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</button>
<span class="navbar-brand">
<a class="navbar-link" href="https://msberends.github.io/AMR/index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.8.0.9010</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.8.1</span>
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@ -205,14 +205,12 @@ Content not found. Please use links in the navbar.
<footer><div class="copyright">
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<p>Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein,
Erwin E. A. Hassing.</p>
<p>Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein, Erwin E. A. Hassing.</p>
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</button>
<span class="navbar-brand">
<a class="navbar-link" href="index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.8.0.9010</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.8.1</span>
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@ -416,13 +416,11 @@ END OF TERMS AND CONDITIONS
<footer><div class="copyright">
<p></p><p>Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein,
Erwin E. A. Hassing.</p>
<p></p><p>Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein, Erwin E. A. Hassing.</p>
</div>
<div class="pkgdown">
<p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a>
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</header><div class="row">
</header><script src="EUCAST_files/accessible-code-block-0.0.1/empty-anchor.js"></script><div class="row">
<div class="col-md-9 contents">
<div class="page-header toc-ignore">
<h1 data-toc-skip>How to apply EUCAST rules</h1>
@ -201,39 +201,18 @@
<div class="section level2">
<h2 id="introduction">Introduction<a class="anchor" aria-label="anchor" href="#introduction"></a>
</h2>
<p>What are EUCAST rules? The European Committee on Antimicrobial
Susceptibility Testing (EUCAST) states <a href="https://www.eucast.org/expert_rules_and_intrinsic_resistance/" class="external-link">on
their website</a>:</p>
<p>What are EUCAST rules? The European Committee on Antimicrobial Susceptibility Testing (EUCAST) states <a href="https://www.eucast.org/expert_rules_and_intrinsic_resistance/" class="external-link">on their website</a>:</p>
<blockquote>
<p><em>EUCAST expert rules are a tabulated collection of expert
knowledge on intrinsic resistances, exceptional resistance phenotypes
and interpretive rules that may be applied to antimicrobial
susceptibility testing in order to reduce errors and make appropriate
recommendations for reporting particular resistances.</em></p>
<p><em>EUCAST expert rules are a tabulated collection of expert knowledge on intrinsic resistances, exceptional resistance phenotypes and interpretive rules that may be applied to antimicrobial susceptibility testing in order to reduce errors and make appropriate recommendations for reporting particular resistances.</em></p>
</blockquote>
<p>In Europe, a lot of medical microbiological laboratories already
apply these rules (<a href="https://www.eurosurveillance.org/content/10.2807/1560-7917.ES2015.20.2.21008" class="external-link">Brown
<em>et al.</em>, 2015</a>). Our package features their latest insights
on intrinsic resistance and unusual phenotypes (v3.3, 2021).</p>
<p>Moreover, the <code><a href="../reference/eucast_rules.html">eucast_rules()</a></code> function we use for this
purpose can also apply additional rules, like forcing
<help title="ATC: J01CA01">ampicillin</help> = R in isolates when
<help title="ATC: J01CR02">amoxicillin/clavulanic acid</help> = R.</p>
<p>In Europe, a lot of medical microbiological laboratories already apply these rules (<a href="https://www.eurosurveillance.org/content/10.2807/1560-7917.ES2015.20.2.21008" class="external-link">Brown <em>et al.</em>, 2015</a>). Our package features their latest insights on intrinsic resistance and unusual phenotypes (v3.3, 2021).</p>
<p>Moreover, the <code><a href="../reference/eucast_rules.html">eucast_rules()</a></code> function we use for this purpose can also apply additional rules, like forcing <help title="ATC: J01CA01">ampicillin</help> = R in isolates when <help title="ATC: J01CR02">amoxicillin/clavulanic acid</help> = R.</p>
</div>
<div class="section level2">
<h2 id="examples">Examples<a class="anchor" aria-label="anchor" href="#examples"></a>
</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>
<p>These rules can be used to discard impossible bug-drug combinations in your data. For example, <em>Klebsiella</em> produces beta-lactamase that prevents ampicillin (or amoxicillin) from working against it. In other words, practically every strain of <em>Klebsiella</em> is resistant to ampicillin.</p>
<p>Sometimes, laboratory data can still contain such strains with ampicillin being susceptible to ampicillin. This could be because an antibiogram is available before an identification is available, and the antibiogram is then not re-interpreted based on the identification (namely, <em>Klebsiella</em>). EUCAST expert rules solve this, that can be applied using <code><a href="../reference/eucast_rules.html">eucast_rules()</a></code>:</p>
<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="va">oops</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html" class="external-link">data.frame</a></span><span class="op">(</span>mo <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"Klebsiella"</span>,
<span class="st">"Escherichia"</span><span class="op">)</span>,
@ -247,10 +226,7 @@ be applied using <code><a href="../reference/eucast_rules.html">eucast_rules()</
<span class="co"># mo ampicillin</span>
<span class="co"># 1 Klebsiella R</span>
<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>
<p>A more convenient function is <code><a href="../reference/mo_property.html">mo_is_intrinsic_resistant()</a></code> that uses the same guideline, but allows to check for one or more specific microorganisms or antibiotics:</p>
<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="fu"><a href="../reference/mo_property.html">mo_is_intrinsic_resistant</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"Klebsiella"</span>, <span class="st">"Escherichia"</span><span class="op">)</span>,
<span class="st">"ampicillin"</span><span class="op">)</span>
@ -259,11 +235,7 @@ antibiotics:</p>
<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" class="external-link">c</a></span><span class="op">(</span><span class="st">"ampicillin"</span>, <span class="st">"kanamycin"</span><span class="op">)</span><span class="op">)</span>
<span class="co"># [1] TRUE FALSE</span></code></pre></div>
<p>EUCAST rules can not only be used for correction, they can also be
used for filling in known resistance and susceptibility based on results
of other antimicrobials drugs. This process is called <em>interpretive
reading</em>, is basically a form of imputation, and is part of the
<code><a href="../reference/eucast_rules.html">eucast_rules()</a></code> function as well:</p>
<p>EUCAST rules can not only be used for correction, they can also be used for filling in known resistance and susceptibility based on results of other antimicrobials drugs. This process is called <em>interpretive reading</em>, is basically a form of imputation, and is part of the <code><a href="../reference/eucast_rules.html">eucast_rules()</a></code> function as well:</p>
<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="va">data</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html" class="external-link">data.frame</a></span><span class="op">(</span>mo <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"Staphylococcus aureus"</span>,
<span class="st">"Enterococcus faecalis"</span>,
@ -425,14 +397,12 @@ reading</em>, is basically a form of imputation, and is part of the
<footer><div class="copyright">
<p></p>
<p>Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein,
Erwin E. A. Hassing.</p>
<p>Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein, Erwin E. A. Hassing.</p>
</div>
<div class="pkgdown">
<p></p>
<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a>
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<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.2.</p>
</div>
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// Written by JooYoung Seo (jooyoung@psu.edu) and Atsushi Yasumoto on June 1st, 2020.
