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(v1.7.1.9022) rely on vctrs for ab selectors

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dr. M.S. (Matthijs) Berends
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@ -27,6 +27,8 @@
<![endif]-->
</head>
<body data-spy="scroll" data-target="#toc">
<div class="container template-article">
<header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
<div class="container">
@ -39,7 +41,7 @@
</button>
<span class="navbar-brand">
<a class="navbar-link" href="../index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.7.1</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.7.1.9022</span>
</span>
</div>
@ -47,14 +49,14 @@
<ul class="nav navbar-nav">
<li>
<a href="../index.html">
<span class="fas fa-home"></span>
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Home
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<span class="fa fa-question-circle"></span>
How to
@ -63,77 +65,77 @@
<ul class="dropdown-menu" role="menu">
<li>
<a href="../articles/AMR.html">
<span class="fas fa-directions"></span>
<span class="fa fa-directions"></span>
Conduct AMR analysis
</a>
</li>
<li>
<a href="../articles/resistance_predict.html">
<span class="fas fa-dice"></span>
<span class="fa fa-dice"></span>
Predict antimicrobial resistance
</a>
</li>
<li>
<a href="../articles/datasets.html">
<span class="fas fa-database"></span>
<span class="fa fa-database"></span>
Data sets for download / own use
</a>
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<li>
<a href="../articles/PCA.html">
<span class="fas fa-compress"></span>
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Conduct principal component analysis for AMR
</a>
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<li>
<a href="../articles/MDR.html">
<span class="fas fa-skull-crossbones"></span>
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Determine multi-drug resistance (MDR)
</a>
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<a href="../articles/WHONET.html">
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Apply EUCAST rules
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Get properties of a microorganism
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Get properties of an antibiotic
</a>
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<a href="../articles/benchmarks.html">
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Other: benchmarks
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@ -142,14 +144,14 @@
</li>
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@ -164,7 +166,7 @@
</ul>
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@ -172,7 +174,7 @@
</li>
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<a href="../survey.html">
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Survey
</a>
@ -187,13 +189,13 @@
</header><script src="EUCAST_files/header-attrs-2.8/header-attrs.js"></script><div class="row">
</header><script src="EUCAST_files/header-attrs-2.9/header-attrs.js"></script><div class="row">
<div class="col-md-9 contents">
<div class="page-header toc-ignore">
<h1 data-toc-skip>How to apply EUCAST rules</h1>
<small class="dont-index">Source: <a href="https://github.com/msberends/AMR/blob/master/vignettes/EUCAST.Rmd"><code>vignettes/EUCAST.Rmd</code></a></small>
<small class="dont-index">Source: <a href="https://github.com/msberends/AMR/blob/master/vignettes/EUCAST.Rmd" class="external-link"><code>vignettes/EUCAST.Rmd</code></a></small>
<div class="hidden name"><code>EUCAST.Rmd</code></div>
</div>
@ -202,21 +204,21 @@
<div id="introduction" class="section level2">
<h2 class="hasAnchor">
<a href="#introduction" class="anchor"></a>Introduction</h2>
<p>What are EUCAST rules? The European Committee on Antimicrobial Susceptibility Testing (EUCAST) states <a href="https://www.eucast.org/expert_rules_and_intrinsic_resistance/">on their website</a>:</p>
<a href="#introduction" class="anchor" aria-hidden="true"></a>Introduction</h2>
<p>What are EUCAST rules? The European Committee on Antimicrobial Susceptibility Testing (EUCAST) states <a href="https://www.eucast.org/expert_rules_and_intrinsic_resistance/" class="external-link">on their website</a>:</p>
<blockquote>
<p><em>EUCAST expert rules are a tabulated collection of expert knowledge on intrinsic resistances, exceptional resistance phenotypes and interpretive rules that may be applied to antimicrobial susceptibility testing in order to reduce errors and make appropriate recommendations for reporting particular resistances.</em></p>
</blockquote>
<p>In Europe, a lot of medical microbiological laboratories already apply these rules (<a href="https://www.eurosurveillance.org/content/10.2807/1560-7917.ES2015.20.2.21008">Brown <em>et al.</em>, 2015</a>). Our package features their latest insights on intrinsic resistance and unusual phenotypes (v3.2, 2020).</p>
<p>In Europe, a lot of medical microbiological laboratories already apply these rules (<a href="https://www.eurosurveillance.org/content/10.2807/1560-7917.ES2015.20.2.21008" class="external-link">Brown <em>et al.</em>, 2015</a>). Our package features their latest insights on intrinsic resistance and unusual phenotypes (v3.2, 2020).</p>
<p>Moreover, the <code><a href="../reference/eucast_rules.html">eucast_rules()</a></code> function we use for this purpose can also apply additional rules, like forcing <help title="ATC: J01CA01">ampicillin</help> = R in isolates when <help title="ATC: J01CR02">amoxicillin/clavulanic acid</help> = R.</p>
</div>
<div id="examples" class="section level2">
<h2 class="hasAnchor">
<a href="#examples" class="anchor"></a>Examples</h2>
<a href="#examples" class="anchor" aria-hidden="true"></a>Examples</h2>
<p>These rules can be used to discard impossible bug-drug combinations in your data. For example, <em>Klebsiella</em> produces beta-lactamase that prevents ampicillin (or amoxicillin) from working against it. In other words, practically every strain of <em>Klebsiella</em> is resistant to ampicillin.</p>
<p>Sometimes, laboratory data can still contain such strains with ampicillin being susceptible to ampicillin. This could be because an antibiogram is available before an identification is available, and the antibiogram is then not re-interpreted based on the identification (namely, <em>Klebsiella</em>). EUCAST expert rules solve this, that can be applied using <code><a href="../reference/eucast_rules.html">eucast_rules()</a></code>:</p>
<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="va">oops</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html">data.frame</a></span><span class="op">(</span>mo <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="st">"Klebsiella"</span>,
<code class="sourceCode R"><span class="va">oops</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html" class="external-link">data.frame</a></span><span class="op">(</span>mo <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"Klebsiella"</span>,
<span class="st">"Escherichia"</span><span class="op">)</span>,
ampicillin <span class="op">=</span> <span class="st">"S"</span><span class="op">)</span>
<span class="va">oops</span>
@ -230,16 +232,16 @@
<span class="co"># 2 Escherichia S</span></code></pre></div>
<p>A more convenient function is <code><a href="../reference/mo_property.html">mo_is_intrinsic_resistant()</a></code> that uses the same guideline, but allows to check for one or more specific microorganisms or antibiotics:</p>
<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="fu"><a href="../reference/mo_property.html">mo_is_intrinsic_resistant</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="st">"Klebsiella"</span>, <span class="st">"Escherichia"</span><span class="op">)</span>,
<code class="sourceCode R"><span class="fu"><a href="../reference/mo_property.html">mo_is_intrinsic_resistant</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"Klebsiella"</span>, <span class="st">"Escherichia"</span><span class="op">)</span>,
<span class="st">"ampicillin"</span><span class="op">)</span>
<span class="co"># [1] TRUE FALSE</span>
<span class="fu"><a href="../reference/mo_property.html">mo_is_intrinsic_resistant</a></span><span class="op">(</span><span class="st">"Klebsiella"</span>,
<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="st">"ampicillin"</span>, <span class="st">"kanamycin"</span><span class="op">)</span><span class="op">)</span>
<span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"ampicillin"</span>, <span class="st">"kanamycin"</span><span class="op">)</span><span class="op">)</span>
<span class="co"># [1] TRUE FALSE</span></code></pre></div>
<p>EUCAST rules can not only be used for correction, they can also be used for filling in known resistance and susceptibility based on results of other antimicrobials drugs. This process is called <em>interpretive reading</em>, is basically a form of imputation, and is part of the <code><a href="../reference/eucast_rules.html">eucast_rules()</a></code> function as well:</p>
<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="va">data</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html">data.frame</a></span><span class="op">(</span>mo <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="st">"Staphylococcus aureus"</span>,
<code class="sourceCode R"><span class="va">data</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html" class="external-link">data.frame</a></span><span class="op">(</span>mo <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"Staphylococcus aureus"</span>,
<span class="st">"Enterococcus faecalis"</span>,
<span class="st">"Escherichia coli"</span>,
<span class="st">"Klebsiella pneumoniae"</span>,
@ -398,11 +400,13 @@
<footer><div class="copyright">
<p>Developed by <a href="https://www.rug.nl/staff/m.s.berends/">Matthijs S. Berends</a>, <a href="https://www.rug.nl/staff/c.f.luz/">Christian F. Luz</a>, <a href="https://www.rug.nl/staff/a.w.friedrich/">Alexander W. Friedrich</a>, <a href="https://www.rug.nl/staff/b.sinha/">Bhanu N. M. Sinha</a>, <a href="https://www.rug.nl/staff/c.j.albers/">Casper J. Albers</a>, <a href="https://www.rug.nl/staff/c.glasner/">Corinna Glasner</a>.</p>
<p></p>
<p>Developed by <a href="https://www.rug.nl/staff/m.s.berends/" class="external-link external-link">Matthijs S. Berends</a>, <a href="https://www.rug.nl/staff/c.f.luz/" class="external-link external-link">Christian F. Luz</a>, <a href="https://www.rug.nl/staff/a.w.friedrich/" class="external-link external-link">Alexander W. Friedrich</a>, <a href="https://www.rug.nl/staff/b.sinha/" class="external-link external-link">Bhanu N. M. Sinha</a>, <a href="https://www.rug.nl/staff/c.j.albers/" class="external-link external-link">Casper J. Albers</a>, <a href="https://www.rug.nl/staff/c.glasner/" class="external-link external-link">Corinna Glasner</a>.</p>
</div>
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<p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.6.1.</p>
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<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link external-link">pkgdown</a> 1.6.1.9001.</p>
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@ -411,5 +415,7 @@
</body>
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12
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@ -0,0 +1,12 @@
// Pandoc 2.9 adds attributes on both header and div. We remove the former (to
// be compatible with the behavior of Pandoc < 2.8).
document.addEventListener('DOMContentLoaded', function(e) {
var hs = document.querySelectorAll("div.section[class*='level'] > :first-child");
var i, h, a;
for (i = 0; i < hs.length; i++) {
h = hs[i];
if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6
a = h.attributes;
while (a.length > 0) h.removeAttribute(a[0].name);
}
});

200
docs/articles/MDR.html
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@ -27,6 +27,8 @@
<![endif]-->
</head>
<body data-spy="scroll" data-target="#toc">
<div class="container template-article">
<header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
<div class="container">
@ -39,7 +41,7 @@
</button>
<span class="navbar-brand">
<a class="navbar-link" href="../index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.7.1</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.7.1.9022</span>
</span>
</div>
@ -47,14 +49,14 @@
<ul class="nav navbar-nav">
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<a href="../index.html">
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How to
@ -63,77 +65,77 @@
<ul class="dropdown-menu" role="menu">
<li>
<a href="../articles/AMR.html">
<span class="fas fa-directions"></span>
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Conduct AMR analysis
</a>
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<span class="fas fa-dice"></span>
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Predict antimicrobial resistance
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Conduct principal component analysis for AMR
</a>
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Determine multi-drug resistance (MDR)
</a>
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<a href="../articles/WHONET.html">
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Work with WHONET data
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Apply EUCAST rules
</a>
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<a href="../reference/mo_property.html">
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Get properties of a microorganism
</a>
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<li>
<a href="../reference/ab_property.html">
<span class="fas fa-capsules"></span>
<span class="fa fa-capsules"></span>
Get properties of an antibiotic
</a>
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<a href="../articles/benchmarks.html">
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Other: benchmarks
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@ -142,14 +144,14 @@
</li>
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@ -164,7 +166,7 @@
</ul>
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@ -172,7 +174,7 @@
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<a href="../survey.html">
<span class="fas fa-clipboard-list"></span>
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Survey
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@ -187,13 +189,13 @@
</header><script src="MDR_files/header-attrs-2.8/header-attrs.js"></script><div class="row">
</header><script src="MDR_files/header-attrs-2.9/header-attrs.js"></script><div class="row">
<div class="col-md-9 contents">
<div class="page-header toc-ignore">
<h1 data-toc-skip>How to determine multi-drug resistance (MDR)</h1>
<small class="dont-index">Source: <a href="https://github.com/msberends/AMR/blob/master/vignettes/MDR.Rmd"><code>vignettes/MDR.Rmd</code></a></small>
<small class="dont-index">Source: <a href="https://github.com/msberends/AMR/blob/master/vignettes/MDR.Rmd" class="external-link"><code>vignettes/MDR.Rmd</code></a></small>
<div class="hidden name"><code>MDR.Rmd</code></div>
</div>
@ -203,50 +205,50 @@
<p>With the function <code><a href="../reference/mdro.html">mdro()</a></code>, you can determine which micro-organisms are multi-drug resistant organisms (MDRO).</p>
<div id="type-of-input" class="section level3">
<h3 class="hasAnchor">
<a href="#type-of-input" class="anchor"></a>Type of input</h3>
<p>The <code><a href="../reference/mdro.html">mdro()</a></code> function takes a data set as input, such as a regular <code>data.frame</code>. It tries to automatically determine the right columns for info about your isolates, like the name of the species and all columns with results of antimicrobial agents. See the help page for more info about how to set the right settings for your data with the command <code><a href="../reference/mdro.html">?mdro</a></code>.</p>
<a href="#type-of-input" class="anchor" aria-hidden="true"></a>Type of input</h3>
<p>The <code><a href="../reference/mdro.html">mdro()</a></code> function takes a data set as input, such as a regular <code>data.frame</code>. It tries to automatically determine the right columns for info about your isolates, such as the name of the species and all columns with results of antimicrobial agents. See the help page for more info about how to set the right settings for your data with the command <code><a href="../reference/mdro.html">?mdro</a></code>.</p>
<p>For WHONET data (and most other data), all settings are automatically set correctly.</p>
</div>
<div id="guidelines" class="section level3">
<h3 class="hasAnchor">
<a href="#guidelines" class="anchor"></a>Guidelines</h3>
<p>The function support multiple guidelines. You can select a guideline with the <code>guideline</code> parameter. Currently supported guidelines are (case-insensitive):</p>
<a href="#guidelines" class="anchor" aria-hidden="true"></a>Guidelines</h3>
<p>The <code><a href="../reference/mdro.html">mdro()</a></code> function support multiple guidelines. You can select a guideline with the <code>guideline</code> parameter. Currently supported guidelines are (case-insensitive):</p>
<ul>
<li>
<p><code>guideline = "CMI2012"</code> (default)</p>
