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dr. M.S. (Matthijs) Berends
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@ -39,7 +39,7 @@
</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">1.6.0.9011</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.7.0</span>
</span>
</div>
@ -47,14 +47,14 @@
<ul class="nav navbar-nav">
<li>
<a href="../index.html">
<span class="fa fa-home"></span>
<span class="fas fa-home"></span>
Home
</a>
</li>
<li class="dropdown">
<a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false">
<span class="fa fa-question-circle"></span>
<span class="fas fa-question-circle"></span>
How to
@ -63,77 +63,77 @@
<ul class="dropdown-menu" role="menu">
<li>
<a href="../articles/AMR.html">
<span class="fa fa-directions"></span>
<span class="fas fa-directions"></span>
Conduct AMR analysis
</a>
</li>
<li>
<a href="../articles/resistance_predict.html">
<span class="fa fa-dice"></span>
<span class="fas fa-dice"></span>
Predict antimicrobial resistance
</a>
</li>
<li>
<a href="../articles/datasets.html">
<span class="fa fa-database"></span>
<span class="fas fa-database"></span>
Data sets for download / own use
</a>
</li>
<li>
<a href="../articles/PCA.html">
<span class="fa fa-compress"></span>
<span class="fas fa-compress"></span>
Conduct principal component analysis for AMR
</a>
</li>
<li>
<a href="../articles/MDR.html">
<span class="fa fa-skull-crossbones"></span>
<span class="fas fa-skull-crossbones"></span>
Determine multi-drug resistance (MDR)
</a>
</li>
<li>
<a href="../articles/WHONET.html">
<span class="fa fa-globe-americas"></span>
<span class="fas fa-globe-americas"></span>
Work with WHONET data
</a>
</li>
<li>
<a href="../articles/SPSS.html">
<span class="fa fa-file-upload"></span>
<span class="fas fa-file-upload"></span>
Import data from SPSS/SAS/Stata
</a>
</li>
<li>
<a href="../articles/EUCAST.html">
<span class="fa fa-exchange-alt"></span>
<span class="fas fa-exchange-alt"></span>
Apply EUCAST rules
</a>
</li>
<li>
<a href="../reference/mo_property.html">
<span class="fa fa-bug"></span>
<span class="fas fa-bug"></span>
Get properties of a microorganism
</a>
</li>
<li>
<a href="../reference/ab_property.html">
<span class="fa fa-capsules"></span>
<span class="fas fa-capsules"></span>
Get properties of an antibiotic
</a>
</li>
<li>
<a href="../articles/benchmarks.html">
<span class="fa fa-shipping-fast"></span>
<span class="fas fa-shipping-fast"></span>
Other: benchmarks
</a>
@ -142,21 +142,21 @@
</li>
<li>
<a href="../reference/index.html">
<span class="fa fa-book-open"></span>
<span class="fas fa-book-open"></span>
Manual
</a>
</li>
<li>
<a href="../authors.html">
<span class="fa fa-users"></span>
<span class="fas fa-users"></span>
Authors
</a>
</li>
<li>
<a href="../news/index.html">
<span class="far fa far fa-newspaper"></span>
<span class="far fa-newspaper"></span>
Changelog
</a>
@ -165,14 +165,14 @@
<ul class="nav navbar-nav navbar-right">
<li>
<a href="https://github.com/msberends/AMR">
<span class="fab fa fab fa-github"></span>
<span class="fab fa-github"></span>
Source Code
</a>
</li>
<li>
<a href="../survey.html">
<span class="fa fa-clipboard-list"></span>
<span class="fas fa-clipboard-list"></span>
Survey
</a>
@ -187,8 +187,7 @@
</header><link href="EUCAST_files/anchor-sections-1.0/anchor-sections.css" rel="stylesheet">
<script src="EUCAST_files/anchor-sections-1.0/anchor-sections.js"></script><div class="row">
</header><script src="EUCAST_files/header-attrs-2.8/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>
@ -216,8 +215,8 @@
<a href="#examples" class="anchor"></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">
<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>,
<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>,
<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>
@ -228,19 +227,19 @@
<span class="fu"><a href="../reference/eucast_rules.html">eucast_rules</a></span><span class="op">(</span><span class="va">oops</span>, info <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>
<span class="co"># mo ampicillin</span>
<span class="co"># 1 Klebsiella R</span>
<span class="co"># 2 Escherichia S</span></pre></div>
<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">
<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>,
<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>,
<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="co"># [1] TRUE FALSE</span></pre></div>
<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">
<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>,
<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>,
<span class="st">"Enterococcus faecalis"</span>,
<span class="st">"Escherichia coli"</span>,
<span class="st">"Klebsiella pneumoniae"</span>,
@ -252,9 +251,9 @@
CXM <span class="op">=</span> <span class="st">"-"</span>, <span class="co"># Cefuroxime</span>
PEN <span class="op">=</span> <span class="st">"S"</span>, <span class="co"># Benzylenicillin</span>
FOX <span class="op">=</span> <span class="st">"S"</span>, <span class="co"># Cefoxitin</span>
stringsAsFactors <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></pre></div>
<div class="sourceCode" id="cb4"><pre class="downlit">
<span class="va">data</span></pre></div>
stringsAsFactors <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></code></pre></div>
<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="va">data</span></code></pre></div>
<table class="table">
<thead><tr class="header">
<th align="left">mo</th>
@ -319,8 +318,8 @@
</tr>
</tbody>
</table>
<div class="sourceCode" id="cb5"><pre class="downlit">
<span class="fu"><a href="../reference/eucast_rules.html">eucast_rules</a></span><span class="op">(</span><span class="va">data</span><span class="op">)</span></pre></div>
<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="fu"><a href="../reference/eucast_rules.html">eucast_rules</a></span><span class="op">(</span><span class="va">data</span><span class="op">)</span></code></pre></div>
<table class="table">
<thead><tr class="header">
<th align="left">mo</th>

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);
}
});