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</header><div class="row">
</header><script src="MDR_files/accessible-code-block-0.0.1/empty-anchor.js"></script><div class="row">
<div class="col-md-9 contents">
<div class="page-header toc-ignore">
<h1 data-toc-skip>How to determine multi-drug resistance
(MDR)</h1>
<h1 data-toc-skip>How to determine multi-drug resistance (MDR)</h1>
<small class="dont-index">Source: <a href="https://github.com/msberends/AMR/blob/HEAD/vignettes/MDR.Rmd" class="external-link"><code>vignettes/MDR.Rmd</code></a></small>
@ -199,87 +198,54 @@
<p>With the function <code><a href="../reference/mdro.html">mdro()</a></code>, you can determine which
micro-organisms are multi-drug resistant organisms (MDRO).</p>
<p>With the function <code><a href="../reference/mdro.html">mdro()</a></code>, you can determine which micro-organisms are multi-drug resistant organisms (MDRO).</p>
<div class="section level3">
<h3 id="type-of-input">Type of input<a class="anchor" aria-label="anchor" href="#type-of-input"></a>
</h3>
<p>The <code><a href="../reference/mdro.html">mdro()</a></code> function takes a data set as input, such as a
regular <code>data.frame</code>. It tries to automatically determine the
right columns for info about your isolates, such as the name of the
species and all columns with results of antimicrobial agents. See the
help page for more info about how to set the right settings for your
data with the command <code><a href="../reference/mdro.html">?mdro</a></code>.</p>
<p>For WHONET data (and most other data), all settings are automatically
set correctly.</p>
<p>The <code><a href="../reference/mdro.html">mdro()</a></code> function takes a data set as input, such as a regular <code>data.frame</code>. It tries to automatically determine the right columns for info about your isolates, such as the name of the species and all columns with results of antimicrobial agents. See the help page for more info about how to set the right settings for your data with the command <code><a href="../reference/mdro.html">?mdro</a></code>.</p>
<p>For WHONET data (and most other data), all settings are automatically set correctly.</p>
</div>
<div class="section level3">
<h3 id="guidelines">Guidelines<a class="anchor" aria-label="anchor" href="#guidelines"></a>
</h3>
<p>The <code><a href="../reference/mdro.html">mdro()</a></code> function support multiple guidelines. You can
select a guideline with the <code>guideline</code> parameter. Currently
supported guidelines are (case-insensitive):</p>
<p>The <code><a href="../reference/mdro.html">mdro()</a></code> function support multiple guidelines. You can select a guideline with the <code>guideline</code> parameter. Currently supported guidelines are (case-insensitive):</p>
<ul>
<li>
<p><code>guideline = "CMI2012"</code> (default)</p>
<p>Magiorakos AP, Srinivasan A <em>et al.</em> “Multidrug-resistant,
extensively drug-resistant and pandrug-resistant bacteria: an
international expert proposal for interim standard definitions for
acquired resistance.” Clinical Microbiology and Infection (2012) (<a href="https://www.clinicalmicrobiologyandinfection.com/article/S1198-743X(14)61632-3/fulltext" class="external-link">link</a>)</p>
<p>Magiorakos AP, Srinivasan A <em>et al.</em> “Multidrug-resistant, extensively drug-resistant and pandrug-resistant bacteria: an international expert proposal for interim standard definitions for acquired resistance.” Clinical Microbiology and Infection (2012) (<a href="https://www.clinicalmicrobiologyandinfection.com/article/S1198-743X(14)61632-3/fulltext" class="external-link">link</a>)</p>
</li>
<li>
<p><code>guideline = "EUCAST3.2"</code> (or simply
<code>guideline = "EUCAST"</code>)</p>
<p>The European international guideline - EUCAST Expert Rules Version
3.2 “Intrinsic Resistance and Unusual Phenotypes” (<a href="https://www.eucast.org/fileadmin/src/media/PDFs/EUCAST_files/Expert_Rules/2020/Intrinsic_Resistance_and_Unusual_Phenotypes_Tables_v3.2_20200225.pdf" class="external-link">link</a>)</p>
<p><code>guideline = "EUCAST3.2"</code> (or simply <code>guideline = "EUCAST"</code>)</p>
<p>The European international guideline - EUCAST Expert Rules Version 3.2 “Intrinsic Resistance and Unusual Phenotypes” (<a href="https://www.eucast.org/fileadmin/src/media/PDFs/EUCAST_files/Expert_Rules/2020/Intrinsic_Resistance_and_Unusual_Phenotypes_Tables_v3.2_20200225.pdf" class="external-link">link</a>)</p>
</li>
<li>
<p><code>guideline = "EUCAST3.1"</code></p>
<p>The European international guideline - EUCAST Expert Rules Version
3.1 “Intrinsic Resistance and Exceptional Phenotypes Tables” (<a href="https://www.eucast.org/fileadmin/src/media/PDFs/EUCAST_files/Expert_Rules/Expert_rules_intrinsic_exceptional_V3.1.pdf" class="external-link">link</a>)</p>
<p>The European international guideline - EUCAST Expert Rules Version 3.1 “Intrinsic Resistance and Exceptional Phenotypes Tables” (<a href="https://www.eucast.org/fileadmin/src/media/PDFs/EUCAST_files/Expert_Rules/Expert_rules_intrinsic_exceptional_V3.1.pdf" class="external-link">link</a>)</p>
</li>
<li>
<p><code>guideline = "TB"</code></p>
<p>The international guideline for multi-drug resistant tuberculosis -
World Health Organization “Companion handbook to the WHO guidelines for
the programmatic management of drug-resistant tuberculosis” (<a href="https://www.who.int/tb/publications/pmdt_companionhandbook/en/" class="external-link">link</a>)</p>
<p>The international guideline for multi-drug resistant tuberculosis - World Health Organization “Companion handbook to the WHO guidelines for the programmatic management of drug-resistant tuberculosis” (<a href="https://www.who.int/tb/publications/pmdt_companionhandbook/en/" class="external-link">link</a>)</p>
</li>
<li>
<p><code>guideline = "MRGN"</code></p>
<p>The German national guideline - Mueller <em>et al.</em> (2015)
Antimicrobial Resistance and Infection Control 4:7. DOI:
10.1186/s13756-015-0047-6</p>
<p>The German national guideline - Mueller <em>et al.</em> (2015) Antimicrobial Resistance and Infection Control 4:7. DOI: 10.1186/s13756-015-0047-6</p>
</li>
<li>
<p><code>guideline = "BRMO"</code></p>
<p>The Dutch national guideline - Rijksinstituut voor Volksgezondheid en
Milieu “WIP-richtlijn BRMO (Bijzonder Resistente Micro-Organismen)
(ZKH)” (<a href="https://www.rivm.nl/wip-richtlijn-brmo-bijzonder-resistente-micro-organismen-zkh" class="external-link">link</a>)</p>
<p>The Dutch national guideline - Rijksinstituut voor Volksgezondheid en Milieu “WIP-richtlijn BRMO (Bijzonder Resistente Micro-Organismen) (ZKH)” (<a href="https://www.rivm.nl/wip-richtlijn-brmo-bijzonder-resistente-micro-organismen-zkh" class="external-link">link</a>)</p>
</li>
</ul>
<p>Please suggest your own (country-specific) guidelines by letting us
know: <a href="https://github.com/msberends/AMR/issues/new" class="external-link uri">https://github.com/msberends/AMR/issues/new</a>.</p>
<p>Please suggest your own (country-specific) guidelines by letting us know: <a href="https://github.com/msberends/AMR/issues/new" class="external-link uri">https://github.com/msberends/AMR/issues/new</a>.</p>
<div class="section level4">
<h4 id="custom-guidelines">Custom Guidelines<a class="anchor" aria-label="anchor" href="#custom-guidelines"></a>
</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" class="external-link">case_when()</a></code> of the
<code>dplyr</code> package, you will recognise the input method to set
your own rules. Rules must be set using what R considers to be the
‘formula notation’:</p>
<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" class="external-link">case_when()</a></code> of the <code>dplyr</code> package, you will recognise the input method to set your own rules. Rules must be set using what R considers to be the ‘formula notation’:</p>
<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="va">custom</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/mdro.html">custom_mdro_guideline</a></span><span class="op">(</span><span class="va">CIP</span> <span class="op">==</span> <span class="st">"R"</span> <span class="op">&amp;</span> <span class="va">age</span> <span class="op">&gt;</span> <span class="fl">60</span> <span class="op">~</span> <span class="st">"Elderly Type A"</span>,
<span class="va">ERY</span> <span class="op">==</span> <span class="st">"R"</span> <span class="op">&amp;</span> <span class="va">age</span> <span class="op">&gt;</span> <span class="fl">60</span> <span class="op">~</span> <span class="st">"Elderly Type B"</span><span class="op">)</span></code></pre></div>
<p>If a row/an isolate matches the first rule, the value after the first
<code>~</code> (in this case <em>‘Elderly Type A’</em>) will be set as
MDRO value. Otherwise, the second rule will be tried and so on. The
maximum number of rules is unlimited.</p>
<p>You can print the rules set in the console for an overview. Colours
will help reading it if your console supports colours.</p>
<p>If a row/an isolate matches the first rule, the value after the first <code>~</code> (in this case <em>‘Elderly Type A’</em>) will be set as MDRO value. Otherwise, the second rule will be tried and so on. The maximum number of rules is unlimited.</p>
<p>You can print the rules set in the console for an overview. Colours will help reading it if your console supports colours.</p>
<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="va">custom</span>
<span class="co"># A set of custom MDRO rules:</span>
@ -289,8 +255,7 @@ will help reading it if your console supports colours.</p>
<span class="co"># </span>
<span class="co"># Unmatched rows will return NA.</span>
<span class="co"># Results will be of class &lt;factor&gt;, with ordered levels: Negative &lt; Elderly Type A &lt; Elderly Type B</span></code></pre></div>
<p>The outcome of the function can be used for the
<code>guideline</code> argument in the <code><a href="../reference/mdro.html">mdro()</a></code> function:</p>
<p>The outcome of the function can be used for the <code>guideline</code> argument in the <code><a href="../reference/mdro.html">mdro()</a></code> function:</p>
<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="va">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/mdro.html">mdro</a></span><span class="op">(</span><span class="va">example_isolates</span>, guideline <span class="op">=</span> <span class="va">custom</span><span class="op">)</span>
<span class="co"># Determining MDROs based on custom rules, resulting in factor levels:</span>
@ -301,25 +266,14 @@ will help reading it if your console supports colours.</p>
<span class="co"># x</span>
<span class="co"># Negative Elderly Type A Elderly Type B </span>
<span class="co"># 1070 198 732</span></code></pre></div>
<p>The rules set (the <code>custom</code> object in this case) could be
exported to a shared file location using <code><a href="https://rdrr.io/r/base/readRDS.html" class="external-link">saveRDS()</a></code> if you
collaborate with multiple users. The custom rules set could then be
imported using <code><a href="https://rdrr.io/r/base/readRDS.html" class="external-link">readRDS()</a></code>.</p>
<p>The rules set (the <code>custom</code> object in this case) could be exported to a shared file location using <code><a href="https://rdrr.io/r/base/readRDS.html" class="external-link">saveRDS()</a></code> if you collaborate with multiple users. The custom rules set could then be imported using <code><a href="https://rdrr.io/r/base/readRDS.html" class="external-link">readRDS()</a></code>.</p>
</div>
</div>
<div class="section level3">
<h3 id="examples">Examples<a class="anchor" aria-label="anchor" href="#examples"></a>
</h3>
<p>The <code><a href="../reference/mdro.html">mdro()</a></code> function always returns an ordered
<code>factor</code> for predefined guidelines. For example, the output
of the default guideline by Magiorakos <em>et al.</em> returns a
<code>factor</code> with levels ‘Negative’, ‘MDR’, ‘XDR’ or ‘PDR’ in
that order.</p>
<p>The next example uses the <code>example_isolates</code> data set.