<p>Magiorakos AP, Srinivasan A <em>et al.</em> “Multidrug-resistant, extensively drug-resistant and pandrug-resistant bacteria: an international expert proposal for interim standard definitions for acquired resistance.” Clinical Microbiology and Infection (2012) (<a href="https://www.clinicalmicrobiologyandinfection.com/article/S1198-743X(14)61632-3/fulltext">link</a>)</p>
<p>Magiorakos AP, Srinivasan A <em>et al.</em> “Multidrug-resistant, extensively drug-resistant and pandrug-resistant bacteria: an international expert proposal for interim standard definitions for acquired resistance.” Clinical Microbiology and Infection (2012) (<a href="https://www.clinicalmicrobiologyandinfection.com/article/S1198-743X(14)61632-3/fulltext" class="external-link">link</a>)</p>
</li>
<li>
<p><code>guideline = "EUCAST3.2"</code> (or simply <code>guideline = "EUCAST"</code>)</p>
<p>The European international guideline - EUCAST Expert Rules Version 3.2 “Intrinsic Resistance and Unusual Phenotypes” (<a href="https://www.eucast.org/fileadmin/src/media/PDFs/EUCAST_files/Expert_Rules/2020/Intrinsic_Resistance_and_Unusual_Phenotypes_Tables_v3.2_20200225.pdf">link</a>)</p>
<p>The European international guideline - EUCAST Expert Rules Version 3.2 “Intrinsic Resistance and Unusual Phenotypes” (<a href="https://www.eucast.org/fileadmin/src/media/PDFs/EUCAST_files/Expert_Rules/2020/Intrinsic_Resistance_and_Unusual_Phenotypes_Tables_v3.2_20200225.pdf" class="external-link">link</a>)</p>
</li>
<li>
<p><code>guideline = "EUCAST3.1"</code></p>
<p>The European international guideline - EUCAST Expert Rules Version 3.1 “Intrinsic Resistance and Exceptional Phenotypes Tables” (<a href="https://www.eucast.org/fileadmin/src/media/PDFs/EUCAST_files/Expert_Rules/Expert_rules_intrinsic_exceptional_V3.1.pdf">link</a>)</p>
<p>The European international guideline - EUCAST Expert Rules Version 3.1 “Intrinsic Resistance and Exceptional Phenotypes Tables” (<a href="https://www.eucast.org/fileadmin/src/media/PDFs/EUCAST_files/Expert_Rules/Expert_rules_intrinsic_exceptional_V3.1.pdf" class="external-link">link</a>)</p>
</li>
<li>
<p><code>guideline = "TB"</code></p>
<p>The international guideline for multi-drug resistant tuberculosis - World Health Organization “Companion handbook to the WHO guidelines for the programmatic management of drug-resistant tuberculosis” (<a href="https://www.who.int/tb/publications/pmdt_companionhandbook/en/">link</a>)</p>
<p>The international guideline for multi-drug resistant tuberculosis - World Health Organization “Companion handbook to the WHO guidelines for the programmatic management of drug-resistant tuberculosis” (<a href="https://www.who.int/tb/publications/pmdt_companionhandbook/en/" class="external-link">link</a>)</p>
</li>
<li>
<p><code>guideline = "MRGN"</code></p>
<p>The German national guideline - Mueller et al. (2015) Antimicrobial Resistance and Infection Control 4:7. DOI: 10.1186/s13756-015-0047-6</p>
<p>The German national guideline - Mueller <em>et al.</em> (2015) Antimicrobial Resistance and Infection Control 4:7. DOI: 10.1186/s13756-015-0047-6</p>
</li>
<li>
<p><code>guideline = "BRMO"</code></p>
<p>The Dutch national guideline - Rijksinstituut voor Volksgezondheid en Milieu “WIP-richtlijn BRMO (Bijzonder Resistente Micro-Organismen) (ZKH)” (<a href="https://www.rivm.nl/wip-richtlijn-brmo-bijzonder-resistente-micro-organismen-zkh">link</a>)</p>
<p>The Dutch national guideline - Rijksinstituut voor Volksgezondheid en Milieu “WIP-richtlijn BRMO (Bijzonder Resistente Micro-Organismen) (ZKH)” (<a href="https://www.rivm.nl/wip-richtlijn-brmo-bijzonder-resistente-micro-organismen-zkh" class="external-link">link</a>)</p>
</li>
</ul>
<p>Please suggest your own (country-specific) guidelines by letting us know: <a href="https://github.com/msberends/AMR/issues/new" class="uri">https://github.com/msberends/AMR/issues/new</a>.</p>
<p>Please suggest your own (country-specific) guidelines by letting us know: <a href="https://github.com/msberends/AMR/issues/new" class="external-link uri">https://github.com/msberends/AMR/issues/new</a>.</p>
<div id="custom-guidelines" class="section level4">
<h4 class="hasAnchor">
<a href="#custom-guidelines" class="anchor"></a>Custom Guidelines</h4>
<a href="#custom-guidelines" class="anchor" aria-hidden="true"></a>Custom Guidelines</h4>
<p>You can also use your own custom guideline. Custom guidelines can be set with the <code><a href="../reference/mdro.html">custom_mdro_guideline()</a></code> function. This is of great importance if you have custom rules to determine MDROs in your hospital, e.g., rules that are dependent on ward, state of contact isolation or other variables in your data.</p>
<p>If you are familiar with <code><a href="https://dplyr.tidyverse.org/reference/case_when.html">case_when()</a></code> of the <code>dplyr</code> package, you will recognise the input method to set your own rules. Rules must be set using what considers to be the ‘formula notation’:</p>
<p>If you are familiar with <code><a href="https://dplyr.tidyverse.org/reference/case_when.html" class="external-link">case_when()</a></code> of the <code>dplyr</code> package, you will recognise the input method to set your own rules. Rules must be set using what R considers to be the ‘formula notation’:</p>
<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="va">custom</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/mdro.html">custom_mdro_guideline</a></span><span class="op">(</span><span class="va">CIP</span> <span class="op">==</span> <span class="st">"R"</span> <span class="op">&amp;</span> <span class="va">age</span> <span class="op">&gt;</span> <span class="fl">60</span> <span class="op">~</span> <span class="st">"Elderly Type A"</span>,
<span class="va">ERY</span> <span class="op">==</span> <span class="st">"R"</span> <span class="op">&amp;</span> <span class="va">age</span> <span class="op">&gt;</span> <span class="fl">60</span> <span class="op">~</span> <span class="st">"Elderly Type B"</span><span class="op">)</span></code></pre></div>
<p>If a row/an isolate matches the first rule, the value after the first <code><a href="https://rdrr.io/r/base/tilde.html">~</a></code> (in this case <em>‘Elderly Type A’</em>) will be set as MDRO value. Otherwise, the second rule will be tried and so on. The number of rules is unlimited.</p>
<p>If a row/an isolate matches the first rule, the value after the first <code><a href="https://rdrr.io/r/base/tilde.html" class="external-link">~</a></code> (in this case <em>‘Elderly Type A’</em>) will be set as MDRO value. Otherwise, the second rule will be tried and so on. The maximum number of rules is unlimited.</p>
<p>You can print the rules set in the console for an overview. Colours will help reading it if your console supports colours.</p>
<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="va">custom</span>
@ -257,30 +259,50 @@
<span class="co"># </span>
<span class="co"># Unmatched rows will return NA.</span>
<span class="co"># Results will be of class &lt;factor&gt;, with ordered levels: Negative &lt; Elderly Type A &lt; Elderly Type B</span></code></pre></div>
<p>The outcome of the function can be used for the <code>guideline</code> argument in the [mdro()] function:</p>
<p>The outcome of the function can be used for the <code>guideline</code> argument in the <code><a href="../reference/mdro.html">mdro()</a></code> function:</p>
<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="va">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/mdro.html">mdro</a></span><span class="op">(</span><span class="va">example_isolates</span>, guideline <span class="op">=</span> <span class="va">custom</span><span class="op">)</span>
<span class="fu"><a href="https://rdrr.io/r/base/table.html">table</a></span><span class="op">(</span><span class="va">x</span><span class="op">)</span>
<span class="co"># Determining MDROs based on custom rules, resulting in [factor] levels:</span>
<span class="co"># Negative &lt; Elderly Type A &lt; Elderly Type B.</span>
<span class="co"># - Custom MDRO rule 1: `CIP == "R" &amp; age &gt; 60` (198 rows matched)</span>
<span class="co"># - Custom MDRO rule 2: `ERY == "R" &amp; age &gt; 60` (732 rows matched)</span>
<span class="fu"><a href="https://rdrr.io/r/base/table.html" class="external-link">table</a></span><span class="op">(</span><span class="va">x</span><span class="op">)</span>
<span class="co"># x</span>
<span class="co"># Negative Elderly Type A Elderly Type B </span>
<span class="co"># 1070 198 732</span></code></pre></div>
<p>The rules set (the <code>custom</code> object in this case) could be exported to a shared file location using <code><a href="https://rdrr.io/r/base/readRDS.html">saveRDS()</a></code> if you collaborate with multiple users. The custom rules set could then be imported using <code><a href="https://rdrr.io/r/base/readRDS.html">readRDS()</a></code>.</p>
<p>The rules set (the <code>custom</code> object in this case) could be exported to a shared file location using <code><a href="https://rdrr.io/r/base/readRDS.html" class="external-link">saveRDS()</a></code> if you collaborate with multiple users. The custom rules set could then be imported using <code><a href="https://rdrr.io/r/base/readRDS.html" class="external-link">readRDS()</a></code>.</p>
</div>
</div>
<div id="examples" class="section level3">
<h3 class="hasAnchor">
<a href="#examples" class="anchor"></a>Examples</h3>
<p>The <code><a href="../reference/mdro.html">mdro()</a></code> function always returns an ordered <code>factor</code>. For example, the output of the default guideline by Magiorakos <em>et al.</em> returns a <code>factor</code> with levels ‘Negative’, ‘MDR’, ‘XDR’ or ‘PDR’ in that order.</p>
<p>The next example uses the <code>example_isolates</code> data set. This is a data set included with this package and contains 2,000 microbial isolates with their full antibiograms. It reflects reality and can be used to practice AMR data analysis. If we test the MDR/XDR/PDR guideline on this data set, we get:</p>
<a href="#examples" class="anchor" aria-hidden="true"></a>Examples</h3>
<p>The <code><a href="../reference/mdro.html">mdro()</a></code> function always returns an ordered <code>factor</code> for predefined guidelines. For example, the output of the default guideline by Magiorakos <em>et al.</em> returns a <code>factor</code> with levels ‘Negative’, ‘MDR’, ‘XDR’ or ‘PDR’ in that order.</p>
<p>The next example uses the <code>example_isolates</code> data set. This is a data set included with this package and contains full antibiograms of 2,000 microbial isolates. It reflects reality and can be used to practise AMR data analysis. If we test the MDR/XDR/PDR guideline on this data set, we get:</p>
<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://dplyr.tidyverse.org">dplyr</a></span><span class="op">)</span> <span class="co"># to support pipes: %&gt;%</span>
<span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://github.com/msberends/cleaner">cleaner</a></span><span class="op">)</span> <span class="co"># to create frequency tables</span></code></pre></div>
<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"># to support pipes: %&gt;%</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>
<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="va">example_isolates</span> <span class="op">%&gt;%</span>
<span class="fu"><a href="../reference/mdro.html">mdro</a></span><span class="op">(</span><span class="op">)</span> <span class="op">%&gt;%</span>
<span class="fu"><a href="https://rdrr.io/pkg/cleaner/man/freq.html">freq</a></span><span class="op">(</span><span class="op">)</span> <span class="co"># show frequency table of the result</span>
<span class="fu"><a href="https://rdrr.io/pkg/cleaner/man/freq.html" class="external-link">freq</a></span><span class="op">(</span><span class="op">)</span> <span class="co"># show frequency table of the result</span>
<span class="co"># ℹ Using column 'mo' as input for `col_mo`.</span>
<span class="co"># Auto-guessing columns suitable for analysis... OK.</span>
<span class="co"># ℹ Reliability would be improved if these antimicrobial results would be</span>
<span class="co"># available too: ampicillin/sulbactam (SAM), aztreonam (ATM), cefotetan</span>
<span class="co"># (CTT), ceftaroline (CPT), daptomycin (DAP), doripenem (DOR), ertapenem</span>
<span class="co"># (ETP), fusidic acid (FUS), gentamicin-high (GEH), levofloxacin (LVX),</span>
<span class="co"># minocycline (MNO), netilmicin (NET), polymyxin B (PLB),</span>
<span class="co"># quinupristin/dalfopristin (QDA), streptomycin-high (STH), telavancin (TLV)</span>
<span class="co"># and ticarcillin/clavulanic acid (TCC)</span>
<span class="co"># Table 1 - Staphylococcus aureus... OK.</span>
<span class="co"># Table 2 - Enterococcus spp.... OK.</span>
<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>
<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><strong>Frequency table</strong></p>
<p>Class: factor &gt; ordered (numeric)<br>
Length: 2,000<br>
@ -319,16 +341,16 @@ Unique: 2</p>
<div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="co"># random_rsi() is a helper function to generate</span>
<span class="co"># a random vector with values S, I and R</span>
<span class="va">my_TB_data</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html">data.frame</a></span><span class="op">(</span>rifampicin <span class="op">=</span> <span class="fu"><a href="../reference/random.html">random_rsi</a></span><span class="op">(</span><span class="fl">5000</span><span class="op">)</span>,
<span class="va">my_TB_data</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html" class="external-link">data.frame</a></span><span class="op">(</span>rifampicin <span class="op">=</span> <span class="fu"><a href="../reference/random.html">random_rsi</a></span><span class="op">(</span><span class="fl">5000</span><span class="op">)</span>,
isoniazid <span class="op">=</span> <span class="fu"><a href="../reference/random.html">random_rsi</a></span><span class="op">(</span><span class="fl">5000</span><span class="op">)</span>,
gatifloxacin <span class="op">=</span> <span class="fu"><a href="../reference/random.html">random_rsi</a></span><span class="op">(</span><span class="fl">5000</span><span class="op">)</span>,
ethambutol <span class="op">=</span> <span class="fu"><a href="../reference/random.html">random_rsi</a></span><span class="op">(</span><span class="fl">5000</span><span class="op">)</span>,
pyrazinamide <span class="op">=</span> <span class="fu"><a href="../reference/random.html">random_rsi</a></span><span class="op">(</span><span class="fl">5000</span><span class="op">)</span>,
moxifloxacin <span class="op">=</span> <span class="fu"><a href="../reference/random.html">random_rsi</a></span><span class="op">(</span><span class="fl">5000</span><span class="op">)</span>,
kanamycin <span class="op">=</span> <span class="fu"><a href="../reference/random.html">random_rsi</a></span><span class="op">(</span><span class="fl">5000</span><span class="op">)</span><span class="op">)</span></code></pre></div>
<p>Because all column names are automatically verified for valid drug names or codes, this would have worked exactly the same:</p>
<p>Because all column names are automatically verified for valid drug names or codes, this would have worked exactly the same way:</p>
<div class="sourceCode" id="cb7"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="va">my_TB_data</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html">data.frame</a></span><span class="op">(</span>RIF <span class="op">=</span> <span class="fu"><a href="../reference/random.html">random_rsi</a></span><span class="op">(</span><span class="fl">5000</span><span class="op">)</span>,
<code class="sourceCode R"><span class="va">my_TB_data</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html" class="external-link">data.frame</a></span><span class="op">(</span>RIF <span class="op">=</span> <span class="fu"><a href="../reference/random.html">random_rsi</a></span><span class="op">(</span><span class="fl">5000</span><span class="op">)</span>,
INH <span class="op">=</span> <span class="fu"><a href="../reference/random.html">random_rsi</a></span><span class="op">(</span><span class="fl">5000</span><span class="op">)</span>,
GAT <span class="op">=</span> <span class="fu"><a href="../reference/random.html">random_rsi</a></span><span class="op">(</span><span class="fl">5000</span><span class="op">)</span>,
ETH <span class="op">=</span> <span class="fu"><a href="../reference/random.html">random_rsi</a></span><span class="op">(</span><span class="fl">5000</span><span class="op">)</span>,
@ -337,21 +359,21 @@ Unique: 2</p>
KAN <span class="op">=</span> <span class="fu"><a href="../reference/random.html">random_rsi</a></span><span class="op">(</span><span class="fl">5000</span><span class="op">)</span><span class="op">)</span></code></pre></div>
<p>The data set now looks like this:</p>
<div class="sourceCode" id="cb8"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/utils/head.html">head</a></span><span class="op">(</span><span class="va">my_TB_data</span><span class="op">)</span>
