-27" data-line-number="27"><span class="co"># as.mo("Staphylococcus aureus") 28.0 29.0 30.0 29.0 30.0 32</span></a>
<a class="sourceLine" id="cb2-28" data-line-number="28"><span class="co"># as.mo("Staphylococcus aureus (MRSA)") 600.0 620.0 640.0 620.0 640.0 800</span></a>
<a class="sourceLine" id="cb2-29" data-line-number="29"><span class="co"># as.mo("Sthafilokkockus aaureuz") 320.0 340.0 370.0 350.0 400.0 450</span></a>
<a class="sourceLine" id="cb2-30" data-line-number="30"><span class="co"># as.mo("MRSA") 8.7 8.7 11.0 9.2 9.9 31</span></a>
<a class="sourceLine" id="cb2-31" data-line-number="31"><span class="co"># as.mo("VISA") 19.0 19.0 20.0 19.0 20.0 23</span></a>
<a class="sourceLine" id="cb2-32" data-line-number="32"><span class="co"># as.mo("VRSA") 19.0 19.0 25.0 20.0 28.0 44</span></a>
<a class="sourceLine" id="cb2-33" data-line-number="33"><span class="co"># as.mo(22242419) 18.0 19.0 47.0 31.0 48.0 190</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>
<a class="sourceLine" id="cb2-37" data-line-number="37"><span class="co"># 10</span></a>
<a class="sourceLine" id="cb2-38" data-line-number="38"><span class="co"># 10</span></a>
<a class="sourceLine" id="cb2-39" data-line-number="39"><span class="co"># 10</span></a>
<a class="sourceLine" id="cb2-40" data-line-number="40"><span class="co"># 10</span></a>
<a class="sourceLine" id="cb2-41" data-line-number="41"><span class="co"># 10</span></a>
<a class="sourceLine" id="cb2-42" data-line-number="42"><span class="co"># 10</span></a>
<a class="sourceLine" id="cb2-43" data-line-number="43"><span class="co"># 10</span></a>
<a class="sourceLine" id="cb2-44" data-line-number="44"><span class="co"># 10</span></a>
<a class="sourceLine" id="cb2-45" data-line-number="45"><span class="co"># 10</span></a>
<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-4-1.png" width="562.5"></p>
<p>In the table above, all measurements are shown in milliseconds (thousands of seconds). A value of 5 milliseconds means it can determine 200 input values per second. It case of 100 milliseconds, this is only 10 input values per second. 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>Thermus islandicus</em> (<code>B_THERMS_ISLN</code>), a bug probably never found before in humans:</p>
@ -258,93 +258,25 @@
<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">"T. islandicus"</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">"Thermus islandicus"</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="co"># Warning: </span></a>
<a class="sourceLine" id="cb3-8" data-line-number="8"><span class="co"># Result of one value was guessed with uncertainty. Use mo_uncertainties() to review it.</span></a>
<a class="sourceLine" id="cb3-9" data-line-number="9"></a>
<a class="sourceLine" id="cb3-10" data-line-number="10"><span class="co"># Warning: </span></a>
<a class="sourceLine" id="cb3-11" data-line-number="11"><span class="co"># Result of one value was guessed with uncertainty. Use mo_uncertainties() to review it.</span></a>
<a class="sourceLine" id="cb3-12" data-line-number="12"></a>
<a class="sourceLine" id="cb3-13" data-line-number="13"><span class="co"># Warning: </span></a>
<a class="sourceLine" id="cb3-14" data-line-number="14"><span class="co"># Result of one value was guessed with uncertainty. Use mo_uncertainties() to review it.</span></a>
<a class="sourceLine" id="cb3-15" data-line-number="15"></a>
<a class="sourceLine" id="cb3-16" data-line-number="16"><span class="co"># Warning: </span></a>
<a class="sourceLine" id="cb3-17" data-line-number="17"><span class="co"># Result of one value was guessed with uncertainty. Use mo_uncertainties() to review it.</span></a>
<a class="sourceLine" id="cb3-18" data-line-number="18"></a>
<a class="sourceLine" id="cb3-19" data-line-number="19"><span class="co"># Warning: </span></a>