This is a data set included with this package and contains full
antibiograms of 2,000 microbial isolates. It reflects reality and can be
used to practise AMR data analysis. If we test the MDR/XDR/PDR guideline
on this data set, we get:</p>
<p>The <code><a href="../reference/mdro.html">mdro()</a></code> function always returns an ordered <code>factor</code> for predefined guidelines. For example, the output of the default guideline by Magiorakos <em>et al.</em> returns a <code>factor</code> with levels ‘Negative’, ‘MDR’, ‘XDR’ or ‘PDR’ in that order.</p>
<p>The next example uses the <code>example_isolates</code> data set. This is a data set included with this package and contains full antibiograms of 2,000 microbial isolates. It reflects reality and can be used to practise AMR data analysis. If we test the MDR/XDR/PDR guideline on this data set, we get:</p>
<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://dplyr.tidyverse.org" class="external-link">dplyr</a></span><span class="op">)</span> <span class="co"># to support pipes: %&gt;%</span>
<span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://github.com/msberends/cleaner" class="external-link">cleaner</a></span><span class="op">)</span> <span class="co"># to create frequency tables</span></code></pre></div>
@ -343,32 +297,16 @@ on this data set, we get:</p>
<span class="co"># Table 5 - Acinetobacter spp.... OK.</span>
<span class="co"># Warning: in `mdro()`: NA introduced for isolates where the available percentage of</span>
<span class="co"># antimicrobial classes was below 50% (set with `pct_required_classes`)</span></code></pre></div>
<p>Only results with ‘R’ are considered as resistance. Use
<code>combine_SI = FALSE</code> to also consider ‘I’ as resistance.</p>
<p>Determining multidrug-resistant organisms (MDRO), according to:
Guideline: Multidrug-resistant, extensively drug-resistant and
pandrug-resistant bacteria: an international expert proposal for interim
standard definitions for acquired resistance. Author(s): Magiorakos AP,
Srinivasan A, Carey RB, …, Vatopoulos A, Weber JT, Monnet DL Source:
Clinical Microbiology and Infection 18:3, 2012; doi:
10.1111/j.1469-0691.2011.03570.x</p>
<p>Only results with ‘R’ are considered as resistance. Use <code>combine_SI = FALSE</code> to also consider ‘I’ as resistance.</p>
<p>Determining multidrug-resistant organisms (MDRO), according to: Guideline: Multidrug-resistant, extensively drug-resistant and pandrug-resistant bacteria: an international expert proposal for interim standard definitions for acquired resistance. Author(s): Magiorakos AP, Srinivasan A, Carey RB, …, Vatopoulos A, Weber JT, Monnet DL Source: Clinical Microbiology and Infection 18:3, 2012; doi: 10.1111/j.1469-0691.2011.03570.x</p>
<p>(16 isolates had no test results)</p>
<p><strong>Frequency table</strong></p>
<p>Class: factor &gt; ordered (numeric)<br>
Length: 2,000<br>
Levels: 4: Negative &lt; Multi-drug-resistant (MDR) &lt; Extensively
drug-resistant …<br>
Levels: 4: Negative &lt; Multi-drug-resistant (MDR) &lt; Extensively drug-resistant …<br>
Available: 1,729 (86.45%, NA: 271 = 13.55%)<br>
Unique: 2</p>
<table style="width:100%;" class="table">
<colgroup>
<col width="4%">
<col width="38%">
<col width="9%">
<col width="12%">
<col width="16%">
<col width="19%">
</colgroup>
<table class="table">
<thead><tr class="header">
<th align="left"></th>
<th align="left">Item</th>
@ -396,8 +334,7 @@ Unique: 2</p>
</tr>
</tbody>
</table>
<p>For another example, I will create a data set to determine multi-drug
resistant TB:</p>
<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 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>
@ -408,8 +345,7 @@ resistant TB:</p>
pyrazinamide <span class="op">=</span> <span class="fu"><a href="../reference/random.html">random_rsi</a></span><span class="op">(</span><span class="fl">5000</span><span class="op">)</span>,
moxifloxacin <span class="op">=</span> <span class="fu"><a href="../reference/random.html">random_rsi</a></span><span class="op">(</span><span class="fl">5000</span><span class="op">)</span>,
kanamycin <span class="op">=</span> <span class="fu"><a href="../reference/random.html">random_rsi</a></span><span class="op">(</span><span class="fl">5000</span><span class="op">)</span><span class="op">)</span></code></pre></div>
<p>Because all column names are automatically verified for valid drug
names or codes, this would have worked exactly the same way:</p>
<p>Because all column names are automatically verified for valid drug names or codes, this would have worked exactly the same way:</p>
<div class="sourceCode" id="cb7"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="va">my_TB_data</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html" class="external-link">data.frame</a></span><span class="op">(</span>RIF <span class="op">=</span> <span class="fu"><a href="../reference/random.html">random_rsi</a></span><span class="op">(</span><span class="fl">5000</span><span class="op">)</span>,
INH <span class="op">=</span> <span class="fu"><a href="../reference/random.html">random_rsi</a></span><span class="op">(</span><span class="fl">5000</span><span class="op">)</span>,
@ -422,21 +358,20 @@ names or codes, this would have worked exactly the same way:</p>
<div class="sourceCode" id="cb8"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/utils/head.html" class="external-link">head</a></span><span class="op">(</span><span class="va">my_TB_data</span><span class="op">)</span>
<span class="co"># rifampicin isoniazid gatifloxacin ethambutol pyrazinamide moxifloxacin</span>
<span class="co"># 1 I S I I S S</span>
<span class="co"># 2 R S I S R R</span>
<span class="co"># 3 I R I S R S</span>
<span class="co"># 4 I I I R S R</span>
<span class="co"># 5 R S I R S I</span>
<span class="co"># 6 S S I S R R</span>
<span class="co"># 1 S R S I R S</span>
<span class="co"># 2 I I S S I I</span>
<span class="co"># 3 I S R R R S</span>
<span class="co"># 4 S S I S R S</span>
<span class="co"># 5 R R S I R I</span>
<span class="co"># 6 R S R S S I</span>
<span class="co"># kanamycin</span>
<span class="co"># 1 R</span>
<span class="co"># 2 I</span>
<span class="co"># 1 I</span>
<span class="co"># 2 S</span>
<span class="co"># 3 S</span>
<span class="co"># 4 S</span>
<span class="co"># 5 S</span>
<span class="co"># 6 R</span></code></pre></div>
<p>We can now add the interpretation of MDR-TB to our data set. You can
use:</p>
<span class="co"># 4 I</span>
<span class="co"># 5 I</span>
<span class="co"># 6 I</span></code></pre></div>
<p>We can now add the interpretation of MDR-TB to our data set. You can use:</p>
<div class="sourceCode" id="cb9"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="fu"><a href="../reference/mdro.html">mdro</a></span><span class="op">(</span><span class="va">my_TB_data</span>, guideline <span class="op">=</span> <span class="st">"TB"</span><span class="op">)</span></code></pre></div>
<p>or its shortcut <code><a href="../reference/mdro.html">mdr_tb()</a></code>:</p>
@ -455,26 +390,17 @@ use:</p>
<span class="co"># management of drug-resistant tuberculosis</span>
<span class="co"># Author(s): WHO (World Health Organization)</span>
<span class="co"># Version: WHO/HTM/TB/2014.11, 2014</span>
<span class="co"># Source: https://www.who.int/tb/publications/pmdt_companionhandbook/en/</span></code></pre></div>
<span class="co"># Source: https://www.who.int/publications/i/item/9789241548809</span></code></pre></div>
<p>Create a frequency table of the results:</p>
<div class="sourceCode" id="cb11"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/pkg/cleaner/man/freq.html" class="external-link">freq</a></span><span class="op">(</span><span class="va">my_TB_data</span><span class="op">$</span><span class="va">mdr</span><span class="op">)</span></code></pre></div>
<p><strong>Frequency table</strong></p>
<p>Class: factor &gt; ordered (numeric)<br>
Length: 5,000<br>
Levels: 5: Negative &lt; Mono-resistant &lt; Poly-resistant &lt;
Multi-drug-resistant &lt;…<br>
Levels: 5: Negative &lt; Mono-resistant &lt; Poly-resistant &lt; Multi-drug-resistant &lt;…<br>
Available: 5,000 (100%, NA: 0 = 0%)<br>
Unique: 5</p>
<table style="width:100%;" class="table">
<colgroup>
<col width="4%">
<col width="38%">
<col width="9%">
<col width="12%">
<col width="16%">
<col width="19%">
</colgroup>
<table class="table">
<thead><tr class="header">
<th align="left"></th>
<th align="left">Item</th>
@ -487,40 +413,40 @@ Unique: 5</p>
<tr class="odd">
<td align="left">1</td>
<td align="left">Mono-resistant</td>
<td align="right">3261</td>
<td align="right">65.22%</td>
<td align="right">3261</td>
<td align="right">65.22%</td>
<td align="right">3244</td>
<td align="right">64.88%</td>
<td align="right">3244</td>
<td align="right">64.88%</td>
</tr>
<tr class="even">
<td align="left">2</td>
<td align="left">Negative</td>
<td align="right">987</td>
<td align="right">19.74%</td>
<td align="right">4248</td>
<td align="right">84.96%</td>
<td align="right">990</td>
<td align="right">19.80%</td>
<td align="right">4234</td>
<td align="right">84.68%</td>
</tr>
<tr class="odd">
<td align="left">3</td>
<td align="left">Multi-drug-resistant</td>
<td align="right">437</td>
<td align="right">8.74%</td>
<td align="right">4685</td>
<td align="right">93.70%</td>
<td align="right">417</td>
<td align="right">8.34%</td>
<td align="right">4651</td>
<td align="right">93.02%</td>
</tr>
<tr class="even">
<td align="left">4</td>
<td align="left">Poly-resistant</td>
<td align="right">218</td>
<td align="right">4.36%</td>
<td align="right">4903</td>
<td align="right">98.06%</td>
<td align="right">248</td>
<td align="right">4.96%</td>
<td align="right">4899</td>
<td align="right">97.98%</td>
</tr>
<tr class="odd">
<td align="left">5</td>
<td align="left">Extensively drug-resistant</td>
<td align="right">97</td>
<td align="right">1.94%</td>
<td align="right">101</td>
<td align="right">2.02%</td>
<td align="right">5000</td>
<td align="right">100.00%</td>
</tr>
@ -539,14 +465,12 @@ Unique: 5</p>
<footer><div class="copyright">
<p></p>
<p>Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein,
Erwin E. A. Hassing.</p>
<p>Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein, Erwin E. A. Hassing.</p>
</div>
<div class="pkgdown">
<p></p>
<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a>
2.0.2.</p>
<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.2.</p>
</div>
</footer>

15
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@ -0,0 +1,15 @@
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39
docs/articles/PCA.html
View File

@ -185,11 +185,10 @@
</header><div class="row">
</header><script src="PCA_files/accessible-code-block-0.0.1/empty-anchor.js"></script><div class="row">
<div class="col-md-9 contents">
<div class="page-header toc-ignore">
<h1 data-toc-skip>How to conduct principal component analysis