163
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@ -39,7 +39,7 @@
</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">1.6.0.9011</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.7.0</span>
</span>
</div>
@ -47,14 +47,14 @@
<ul class="nav navbar-nav">
<li>
<a href="../index.html">
<span class="fa fa-home"></span>
<span class="fas fa-home"></span>
Home
</a>
</li>
<li class="dropdown">
<a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false">
<span class="fa fa-question-circle"></span>
<span class="fas fa-question-circle"></span>
How to
@ -63,77 +63,77 @@
<ul class="dropdown-menu" role="menu">
<li>
<a href="../articles/AMR.html">
<span class="fa fa-directions"></span>
<span class="fas fa-directions"></span>
Conduct AMR analysis
</a>
</li>
<li>
<a href="../articles/resistance_predict.html">
<span class="fa fa-dice"></span>
<span class="fas fa-dice"></span>
Predict antimicrobial resistance
</a>
</li>
<li>
<a href="../articles/datasets.html">
<span class="fa fa-database"></span>
<span class="fas fa-database"></span>
Data sets for download / own use
</a>
</li>
<li>
<a href="../articles/PCA.html">
<span class="fa fa-compress"></span>
<span class="fas fa-compress"></span>
Conduct principal component analysis for AMR
</a>
</li>
<li>
<a href="../articles/MDR.html">
<span class="fa fa-skull-crossbones"></span>
<span class="fas fa-skull-crossbones"></span>
Determine multi-drug resistance (MDR)
</a>
</li>
<li>
<a href="../articles/WHONET.html">
<span class="fa fa-globe-americas"></span>
<span class="fas fa-globe-americas"></span>
Work with WHONET data
</a>
</li>
<li>
<a href="../articles/SPSS.html">
<span class="fa fa-file-upload"></span>
<span class="fas fa-file-upload"></span>
Import data from SPSS/SAS/Stata
</a>
</li>
<li>
<a href="../articles/EUCAST.html">
<span class="fa fa-exchange-alt"></span>
<span class="fas fa-exchange-alt"></span>
Apply EUCAST rules
</a>
</li>
<li>
<a href="../reference/mo_property.html">
<span class="fa fa-bug"></span>
<span class="fas fa-bug"></span>
Get properties of a microorganism
</a>
</li>
<li>
<a href="../reference/ab_property.html">
<span class="fa fa-capsules"></span>
<span class="fas fa-capsules"></span>
Get properties of an antibiotic
</a>
</li>
<li>
<a href="../articles/benchmarks.html">
<span class="fa fa-shipping-fast"></span>
<span class="fas fa-shipping-fast"></span>
Other: benchmarks
</a>
@ -142,21 +142,21 @@
</li>
<li>
<a href="../reference/index.html">
<span class="fa fa-book-open"></span>
<span class="fas fa-book-open"></span>
Manual
</a>
</li>
<li>
<a href="../authors.html">
<span class="fa fa-users"></span>
<span class="fas fa-users"></span>
Authors
</a>
</li>
<li>
<a href="../news/index.html">
<span class="far fa far fa-newspaper"></span>
<span class="far fa-newspaper"></span>
Changelog
</a>
@ -165,14 +165,14 @@
<ul class="nav navbar-nav navbar-right">
<li>
<a href="https://github.com/msberends/AMR">
<span class="fab fa fab fa-github"></span>
<span class="fab fa-github"></span>
Source Code
</a>
</li>
<li>
<a href="../survey.html">
<span class="fa fa-clipboard-list"></span>
<span class="fas fa-clipboard-list"></span>
Survey
</a>
@ -187,8 +187,7 @@
</header><link href="MDR_files/anchor-sections-1.0/anchor-sections.css" rel="stylesheet">
<script src="MDR_files/anchor-sections-1.0/anchor-sections.js"></script><div class="row">
</header><script src="MDR_files/header-attrs-2.8/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>
@ -244,27 +243,27 @@
<a href="#custom-guidelines" class="anchor"></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>
<div class="sourceCode" id="cb1"><pre class="downlit">
<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></pre></div>
<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>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">
<span class="va">custom</span>
<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="va">custom</span>
<span class="co"># A set of custom MDRO rules:</span>
<span class="co"># 1. If CIP is "R" and age is higher than 60 then: Elderly Type A</span>
<span class="co"># 2. If ERY is "R" and age is higher than 60 then: Elderly Type B</span>
<span class="co"># 3. Otherwise: Negative</span>
<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></pre></div>
<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>
<div class="sourceCode" id="cb3"><pre class="downlit">
<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>
<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"># x</span>
<span class="co"># Negative Elderly Type A Elderly Type B </span>
<span class="co"># 1070 198 732</span></pre></div>
<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>
</div>
</div>
@ -273,15 +272,15 @@
<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>
<div class="sourceCode" id="cb4"><pre class="downlit">
<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></pre></div>
<div class="sourceCode" id="cb5"><pre class="downlit">
<span class="va">example_isolates</span> <span class="op">%&gt;%</span>
<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>
<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="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></pre></div>
<span class="co"># classes was below 50% (set with `pct_required_classes`)</span></code></pre></div>
<p><strong>Frequency table</strong></p>
<p>Class: factor &gt; ordered (numeric)<br>
Length: 2,000<br>
@ -317,8 +316,8 @@ Unique: 2</p>
</tbody>
</table>
<p>For another example, I will create a data set to determine multi-drug resistant TB:</p>
<div class="sourceCode" id="cb6"><pre class="downlit">
<span class="co"># random_rsi() is a helper function to generate</span>
<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>,
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>,
@ -326,44 +325,44 @@ Unique: 2</p>
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></pre></div>
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>
<div class="sourceCode" id="cb7"><pre class="downlit">
<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>,
<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>,
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>,
PZA <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>,
MFX <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>,
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></pre></div>
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">
<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>
<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>
<span class="co"># rifampicin isoniazid gatifloxacin ethambutol pyrazinamide moxifloxacin</span>
<span class="co"># 1 S S S S R R</span>
<span class="co"># 2 R S S S I R</span>
<span class="co"># 3 R R I R I R</span>
<span class="co"># 4 R R S S R I</span>
<span class="co"># 5 R R R I I I</span>
<span class="co"># 6 R I R R I I</span>
<span class="co"># 1 R I I I S R</span>
<span class="co"># 2 R R S R S S</span>
<span class="co"># 3 I R S S S R</span>
<span class="co"># 4 I I I R S I</span>
<span class="co"># 5 I R R I R S</span>
<span class="co"># 6 R R R R I S</span>
<span class="co"># kanamycin</span>
<span class="co"># 1 I</span>
<span class="co"># 2 R</span>
<span class="co"># 3 I</span>
<span class="co"># 4 I</span>
<span class="co"># 5 S</span>
<span class="co"># 6 I</span></pre></div>
<span class="co"># 1 S</span>
<span class="co"># 2 I</span>
<span class="co"># 3 R</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">
<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></pre></div>
<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>
<p>or its shortcut <code><a href="../reference/mdro.html">mdr_tb()</a></code>:</p>
<div class="sourceCode" id="cb10"><pre class="downlit">
<span class="va">my_TB_data</span><span class="op">$</span><span class="va">mdr</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/mdro.html">mdr_tb</a></span><span class="op">(</span><span class="va">my_TB_data</span><span class="op">)</span>
<span class="co"># ℹ No column found as input for `col_mo`, assuming all records</span>
<span class="co"># containMycobacterium tuberculosis.</span></pre></div>
<div class="sourceCode" id="cb10"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="va">my_TB_data</span><span class="op">$</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>
<p>Create a frequency table of the results:</p>
<div class="sourceCode" id="cb11"><pre class="downlit">
<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">$</span><span class="va">mdr</span><span class="op">)</span></pre></div>
<div class="sourceCode" id="cb11"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/pkg/cleaner/man/freq.html">freq</a></span><span class="op">(</span><span class="va">my_TB_data</span><span class="op">$</span><span class="va">mdr</span><span class="op">)</span></code></pre></div>
<p><strong>Frequency table</strong></p>
<p>Class: factor &gt; ordered (numeric)<br>
Length: 5,000<br>
@ -383,40 +382,40 @@ Unique: 5</p>
<tr class="odd">
<td align="left">1</td>
<td align="left">Mono-resistant</td>
<td align="right">3165</td>
<td align="right">63.30%</td>
<td align="right">3165</td>
<td align="right">63.30%</td>
<td align="right">3200</td>
<td align="right">64.00%</td>
<td align="right">3200</td>
<td align="right">64.00%</td>
</tr>
<tr class="even">
<td align="left">2</td>
<td align="left">Negative</td>
<td align="right">1000</td>
<td align="right">20.00%</td>
<td align="right">4165</td>
<td align="right">83.30%</td>
<td align="right">1014</td>
<td align="right">20.28%</td>
<td align="right">4214</td>
<td align="right">84.28%</td>
</tr>
<tr class="odd">
<td align="left">3</td>
<td align="left">Multi-drug-resistant</td>
<td align="right">463</td>
<td align="right">9.26%</td>
<td align="right">4628</td>
<td align="right">92.56%</td>
<td align="right">455</td>
<td align="right">9.10%</td>
<td align="right">4669</td>
<td align="right">93.38%</td>
</tr>
<tr class="even">
<td align="left">4</td>
<td align="left">Poly-resistant</td>
<td align="right">255</td>
<td align="right">5.10%</td>
<td align="right">4883</td>
<td align="right">97.66%</td>
<td align="right">252</td>
<td align="right">5.04%</td>
<td align="right">4921</td>
<td align="right">98.42%</td>
</tr>
<tr class="odd">
<td align="left">5</td>
<td align="left">Extensively drug-resistant</td>
<td align="right">117</td>
<td align="right">2.34%</td>
<td align="right">79</td>
<td align="right">1.58%</td>
<td align="right">5000</td>
<td align="right">100.00%</td>
</tr>

12
docs/articles/MDR_files/header-attrs-2.8/header-attrs.js Normal file
View File

@ -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);
}
});