<a class="sourceLine" id="cb3-20" data-line-number="20"><span class="co"># Result of one value was guessed with uncertainty. Use mo_uncertainties() to review it.</span></a>
<a class="sourceLine" id="cb3-21" data-line-number="21"></a>
<a class="sourceLine" id="cb3-22" data-line-number="22"><span class="co"># Warning: </span></a>
<a class="sourceLine" id="cb3-23" data-line-number="23"><span class="co"># Result of one value was guessed with uncertainty. Use mo_uncertainties() to review it.</span></a>
<a class="sourceLine" id="cb3-24" data-line-number="24"></a>
<a class="sourceLine" id="cb3-25" data-line-number="25"><span class="co"># Warning: </span></a>
<a class="sourceLine" id="cb3-26" data-line-number="26"><span class="co"># Result of one value was guessed with uncertainty. Use mo_uncertainties() to review it.</span></a>
<a class="sourceLine" id="cb3-27" data-line-number="27"></a>
<a class="sourceLine" id="cb3-28" data-line-number="28"><span class="co"># Warning: </span></a>
<a class="sourceLine" id="cb3-29" data-line-number="29"><span class="co"># Result of one value was guessed with uncertainty. Use mo_uncertainties() to review it.</span></a>
<a class="sourceLine" id="cb3-30" data-line-number="30"></a>
<a class="sourceLine" id="cb3-31" data-line-number="31"><span class="co"># Warning: </span></a>
<a class="sourceLine" id="cb3-32" data-line-number="32"><span class="co"># Result of one value was guessed with uncertainty. Use mo_uncertainties() to review it.</span></a>
<a class="sourceLine" id="cb3-33" data-line-number="33"></a>
<a class="sourceLine" id="cb3-34" data-line-number="34"><span class="co"># Warning: </span></a>
<a class="sourceLine" id="cb3-35" data-line-number="35"><span class="co"># Result of one value was guessed with uncertainty. Use mo_uncertainties() to review it.</span></a>
<a class="sourceLine" id="cb3-36" data-line-number="36"></a>
<a class="sourceLine" id="cb3-37" data-line-number="37"><span class="co"># Warning: </span></a>
<a class="sourceLine" id="cb3-38" data-line-number="38"><span class="co"># Result of one value was guessed with uncertainty. Use mo_uncertainties() to review it.</span></a>
<a class="sourceLine" id="cb3-39" data-line-number="39"></a>
<a class="sourceLine" id="cb3-40" data-line-number="40"><span class="co"># Warning: </span></a>
<a class="sourceLine" id="cb3-41" data-line-number="41"><span class="co"># Result of one value was guessed with uncertainty. Use mo_uncertainties() to review it.</span></a>
<a class="sourceLine" id="cb3-42" data-line-number="42"></a>
<a class="sourceLine" id="cb3-43" data-line-number="43"><span class="co"># Warning: </span></a>
<a class="sourceLine" id="cb3-44" data-line-number="44"><span class="co"># Result of one value was guessed with uncertainty. Use mo_uncertainties() to review it.</span></a>
<a class="sourceLine" id="cb3-45" data-line-number="45"></a>
<a class="sourceLine" id="cb3-46" data-line-number="46"><span class="co"># Warning: </span></a>
<a class="sourceLine" id="cb3-47" data-line-number="47"><span class="co"># Result of one value was guessed with uncertainty. Use mo_uncertainties() to review it.</span></a>
<a class="sourceLine" id="cb3-48" data-line-number="48"></a>
<a class="sourceLine" id="cb3-49" data-line-number="49"><span class="co"># Warning: </span></a>
<a class="sourceLine" id="cb3-50" data-line-number="50"><span class="co"># Result of one value was guessed with uncertainty. Use mo_uncertainties() to review it.</span></a>
<a class="sourceLine" id="cb3-51" data-line-number="51"></a>
<a class="sourceLine" id="cb3-52" data-line-number="52"><span class="co"># Warning: </span></a>
<a class="sourceLine" id="cb3-53" data-line-number="53"><span class="co"># Result of one value was guessed with uncertainty. Use mo_uncertainties() to review it.</span></a>