(PCA) for AMR</h1>
<h1 data-toc-skip>How to conduct principal component analysis (PCA) for AMR</h1>
<small class="dont-index">Source: <a href="https://github.com/msberends/AMR/blob/HEAD/vignettes/PCA.Rmd" class="external-link"><code>vignettes/PCA.Rmd</code></a></small>
@ -199,8 +198,7 @@
<p><strong>NOTE: This page will be updated soon, as the pca() function
is currently being developed.</strong></p>
<p><strong>NOTE: This page will be updated soon, as the pca() function is currently being developed.</strong></p>
<div class="section level2">
<h2 id="introduction">Introduction<a class="anchor" aria-label="anchor" href="#introduction"></a>
</h2>
@ -208,12 +206,11 @@ is currently being developed.</strong></p>
<div class="section level2">
<h2 id="transforming">Transforming<a class="anchor" aria-label="anchor" href="#transforming"></a>
</h2>
<p>For PCA, we need to transform our AMR data first. This is what the
<code>example_isolates</code> data set in this package looks like:</p>
<p>For PCA, we need to transform our AMR data first. This is what the <code>example_isolates</code> data set in this package looks like:</p>
<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://msberends.github.io/AMR/">AMR</a></span><span class="op">)</span>
<span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://dplyr.tidyverse.org" class="external-link">dplyr</a></span><span class="op">)</span>
<span class="fu"><a href="https://pillar.r-lib.org/reference/glimpse.html" class="external-link">glimpse</a></span><span class="op">(</span><span class="va">example_isolates</span><span class="op">)</span>
<span class="fu"><a href="https://dplyr.tidyverse.org/reference/glimpse.html" class="external-link">glimpse</a></span><span class="op">(</span><span class="va">example_isolates</span><span class="op">)</span>
<span class="co"># Rows: 2,000</span>
<span class="co"># Columns: 49</span>
<span class="co"># $ date <span style="color: #949494; font-style: italic;">&lt;date&gt;</span> 2002-01-02, 2002-01-03, 2002-01-07, 2002-01-07, 2002-…</span>
@ -265,8 +262,7 @@ is currently being developed.</strong></p>
<span class="co"># $ COL <span style="color: #949494; font-style: italic;">&lt;rsi&gt;</span> NA, NA, R, R, R, R, R, R, R, R, R, R, NA, NA, NA, R, R…</span>
<span class="co"># $ MUP <span style="color: #949494; font-style: italic;">&lt;rsi&gt;</span> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…</span>
<span class="co"># $ RIF <span style="color: #949494; font-style: italic;">&lt;rsi&gt;</span> 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>
<p>Now to transform this to a data set with only resistance percentages per taxonomic order and genus:</p>
<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="va">resistance_data</span> <span class="op">&lt;-</span> <span class="va">example_isolates</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span>
<span class="fu"><a href="https://dplyr.tidyverse.org/reference/group_by.html" class="external-link">group_by</a></span><span class="op">(</span>order <span class="op">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_order</a></span><span class="op">(</span><span class="va">mo</span><span class="op">)</span>, <span class="co"># group on anything, like order</span>
@ -290,15 +286,12 @@ per taxonomic order and genus:</p>
<div class="section level2">
<h2 id="perform-principal-component-analysis">Perform principal component analysis<a class="anchor" aria-label="anchor" href="#perform-principal-component-analysis"></a>
</h2>
<p>The new <code><a href="../reference/pca.html">pca()</a></code> function will automatically filter on rows
that contain numeric values in all selected variables, so we now only
need to do:</p>
<p>The new <code><a href="../reference/pca.html">pca()</a></code> function will automatically filter on rows that contain numeric values in all selected variables, so we now only need to do:</p>
<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="va">pca_result</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/pca.html">pca</a></span><span class="op">(</span><span class="va">resistance_data</span><span class="op">)</span>
<span class="co"># ℹ Columns selected for PCA: "AMC", "CAZ", "CTX", "CXM", "GEN", "SXT", "TMP"</span>
<span class="co"># and "TOB". Total observations available: 7.</span></code></pre></div>
<p>The result can be reviewed with the good old <code><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary()</a></code>
function:</p>
<p>The result can be reviewed with the good old <code><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary()</a></code> function:</p>
<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">pca_result</span><span class="op">)</span>
<span class="co"># Groups (n=4, named as 'order'):</span>
@ -310,11 +303,7 @@ function:</p>
<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><span class="co"># Groups (n=4, named as 'order'):</span>
<span class="co"># [1] "Caryophanales" "Enterobacterales" "Lactobacillales" "Pseudomonadales"</span></code></pre>
<p>Good news. The first two components explain a total of 93.3% of the
variance (see the PC1 and PC2 values of the <em>Proportion of
Variance</em>. We can create a so-called biplot with the base R
<code><a href="https://rdrr.io/r/stats/biplot.html" class="external-link">biplot()</a></code> function, to see which antimicrobial resistance
per drug explain the difference per microorganism.</p>
<p>Good news. The first two components explain a total of 93.3% of the variance (see the PC1 and PC2 values of the <em>Proportion of Variance</em>. We can create a so-called biplot with the base R <code><a href="https://rdrr.io/r/stats/biplot.html" class="external-link">biplot()</a></code> function, to see which antimicrobial resistance per drug explain the difference per microorganism.</p>
</div>
<div class="section level2">
<h2 id="plotting-the-results">Plotting the results<a class="anchor" aria-label="anchor" href="#plotting-the-results"></a>
@ -322,9 +311,7 @@ per drug explain the difference per microorganism.</p>
<div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/stats/biplot.html" class="external-link">biplot</a></span><span class="op">(</span><span class="va">pca_result</span><span class="op">)</span></code></pre></div>
<p><img src="PCA_files/figure-html/unnamed-chunk-5-1.png" width="750"></p>
<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>
<p>But we can’t see the explanation of the points. Perhaps this works better with our new <code><a href="../reference/ggplot_pca.html">ggplot_pca()</a></code> function, that automatically adds the right labels and even groups:</p>
<div class="sourceCode" id="cb7"><pre class="downlit sourceCode r">
<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>
@ -348,14 +335,12 @@ automatically adds the right labels and even groups:</p>
<footer><div class="copyright">
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<p>Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein,
Erwin E. A. Hassing.</p>
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<div class="pkgdown">
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<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a>
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@ -185,14 +185,13 @@
</header><div class="row">
</header><script src="SPSS_files/accessible-code-block-0.0.1/empty-anchor.js"></script><div class="row">
<div class="col-md-9 contents">
<div class="page-header toc-ignore">
<h1 data-toc-skip>How to import data from SPSS / SAS / Stata</h1>
<h4 data-toc-skip class="author">Dr. Matthijs
Berends</h4>
<h4 data-toc-skip class="author">Dr. Matthijs Berends</h4>
<h4 data-toc-skip class="date">14 March 2022</h4>
<h4 data-toc-skip class="date">27 March 2022</h4>
<small class="dont-index">Source: <a href="https://github.com/msberends/AMR/blob/HEAD/vignettes/SPSS.Rmd" class="external-link"><code>vignettes/SPSS.Rmd</code></a></small>
<div class="hidden name"><code>SPSS.Rmd</code></div>
@ -204,135 +203,45 @@ Berends</h4>
<div class="section level2">
<h2 id="spss-sas-stata">SPSS / SAS / Stata<a class="anchor" aria-label="anchor" href="#spss-sas-stata"></a>
</h2>
<p>SPSS (Statistical Package for the Social Sciences) is probably the
most well-known software package for statistical analysis. SPSS is
easier to learn than R, because in SPSS you only have to click a menu to
run parts of your analysis. Because of its user-friendliness, it is
taught at universities and particularly useful for students who are new
to statistics. From my experience, I would guess that pretty much all
(bio)medical students know it at the time they graduate. SAS and Stata
are comparable statistical packages popular in big industries.</p>
<p>SPSS (Statistical Package for the Social Sciences) is probably the most well-known software package for statistical analysis. SPSS is easier to learn than R, because in SPSS you only have to click a menu to run parts of your analysis. Because of its user-friendliness, it is taught at universities and particularly useful for students who are new to statistics. From my experience, I would guess that pretty much all (bio)medical students know it at the time they graduate. SAS and Stata are comparable statistical packages popular in big industries.</p>
</div>
<div class="section level2">
<h2 id="compared-to-r">Compared to R<a class="anchor" aria-label="anchor" href="#compared-to-r"></a>
</h2>
<p>As said, SPSS is easier to learn than R. But SPSS, SAS and Stata come
with major downsides when comparing it with R:</p>
<p>As said, SPSS is easier to learn than R. But SPSS, SAS and Stata come with major downsides when comparing it with R:</p>
<ul>
<li>
<p><strong>R is highly modular.</strong></p>
<p>The <a href="https://cran.r-project.org/" class="external-link">official R network
(CRAN)</a> features more than 16,000 packages at the time of writing,
our <code>AMR</code> package being one of them. All these packages were
peer-reviewed before publication. Aside from this official channel,
there are also developers who choose not to submit to CRAN, but rather
keep it on their own public repository, like GitHub. So there may even
be a lot more than 14,000 packages out there.</p>
<p>Bottom line is, you can really extend it yourself or ask somebody to
do this for you. Take for example our <code>AMR</code> package. Among
other things, it adds reliable reference data to R to help you with the
data cleaning and analysis. SPSS, SAS and Stata will never know what a
valid MIC value is or what the Gram stain of <em>E. coli</em> is. Or
that all species of <em>Klebiella</em> are resistant to amoxicillin and
that Floxapen<sup>®</sup> is a trade name of flucloxacillin. These facts
and properties are often needed to clean existing data, which would be
very inconvenient in a software package without reliable reference data.
