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@ -44,7 +44,7 @@
</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="Released version">1.8.0</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.8.0.9005</span>
</span>
</div>
@ -185,7 +185,7 @@
</header><div class="row">
</header><script src="EUCAST_files/accessible-code-block-0.0.1/empty-anchor.js"></script><div class="row">
<div class="col-md-9 contents">
<div class="page-header toc-ignore">
<h1 data-toc-skip>How to apply EUCAST rules</h1>
@ -402,7 +402,7 @@
<div class="pkgdown">
<p></p>
<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.0.</p>
<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.2.</p>
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@ -44,7 +44,7 @@
</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="Released version">1.8.0</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.8.0.9005</span>
</span>
</div>
@ -185,7 +185,7 @@
</header><div class="row">
</header><script src="MDR_files/accessible-code-block-0.0.1/empty-anchor.js"></script><div class="row">
<div class="col-md-9 contents">
<div class="page-header toc-ignore">
<h1 data-toc-skip>How to determine multi-drug resistance (MDR)</h1>
@ -295,8 +295,8 @@
<span class="co"># Table 3 - Enterobacteriaceae... OK.</span>
<span class="co"># Table 4 - Pseudomonas aeruginosa... OK.</span>
<span class="co"># Table 5 - Acinetobacter spp.... OK.</span>
<span class="co"># Warning: NA introduced for isolates where the available percentage of antimicrobial</span>
<span class="co"># classes was below 50% (set with `pct_required_classes`)</span></code></pre></div>
<span class="co"># Warning: in `mdro()`: NA introduced for isolates where the available percentage of</span>
<span class="co"># antimicrobial classes was below 50% (set with `pct_required_classes`)</span></code></pre></div>
<p>Only results with ‘R’ are considered as resistance. Use <code>combine_SI = FALSE</code> to also consider ‘I’ as resistance.</p>
<p>Determining multidrug-resistant organisms (MDRO), according to: Guideline: Multidrug-resistant, extensively drug-resistant and pandrug-resistant bacteria: an international expert proposal for interim standard definitions for acquired resistance. Author(s): Magiorakos AP, Srinivasan A, Carey RB, …, Vatopoulos A, Weber JT, Monnet DL Source: Clinical Microbiology and Infection 18:3, 2012; doi: 10.1111/j.1469-0691.2011.03570.x</p>
<p>(16 isolates had no test results)</p>
@ -320,17 +320,17 @@ Unique: 2</p>
<td align="left">1</td>
<td align="left">Negative</td>
<td align="right">1601</td>
<td align="right">92.60%</td>
<td align="right">92.6%</td>
<td align="right">1601</td>
<td align="right">92.60%</td>
<td align="right">92.6%</td>
</tr>
<tr class="even">
<td align="left">2</td>
<td align="left">Multi-drug-resistant (MDR)</td>
<td align="right">128</td>
<td align="right">7.40%</td>
<td align="right">7.4%</td>
<td align="right">1729</td>
<td align="right">100.00%</td>
<td align="right">100.0%</td>
</tr>
</tbody>
</table>
@ -358,18 +358,18 @@ Unique: 2</p>
<div class="sourceCode" id="cb8"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/utils/head.html" class="external-link">head</a></span><span class="op">(</span><span class="va">my_TB_data</span><span class="op">)</span>
<span class="co"># rifampicin isoniazid gatifloxacin ethambutol pyrazinamide moxifloxacin</span>
<span class="co"># 1 I R I S R S</span>
<span class="co"># 2 I S R R S R</span>
<span class="co"># 3 R I S I R S</span>
<span class="co"># 4 S S I R I S</span>
<span class="co"># 5 S R S S S I</span>
<span class="co"># 6 S R R S R I</span>
<span class="co"># 1 I R I R I I</span>
<span class="co"># 2 I R I I I I</span>
<span class="co"># 3 S I I R S S</span>
<span class="co"># 4 R I R I R I</span>
<span class="co"># 5 S I S S R S</span>
<span class="co"># 6 S S I I I S</span>
<span class="co"># kanamycin</span>
<span class="co"># 1 S</span>
<span class="co"># 2 I</span>
<span class="co"># 3 R</span>
<span class="co"># 4 R</span>
<span class="co"># 5 I</span>
<span class="co"># 1 I</span>
<span class="co"># 2 R</span>
<span class="co"># 3 I</span>
<span class="co"># 4 I</span>
<span class="co"># 5 R</span>
<span class="co"># 6 R</span></code></pre></div>
<p>We can now add the interpretation of MDR-TB to our data set. You can use:</p>
<div class="sourceCode" id="cb9"><pre class="downlit sourceCode r">
@ -398,7 +398,7 @@ Unique: 2</p>
<p>Class: factor &gt; ordered (numeric)<br>
Length: 5,000<br>
Levels: 5: Negative &lt; Mono-resistant &lt; Poly-resistant &lt; Multi-drug-resistant &lt;…<br>
Available: 5,000 (100.0%, NA: 0 = 0.0%)<br>
Available: 5,000 (100%, NA: 0 = 0%)<br>
Unique: 5</p>
<table class="table">
<thead><tr class="header">
@ -413,40 +413,40 @@ 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">3243</td>
<td align="right">64.86%</td>
<td align="right">3243</td>
<td align="right">64.86%</td>
</tr>
<tr class="even">
<td align="left">2</td>
<td align="left">Negative</td>
<td align="right">975</td>
<td align="right">19.50%</td>
<td align="right">4225</td>
<td align="right">84.50%</td>
<td align="right">969</td>
<td align="right">19.38%</td>
<td align="right">4212</td>
<td align="right">84.24%</td>
</tr>
<tr class="odd">
<td align="left">3</td>
<td align="left">Multi-drug-resistant</td>
<td align="right">474</td>
<td align="right">9.48%</td>
<td align="right">4699</td>
<td align="right">93.98%</td>
<td align="right">425</td>
<td align="right">8.50%</td>
<td align="right">4637</td>
<td align="right">92.74%</td>
</tr>
<tr class="even">
<td align="left">4</td>
<td align="left">Poly-resistant</td>
<td align="right">203</td>
<td align="right">4.06%</td>
<td align="right">4902</td>
<td align="right">98.04%</td>
<td align="right">263</td>
<td align="right">5.26%</td>
<td align="right">4900</td>
<td align="right">98.00%</td>
</tr>
<tr class="odd">
<td align="left">5</td>
<td align="left">Extensively drug-resistant</td>
<td align="right">98</td>
<td align="right">1.96%</td>
<td align="right">100</td>
<td align="right">2.00%</td>
<td align="right">5000</td>
<td align="right">100.00%</td>
</tr>
@ -470,7 +470,7 @@ Unique: 5</p>
<div class="pkgdown">
<p></p>
<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.0.</p>
<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.2.</p>
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docs/articles/PCA.html
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</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="Released version">1.8.0</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.8.0.9005</span>
</span>
</div>
@ -185,7 +185,7 @@
</header><div class="row">
</header><script src="PCA_files/accessible-code-block-0.0.1/empty-anchor.js"></script><div class="row">
<div class="col-md-9 contents">
<div class="page-header toc-ignore">
<h1 data-toc-skip>How to conduct principal component analysis (PCA) for AMR</h1>
@ -199,18 +199,18 @@
<p><strong>NOTE: This page will be updated soon, as the pca() function is currently being developed.</strong></p>
<div class="section level1">
<h1 id="introduction">Introduction<a class="anchor" aria-label="anchor" href="#introduction"></a>
</h1>
<div class="section level2">
<h2 id="introduction">Introduction<a class="anchor" aria-label="anchor" href="#introduction"></a>
</h2>
</div>
<div class="section level1">
<h1 id="transforming">Transforming<a class="anchor" aria-label="anchor" href="#transforming"></a>
</h1>
<div class="section level2">
<h2 id="transforming">Transforming<a class="anchor" aria-label="anchor" href="#transforming"></a>
</h2>
<p>For PCA, we need to transform our AMR data first. This is what the <code>example_isolates</code> data set in this package looks like:</p>
<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://msberends.github.io/AMR">AMR</a></span><span class="op">)</span>
<code class="sourceCode R"><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://msberends.github.io/AMR/">AMR</a></span><span class="op">)</span>
<span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://dplyr.tidyverse.org" class="external-link">dplyr</a></span><span class="op">)</span>
<span class="fu"><a href="https://pillar.r-lib.org/reference/glimpse.html" class="external-link">glimpse</a></span><span class="op">(</span><span class="va">example_isolates</span><span class="op">)</span>
<span class="fu"><a href="https://dplyr.tidyverse.org/reference/glimpse.html" class="external-link">glimpse</a></span><span class="op">(</span><span class="va">example_isolates</span><span class="op">)</span>
<span class="co"># Rows: 2,000</span>
<span class="co"># Columns: 49</span>
<span class="co"># $ date <span style="color: #949494; font-style: italic;">&lt;date&gt;</span> 2002-01-02, 2002-01-03, 2002-01-07, 2002-01-07, 2002-…</span>
@ -283,9 +283,9 @@
<span class="co"># <span style="color: #BCBCBC;">5</span> Caryophanales Gemella <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span></span>
<span class="co"># <span style="color: #BCBCBC;">6</span> Caryophanales Listeria <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span></span></code></pre></div>
</div>