<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 S I R R R S</span>
<span class="co"># 2 I R R R R R</span>
<span class="co"># 3 S S S R I R</span>
<span class="co"># 4 R I R R S S</span>
<span class="co"># 5 I R S S R I</span>
<span class="co"># 6 I S S R R R</span>
<span class="co"># 1 I S S R I S</span>
<span class="co"># 2 R R R R S R</span>
<span class="co"># 3 R R R I S S</span>
<span class="co"># 4 R S I R R R</span>
<span class="co"># 5 R S S S I R</span>
<span class="co"># 6 S I S S S I</span>
<span class="co"># kanamycin</span>
<span class="co"># 1 I</span>
<span class="co"># 2 R</span>
<span class="co"># 3 R</span>
<span class="co"># 4 R</span>
<span class="co"># 5 S</span>
<span class="co"># 6 S</span></code></pre></div>
<span class="co"># 1 S</span>
<span class="co"># 2 S</span>
<span class="co"># 3 I</span>
<span class="co"># 4 S</span>
<span class="co"># 5 I</span>
<span class="co"># 6 I</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">
<code class="sourceCode R"><span class="fu"><a href="../reference/mdro.html">mdro</a></span><span class="op">(</span><span class="va">my_TB_data</span>, guideline <span class="op">=</span> <span class="st">"TB"</span><span class="op">)</span></code></pre></div>
@ -359,10 +381,22 @@ Unique: 2</p>
<div class="sourceCode" id="cb10"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="va">my_TB_data</span><span class="op">$</span><span class="va">mdr</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/mdro.html">mdr_tb</a></span><span class="op">(</span><span class="va">my_TB_data</span><span class="op">)</span>
<span class="co"># ℹ No column found as input for `col_mo`, assuming all rows contain</span>
<span class="co"># Mycobacterium tuberculosis.</span></code></pre></div>
<span class="co"># Mycobacterium tuberculosis.</span>
<span class="co"># Auto-guessing columns suitable for analysis... OK.</span>
<span class="co"># ℹ Reliability would be improved if these antimicrobial results would be</span>
<span class="co"># available too: capreomycin (CAP), rifabutin (RIB) and rifapentine (RFP)</span>
<span class="co"># </span>
<span class="co"># Only results with 'R' are considered as resistance. Use `combine_SI = FALSE` to also consider 'I' as resistance.</span>
<span class="co"># </span>
<span class="co"># Determining multidrug-resistant organisms (MDRO), according to:</span>
<span class="co"># Guideline: Companion handbook to the WHO guidelines for the programmatic</span>
<span class="co"># management of drug-resistant tuberculosis</span>
<span class="co"># Author(s): WHO (World Health Organization)</span>
<span class="co"># Version: WHO/HTM/TB/2014.11, 2014</span>
<span class="co"># Source: https://www.who.int/tb/publications/pmdt_companionhandbook/en/</span></code></pre></div>
<p>Create a frequency table of the results:</p>
<div class="sourceCode" id="cb11"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/pkg/cleaner/man/freq.html">freq</a></span><span class="op">(</span><span class="va">my_TB_data</span><span class="op">$</span><span class="va">mdr</span><span class="op">)</span></code></pre></div>
<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/pkg/cleaner/man/freq.html" class="external-link">freq</a></span><span class="op">(</span><span class="va">my_TB_data</span><span class="op">$</span><span class="va">mdr</span><span class="op">)</span></code></pre></div>
<p><strong>Frequency table</strong></p>
<p>Class: factor &gt; ordered (numeric)<br>
Length: 5,000<br>
@ -382,40 +416,40 @@ Unique: 5</p>
<tr class="odd">
<td align="left">1</td>
<td align="left">Mono-resistant</td>
<td align="right">3187</td>
<td align="right">63.74%</td>
<td align="right">3187</td>
<td align="right">63.74%</td>
<td align="right">3175</td>
<td align="right">63.50%</td>
<td align="right">3175</td>
<td align="right">63.50%</td>
</tr>
<tr class="even">
<td align="left">2</td>
<td align="left">Negative</td>
<td align="right">1027</td>
<td align="right">20.54%</td>
<td align="right">4214</td>
<td align="right">84.28%</td>
<td align="right">1014</td>
<td align="right">20.28%</td>
<td align="right">4189</td>
<td align="right">83.78%</td>
</tr>
<tr class="odd">
<td align="left">3</td>
<td align="left">Multi-drug-resistant</td>
<td align="right">430</td>
<td align="right">8.60%</td>
<td align="right">4644</td>
<td align="right">92.88%</td>
<td align="right">445</td>
<td align="right">8.90%</td>
<td align="right">4634</td>
<td align="right">92.68%</td>
</tr>
<tr class="even">
<td align="left">4</td>
<td align="left">Poly-resistant</td>
<td align="right">245</td>
<td align="right">4.90%</td>
<td align="right">4889</td>
<td align="right">97.78%</td>
<td align="right">272</td>
<td align="right">5.44%</td>
<td align="right">4906</td>
<td align="right">98.12%</td>
</tr>
<tr class="odd">
<td align="left">5</td>
<td align="left">Extensively drug-resistant</td>
<td align="right">111</td>
<td align="right">2.22%</td>
<td align="right">94</td>
<td align="right">1.88%</td>
<td align="right">5000</td>
<td align="right">100.00%</td>
</tr>
@ -433,11 +467,13 @@ Unique: 5</p>
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<p>Developed by <a href="https://www.rug.nl/staff/m.s.berends/">Matthijs S. Berends</a>, <a href="https://www.rug.nl/staff/c.f.luz/">Christian F. Luz</a>, <a href="https://www.rug.nl/staff/a.w.friedrich/">Alexander W. Friedrich</a>, <a href="https://www.rug.nl/staff/b.sinha/">Bhanu N. M. Sinha</a>, <a href="https://www.rug.nl/staff/c.j.albers/">Casper J. Albers</a>, <a href="https://www.rug.nl/staff/c.glasner/">Corinna Glasner</a>.</p>
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</button>
<span class="navbar-brand">
<a class="navbar-link" href="../index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.7.1</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.7.1.9022</span>
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@ -47,14 +49,14 @@
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@ -63,77 +65,77 @@
<ul class="dropdown-menu" role="menu">
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@ -172,7 +174,7 @@
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@ -187,13 +189,13 @@
</header><script src="PCA_files/header-attrs-2.8/header-attrs.js"></script><div class="row">
</header><script src="PCA_files/header-attrs-2.9/header-attrs.js"></script><div class="row">
<div class="col-md-9 contents">
<div class="page-header toc-ignore">
<h1 data-toc-skip>How to conduct principal component analysis (PCA) for AMR</h1>
<small class="dont-index">Source: <a href="https://github.com/msberends/AMR/blob/master/vignettes/PCA.Rmd"><code>vignettes/PCA.Rmd</code></a></small>
<small class="dont-index">Source: <a href="https://github.com/msberends/AMR/blob/master/vignettes/PCA.Rmd" class="external-link"><code>vignettes/PCA.Rmd</code></a></small>
<div class="hidden name"><code>PCA.Rmd</code></div>
</div>
@ -203,16 +205,16 @@
<p><strong>NOTE: This page will be updated soon, as the pca() function is currently being developed.</strong></p>
<div id="introduction" class="section level1">
<h1 class="hasAnchor">
<a href="#introduction" class="anchor"></a>Introduction</h1>
<a href="#introduction" class="anchor" aria-hidden="true"></a>Introduction</h1>
</div>
<div id="transforming" class="section level1">
<h1 class="hasAnchor">
<a href="#transforming" class="anchor"></a>Transforming</h1>
<a href="#transforming" class="anchor" aria-hidden="true"></a>Transforming</h1>
<p>For PCA, we need to transform our AMR data first. This is what the <code>example_isolates</code> data set in this package looks like:</p>
<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://msberends.github.io/AMR/">AMR</a></span><span class="op">)</span>
<span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://dplyr.tidyverse.org">dplyr</a></span><span class="op">)</span>
<span class="fu"><a href="https://pillar.r-lib.org/reference/glimpse.html">glimpse</a></span><span class="op">(</span><span class="va">example_isolates</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://github.com/msberends/AMR" class="external-link">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="co"># Rows: 2,000</span>
<span class="co"># Columns: 49</span>
<span class="co"># $ date &lt;date&gt; 2002-01-02, 2002-01-03, 2002-01-07, 2002-01-07, 2002-…</span>
@ -267,13 +269,13 @@
<p>Now to transform this to a data set with only resistance percentages per taxonomic order and genus:</p>
<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="va">resistance_data</span> <span class="op">&lt;-</span> <span class="va">example_isolates</span> <span class="op">%&gt;%</span>
<span class="fu"><a href="https://dplyr.tidyverse.org/reference/group_by.html">group_by</a></span><span class="op">(</span>order <span class="op">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_order</a></span><span class="op">(</span><span class="va">mo</span><span class="op">)</span>, <span class="co"># group on anything, like order</span>
<span class="fu"><a href="https://dplyr.tidyverse.org/reference/group_by.html" class="external-link">group_by</a></span><span class="op">(</span>order <span class="op">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_order</a></span><span class="op">(</span><span class="va">mo</span><span class="op">)</span>, <span class="co"># group on anything, like order</span>
genus <span class="op">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_genus</a></span><span class="op">(</span><span class="va">mo</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="co"># and genus as we do here</span>
<span class="fu"><a href="https://dplyr.tidyverse.org/reference/summarise_all.html">summarise_if</a></span><span class="op">(</span><span class="va">is.rsi</span>, <span class="va">resistance</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="co"># then get resistance of all drugs</span>
<span class="fu"><a href="https://dplyr.tidyverse.org/reference/select.html">select</a></span><span class="op">(</span><span class="va">order</span>, <span class="va">genus</span>, <span class="va">AMC</span>, <span class="va">CXM</span>, <span class="va">CTX</span>,
<span class="fu"><a href="https://dplyr.tidyverse.org/reference/summarise_all.html" class="external-link">summarise_if</a></span><span class="op">(</span><span class="va">is.rsi</span>, <span class="va">resistance</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="co"># then get resistance of all drugs</span>
<span class="fu"><a href="https://dplyr.tidyverse.org/reference/select.html" class="external-link">select</a></span><span class="op">(</span><span class="va">order</span>, <span class="va">genus</span>, <span class="va">AMC</span>, <span class="va">CXM</span>, <span class="va">CTX</span>,
<span class="va">CAZ</span>, <span class="va">GEN</span>, <span class="va">TOB</span>, <span class="va">TMP</span>, <span class="va">SXT</span><span class="op">)</span> <span class="co"># and select only relevant columns</span>
<span class="fu"><a href="https://rdrr.io/r/utils/head.html">head</a></span><span class="op">(</span><span class="va">resistance_data</span><span class="op">)</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">resistance_data</span><span class="op">)</span>
<span class="co"># # A tibble: 6 x 10</span>
<span class="co"># # Groups: order [5]</span>
<span class="co"># order genus AMC CXM CTX CAZ GEN TOB TMP SXT</span>
@ -287,15 +289,15 @@
</div>
<div id="perform-principal-component-analysis" class="section level1">
<h1 class="hasAnchor">
<a href="#perform-principal-component-analysis" class="anchor"></a>Perform principal component analysis</h1>
<a href="#perform-principal-component-analysis" class="anchor" aria-hidden="true"></a>Perform principal component analysis</h1>
<p>The new <code><a href="../reference/pca.html">pca()</a></code> function will automatically filter on rows that contain numeric values in all selected variables, so we now only need to do:</p>
<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="va">pca_result</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/pca.html">pca</a></span><span class="op">(</span><span class="va">resistance_data</span><span class="op">)</span>
<span class="co"># ℹ Columns selected for PCA: "AMC", "CAZ", "CTX", "CXM", "GEN", "SXT", "TMP"</span>
<span class="co"># and "TOB". Total observations available: 7.</span></code></pre></div>
<p>The result can be reviewed with the good old <code><a href="https://rdrr.io/r/base/summary.html">summary()</a></code> function:</p>
<p>The result can be reviewed with the good old <code><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary()</a></code> function:</p>
<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/base/summary.html">summary</a></span><span class="op">(</span><span class="va">pca_result</span><span class="op">)</span>
<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">pca_result</span><span class="op">)</span>
<span class="co"># Groups (n=4, named as 'order'):</span>
<span class="co"># [1] "Caryophanales" "Enterobacterales" "Lactobacillales" "Pseudomonadales"</span>
<span class="co"># Importance of components:</span>
@ -305,13 +307,13 @@
<span class="co"># Cumulative Proportion 0.5799 0.9330 0.9801 0.99446 0.99988 1.00000 1.000e+00</span></code></pre></div>
<pre><code># Groups (n=4, named as 'order'):
# [1] "Caryophanales" "Enterobacterales" "Lactobacillales" "Pseudomonadales"</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">biplot()</a></code> function, to see which antimicrobial resistance per drug explain the difference per microorganism.</p>
<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 id="plotting-the-results" class="section level1">
<h1 class="hasAnchor">
<a href="#plotting-the-results" class="anchor"></a>Plotting the results</h1>
<a href="#plotting-the-results" class="anchor" aria-hidden="true"></a>Plotting the results</h1>
<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">biplot</a></span><span class="op">(</span><span class="va">pca_result</span><span class="op">)</span></code></pre></div>
<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>
<p>But we can’t see the explanation of the points. Perhaps this works better with our new <code><a href="../reference/ggplot_pca.html">ggplot_pca()</a></code> function, that automatically adds the right labels and even groups:</p>
<div class="sourceCode" id="cb7"><pre class="downlit sourceCode r">
@ -320,7 +322,7 @@
<p>You can also print an ellipse per group, and edit the appearance:</p>
<div class="sourceCode" id="cb8"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="fu"><a href="../reference/ggplot_pca.html">ggplot_pca</a></span><span class="op">(</span><span class="va">pca_result</span>, ellipse <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span> <span class="op">+</span>
<span class="fu">ggplot2</span><span class="fu">::</span><span class="fu"><a href="https://ggplot2.tidyverse.org/reference/labs.html">labs</a></span><span class="op">(</span>title <span class="op">=</span> <span class="st">"An AMR/PCA biplot!"</span><span class="op">)</span></code></pre></div>
<span class="fu">ggplot2</span><span class="fu">::</span><span class="fu"><a href="https://ggplot2.tidyverse.org/reference/labs.html" class="external-link">labs</a></span><span class="op">(</span>title <span class="op">=</span> <span class="st">"An AMR/PCA biplot!"</span><span class="op">)</span></code></pre></div>
<p><img src="PCA_files/figure-html/unnamed-chunk-7-1.png" width="750"></p>
</div>
</div>
@ -336,11 +338,13 @@
<footer><div class="copyright">
<p>Developed by <a href="https://www.rug.nl/staff/m.s.berends/">Matthijs S. Berends</a>, <a href="https://www.rug.nl/staff/c.f.luz/">Christian F. Luz</a>, <a href="https://www.rug.nl/staff/a.w.friedrich/">Alexander W. Friedrich</a>, <a href="https://www.rug.nl/staff/b.sinha/">Bhanu N. M. Sinha</a>, <a href="https://www.rug.nl/staff/c.j.albers/">Casper J. Albers</a>, <a href="https://www.rug.nl/staff/c.glasner/">Corinna Glasner</a>.</p>
<p></p>
<p>Developed by <a href="https://www.rug.nl/staff/m.s.berends/" class="external-link external-link">Matthijs S. Berends</a>, <a href="https://www.rug.nl/staff/c.f.luz/" class="external-link external-link">Christian F. Luz</a>, <a href="https://www.rug.nl/staff/a.w.friedrich/" class="external-link external-link">Alexander W. Friedrich</a>, <a href="https://www.rug.nl/staff/b.sinha/" class="external-link external-link">Bhanu N. M. Sinha</a>, <a href="https://www.rug.nl/staff/c.j.albers/" class="external-link external-link">Casper J. Albers</a>, <a href="https://www.rug.nl/staff/c.glasner/" class="external-link external-link">Corinna Glasner</a>.</p>
</div>
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<p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.6.1.</p>
<p></p>
<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link external-link">pkgdown</a> 1.6.1.9001.</p>
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@ -349,5 +353,7 @@
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@ -0,0 +1,12 @@
// Pandoc 2.9 adds attributes on both header and div. We remove the former (to
// be compatible with the behavior of Pandoc < 2.8).