See below for a demonstration.</p>
<p>The <a href="https://cran.r-project.org/" class="external-link">official R network (CRAN)</a> features more than 16,000 packages at the time of writing, our <code>AMR</code> package being one of them. All these packages were peer-reviewed before publication. Aside from this official channel, there are also developers who choose not to submit to CRAN, but rather keep it on their own public repository, like GitHub. So there may even be a lot more than 14,000 packages out there.</p>
<p>Bottom line is, you can really extend it yourself or ask somebody to do this for you. Take for example our <code>AMR</code> package. Among other things, it adds reliable reference data to R to help you with the data cleaning and analysis. SPSS, SAS and Stata will never know what a valid MIC value is or what the Gram stain of <em>E. coli</em> is. Or that all species of <em>Klebiella</em> are resistant to amoxicillin and that Floxapen<sup>®</sup> is a trade name of flucloxacillin. These facts and properties are often needed to clean existing data, which would be very inconvenient in a software package without reliable reference data. See below for a demonstration.</p>
</li>
<li>
<p><strong>R is extremely flexible.</strong></p>
<p>Because you write the syntax yourself, you can do anything you want.
The flexibility in transforming, arranging, grouping and summarising
data, or drawing plots, is endless - with SPSS, SAS or Stata you are
bound to their algorithms and format styles. They may be a bit flexible,
but you can probably never create that very specific publication-ready
plot without using other (paid) software. If you sometimes write
syntaxes in SPSS to run a complete analysis or to ‘automate’ some of
your work, you could do this a lot less time in R. You will notice that
writing syntaxes in R is a lot more nifty and clever than in SPSS.
Still, as working with any statistical package, you will have to have
knowledge about what you are doing (statistically) and what you are
willing to accomplish.</p>
<p>Because you write the syntax yourself, you can do anything you want. The flexibility in transforming, arranging, grouping and summarising data, or drawing plots, is endless - with SPSS, SAS or Stata you are bound to their algorithms and format styles. They may be a bit flexible, but you can probably never create that very specific publication-ready plot without using other (paid) software. If you sometimes write syntaxes in SPSS to run a complete analysis or to ‘automate’ some of your work, you could do this a lot less time in R. You will notice that writing syntaxes in R is a lot more nifty and clever than in SPSS. Still, as working with any statistical package, you will have to have knowledge about what you are doing (statistically) and what you are willing to accomplish.</p>
</li>
<li>
<p><strong>R can be easily automated.</strong></p>
<p>Over the last years, <a href="https://rmarkdown.rstudio.com/" class="external-link">R
Markdown</a> has really made an interesting development. With R
Markdown, you can very easily produce reports, whether the format has to
be Word, PowerPoint, a website, a PDF document or just the raw data to
Excel. It even allows the use of a reference file containing the layout
style (e.g. fonts and colours) of your organisation. I use this a lot to
generate weekly and monthly reports automatically. Just write the code
once and enjoy the automatically updated reports at any interval you
like.</p>
<p>For an even more professional environment, you could create <a href="https://shiny.rstudio.com/" class="external-link">Shiny apps</a>: live manipulation of
data using a custom made website. The webdesign knowledge needed
(JavaScript, CSS, HTML) is almost <em>zero</em>.</p>
<p>Over the last years, <a href="https://rmarkdown.rstudio.com/" class="external-link">R Markdown</a> has really made an interesting development. With R Markdown, you can very easily produce reports, whether the format has to be Word, PowerPoint, a website, a PDF document or just the raw data to Excel. It even allows the use of a reference file containing the layout style (e.g. fonts and colours) of your organisation. I use this a lot to generate weekly and monthly reports automatically. Just write the code once and enjoy the automatically updated reports at any interval you like.</p>
<p>For an even more professional environment, you could create <a href="https://shiny.rstudio.com/" class="external-link">Shiny apps</a>: live manipulation of data using a custom made website. The webdesign knowledge needed (JavaScript, CSS, HTML) is almost <em>zero</em>.</p>
</li>
<li>
<p><strong>R has a huge community.</strong></p>
<p>Many R users just ask questions on websites like <a href="https://stackoverflow.com" class="external-link">StackOverflow.com</a>, the largest
online community for programmers. At the time of writing, <a href="https://stackoverflow.com/questions/tagged/r?sort=votes" class="external-link">439,954
R-related questions</a> have already been asked on this platform (that
covers questions and answers for any programming language). In my own
experience, most questions are answered within a couple of
minutes.</p>
<p>Many R users just ask questions on websites like <a href="https://stackoverflow.com" class="external-link">StackOverflow.com</a>, the largest online community for programmers. At the time of writing, <a href="https://stackoverflow.com/questions/tagged/r?sort=votes" class="external-link">440,893 R-related questions</a> have already been asked on this platform (that covers questions and answers for any programming language). In my own experience, most questions are answered within a couple of minutes.</p>
</li>
<li>
<p><strong>R understands any data type, including
SPSS/SAS/Stata.</strong></p>
<p>And that’s not vice versa I’m afraid. You can import data from any
source into R. For example from SPSS, SAS and Stata (<a href="https://haven.tidyverse.org/" class="external-link">link</a>), from Minitab, Epi Info
and EpiData (<a href="https://cran.r-project.org/package=foreign" class="external-link">link</a>), from Excel
(<a href="https://readxl.tidyverse.org/" class="external-link">link</a>), from flat files like
CSV, TXT or TSV (<a href="https://readr.tidyverse.org/" class="external-link">link</a>), or
directly from databases and datawarehouses from anywhere on the world
(<a href="https://dbplyr.tidyverse.org/" class="external-link">link</a>). You can even scrape
websites to download tables that are live on the internet (<a href="https://github.com/hadley/rvest" class="external-link">link</a>) or get the results of
an API call and transform it into data in only one command (<a href="https://github.com/Rdatatable/data.table/wiki/Convenience-features-of-fread" class="external-link">link</a>).</p>
<p>And the best part - you can export from R to most data formats as
well. So you can import an SPSS file, do your analysis neatly in R and
export the resulting tables to Excel files for sharing.</p>
<p><strong>R understands any data type, including SPSS/SAS/Stata.</strong></p>
<p>And that’s not vice versa I’m afraid. You can import data from any source into R. For example from SPSS, SAS and Stata (<a href="https://haven.tidyverse.org/" class="external-link">link</a>), from Minitab, Epi Info and EpiData (<a href="https://cran.r-project.org/package=foreign" class="external-link">link</a>), from Excel (<a href="https://readxl.tidyverse.org/" class="external-link">link</a>), from flat files like CSV, TXT or TSV (<a href="https://readr.tidyverse.org/" class="external-link">link</a>), or directly from databases and datawarehouses from anywhere on the world (<a href="https://dbplyr.tidyverse.org/" class="external-link">link</a>). You can even scrape websites to download tables that are live on the internet (<a href="https://github.com/hadley/rvest" class="external-link">link</a>) or get the results of an API call and transform it into data in only one command (<a href="https://github.com/Rdatatable/data.table/wiki/Convenience-features-of-fread" class="external-link">link</a>).</p>
<p>And the best part - you can export from R to most data formats as well. So you can import an SPSS file, do your analysis neatly in R and export the resulting tables to Excel files for sharing.</p>
</li>
<li>
<p><strong>R is completely free and open-source.</strong></p>
<p>No strings attached. It was created and is being maintained by
volunteers who believe that (data) science should be open and publicly
available to everybody. SPSS, SAS and Stata are quite expensive. IBM
SPSS Staticstics only comes with subscriptions nowadays, varying <a href="https://www.ibm.com/products/spss-statistics/pricing" class="external-link">between USD
1,300 and USD 8,500</a> per user <em>per year</em>. SAS Analytics Pro
costs <a href="https://www.sas.com/store/products-solutions/sas-analytics-pro/prodPERSANL.html" class="external-link">around
USD 10,000</a> per computer. Stata also has a business model with
subscription fees, varying <a href="https://www.stata.com/order/new/bus/single-user-licenses/dl/" class="external-link">between
USD 600 and USD 2,800</a> per computer per year, but lower prices come
with a limitation of the number of variables you can work with. And
still they do not offer the above benefits of R.</p>
<p>If you are working at a midsized or small company, you can save it
tens of thousands of dollars by using R instead of e.g. SPSS - gaining
even more functions and flexibility. And all R enthousiasts can do as
much PR as they want (like I do here), because nobody is officially
associated with or affiliated by R. It is really free.</p>
<p>No strings attached. It was created and is being maintained by volunteers who believe that (data) science should be open and publicly available to everybody. SPSS, SAS and Stata are quite expensive. IBM SPSS Staticstics only comes with subscriptions nowadays, varying <a href="https://www.ibm.com/products/spss-statistics/pricing" class="external-link">between USD 1,300 and USD 8,500</a> per user <em>per year</em>. SAS Analytics Pro costs <a href="https://www.sas.com/store/products-solutions/sas-analytics-pro/prodPERSANL.html" class="external-link">around USD 10,000</a> per computer. Stata also has a business model with subscription fees, varying <a href="https://www.stata.com/order/new/bus/single-user-licenses/dl/" class="external-link">between USD 600 and USD 2,800</a> per computer per year, but lower prices come with a limitation of the number of variables you can work with. And still they do not offer the above benefits of R.</p>