<div class="section level1">
<h1 id="perform-principal-component-analysis">Perform principal component analysis<a class="anchor" aria-label="anchor" href="#perform-principal-component-analysis"></a>
</h1>
<div class="section level2">
<h2 id="perform-principal-component-analysis">Perform principal component analysis<a class="anchor" aria-label="anchor" href="#perform-principal-component-analysis"></a>
</h2>
<p>The new <code><a href="../reference/pca.html">pca()</a></code> function will automatically filter on rows that contain numeric values in all selected variables, so we now only need to do:</p>
<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="va">pca_result</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/pca.html">pca</a></span><span class="op">(</span><span class="va">resistance_data</span><span class="op">)</span>
@ -305,9 +305,9 @@
<span class="co"># [1] "Caryophanales" "Enterobacterales" "Lactobacillales" "Pseudomonadales"</span></code></pre>
<p>Good news. The first two components explain a total of 93.3% of the variance (see the PC1 and PC2 values of the <em>Proportion of Variance</em>. We can create a so-called biplot with the base R <code><a href="https://rdrr.io/r/stats/biplot.html" class="external-link">biplot()</a></code> function, to see which antimicrobial resistance per drug explain the difference per microorganism.</p>
</div>
<div class="section level1">
<h1 id="plotting-the-results">Plotting the results<a class="anchor" aria-label="anchor" href="#plotting-the-results"></a>
</h1>
<div class="section level2">
<h2 id="plotting-the-results">Plotting the results<a class="anchor" aria-label="anchor" href="#plotting-the-results"></a>
</h2>
<div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/stats/biplot.html" class="external-link">biplot</a></span><span class="op">(</span><span class="va">pca_result</span><span class="op">)</span></code></pre></div>
<p><img src="PCA_files/figure-html/unnamed-chunk-5-1.png" width="750"></p>
@ -340,7 +340,7 @@
<div class="pkgdown">
<p></p>
<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.0.</p>
<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.2.</p>
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docs/articles/SPSS.html
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@ -44,7 +44,7 @@
</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="Released version">1.8.0</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.8.0.9005</span>
</span>
</div>
@ -185,13 +185,13 @@
</header><div class="row">
</header><script src="SPSS_files/accessible-code-block-0.0.1/empty-anchor.js"></script><div class="row">
<div class="col-md-9 contents">
<div class="page-header toc-ignore">
<h1 data-toc-skip>How to import data from SPSS / SAS / Stata</h1>
<h4 data-toc-skip class="author">Dr. Matthijs Berends</h4>
<h4 data-toc-skip class="date">23 December 2021</h4>
<h4 data-toc-skip class="date">12 March 2022</h4>
<small class="dont-index">Source: <a href="https://github.com/msberends/AMR/blob/HEAD/vignettes/SPSS.Rmd" class="external-link"><code>vignettes/SPSS.Rmd</code></a></small>
<div class="hidden name"><code>SPSS.Rmd</code></div>
@ -226,7 +226,7 @@
</li>
<li>
<p><strong>R has a huge community.</strong></p>
<p>Many R users just ask questions on websites like <a href="https://stackoverflow.com" class="external-link">StackOverflow.com</a>, the largest online community for programmers. At the time of writing, <a href="https://stackoverflow.com/questions/tagged/r?sort=votes" class="external-link">430,288 R-related questions</a> have already been asked on this platform (that covers questions and answers for any programming language). In my own experience, most questions are answered within a couple of minutes.</p>
<p>Many R users just ask questions on websites like <a href="https://stackoverflow.com" class="external-link">StackOverflow.com</a>, the largest online community for programmers. At the time of writing, <a href="https://stackoverflow.com/questions/tagged/r?sort=votes" class="external-link">439,030 R-related questions</a> have already been asked on this platform (that covers questions and answers for any programming language). In my own experience, most questions are answered within a couple of minutes.</p>
</li>
<li>
<p><strong>R understands any data type, including SPSS/SAS/Stata.</strong></p>
@ -241,7 +241,7 @@
<li>
<p><strong>R is (nowadays) the preferred analysis software in academic papers.</strong></p>
<p>At present, R is among the world most powerful statistical languages, and it is generally very popular in science (Bollmann <em>et al.</em>, 2017). For all the above reasons, the number of references to R as an analysis method in academic papers <a href="https://r4stats.com/2014/08/20/r-passes-spss-in-scholarly-use-stata-growing-rapidly/" class="external-link">is rising continuously</a> and has even surpassed SPSS for academic use (Muenchen, 2014).</p>
<p>I believe that the thing with SPSS is, that it has always had a great user interface which is very easy to learn and use. Back when they developed it, they had very little competition, let alone from R. R didn’t even had a professional user interface until the last decade (called RStudio, see below). How people used R between the nineties and 2010 is almost completely incomparable to how R is being used now. The language itself <a href="https://www.tidyverse.org/packages/" class="external-link">has been restyled completely</a> by volunteers who are dedicated professionals in the field of data science. SPSS was great when there was nothing else that could compete. But now in 2021, I don’t see any reason why SPSS would be of any better use than R.</p>
<p>I believe that the thing with SPSS is, that it has always had a great user interface which is very easy to learn and use. Back when they developed it, they had very little competition, let alone from R. R didn’t even had a professional user interface until the last decade (called RStudio, see below). How people used R between the nineties and 2010 is almost completely incomparable to how R is being used now. The language itself <a href="https://www.tidyverse.org/packages/" class="external-link">has been restyled completely</a> by volunteers who are dedicated professionals in the field of data science. SPSS was great when there was nothing else that could compete. But now in 2022, I don’t see any reason why SPSS would be of any better use than R.</p>
</li>
</ul>
<p>To demonstrate the first point:</p>
@ -414,7 +414,7 @@
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@ -44,7 +44,7 @@
</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="Released version">1.8.0</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.8.0.9005</span>
</span>
</div>
@ -185,7 +185,7 @@
</header><div class="row">
</header><script src="WHONET_files/accessible-code-block-0.0.1/empty-anchor.js"></script><div class="row">
<div class="col-md-9 contents">
<div class="page-header toc-ignore">
<h1 data-toc-skip>How to work with WHONET data</h1>
@ -215,7 +215,7 @@
<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://dplyr.tidyverse.org" class="external-link">dplyr</a></span><span class="op">)</span> <span class="co"># part of tidyverse</span>
<span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://ggplot2.tidyverse.org" class="external-link">ggplot2</a></span><span class="op">)</span> <span class="co"># part of tidyverse</span>
<span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://msberends.github.io/AMR">AMR</a></span><span class="op">)</span> <span class="co"># this package</span>
<span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://msberends.github.io/AMR/">AMR</a></span><span class="op">)</span> <span class="co"># this package</span>
<span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://github.com/msberends/cleaner" class="external-link">cleaner</a></span><span class="op">)</span> <span class="co"># to create frequency tables</span></code></pre></div>
<p>We will have to transform some variables to simplify and automate the analysis:</p>
<ul>
@ -238,7 +238,7 @@
<p><strong>Frequency table</strong></p>
<p>Class: character<br>
Length: 500<br>
Available: 500 (100.0%, NA: 0 = 0.0%)<br>
Available: 500 (100%, NA: 0 = 0%)<br>
Unique: 37</p>
<p>Shortest: 11<br>
Longest: 40</p>
@ -334,7 +334,7 @@ Longest: 40</p>
</tr>
</tbody>
</table>
<p>(omitted 27 entries, n = 56 [11.20%])</p>
<p>(omitted 27 entries, n = 56 [11.2%])</p>
<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="co"># our transformed antibiotic columns</span>
<span class="co"># amoxicillin/clavulanic acid (J01CR02) as an example</span>
@ -413,7 +413,7 @@ Drug group: Beta-lactams/penicillins<br>
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@ -44,7 +44,7 @@
</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="Released version">1.8.0</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.8.0.9005</span>
</span>
</div>
@ -185,7 +185,7 @@
</header><div class="row">
</header><script src="benchmarks_files/accessible-code-block-0.0.1/empty-anchor.js"></script><div class="row">
<div class="col-md-9 contents">