<a class="sourceLine" id="cb3-54" data-line-number="54"></a>
<a class="sourceLine" id="cb3-55" data-line-number="55"><span class="co"># Warning: </span></a>
<a class="sourceLine" id="cb3-56" data-line-number="56"><span class="co"># Result of one value was guessed with uncertainty. Use mo_uncertainties() to review it.</span></a>
<a class="sourceLine" id="cb3-57" data-line-number="57"></a>
<a class="sourceLine" id="cb3-58" data-line-number="58"><span class="co"># Warning: </span></a>
<a class="sourceLine" id="cb3-59" data-line-number="59"><span class="co"># Result of one value was guessed with uncertainty. Use mo_uncertainties() to review it.</span></a>
<a class="sourceLine" id="cb3-60" data-line-number="60"></a>
<a class="sourceLine" id="cb3-61" data-line-number="61"><span class="co"># Warning: </span></a>
<a class="sourceLine" id="cb3-62" data-line-number="62"><span class="co"># Result of one value was guessed with uncertainty. Use mo_uncertainties() to review it.</span></a>
<a class="sourceLine" id="cb3-63" data-line-number="63"></a>
<a class="sourceLine" id="cb3-64" data-line-number="64"><span class="co"># Warning: </span></a>
<a class="sourceLine" id="cb3-65" data-line-number="65"><span class="co"># Result of one value was guessed with uncertainty. Use mo_uncertainties() to review it.</span></a>
<a class="sourceLine" id="cb3-66" data-line-number="66"><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/print">print</a></span>(T.islandicus, <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="cb3-67" data-line-number="67"><span class="co"># Unit: milliseconds</span></a>
<a class="sourceLine" id="cb3-68" data-line-number="68"><span class="co"># expr min lq mean median uq max neval</span></a>
<a class="sourceLine" id="cb3-69" data-line-number="69"><span class="co"># as.mo("theisl") 1300 1400 1400 1400 1500 1600 10</span></a>
<a class="sourceLine" id="cb3-70" data-line-number="70"><span class="co"># as.mo("THEISL") 1400 1400 1400 1400 1500 1600 10</span></a>
<a class="sourceLine" id="cb3-71" data-line-number="71"><span class="co"># as.mo("T. islandicus") 370 400 410 410 420 450 10</span></a>
<a class="sourceLine" id="cb3-72" data-line-number="72"><span class="co"># as.mo("T. islandicus") 360 370 400 380 410 490 10</span></a>
<a class="sourceLine" id="cb3-73" data-line-number="73"><span class="co"># as.mo("Thermus islandicus") 28 30 35 32 35 59 10</span></a></code></pre></div>
<p>That takes 8.5 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>Thermus islandicus</em>) are almost fast - these are the most probable input from most data sets.</p>
<a class="sourceLine" id="cb3-7" data-line-number="7"><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/print">print</a></span>(T.islandicus, <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="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 max neval</span></a>
<a class="sourceLine" id="cb3-10" data-line-number="10"><span class="co"># as.mo("theisl") 1300 1500 1500 1500 1500 1600 10</span></a>
<a class="sourceLine" id="cb3-11" data-line-number="11"><span class="co"># as.mo("THEISL") 1400 1500 1500 1500 1500 1600 10</span></a>
<a class="sourceLine" id="cb3-12" data-line-number="12"><span class="co"># as.mo("T. islandicus") 410 410 430 410 450 500 10</span></a>
<a class="sourceLine" id="cb3-13" data-line-number="13"><span class="co"># as.mo("T. islandicus") 410 420 430 420 440 440 10</span></a>
<a class="sourceLine" id="cb3-14" data-line-number="14"><span class="co"># as.mo("Thermus islandicus") 30 30 31 31 32 35 10</span></a></code></pre></div>
<p>That takes 8.2 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>Thermus islandicus</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>Thermus islandicus</em> (which is uncommon):</p>
<pre><code># Warning:
# Result of one value was guessed with uncertainty. Use mo_uncertainties() to review it.
# NOTE: Prevotella ruminicola brevis (Shah et al., 1990) was renamed Prevotella brevis (Avgustin et al., 2016) [B_PRVTL_BRVS]
# Warning:
# Result of one value was guessed with uncertainty. Use mo_uncertainties() to review it.
# NOTE: Prevotella ruminicola brevis (Shah et al., 1990) was renamed Prevotella brevis (Avgustin et al., 2016) [B_PRVTL_BRVS]
# NOTE: Prevotella ruminicola brevis (Shah et al., 1990) was renamed Prevotella brevis (Avgustin et al., 2016) [B_PRVTL_BRVS]
# Warning:
# Result of one value was guessed with uncertainty. Use mo_uncertainties() to review it.
# Warning:
# Result of one value was guessed with uncertainty. Use mo_uncertainties() to review it.
# NOTE: Prevotella ruminicola brevis (Shah et al., 1990) was renamed Prevotella brevis (Avgustin et al., 2016) [B_PRVTL_BRVS]
# NOTE: Prevotella ruminicola brevis (Shah et al., 1990) was renamed Prevotella brevis (Avgustin et al., 2016) [B_PRVTL_BRVS]
# Warning:
# Result of one value was guessed with uncertainty. Use mo_uncertainties() to review it.
# NOTE: Prevotella ruminicola brevis (Shah et al., 1990) was renamed Prevotella brevis (Avgustin et al., 2016) [B_PRVTL_BRVS]
# NOTE: Prevotella ruminicola brevis (Shah et al., 1990) was renamed Prevotella brevis (Avgustin et al., 2016) [B_PRVTL_BRVS]
# Warning:
# Result of one value was guessed with uncertainty. Use mo_uncertainties() to review it.
@ -356,15 +288,18 @@
# Warning:
# Result of one value was guessed with uncertainty. Use mo_uncertainties() to review it.
# NOTE: Prevotella ruminicola brevis (Shah et al., 1990) was renamed Prevotella brevis (Avgustin et al., 2016) [B_PRVTL_BRVS]
# NOTE: Prevotella ruminicola brevis (Shah et al., 1990) was renamed Prevotella brevis (Avgustin et al., 2016) [B_PRVTL_BRVS]
# NOTE: Prevotella ruminicola brevis (Shah et al., 1990) was renamed Prevotella brevis (Avgustin et al., 2016) [B_PRVTL_BRVS]
# Warning:
# Result of one value was guessed with uncertainty. Use mo_uncertainties() to review it.
# Warning:
# Result of one value was guessed with uncertainty. Use mo_uncertainties() to review it.
# Warning:
# Result of one value was guessed with uncertainty. Use mo_uncertainties() to review it.</code></pre>
<p><img src="benchmarks_files/figure-html/unnamed-chunk-6-1.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>
<pre><code># NOTE: results are saved to /Users/msberends/Library/R/3.6/library/AMR/mo_history/mo_history.csv.