<p>If you are working at a midsized or small company, you can save it tens of thousands of dollars by using R instead of e.g. SPSS - gaining even more functions and flexibility. And all R enthousiasts can do as much PR as they want (like I do here), because nobody is officially associated with or affiliated by R. It is really free.</p>
</li>
<li>
<p><strong>R is (nowadays) the preferred analysis software in
academic papers.</strong></p>
<p>At present, R is among the world most powerful statistical languages,
and it is generally very popular in science (Bollmann <em>et al.</em>,
2017). For all the above reasons, the number of references to R as an
analysis method in academic papers <a href="https://r4stats.com/2014/08/20/r-passes-spss-in-scholarly-use-stata-growing-rapidly/" class="external-link">is
rising continuously</a> and has even surpassed SPSS for academic use
(Muenchen, 2014).</p>
<p>I believe that the thing with SPSS is, that it has always had a great
user interface which is very easy to learn and use. Back when they
developed it, they had very little competition, let alone from R. R
didn’t even had a professional user interface until the last decade
(called RStudio, see below). How people used R between the nineties and
2010 is almost completely incomparable to how R is being used now. The
language itself <a href="https://www.tidyverse.org/packages/" class="external-link">has been
restyled completely</a> by volunteers who are dedicated professionals in
the field of data science. SPSS was great when there was nothing else
that could compete. But now in 2022, I don’t see any reason why SPSS
would be of any better use than R.</p>
<p><strong>R is (nowadays) the preferred analysis software in academic papers.</strong></p>
<p>At present, R is among the world most powerful statistical languages, and it is generally very popular in science (Bollmann <em>et al.</em>, 2017). For all the above reasons, the number of references to R as an analysis method in academic papers <a href="https://r4stats.com/2014/08/20/r-passes-spss-in-scholarly-use-stata-growing-rapidly/" class="external-link">is rising continuously</a> and has even surpassed SPSS for academic use (Muenchen, 2014).</p>
<p>I believe that the thing with SPSS is, that it has always had a great user interface which is very easy to learn and use. Back when they developed it, they had very little competition, let alone from R. R didn’t even had a professional user interface until the last decade (called RStudio, see below). How people used R between the nineties and 2010 is almost completely incomparable to how R is being used now. The language itself <a href="https://www.tidyverse.org/packages/" class="external-link">has been restyled completely</a> by volunteers who are dedicated professionals in the field of data science. SPSS was great when there was nothing else that could compete. But now in 2022, I don’t see any reason why SPSS would be of any better use than R.</p>
</li>
</ul>
<p>To demonstrate the first point:</p>
@ -376,23 +285,13 @@ would be of any better use than R.</p>
<div class="section level3">
<h3 id="rstudio">RStudio<a class="anchor" aria-label="anchor" href="#rstudio"></a>
</h3>
<p>To work with R, probably the best option is to use <a href="https://www.rstudio.com/products/rstudio/" class="external-link">RStudio</a>. It is an
open-source and free desktop environment which not only allows you to
run R code, but also supports project management, version management,
package management and convenient import menus to work with other data
sources. You can also install <a href="https://www.rstudio.com/products/rstudio/" class="external-link">RStudio Server</a> on a
private or corporate server, which brings nothing less than the complete
RStudio software to you as a website (at home or at work).</p>
<p>To import a data file, just click <em>Import Dataset</em> in the
Environment tab:</p>
<p>To work with R, probably the best option is to use <a href="https://www.rstudio.com/products/rstudio/" class="external-link">RStudio</a>. It is an open-source and free desktop environment which not only allows you to run R code, but also supports project management, version management, package management and convenient import menus to work with other data sources. You can also install <a href="https://www.rstudio.com/products/rstudio/" class="external-link">RStudio Server</a> on a private or corporate server, which brings nothing less than the complete RStudio software to you as a website (at home or at work).</p>
<p>To import a data file, just click <em>Import Dataset</em> in the Environment tab:</p>
<p><img src="https://github.com/msberends/AMR/raw/main/docs/import1.png"></p>
<p>If additional packages are needed, RStudio will ask you if they
should be installed on beforehand.</p>
<p>In the the window that opens, you can define all options (parameters)
that should be used for import and you’re ready to go:</p>
<p>If additional packages are needed, RStudio will ask you if they should be installed on beforehand.</p>
<p>In the the window that opens, you can define all options (parameters) that should be used for import and you’re ready to go:</p>
<p><img src="https://github.com/msberends/AMR/raw/main/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" class="external-link">as_factor()</a></code>.</p>
<p>If you want named variables to be imported as factors so it resembles SPSS more, use <code><a href="https://haven.tidyverse.org/reference/as_factor.html" class="external-link">as_factor()</a></code>.</p>
<p>The difference is this:</p>
<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="va">SPSS_data</span>
@ -430,8 +329,7 @@ SPSS more, use <code><a href="https://haven.tidyverse.org/reference/as_factor.ht
<div class="section level3">
<h3 id="base-r">Base R<a class="anchor" aria-label="anchor" href="#base-r"></a>
</h3>
<p>To import data from SPSS, SAS or Stata, you can use the <a href="https://haven.tidyverse.org/" class="external-link">great <code>haven</code> package</a>
yourself:</p>
<p>To import data from SPSS, SAS or Stata, you can use the <a href="https://haven.tidyverse.org/" class="external-link">great <code>haven</code> package</a> yourself:</p>
<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="co"># download and install the latest version:</span>
<span class="fu"><a href="https://rdrr.io/r/utils/install.packages.html" class="external-link">install.packages</a></span><span class="op">(</span><span class="st">"haven"</span><span class="op">)</span>
@ -511,14 +409,12 @@ yourself:</p>
<footer><div class="copyright">
<p></p>
<p>Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein,
Erwin E. A. Hassing.</p>
<p>Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein, Erwin E. A. Hassing.</p>
</div>
<div class="pkgdown">
<p></p>
<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a>
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<div class="col-md-9 contents">
<div class="page-header toc-ignore">
<h1 data-toc-skip>How to work with WHONET data</h1>
@ -201,43 +201,26 @@
<div class="section level3">
<h3 id="import-of-data">Import of data<a class="anchor" aria-label="anchor" href="#import-of-data"></a>
</h3>
<p>This tutorial assumes you already imported the WHONET data with
e.g. the <a href="https://readxl.tidyverse.org/" class="external-link"><code>readxl</code>
package</a>. In RStudio, this can be done using the menu button ‘Import
Dataset’ in the tab ‘Environment’. Choose the option ‘From Excel’ and
select your exported file. Make sure date fields are imported
correctly.</p>
<p>This tutorial assumes you already imported the WHONET data with e.g. the <a href="https://readxl.tidyverse.org/" class="external-link"><code>readxl</code> package</a>. In RStudio, this can be done using the menu button ‘Import Dataset’ in the tab ‘Environment’. Choose the option ‘From Excel’ and select your exported file. Make sure date fields are imported correctly.</p>
<p>An example syntax could look like this:</p>
<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://readxl.tidyverse.org" class="external-link">readxl</a></span><span class="op">)</span>
<span class="va">data</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://readxl.tidyverse.org/reference/read_excel.html" class="external-link">read_excel</a></span><span class="op">(</span>path <span class="op">=</span> <span class="st">"path/to/your/file.xlsx"</span><span class="op">)</span></code></pre></div>
<p>This package comes with an <a href="https://msberends.github.io/AMR/reference/WHONET.html">example
data set <code>WHONET</code></a>. We will use it for this analysis.</p>
<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 class="section level3">
<h3 id="preparation">Preparation<a class="anchor" aria-label="anchor" href="#preparation"></a>
</h3>
<p>First, load the relevant packages if you did not yet did this. I use
the tidyverse for all of my analyses. All of them. If you don’t know it
yet, I suggest you read about it on their website: <a href="https://www.tidyverse.org/" class="external-link uri">https://www.tidyverse.org/</a>.</p>
<p>First, load the relevant packages if you did not yet did this. I use the tidyverse for all of my analyses. All of them. If you don’t know it yet, I suggest you read about it on their website: <a href="https://www.tidyverse.org/" class="external-link uri">https://www.tidyverse.org/</a>.</p>
<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://dplyr.tidyverse.org" class="external-link">dplyr</a></span><span class="op">)</span> <span class="co"># part of tidyverse</span>
<span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://ggplot2.tidyverse.org" class="external-link">ggplot2</a></span><span class="op">)</span> <span class="co"># part of tidyverse</span>
<span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://msberends.github.io/AMR/">AMR</a></span><span class="op">)</span> <span class="co"># this package</span>
<span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://github.com/msberends/cleaner" class="external-link">cleaner</a></span><span class="op">)</span> <span class="co"># to create frequency tables</span></code></pre></div>
<p>We will have to transform some variables to simplify and automate the
analysis:</p>
<p>We will have to transform some variables to simplify and automate the analysis:</p>
<ul>
<li>Microorganisms should be transformed to our own microorganism codes
(called an <code>mo</code>) using <a href="https://msberends.github.io/AMR/reference/catalogue_of_life">our
Catalogue of Life reference data set</a>, which contains all ~70,000
microorganisms from the taxonomic kingdoms Bacteria, Fungi and Protozoa.