<div class="page-header toc-ignore">
<h1 data-toc-skip>Benchmarks</h1>
@ -202,7 +202,7 @@
<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" class="external-link">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="downlit sourceCode r">
<code class="sourceCode R"><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://github.com/joshuaulrich/microbenchmark/" class="external-link">microbenchmark</a></span><span class="op">)</span>
<span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://msberends.github.io/AMR">AMR</a></span><span class="op">)</span>
<span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://msberends.github.io/AMR/">AMR</a></span><span class="op">)</span>
<span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://dplyr.tidyverse.org" class="external-link">dplyr</a></span><span class="op">)</span></code></pre></div>
<p>In the next test, we try to ‘coerce’ different input values into the microbial code of <em>Staphylococcus aureus</em>. Coercion is a computational process of forcing output based on an input. For microorganism names, coercing user input to taxonomically valid microorganism names is crucial to ensure correct interpretation and to enable grouping based on taxonomic properties.</p>
<p>The actual result is the same every time: it returns its microorganism code <code>B_STPHY_AURS</code> (<em>B</em> stands for <em>Bacteria</em>, its taxonomic kingdom).</p>
@ -222,23 +222,23 @@
<span class="fu"><a href="../reference/as.mo.html">as.mo</a></span><span class="op">(</span><span class="st">"MRSA"</span><span class="op">)</span>, <span class="co"># Methicillin Resistant S. aureus</span>
<span class="fu"><a href="../reference/as.mo.html">as.mo</a></span><span class="op">(</span><span class="st">"VISA"</span><span class="op">)</span>, <span class="co"># Vancomycin Intermediate S. aureus</span>
times <span class="op">=</span> <span class="fl">25</span><span class="op">)</span>
<span class="fu"><a href="https://docs.ropensci.org/skimr/reference/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">S.aureus</span>, unit <span class="op">=</span> <span class="st">"ms"</span>, signif <span class="op">=</span> <span class="fl">2</span><span class="op">)</span>
<span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">S.aureus</span>, unit <span class="op">=</span> <span class="st">"ms"</span>, signif <span class="op">=</span> <span class="fl">2</span><span class="op">)</span>
<span class="co"># Unit: milliseconds</span>
<span class="co"># expr min lq mean median uq max neval</span>
<span class="co"># as.mo("sau") 10.0 12.0 20.0 12.0 15.0 53 25</span>
<span class="co"># as.mo("stau") 49.0 54.0 72.0 58.0 91.0 97 25</span>
<span class="co"># as.mo("STAU") 50.0 54.0 71.0 57.0 90.0 110 25</span>
<span class="co"># as.mo("staaur") 10.0 12.0 17.0 12.0 14.0 51 25</span>
<span class="co"># as.mo("STAAUR") 10.0 12.0 17.0 12.0 14.0 54 25</span>
<span class="co"># as.mo("S. aureus") 26.0 27.0 40.0 31.0 56.0 74 25</span>
<span class="co"># as.mo("S aureus") 26.0 27.0 39.0 29.0 58.0 68 25</span>
<span class="co"># as.mo("Staphylococcus aureus") 3.5 3.9 6.6 4.1 4.8 38 25</span>
<span class="co"># as.mo("Staphylococcus aureus (MRSA)") 230.0 240.0 250.0 240.0 250.0 280 25</span>
<span class="co"># as.mo("Sthafilokkockus aaureuz") 180.0 190.0 200.0 190.0 200.0 290 25</span>
<span class="co"># as.mo("MRSA") 11.0 12.0 19.0 13.0 14.0 50 25</span>
<span class="co"># as.mo("VISA") 21.0 22.0 32.0 25.0 50.0 60 25</span></code></pre></div>
<span class="co"># expr min lq mean median uq max neval</span>
<span class="co"># as.mo("sau") 20.0 20.0 25.0 20.0 21 57 25</span>
<span class="co"># as.mo("stau") 94.0 94.0 110.0 95.0 130 130 25</span>
<span class="co"># as.mo("STAU") 94.0 95.0 110.0 130.0 130 130 25</span>
<span class="co"># as.mo("staaur") 20.0 20.0 28.0 20.0 21 58 25</span>
<span class="co"># as.mo("STAAUR") 20.0 20.0 25.0 20.0 21 57 25</span>
<span class="co"># as.mo("S. aureus") 56.0 56.0 72.0 57.0 92 97 25</span>
<span class="co"># as.mo("S aureus") 55.0 56.0 66.0 56.0 75 93 25</span>
<span class="co"># as.mo("Staphylococcus aureus") 5.7 5.8 7.3 5.9 6 41 25</span>
<span class="co"># as.mo("Staphylococcus aureus (MRSA)") 370.0 370.0 390.0 380.0 410 410 25</span>
<span class="co"># as.mo("Sthafilokkockus aaureuz") 260.0 290.0 300.0 290.0 300 330 25</span>
<span class="co"># as.mo("MRSA") 20.0 20.0 23.0 20.0 20 56 25</span>
<span class="co"># as.mo("VISA") 34.0 35.0 48.0 35.0 35 220 25</span></code></pre></div>
<p><img src="benchmarks_files/figure-html/unnamed-chunk-4-1.png" width="750"></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 200 milliseconds, this is only 5 input values per second. It is clear that accepted taxonomic names are extremely fast, but some variations are up to 47 times slower to determine.</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 200 milliseconds, this is only 5 input values per second. It is clear that accepted taxonomic names are extremely fast, but some variations are up to 65 times slower to determine.</p>
<p>To improve performance, we implemented two important algorithms to save unnecessary calculations: <strong>repetitive results</strong> and <strong>already precalculated results</strong>.</p>
<div class="section level3">
<h3 id="repetitive-results">Repetitive results<a class="anchor" aria-label="anchor" href="#repetitive-results"></a>
@ -258,8 +258,8 @@
<span class="co"># what do these values look like? They are of class &lt;mo&gt;:</span>
<span class="fu"><a href="https://rdrr.io/r/utils/head.html" class="external-link">head</a></span><span class="op">(</span><span class="va">x</span><span class="op">)</span>
<span class="co"># Class &lt;mo&gt;</span>
<span class="co"># [1] B_STPHY_AURS B_STRPT_EQNS B_KLBSL_PNMN B_STPHY_EPDR B_STPHY_AURS</span>
<span class="co"># [6] B_CRYNB_STRT</span>
<span class="co"># [1] B_PROTS_MRBL B_STPHY_CONS B_ESCHR_COLI B_PSDMN_AERG B_ENTRC_FCLS</span>
<span class="co"># [6] B_STPHY_AURS</span>
<span class="co"># as the example_isolates data set has 2,000 rows, we should have 2 million items</span>
<span class="fu"><a href="https://rdrr.io/r/base/length.html" class="external-link">length</a></span><span class="op">(</span><span class="va">x</span><span class="op">)</span>
@ -272,11 +272,11 @@
<span class="co"># now let's see:</span>
<span class="va">run_it</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/microbenchmark/man/microbenchmark.html" class="external-link">microbenchmark</a></span><span class="op">(</span><span class="fu"><a href="../reference/mo_property.html">mo_name</a></span><span class="op">(</span><span class="va">x</span><span class="op">)</span>,
times <span class="op">=</span> <span class="fl">10</span><span class="op">)</span>
<span class="fu"><a href="https://docs.ropensci.org/skimr/reference/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">run_it</span>, unit <span class="op">=</span> <span class="st">"ms"</span>, signif <span class="op">=</span> <span class="fl">3</span><span class="op">)</span>
<span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">run_it</span>, unit <span class="op">=</span> <span class="st">"ms"</span>, signif <span class="op">=</span> <span class="fl">3</span><span class="op">)</span>
<span class="co"># Unit: milliseconds</span>
<span class="co"># expr min lq mean median uq max neval</span>
<span class="co"># mo_name(x) 196 209 274 223 364 388 10</span></code></pre></div>
<p>So getting official taxonomic names of 2,000,000 (!!) items consisting of 90 unique values only takes 0.223 seconds. That is 112 nanoseconds on average. You only lose time on your unique input values.</p>
<span class="co"># mo_name(x) 265 269 357 298 471 516 10</span></code></pre></div>
<p>So getting official taxonomic names of 2,000,000 (!!) items consisting of 90 unique values only takes 0.298 seconds. That is 149 nanoseconds on average. You only lose time on your unique input values.</p>
</div>
<div class="section level3">
<h3 id="precalculated-results">Precalculated results<a class="anchor" aria-label="anchor" href="#precalculated-results"></a>
@ -287,13 +287,13 @@
B <span class="op">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_name</a></span><span class="op">(</span><span class="st">"S. aureus"</span><span class="op">)</span>,
C <span class="op">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_name</a></span><span class="op">(</span><span class="st">"Staphylococcus aureus"</span><span class="op">)</span>,
times <span class="op">=</span> <span class="fl">10</span><span class="op">)</span>
<span class="fu"><a href="https://docs.ropensci.org/skimr/reference/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">run_it</span>, unit <span class="op">=</span> <span class="st">"ms"</span>, signif <span class="op">=</span> <span class="fl">3</span><span class="op">)</span>
<span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">run_it</span>, unit <span class="op">=</span> <span class="st">"ms"</span>, signif <span class="op">=</span> <span class="fl">3</span><span class="op">)</span>
<span class="co"># Unit: milliseconds</span>