177
docs/articles/PCA.html
View File

@ -39,7 +39,7 @@
</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">1.6.0.9011</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.7.0</span>
</span>
</div>
@ -47,14 +47,14 @@
<ul class="nav navbar-nav">
<li>
<a href="../index.html">
<span class="fa fa-home"></span>
<span class="fas fa-home"></span>
Home
</a>
</li>
<li class="dropdown">
<a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false">
<span class="fa fa-question-circle"></span>
<span class="fas fa-question-circle"></span>
How to
@ -63,77 +63,77 @@
<ul class="dropdown-menu" role="menu">
<li>
<a href="../articles/AMR.html">
<span class="fa fa-directions"></span>
<span class="fas fa-directions"></span>
Conduct AMR analysis
</a>
</li>
<li>
<a href="../articles/resistance_predict.html">
<span class="fa fa-dice"></span>
<span class="fas fa-dice"></span>
Predict antimicrobial resistance
</a>
</li>
<li>
<a href="../articles/datasets.html">
<span class="fa fa-database"></span>
<span class="fas fa-database"></span>
Data sets for download / own use
</a>
</li>
<li>
<a href="../articles/PCA.html">
<span class="fa fa-compress"></span>
<span class="fas fa-compress"></span>
Conduct principal component analysis for AMR
</a>
</li>
<li>
<a href="../articles/MDR.html">
<span class="fa fa-skull-crossbones"></span>
<span class="fas fa-skull-crossbones"></span>
Determine multi-drug resistance (MDR)
</a>
</li>
<li>
<a href="../articles/WHONET.html">
<span class="fa fa-globe-americas"></span>
<span class="fas fa-globe-americas"></span>
Work with WHONET data
</a>
</li>
<li>
<a href="../articles/SPSS.html">
<span class="fa fa-file-upload"></span>
<span class="fas fa-file-upload"></span>
Import data from SPSS/SAS/Stata
</a>
</li>
<li>
<a href="../articles/EUCAST.html">
<span class="fa fa-exchange-alt"></span>
<span class="fas fa-exchange-alt"></span>
Apply EUCAST rules
</a>
</li>
<li>
<a href="../reference/mo_property.html">
<span class="fa fa-bug"></span>
<span class="fas fa-bug"></span>
Get properties of a microorganism
</a>
</li>
<li>
<a href="../reference/ab_property.html">
<span class="fa fa-capsules"></span>
<span class="fas fa-capsules"></span>
Get properties of an antibiotic
</a>
</li>
<li>
<a href="../articles/benchmarks.html">
<span class="fa fa-shipping-fast"></span>
<span class="fas fa-shipping-fast"></span>
Other: benchmarks
</a>
@ -142,21 +142,21 @@
</li>
<li>
<a href="../reference/index.html">
<span class="fa fa-book-open"></span>
<span class="fas fa-book-open"></span>
Manual
</a>
</li>
<li>
<a href="../authors.html">
<span class="fa fa-users"></span>
<span class="fas fa-users"></span>
Authors
</a>
</li>
<li>
<a href="../news/index.html">
<span class="far fa far fa-newspaper"></span>
<span class="far fa-newspaper"></span>
Changelog
</a>
@ -165,14 +165,14 @@
<ul class="nav navbar-nav navbar-right">
<li>
<a href="https://github.com/msberends/AMR">
<span class="fab fa fab fa-github"></span>
<span class="fab fa-github"></span>
Source Code
</a>
</li>
<li>
<a href="../survey.html">
<span class="fa fa-clipboard-list"></span>
<span class="fas fa-clipboard-list"></span>
Survey
</a>
@ -187,8 +187,7 @@
</header><link href="PCA_files/anchor-sections-1.0/anchor-sections.css" rel="stylesheet">
<script src="PCA_files/anchor-sections-1.0/anchor-sections.js"></script><div class="row">
</header><script src="PCA_files/header-attrs-2.8/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>
@ -210,64 +209,64 @@
<h1 class="hasAnchor">
<a href="#transforming" class="anchor"></a>Transforming</h1>
<p>For PCA, we need to transform our AMR data first. This is what the <code>example_isolates</code> data set in this package looks like:</p>
<div class="sourceCode" id="cb1"><pre class="downlit">
<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>
<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://tibble.tidyverse.org/reference/glimpse.html">glimpse</a></span><span class="op">(</span><span class="va">example_isolates</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>
<span class="co"># Rows: 2,000</span>
<span class="co"># Columns: 49</span>
<span class="co"># $ date &lt;date&gt; 2002-01-02, 2002-01-03, 2002-01-07, 2002-01-07, 2002…</span>
<span class="co"># $ hospital_id &lt;fct&gt; D, D, B, B, B, B, D, D, B, B, D, D, D, D, D, B, B, B,…</span>
<span class="co"># $ ward_icu &lt;lgl&gt; FALSE, FALSE, TRUE, TRUE, TRUE, TRUE, FALSE, FALSE, T…</span>
<span class="co"># $ ward_clinical &lt;lgl&gt; TRUE, TRUE, FALSE, FALSE, FALSE, FALSE, TRUE, TRUE, F…</span>
<span class="co"># $ ward_outpatient &lt;lgl&gt; FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALS…</span>
<span class="co"># $ age &lt;dbl&gt; 65, 65, 45, 45, 45, 45, 78, 78, 45, 79, 67, 67, 71, 7…</span>
<span class="co"># $ gender &lt;chr&gt; "F", "F", "F", "F", "F", "F", "M", "M", "F", "F", "M"…</span>
<span class="co"># $ patient_id &lt;chr&gt; "A77334", "A77334", "067927", "067927", "067927", "06…</span>
<span class="co"># $ mo &lt;mo&gt; "B_ESCHR_COLI", "B_ESCHR_COLI", "B_STPHY_EPDR", "B_STP…</span>
<span class="co"># $ PEN &lt;rsi&gt; R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R,…</span>
<span class="co"># $ OXA &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span>
<span class="co"># $ FLC &lt;rsi&gt; NA, NA, R, R, R, R, S, S, R, S, S, S, NA, NA, NA, NA,…</span>
<span class="co"># $ AMX &lt;rsi&gt; NA, NA, NA, NA, NA, NA, R, R, NA, NA, NA, NA, NA, NA,…</span>
<span class="co"># $ AMC &lt;rsi&gt; I, I, NA, NA, NA, NA, S, S, NA, NA, S, S, I, I, R, I,…</span>
<span class="co"># $ AMP &lt;rsi&gt; NA, NA, NA, NA, NA, NA, R, R, NA, NA, NA, NA, NA, NA,…</span>
<span class="co"># $ TZP &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span>
<span class="co"># $ CZO &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span>
<span class="co"># $ FEP &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span>
<span class="co"># $ CXM &lt;rsi&gt; I, I, R, R, R, R, S, S, R, S, S, S, S, S, NA, S, S, R…</span>
<span class="co"># $ FOX &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span>
<span class="co"># $ CTX &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, S, S,…</span>
<span class="co"># $ CAZ &lt;rsi&gt; NA, NA, R, R, R, R, R, R, R, R, R, R, NA, NA, NA, S, …</span>
<span class="co"># $ CRO &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, S, S,…</span>
<span class="co"># $ GEN &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span>
<span class="co"># $ TOB &lt;rsi&gt; NA, NA, NA, NA, NA, NA, S, S, NA, NA, NA, NA, S, S, N…</span>
<span class="co"># $ AMK &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span>
<span class="co"># $ KAN &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span>
<span class="co"># $ TMP &lt;rsi&gt; R, R, S, S, R, R, R, R, S, S, NA, NA, S, S, S, S, S, …</span>
<span class="co"># $ SXT &lt;rsi&gt; R, R, S, S, NA, NA, NA, NA, S, S, NA, NA, S, S, S, S,…</span>
<span class="co"># $ NIT &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span>
<span class="co"># $ FOS &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span>
<span class="co"># $ LNZ &lt;rsi&gt; R, R, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, R, R…</span>
<span class="co"># $ CIP &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, S, S, NA, NA, NA, NA,…</span>
<span class="co"># $ MFX &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span>
<span class="co"># $ VAN &lt;rsi&gt; R, R, S, S, S, S, S, S, S, S, NA, NA, R, R, R, R, R, …</span>
<span class="co"># $ TEC &lt;rsi&gt; R, R, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, R, R…</span>
<span class="co"># $ TCY &lt;rsi&gt; R, R, S, S, S, S, S, S, S, I, S, S, NA, NA, I, R, R, …</span>
<span class="co"># $ TGC &lt;rsi&gt; NA, NA, S, S, S, S, S, S, S, NA, S, S, NA, NA, NA, R,…</span>
<span class="co"># $ DOX &lt;rsi&gt; NA, NA, S, S, S, S, S, S, S, NA, S, S, NA, NA, NA, R,…</span>
<span class="co"># $ ERY &lt;rsi&gt; R, R, R, R, R, R, S, S, R, S, S, S, R, R, R, R, R, R,…</span>
<span class="co"># $ CLI &lt;rsi&gt; R, R, NA, NA, NA, R, NA, NA, NA, NA, NA, NA, R, R, R,…</span>
<span class="co"># $ AZM &lt;rsi&gt; R, R, R, R, R, R, S, S, R, S, S, S, R, R, R, R, R, R,…</span>
<span class="co"># $ IPM &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, S, S,…</span>
<span class="co"># $ MEM &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span>
<span class="co"># $ MTR &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span>
<span class="co"># $ CHL &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span>
<span class="co"># $ COL &lt;rsi&gt; NA, NA, R, R, R, R, R, R, R, R, R, R, NA, NA, NA, R, …</span>
<span class="co"># $ MUP &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span>
<span class="co"># $ RIF &lt;rsi&gt; R, R, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, R, R…</span></pre></div>
<span class="co"># $ date &lt;date&gt; 2002-01-02, 2002-01-03, 2002-01-07, 2002-01-07, 2002-…</span>
<span class="co"># $ hospital_id &lt;fct&gt; D, D, B, B, B, B, D, D, B, B, D, D, D, D, D, B, B, B, …</span>
<span class="co"># $ ward_icu &lt;lgl&gt; FALSE, FALSE, TRUE, TRUE, TRUE, TRUE, FALSE, FALSE, TR…</span>
<span class="co"># $ ward_clinical &lt;lgl&gt; TRUE, TRUE, FALSE, FALSE, FALSE, FALSE, TRUE, TRUE, FA…</span>
<span class="co"># $ ward_outpatient &lt;lgl&gt; FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE…</span>
<span class="co"># $ age &lt;dbl&gt; 65, 65, 45, 45, 45, 45, 78, 78, 45, 79, 67, 67, 71, 71…</span>
<span class="co"># $ gender &lt;chr&gt; "F", "F", "F", "F", "F", "F", "M", "M", "F", "F", "M",…</span>
<span class="co"># $ patient_id &lt;chr&gt; "A77334", "A77334", "067927", "067927", "067927", "067…</span>
<span class="co"># $ mo &lt;mo&gt; "B_ESCHR_COLI", "B_ESCHR_COLI", "B_STPHY_EPDR", "B_STPH…</span>
<span class="co"># $ PEN &lt;rsi&gt; R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, …</span>
<span class="co"># $ OXA &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…</span>
<span class="co"># $ FLC &lt;rsi&gt; NA, NA, R, R, R, R, S, S, R, S, S, S, NA, NA, NA, NA, …</span>
<span class="co"># $ AMX &lt;rsi&gt; NA, NA, NA, NA, NA, NA, R, R, NA, NA, NA, NA, NA, NA, …</span>
<span class="co"># $ AMC &lt;rsi&gt; I, I, NA, NA, NA, NA, S, S, NA, NA, S, S, I, I, R, I, …</span>
<span class="co"># $ AMP &lt;rsi&gt; NA, NA, NA, NA, NA, NA, R, R, NA, NA, NA, NA, NA, NA, …</span>
<span class="co"># $ TZP &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…</span>
<span class="co"># $ CZO &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…</span>
<span class="co"># $ FEP &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…</span>
<span class="co"># $ CXM &lt;rsi&gt; I, I, R, R, R, R, S, S, R, S, S, S, S, S, NA, S, S, R,…</span>
<span class="co"># $ FOX &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…</span>
<span class="co"># $ CTX &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, S, S, …</span>
<span class="co"># $ CAZ &lt;rsi&gt; NA, NA, R, R, R, R, R, R, R, R, R, R, NA, NA, NA, S, S…</span>
<span class="co"># $ CRO &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, S, S, …</span>
<span class="co"># $ GEN &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…</span>
<span class="co"># $ TOB &lt;rsi&gt; NA, NA, NA, NA, NA, NA, S, S, NA, NA, NA, NA, S, S, NA…</span>