document.addEventListener('DOMContentLoaded', function(e) {
var hs = document.querySelectorAll("div.section[class*='level'] > :first-child");
var i, h, a;
for (i = 0; i < hs.length; i++) {
h = hs[i];
if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6
a = h.attributes;
while (a.length > 0) h.removeAttribute(a[0].name);
}
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@ -27,6 +27,8 @@
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<header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
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@ -39,7 +41,7 @@
</button>
<span class="navbar-brand">
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<h1 data-toc-skip>How to import data from SPSS / SAS / Stata</h1>
<h4 class="author">Matthijs S. Berends</h4>
<h4 data-toc-skip class="author">Matthijs S. Berends</h4>
<h4 class="date">03 June 2021</h4>
<h4 data-toc-skip class="date">23 July 2021</h4>
<small class="dont-index">Source: <a href="https://github.com/msberends/AMR/blob/master/vignettes/SPSS.Rmd"><code>vignettes/SPSS.Rmd</code></a></small>
<small class="dont-index">Source: <a href="https://github.com/msberends/AMR/blob/master/vignettes/SPSS.Rmd" class="external-link"><code>vignettes/SPSS.Rmd</code></a></small>
<div class="hidden name"><code>SPSS.Rmd</code></div>
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<div id="spss-sas-stata" class="section level2">
<h2 class="hasAnchor">
<a href="#spss-sas-stata" class="anchor"></a>SPSS / SAS / Stata</h2>
<a href="#spss-sas-stata" class="anchor" aria-hidden="true"></a>SPSS / SAS / Stata</h2>
<p>SPSS (Statistical Package for the Social Sciences) is probably the most well-known software package for statistical analysis. SPSS is easier to learn than R, because in SPSS you only have to click a menu to run parts of your analysis. Because of its user-friendliness, it is taught at universities and particularly useful for students who are new to statistics. From my experience, I would guess that pretty much all (bio)medical students know it at the time they graduate. SAS and Stata are comparable statistical packages popular in big industries.</p>
</div>
<div id="compared-to-r" class="section level2">
<h2 class="hasAnchor">
<a href="#compared-to-r" class="anchor"></a>Compared to R</h2>
<a href="#compared-to-r" class="anchor" aria-hidden="true"></a>Compared to R</h2>
<p>As said, SPSS is easier to learn than R. But SPSS, SAS and Stata come with major downsides when comparing it with R:</p>
<ul>
<li>
<p><strong>R is highly modular.</strong></p>
<p>The <a href="https://cran.r-project.org/">official R network (CRAN)</a> features more than 16,000 packages at the time of writing, our <code>AMR</code> package being one of them. All these packages were peer-reviewed before publication. Aside from this official channel, there are also developers who choose not to submit to CRAN, but rather keep it on their own public repository, like GitHub. So there may even be a lot more than 14,000 packages out there.</p>
<p>The <a href="https://cran.r-project.org/" class="external-link">official R network (CRAN)</a> features more than 16,000 packages at the time of writing, our <code>AMR</code> package being one of them. All these packages were peer-reviewed before publication. Aside from this official channel, there are also developers who choose not to submit to CRAN, but rather keep it on their own public repository, like GitHub. So there may even be a lot more than 14,000 packages out there.</p>
<p>Bottom line is, you can really extend it yourself or ask somebody to do this for you. Take for example our <code>AMR</code> package. Among other things, it adds reliable reference data to R to help you with the data cleaning and analysis. SPSS, SAS and Stata will never know what a valid MIC value is or what the Gram stain of <em>E. coli</em> is. Or that all species of <em>Klebiella</em> are resistant to amoxicillin and that Floxapen<sup>®</sup> is a trade name of flucloxacillin. These facts and properties are often needed to clean existing data, which would be very inconvenient in a software package without reliable reference data. See below for a demonstration.</p>
</li>
<li>
@ -223,27 +225,27 @@
</li>
<li>
<p><strong>R can be easily automated.</strong></p>
<p>Over the last years, <a href="https://rmarkdown.rstudio.com/">R Markdown</a> has really made an interesting development. With R Markdown, you can very easily produce reports, whether the format has to be Word, PowerPoint, a website, a PDF document or just the raw data to Excel. It even allows the use of a reference file containing the layout style (e.g. fonts and colours) of your organisation. I use this a lot to generate weekly and monthly reports automatically. Just write the code once and enjoy the automatically updated reports at any interval you like.</p>
<p>For an even more professional environment, you could create <a href="https://shiny.rstudio.com/">Shiny apps</a>: live manipulation of data using a custom made website. The webdesign knowledge needed (JavaScript, CSS, HTML) is almost <em>zero</em>.</p>
<p>Over the last years, <a href="https://rmarkdown.rstudio.com/" class="external-link">R Markdown</a> has really made an interesting development. With R Markdown, you can very easily produce reports, whether the format has to be Word, PowerPoint, a website, a PDF document or just the raw data to Excel. It even allows the use of a reference file containing the layout style (e.g. fonts and colours) of your organisation. I use this a lot to generate weekly and monthly reports automatically. Just write the code once and enjoy the automatically updated reports at any interval you like.</p>
<p>For an even more professional environment, you could create <a href="https://shiny.rstudio.com/" class="external-link">Shiny apps</a>: live manipulation of data using a custom made website. The webdesign knowledge needed (JavaScript, CSS, HTML) is almost <em>zero</em>.</p>
</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">StackOverflow.com</a>, the largest online community for programmers. At the time of writing, <a href="https://stackoverflow.com/questions/tagged/r?sort=votes">404,559 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">411,199 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>
<p>And that’s not vice versa I’m afraid. You can import data from any source into R. For example from SPSS, SAS and Stata (<a href="https://haven.tidyverse.org/">link</a>), from Minitab, Epi Info and EpiData (<a href="https://cran.r-project.org/package=foreign">link</a>), from Excel (<a href="https://readxl.tidyverse.org/">link</a>), from flat files like CSV, TXT or TSV (<a href="https://readr.tidyverse.org/">link</a>), or directly from databases and datawarehouses from anywhere on the world (<a href="https://dbplyr.tidyverse.org/">link</a>). You can even scrape websites to download tables that are live on the internet (<a href="https://github.com/hadley/rvest">link</a>) or get the results of an API call and transform it into data in only one command (<a href="https://github.com/Rdatatable/data.table/wiki/Convenience-features-of-fread">link</a>).</p>
<p>And that’s not vice versa I’m afraid. You can import data from any source into R. For example from SPSS, SAS and Stata (<a href="https://haven.tidyverse.org/" class="external-link">link</a>), from Minitab, Epi Info and EpiData (<a href="https://cran.r-project.org/package=foreign" class="external-link">link</a>), from Excel (<a href="https://readxl.tidyverse.org/" class="external-link">link</a>), from flat files like CSV, TXT or TSV (<a href="https://readr.tidyverse.org/" class="external-link">link</a>), or directly from databases and datawarehouses from anywhere on the world (<a href="https://dbplyr.tidyverse.org/" class="external-link">link</a>). You can even scrape websites to download tables that are live on the internet (<a href="https://github.com/hadley/rvest" class="external-link">link</a>) or get the results of an API call and transform it into data in only one command (<a href="https://github.com/Rdatatable/data.table/wiki/Convenience-features-of-fread" class="external-link">link</a>).</p>
<p>And the best part - you can export from R to most data formats as well. So you can import an SPSS file, do your analysis neatly in R and export the resulting tables to Excel files for sharing.</p>
</li>
<li>
<p><strong>R is completely free and open-source.</strong></p>
<p>No strings attached. It was created and is being maintained by volunteers who believe that (data) science should be open and publicly available to everybody. SPSS, SAS and Stata are quite expensive. IBM SPSS Staticstics only comes with subscriptions nowadays, varying <a href="https://www.ibm.com/products/spss-statistics/pricing">between USD 1,300 and USD 8,500</a> per user <em>per year</em>. SAS Analytics Pro costs <a href="https://www.sas.com/store/products-solutions/sas-analytics-pro/prodPERSANL.html">around USD 10,000</a> per computer. Stata also has a business model with subscription fees, varying <a href="https://www.stata.com/order/new/bus/single-user-licenses/dl/">between USD 600 and USD 2,800</a> per computer per year, but lower prices come with a limitation of the number of variables you can work with. And still they do not offer the above benefits of R.</p>
<p>No strings attached. It was created and is being maintained by volunteers who believe that (data) science should be open and publicly available to everybody. SPSS, SAS and Stata are quite expensive. IBM SPSS Staticstics only comes with subscriptions nowadays, varying <a href="https://www.ibm.com/products/spss-statistics/pricing" class="external-link">between USD 1,300 and USD 8,500</a> per user <em>per year</em>. SAS Analytics Pro costs <a href="https://www.sas.com/store/products-solutions/sas-analytics-pro/prodPERSANL.html" class="external-link">around USD 10,000</a> per computer. Stata also has a business model with subscription fees, varying <a href="https://www.stata.com/order/new/bus/single-user-licenses/dl/" class="external-link">between USD 600 and USD 2,800</a> per computer per year, but lower prices come with a limitation of the number of variables you can work with. And still they do not offer the above benefits of R.</p>
<p>If you are working at a midsized or small company, you can save it tens of thousands of dollars by using R instead of e.g. SPSS - gaining even more functions and flexibility. And all R enthousiasts can do as much PR as they want (like I do here), because nobody is officially associated with or affiliated by R. It is really free.</p>
</li>
<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/">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/">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>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>
</li>
</ul>
<p>To demonstrate the first point:</p>
@ -261,7 +263,7 @@
<span class="co"># [1] "Gram-negative"</span>
<span class="co"># Klebsiella is intrinsic resistant to amoxicillin, according to EUCAST:</span>
<span class="va">klebsiella_test</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html">data.frame</a></span><span class="op">(</span>mo <span class="op">=</span> <span class="st">"klebsiella"</span>,
<span class="va">klebsiella_test</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html" class="external-link">data.frame</a></span><span class="op">(</span>mo <span class="op">=</span> <span class="st">"klebsiella"</span>,
amox <span class="op">=</span> <span class="st">"S"</span>,
stringsAsFactors <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>
<span class="va">klebsiella_test</span> <span class="co"># (our original data)</span>
@ -283,17 +285,17 @@
</div>
<div id="import-data-from-spsssasstata" class="section level2">
<h2 class="hasAnchor">
<a href="#import-data-from-spsssasstata" class="anchor"></a>Import data from SPSS/SAS/Stata</h2>
<a href="#import-data-from-spsssasstata" class="anchor" aria-hidden="true"></a>Import data from SPSS/SAS/Stata</h2>
<div id="rstudio" class="section level3">
<h3 class="hasAnchor">
<a href="#rstudio" class="anchor"></a>RStudio</h3>
<p>To work with R, probably the best option is to use <a href="https://www.rstudio.com/products/rstudio/">RStudio</a>. It is an open-source and free desktop environment which not only allows you to run R code, but also supports project management, version management, package management and convenient import menus to work with other data sources. You can also install <a href="https://www.rstudio.com/products/rstudio/">RStudio Server</a> on a private or corporate server, which brings nothing less than the complete RStudio software to you as a website (at home or at work).</p>
<a href="#rstudio" class="anchor" aria-hidden="true"></a>RStudio</h3>
<p>To work with R, probably the best option is to use <a href="https://www.rstudio.com/products/rstudio/" class="external-link">RStudio</a>. It is an open-source and free desktop environment which not only allows you to run R code, but also supports project management, version management, package management and convenient import menus to work with other data sources. You can also install <a href="https://www.rstudio.com/products/rstudio/" class="external-link">RStudio Server</a> on a private or corporate server, which brings nothing less than the complete RStudio software to you as a website (at home or at work).</p>
<p>To import a data file, just click <em>Import Dataset</em> in the Environment tab:</p>
<p><img src="https://github.com/msberends/AMR/raw/master/docs/import1.png"></p>
<p>If additional packages are needed, RStudio will ask you if they should be installed on beforehand.</p>
<p>In the the window that opens, you can define all options (parameters) that should be used for import and you’re ready to go:</p>
<p><img src="https://github.com/msberends/AMR/raw/master/docs/import2.png"></p>
<p>If you want named variables to be imported as factors so it resembles SPSS more, use <code><a href="https://haven.tidyverse.org/reference/as_factor.html">as_factor()</a></code>.</p>
<p>If you want named variables to be imported as factors so it resembles SPSS more, use <code><a href="https://haven.tidyverse.org/reference/as_factor.html" class="external-link">as_factor()</a></code>.</p>
<p>The difference is this:</p>
<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="va">SPSS_data</span>
@ -330,70 +332,70 @@
</div>
<div id="base-r" class="section level3">
<h3 class="hasAnchor">
<a href="#base-r" class="anchor"></a>Base R</h3>
<p>To import data from SPSS, SAS or Stata, you can use the <a href="https://haven.tidyverse.org/">great <code>haven</code> package</a> yourself:</p>
<a href="#base-r" class="anchor" aria-hidden="true"></a>Base R</h3>
<p>To import data from SPSS, SAS or Stata, you can use the <a href="https://haven.tidyverse.org/" class="external-link">great <code>haven</code> package</a> yourself:</p>
<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="co"># download and install the latest version:</span>
<span class="fu"><a href="https://rdrr.io/r/utils/install.packages.html">install.packages</a></span><span class="op">(</span><span class="st">"haven"</span><span class="op">)</span>
<span class="fu"><a href="https://rdrr.io/r/utils/install.packages.html" class="external-link">install.packages</a></span><span class="op">(</span><span class="st">"haven"</span><span class="op">)</span>
<span class="co"># load the package you just installed:</span>
<span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://haven.tidyverse.org">haven</a></span><span class="op">)</span> </code></pre></div>
<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://haven.tidyverse.org" class="external-link">haven</a></span><span class="op">)</span> </code></pre></div>
<p>You can now import files as follows:</p>
<div id="spss" class="section level4">
<h4 class="hasAnchor">
<a href="#spss" class="anchor"></a>SPSS</h4>
<a href="#spss" class="anchor" aria-hidden="true"></a>SPSS</h4>
<p>To read files from SPSS into R:</p>
<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="co"># read any SPSS file based on file extension (best way):</span>
<span class="fu"><a href="https://haven.tidyverse.org/reference/read_spss.html">read_spss</a></span><span class="op">(</span>file <span class="op">=</span> <span class="st">"path/to/file"</span><span class="op">)</span>
<span class="fu"><a href="https://haven.tidyverse.org/reference/read_spss.html" class="external-link">read_spss</a></span><span class="op">(</span>file <span class="op">=</span> <span class="st">"path/to/file"</span><span class="op">)</span>
<span class="co"># read .sav or .zsav file:</span>
<span class="fu"><a href="https://haven.tidyverse.org/reference/read_spss.html">read_sav</a></span><span class="op">(</span>file <span class="op">=</span> <span class="st">"path/to/file"</span><span class="op">)</span>
<span class="fu"><a href="https://haven.tidyverse.org/reference/read_spss.html" class="external-link">read_sav</a></span><span class="op">(</span>file <span class="op">=</span> <span class="st">"path/to/file"</span><span class="op">)</span>
<span class="co"># read .por file:</span>
<span class="fu"><a href="https://haven.tidyverse.org/reference/read_spss.html">read_por</a></span><span class="op">(</span>file <span class="op">=</span> <span class="st">"path/to/file"</span><span class="op">)</span></code></pre></div>
<p>Do not forget about <code><a href="https://haven.tidyverse.org/reference/as_factor.html">as_factor()</a></code>, as mentioned above.</p>
<span class="fu"><a href="https://haven.tidyverse.org/reference/read_spss.html" class="external-link">read_por</a></span><span class="op">(</span>file <span class="op">=</span> <span class="st">"path/to/file"</span><span class="op">)</span></code></pre></div>
<p>Do not forget about <code><a href="https://haven.tidyverse.org/reference/as_factor.html" class="external-link">as_factor()</a></code>, as mentioned above.</p>
<p>To export your R objects to the SPSS file format:</p>
<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="co"># save as .sav file:</span>
<span class="fu"><a href="https://haven.tidyverse.org/reference/read_spss.html">write_sav</a></span><span class="op">(</span>data <span class="op">=</span> <span class="va">yourdata</span>, path <span class="op">=</span> <span class="st">"path/to/file"</span><span class="op">)</span>
<span class="fu"><a href="https://haven.tidyverse.org/reference/read_spss.html" class="external-link">write_sav</a></span><span class="op">(</span>data <span class="op">=</span> <span class="va">yourdata</span>, path <span class="op">=</span> <span class="st">"path/to/file"</span><span class="op">)</span>
<span class="co"># save as compressed .zsav file:</span>
<span class="fu"><a href="https://haven.tidyverse.org/reference/read_spss.html">write_sav</a></span><span class="op">(</span>data <span class="op">=</span> <span class="va">yourdata</span>, path <span class="op">=</span> <span class="st">"path/to/file"</span>, compress <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></code></pre></div>
<span class="fu"><a href="https://haven.tidyverse.org/reference/read_spss.html" class="external-link">write_sav</a></span><span class="op">(</span>data <span class="op">=</span> <span class="va">yourdata</span>, path <span class="op">=</span> <span class="st">"path/to/file"</span>, compress <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></code></pre></div>
</div>
<div id="sas" class="section level4">
<h4 class="hasAnchor">
<a href="#sas" class="anchor"></a>SAS</h4>
<a href="#sas" class="anchor" aria-hidden="true"></a>SAS</h4>
<p>To read files from SAS into R:</p>
<div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="co"># read .sas7bdat + .sas7bcat files:</span>
<span class="fu"><a href="https://haven.tidyverse.org/reference/read_sas.html">read_sas</a></span><span class="op">(</span>data_file <span class="op">=</span> <span class="st">"path/to/file"</span>, catalog_file <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span>
<span class="fu"><a href="https://haven.tidyverse.org/reference/read_sas.html" class="external-link">read_sas</a></span><span class="op">(</span>data_file <span class="op">=</span> <span class="st">"path/to/file"</span>, catalog_file <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span>
<span class="co"># read SAS transport files (version 5 and version 8):</span>
<span class="fu"><a href="https://haven.tidyverse.org/reference/read_xpt.html">read_xpt</a></span><span class="op">(</span>file <span class="op">=</span> <span class="st">"path/to/file"</span><span class="op">)</span></code></pre></div>
<span class="fu"><a href="https://haven.tidyverse.org/reference/read_xpt.html" class="external-link">read_xpt</a></span><span class="op">(</span>file <span class="op">=</span> <span class="st">"path/to/file"</span><span class="op">)</span></code></pre></div>
<p>To export your R objects to the SAS file format:</p>
<div class="sourceCode" id="cb7"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="co"># save as regular SAS file:</span>
<span class="fu"><a href="https://haven.tidyverse.org/reference/read_sas.html">write_sas</a></span><span class="op">(</span>data <span class="op">=</span> <span class="va">yourdata</span>, path <span class="op">=</span> <span class="st">"path/to/file"</span><span class="op">)</span>
<span class="fu"><a href="https://haven.tidyverse.org/reference/read_sas.html" class="external-link">write_sas</a></span><span class="op">(</span>data <span class="op">=</span> <span class="va">yourdata</span>, path <span class="op">=</span> <span class="st">"path/to/file"</span><span class="op">)</span>
<span class="co"># the SAS transport format is an open format </span>
<span class="co"># (required for submission of the data to the FDA)</span>
<span class="fu"><a href="https://haven.tidyverse.org/reference/read_xpt.html">write_xpt</a></span><span class="op">(</span>data <span class="op">=</span> <span class="va">yourdata</span>, path <span class="op">=</span> <span class="st">"path/to/file"</span>, version <span class="op">=</span> <span class="fl">8</span><span class="op">)</span></code></pre></div>
<span class="fu"><a href="https://haven.tidyverse.org/reference/read_xpt.html" class="external-link">write_xpt</a></span><span class="op">(</span>data <span class="op">=</span> <span class="va">yourdata</span>, path <span class="op">=</span> <span class="st">"path/to/file"</span>, version <span class="op">=</span> <span class="fl">8</span><span class="op">)</span></code></pre></div>
</div>
<div id="stata" class="section level4">
<h4 class="hasAnchor">
<a href="#stata" class="anchor"></a>Stata</h4>
<a href="#stata" class="anchor" aria-hidden="true"></a>Stata</h4>
<p>To read files from Stata into R:</p>
<div class="sourceCode" id="cb8"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="co"># read .dta file:</span>
<span class="fu"><a href="https://haven.tidyverse.org/reference/read_dta.html">read_stata</a></span><span class="op">(</span>file <span class="op">=</span> <span class="st">"/path/to/file"</span><span class="op">)</span>
<span class="fu"><a href="https://haven.tidyverse.org/reference/read_dta.html" class="external-link">read_stata</a></span><span class="op">(</span>file <span class="op">=</span> <span class="st">"/path/to/file"</span><span class="op">)</span>
<span class="co"># works exactly the same:</span>
<span class="fu"><a href="https://haven.tidyverse.org/reference/read_dta.html">read_dta</a></span><span class="op">(</span>file <span class="op">=</span> <span class="st">"/path/to/file"</span><span class="op">)</span></code></pre></div>
<span class="fu"><a href="https://haven.tidyverse.org/reference/read_dta.html" class="external-link">read_dta</a></span><span class="op">(</span>file <span class="op">=</span> <span class="st">"/path/to/file"</span><span class="op">)</span></code></pre></div>
<p>To export your R objects to the Stata file format:</p>
<div class="sourceCode" id="cb9"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="co"># save as .dta file, Stata version 14:</span>
<span class="co"># (supports Stata v8 until v15 at the time of writing)</span>
<span class="fu"><a href="https://haven.tidyverse.org/reference/read_dta.html">write_dta</a></span><span class="op">(</span>data <span class="op">=</span> <span class="va">yourdata</span>, path <span class="op">=</span> <span class="st">"/path/to/file"</span>, version <span class="op">=</span> <span class="fl">14</span><span class="op">)</span></code></pre></div>
<span class="fu"><a href="https://haven.tidyverse.org/reference/read_dta.html" class="external-link">write_dta</a></span><span class="op">(</span>data <span class="op">=</span> <span class="va">yourdata</span>, path <span class="op">=</span> <span class="st">"/path/to/file"</span>, version <span class="op">=</span> <span class="fl">14</span><span class="op">)</span></code></pre></div>
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@ -410,11 +412,13 @@
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<p>Developed by <a href="https://www.rug.nl/staff/m.s.berends/">Matthijs S. Berends</a>, <a href="https://www.rug.nl/staff/c.f.luz/">Christian F. Luz</a>, <a href="https://www.rug.nl/staff/a.w.friedrich/">Alexander W. Friedrich</a>, <a href="https://www.rug.nl/staff/b.sinha/">Bhanu N. M. Sinha</a>, <a href="https://www.rug.nl/staff/c.j.albers/">Casper J. Albers</a>, <a href="https://www.rug.nl/staff/c.glasner/">Corinna Glasner</a>.</p>
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@ -423,5 +427,7 @@
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@ -0,0 +1,12 @@
// Pandoc 2.9 adds attributes on both header and div. We remove the former (to
// be compatible with the behavior of Pandoc < 2.8).