<span class="co"># expr min lq mean median uq max neval</span>
<span class="co"># A 8.00 9.16 9.19 9.28 9.46 9.73 10</span>
<span class="co"># B 23.40 27.20 32.60 27.90 28.10 80.20 10</span>
<span class="co"># C 1.85 2.25 2.40 2.47 2.62 2.90 10</span></code></pre></div>
<p>So going from <code>mo_name("Staphylococcus aureus")</code> to <code>"Staphylococcus aureus"</code> takes 0.0025 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>
<span class="co"># expr min lq mean median uq max neval</span>
<span class="co"># A 12.30 12.30 12.60 12.50 12.60 14.2 10</span>
<span class="co"># B 61.70 61.90 62.70 62.40 62.50 66.2 10</span>
<span class="co"># C 3.04 3.05 6.92 3.12 3.21 40.9 10</span></code></pre></div>
<p>So going from <code>mo_name("Staphylococcus aureus")</code> to <code>"Staphylococcus aureus"</code> takes 0.0031 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="cb5"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="va">run_it</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/microbenchmark/man/microbenchmark.html" class="external-link">microbenchmark</a></span><span class="op">(</span>A <span class="op">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_species</a></span><span class="op">(</span><span class="st">"aureus"</span><span class="op">)</span>,
B <span class="op">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_genus</a></span><span class="op">(</span><span class="st">"Staphylococcus"</span><span class="op">)</span>,
@ -304,17 +304,17 @@
G <span class="op">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_phylum</a></span><span class="op">(</span><span class="st">"Firmicutes"</span><span class="op">)</span>,
H <span class="op">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_kingdom</a></span><span class="op">(</span><span class="st">"Bacteria"</span><span class="op">)</span>,
times <span class="op">=</span> <span class="fl">10</span><span class="op">)</span>
<span class="fu"><a href="https://docs.ropensci.org/skimr/reference/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">run_it</span>, unit <span class="op">=</span> <span class="st">"ms"</span>, signif <span class="op">=</span> <span class="fl">3</span><span class="op">)</span>
<span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">run_it</span>, unit <span class="op">=</span> <span class="st">"ms"</span>, signif <span class="op">=</span> <span class="fl">3</span><span class="op">)</span>
<span class="co"># Unit: milliseconds</span>
<span class="co"># expr min lq mean median uq max neval</span>
<span class="co"># A 1.76 1.80 2.07 1.95 2.26 2.90 10</span>
<span class="co"># B 1.69 1.73 1.90 1.81 2.03 2.48 10</span>
<span class="co"># C 1.71 1.77 1.92 1.91 2.05 2.17 10</span>
<span class="co"># D 1.68 1.71 1.76 1.76 1.82 1.88 10</span>
<span class="co"># E 1.68 1.70 1.89 1.89 2.04 2.26 10</span>
<span class="co"># F 1.67 1.75 1.93 1.89 2.11 2.35 10</span>
<span class="co"># G 1.70 1.76 1.97 1.88 2.12 2.43 10</span>
<span class="co"># H 1.67 1.71 1.83 1.75 1.98 2.14 10</span></code></pre></div>
<span class="co"># A 3.02 3.09 3.22 3.10 3.46 3.55 10</span>
<span class="co"># B 3.03 3.04 3.21 3.12 3.42 3.48 10</span>
<span class="co"># C 3.03 3.05 3.08 3.06 3.12 3.16 10</span>
<span class="co"># D 2.98 3.01 3.17 3.09 3.40 3.47 10</span>
<span class="co"># E 3.01 3.06 3.19 3.12 3.43 3.43 10</span>
<span class="co"># F 2.95 2.99 3.11 3.05 3.16 3.46 10</span>
<span class="co"># G 2.94 3.06 3.19 3.13 3.41 3.54 10</span>
<span class="co"># H 2.93 3.01 3.12 3.08 3.19 3.42 10</span></code></pre></div>
<p>Of course, when running <code>mo_phylum("Firmicutes")</code> the function has zero knowledge about the actual microorganism, namely <em>S. aureus</em>. But since the result would be <code>"Firmicutes"</code> anyway, there is no point in calculating the result. And because this package contains all phyla of all known bacteria, it can just return the initial value immediately.</p>
</div>
<div class="section level3">
@ -347,19 +347,19 @@
ru <span class="op">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_name</a></span><span class="op">(</span><span class="va">CoNS</span>, language <span class="op">=</span> <span class="st">"ru"</span><span class="op">)</span>,
sv <span class="op">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_name</a></span><span class="op">(</span><span class="va">CoNS</span>, language <span class="op">=</span> <span class="st">"sv"</span><span class="op">)</span>,
times <span class="op">=</span> <span class="fl">100</span><span class="op">)</span>
<span class="fu"><a href="https://docs.ropensci.org/skimr/reference/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">run_it</span>, unit <span class="op">=</span> <span class="st">"ms"</span>, signif <span class="op">=</span> <span class="fl">4</span><span class="op">)</span>
<span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">run_it</span>, unit <span class="op">=</span> <span class="st">"ms"</span>, signif <span class="op">=</span> <span class="fl">4</span><span class="op">)</span>
<span class="co"># Unit: milliseconds</span>
<span class="co"># expr min lq mean median uq max neval</span>
<span class="co"># da 1.9470 2.0220 2.190 2.0720 2.358 3.234 100</span>
<span class="co"># de 1.9560 2.0330 3.649 2.1610 2.401 50.670 100</span>
<span class="co"># en 0.8937 0.9124 1.022 0.9776 1.120 1.748 100</span>
<span class="co"># es 1.9710 2.0290 2.216 2.1000 2.391 3.109 100</span>
<span class="co"># fr 1.8280 1.8960 3.214 1.9420 2.237 71.550 100</span>
<span class="co"># it 1.9370 1.9970 2.163 2.0610 2.339 3.210 100</span>
<span class="co"># nl 1.9710 2.0280 2.698 2.1110 2.421 49.340 100</span>
<span class="co"># pt 1.8920 1.9600 2.119 2.0200 2.261 3.265 100</span>
<span class="co"># ru 1.8630 1.9420 2.779 2.0270 2.335 66.660 100</span>
<span class="co"># sv 1.8680 1.9190 4.062 1.9890 2.263 78.870 100</span></code></pre></div>
<span class="co"># expr min lq mean median uq max neval</span>
<span class="co"># da 3.579 3.689 3.811 3.737 3.894 4.626 100</span>
<span class="co"># de 3.600 3.694 4.205 3.750 3.869 43.820 100</span>
<span class="co"># en 1.686 1.720 1.828 1.766 1.822 2.244 100</span>
<span class="co"># es 3.618 3.688 4.633 3.772 4.083 43.890 100</span>
<span class="co"># fr 3.493 3.602 4.104 3.658 3.785 41.860 100</span>
<span class="co"># it 3.543 3.628 4.152 3.702 3.810 44.040 100</span>
<span class="co"># nl 3.625 3.716 5.024 3.763 3.924 44.220 100</span>
<span class="co"># pt 3.510 3.610 3.742 3.684 3.861 4.096 100</span>
<span class="co"># ru 3.568 3.680 4.534 3.742 3.871 41.170 100</span>
<span class="co"># sv 3.585 3.664 3.833 3.748 4.046 4.987 100</span></code></pre></div>
<p>Currently supported languages are Danish, Dutch, English, French, German, Italian, Portuguese, Russian, Spanish and Swedish.</p>
</div>
</div>
@ -379,7 +379,7 @@
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<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.0.</p>
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@ -185,12 +185,12 @@
</header><div class="row">
</header><script src="datasets_files/accessible-code-block-0.0.1/empty-anchor.js"></script><div class="row">
<div class="col-md-9 contents">
<div class="page-header toc-ignore">
<h1 data-toc-skip>Data sets for download / own use</h1>
<h4 data-toc-skip class="date">10 March 2022</h4>
<h4 data-toc-skip class="date">12 March 2022</h4>
<small class="dont-index">Source: <a href="https://github.com/msberends/AMR/blob/HEAD/vignettes/datasets.Rmd" class="external-link"><code>vignettes/datasets.Rmd</code></a></small>
<div class="hidden name"><code>datasets.Rmd</code></div>
@ -199,71 +199,42 @@
<p>All reference data (about microorganisms, antibiotics, R/SI
interpretation, EUCAST rules, etc.) in this <code>AMR</code> package are
reliable, up-to-date and freely available. We continually export our
data sets to formats for use in R, SPSS, SAS, Stata and Excel. We also
supply tab separated files that are machine-readable and suitable for
input in any software program, such as laboratory information
systems.</p>
<p>On this page, we explain how to download them and how the structure
of the data sets look like.</p>
<p>All reference data (about microorganisms, antibiotics, R/SI interpretation, EUCAST rules, etc.) in this <code>AMR</code> package are reliable, up-to-date and freely available. We continually export our data sets to formats for use in R, SPSS, SAS, Stata and Excel. We also supply tab separated files that are machine-readable and suitable for input in any software program, such as laboratory information systems.</p>
<p>On this page, we explain how to download them and how the structure of the data sets look like.</p>
<p class="dataset-within-r">
If you are reading this page from within R, please
<a href="https://msberends.github.io/AMR/articles/datasets.html">visit
our website</a>, which is automatically updated with every code change.
If you are reading this page from within R, please <a href="https://msberends.github.io/AMR/articles/datasets.html">visit our website</a>, which is automatically updated with every code change.