We do the tranformation with <code><a href="../reference/as.mo.html">as.mo()</a></code>. This function also
recognises almost all WHONET abbreviations of microorganisms.</li>
<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>
<li>Microorganisms should be transformed to our own microorganism codes (called an <code>mo</code>) using <a href="https://msberends.github.io/AMR/reference/catalogue_of_life">our Catalogue of Life reference data set</a>, which contains all ~70,000 microorganisms from the taxonomic kingdoms Bacteria, Fungi and Protozoa. We do the tranformation with <code><a href="../reference/as.mo.html">as.mo()</a></code>. This function also recognises almost all WHONET abbreviations of microorganisms.</li>
<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 sourceCode r">
<code class="sourceCode R"><span class="co"># transform variables</span>
@ -247,9 +230,7 @@ In other words, they should only contain values <code>"S"</code>,
<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" class="external-link">mutate_at</a></span><span class="op">(</span><span class="fu"><a href="https://dplyr.tidyverse.org/reference/vars.html" class="external-link">vars</a></span><span class="op">(</span><span class="va">AMP_ND10</span><span class="op">:</span><span class="va">CIP_EE</span><span class="op">)</span>, <span class="va">as.rsi</span><span class="op">)</span></code></pre></div>
<p>No errors or warnings, so all values are transformed succesfully.</p>
<p>We also created a package dedicated to data cleaning and checking,
called the <code>cleaner</code> package. Its <code><a href="https://rdrr.io/pkg/cleaner/man/freq.html" class="external-link">freq()</a></code>
function can be used to create frequency tables.</p>
<p>We also created a package dedicated to data cleaning and checking, called the <code>cleaner</code> package. Its <code><a href="https://rdrr.io/pkg/cleaner/man/freq.html" class="external-link">freq()</a></code> function can be used to create frequency tables.</p>
<p>So let’s check our data, with a couple of frequency tables:</p>
<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="co"># our newly created `mo` variable, put in the mo_name() function</span>
@ -262,14 +243,6 @@ Unique: 37</p>
<p>Shortest: 11<br>
Longest: 40</p>
<table class="table">
<colgroup>
<col width="4%">
<col width="47%">
<col width="7%">
<col width="10%">
<col width="13%">
<col width="15%">
</colgroup>
<thead><tr class="header">
<th align="left"></th>
<th align="left">Item</th>
@ -415,8 +388,7 @@ Drug group: Beta-lactams/penicillins<br>
<div class="section level3">
<h3 id="a-first-glimpse-at-results">A first glimpse at results<a class="anchor" aria-label="anchor" href="#a-first-glimpse-at-results"></a>
</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>
<p>An easy <code>ggplot</code> will already give a lot of information, using the included <code><a href="../reference/ggplot_rsi.html">ggplot_rsi()</a></code> function:</p>
<div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="va">data</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span>
<span class="fu"><a href="https://dplyr.tidyverse.org/reference/group_by.html" class="external-link">group_by</a></span><span class="op">(</span><span class="va">Country</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span>
@ -436,14 +408,12 @@ using the included <code><a href="../reference/ggplot_rsi.html">ggplot_rsi()</a>
<footer><div class="copyright">
<p></p>
<p>Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein,
Erwin E. A. Hassing.</p>
<p>Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein, Erwin E. A. Hassing.</p>
</div>
<div class="pkgdown">
<p></p>
<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a>
2.0.2.</p>
<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.2.</p>
</div>
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</header><div class="row">
</header><script src="benchmarks_files/accessible-code-block-0.0.1/empty-anchor.js"></script><div class="row">
<div class="col-md-9 contents">
<div class="page-header toc-ignore">
<h1 data-toc-skip>Benchmarks</h1>
@ -198,30 +198,14 @@
<p>One of the most important features of this package is the complete
microbial taxonomic database, supplied by the <a href="http://www.catalogueoflife.org" class="external-link">Catalogue of Life</a> (CoL) and
the <a href="https://lpsn.dsmz.de" class="external-link">List of Prokaryotic names with
Standing in Nomenclature</a> (LPSN). We created a function
<code><a href="../reference/as.mo.html">as.mo()</a></code> that transforms any user input value to a valid
microbial ID by using intelligent rules combined with the microbial
taxonomy.</p>
<p>Using the <code>microbenchmark</code> package, we can review the
calculation performance of this function. Its function
<code><a href="https://rdrr.io/pkg/microbenchmark/man/microbenchmark.html" class="external-link">microbenchmark()</a></code> runs different input expressions
independently of each other and measures their time-to-result.</p>
<p>One of the most important features of this package is the complete microbial taxonomic database, supplied by the <a href="http://www.catalogueoflife.org" class="external-link">Catalogue of Life</a> (CoL) and the <a href="https://lpsn.dsmz.de" class="external-link">List of Prokaryotic names with Standing in Nomenclature</a> (LPSN). We created a function <code><a href="../reference/as.mo.html">as.mo()</a></code> that transforms any user input value to a valid microbial ID by using intelligent rules combined with the microbial taxonomy.</p>
<p>Using the <code>microbenchmark</code> package, we can review the calculation performance of this function. Its function <code><a href="https://rdrr.io/pkg/microbenchmark/man/microbenchmark.html" class="external-link">microbenchmark()</a></code> runs different input expressions independently of each other and measures their time-to-result.</p>
<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://github.com/joshuaulrich/microbenchmark/" class="external-link">microbenchmark</a></span><span class="op">)</span>
<span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://msberends.github.io/AMR/">AMR</a></span><span class="op">)</span>
<span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://dplyr.tidyverse.org" class="external-link">dplyr</a></span><span class="op">)</span></code></pre></div>
<p>In the next test, we try to ‘coerce’ different input values into the
microbial code of <em>Staphylococcus aureus</em>. Coercion is a
computational process of forcing output based on an input. For
microorganism names, coercing user input to taxonomically valid
microorganism names is crucial to ensure correct interpretation and to
enable grouping based on taxonomic properties.</p>
<p>The actual result is the same every time: it returns its
microorganism code <code>B_STPHY_AURS</code> (<em>B</em> stands for
<em>Bacteria</em>, its taxonomic kingdom).</p>
<p>In the next test, we try to ‘coerce’ different input values into the microbial code of <em>Staphylococcus aureus</em>. Coercion is a computational process of forcing output based on an input. For microorganism names, coercing user input to taxonomically valid microorganism names is crucial to ensure correct interpretation and to enable grouping based on taxonomic properties.</p>
<p>The actual result is the same every time: it returns its microorganism code <code>B_STPHY_AURS</code> (<em>B</em> stands for <em>Bacteria</em>, its taxonomic kingdom).</p>
<p>But the calculation time differs a lot:</p>
<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="va">S.aureus</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/microbenchmark/man/microbenchmark.html" class="external-link">microbenchmark</a></span><span class="op">(</span>
@ -238,42 +222,29 @@ microorganism code <code>B_STPHY_AURS</code> (<em>B</em> stands for
<span class="fu"><a href="../reference/as.mo.html">as.mo</a></span><span class="op">(</span><span class="st">"MRSA"</span><span class="op">)</span>, <span class="co"># Methicillin Resistant S. aureus</span>
<span class="fu"><a href="../reference/as.mo.html">as.mo</a></span><span class="op">(</span><span class="st">"VISA"</span><span class="op">)</span>, <span class="co"># Vancomycin Intermediate S. aureus</span>
times <span class="op">=</span> <span class="fl">25</span><span class="op">)</span>
<span class="fu"><a href="https://docs.ropensci.org/skimr/reference/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">S.aureus</span>, unit <span class="op">=</span> <span class="st">"ms"</span>, signif <span class="op">=</span> <span class="fl">2</span><span class="op">)</span>
<span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">S.aureus</span>, unit <span class="op">=</span> <span class="st">"ms"</span>, signif <span class="op">=</span> <span class="fl">2</span><span class="op">)</span>
<span class="co"># Unit: milliseconds</span>
<span class="co"># expr min lq mean median uq max neval</span>
<span class="co"># as.mo("sau") 12.0 13.0 17.0 14.0 15 53 25</span>
<span class="co"># as.mo("stau") 51.0 59.0 74.0 71.0 90 97 25</span>
<span class="co"># as.mo("STAU") 53.0 60.0 77.0 87.0 91 96 25</span>
<span class="co"># as.mo("staaur") 11.0 13.0 16.0 14.0 15 48 25</span>
<span class="co"># as.mo("STAAUR") 13.0 14.0 19.0 15.0 16 48 25</span>
<span class="co"># as.mo("S. aureus") 28.0 31.0 48.0 59.0 63 70 25</span>
<span class="co"># as.mo("S aureus") 28.0 29.0 42.0 33.0 58 83 25</span>
<span class="co"># as.mo("Staphylococcus aureus") 3.9 4.1 6.1 4.4 5 43 25</span>
<span class="co"># as.mo("Staphylococcus aureus (MRSA)") 250.0 260.0 270.0 270.0 270 390 25</span>
<span class="co"># as.mo("Sthafilokkockus aaureuz") 160.0 190.0 200.0 200.0 220 240 25</span>
<span class="co"># as.mo("MRSA") 13.0 13.0 19.0 14.0 15 50 25</span>
<span class="co"># as.mo("VISA") 21.0 22.0 29.0 24.0 27 61 25</span></code></pre></div>
<span class="co"># expr min lq mean median uq max neval</span>
<span class="co"># as.mo("sau") 19.0 20.0 25.0 20.0 26.0 55 25</span>
<span class="co"># as.mo("stau") 94.0 95.0 110.0 100.0 130.0 140 25</span>
<span class="co"># as.mo("STAU") 92.0 97.0 110.0 110.0 120.0 140 25</span>
<span class="co"># as.mo("staaur") 19.0 19.0 24.0 20.0 21.0 56 25</span>