# NOTE: Prevotella ruminicola brevis (Shah et al., 1990) was renamed Prevotella brevis (Avgustin et al., 2016) [B_PRVTL_BRVS]
# Warning:
# Result of one value was guessed with uncertainty. Use mo_uncertainties() to review it.</code></pre>
<p><img src="benchmarks_files/figure-html/unnamed-chunk-7-1.png" width="562.5"></p>
@ -400,8 +335,8 @@
<a class="sourceLine" id="cb6-24" data-line-number="24"><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/print">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="cb6-25" data-line-number="25"><span class="co"># Unit: milliseconds</span></a>
<a class="sourceLine" id="cb6-26" data-line-number="26"><span class="co"># expr min lq mean median uq max neval</span></a>
<a class="sourceLine" id="cb6-27" data-line-number="27"><span class="co"># mo_name(x) 610 644 669 665 684 748 10</span></a></code></pre></div>
<p>So transforming 500,000 values (!!) of 50 unique values only takes 0.66 seconds (664 ms). You only lose time on your unique input values.</p>
<a class="sourceLine" id="cb6-27" data-line-number="27"><span class="co"># mo_name(x) 604 632 655 644 660 764 10</span></a></code></pre></div>
<p>So transforming 500,000 values (!!) of 50 unique values only takes 0.64 seconds (644 ms). You only lose time on your unique input values.</p>
</div>
<div id="precalculated-results" class="section level3">
<h3 class="hasAnchor">
@ -414,9 +349,9 @@
<a class="sourceLine" id="cb7-5" data-line-number="5"><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/print">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="cb7-6" data-line-number="6"><span class="co"># Unit: milliseconds</span></a>
<a class="sourceLine" id="cb7-7" data-line-number="7"><span class="co"># expr min lq mean median uq max neval</span></a>
<a class="sourceLine" id="cb7-8" data-line-number="8"><span class="co"># A 6.150 6.260 6.400 6.390 6.520 6.710 10</span></a>
<a class="sourceLine" id="cb7-9" data-line-number="9"><span class="co"># B 22.200 22.500 26.400 22.700 24.800 53.100 10</span></a>
<a class="sourceLine" id="cb7-10" data-line-number="10"><span class="co"># C 0.645 0.774 0.801 0.803 0.812 0.911 10</span></a></code></pre></div>
<a class="sourceLine" id="cb7-8" data-line-number="8"><span class="co"># A 6.190 6.270 6.560 6.290 7.160 7.280 10</span></a>
<a class="sourceLine" id="cb7-9" data-line-number="9"><span class="co"># B 22.600 23.100 26.700 23.400 24.300 51.600 10</span></a>
<a class="sourceLine" id="cb7-10" data-line-number="10"><span class="co"># C 0.704 0.765 0.827 0.829 0.906 0.913 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 0.0008 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="cb8"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb8-1" data-line-number="1">run_it &lt;-<span class="st"> </span><span class="kw"><a href="https://www.rdocumentation.org/packages/microbenchmark/topics/microbenchmark">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="cb8-2" 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>
@ -430,14 +365,14 @@
<a class="sourceLine" id="cb8-10" data-line-number="10"><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/print">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="cb8-11" data-line-number="11"><span class="co"># Unit: milliseconds</span></a>
<a class="sourceLine" id="cb8-12" data-line-number="12"><span class="co"># expr min lq mean median uq max neval</span></a>
<a class="sourceLine" id="cb8-13" data-line-number="13"><span class="co"># A 0.467 0.471 0.509 0.492 0.512 0.680 10</span></a>
<a class="sourceLine" id="cb8-14" data-line-number="14"><span class="co"># B 0.628 0.634 0.664 0.646 0.685 0.748 10</span></a>
<a class="sourceLine" id="cb8-15" data-line-number="15"><span class="co"># C 0.712 0.723 0.771 0.755 0.797 0.906 10</span></a>
<a class="sourceLine" id="cb8-16" data-line-number="16"><span class="co"># D 0.444 0.455 0.475 0.464 0.501 0.518 10</span></a>
<a class="sourceLine" id="cb8-17" data-line-number="17"><span class="co"># E 0.452 0.453 0.468 0.457 0.487 0.510 10</span></a>