</p>
<div class="section level2">
<h2 id="microorganisms-currently-accepted-names">Microorganisms (currently accepted names)<a class="anchor" aria-label="anchor" href="#microorganisms-currently-accepted-names"></a>
</h2>
<p>A data set with 70,760 rows and 16 columns, containing the following
column names:<br><em>mo</em>, <em>fullname</em>, <em>kingdom</em>, <em>phylum</em>,
<em>class</em>, <em>order</em>, <em>family</em>, <em>genus</em>,
<em>species</em>, <em>subspecies</em>, <em>rank</em>, <em>ref</em>,
<em>species_id</em>, <em>source</em>, <em>prevalence</em> and
<em>snomed</em>.</p>
<p>This data set is in R available as <code>microorganisms</code>, after
you load the <code>AMR</code> package.</p>
<p>It was last updated on 29 November 2021 11:38:23 UTC. Find more info
about the structure of this data set <a href="https://msberends.github.io/AMR/reference/microorganisms.html">here</a>.</p>
<p>A data set with 70,760 rows and 16 columns, containing the following column names:<br><em>mo</em>, <em>fullname</em>, <em>kingdom</em>, <em>phylum</em>, <em>class</em>, <em>order</em>, <em>family</em>, <em>genus</em>, <em>species</em>, <em>subspecies</em>, <em>rank</em>, <em>ref</em>, <em>species_id</em>, <em>source</em>, <em>prevalence</em> and <em>snomed</em>.</p>
<p>This data set is in R available as <code>microorganisms</code>, after you load the <code>AMR</code> package.</p>
<p>It was last updated on 1 February 2022 22:08:20 UTC. Find more info about the structure of this data set <a href="https://msberends.github.io/AMR/reference/microorganisms.html">here</a>.</p>
<p><strong>Direct download links:</strong></p>
<ul>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/microorganisms.rds" class="external-link">R
file</a> (1.3 MB)<br>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/microorganisms.rds" class="external-link">R file</a> (1.3 MB)<br>
</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/microorganisms.xlsx" class="external-link">Excel
file</a> (6.4 MB)<br>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/microorganisms.xlsx" class="external-link">Excel file</a> (6.4 MB)<br>
</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/microorganisms.txt" class="external-link">plain
text file</a> (13.1 MB)<br>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/microorganisms.txt" class="external-link">plain text file</a> (13.1 MB)<br>
</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/microorganisms.sas" class="external-link">SAS
file</a> (30.7 MB)<br>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/microorganisms.sas" class="external-link">SAS file</a> (30.7 MB)<br>
</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/microorganisms.sav" class="external-link">SPSS
file</a> (16.3 MB)<br>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/microorganisms.sav" class="external-link">SPSS file</a> (16.3 MB)<br>
</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/microorganisms.dta" class="external-link">Stata
file</a> (27.5 MB)</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/microorganisms.dta" class="external-link">Stata file</a> (27.5 MB)</li>
</ul>
<p><strong>NOTE: The exported files for SAS, SPSS and Stata do not
contain SNOMED codes, as their file size would exceed 100 MB; the file
size limit of GitHub.</strong> Advice? Use R instead.</p>
<p><strong>NOTE: The exported files for SAS, SPSS and Stata do not contain SNOMED codes, as their file size would exceed 100 MB; the file size limit of GitHub.</strong> Advice? Use R instead.</p>
<div class="section level3">
<h3 id="source">Source<a class="anchor" aria-label="anchor" href="#source"></a>
</h3>
<p>Our full taxonomy of microorganisms is based on the authoritative and
comprehensive:</p>
<p>Our full taxonomy of microorganisms is based on the authoritative and comprehensive:</p>
<ul>
<li>
<a href="http://www.catalogueoflife.org" class="external-link">Catalogue of Life</a>
(included version: 2019)</li>
<a href="http://www.catalogueoflife.org" class="external-link">Catalogue of Life</a> (included version: 2019)</li>
<li>
<a href="https://lpsn.dsmz.de" class="external-link">List of Prokaryotic names with
Standing in Nomenclature</a> (LPSN, last updated: 5 October 2021)</li>
<li>US Edition of SNOMED CT from 1 September 2020, retrieved from the <a href="https://phinvads.cdc.gov/vads/ViewValueSet.action?oid=2.16.840.1.114222.4.11.1009" class="external-link">Public
Health Information Network Vocabulary Access and Distribution System
(PHIN VADS)</a>, OID 2.16.840.1.114222.4.11.1009, version 12</li>
<a href="https://lpsn.dsmz.de" class="external-link">List of Prokaryotic names with Standing in Nomenclature</a> (LPSN, last updated: 5 October 2021)</li>
<li>US Edition of SNOMED CT from 1 September 2020, retrieved from the <a href="https://phinvads.cdc.gov/vads/ViewValueSet.action?oid=2.16.840.1.114222.4.11.1009" class="external-link">Public Health Information Network Vocabulary Access and Distribution System (PHIN VADS)</a>, OID 2.16.840.1.114222.4.11.1009, version 12</li>
</ul>
</div>
<div class="section level3">
@ -456,64 +427,40 @@ Health Information Network Vocabulary Access and Distribution System
<div class="section level2">
<h2 id="microorganisms-previously-accepted-names">Microorganisms (previously accepted names)<a class="anchor" aria-label="anchor" href="#microorganisms-previously-accepted-names"></a>
</h2>
<p>A data set with 14,338 rows and 4 columns, containing the following
column names:<br><em>fullname</em>, <em>fullname_new</em>, <em>ref</em> and
<em>prevalence</em>.</p>
<p><strong>Note:</strong> remember that the ‘ref’ columns contains the
scientific reference to the old taxonomic entries, i.e. of column
<em>‘fullname’</em>. For the scientific reference of the new names,
i.e. of column <em>‘fullname_new’</em>, see the
<code>microorganisms</code> data set.</p>
<p>This data set is in R available as <code>microorganisms.old</code>,
after you load the <code>AMR</code> package.</p>
<p>It was last updated on 6 October 2021 14:38:29 UTC. Find more info
about the structure of this data set <a href="https://msberends.github.io/AMR/reference/microorganisms.old.html">here</a>.</p>
<p>A data set with 14,338 rows and 4 columns, containing the following column names:<br><em>fullname</em>, <em>fullname_new</em>, <em>ref</em> and <em>prevalence</em>.</p>
<p><strong>Note:</strong> remember that the ‘ref’ columns contains the scientific reference to the old taxonomic entries, i.e. of column <em>‘fullname’</em>. For the scientific reference of the new names, i.e. of column <em>‘fullname_new’</em>, see the <code>microorganisms</code> data set.</p>
<p>This data set is in R available as <code>microorganisms.old</code>, after you load the <code>AMR</code> package.</p>
<p>It was last updated on 1 February 2022 22:08:19 UTC. Find more info about the structure of this data set <a href="https://msberends.github.io/AMR/reference/microorganisms.old.html">here</a>.</p>
<p><strong>Direct download links:</strong></p>
<ul>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/microorganisms.old.rds" class="external-link">R
file</a> (0.2 MB)<br>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/microorganisms.old.rds" class="external-link">R file</a> (0.2 MB)<br>
</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/microorganisms.old.xlsx" class="external-link">Excel
file</a> (0.5 MB)<br>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/microorganisms.old.xlsx" class="external-link">Excel file</a> (0.5 MB)<br>
</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/microorganisms.old.txt" class="external-link">plain
text file</a> (1 MB)<br>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/microorganisms.old.txt" class="external-link">plain text file</a> (1 MB)<br>
</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/microorganisms.old.sas" class="external-link">SAS
file</a> (2.1 MB)<br>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/microorganisms.old.sas" class="external-link">SAS file</a> (2.1 MB)<br>
</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/microorganisms.old.sav" class="external-link">SPSS
file</a> (1.3 MB)<br>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/microorganisms.old.sav" class="external-link">SPSS file</a> (1.3 MB)<br>
</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/microorganisms.old.dta" class="external-link">Stata
file</a> (2 MB)</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/microorganisms.old.dta" class="external-link">Stata file</a> (2 MB)</li>
</ul>
<div class="section level3">
<h3 id="source-1">Source<a class="anchor" aria-label="anchor" href="#source-1"></a>
</h3>
<p>This data set contains old, previously accepted taxonomic names. The
data sources are the same as the <code>microorganisms</code> data
set:</p>
<p>This data set contains old, previously accepted taxonomic names. The data sources are the same as the <code>microorganisms</code> data set:</p>
<ul>
<li>
<a href="http://www.catalogueoflife.org" class="external-link">Catalogue of Life</a>
(included version: 2019)</li>
<a href="http://www.catalogueoflife.org" class="external-link">Catalogue of Life</a> (included version: 2019)</li>
<li>
<a href="https://lpsn.dsmz.de" class="external-link">List of Prokaryotic names with
Standing in Nomenclature</a> (LPSN, last updated: 5 October 2021)</li>
<a href="https://lpsn.dsmz.de" class="external-link">List of Prokaryotic names with Standing in Nomenclature</a> (LPSN, last updated: 5 October 2021)</li>
</ul>
</div>
<div class="section level3">
<h3 id="example-content-1">Example content<a class="anchor" aria-label="anchor" href="#example-content-1"></a>
</h3>
<p>Example rows when filtering on <em>Escherichia</em>:</p>
<table style="width:100%;" class="table">
<colgroup>
<col width="31%">
<col width="30%">
<col width="24%">