<span class="co"># $ AMK &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…</span>
<span class="co"># $ KAN &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…</span>
<span class="co"># $ TMP &lt;rsi&gt; R, R, S, S, R, R, R, R, S, S, NA, NA, S, S, S, S, S, R…</span>
<span class="co"># $ SXT &lt;rsi&gt; R, R, S, S, NA, NA, NA, NA, S, S, NA, NA, S, S, S, S, …</span>
<span class="co"># $ NIT &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…</span>
<span class="co"># $ FOS &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…</span>
<span class="co"># $ LNZ &lt;rsi&gt; R, R, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, R, R,…</span>
<span class="co"># $ CIP &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, S, S, NA, NA, NA, NA, …</span>
<span class="co"># $ MFX &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…</span>
<span class="co"># $ VAN &lt;rsi&gt; R, R, S, S, S, S, S, S, S, S, NA, NA, R, R, R, R, R, S…</span>
<span class="co"># $ TEC &lt;rsi&gt; R, R, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, R, R,…</span>
<span class="co"># $ TCY &lt;rsi&gt; R, R, S, S, S, S, S, S, S, I, S, S, NA, NA, I, R, R, S…</span>
<span class="co"># $ TGC &lt;rsi&gt; NA, NA, S, S, S, S, S, S, S, NA, S, S, NA, NA, NA, R, …</span>
<span class="co"># $ DOX &lt;rsi&gt; NA, NA, S, S, S, S, S, S, S, NA, S, S, NA, NA, NA, R, …</span>
<span class="co"># $ ERY &lt;rsi&gt; R, R, R, R, R, R, S, S, R, S, S, S, R, R, R, R, R, R, …</span>
<span class="co"># $ CLI &lt;rsi&gt; R, R, NA, NA, NA, R, NA, NA, NA, NA, NA, NA, R, R, R, …</span>
<span class="co"># $ AZM &lt;rsi&gt; R, R, R, R, R, R, S, S, R, S, S, S, R, R, R, R, R, R, …</span>
<span class="co"># $ IPM &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, S, S, …</span>
<span class="co"># $ MEM &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…</span>
<span class="co"># $ MTR &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…</span>
<span class="co"># $ CHL &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…</span>
<span class="co"># $ COL &lt;rsi&gt; NA, NA, R, R, R, R, R, R, R, R, R, R, NA, NA, NA, R, R…</span>
<span class="co"># $ MUP &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…</span>
<span class="co"># $ RIF &lt;rsi&gt; R, R, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, R, R,…</span></code></pre></div>
<p>Now to transform this to a data set with only resistance percentages per taxonomic order and genus:</p>
<div class="sourceCode" id="cb2"><pre class="downlit">
<span class="va">resistance_data</span> <span class="op">&lt;-</span> <span class="va">example_isolates</span> <span class="op">%&gt;%</span>
<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>
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>
@ -284,26 +283,26 @@
<span class="co"># 3 Bacteroidales Bacteroides NA NA NA NA NA NA NA NA</span>
<span class="co"># 4 Campylobacteral… Campylobacter NA NA NA NA NA NA NA NA</span>
<span class="co"># 5 Caryophanales Gemella NA NA NA NA NA NA NA NA</span>
<span class="co"># 6 Caryophanales Listeria NA NA NA NA NA NA NA NA</span></pre></div>
<span class="co"># 6 Caryophanales Listeria NA NA NA NA NA NA NA NA</span></code></pre></div>
</div>
<div id="perform-principal-component-analysis" class="section level1">
<h1 class="hasAnchor">
<a href="#perform-principal-component-analysis" class="anchor"></a>Perform principal component analysis</h1>
<p>The new <code><a href="../reference/pca.html">pca()</a></code> function will automatically filter on rows that contain numeric values in all selected variables, so we now only need to do:</p>
<div class="sourceCode" id="cb3"><pre class="downlit">
<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>
<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></pre></div>
<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>
<div class="sourceCode" id="cb4"><pre class="downlit">
<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>
<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>
<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>
<span class="co"># PC1 PC2 PC3 PC4 PC5 PC6 PC7</span>
<span class="co"># Standard deviation 2.1539 1.6807 0.6138 0.33879 0.20808 0.03140 5.121e-17</span>
<span class="co"># Proportion of Variance 0.5799 0.3531 0.0471 0.01435 0.00541 0.00012 0.000e+00</span>
<span class="co"># Cumulative Proportion 0.5799 0.9330 0.9801 0.99446 0.99988 1.00000 1.000e+00</span></pre></div>
<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>
@ -311,17 +310,17 @@
<div id="plotting-the-results" class="section level1">
<h1 class="hasAnchor">
<a href="#plotting-the-results" class="anchor"></a>Plotting the results</h1>
<div class="sourceCode" id="cb6"><pre class="downlit">
<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></pre></div>
<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>
<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">
<span class="fu"><a href="../reference/ggplot_pca.html">ggplot_pca</a></span><span class="op">(</span><span class="va">pca_result</span><span class="op">)</span></pre></div>
<div class="sourceCode" id="cb7"><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><span class="op">)</span></code></pre></div>
<p><img src="PCA_files/figure-html/unnamed-chunk-6-1.png" width="750"></p>
<p>You can also print an ellipse per group, and edit the appearance:</p>
<div class="sourceCode" id="cb8"><pre class="downlit">
<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></pre></div>
<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>
<p><img src="PCA_files/figure-html/unnamed-chunk-7-1.png" width="750"></p>
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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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@ -39,7 +39,7 @@
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@ -187,14 +187,13 @@
</header><link href="SPSS_files/anchor-sections-1.0/anchor-sections.css" rel="stylesheet">
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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 class="date">27 April 2021</h4>
<h4 class="date">24 May 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>
<div class="hidden name"><code>SPSS.Rmd</code></div>
@ -229,7 +228,7 @@
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<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">398,952 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">StackOverflow.com</a>, the largest online community for programmers. At the time of writing, <a href="https://stackoverflow.com/questions/tagged/r?sort=votes">403,383 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>
@ -248,8 +247,8 @@
</li>
</ul>
<p>To demonstrate the first point:</p>
<div class="sourceCode" id="cb1"><pre class="downlit">
<span class="co"># not all values are valid MIC values:</span>
<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="co"># not all values are valid MIC values:</span>
<span class="fu"><a href="../reference/as.mic.html">as.mic</a></span><span class="op">(</span><span class="fl">0.125</span><span class="op">)</span>
<span class="co"># Class &lt;mic&gt;</span>
<span class="co"># [1] 0.125</span>
@ -280,7 +279,7 @@
<span class="co"># [4] "fluclox" "flucloxacilina" "flucloxacillin" </span>
<span class="co"># [7] "flucloxacilline" "flucloxacillinum" "fluorochloroxacillin"</span>
<span class="fu"><a href="../reference/ab_property.html">ab_atc</a></span><span class="op">(</span><span class="st">"floxapen"</span><span class="op">)</span>
<span class="co"># [1] "J01CF05"</span></pre></div>
<span class="co"># [1] "J01CF05"</span></code></pre></div>
</div>
<div id="import-data-from-spsssasstata" class="section level2">
<h2 class="hasAnchor">
@ -296,8 +295,8 @@
<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>The difference is this:</p>
<div class="sourceCode" id="cb2"><pre class="downlit">
<span class="va">SPSS_data</span>
<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="va">SPSS_data</span>
<span class="co"># # A tibble: 4,203 x 4</span>
<span class="co"># v001 sex status statusage</span>
<span class="co"># &lt;dbl&gt; &lt;dbl+lbl&gt; &lt;dbl+lbl&gt; &lt;dbl&gt;</span>
@ -327,74 +326,74 @@
<span class="co"># 8 10011 Male alive 73.1</span>
<span class="co"># 9 10017 Male alive 56.7</span>
<span class="co"># 10 10018 Female alive 66.6</span>
<span class="co"># # … with 4,193 more rows</span></pre></div>
<span class="co"># # … with 4,193 more rows</span></code></pre></div>
</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>
<div class="sourceCode" id="cb3"><pre class="downlit">
<span class="co"># download and install the latest version:</span>
<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="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="http://haven.tidyverse.org">haven</a></span><span class="op">)</span> </pre></div>
<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>
<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>
<p>To read files from SPSS into R:</p>
<div class="sourceCode" id="cb4"><pre class="downlit">
<span class="co"># read any SPSS file based on file extension (best way):</span>
<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="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="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></pre></div>
<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>
<p>To export your R objects to the SPSS file format:</p>
<div class="sourceCode" id="cb5"><pre class="downlit">
<span class="co"># save as .sav file:</span>
<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="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></pre></div>
<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>
</div>
<div id="sas" class="section level4">
<h4 class="hasAnchor">
<a href="#sas" class="anchor"></a>SAS</h4>
<p>To read files from SAS into R:</p>
<div class="sourceCode" id="cb6"><pre class="downlit">
<span class="co"># read .sas7bdat + .sas7bcat files:</span>
<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="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></pre></div>
<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>
<p>To export your R objects to the SAS file format:</p>
<div class="sourceCode" id="cb7"><pre class="downlit">
<span class="co"># save as regular SAS file:</span>
<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="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></pre></div>
<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>
</div>
<div id="stata" class="section level4">
<h4 class="hasAnchor">
<a href="#stata" class="anchor"></a>Stata</h4>
<p>To read files from Stata into R:</p>
<div class="sourceCode" id="cb8"><pre class="downlit">
<span class="co"># read .dta file:</span>
<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="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></pre></div>
<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>
<p>To export your R objects to the Stata file format:</p>
<div class="sourceCode" id="cb9"><pre class="downlit">
<span class="co"># save as .dta file, Stata version 14:</span>
<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></pre></div>
<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>
</div>
</div>
</div>