document.addEventListener('DOMContentLoaded', function(e) {
var hs = document.querySelectorAll("div.section[class*='level'] > :first-child");
var i, h, a;
for (i = 0; i < hs.length; i++) {
h = hs[i];
if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6
a = h.attributes;
while (a.length > 0) h.removeAttribute(a[0].name);
}
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86
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@ -27,6 +27,8 @@
<![endif]-->
</head>
<body data-spy="scroll" data-target="#toc">
<div class="container template-article">
<header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
<div class="container">
@ -39,7 +41,7 @@
</button>
<span class="navbar-brand">
<a class="navbar-link" href="../index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.7.1</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.7.1.9022</span>
</span>
</div>
@ -47,14 +49,14 @@
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@ -63,77 +65,77 @@
<ul class="dropdown-menu" role="menu">
<li>
<a href="../articles/AMR.html">
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Conduct AMR analysis
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Predict antimicrobial resistance
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Conduct principal component analysis for AMR
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Determine multi-drug resistance (MDR)
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Work with WHONET data
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@ -142,14 +144,14 @@
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@ -164,7 +166,7 @@
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@ -172,7 +174,7 @@
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@ -187,13 +189,13 @@
</header><script src="WHONET_files/header-attrs-2.8/header-attrs.js"></script><div class="row">
</header><script src="WHONET_files/header-attrs-2.9/header-attrs.js"></script><div class="row">
<div class="col-md-9 contents">
<div class="page-header toc-ignore">
<h1 data-toc-skip>How to work with WHONET data</h1>
<small class="dont-index">Source: <a href="https://github.com/msberends/AMR/blob/master/vignettes/WHONET.Rmd"><code>vignettes/WHONET.Rmd</code></a></small>
<small class="dont-index">Source: <a href="https://github.com/msberends/AMR/blob/master/vignettes/WHONET.Rmd" class="external-link"><code>vignettes/WHONET.Rmd</code></a></small>
<div class="hidden name"><code>WHONET.Rmd</code></div>
</div>
@ -202,23 +204,23 @@
<div id="import-of-data" class="section level3">
<h3 class="hasAnchor">
<a href="#import-of-data" class="anchor"></a>Import of data</h3>
<p>This tutorial assumes you already imported the WHONET data with e.g. the <a href="https://readxl.tidyverse.org/"><code>readxl</code> package</a>. In RStudio, this can be done using the menu button ‘Import Dataset’ in the tab ‘Environment’. Choose the option ‘From Excel’ and select your exported file. Make sure date fields are imported correctly.</p>
<a href="#import-of-data" class="anchor" aria-hidden="true"></a>Import of data</h3>
<p>This tutorial assumes you already imported the WHONET data with e.g. the <a href="https://readxl.tidyverse.org/" class="external-link"><code>readxl</code> package</a>. In RStudio, this can be done using the menu button ‘Import Dataset’ in the tab ‘Environment’. Choose the option ‘From Excel’ and select your exported file. Make sure date fields are imported correctly.</p>
<p>An example syntax could look like this:</p>
<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://readxl.tidyverse.org">readxl</a></span><span class="op">)</span>
<span class="va">data</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://readxl.tidyverse.org/reference/read_excel.html">read_excel</a></span><span class="op">(</span>path <span class="op">=</span> <span class="st">"path/to/your/file.xlsx"</span><span class="op">)</span></code></pre></div>
<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://readxl.tidyverse.org" class="external-link">readxl</a></span><span class="op">)</span>
<span class="va">data</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://readxl.tidyverse.org/reference/read_excel.html" class="external-link">read_excel</a></span><span class="op">(</span>path <span class="op">=</span> <span class="st">"path/to/your/file.xlsx"</span><span class="op">)</span></code></pre></div>
<p>This package comes with an <a href="https://msberends.github.io/AMR/reference/WHONET.html">example data set <code>WHONET</code></a>. We will use it for this analysis.</p>
</div>
<div id="preparation" class="section level3">
<h3 class="hasAnchor">
<a href="#preparation" class="anchor"></a>Preparation</h3>
<p>First, load the relevant packages if you did not yet did this. I use the tidyverse for all of my analyses. All of them. If you don’t know it yet, I suggest you read about it on their website: <a href="https://www.tidyverse.org/" class="uri">https://www.tidyverse.org/</a>.</p>
<a href="#preparation" class="anchor" aria-hidden="true"></a>Preparation</h3>
<p>First, load the relevant packages if you did not yet did this. I use the tidyverse for all of my analyses. All of them. If you don’t know it yet, I suggest you read about it on their website: <a href="https://www.tidyverse.org/" class="external-link uri">https://www.tidyverse.org/</a>.</p>
<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://dplyr.tidyverse.org">dplyr</a></span><span class="op">)</span> <span class="co"># part of tidyverse</span>
<span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://ggplot2.tidyverse.org">ggplot2</a></span><span class="op">)</span> <span class="co"># part of tidyverse</span>
<span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://msberends.github.io/AMR/">AMR</a></span><span class="op">)</span> <span class="co"># this package</span>
<span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://github.com/msberends/cleaner">cleaner</a></span><span class="op">)</span> <span class="co"># to create frequency tables</span></code></pre></div>
<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://github.com/msberends/AMR" class="external-link">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>
<li>Microorganisms should be transformed to our own microorganism codes (called an <code>mo</code>) using <a href="https://msberends.github.io/AMR/reference/catalogue_of_life">our Catalogue of Life reference data set</a>, which contains all ~70,000 microorganisms from the taxonomic kingdoms Bacteria, Fungi and Protozoa. We do the tranformation with <code><a href="../reference/as.mo.html">as.mo()</a></code>. This function also recognises almost all WHONET abbreviations of microorganisms.</li>
@ -228,15 +230,15 @@
<code class="sourceCode R"><span class="co"># transform variables</span>
<span class="va">data</span> <span class="op">&lt;-</span> <span class="va">WHONET</span> <span class="op">%&gt;%</span>
<span class="co"># get microbial ID based on given organism</span>
<span class="fu"><a href="https://dplyr.tidyverse.org/reference/mutate.html">mutate</a></span><span class="op">(</span>mo <span class="op">=</span> <span class="fu"><a href="../reference/as.mo.html">as.mo</a></span><span class="op">(</span><span class="va">Organism</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span>
<span class="fu"><a href="https://dplyr.tidyverse.org/reference/mutate.html" class="external-link">mutate</a></span><span class="op">(</span>mo <span class="op">=</span> <span class="fu"><a href="../reference/as.mo.html">as.mo</a></span><span class="op">(</span><span class="va">Organism</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span>
<span class="co"># transform everything from "AMP_ND10" to "CIP_EE" to the new `rsi` class</span>
<span class="fu"><a href="https://dplyr.tidyverse.org/reference/mutate_all.html">mutate_at</a></span><span class="op">(</span><span class="fu"><a href="https://dplyr.tidyverse.org/reference/vars.html">vars</a></span><span class="op">(</span><span class="va">AMP_ND10</span><span class="op">:</span><span class="va">CIP_EE</span><span class="op">)</span>, <span class="va">as.rsi</span><span class="op">)</span></code></pre></div>
<span class="fu"><a href="https://dplyr.tidyverse.org/reference/mutate_all.html" class="external-link">mutate_at</a></span><span class="op">(</span><span class="fu"><a href="https://dplyr.tidyverse.org/reference/vars.html" class="external-link">vars</a></span><span class="op">(</span><span class="va">AMP_ND10</span><span class="op">:</span><span class="va">CIP_EE</span><span class="op">)</span>, <span class="va">as.rsi</span><span class="op">)</span></code></pre></div>
<p>No errors or warnings, so all values are transformed succesfully.</p>
<p>We also created a package dedicated to data cleaning and checking, called the <code>cleaner</code> package. Its <code><a href="https://rdrr.io/pkg/cleaner/man/freq.html">freq()</a></code> function can be used to create frequency tables.</p>
<p>We also created a package dedicated to data cleaning and checking, called the <code>cleaner</code> package. Its <code><a href="https://rdrr.io/pkg/cleaner/man/freq.html" class="external-link">freq()</a></code> function can be used to create frequency tables.</p>
<p>So let’s check our data, with a couple of frequency tables:</p>
<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="co"># our newly created `mo` variable, put in the mo_name() function</span>
<span class="va">data</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/pkg/cleaner/man/freq.html">freq</a></span><span class="op">(</span><span class="fu"><a href="../reference/mo_property.html">mo_name</a></span><span class="op">(</span><span class="va">mo</span><span class="op">)</span>, nmax <span class="op">=</span> <span class="fl">10</span><span class="op">)</span></code></pre></div>
<span class="va">data</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/pkg/cleaner/man/freq.html" class="external-link">freq</a></span><span class="op">(</span><span class="fu"><a href="../reference/mo_property.html">mo_name</a></span><span class="op">(</span><span class="va">mo</span><span class="op">)</span>, nmax <span class="op">=</span> <span class="fl">10</span><span class="op">)</span></code></pre></div>
<p><strong>Frequency table</strong></p>
<p>Class: character<br>
Length: 500<br>
@ -340,7 +342,7 @@ Longest: 40</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>
<span class="va">data</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/pkg/cleaner/man/freq.html">freq</a></span><span class="op">(</span><span class="va">AMC_ND2</span><span class="op">)</span></code></pre></div>
<span class="va">data</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/pkg/cleaner/man/freq.html" class="external-link">freq</a></span><span class="op">(</span><span class="va">AMC_ND2</span><span class="op">)</span></code></pre></div>
<p><strong>Frequency table</strong></p>
<p>Class: factor &gt; ordered &gt; rsi (numeric)<br>
Length: 500<br>
@ -389,12 +391,12 @@ Drug group: Beta-lactams/penicillins<br>
</div>
<div id="a-first-glimpse-at-results" class="section level3">
<h3 class="hasAnchor">
<a href="#a-first-glimpse-at-results" class="anchor"></a>A first glimpse at results</h3>
<a href="#a-first-glimpse-at-results" class="anchor" aria-hidden="true"></a>A first glimpse at results</h3>
<p>An easy <code>ggplot</code> will already give a lot of information, using the included <code><a href="../reference/ggplot_rsi.html">ggplot_rsi()</a></code> function:</p>
<div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="va">data</span> <span class="op">%&gt;%</span>
<span class="fu"><a href="https://dplyr.tidyverse.org/reference/group_by.html">group_by</a></span><span class="op">(</span><span class="va">Country</span><span class="op">)</span> <span class="op">%&gt;%</span>
<span class="fu"><a href="https://dplyr.tidyverse.org/reference/select.html">select</a></span><span class="op">(</span><span class="va">Country</span>, <span class="va">AMP_ND2</span>, <span class="va">AMC_ED20</span>, <span class="va">CAZ_ED10</span>, <span class="va">CIP_ED5</span><span class="op">)</span> <span class="op">%&gt;%</span>
<span class="fu"><a href="https://dplyr.tidyverse.org/reference/group_by.html" class="external-link">group_by</a></span><span class="op">(</span><span class="va">Country</span><span class="op">)</span> <span class="op">%&gt;%</span>
<span class="fu"><a href="https://dplyr.tidyverse.org/reference/select.html" class="external-link">select</a></span><span class="op">(</span><span class="va">Country</span>, <span class="va">AMP_ND2</span>, <span class="va">AMC_ED20</span>, <span class="va">CAZ_ED10</span>, <span class="va">CIP_ED5</span><span class="op">)</span> <span class="op">%&gt;%</span>
<span class="fu"><a href="../reference/ggplot_rsi.html">ggplot_rsi</a></span><span class="op">(</span>translate_ab <span class="op">=</span> <span class="st">'ab'</span>, facet <span class="op">=</span> <span class="st">"Country"</span>, datalabels <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></code></pre></div>
<p><img src="WHONET_files/figure-html/unnamed-chunk-7-1.png" width="720"></p>
</div>
@ -409,11 +411,13 @@ Drug group: Beta-lactams/penicillins<br>
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@ -422,5 +426,7 @@ Drug group: Beta-lactams/penicillins<br>
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@ -27,6 +27,8 @@
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@ -39,7 +41,7 @@
</button>
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<a class="navbar-link" href="../index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.7.1</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.7.1.9022</span>
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@ -47,14 +49,14 @@
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</header><script src="benchmarks_files/header-attrs-2.8/header-attrs.js"></script><div class="row">
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<h1 data-toc-skip>Benchmarks</h1>
<small class="dont-index">Source: <a href="https://github.com/msberends/AMR/blob/master/vignettes/benchmarks.Rmd"><code>vignettes/benchmarks.Rmd</code></a></small>
<small class="dont-index">Source: <a href="https://github.com/msberends/AMR/blob/master/vignettes/benchmarks.Rmd" class="external-link"><code>vignettes/benchmarks.Rmd</code></a></small>
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<p>One of the most important features of this package is the complete microbial taxonomic database, supplied by the <a href="http://www.catalogueoflife.org">Catalogue of Life</a> (CoL) and the <a href="https://lpsn.dsmz.de">List of Prokaryotic names with Standing in Nomenclature</a> (LPSN). We created a function <code><a href="../reference/as.mo.html">as.mo()</a></code> that transforms any user input value to a valid microbial ID by using intelligent rules combined with the microbial taxonomy.</p>
<p>Using the <code>microbenchmark</code> package, we can review the calculation performance of this function. Its function <code><a href="https://rdrr.io/pkg/microbenchmark/man/microbenchmark.html">microbenchmark()</a></code> runs different input expressions independently of each other and measures their time-to-result.</p>
<p>One of the most important features of this package is the complete microbial taxonomic database, supplied by the <a href="http://www.catalogueoflife.org" class="external-link">Catalogue of Life</a> (CoL) and the <a href="https://lpsn.dsmz.de" class="external-link">List of Prokaryotic names with Standing in Nomenclature</a> (LPSN). We created a function <code><a href="../reference/as.mo.html">as.mo()</a></code> that transforms any user input value to a valid microbial ID by using intelligent rules combined with the microbial taxonomy.</p>
<p>Using the <code>microbenchmark</code> package, we can review the calculation performance of this function. Its function <code><a href="https://rdrr.io/pkg/microbenchmark/man/microbenchmark.html" 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">library</a></span><span class="op">(</span><span class="va"><a href="https://github.com/joshuaulrich/microbenchmark/">microbenchmark</a></span><span class="op">)</span>
<span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://msberends.github.io/AMR/">AMR</a></span><span class="op">)</span>
<span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://dplyr.tidyverse.org">dplyr</a></span><span class="op">)</span></code></pre></div>
<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://github.com/msberends/AMR" class="external-link">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>
<p>But the calculation time differs a lot:</p>
<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="va">S.aureus</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/microbenchmark/man/microbenchmark.html">microbenchmark</a></span><span class="op">(</span>
<code class="sourceCode R"><span class="va">S.aureus</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/as.mo.html">as.mo</a></span><span class="op">(</span><span class="st">"sau"</span><span class="op">)</span>, <span class="co"># WHONET code</span>
<span class="fu"><a href="../reference/as.mo.html">as.mo</a></span><span class="op">(</span><span class="st">"stau"</span><span class="op">)</span>,
<span class="fu"><a href="../reference/as.mo.html">as.mo</a></span><span class="op">(</span><span class="st">"STAU"</span><span class="op">)</span>,
@ -224,80 +226,80 @@
<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">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://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="co"># Unit: milliseconds</span>