<col width="13%">
</colgroup>
<table class="table">
<thead><tr class="header">
<th align="center">fullname</th>
<th align="center">fullname_new</th>
@ -546,50 +493,31 @@ Standing in Nomenclature</a> (LPSN, last updated: 5 October 2021)</li>
<div class="section level2">
<h2 id="antibiotic-agents">Antibiotic agents<a class="anchor" aria-label="anchor" href="#antibiotic-agents"></a>
</h2>
<p>A data set with 464 rows and 14 columns, containing the following
column names:<br><em>ab</em>, <em>cid</em>, <em>name</em>, <em>group</em>, <em>atc</em>,
<em>atc_group1</em>, <em>atc_group2</em>, <em>abbreviations</em>,
<em>synonyms</em>, <em>oral_ddd</em>, <em>oral_units</em>,
<em>iv_ddd</em>, <em>iv_units</em> and <em>loinc</em>.</p>
<p>This data set is in R available as <code>antibiotics</code>, after
you load the <code>AMR</code> package.</p>
<p>It was last updated on 14 December 2021 21:59:33 UTC. Find more info
about the structure of this data set <a href="https://msberends.github.io/AMR/reference/antibiotics.html">here</a>.</p>
<p>A data set with 464 rows and 14 columns, containing the following column names:<br><em>ab</em>, <em>cid</em>, <em>name</em>, <em>group</em>, <em>atc</em>, <em>atc_group1</em>, <em>atc_group2</em>, <em>abbreviations</em>, <em>synonyms</em>, <em>oral_ddd</em>, <em>oral_units</em>, <em>iv_ddd</em>, <em>iv_units</em> and <em>loinc</em>.</p>
<p>This data set is in R available as <code>antibiotics</code>, after you load the <code>AMR</code> package.</p>
<p>It was last updated on 1 February 2022 22:08:19 UTC. Find more info about the structure of this data set <a href="https://msberends.github.io/AMR/reference/antibiotics.html">here</a>.</p>
<p><strong>Direct download links:</strong></p>
<ul>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/antibiotics.rds" class="external-link">R
file</a> (33 kB)<br>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/antibiotics.rds" class="external-link">R file</a> (33 kB)<br>
</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/antibiotics.xlsx" class="external-link">Excel
file</a> (65 kB)<br>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/antibiotics.xlsx" class="external-link">Excel file</a> (65 kB)<br>
</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/antibiotics.txt" class="external-link">plain
text file</a> (0.1 MB)<br>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/antibiotics.txt" class="external-link">plain text file</a> (0.1 MB)<br>
</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/antibiotics.sas" class="external-link">SAS
file</a> (1.8 MB)<br>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/antibiotics.sas" class="external-link">SAS file</a> (1.8 MB)<br>
</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/antibiotics.sav" class="external-link">SPSS
file</a> (0.3 MB)<br>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/antibiotics.sav" class="external-link">SPSS file</a> (0.3 MB)<br>
</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/antibiotics.dta" class="external-link">Stata
file</a> (0.3 MB)</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/antibiotics.dta" class="external-link">Stata file</a> (0.3 MB)</li>
</ul>
<div class="section level3">
<h3 id="source-2">Source<a class="anchor" aria-label="anchor" href="#source-2"></a>
</h3>
<p>This data set contains all EARS-Net and ATC codes gathered from WHO
and WHONET, and all compound IDs from PubChem. It also contains all
brand names (synonyms) as found on PubChem and Defined Daily Doses
(DDDs) for oral and parenteral administration.</p>
<p>This data set contains all EARS-Net and ATC codes gathered from WHO and WHONET, and all compound IDs from PubChem. It also contains all brand names (synonyms) as found on PubChem and Defined Daily Doses (DDDs) for oral and parenteral administration.</p>
<ul>
<li>
<a href="https://www.whocc.no/atc_ddd_index/" class="external-link">ATC/DDD index from WHO
Collaborating Centre for Drug Statistics Methodology</a> (note: this may
not be used for commercial purposes, but is freely available from the
WHO CC website for personal use)</li>
<li><a href="https://pubchem.ncbi.nlm.nih.gov" class="external-link">PubChem by the US
National Library of Medicine</a></li>
<a href="https://www.whocc.no/atc_ddd_index/" class="external-link">ATC/DDD index from WHO Collaborating Centre for Drug Statistics Methodology</a> (note: this may not be used for commercial purposes, but is freely available from the WHO CC website for personal use)</li>
<li><a href="https://pubchem.ncbi.nlm.nih.gov" class="external-link">PubChem by the US National Library of Medicine</a></li>
<li><a href="https://whonet.org" class="external-link">WHONET software 2019</a></li>
</ul>
</div>
@ -669,8 +597,7 @@ National Library of Medicine</a></li>
<td align="center">Beta-lactams/penicillins</td>
<td align="center">J01CR02</td>
<td align="center">Beta-lactam antibacterials, penicillins</td>
<td align="center">Combinations of penicillins, incl. beta-lactamase
inhibitors</td>
<td align="center">Combinations of penicillins, incl. beta-lactamase inhibitors</td>
<td align="center">a/c, amcl, aml, …</td>
<td align="center">amocla, amoclan, amoclav, …</td>
<td align="center">1.5</td>
@ -734,49 +661,31 @@ inhibitors</td>
<div class="section level2">
<h2 id="antiviral-agents">Antiviral agents<a class="anchor" aria-label="anchor" href="#antiviral-agents"></a>
</h2>
<p>A data set with 102 rows and 9 columns, containing the following
column names:<br><em>atc</em>, <em>cid</em>, <em>name</em>, <em>atc_group</em>,
<em>synonyms</em>, <em>oral_ddd</em>, <em>oral_units</em>,
<em>iv_ddd</em> and <em>iv_units</em>.</p>
<p>This data set is in R available as <code>antivirals</code>, after you
load the <code>AMR</code> package.</p>
<p>It was last updated on 29 August 2020 19:53:07 UTC. Find more info
about the structure of this data set <a href="https://msberends.github.io/AMR/reference/antibiotics.html">here</a>.</p>
<p>A data set with 102 rows and 9 columns, containing the following column names:<br><em>atc</em>, <em>cid</em>, <em>name</em>, <em>atc_group</em>, <em>synonyms</em>, <em>oral_ddd</em>, <em>oral_units</em>, <em>iv_ddd</em> and <em>iv_units</em>.</p>
<p>This data set is in R available as <code>antivirals</code>, after you load the <code>AMR</code> package.</p>
<p>It was last updated on 23 July 2021 20:35:47 UTC. Find more info about the structure of this data set <a href="https://msberends.github.io/AMR/reference/antibiotics.html">here</a>.</p>
<p><strong>Direct download links:</strong></p>
<ul>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/antivirals.rds" class="external-link">R
file</a> (5 kB)<br>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/antivirals.rds" class="external-link">R file</a> (5 kB)<br>
</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/antivirals.xlsx" class="external-link">Excel
file</a> (14 kB)<br>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/antivirals.xlsx" class="external-link">Excel file</a> (14 kB)<br>
</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/antivirals.txt" class="external-link">plain
text file</a> (16 kB)<br>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/antivirals.txt" class="external-link">plain text file</a> (16 kB)<br>
</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/antivirals.sas" class="external-link">SAS
file</a> (80 kB)<br>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/antivirals.sas" class="external-link">SAS file</a> (80 kB)<br>
</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/antivirals.sav" class="external-link">SPSS
file</a> (68 kB)<br>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/antivirals.sav" class="external-link">SPSS file</a> (68 kB)<br>
</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/antivirals.dta" class="external-link">Stata
file</a> (67 kB)</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/antivirals.dta" class="external-link">Stata file</a> (67 kB)</li>
</ul>
<div class="section level3">
<h3 id="source-3">Source<a class="anchor" aria-label="anchor" href="#source-3"></a>
</h3>
<p>This data set contains all ATC codes gathered from WHO and all
compound IDs from PubChem. It also contains all brand names (synonyms)
as found on PubChem and Defined Daily Doses (DDDs) for oral and
parenteral administration.</p>
<p>This data set contains all ATC codes gathered from WHO and all compound IDs from PubChem. It also contains all brand names (synonyms) as found on PubChem and Defined Daily Doses (DDDs) for oral and parenteral administration.</p>
<ul>
<li>
<a href="https://www.whocc.no/atc_ddd_index/" class="external-link">ATC/DDD index from WHO
Collaborating Centre for Drug Statistics Methodology</a> (note: this may
not be used for commercial purposes, but is freely available from the
WHO CC website for personal use)</li>
<li><a href="https://pubchem.ncbi.nlm.nih.gov" class="external-link">PubChem by the US
National Library of Medicine</a></li>
<a href="https://www.whocc.no/atc_ddd_index/" class="external-link">ATC/DDD index from WHO Collaborating Centre for Drug Statistics Methodology</a> (note: this may not be used for commercial purposes, but is freely available from the WHO CC website for personal use)</li>
<li><a href="https://pubchem.ncbi.nlm.nih.gov" class="external-link">PubChem by the US National Library of Medicine</a></li>
</ul>
</div>
<div class="section level3">
@ -810,8 +719,7 @@ National Library of Medicine</a></li>
<td align="center">J05AF06</td>
<td align="center">441300</td>
<td align="center">Abacavir</td>
<td align="center">Nucleoside and nucleotide reverse transcriptase
inhibitors</td>
<td align="center">Nucleoside and nucleotide reverse transcriptase inhibitors</td>
<td align="center">Abacavir, Abacavir sulfate, Ziagen</td>
<td align="center">0.6</td>
<td align="center">g</td>