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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;
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a = h.attributes;
while (a.length > 0) h.removeAttribute(a[0].name);
}
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79
docs/articles/WHONET.html
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@ -39,7 +39,7 @@
</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">1.6.0.9011</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.7.0</span>
</span>
</div>
@ -47,14 +47,14 @@
<ul class="nav navbar-nav">
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@ -63,77 +63,77 @@
<ul class="dropdown-menu" role="menu">
<li>
<a href="../articles/AMR.html">
<span class="fa fa-directions"></span>
<span class="fas fa-directions"></span>
Conduct AMR analysis
</a>
</li>
<li>
<a href="../articles/resistance_predict.html">
<span class="fa fa-dice"></span>
<span class="fas fa-dice"></span>
Predict antimicrobial resistance
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<a href="../articles/datasets.html">
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Data sets for download / own use
</a>
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<a href="../articles/PCA.html">
<span class="fa 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="fa fa-skull-crossbones"></span>
<span class="fas fa-skull-crossbones"></span>
Determine multi-drug resistance (MDR)
</a>
</li>
<li>
<a href="../articles/WHONET.html">
<span class="fa fa-globe-americas"></span>
<span class="fas fa-globe-americas"></span>
Work with WHONET data
</a>
</li>
<li>
<a href="../articles/SPSS.html">
<span class="fa fa-file-upload"></span>
<span class="fas fa-file-upload"></span>
Import data from SPSS/SAS/Stata
</a>
</li>
<li>
<a href="../articles/EUCAST.html">
<span class="fa fa-exchange-alt"></span>
<span class="fas fa-exchange-alt"></span>
Apply EUCAST rules
</a>
</li>
<li>
<a href="../reference/mo_property.html">
<span class="fa fa-bug"></span>
<span class="fas fa-bug"></span>
Get properties of a microorganism
</a>
</li>
<li>
<a href="../reference/ab_property.html">
<span class="fa fa-capsules"></span>
<span class="fas fa-capsules"></span>
Get properties of an antibiotic
</a>
</li>
<li>
<a href="../articles/benchmarks.html">
<span class="fa fa-shipping-fast"></span>
<span class="fas fa-shipping-fast"></span>
Other: benchmarks
</a>
@ -142,21 +142,21 @@
</li>
<li>
<a href="../reference/index.html">
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@ -165,14 +165,14 @@
<ul class="nav navbar-nav navbar-right">
<li>
<a href="https://github.com/msberends/AMR">
<span class="fab fa fab fa-github"></span>
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<a href="../survey.html">
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</a>
@ -187,8 +187,7 @@
</header><link href="WHONET_files/anchor-sections-1.0/anchor-sections.css" rel="stylesheet">
<script src="WHONET_files/anchor-sections-1.0/anchor-sections.js"></script><div class="row">
</header><script src="WHONET_files/header-attrs-2.8/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>
@ -206,38 +205,38 @@
<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>
<p>An example syntax could look like this:</p>
<div class="sourceCode" id="cb1"><pre class="downlit">
<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></pre></div>
<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>
<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>
<div class="sourceCode" id="cb2"><pre class="downlit">
<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="http://ggplot2.tidyverse.org">ggplot2</a></span><span class="op">)</span> <span class="co"># part of tidyverse</span>
<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></pre></div>
<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>
<p>We will have to transform some variables to simplify and automate the analysis:</p>
<ul>
<li>Microorganisms should be transformed to our own microorganism IDs (called an <code>mo</code>) using <a href="https://msberends.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>
<li>Antimicrobial results or interpretations have to be clean and valid. In other words, they should only contain values <code>"S"</code>, <code>"I"</code> or <code>"R"</code>. That is exactly where the <code><a href="../reference/as.rsi.html">as.rsi()</a></code> function is for.</li>
</ul>
<div class="sourceCode" id="cb3"><pre class="downlit">
<span class="co"># transform variables</span>
<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
<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="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></pre></div>
<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>
<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>So let’s check our data, with a couple of frequency tables:</p>
<div class="sourceCode" id="cb4"><pre class="downlit">
<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></pre></div>
<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>
<p><strong>Frequency table</strong></p>
<p>Class: character<br>
Length: 500<br>
@ -338,10 +337,10 @@ Longest: 40</p>
</tbody>
</table>
<p>(omitted 27 entries, n = 56 [11.20%])</p>
<div class="sourceCode" id="cb5"><pre class="downlit">
<span class="co"># our transformed antibiotic columns</span>
<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></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">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>
@ -392,11 +391,11 @@ Drug group: Beta-lactams/penicillins<br>
<h3 class="hasAnchor">
<a href="#a-first-glimpse-at-results" class="anchor"></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">
<span class="va">data</span> <span class="op">%&gt;%</span>
<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="../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></pre></div>
<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>
</div>