<span class="co"># expr min lq mean median uq max neval</span>
<span class="co"># as.mo("sau") 13.0 13.0 16.0 15.0 16.0 44 25</span>
<span class="co"># as.mo("stau") 55.0 58.0 75.0 62.0 94.0 110 25</span>
<span class="co"># as.mo("STAU") 55.0 59.0 77.0 89.0 94.0 100 25</span>
<span class="co"># as.mo("staaur") 11.0 13.0 20.0 14.0 16.0 48 25</span>
<span class="co"># as.mo("STAAUR") 11.0 13.0 17.0 15.0 16.0 49 25</span>
<span class="co"># as.mo("S. aureus") 26.0 30.0 41.0 32.0 60.0 68 25</span>
<span class="co"># as.mo("S aureus") 27.0 29.0 44.0 32.0 58.0 160 25</span>
<span class="co"># as.mo("Staphylococcus aureus") 3.3 3.9 5.5 4.2 4.7 37 25</span>
<span class="co"># as.mo("Staphylococcus aureus (MRSA)") 250.0 260.0 280.0 280.0 290.0 320 25</span>
<span class="co"># as.mo("Sthafilokkockus aaureuz") 170.0 200.0 210.0 200.0 220.0 250 25</span>
<span class="co"># as.mo("MRSA") 12.0 14.0 21.0 15.0 17.0 56 25</span>
<span class="co"># as.mo("VISA") 20.0 23.0 33.0 25.0 51.0 59 25</span></code></pre></div>
<span class="co"># as.mo("sau") 12.0 13.0 19.0 14 16.0 49 25</span>
<span class="co"># as.mo("stau") 55.0 58.0 80.0 91 96.0 110 25</span>
<span class="co"># as.mo("STAU") 57.0 63.0 79.0 66 96.0 110 25</span>
<span class="co"># as.mo("staaur") 13.0 13.0 19.0 14 16.0 48 25</span>
<span class="co"># as.mo("STAAUR") 13.0 13.0 22.0 15 17.0 48 25</span>
<span class="co"># as.mo("S. aureus") 27.0 30.0 42.0 33 62.0 67 25</span>
<span class="co"># as.mo("S aureus") 28.0 30.0 37.0 32 34.0 64 25</span>
<span class="co"># as.mo("Staphylococcus aureus") 3.5 3.9 5.5 4 4.4 38 25</span>
<span class="co"># as.mo("Staphylococcus aureus (MRSA)") 260.0 270.0 280.0 270 290.0 370 25</span>
<span class="co"># as.mo("Sthafilokkockus aaureuz") 170.0 200.0 210.0 210 230.0 250 25</span>
<span class="co"># as.mo("MRSA") 13.0 14.0 20.0 15 15.0 51 25</span>
<span class="co"># as.mo("VISA") 22.0 23.0 33.0 25 27.0 64 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 200 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 id="repetitive-results" class="section level3">
<h3 class="hasAnchor">
<a href="#repetitive-results" class="anchor"></a>Repetitive results</h3>
<a href="#repetitive-results" class="anchor" aria-hidden="true"></a>Repetitive results</h3>
<p>Repetitive results are values that are present more than once in a vector. Unique values will only be calculated once by <code><a href="../reference/as.mo.html">as.mo()</a></code>. So running <code><a href="../reference/as.mo.html">as.mo(c("E. coli", "E. coli"))</a></code> will check the value <code>"E. coli"</code> only once.</p>
<p>To prove this, we will use <code><a href="../reference/mo_property.html">mo_name()</a></code> for testing - a helper function that returns the full microbial name (genus, species and possibly subspecies) which uses <code><a href="../reference/as.mo.html">as.mo()</a></code> internally.</p>
<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="co"># start with the example_isolates data set</span>
<span class="va">x</span> <span class="op">&lt;-</span> <span class="va">example_isolates</span> <span class="op">%&gt;%</span>
<span class="co"># take all MO codes from the 'mo' column</span>
<span class="fu"><a href="https://dplyr.tidyverse.org/reference/pull.html">pull</a></span><span class="op">(</span><span class="va">mo</span><span class="op">)</span> <span class="op">%&gt;%</span>
<span class="fu"><a href="https://dplyr.tidyverse.org/reference/pull.html" class="external-link">pull</a></span><span class="op">(</span><span class="va">mo</span><span class="op">)</span> <span class="op">%&gt;%</span>
<span class="co"># and copy them a thousand times</span>
<span class="fu"><a href="https://rdrr.io/r/base/rep.html">rep</a></span><span class="op">(</span><span class="fl">1000</span><span class="op">)</span> <span class="op">%&gt;%</span>
<span class="fu"><a href="https://rdrr.io/r/base/rep.html" class="external-link">rep</a></span><span class="op">(</span><span class="fl">1000</span><span class="op">)</span> <span class="op">%&gt;%</span>
<span class="co"># then scramble them</span>
<span class="fu"><a href="https://rdrr.io/r/base/sample.html">sample</a></span><span class="op">(</span><span class="op">)</span>
<span class="fu"><a href="https://rdrr.io/r/base/sample.html" class="external-link">sample</a></span><span class="op">(</span><span class="op">)</span>
<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">head</a></span><span class="op">(</span><span class="va">x</span><span class="op">)</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_CONS B_ESCHR_COLI B_STPHY_AURS B_STRPT_PYGN B_ESCHR_COLI</span>
<span class="co"># [6] B_HMPHL_INFL</span>
<span class="co"># [1] B_SERRT_MRCS B_STRPT_ORLS B_STPHY_CPTS B_STPHY_HMNS B_ESCHR_COLI</span>
<span class="co"># [6] B_ESCHR_COLI</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">length</a></span><span class="op">(</span><span class="va">x</span><span class="op">)</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>
<span class="co"># [1] 2000000</span>
<span class="co"># and how many unique values do we have?</span>
<span class="fu"><a href="https://dplyr.tidyverse.org/reference/n_distinct.html">n_distinct</a></span><span class="op">(</span><span class="va">x</span><span class="op">)</span>
<span class="fu"><a href="https://dplyr.tidyverse.org/reference/n_distinct.html" class="external-link">n_distinct</a></span><span class="op">(</span><span class="va">x</span><span class="op">)</span>
<span class="co"># [1] 90</span>
<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">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>,
<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">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://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="co"># Unit: milliseconds</span>
<span class="co"># expr min lq mean median uq max neval</span>
<span class="co"># mo_name(x) 165 238 258 246 253 369 10</span></code></pre></div>
<p>So getting official taxonomic names of 2,000,000 (!!) items consisting of 90 unique values only takes 0.246 seconds. That is 123 nanoseconds on average. You only lose time on your unique input values.</p>
<span class="co"># mo_name(x) 190 210 273 248 347 419 10</span></code></pre></div>
<p>So getting official taxonomic names of 2,000,000 (!!) items consisting of 90 unique values only takes 0.248 seconds. That is 124 nanoseconds on average. You only lose time on your unique input values.</p>
</div>
<div id="precalculated-results" class="section level3">
<h3 class="hasAnchor">
<a href="#precalculated-results" class="anchor"></a>Precalculated results</h3>
<a href="#precalculated-results" class="anchor" aria-hidden="true"></a>Precalculated results</h3>
<p>What about precalculated results? If the input is an already precalculated result of a helper function such as <code><a href="../reference/mo_property.html">mo_name()</a></code>, it almost doesn’t take any time at all. In other words, if you run <code><a href="../reference/mo_property.html">mo_name()</a></code> on a valid taxonomic name, it will return the results immediately (see ‘C’ below):</p>
<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="va">run_it</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/microbenchmark/man/microbenchmark.html">microbenchmark</a></span><span class="op">(</span>A <span class="op">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_name</a></span><span class="op">(</span><span class="st">"STAAUR"</span><span class="op">)</span>,
<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_name</a></span><span class="op">(</span><span class="st">"STAAUR"</span><span class="op">)</span>,
B <span class="op">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_name</a></span><span class="op">(</span><span class="st">"S. aureus"</span><span class="op">)</span>,
C <span class="op">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_name</a></span><span class="op">(</span><span class="st">"Staphylococcus aureus"</span><span class="op">)</span>,
times <span class="op">=</span> <span class="fl">10</span><span class="op">)</span>
<span class="fu"><a href="https://docs.ropensci.org/skimr/reference/print.html">print</a></span><span class="op">(</span><span class="va">run_it</span>, unit <span class="op">=</span> <span class="st">"ms"</span>, signif <span class="op">=</span> <span class="fl">3</span><span class="op">)</span>
<span class="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="co"># Unit: milliseconds</span>
<span class="co"># expr min lq mean median uq max neval</span>
<span class="co"># A 8.20 8.33 13.30 8.53 9.71 53.2 10</span>
<span class="co"># B 22.80 23.50 29.20 24.60 26.80 69.3 10</span>
<span class="co"># C 1.82 2.04 2.25 2.16 2.38 2.9 10</span></code></pre></div>
<span class="co"># expr min lq mean median uq max neval</span>
<span class="co"># A 8.72 9.35 9.96 10.20 10.40 11.10 10</span>
<span class="co"># B 24.20 25.00 31.40 27.10 27.50 77.90 10</span>
<span class="co"># C 1.80 1.84 2.20 2.18 2.59 2.67 10</span></code></pre></div>
<p>So going from <code><a href="../reference/mo_property.html">mo_name("Staphylococcus aureus")</a></code> to <code>"Staphylococcus aureus"</code> takes 0.0022 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">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>,
<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>,
C <span class="op">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_name</a></span><span class="op">(</span><span class="st">"Staphylococcus aureus"</span><span class="op">)</span>,
D <span class="op">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_family</a></span><span class="op">(</span><span class="st">"Staphylococcaceae"</span><span class="op">)</span>,
@ -306,22 +308,22 @@
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">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://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="co"># Unit: milliseconds</span>
<span class="co"># expr min lq mean median uq max neval</span>
<span class="co"># A 1.66 1.70 1.86 1.77 1.90 2.41 10</span>
<span class="co"># B 1.61 1.71 1.99 1.84 2.37 2.56 10</span>
<span class="co"># C 1.64 1.72 1.79 1.74 1.77 2.14 10</span>
<span class="co"># D 1.68 1.71 2.06 2.06 2.23 2.77 10</span>
<span class="co"># E 1.63 1.71 1.85 1.75 1.92 2.43 10</span>
<span class="co"># F 1.61 1.67 1.80 1.75 1.86 2.22 10</span>
<span class="co"># G 1.63 1.67 4.37 1.96 2.58 25.70 10</span>
<span class="co"># H 1.67 1.73 1.88 1.83 2.01 2.28 10</span></code></pre></div>
<span class="co"># expr min lq mean median uq max neval</span>
<span class="co"># A 1.73 1.79 1.89 1.84 1.95 2.31 10</span>
<span class="co"># B 1.73 1.75 1.91 1.89 1.99 2.26 10</span>
<span class="co"># C 1.76 1.81 1.90 1.83 1.98 2.17 10</span>
<span class="co"># D 1.71 1.78 1.97 1.83 2.12 2.77 10</span>
<span class="co"># E 1.70 1.74 1.80 1.80 1.85 1.89 10</span>
<span class="co"># F 1.73 1.74 1.82 1.77 1.84 2.20 10</span>
<span class="co"># G 1.69 1.75 1.91 1.79 1.85 2.70 10</span>
<span class="co"># H 1.66 1.74 1.84 1.81 1.82 2.27 10</span></code></pre></div>
<p>Of course, when running <code><a href="../reference/mo_property.html">mo_phylum("Firmicutes")</a></code> the function has zero knowledge about the actual microorganism, namely <em>S. aureus</em>. But since the result would be <code>"Firmicutes"</code> anyway, there is no point in calculating the result. And because this package contains all phyla of all known bacteria, it can just return the initial value immediately.</p>
</div>
<div id="results-in-other-languages" class="section level3">
<h3 class="hasAnchor">
<a href="#results-in-other-languages" class="anchor"></a>Results in other languages</h3>
<a href="#results-in-other-languages" class="anchor" aria-hidden="true"></a>Results in other languages</h3>
<p>When the system language is non-English and supported by this <code>AMR</code> package, some functions will have a translated result. This almost does’t take extra time:</p>
<div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="fu"><a href="../reference/mo_property.html">mo_name</a></span><span class="op">(</span><span class="st">"CoNS"</span>, language <span class="op">=</span> <span class="st">"en"</span><span class="op">)</span> <span class="co"># or just mo_name("CoNS") on an English system</span>
@ -333,7 +335,7 @@
<span class="fu"><a href="../reference/mo_property.html">mo_name</a></span><span class="op">(</span><span class="st">"CoNS"</span>, language <span class="op">=</span> <span class="st">"nl"</span><span class="op">)</span> <span class="co"># or just mo_name("CoNS") on a Dutch system</span>
<span class="co"># [1] "Coagulase-negatieve Staphylococcus (CNS)"</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">microbenchmark</a></span><span class="op">(</span>en <span class="op">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_name</a></span><span class="op">(</span><span class="st">"CoNS"</span>, language <span class="op">=</span> <span class="st">"en"</span><span class="op">)</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>en <span class="op">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_name</a></span><span class="op">(</span><span class="st">"CoNS"</span>, language <span class="op">=</span> <span class="st">"en"</span><span class="op">)</span>,
de <span class="op">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_name</a></span><span class="op">(</span><span class="st">"CoNS"</span>, language <span class="op">=</span> <span class="st">"de"</span><span class="op">)</span>,
nl <span class="op">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_name</a></span><span class="op">(</span><span class="st">"CoNS"</span>, language <span class="op">=</span> <span class="st">"nl"</span><span class="op">)</span>,
es <span class="op">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_name</a></span><span class="op">(</span><span class="st">"CoNS"</span>, language <span class="op">=</span> <span class="st">"es"</span><span class="op">)</span>,
@ -341,16 +343,16 @@
fr <span class="op">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_name</a></span><span class="op">(</span><span class="st">"CoNS"</span>, language <span class="op">=</span> <span class="st">"fr"</span><span class="op">)</span>,
pt <span class="op">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_name</a></span><span class="op">(</span><span class="st">"CoNS"</span>, language <span class="op">=</span> <span class="st">"pt"</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">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://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="co"># Unit: milliseconds</span>
<span class="co"># expr min lq mean median uq max neval</span>
<span class="co"># en 19.99 20.43 25.64 21.15 22.38 82.52 100</span>
<span class="co"># de 31.03 31.96 38.34 32.88 35.27 82.31 100</span>
<span class="co"># nl 35.25 36.19 43.25 37.63 39.66 85.19 100</span>
<span class="co"># es 35.01 35.85 40.89 36.91 38.58 83.68 100</span>
<span class="co"># it 24.35 24.90 30.43 25.81 28.03 78.90 100</span>
<span class="co"># fr 23.87 25.02 31.72 26.03 27.46 83.88 100</span>
<span class="co"># pt 24.00 24.99 31.16 26.05 28.06 80.74 100</span></code></pre></div>
<span class="co"># expr min lq mean median uq max neval</span>
<span class="co"># en 20.17 20.83 23.58 21.44 22.59 69.64 100</span>
<span class="co"># de 31.48 32.42 42.10 34.02 36.34 204.60 100</span>
<span class="co"># nl 35.21 36.63 45.71 37.75 40.25 97.01 100</span>
<span class="co"># es 35.09 36.14 43.52 37.44 39.09 90.89 100</span>
<span class="co"># it 24.46 25.36 31.24 26.06 28.11 77.93 100</span>
<span class="co"># fr 24.11 25.02 29.21 25.70 27.61 74.69 100</span>
<span class="co"># pt 24.44 25.27 29.79 26.05 28.12 70.28 100</span></code></pre></div>
<p>Currently supported non-English languages are German, Dutch, Spanish, Italian, French and Portuguese.</p>
</div>
</div>
@ -364,11 +366,13 @@
<footer><div class="copyright">
<p>Developed by <a href="https://www.rug.nl/staff/m.s.berends/">Matthijs S. Berends</a>, <a href="https://www.rug.nl/staff/c.f.luz/">Christian F. Luz</a>, <a href="https://www.rug.nl/staff/a.w.friedrich/">Alexander W. Friedrich</a>, <a href="https://www.rug.nl/staff/b.sinha/">Bhanu N. M. Sinha</a>, <a href="https://www.rug.nl/staff/c.j.albers/">Casper J. Albers</a>, <a href="https://www.rug.nl/staff/c.glasner/">Corinna Glasner</a>.</p>
<p></p>
<p>Developed by <a href="https://www.rug.nl/staff/m.s.berends/" class="external-link external-link">Matthijs S. Berends</a>, <a href="https://www.rug.nl/staff/c.f.luz/" class="external-link external-link">Christian F. Luz</a>, <a href="https://www.rug.nl/staff/a.w.friedrich/" class="external-link external-link">Alexander W. Friedrich</a>, <a href="https://www.rug.nl/staff/b.sinha/" class="external-link external-link">Bhanu N. M. Sinha</a>, <a href="https://www.rug.nl/staff/c.j.albers/" class="external-link external-link">Casper J. Albers</a>, <a href="https://www.rug.nl/staff/c.glasner/" class="external-link external-link">Corinna Glasner</a>.</p>
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<p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.6.1.</p>
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<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link external-link">pkgdown</a> 1.6.1.9001.</p>
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@ -377,5 +381,7 @@
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@ -0,0 +1,12 @@
// Pandoc 2.9 adds attributes on both header and div. We remove the former (to
// be compatible with the behavior of Pandoc < 2.8).