@ -822,8 +730,7 @@ inhibitors</td>
<td align="center">J05AB01</td>
<td align="center">135398513</td>
<td align="center">Aciclovir</td>
<td align="center">Nucleosides and nucleotides excl. reverse
transcriptase inhibitors</td>
<td align="center">Nucleosides and nucleotides excl. reverse transcriptase inhibitors</td>
<td align="center">Acicloftal, Aciclovier, Aciclovir, …</td>
<td align="center">4.0</td>
<td align="center">g</td>
@ -834,10 +741,8 @@ transcriptase inhibitors</td>
<td align="center">J05AF08</td>
<td align="center">60871</td>
<td align="center">Adefovir dipivoxil</td>
<td align="center">Nucleoside and nucleotide reverse transcriptase
inhibitors</td>
<td align="center">Adefovir di ester, Adefovir dipivoxil, Adefovir
Dipivoxil, …</td>
<td align="center">Nucleoside and nucleotide reverse transcriptase inhibitors</td>
<td align="center">Adefovir di ester, Adefovir dipivoxil, Adefovir Dipivoxil, …</td>
<td align="center">10.0</td>
<td align="center">mg</td>
<td align="center"></td>
@ -883,39 +788,27 @@ Dipivoxil, …</td>
<div class="section level2">
<h2 id="intrinsic-bacterial-resistance">Intrinsic bacterial resistance<a class="anchor" aria-label="anchor" href="#intrinsic-bacterial-resistance"></a>
</h2>
<p>A data set with 134,956 rows and 2 columns, containing the following
column names:<br><em>mo</em> and <em>ab</em>.</p>
<p>This data set is in R available as <code>intrinsic_resistant</code>,
after you load the <code>AMR</code> package.</p>
<p>It was last updated on 14 December 2021 21:59:33 UTC. Find more info
about the structure of this data set <a href="https://msberends.github.io/AMR/reference/intrinsic_resistant.html">here</a>.</p>
<p>A data set with 134,956 rows and 2 columns, containing the following column names:<br><em>mo</em> and <em>ab</em>.</p>
<p>This data set is in R available as <code>intrinsic_resistant</code>, after you load the <code>AMR</code> package.</p>
<p>It was last updated on 1 February 2022 22:08:19 UTC. Find more info about the structure of this data set <a href="https://msberends.github.io/AMR/reference/intrinsic_resistant.html">here</a>.</p>
<p><strong>Direct download links:</strong></p>
<ul>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/intrinsic_resistant.rds" class="external-link">R
file</a> (78 kB)<br>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/intrinsic_resistant.rds" class="external-link">R file</a> (78 kB)<br>
</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/intrinsic_resistant.xlsx" class="external-link">Excel
file</a> (0.9 MB)<br>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/intrinsic_resistant.xlsx" class="external-link">Excel file</a> (0.9 MB)<br>
</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/intrinsic_resistant.txt" class="external-link">plain
text file</a> (5.1 MB)<br>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/intrinsic_resistant.txt" class="external-link">plain text file</a> (5.1 MB)<br>
</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/intrinsic_resistant.sas" class="external-link">SAS
file</a> (10.4 MB)<br>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/intrinsic_resistant.sas" class="external-link">SAS file</a> (10.4 MB)<br>
</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/intrinsic_resistant.sav" class="external-link">SPSS
file</a> (7.4 MB)<br>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/intrinsic_resistant.sav" class="external-link">SPSS file</a> (7.4 MB)<br>
</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/intrinsic_resistant.dta" class="external-link">Stata
file</a> (10.2 MB)</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/intrinsic_resistant.dta" class="external-link">Stata file</a> (10.2 MB)</li>
</ul>
<div class="section level3">
<h3 id="source-4">Source<a class="anchor" aria-label="anchor" href="#source-4"></a>
</h3>
<p>This data set contains all defined intrinsic resistance by EUCAST of
all bug-drug combinations, and is based on <a href="https://www.eucast.org/expert_rules_and_intrinsic_resistance/" class="external-link">‘EUCAST
Expert Rules’ and ‘EUCAST Intrinsic Resistance and Unusual Phenotypes’
v3.3</a> (2021).</p>
<p>This data set contains all defined intrinsic resistance by EUCAST of all bug-drug combinations, and is based on <a href="https://www.eucast.org/expert_rules_and_intrinsic_resistance/" class="external-link">‘EUCAST Expert Rules’ and ‘EUCAST Intrinsic Resistance and Unusual Phenotypes’ v3.3</a> (2021).</p>
</div>
<div class="section level3">
<h3 id="example-content-4">Example content<a class="anchor" aria-label="anchor" href="#example-content-4"></a>
@ -1162,40 +1055,27 @@ v3.3</a> (2021).</p>
<div class="section level2">
<h2 id="interpretation-from-mic-values-disk-diameters-to-rsi">Interpretation from MIC values / disk diameters to R/SI<a class="anchor" aria-label="anchor" href="#interpretation-from-mic-values-disk-diameters-to-rsi"></a>
</h2>
<p>A data set with 20,318 rows and 11 columns, containing the following
column names:<br><em>guideline</em>, <em>method</em>, <em>site</em>, <em>mo</em>,
<em>rank_index</em>, <em>ab</em>, <em>ref_tbl</em>, <em>disk_dose</em>,
<em>breakpoint_S</em>, <em>breakpoint_R</em> and <em>uti</em>.</p>
<p>This data set is in R available as <code>rsi_translation</code>,
after you load the <code>AMR</code> package.</p>
<p>It was last updated on 14 December 2021 21:59:33 UTC. Find more info
about the structure of this data set <a href="https://msberends.github.io/AMR/reference/rsi_translation.html">here</a>.</p>
<p>A data set with 20,318 rows and 11 columns, containing the following column names:<br><em>guideline</em>, <em>method</em>, <em>site</em>, <em>mo</em>, <em>rank_index</em>, <em>ab</em>, <em>ref_tbl</em>, <em>disk_dose</em>, <em>breakpoint_S</em>, <em>breakpoint_R</em> and <em>uti</em>.</p>
<p>This data set is in R available as <code>rsi_translation</code>, after you load the <code>AMR</code> package.</p>
<p>It was last updated on 1 February 2022 22:08:20 UTC. Find more info about the structure of this data set <a href="https://msberends.github.io/AMR/reference/rsi_translation.html">here</a>.</p>
<p><strong>Direct download links:</strong></p>
<ul>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/rsi_translation.rds" class="external-link">R
file</a> (39 kB)<br>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/rsi_translation.rds" class="external-link">R file</a> (39 kB)<br>
</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/rsi_translation.xlsx" class="external-link">Excel
file</a> (0.7 MB)<br>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/rsi_translation.xlsx" class="external-link">Excel file</a> (0.7 MB)<br>
</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/rsi_translation.txt" class="external-link">plain
text file</a> (1.7 MB)<br>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/rsi_translation.txt" class="external-link">plain text file</a> (1.7 MB)<br>
</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/rsi_translation.sas" class="external-link">SAS
file</a> (3.6 MB)<br>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/rsi_translation.sas" class="external-link">SAS file</a> (3.6 MB)<br>
</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/rsi_translation.sav" class="external-link">SPSS
file</a> (2.2 MB)<br>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/rsi_translation.sav" class="external-link">SPSS file</a> (2.2 MB)<br>
</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/rsi_translation.dta" class="external-link">Stata
file</a> (3.4 MB)</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/rsi_translation.dta" class="external-link">Stata file</a> (3.4 MB)</li>
</ul>
<div class="section level3">
<h3 id="source-5">Source<a class="anchor" aria-label="anchor" href="#source-5"></a>
</h3>
<p>This data set contains interpretation rules for MIC values and disk
diffusion diameters. Included guidelines are CLSI (2010-2021) and EUCAST
(2011-2021).</p>
<p>This data set contains interpretation rules for MIC values and disk diffusion diameters. Included guidelines are CLSI (2010-2021) and EUCAST (2011-2021).</p>
</div>
<div class="section level3">
<h3 id="example-content-5">Example content<a class="anchor" aria-label="anchor" href="#example-content-5"></a>
@ -1313,57 +1193,33 @@ diffusion diameters. Included guidelines are CLSI (2010-2021) and EUCAST
<div class="section level2">
<h2 id="dosage-guidelines-from-eucast">Dosage guidelines from EUCAST<a class="anchor" aria-label="anchor" href="#dosage-guidelines-from-eucast"></a>
</h2>
<p>A data set with 169 rows and 9 columns, containing the following
column names:<br><em>ab</em>, <em>name</em>, <em>type</em>, <em>dose</em>,
<em>dose_times</em>, <em>administration</em>, <em>notes</em>,
<em>original_txt</em> and <em>eucast_version</em>.</p>
<p>This data set is in R available as <code>dosage</code>, after you
load the <code>AMR</code> package.</p>
<p>It was last updated on 25 January 2021 20:58:20 UTC. Find more info
about the structure of this data set <a href="https://msberends.github.io/AMR/reference/dosage.html">here</a>.</p>
<p>A data set with 169 rows and 9 columns, containing the following column names:<br><em>ab</em>, <em>name</em>, <em>type</em>, <em>dose</em>, <em>dose_times</em>, <em>administration</em>, <em>notes</em>, <em>original_txt</em> and <em>eucast_version</em>.</p>
<p>This data set is in R available as <code>dosage</code>, after you load the <code>AMR</code> package.</p>
<p>It was last updated on 23 July 2021 20:35:47 UTC. Find more info about the structure of this data set <a href="https://msberends.github.io/AMR/reference/dosage.html">here</a>.</p>
<p><strong>Direct download links:</strong></p>
<ul>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/dosage.rds" class="external-link">R