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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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@ -39,7 +39,7 @@
</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">1.6.0.9015</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.7.0</span>
</span>
</div>
@ -187,7 +187,7 @@
</header><script src="benchmarks_files/header-attrs-2.7/header-attrs.js"></script><div class="row">
</header><script src="benchmarks_files/header-attrs-2.8/header-attrs.js"></script><div class="row">
<div class="col-md-9 contents">
<div class="page-header toc-ignore">
<h1 data-toc-skip>Benchmarks</h1>
@ -224,21 +224,34 @@
<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://rdrr.io/r/base/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">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") 9.6 10.0 12.0 10 11.0 42 25</span>
<span class="co"># as.mo("stau") 52.0 55.0 74.0 59 94.0 110 25</span>
<span class="co"># as.mo("STAU") 51.0 53.0 73.0 57 91.0 100 25</span>
<span class="co"># as.mo("staaur") 9.5 9.9 11.0 10 12.0 13 25</span>
<span class="co"># as.mo("STAAUR") 9.4 10.0 18.0 10 12.0 52 25</span>
<span class="co"># as.mo("S. aureus") 24.0 26.0 31.0 26 29.0 62 25</span>
<span class="co"># as.mo("S aureus") 25.0 25.0 42.0 29 62.0 68 25</span>
<span class="co"># as.mo("Staphylococcus aureus") 2.5 2.9 3.2 3 3.5 4 25</span>
<span class="co"># as.mo("Staphylococcus aureus (MRSA)") 240.0 240.0 260.0 250 260.0 390 25</span>
<span class="co"># as.mo("Sthafilokkockus aaureuz") 150.0 190.0 190.0 190 190.0 250 25</span>
<span class="co"># as.mo("MRSA") 8.7 10.0 15.0 11 12.0 49 25</span>
<span class="co"># as.mo("VISA") 17.0 19.0 25.0 21 22.0 57 25</span></code></pre></div>
<span class="co"># expr min lq mean median uq max</span>
<span class="co"># as.mo("sau") 10.0 11.0 14.0 12.0 13.0 47.0</span>
<span class="co"># as.mo("stau") 53.0 56.0 78.0 58.0 94.0 220.0</span>
<span class="co"># as.mo("STAU") 53.0 54.0 69.0 58.0 92.0 99.0</span>
<span class="co"># as.mo("staaur") 10.0 11.0 15.0 11.0 12.0 47.0</span>
<span class="co"># as.mo("STAAUR") 9.4 11.0 14.0 11.0 12.0 64.0</span>
<span class="co"># as.mo("S. aureus") 24.0 26.0 44.0 33.0 63.0 69.0</span>
<span class="co"># as.mo("S aureus") 24.0 27.0 40.0 30.0 61.0 70.0</span>
<span class="co"># as.mo("Staphylococcus aureus") 2.6 3.1 3.2 3.2 3.3 3.8</span>
<span class="co"># as.mo("Staphylococcus aureus (MRSA)") 230.0 250.0 250.0 250.0 250.0 280.0</span>
<span class="co"># as.mo("Sthafilokkockus aaureuz") 180.0 190.0 200.0 190.0 200.0 330.0</span>
<span class="co"># as.mo("MRSA") 10.0 11.0 16.0 12.0 13.0 47.0</span>
<span class="co"># as.mo("VISA") 17.0 19.0 22.0 19.0 22.0 63.0</span>
<span class="co"># neval</span>
<span class="co"># 25</span>
<span class="co"># 25</span>
<span class="co"># 25</span>
<span class="co"># 25</span>
<span class="co"># 25</span>
<span class="co"># 25</span>
<span class="co"># 25</span>
<span class="co"># 25</span>
<span class="co"># 25</span>
<span class="co"># 25</span>
<span class="co"># 25</span>
<span class="co"># 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>
@ -260,8 +273,8 @@
<span class="co"># what do these values look like? They are of class &lt;mo&gt;:</span>
<span class="fu"><a href="https://rdrr.io/r/utils/head.html">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_KLBSL_PNMN B_STPHY_EPDR B_STRPT_PNMN B_STRPT_EQNS B_ESCHR_COLI</span>
<span class="co"># [6] B_KLBSL_PNMN</span>
<span class="co"># [1] B_STRPT_PNMN B_STPHY_CONS B_ESCHR_COLI B_STRPT_PNMN B_ESCHR_COLI</span>
<span class="co"># [6] B_ENTRC_FCLS</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>
@ -274,11 +287,11 @@
<span class="co"># now let's see:</span>
<span class="va">run_it</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/microbenchmark/man/microbenchmark.html">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://rdrr.io/r/base/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">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) 155 182 233 228 242 350 10</span></code></pre></div>
<p>So getting official taxonomic names of 2,000,000 (!!) items consisting of 90 unique values only takes 0.228 seconds. That is 114 nanoseconds on average. You only lose time on your unique input values.</p>
<span class="co"># mo_name(x) 180 186 215 221 222 281 10</span></code></pre></div>
<p>So getting official taxonomic names of 2,000,000 (!!) items consisting of 90 unique values only takes 0.221 seconds. That is 110 nanoseconds on average. You only lose time on your unique input values.</p>
</div>
<div id="precalculated-results" class="section level3">
<h3 class="hasAnchor">
@ -289,13 +302,13 @@
B <span class="op">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_name</a></span><span class="op">(</span><span class="st">"S. aureus"</span><span class="op">)</span>,
C <span class="op">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_name</a></span><span class="op">(</span><span class="st">"Staphylococcus aureus"</span><span class="op">)</span>,
times <span class="op">=</span> <span class="fl">10</span><span class="op">)</span>
<span class="fu"><a href="https://rdrr.io/r/base/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">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 6.62 6.84 7.30 6.91 8.10 8.74 10</span>
<span class="co"># B 22.20 23.10 33.20 24.50 27.70 70.80 10</span>
<span class="co"># C 1.37 1.50 1.66 1.71 1.81 1.84 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 0.0017 seconds - it doesn’t even start calculating <em>if the result would be the same as the expected resulting value</em>. That goes for all helper functions:</p>
<span class="co"># A 7.45 7.68 8.13 8.23 8.39 8.95 10</span>
<span class="co"># B 22.70 22.90 31.30 23.90 26.50 62.00 10</span>
<span class="co"># C 1.51 1.58 1.79 1.84 1.97 1.99 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 0.0018 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>,
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>,
@ -306,17 +319,17 @@
G <span class="op">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_phylum</a></span><span class="op">(</span><span class="st">"Firmicutes"</span><span class="op">)</span>,
H <span class="op">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_kingdom</a></span><span class="op">(</span><span class="st">"Bacteria"</span><span class="op">)</span>,
times <span class="op">=</span> <span class="fl">10</span><span class="op">)</span>
<span class="fu"><a href="https://rdrr.io/r/base/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">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 1.19 1.21 1.43 1.28 1.65 2.03 10</span>
<span class="co"># B 1.19 1.24 1.38 1.26 1.53 1.83 10</span>
<span class="co"># C 1.24 1.28 1.45 1.36 1.41 2.24 10</span>
<span class="co"># D 1.20 1.22 1.33 1.23 1.41 1.77 10</span>
<span class="co"># E 1.21 1.22 1.35 1.27 1.38 1.69 10</span>
<span class="co"># F 1.20 1.21 1.34 1.26 1.37 1.71 10</span>
<span class="co"># G 1.17 1.22 1.31 1.27 1.31 1.71 10</span>
<span class="co"># H 1.21 1.27 1.42 1.37 1.47 1.84 10</span></code></pre></div>
<span class="co"># A 1.44 1.44 1.51 1.46 1.56 1.68 10</span>
<span class="co"># B 1.37 1.40 1.53 1.48 1.67 1.81 10</span>
<span class="co"># C 1.42 1.47 1.61 1.59 1.68 1.84 10</span>
<span class="co"># D 1.39 1.43 1.52 1.46 1.53 1.92 10</span>
<span class="co"># E 1.41 1.46 1.58 1.50 1.70 1.91 10</span>
<span class="co"># F 1.36 1.44 1.49 1.46 1.57 1.64 10</span>
<span class="co"># G 1.37 1.44 1.64 1.48 1.82 2.40 10</span>
<span class="co"># H 1.41 1.43 1.52 1.48 1.54 1.87 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">
@ -341,16 +354,16 @@
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://rdrr.io/r/base/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">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 17.19 17.50 22.00 17.76 18.54 61.02 100</span>
<span class="co"># de 31.08 31.53 39.66 32.04 35.34 76.23 100</span>
<span class="co"># nl 30.19 31.41 40.64 32.30 35.93 79.53 100</span>
<span class="co"># es 30.50 31.33 41.70 31.84 35.90 181.80 100</span>
<span class="co"># it 20.94 21.37 25.27 21.63 22.44 69.84 100</span>
<span class="co"># fr 20.62 21.00 27.09 21.41 23.12 79.50 100</span>
<span class="co"># pt 20.41 21.23 25.33 21.56 22.22 70.65 100</span></code></pre></div>
<span class="co"># en 17.63 17.96 22.55 18.30 18.78 68.16 100</span>
<span class="co"># de 28.48 28.93 33.83 29.18 29.85 76.90 100</span>
<span class="co"># nl 32.10 33.11 43.48 33.70 35.84 202.50 100</span>
<span class="co"># es 32.17 32.67 43.51 33.33 66.07 78.29 100</span>
<span class="co"># it 21.95 22.29 26.55 22.59 23.13 70.41 100</span>
<span class="co"># fr 21.59 21.94 25.36 22.31 22.88 63.46 100</span>
<span class="co"># pt 21.75 22.15 28.50 22.48 23.01 68.10 100</span></code></pre></div>
<p>Currently supported non-English languages are German, Dutch, Spanish, Italian, French and Portuguese.</p>
</div>
</div>