<a class="sourceLine" id="cb8-18" data-line-number="18"><span class="co"># F 0.439 0.450 0.462 0.459 0.470 0.501 10</span></a>
<a class="sourceLine" id="cb8-19" data-line-number="19"><span class="co"># G 0.450 0.460 0.476 0.480 0.492 0.496 10</span></a>
<a class="sourceLine" id="cb8-20" data-line-number="20"><span class="co"># H 0.443 0.455 0.461 0.456 0.466 0.495 10</span></a></code></pre></div>
<a class="sourceLine" id="cb8-13" data-line-number="13"><span class="co"># A 0.456 0.476 0.484 0.483 0.488 0.516 10</span></a>
<a class="sourceLine" id="cb8-14" data-line-number="14"><span class="co"># B 0.613 0.620 0.639 0.628 0.642 0.723 10</span></a>
<a class="sourceLine" id="cb8-15" data-line-number="15"><span class="co"># C 0.675 0.700 0.763 0.796 0.807 0.816 10</span></a>
<a class="sourceLine" id="cb8-16" data-line-number="16"><span class="co"># D 0.443 0.454 0.466 0.467 0.477 0.497 10</span></a>
<a class="sourceLine" id="cb8-17" data-line-number="17"><span class="co"># E 0.453 0.459 0.465 0.464 0.472 0.483 10</span></a>
<a class="sourceLine" id="cb8-18" data-line-number="18"><span class="co"># F 0.433 0.447 0.464 0.463 0.484 0.498 10</span></a>
<a class="sourceLine" id="cb8-19" data-line-number="19"><span class="co"># G 0.453 0.460 0.469 0.464 0.478 0.502 10</span></a>
<a class="sourceLine" id="cb8-20" data-line-number="20"><span class="co"># H 0.433 0.451 0.477 0.459 0.470 0.662 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">
@ -464,13 +399,13 @@
<a class="sourceLine" id="cb9-18" data-line-number="18"><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/print">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="cb9-19" data-line-number="19"><span class="co"># Unit: milliseconds</span></a>
<a class="sourceLine" id="cb9-20" data-line-number="20"><span class="co"># expr min lq mean median uq max neval</span></a>
<a class="sourceLine" id="cb9-21" data-line-number="21"><span class="co"># en 18.19 18.35 18.84 18.65 18.99 20.91 10</span></a>
<a class="sourceLine" id="cb9-22" data-line-number="22"><span class="co"># de 19.31 19.71 20.39 20.29 20.92 21.83 10</span></a>
<a class="sourceLine" id="cb9-23" data-line-number="23"><span class="co"># nl 24.43 24.92 25.51 25.37 25.65 27.97 10</span></a>
<a class="sourceLine" id="cb9-24" data-line-number="24"><span class="co"># es 19.22 19.53 20.06 19.82 20.47 21.81 10</span></a>
<a class="sourceLine" id="cb9-25" data-line-number="25"><span class="co"># it 19.36 20.03 24.77 20.26 20.96 45.31 10</span></a>
<a class="sourceLine" id="cb9-26" data-line-number="26"><span class="co"># fr 19.11 19.30 19.71 19.72 20.11 20.37 10</span></a>
<a class="sourceLine" id="cb9-27" data-line-number="27"><span class="co"># pt 19.40 19.90 27.80 21.38 41.88 45.37 10</span></a></code></pre></div>
<a class="sourceLine" id="cb9-21" data-line-number="21"><span class="co"># en 18.18 18.24 18.54 18.44 18.58 19.41 10</span></a>
<a class="sourceLine" id="cb9-22" data-line-number="22"><span class="co"># de 19.52 19.79 20.03 19.90 20.15 20.95 10</span></a>
<a class="sourceLine" id="cb9-23" data-line-number="23"><span class="co"># nl 24.54 24.94 26.29 25.70 26.35 31.90 10</span></a>
<a class="sourceLine" id="cb9-24" data-line-number="24"><span class="co"># es 19.52 19.69 22.29 19.86 20.27 44.03 10</span></a>
<a class="sourceLine" id="cb9-25" data-line-number="25"><span class="co"># it 19.52 19.57 22.05 19.74 20.46 41.43 10</span></a>
<a class="sourceLine" id="cb9-26" data-line-number="26"><span class="co"># fr 19.61 19.67 22.28 20.04 20.30 42.90 10</span></a>
<a class="sourceLine" id="cb9-27" data-line-number="27"><span class="co"># pt 19.51 19.79 25.16 20.01 20.73 49.48 10</span></a></code></pre></div>
<p>Currently supported are German, Dutch, Spanish, Italian, French and Portuguese.</p>
</div>
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@ -78,7 +78,7 @@
</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">0.7.1.9075</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.7.1.9076</span>
</span>
</div>