file</a> (3 kB)<br>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/dosage.rds" class="external-link">R file</a> (3 kB)<br>
</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/dosage.xlsx" class="external-link">Excel
file</a> (14 kB)<br>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/dosage.xlsx" class="external-link">Excel file</a> (14 kB)<br>
</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/dosage.txt" class="external-link">plain
text file</a> (15 kB)<br>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/dosage.txt" class="external-link">plain text file</a> (15 kB)<br>
</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/dosage.sas" class="external-link">SAS
file</a> (52 kB)<br>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/dosage.sas" class="external-link">SAS file</a> (52 kB)<br>
</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/dosage.sav" class="external-link">SPSS
file</a> (45 kB)<br>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/dosage.sav" class="external-link">SPSS file</a> (45 kB)<br>
</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/dosage.dta" class="external-link">Stata
file</a> (44 kB)</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/dosage.dta" class="external-link">Stata file</a> (44 kB)</li>
</ul>
<div class="section level3">
<h3 id="source-6">Source<a class="anchor" aria-label="anchor" href="#source-6"></a>
</h3>
<p>EUCAST breakpoints used in this package are based on the dosages in
this data set.</p>
<p>Currently included dosages in the data set are meant for: <a href="https://www.eucast.org/clinical_breakpoints/" class="external-link">‘EUCAST Clinical
Breakpoint Tables’ v11.0</a> (2021).</p>
<p>EUCAST breakpoints used in this package are based on the dosages in this data set.</p>
<p>Currently included dosages in the data set are meant for: <a href="https://www.eucast.org/clinical_breakpoints/" class="external-link">‘EUCAST Clinical Breakpoint Tables’ v11.0</a> (2021).</p>
</div>
<div class="section level3">
<h3 id="example-content-6">Example content<a class="anchor" aria-label="anchor" href="#example-content-6"></a>
</h3>
<table class="table">
<colgroup>
<col width="4%">
<col width="10%">
<col width="15%">
<col width="10%">
<col width="9%">
<col width="13%">
<col width="5%">
<col width="16%">
<col width="13%">
</colgroup>
<thead><tr class="header">
<th align="center">ab</th>
<th align="center">name</th>
@ -1460,14 +1316,12 @@ Breakpoint Tables’ v11.0</a> (2021).</p>
<footer><div class="copyright">
<p></p>
<p>Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein,
Erwin E. A. Hassing.</p>
<p>Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein, Erwin E. A. Hassing.</p>
</div>
<div class="pkgdown">
<p></p>
<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a>
2.0.2.</p>
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</div>
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@ -185,13 +185,11 @@
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<span class="navbar-brand">
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<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.8.0</span>
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</header><div class="row">
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<h1 data-toc-skip>How to predict antimicrobial resistance</h1>
@ -206,7 +206,7 @@
<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://dplyr.tidyverse.org" class="external-link">dplyr</a></span><span class="op">)</span>
<span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://ggplot2.tidyverse.org" class="external-link">ggplot2</a></span><span class="op">)</span>
<span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://msberends.github.io/AMR">AMR</a></span><span class="op">)</span>
<span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://msberends.github.io/AMR/">AMR</a></span><span class="op">)</span>
<span class="co"># (if not yet installed, install with:)</span>
<span class="co"># install.packages(c("tidyverse", "AMR"))</span></code></pre></div>
@ -216,18 +216,18 @@
</h2>
<p>Our package contains a function <code><a href="../reference/resistance_predict.html">resistance_predict()</a></code>, which takes the same input as functions for <a href="./AMR.html">other AMR data analysis</a>. Based on a date column, it calculates cases per year and uses a regression model to predict antimicrobial resistance.</p>
<p>It is basically as easy as:</p>
<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a><span class="co"># resistance prediction of piperacillin/tazobactam (TZP):</span></span>
<span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a><span class="fu">resistance_predict</span>(<span class="at">tbl =</span> example_isolates, <span class="at">col_date =</span> <span class="st">"date"</span>, <span class="at">col_ab =</span> <span class="st">"TZP"</span>, <span class="at">model =</span> <span class="st">"binomial"</span>)</span>
<span id="cb2-3"><a href="#cb2-3" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb2-4"><a href="#cb2-4" aria-hidden="true" tabindex="-1"></a><span class="co"># or:</span></span>
<span id="cb2-5"><a href="#cb2-5" aria-hidden="true" tabindex="-1"></a>example_isolates <span class="sc">%&gt;%</span> </span>
<span id="cb2-6"><a href="#cb2-6" aria-hidden="true" tabindex="-1"></a> <span class="fu">resistance_predict</span>(<span class="at">col_ab =</span> <span class="st">"TZP"</span>,</span>
<span id="cb2-7"><a href="#cb2-7" aria-hidden="true" tabindex="-1"></a> model <span class="st">"binomial"</span>)</span>
<span id="cb2-8"><a href="#cb2-8" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb2-9"><a href="#cb2-9" aria-hidden="true" tabindex="-1"></a><span class="co"># to bind it to object 'predict_TZP' for example:</span></span>
<span id="cb2-10"><a href="#cb2-10" aria-hidden="true" tabindex="-1"></a>predict_TZP <span class="ot">&lt;-</span> example_isolates <span class="sc">%&gt;%</span> </span>
<span id="cb2-11"><a href="#cb2-11" aria-hidden="true" tabindex="-1"></a> <span class="fu">resistance_predict</span>(<span class="at">col_ab =</span> <span class="st">"TZP"</span>,</span>
<span id="cb2-12"><a href="#cb2-12" aria-hidden="true" tabindex="-1"></a> <span class="at">model =</span> <span class="st">"binomial"</span>)</span></code></pre></div>
<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true"></a><span class="co"># resistance prediction of piperacillin/tazobactam (TZP):</span></span>
<span id="cb2-2"><a href="#cb2-2" aria-hidden="true"></a><span class="kw">resistance_predict</span>(<span class="dt">tbl =</span> example_isolates, <span class="dt">col_date =</span> <span class="st">"date"</span>, <span class="dt">col_ab =</span> <span class="st">"TZP"</span>, <span class="dt">model =</span> <span class="st">"binomial"</span>)</span>
<span id="cb2-3"><a href="#cb2-3" aria-hidden="true"></a></span>
<span id="cb2-4"><a href="#cb2-4" aria-hidden="true"></a><span class="co"># or:</span></span>
<span id="cb2-5"><a href="#cb2-5" aria-hidden="true"></a>example_isolates <span class="op">%&gt;%</span><span class="st"> </span></span>
<span id="cb2-6"><a href="#cb2-6" aria-hidden="true"></a><span class="st"> </span><span class="kw">resistance_predict</span>(<span class="dt">col_ab =</span> <span class="st">"TZP"</span>,</span>
<span id="cb2-7"><a href="#cb2-7" aria-hidden="true"></a> model <span class="st">"binomial"</span>)</span>
<span id="cb2-8"><a href="#cb2-8" aria-hidden="true"></a></span>
<span id="cb2-9"><a href="#cb2-9" aria-hidden="true"></a><span class="co"># to bind it to object 'predict_TZP' for example:</span></span>
<span id="cb2-10"><a href="#cb2-10" aria-hidden="true"></a>predict_TZP &lt;-<span class="st"> </span>example_isolates <span class="op">%&gt;%</span><span class="st"> </span></span>
<span id="cb2-11"><a href="#cb2-11" aria-hidden="true"></a><span class="st"> </span><span class="kw">resistance_predict</span>(<span class="dt">col_ab =</span> <span class="st">"TZP"</span>,</span>
<span id="cb2-12"><a href="#cb2-12" aria-hidden="true"></a> <span class="dt">model =</span> <span class="st">"binomial"</span>)</span></code></pre></div>
<p>The function will look for a date column itself if <code>col_date</code> is not set.</p>
<p>When running any of these commands, a summary of the regression model will be printed unless using <code>resistance_predict(..., info = FALSE)</code>.</p>
<pre><code><span class="co"># ℹ Using column 'date' as input for `col_date`.</span></code></pre>
@ -264,7 +264,8 @@
<span class="co"># 27 2028 0.43730688 0.3418075 0.5328063 NA NA 0.43730688</span>
<span class="co"># 28 2029 0.46175755 0.3597639 0.5637512 NA NA 0.46175755</span>
<span class="co"># 29 2030 0.48639359 0.3782932 0.5944939 NA NA 0.48639359</span>
<span class="co"># 30 2031 0.51109592 0.3973697 0.6248221 NA NA 0.51109592</span></code></pre></div>
<span class="co"># 30 2031 0.51109592 0.3973697 0.6248221 NA NA 0.51109592</span>
<span class="co"># 31 2032 0.53574417 0.4169574 0.6545309 NA NA 0.53574417</span></code></pre></div>
<p>The function <code>plot</code> is available in base R, and can be extended by other packages to depend the output based on the type of input. We extended its function to cope with resistance predictions:</p>
<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="fu"><a href="../reference/plot.html">plot</a></span><span class="op">(</span><span class="va">predict_TZP</span><span class="op">)</span></code></pre></div>
@ -371,7 +372,7 @@
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<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="Released version">1.8.0</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.8.0.9005</span>
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<h1 data-toc-skip>Welcome to the <code>AMR</code> package</h1>
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