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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);
}
});

2
docs/articles/datasets.html
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@ -39,7 +39,7 @@
</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">1.6.0.9065</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.7.0</span>
</span>
</div>

2
docs/articles/index.html
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@ -81,7 +81,7 @@
</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">1.6.0.9065</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.7.0</span>
</span>
</div>

113
docs/articles/resistance_predict.html
View File

@ -39,7 +39,7 @@
</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">1.6.0.9011</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.7.0</span>
</span>
</div>
@ -47,14 +47,14 @@
<ul class="nav navbar-nav">
<li>
<a href="../index.html">
<span class="fa fa-home"></span>
<span class="fas fa-home"></span>
Home
</a>
</li>
<li class="dropdown">
<a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false">
<span class="fa fa-question-circle"></span>
<span class="fas fa-question-circle"></span>
How to
@ -63,77 +63,77 @@
<ul class="dropdown-menu" role="menu">
<li>
<a href="../articles/AMR.html">
<span class="fa fa-directions"></span>
<span class="fas fa-directions"></span>
Conduct AMR analysis
</a>
</li>
<li>
<a href="../articles/resistance_predict.html">
<span class="fa fa-dice"></span>
<span class="fas fa-dice"></span>
Predict antimicrobial resistance
</a>
</li>
<li>
<a href="../articles/datasets.html">
<span class="fa fa-database"></span>
<span class="fas fa-database"></span>
Data sets for download / own use
</a>
</li>
<li>
<a href="../articles/PCA.html">
<span class="fa fa-compress"></span>
<span class="fas fa-compress"></span>
Conduct principal component analysis for AMR
</a>
</li>
<li>
<a href="../articles/MDR.html">
<span class="fa fa-skull-crossbones"></span>
<span class="fas fa-skull-crossbones"></span>
Determine multi-drug resistance (MDR)
</a>
</li>
<li>
<a href="../articles/WHONET.html">
<span class="fa fa-globe-americas"></span>
<span class="fas fa-globe-americas"></span>
Work with WHONET data
</a>
</li>
<li>
<a href="../articles/SPSS.html">
<span class="fa fa-file-upload"></span>
<span class="fas fa-file-upload"></span>
Import data from SPSS/SAS/Stata
</a>
</li>
<li>
<a href="../articles/EUCAST.html">
<span class="fa fa-exchange-alt"></span>
<span class="fas fa-exchange-alt"></span>
Apply EUCAST rules
</a>
</li>
<li>
<a href="../reference/mo_property.html">
<span class="fa fa-bug"></span>
<span class="fas fa-bug"></span>
Get properties of a microorganism
</a>
</li>
<li>
<a href="../reference/ab_property.html">
<span class="fa fa-capsules"></span>
<span class="fas fa-capsules"></span>
Get properties of an antibiotic
</a>
</li>
<li>
<a href="../articles/benchmarks.html">
<span class="fa fa-shipping-fast"></span>
<span class="fas fa-shipping-fast"></span>
Other: benchmarks
</a>
@ -142,21 +142,21 @@
</li>
<li>
<a href="../reference/index.html">
<span class="fa fa-book-open"></span>
<span class="fas fa-book-open"></span>
Manual
</a>
</li>
<li>
<a href="../authors.html">
<span class="fa fa-users"></span>
<span class="fas fa-users"></span>
Authors
</a>
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<li>
<a href="../news/index.html">
<span class="far fa far fa-newspaper"></span>
<span class="far fa-newspaper"></span>
Changelog
</a>
@ -165,14 +165,14 @@
<ul class="nav navbar-nav navbar-right">
<li>
<a href="https://github.com/msberends/AMR">
<span class="fab fa fab fa-github"></span>
<span class="fab fa-github"></span>
Source Code
</a>
</li>
<li>
<a href="../survey.html">
<span class="fa fa-clipboard-list"></span>
<span class="fas fa-clipboard-list"></span>
Survey
</a>
@ -187,8 +187,7 @@
</header><link href="resistance_predict_files/anchor-sections-1.0/anchor-sections.css" rel="stylesheet">
<script src="resistance_predict_files/anchor-sections-1.0/anchor-sections.js"></script><div class="row">
</header><script src="resistance_predict_files/header-attrs-2.8/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 predict antimicrobial resistance</h1>
@ -206,37 +205,37 @@
<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>
<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">
<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="http://ggplot2.tidyverse.org">ggplot2</a></span><span class="op">)</span>
<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>
<span class="co"># (if not yet installed, install with:)</span>
<span class="co"># install.packages(c("tidyverse", "AMR"))</span></pre></div>
<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>
<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"><a class="sourceLine" id="cb2-1" title="1"><span class="co"># resistance prediction of piperacillin/tazobactam (TZP):</span></a>
<a class="sourceLine" id="cb2-2" title="2"><span class="kw">resistance_predict</span>(<span class="dt">tbl =</span> example_isolates, <span class="dt">col_date =</span> <span class="st">"date"</span>, <span class="dt">col_ab =</span> <span class="st">"TZP"</span>, <span class="dt">model =</span> <span class="st">"binomial"</span>)</a>
<a class="sourceLine" id="cb2-3" title="3"></a>
<a class="sourceLine" id="cb2-4" title="4"><span class="co"># or:</span></a>
<a class="sourceLine" id="cb2-5" title="5">example_isolates <span class="op">%&gt;%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb2-6" title="6"><span class="st"> </span><span class="kw">resistance_predict</span>(<span class="dt">col_ab =</span> <span class="st">"TZP"</span>,</a>
<a class="sourceLine" id="cb2-7" title="7"> model <span class="st">"binomial"</span>)</a>
<a class="sourceLine" id="cb2-8" title="8"></a>
<a class="sourceLine" id="cb2-9" title="9"><span class="co"># to bind it to object 'predict_TZP' for example:</span></a>
<a class="sourceLine" id="cb2-10" title="10">predict_TZP &lt;-<span class="st"> </span>example_isolates <span class="op">%&gt;%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb2-11" title="11"><span class="st"> </span><span class="kw">resistance_predict</span>(<span class="dt">col_ab =</span> <span class="st">"TZP"</span>,</a>
<a class="sourceLine" id="cb2-12" title="12"> <span class="dt">model =</span> <span class="st">"binomial"</span>)</a></code></pre></div>
<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a><span class="co"># resistance prediction of piperacillin/tazobactam (TZP):</span></span>
<span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a><span class="fu">resistance_predict</span>(<span class="at">tbl =</span> example_isolates, <span class="at">col_date =</span> <span class="st">"date"</span>, <span class="at">col_ab =</span> <span class="st">"TZP"</span>, <span class="at">model =</span> <span class="st">"binomial"</span>)</span>
<span id="cb2-3"><a href="#cb2-3" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb2-4"><a href="#cb2-4" aria-hidden="true" tabindex="-1"></a><span class="co"># or:</span></span>
<span id="cb2-5"><a href="#cb2-5" aria-hidden="true" tabindex="-1"></a>example_isolates <span class="sc">%&gt;%</span> </span>
<span id="cb2-6"><a href="#cb2-6" aria-hidden="true" tabindex="-1"></a> <span class="fu">resistance_predict</span>(<span class="at">col_ab =</span> <span class="st">"TZP"</span>,</span>
<span id="cb2-7"><a href="#cb2-7" aria-hidden="true" tabindex="-1"></a> model <span class="st">"binomial"</span>)</span>
<span id="cb2-8"><a href="#cb2-8" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb2-9"><a href="#cb2-9" aria-hidden="true" tabindex="-1"></a><span class="co"># to bind it to object 'predict_TZP' for example:</span></span>
<span id="cb2-10"><a href="#cb2-10" aria-hidden="true" tabindex="-1"></a>predict_TZP <span class="ot">&lt;-</span> example_isolates <span class="sc">%&gt;%</span> </span>
<span id="cb2-11"><a href="#cb2-11" aria-hidden="true" tabindex="-1"></a> <span class="fu">resistance_predict</span>(<span class="at">col_ab =</span> <span class="st">"TZP"</span>,</span>
<span id="cb2-12"><a href="#cb2-12" aria-hidden="true" tabindex="-1"></a> <span class="at">model =</span> <span class="st">"binomial"</span>)</span></code></pre></div>
<p>The function will look for a date column itself if <code>col_date</code> is not set.</p>
<p>When running any of these commands, a summary of the regression model will be printed unless using <code><a href="../reference/resistance_predict.html">resistance_predict(..., info = FALSE)</a></code>.</p>
<pre><code># ℹ Using column 'date' as input for `col_date`.</code></pre>
<p>This text is only a printed summary - the actual result (output) of the function is a <code>data.frame</code> containing for each year: the number of observations, the actual observed resistance, the estimated resistance and the standard error below and above the estimation:</p>
<div class="sourceCode" id="cb4"><pre class="downlit">
<span class="va">predict_TZP</span>
<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="va">predict_TZP</span>
<span class="co"># year value se_min se_max observations observed estimated</span>
<span class="co"># 1 2002 0.20000000 NA NA 15 0.20000000 0.05616378</span>
<span class="co"># 2 2003 0.06250000 NA NA 32 0.06250000 0.06163839</span>
@ -267,30 +266,31 @@
<span class="co"># 27 2028 0.43730688 0.3418075 0.5328063 NA NA 0.43730688</span>
<span class="co"># 28 2029 0.46175755 0.3597639 0.5637512 NA NA 0.46175755</span>
<span class="co"># 29 2030 0.48639359 0.3782932 0.5944939 NA NA 0.48639359</span>
<span class="co"># 30 2031 0.51109592 0.3973697 0.6248221 NA NA 0.51109592</span></pre></div>
<span class="co"># 30 2031 0.51109592 0.3973697 0.6248221 NA NA 0.51109592</span></code></pre></div>
<p>The function <code>plot</code> is available in base R, and can be extended by other packages to depend the output based on the type of input. We extended its function to cope with resistance predictions:</p>
<div class="sourceCode" id="cb5"><pre class="downlit">
<span class="fu"><a href="../reference/plot.html">plot</a></span><span class="op">(</span><span class="va">predict_TZP</span><span class="op">)</span></pre></div>
<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="fu"><a href="../reference/plot.html">plot</a></span><span class="op">(</span><span class="va">predict_TZP</span><span class="op">)</span></code></pre></div>
<p><img src="resistance_predict_files/figure-html/unnamed-chunk-4-1.png" width="720"></p>
<p>This is the fastest way to plot the result. It automatically adds the right axes, error bars, titles, number of available observations and type of model.</p>
<p>We also support the <code>ggplot2</code> package with our custom function <code><a href="../reference/resistance_predict.html">ggplot_rsi_predict()</a></code> to create more appealing plots:</p>
<div class="sourceCode" id="cb6"><pre class="downlit">
<span class="fu"><a href="../reference/resistance_predict.html">ggplot_rsi_predict</a></span><span class="op">(</span><span class="va">predict_TZP</span><span class="op">)</span></pre></div>
<div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="fu"><a href="../reference/resistance_predict.html">ggplot_rsi_predict</a></span><span class="op">(</span><span class="va">predict_TZP</span><span class="op">)</span></code></pre></div>
<p><img src="resistance_predict_files/figure-html/unnamed-chunk-5-1.png" width="720"></p>
<div class="sourceCode" id="cb7"><pre class="downlit">
<div class="sourceCode" id="cb7"><pre class="downlit sourceCode r">
<code class="sourceCode R">
<span class="co"># choose for error bars instead of a ribbon</span>
<span class="fu"><a href="../reference/resistance_predict.html">ggplot_rsi_predict</a></span><span class="op">(</span><span class="va">predict_TZP</span>, ribbon <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></pre></div>
<span class="fu"><a href="../reference/resistance_predict.html">ggplot_rsi_predict</a></span><span class="op">(</span><span class="va">predict_TZP</span>, ribbon <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></code></pre></div>
<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>
<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">
<span class="va">example_isolates</span> <span class="op">%&gt;%</span>
<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="../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></pre></div>
<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>
<p><img src="resistance_predict_files/figure-html/unnamed-chunk-6-1.png" width="720"></p>
<p>Vancomycin resistance could be 100% in ten years, but might also stay around 0%.</p>
<p>You can define the model with the <code>model</code> parameter. The model chosen above is a generalised linear regression model using a binomial distribution, assuming that a period of zero resistance was followed by a period of increasing resistance leading slowly to more and more resistance.</p>
@ -331,16 +331,17 @@
</tbody>
</table>
<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">
<span class="va">example_isolates</span> <span class="op">%&gt;%</span>
<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="../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></pre></div>
<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>
<p><img src="resistance_predict_files/figure-html/unnamed-chunk-7-1.png" width="720"></p>
<p>This seems more likely, doesn’t it?</p>
<p>The model itself is also available from the object, as an <code>attribute</code>:</p>
<div class="sourceCode" id="cb10"><pre class="downlit">
<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">$</span><span class="va">model</span>
<div class="sourceCode" id="cb10"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="va">model</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/attributes.html">attributes</a></span><span class="op">(</span><span class="va">predict_TZP</span><span class="op">)</span><span class="op">$</span><span class="va">model</span>
<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">$</span><span class="va">family</span>
<span class="co"># </span>
@ -350,7 +351,7 @@
<span class="fu"><a href="https://rdrr.io/r/base/summary.html">summary</a></span><span class="op">(</span><span class="va">model</span><span class="op">)</span><span class="op">$</span><span class="va">coefficients</span>
<span class="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></pre></div>
<span class="co"># year 0.09883005 0.02295317 4.305725 1.664395e-05</span></code></pre></div>
</div>
</div>
</div>