document.addEventListener('DOMContentLoaded', function(e) {
var hs = document.querySelectorAll("div.section[class*='level'] > :first-child");
var i, h, a;
for (i = 0; i < hs.length; i++) {
h = hs[i];
if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6
a = h.attributes;
while (a.length > 0) h.removeAttribute(a[0].name);
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@ -27,6 +27,8 @@
<![endif]-->
</head>
<body data-spy="scroll" data-target="#toc">
<div class="container template-article">
<header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
<div class="container">
@ -39,7 +41,7 @@
</button>
<span class="navbar-brand">
<a class="navbar-link" href="../index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.7.1.9021</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.7.1.9022</span>
</span>
</div>
@ -164,7 +166,7 @@
</ul>
<ul class="nav navbar-nav navbar-right">
<li>
<a href="https://github.com/msberends/AMR">
<a href="https://github.com/msberends/AMR" class="external-link">
<span class="fab fa-github"></span>
Source Code
@ -192,9 +194,9 @@
<div class="page-header toc-ignore">
<h1 data-toc-skip>Data sets for download / own use</h1>
<h4 class="date">12 July 2021</h4>
<h4 data-toc-skip class="date">23 July 2021</h4>
<small class="dont-index">Source: <a href="https://github.com/msberends/AMR/blob/master/vignettes/datasets.Rmd"><code>vignettes/datasets.Rmd</code></a></small>
<small class="dont-index">Source: <a href="https://github.com/msberends/AMR/blob/master/vignettes/datasets.Rmd" class="external-link"><code>vignettes/datasets.Rmd</code></a></small>
<div class="hidden name"><code>datasets.Rmd</code></div>
</div>
@ -208,40 +210,40 @@ If you are reading this page from within R, please <a href="https://msberends.gi
</p>
<div id="microorganisms-currently-accepted-names" class="section level2">
<h2 class="hasAnchor">
<a href="#microorganisms-currently-accepted-names" class="anchor"></a>Microorganisms (currently accepted names)</h2>
<a href="#microorganisms-currently-accepted-names" class="anchor" aria-hidden="true"></a>Microorganisms (currently accepted names)</h2>
<p>A data set with 70,026 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 11 March 2021 20:59:32 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/master/data-raw/../data-raw/microorganisms.rds">R file</a> (2.2 MB)<br>
<li>Download as <a href="https://github.com/msberends/AMR/raw/master/data-raw/../data-raw/microorganisms.rds" class="external-link">R file</a> (2.2 MB)<br>
</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/master/data-raw/../data-raw/microorganisms.xlsx">Excel file</a> (6.4 MB)<br>
<li>Download as <a href="https://github.com/msberends/AMR/raw/master/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/master/data-raw/../data-raw/microorganisms.txt">plain text file</a> (14.8 MB)<br>
<li>Download as <a href="https://github.com/msberends/AMR/raw/master/data-raw/../data-raw/microorganisms.txt" class="external-link">plain text file</a> (14.8 MB)<br>
</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/master/data-raw/../data-raw/microorganisms.sas">SAS file</a> (27.4 MB)<br>
<li>Download as <a href="https://github.com/msberends/AMR/raw/master/data-raw/../data-raw/microorganisms.sas" class="external-link">SAS file</a> (27.4 MB)<br>
</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/master/data-raw/../data-raw/microorganisms.sav">SPSS file</a> (27.8 MB)<br>
<li>Download as <a href="https://github.com/msberends/AMR/raw/master/data-raw/../data-raw/microorganisms.sav" class="external-link">SPSS file</a> (27.8 MB)<br>
</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/master/data-raw/../data-raw/microorganisms.dta">Stata file</a> (25.1 MB)</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/master/data-raw/../data-raw/microorganisms.dta" class="external-link">Stata file</a> (25.1 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>
<div id="source" class="section level3">
<h3 class="hasAnchor">
<a href="#source" class="anchor"></a>Source</h3>
<a href="#source" class="anchor" aria-hidden="true"></a>Source</h3>
<p>Our full taxonomy of microorganisms is based on the authoritative and comprehensive:</p>
<ul>
<li>
<a href="http://www.catalogueoflife.org">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">List of Prokaryotic names with Standing in Nomenclature</a> (LPSN, last updated: March 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">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: March 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 id="example-content" class="section level3">
<h3 class="hasAnchor">
<a href="#example-content" class="anchor"></a>Example content</h3>
<a href="#example-content" class="anchor" aria-hidden="true"></a>Example content</h3>
<p>Included (sub)species per taxonomic kingdom:</p>
<table class="table">
<thead><tr class="header">
@ -428,39 +430,39 @@ If you are reading this page from within R, please <a href="https://msberends.gi
</div>
<div id="microorganisms-previously-accepted-names" class="section level2">
<h2 class="hasAnchor">
<a href="#microorganisms-previously-accepted-names" class="anchor"></a>Microorganisms (previously accepted names)</h2>
<a href="#microorganisms-previously-accepted-names" class="anchor" aria-hidden="true"></a>Microorganisms (previously accepted names)</h2>
<p>A data set with 14,100 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 5 March 2021 09:46:55 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/master/data-raw/../data-raw/microorganisms.old.rds">R file</a> (0.2 MB)<br>
<li>Download as <a href="https://github.com/msberends/AMR/raw/master/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/master/data-raw/../data-raw/microorganisms.old.xlsx">Excel file</a> (0.5 MB)<br>
<li>Download as <a href="https://github.com/msberends/AMR/raw/master/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/master/data-raw/../data-raw/microorganisms.old.txt">plain text file</a> (0.9 MB)<br>
<li>Download as <a href="https://github.com/msberends/AMR/raw/master/data-raw/../data-raw/microorganisms.old.txt" class="external-link">plain text file</a> (0.9 MB)<br>
</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/master/data-raw/../data-raw/microorganisms.old.sas">SAS file</a> (2.1 MB)<br>
<li>Download as <a href="https://github.com/msberends/AMR/raw/master/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/master/data-raw/../data-raw/microorganisms.old.sav">SPSS file</a> (2.2 MB)<br>
<li>Download as <a href="https://github.com/msberends/AMR/raw/master/data-raw/../data-raw/microorganisms.old.sav" class="external-link">SPSS file</a> (2.2 MB)<br>
</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/master/data-raw/../data-raw/microorganisms.old.dta">Stata file</a> (2 MB)</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/master/data-raw/../data-raw/microorganisms.old.dta" class="external-link">Stata file</a> (2 MB)</li>
</ul>
<div id="source-1" class="section level3">
<h3 class="hasAnchor">
<a href="#source-1" class="anchor"></a>Source</h3>
<a href="#source-1" class="anchor" aria-hidden="true"></a>Source</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>
<ul>
<li>
<a href="http://www.catalogueoflife.org">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">List of Prokaryotic names with Standing in Nomenclature</a> (LPSN, last updated: March 2021)</li>
<a href="https://lpsn.dsmz.de" class="external-link">List of Prokaryotic names with Standing in Nomenclature</a> (LPSN, last updated: March 2021)</li>
</ul>
</div>
<div id="example-content-1" class="section level3">
<h3 class="hasAnchor">
<a href="#example-content-1" class="anchor"></a>Example content</h3>
<a href="#example-content-1" class="anchor" aria-hidden="true"></a>Example content</h3>
<p>Example rows when filtering on <em>Escherichia</em>:</p>
<table class="table">
<thead><tr class="header">
@ -494,39 +496,39 @@ If you are reading this page from within R, please <a href="https://msberends.gi
</div>
<div id="antibiotic-agents" class="section level2">
<h2 class="hasAnchor">
<a href="#antibiotic-agents" class="anchor"></a>Antibiotic agents</h2>
<a href="#antibiotic-agents" class="anchor" aria-hidden="true"></a>Antibiotic agents</h2>
<p>A data set with 456 rows and 14 columns, containing the following column names:<br><em>ab</em>, <em>atc</em>, <em>cid</em>, <em>name</em>, <em>group</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 23 June 2021 13:07:57 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/master/data-raw/../data-raw/antibiotics.rds">R file</a> (32 kB)<br>
<li>Download as <a href="https://github.com/msberends/AMR/raw/master/data-raw/../data-raw/antibiotics.rds" class="external-link">R file</a> (32 kB)<br>
</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/master/data-raw/../data-raw/antibiotics.xlsx">Excel file</a> (65 kB)<br>
<li>Download as <a href="https://github.com/msberends/AMR/raw/master/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/master/data-raw/../data-raw/antibiotics.txt">plain text file</a> (0.1 MB)<br>
<li>Download as <a href="https://github.com/msberends/AMR/raw/master/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/master/data-raw/../data-raw/antibiotics.sas">SAS file</a> (1.8 MB)<br>
<li>Download as <a href="https://github.com/msberends/AMR/raw/master/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/master/data-raw/../data-raw/antibiotics.sav">SPSS file</a> (0.3 MB)<br>
<li>Download as <a href="https://github.com/msberends/AMR/raw/master/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/master/data-raw/../data-raw/antibiotics.dta">Stata file</a> (0.3 MB)</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/master/data-raw/../data-raw/antibiotics.dta" class="external-link">Stata file</a> (0.3 MB)</li>
</ul>
<div id="source-2" class="section level3">
<h3 class="hasAnchor">
<a href="#source-2" class="anchor"></a>Source</h3>
<a href="#source-2" class="anchor" aria-hidden="true"></a>Source</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>
<ul>
<li>
<a href="https://www.whocc.no/atc_ddd_index/">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">PubChem by the US National Library of Medicine</a></li>
<li><a href="https://whonet.org">WHONET software 2019</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>
<div id="example-content-2" class="section level3">
<h3 class="hasAnchor">
<a href="#example-content-2" class="anchor"></a>Example content</h3>
<table class="table">
<a href="#example-content-2" class="anchor" aria-hidden="true"></a>Example content</h3>
<table style="width:100%;" class="table">
<colgroup>
<col width="1%">
<col width="2%">
@ -662,37 +664,37 @@ If you are reading this page from within R, please <a href="https://msberends.gi
</div>
<div id="antiviral-agents" class="section level2">
<h2 class="hasAnchor">
<a href="#antiviral-agents" class="anchor"></a>Antiviral agents</h2>
<a href="#antiviral-agents" class="anchor" aria-hidden="true"></a>Antiviral agents</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><strong>Direct download links:</strong></p>
<ul>
<li>Download as <a href="https://github.com/msberends/AMR/raw/master/data-raw/../data-raw/antivirals.rds">R file</a> (5 kB)<br>
<li>Download as <a href="https://github.com/msberends/AMR/raw/master/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/master/data-raw/../data-raw/antivirals.xlsx">Excel file</a> (14 kB)<br>
<li>Download as <a href="https://github.com/msberends/AMR/raw/master/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/master/data-raw/../data-raw/antivirals.txt">plain text file</a> (16 kB)<br>
<li>Download as <a href="https://github.com/msberends/AMR/raw/master/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/master/data-raw/../data-raw/antivirals.sas">SAS file</a> (80 kB)<br>
<li>Download as <a href="https://github.com/msberends/AMR/raw/master/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/master/data-raw/../data-raw/antivirals.sav">SPSS file</a> (68 kB)<br>
<li>Download as <a href="https://github.com/msberends/AMR/raw/master/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/master/data-raw/../data-raw/antivirals.dta">Stata file</a> (67 kB)</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/master/data-raw/../data-raw/antivirals.dta" class="external-link">Stata file</a> (67 kB)</li>
</ul>
<div id="source-3" class="section level3">
<h3 class="hasAnchor">
<a href="#source-3" class="anchor"></a>Source</h3>
<a href="#source-3" class="anchor" aria-hidden="true"></a>Source</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>
<ul>
<li>
<a href="https://www.whocc.no/atc_ddd_index/">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">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 id="example-content-3" class="section level3">
<h3 class="hasAnchor">
<a href="#example-content-3" class="anchor"></a>Example content</h3>
<a href="#example-content-3" class="anchor" aria-hidden="true"></a>Example content</h3>
<table class="table">
<colgroup>
<col width="4%">
@ -789,32 +791,32 @@ If you are reading this page from within R, please <a href="https://msberends.gi
</div>
<div id="intrinsic-bacterial-resistance" class="section level2">
<h2 class="hasAnchor">
<a href="#intrinsic-bacterial-resistance" class="anchor"></a>Intrinsic bacterial resistance</h2>
<a href="#intrinsic-bacterial-resistance" class="anchor" aria-hidden="true"></a>Intrinsic bacterial resistance</h2>
<p>A data set with 93,892 rows and 2 columns, containing the following column names:<br><em>microorganism</em> and <em>antibiotic</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 5 March 2021 09:46:55 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/master/data-raw/../data-raw/intrinsic_resistant.rds">R file</a> (69 kB)<br>
<li>Download as <a href="https://github.com/msberends/AMR/raw/master/data-raw/../data-raw/intrinsic_resistant.rds" class="external-link">R file</a> (69 kB)<br>
</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/master/data-raw/../data-raw/intrinsic_resistant.xlsx">Excel file</a> (0.9 MB)<br>
<li>Download as <a href="https://github.com/msberends/AMR/raw/master/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/master/data-raw/../data-raw/intrinsic_resistant.txt">plain text file</a> (3.5 MB)<br>
<li>Download as <a href="https://github.com/msberends/AMR/raw/master/data-raw/../data-raw/intrinsic_resistant.txt" class="external-link">plain text file</a> (3.5 MB)<br>
</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/master/data-raw/../data-raw/intrinsic_resistant.sas">SAS file</a> (7.1 MB)<br>
<li>Download as <a href="https://github.com/msberends/AMR/raw/master/data-raw/../data-raw/intrinsic_resistant.sas" class="external-link">SAS file</a> (7.1 MB)<br>
</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/master/data-raw/../data-raw/intrinsic_resistant.sav">SPSS file</a> (7.9 MB)<br>
<li>Download as <a href="https://github.com/msberends/AMR/raw/master/data-raw/../data-raw/intrinsic_resistant.sav" class="external-link">SPSS file</a> (7.9 MB)<br>
</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/master/data-raw/../data-raw/intrinsic_resistant.dta">Stata file</a> (7 MB)</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/master/data-raw/../data-raw/intrinsic_resistant.dta" class="external-link">Stata file</a> (7 MB)</li>
</ul>
<div id="source-4" class="section level3">
<h3 class="hasAnchor">
<a href="#source-4" class="anchor"></a>Source</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/">‘EUCAST Expert Rules’ and ‘EUCAST Intrinsic Resistance and Unusual Phenotypes’ v3.2</a> (2020).</p>
<a href="#source-4" class="anchor" aria-hidden="true"></a>Source</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’ v3.2</a> (2020).</p>
</div>
<div id="example-content-4" class="section level3">
<h3 class="hasAnchor">
<a href="#example-content-4" class="anchor"></a>Example content</h3>
<a href="#example-content-4" class="anchor" aria-hidden="true"></a>Example content</h3>
<p>Example rows when filtering on <em>Enterobacter cloacae</em>:</p>
<table class="table">
<thead><tr class="header">
@ -1004,32 +1006,32 @@ If you are reading this page from within R, please <a href="https://msberends.gi
</div>
<div id="interpretation-from-mic-values-disk-diameters-to-rsi" class="section level2">
<h2 class="hasAnchor">
<a href="#interpretation-from-mic-values-disk-diameters-to-rsi" class="anchor"></a>Interpretation from MIC values / disk diameters to R/SI</h2>
<a href="#interpretation-from-mic-values-disk-diameters-to-rsi" class="anchor" aria-hidden="true"></a>Interpretation from MIC values / disk diameters to R/SI</h2>
<p>A data set with 22,000 rows and 10 columns, containing the following column names:<br><em>guideline</em>, <em>method</em>, <em>site</em>, <em>mo</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 12 July 2021 10:54:20 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/master/data-raw/../data-raw/rsi_translation.rds">R file</a> (37 kB)<br>
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<p>This data set contains interpretation rules for MIC values and disk diffusion diameters. Included guidelines are CLSI (2010-2020) and EUCAST (2011-2021).</p>
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<h2 class="hasAnchor">
<a href="#dosage-guidelines-from-eucast" class="anchor"></a>Dosage guidelines from EUCAST</h2>
<a href="#dosage-guidelines-from-eucast" class="anchor" aria-hidden="true"></a>Dosage guidelines from EUCAST</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>
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<div id="source-6" class="section level3">
<h3 class="hasAnchor">
<a href="#source-6" class="anchor"></a>Source</h3>
<a href="#source-6" class="anchor" aria-hidden="true"></a>Source</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/">‘EUCAST Clinical Breakpoint Tables’ v11.0</a> (2021).</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>
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@ -63,77 +65,77 @@
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Determine multi-drug resistance (MDR)
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</header><script src="resistance_predict_files/header-attrs-2.8/header-attrs.js"></script><div class="row">
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<h1 data-toc-skip>How to predict antimicrobial resistance</h1>
<small class="dont-index">Source: <a href="https://github.com/msberends/AMR/blob/master/vignettes/resistance_predict.Rmd"><code>vignettes/resistance_predict.Rmd</code></a></small>
<small class="dont-index">Source: <a href="https://github.com/msberends/AMR/blob/master/vignettes/resistance_predict.Rmd" class="external-link"><code>vignettes/resistance_predict.Rmd</code></a></small>
<div class="hidden name"><code>resistance_predict.Rmd</code></div>
</div>
@ -202,20 +204,20 @@
<div id="needed-r-packages" class="section level2">
<h2 class="hasAnchor">
<a href="#needed-r-packages" class="anchor"></a>Needed R packages</h2>
<p>As with many uses in R, we need some additional packages for AMR data analysis. Our package works closely together with the <a href="https://www.tidyverse.org">tidyverse packages</a> <a href="https://dplyr.tidyverse.org/"><code>dplyr</code></a> and <a href="https://ggplot2.tidyverse.org"><code>ggplot2</code></a> by Dr Hadley Wickham. The tidyverse tremendously improves the way we conduct data science - it allows for a very natural way of writing syntaxes and creating beautiful plots in R.</p>
<a href="#needed-r-packages" class="anchor" aria-hidden="true"></a>Needed R packages</h2>
<p>As with many uses in R, we need some additional packages for AMR data analysis. Our package works closely together with the <a href="https://www.tidyverse.org" class="external-link">tidyverse packages</a> <a href="https://dplyr.tidyverse.org/" class="external-link"><code>dplyr</code></a> and <a href="https://ggplot2.tidyverse.org" class="external-link"><code>ggplot2</code></a> by Dr Hadley Wickham. The tidyverse tremendously improves the way we conduct data science - it allows for a very natural way of writing syntaxes and creating beautiful plots in R.</p>
<p>Our <code>AMR</code> package depends on these packages and even extends their use and functions.</p>
<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://dplyr.tidyverse.org">dplyr</a></span><span class="op">)</span>
<span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://ggplot2.tidyverse.org">ggplot2</a></span><span class="op">)</span>
<span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://msberends.github.io/AMR/">AMR</a></span><span class="op">)</span>
<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://github.com/msberends/AMR" class="external-link">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>
</div>
<div id="prediction-analysis" class="section level2">
<h2 class="hasAnchor">
<a href="#prediction-analysis" class="anchor"></a>Prediction analysis</h2>
<a href="#prediction-analysis" class="anchor" aria-hidden="true"></a>Prediction analysis</h2>
<p>Our package contains a function <code><a href="../reference/resistance_predict.html">resistance_predict()</a></code>, which takes the same input as functions for <a href="./AMR.html">other AMR 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>
@ -283,11 +285,11 @@
<p><img src="resistance_predict_files/figure-html/unnamed-chunk-5-2.png" width="720"></p>
<div id="choosing-the-right-model" class="section level3">
<h3 class="hasAnchor">
<a href="#choosing-the-right-model" class="anchor"></a>Choosing the right model</h3>
<a href="#choosing-the-right-model" class="anchor" aria-hidden="true"></a>Choosing the right model</h3>
<p>Resistance is not easily predicted; if we look at vancomycin resistance in Gram-positive bacteria, the spread (i.e. standard error) is enormous:</p>
<div class="sourceCode" id="cb8"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="va">example_isolates</span> <span class="op">%&gt;%</span>
<span class="fu"><a href="https://dplyr.tidyverse.org/reference/filter.html">filter</a></span><span class="op">(</span><span class="fu"><a href="../reference/mo_property.html">mo_gramstain</a></span><span class="op">(</span><span class="va">mo</span>, language <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span> <span class="op">==</span> <span class="st">"Gram-positive"</span><span class="op">)</span> <span class="op">%&gt;%</span>
<span class="fu"><a href="https://dplyr.tidyverse.org/reference/filter.html" class="external-link">filter</a></span><span class="op">(</span><span class="fu"><a href="../reference/mo_property.html">mo_gramstain</a></span><span class="op">(</span><span class="va">mo</span>, language <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span> <span class="op">==</span> <span class="st">"Gram-positive"</span><span class="op">)</span> <span class="op">%&gt;%</span>
<span class="fu"><a href="../reference/resistance_predict.html">resistance_predict</a></span><span class="op">(</span>col_ab <span class="op">=</span> <span class="st">"VAN"</span>, year_min <span class="op">=</span> <span class="fl">2010</span>, info <span class="op">=</span> <span class="cn">FALSE</span>, model <span class="op">=</span> <span class="st">"binomial"</span><span class="op">)</span> <span class="op">%&gt;%</span>
<span class="fu"><a href="../reference/resistance_predict.html">ggplot_rsi_predict</a></span><span class="op">(</span><span class="op">)</span>
<span class="co"># ℹ Using column 'date' as input for `col_date`.</span></code></pre></div>
@ -311,21 +313,21 @@
<td>
<code>"binomial"</code> or <code>"binom"</code> or <code>"logit"</code>
</td>
<td><code><a href="https://rdrr.io/r/stats/glm.html">glm(..., family = binomial)</a></code></td>
<td><code><a href="https://rdrr.io/r/stats/glm.html" class="external-link">glm(..., family = binomial)</a></code></td>
<td>Generalised linear model with binomial distribution</td>
</tr>
<tr class="even">
<td>
<code>"loglin"</code> or <code>"poisson"</code>
</td>
<td><code><a href="https://rdrr.io/r/stats/glm.html">glm(..., family = poisson)</a></code></td>
<td><code><a href="https://rdrr.io/r/stats/glm.html" class="external-link">glm(..., family = poisson)</a></code></td>
<td>Generalised linear model with poisson distribution</td>
</tr>
<tr class="odd">
<td>
<code>"lin"</code> or <code>"linear"</code>
</td>
<td><code><a href="https://rdrr.io/r/stats/lm.html">lm()</a></code></td>
<td><code><a href="https://rdrr.io/r/stats/lm.html" class="external-link">lm()</a></code></td>
<td>Linear model</td>
</tr>
</tbody>
@ -333,7 +335,7 @@
<p>For the vancomycin resistance in Gram-positive bacteria, a linear model might be more appropriate since no binomial distribution is to be expected based on the observed years:</p>
<div class="sourceCode" id="cb9"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="va">example_isolates</span> <span class="op">%&gt;%</span>
<span class="fu"><a href="https://dplyr.tidyverse.org/reference/filter.html">filter</a></span><span class="op">(</span><span class="fu"><a href="../reference/mo_property.html">mo_gramstain</a></span><span class="op">(</span><span class="va">mo</span>, language <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span> <span class="op">==</span> <span class="st">"Gram-positive"</span><span class="op">)</span> <span class="op">%&gt;%</span>
<span class="fu"><a href="https://dplyr.tidyverse.org/reference/filter.html" class="external-link">filter</a></span><span class="op">(</span><span class="fu"><a href="../reference/mo_property.html">mo_gramstain</a></span><span class="op">(</span><span class="va">mo</span>, language <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span> <span class="op">==</span> <span class="st">"Gram-positive"</span><span class="op">)</span> <span class="op">%&gt;%</span>
<span class="fu"><a href="../reference/resistance_predict.html">resistance_predict</a></span><span class="op">(</span>col_ab <span class="op">=</span> <span class="st">"VAN"</span>, year_min <span class="op">=</span> <span class="fl">2010</span>, info <span class="op">=</span> <span class="cn">FALSE</span>, model <span class="op">=</span> <span class="st">"linear"</span><span class="op">)</span> <span class="op">%&gt;%</span>
<span class="fu"><a href="../reference/resistance_predict.html">ggplot_rsi_predict</a></span><span class="op">(</span><span class="op">)</span>
<span class="co"># ℹ Using column 'date' as input for `col_date`.</span></code></pre></div>
@ -341,14 +343,14 @@
<p>This seems more likely, doesn’t it?</p>
<p>The model itself is also available from the object, as an <code>attribute</code>:</p>
<div class="sourceCode" id="cb10"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="va">model</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/attributes.html">attributes</a></span><span class="op">(</span><span class="va">predict_TZP</span><span class="op">)</span><span class="op">$</span><span class="va">model</span>
<code class="sourceCode R"><span class="va">model</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/attributes.html" class="external-link">attributes</a></span><span class="op">(</span><span class="va">predict_TZP</span><span class="op">)</span><span class="op">$</span><span class="va">model</span>
<span class="fu"><a href="https://rdrr.io/r/base/summary.html">summary</a></span><span class="op">(</span><span class="va">model</span><span class="op">)</span><span class="op">$</span><span class="va">family</span>
<span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">model</span><span class="op">)</span><span class="op">$</span><span class="va">family</span>
<span class="co"># </span>
<span class="co"># Family: binomial </span>
<span class="co"># Link function: logit</span>
<span class="fu"><a href="https://rdrr.io/r/base/summary.html">summary</a></span><span class="op">(</span><span class="va">model</span><span class="op">)</span><span class="op">$</span><span class="va">coefficients</span>
<span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">model</span><span class="op">)</span><span class="op">$</span><span class="va">coefficients</span>
<span class="co"># Estimate Std. Error z value Pr(&gt;|z|)</span>
<span class="co"># (Intercept) -200.67944891 46.17315349 -4.346237 1.384932e-05</span>
<span class="co"># year 0.09883005 0.02295317 4.305725 1.664395e-05</span></code></pre></div>
@ -367,11 +369,13 @@
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@ -380,5 +384,7 @@
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@ -0,0 +1,12 @@
// Pandoc 2.9 adds attributes on both header and div. We remove the former (to
// be compatible with the behavior of Pandoc < 2.8).
document.addEventListener('DOMContentLoaded', function(e) {
var hs = document.querySelectorAll("div.section[class*='level'] > :first-child");
var i, h, a;
for (i = 0; i < hs.length; i++) {
h = hs[i];
if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6
a = h.attributes;
while (a.length > 0) h.removeAttribute(a[0].name);
}
});

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@ -27,6 +27,8 @@
<![endif]-->
</head>
<body data-spy="scroll" data-target="#toc">
<div class="container template-article">
<header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
<div class="container">
@ -39,7 +41,7 @@
</button>
<span class="navbar-brand">
<a class="navbar-link" href="../index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.7.1</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.7.1.9022</span>
</span>
</div>
@ -47,14 +49,14 @@
<ul class="nav navbar-nav">
<li>
<a href="../index.html">
<span class="fas fa-home"></span>
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Home
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How to
@ -63,77 +65,77 @@
<ul class="dropdown-menu" role="menu">
<li>
<a href="../articles/AMR.html">
<span class="fas fa-directions"></span>
<span class="fa fa-directions"></span>
Conduct AMR analysis
</a>
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Predict antimicrobial resistance
</a>
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Data sets for download / own use
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Conduct principal component analysis for AMR
</a>
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<a href="../articles/MDR.html">
<span class="fas fa-skull-crossbones"></span>
<span class="fa fa-skull-crossbones"></span>
Determine multi-drug resistance (MDR)
</a>
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<a href="../articles/WHONET.html">
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Work with WHONET data
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<a href="../articles/SPSS.html">
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Import data from SPSS/SAS/Stata
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Get properties of an antibiotic
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Other: benchmarks
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@ -142,14 +144,14 @@
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@ -164,7 +166,7 @@
</ul>
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Source Code
@ -172,7 +174,7 @@
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<a href="../survey.html">
<span class="fas fa-clipboard-list"></span>
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Survey
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@ -187,13 +189,13 @@
</header><script src="welcome_to_AMR_files/header-attrs-2.8/header-attrs.js"></script><div class="row">
</header><script src="welcome_to_AMR_files/header-attrs-2.9/header-attrs.js"></script><div class="row">
<div class="col-md-9 contents">
<div class="page-header toc-ignore">
<h1 data-toc-skip>Welcome to the AMR package</h1>
<small class="dont-index">Source: <a href="https://github.com/msberends/AMR/blob/master/vignettes/welcome_to_AMR.Rmd"><code>vignettes/welcome_to_AMR.Rmd</code></a></small>
<small class="dont-index">Source: <a href="https://github.com/msberends/AMR/blob/master/vignettes/welcome_to_AMR.Rmd" class="external-link"><code>vignettes/welcome_to_AMR.Rmd</code></a></small>
<div class="hidden name"><code>welcome_to_AMR.Rmd</code></div>
</div>
@ -203,14 +205,14 @@
<p><strong>READ ALL VIGNETTES <a href="https://msberends.github.io/AMR/articles/">ON OUR WEBSITE</a>.</strong></p>
<div id="welcome-to-the-amr-package" class="section level1">
<h1 class="hasAnchor">
<a href="#welcome-to-the-amr-package" class="anchor"></a>Welcome to the AMR package</h1>
<a href="#welcome-to-the-amr-package" class="anchor" aria-hidden="true"></a>Welcome to the AMR package</h1>
<p><code>AMR</code> is a free, open-source and independent R package to simplify the analysis and prediction of Antimicrobial Resistance (AMR) and to work with microbial and antimicrobial data and properties, by using evidence-based methods. <strong>Our aim is to provide a standard</strong> for clean and reproducible antimicrobial resistance data analysis, that can therefore empower epidemiological analyses to continuously enable surveillance and treatment evaluation in any setting.</p>
<p>After installing this package, R knows <strong>~70,000 distinct microbial species</strong> and all <strong>~550 antibiotic, antimycotic and antiviral drugs</strong> by name and code (including ATC, EARS-NET, LOINC and SNOMED CT), and knows all about valid R/SI and MIC values. It supports any data format, including WHONET/EARS-Net data.</p>
<p>This package is fully independent of any other R package and works on Windows, macOS and Linux with all versions of R since R-3.0.0 (April 2013). <strong>It was designed to work in any setting, including those with very limited resources</strong>. It was created for both routine data analysis and academic research at the Faculty of Medical Sciences of the University of Groningen, in collaboration with non-profit organisations Certe Medical Diagnostics and Advice and University Medical Center Groningen. This R package is actively maintained (see Changelog) and is free software (see Copyright).</p>
<p>Since its first public release in early 2018, this package has been downloaded from more than 100 countries.</p>
<div id="usage-examples" class="section level2">
<h2 class="hasAnchor">
<a href="#usage-examples" class="anchor"></a>Usage examples</h2>
<a href="#usage-examples" class="anchor" aria-hidden="true"></a>Usage examples</h2>
<p>This package can be used for:</p>
<ul>
<li>Reference for the taxonomy of microorganisms, since the package contains all microbial (sub)species from the Catalogue of Life and List of Prokaryotic names with Standing in Nomenclature</li>
@ -245,11 +247,13 @@
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<p>Developed by <a href="https://www.rug.nl/staff/m.s.berends/">Matthijs S. Berends</a>, <a href="https://www.rug.nl/staff/c.f.luz/">Christian F. Luz</a>, <a href="https://www.rug.nl/staff/a.w.friedrich/">Alexander W. Friedrich</a>, <a href="https://www.rug.nl/staff/b.sinha/">Bhanu N. M. Sinha</a>, <a href="https://www.rug.nl/staff/c.j.albers/">Casper J. Albers</a>, <a href="https://www.rug.nl/staff/c.glasner/">Corinna Glasner</a>.</p>
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<p>Developed by <a href="https://www.rug.nl/staff/m.s.berends/" class="external-link external-link">Matthijs S. Berends</a>, <a href="https://www.rug.nl/staff/c.f.luz/" class="external-link external-link">Christian F. Luz</a>, <a href="https://www.rug.nl/staff/a.w.friedrich/" class="external-link external-link">Alexander W. Friedrich</a>, <a href="https://www.rug.nl/staff/b.sinha/" class="external-link external-link">Bhanu N. M. Sinha</a>, <a href="https://www.rug.nl/staff/c.j.albers/" class="external-link external-link">Casper J. Albers</a>, <a href="https://www.rug.nl/staff/c.glasner/" class="external-link external-link">Corinna Glasner</a>.</p>
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@ -258,5 +262,7 @@
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// Pandoc 2.9 adds attributes on both header and div. We remove the former (to
// be compatible with the behavior of Pandoc < 2.8).
document.addEventListener('DOMContentLoaded', function(e) {
var hs = document.querySelectorAll("div.section[class*='level'] > :first-child");
var i, h, a;
for (i = 0; i < hs.length; i++) {
h = hs[i];
if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6
a = h.attributes;
while (a.length > 0) h.removeAttribute(a[0].name);
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});