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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);
}
});

41
docs/articles/welcome_to_AMR.html
View File

@ -39,7 +39,7 @@
</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">1.6.0.9011</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.7.0</span>
</span>
</div>
@ -47,14 +47,14 @@
<ul class="nav navbar-nav">
<li>
<a href="../index.html">
<span class="fa fa-home"></span>
<span class="fas fa-home"></span>
Home
</a>
</li>
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<a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false">
<span class="fa fa-question-circle"></span>
<span class="fas fa-question-circle"></span>
How to
@ -63,77 +63,77 @@
<ul class="dropdown-menu" role="menu">
<li>
<a href="../articles/AMR.html">
<span class="fa fa-directions"></span>
<span class="fas fa-directions"></span>
Conduct AMR analysis
</a>
</li>
<li>
<a href="../articles/resistance_predict.html">
<span class="fa fa-dice"></span>
<span class="fas fa-dice"></span>
Predict antimicrobial resistance
</a>
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<li>
<a href="../articles/datasets.html">
<span class="fa fa-database"></span>
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Data sets for download / own use
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<span class="fa 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="fa fa-skull-crossbones"></span>
<span class="fas fa-skull-crossbones"></span>
Determine multi-drug resistance (MDR)
</a>
</li>
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<a href="../articles/WHONET.html">
<span class="fa fa-globe-americas"></span>
<span class="fas fa-globe-americas"></span>
Work with WHONET data
</a>
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<li>
<a href="../articles/SPSS.html">
<span class="fa fa-file-upload"></span>
<span class="fas fa-file-upload"></span>
Import data from SPSS/SAS/Stata
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<a href="../articles/EUCAST.html">
<span class="fa fa-exchange-alt"></span>
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Apply EUCAST rules
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<a href="../reference/mo_property.html">
<span class="fa fa-bug"></span>
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Get properties of a microorganism
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<a href="../reference/ab_property.html">
<span class="fa fa-capsules"></span>
<span class="fas fa-capsules"></span>
Get properties of an antibiotic
</a>
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<li>
<a href="../articles/benchmarks.html">
<span class="fa fa-shipping-fast"></span>
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Other: benchmarks
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@ -142,21 +142,21 @@
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<a href="../reference/index.html">
<span class="fa fa-book-open"></span>
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<a href="../news/index.html">
<span class="far fa far fa-newspaper"></span>
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Changelog
</a>
@ -165,14 +165,14 @@
<ul class="nav navbar-nav navbar-right">
<li>
<a href="https://github.com/msberends/AMR">
<span class="fab fa fab fa-github"></span>
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<li>
<a href="../survey.html">
<span class="fa fa-clipboard-list"></span>
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Survey
</a>
@ -187,8 +187,7 @@
</header><link href="welcome_to_AMR_files/anchor-sections-1.0/anchor-sections.css" rel="stylesheet">
<script src="welcome_to_AMR_files/anchor-sections-1.0/anchor-sections.js"></script><div class="row">
</header><script src="welcome_to_AMR_files/header-attrs-2.8/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>

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);
}
});