<span class="co"># as.mo("STAAUR") 19.0 20.0 21.0 20.0 20.0 49 25</span>
<span class="co"># as.mo("S. aureus") 54.0 57.0 72.0 64.0 86.0 96 25</span>
<span class="co"># as.mo("S aureus") 55.0 55.0 72.0 57.0 90.0 100 25</span>
<span class="co"># as.mo("Staphylococcus aureus") 5.6 5.7 8.5 5.8 6.2 40 25</span>
<span class="co"># as.mo("Staphylococcus aureus (MRSA)") 360.0 370.0 400.0 400.0 420.0 550 25</span>
<span class="co"># as.mo("Sthafilokkockus aaureuz") 280.0 290.0 300.0 300.0 320.0 350 25</span>
<span class="co"># as.mo("MRSA") 19.0 20.0 24.0 20.0 21.0 51 25</span>
<span class="co"># as.mo("VISA") 34.0 34.0 48.0 36.0 65.0 73 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 61 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>
<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 69 times slower to determine.</p>
<p>To improve performance, we implemented two important algorithms to save unnecessary calculations: <strong>repetitive results</strong> and <strong>already precalculated results</strong>.</p>
<div class="section level3">
<h3 id="repetitive-results">Repetitive results<a class="anchor" aria-label="anchor" href="#repetitive-results"></a>
</h3>
<p>Repetitive results are values that are present more than once in a
vector. Unique values will only be calculated once by
<code><a href="../reference/as.mo.html">as.mo()</a></code>. So running
<code>as.mo(c("E. coli", "E. coli"))</code> will check the value
<code>"E. coli"</code> only once.</p>
<p>To prove this, we will use <code><a href="../reference/mo_property.html">mo_name()</a></code> for testing - a
helper function that returns the full microbial name (genus, species and
possibly subspecies) which uses <code><a href="../reference/as.mo.html">as.mo()</a></code> internally.</p>
<p>Repetitive results are values that are present more than once in a vector. Unique values will only be calculated once by <code><a href="../reference/as.mo.html">as.mo()</a></code>. So running <code>as.mo(c("E. coli", "E. coli"))</code> will check the value <code>"E. coli"</code> only once.</p>
<p>To prove this, we will use <code><a href="../reference/mo_property.html">mo_name()</a></code> for testing - a helper function that returns the full microbial name (genus, species and possibly subspecies) which uses <code><a href="../reference/as.mo.html">as.mo()</a></code> internally.</p>
<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="co"># start with the example_isolates data set</span>
<span class="va">x</span> <span class="op">&lt;-</span> <span class="va">example_isolates</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span>
@ -287,8 +258,8 @@ possibly subspecies) which uses <code><a href="../reference/as.mo.html">as.mo()<
<span class="co"># what do these values look like? They are of class &lt;mo&gt;:</span>
<span class="fu"><a href="https://rdrr.io/r/utils/head.html" class="external-link">head</a></span><span class="op">(</span><span class="va">x</span><span class="op">)</span>
<span class="co"># Class &lt;mo&gt;</span>
<span class="co"># [1] B_ACNTB B_ESCHR_COLI B_STRPT_GRPC B_STPHY_HMNS B_STPHY_CONS</span>
<span class="co"># [6] B_ESCHR_COLI</span>
<span class="co"># [1] B_ENTRBC_CLOC B_ESCHR_COLI B_STRPT_PYGN B_STPHY_AURS B_ESCHR_COLI </span>
<span class="co"># [6] B_STRPT_PNMN</span>
<span class="co"># as the example_isolates data set has 2,000 rows, we should have 2 million items</span>
<span class="fu"><a href="https://rdrr.io/r/base/length.html" class="external-link">length</a></span><span class="op">(</span><span class="va">x</span><span class="op">)</span>
@ -301,38 +272,28 @@ possibly subspecies) which uses <code><a href="../reference/as.mo.html">as.mo()<
<span class="co"># now let's see:</span>
<span class="va">run_it</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/microbenchmark/man/microbenchmark.html" class="external-link">microbenchmark</a></span><span class="op">(</span><span class="fu"><a href="../reference/mo_property.html">mo_name</a></span><span class="op">(</span><span class="va">x</span><span class="op">)</span>,
times <span class="op">=</span> <span class="fl">10</span><span class="op">)</span>
<span class="fu"><a href="https://docs.ropensci.org/skimr/reference/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">run_it</span>, unit <span class="op">=</span> <span class="st">"ms"</span>, signif <span class="op">=</span> <span class="fl">3</span><span class="op">)</span>
<span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">run_it</span>, unit <span class="op">=</span> <span class="st">"ms"</span>, signif <span class="op">=</span> <span class="fl">3</span><span class="op">)</span>
<span class="co"># Unit: milliseconds</span>
<span class="co"># expr min lq mean median uq max neval</span>
<span class="co"># mo_name(x) 200 204 265 225 320 392 10</span></code></pre></div>
<p>So getting official taxonomic names of 2,000,000 (!!) items
consisting of 90 unique values only takes 0.225 seconds. That is 112
nanoseconds on average. You only lose time on your unique input
values.</p>
<span class="co"># mo_name(x) 259 264 357 299 451 509 10</span></code></pre></div>
<p>So getting official taxonomic names of 2,000,000 (!!) items consisting of 90 unique values only takes 0.299 seconds. That is 149 nanoseconds on average. You only lose time on your unique input values.</p>
</div>
<div class="section level3">
<h3 id="precalculated-results">Precalculated results<a class="anchor" aria-label="anchor" href="#precalculated-results"></a>
</h3>
<p>What about precalculated results? If the input is an already
precalculated result of a helper function such as
<code><a href="../reference/mo_property.html">mo_name()</a></code>, it almost doesn’t take any time at all. In other
words, if you run <code><a href="../reference/mo_property.html">mo_name()</a></code> on a valid taxonomic name, it
will return the results immediately (see ‘C’ below):</p>
<p>What about precalculated results? If the input is an already precalculated result of a helper function such as <code><a href="../reference/mo_property.html">mo_name()</a></code>, it almost doesn’t take any time at all. In other words, if you run <code><a href="../reference/mo_property.html">mo_name()</a></code> on a valid taxonomic name, it will return the results immediately (see ‘C’ below):</p>
<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="va">run_it</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/microbenchmark/man/microbenchmark.html" class="external-link">microbenchmark</a></span><span class="op">(</span>A <span class="op">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_name</a></span><span class="op">(</span><span class="st">"STAAUR"</span><span class="op">)</span>,
B <span class="op">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_name</a></span><span class="op">(</span><span class="st">"S. aureus"</span><span class="op">)</span>,
C <span class="op">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_name</a></span><span class="op">(</span><span class="st">"Staphylococcus aureus"</span><span class="op">)</span>,
times <span class="op">=</span> <span class="fl">10</span><span class="op">)</span>
<span class="fu"><a href="https://docs.ropensci.org/skimr/reference/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">run_it</span>, unit <span class="op">=</span> <span class="st">"ms"</span>, signif <span class="op">=</span> <span class="fl">3</span><span class="op">)</span>
<span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">run_it</span>, unit <span class="op">=</span> <span class="st">"ms"</span>, signif <span class="op">=</span> <span class="fl">3</span><span class="op">)</span>
<span class="co"># Unit: milliseconds</span>
<span class="co"># expr min lq mean median uq max neval</span>
<span class="co"># A 8.35 8.84 9.10 9.01 9.32 10.20 10</span>
<span class="co"># B 23.00 24.70 30.30 25.00 26.90 72.70 10</span>
<span class="co"># C 2.05 2.07 2.44 2.41 2.83 3.05 10</span></code></pre></div>
<p>So going from <code>mo_name("Staphylococcus aureus")</code> to
<code>"Staphylococcus aureus"</code> takes 0.0024 seconds - it doesn’t
even start calculating <em>if the result would be the same as the
expected resulting value</em>. That goes for all helper functions:</p>
<span class="co"># expr min lq mean median uq max neval</span>
<span class="co"># A 11.90 12.10 13.0 13.50 13.70 13.80 10</span>
<span class="co"># B 60.90 61.20 67.7 66.20 66.90 99.70 10</span>
<span class="co"># C 2.91 2.94 3.2 3.32 3.38 3.46 10</span></code></pre></div>
<p>So going from <code>mo_name("Staphylococcus aureus")</code> to <code>"Staphylococcus aureus"</code> takes 0.0033 seconds - it doesn’t even start calculating <em>if the result would be the same as the expected resulting value</em>. That goes for all helper functions:</p>
<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="va">run_it</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/microbenchmark/man/microbenchmark.html" class="external-link">microbenchmark</a></span><span class="op">(</span>A <span class="op">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_species</a></span><span class="op">(</span><span class="st">"aureus"</span><span class="op">)</span>,
B <span class="op">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_genus</a></span><span class="op">(</span><span class="st">"Staphylococcus"</span><span class="op">)</span>,
@ -343,31 +304,23 @@ expected resulting value</em>. That goes for all helper functions:</p>
G <span class="op">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_phylum</a></span><span class="op">(</span><span class="st">"Firmicutes"</span><span class="op">)</span>,
H <span class="op">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_kingdom</a></span><span class="op">(</span><span class="st">"Bacteria"</span><span class="op">)</span>,
times <span class="op">=</span> <span class="fl">10</span><span class="op">)</span>
<span class="fu"><a href="https://docs.ropensci.org/skimr/reference/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">run_it</span>, unit <span class="op">=</span> <span class="st">"ms"</span>, signif <span class="op">=</span> <span class="fl">3</span><span class="op">)</span>
<span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">run_it</span>, unit <span class="op">=</span> <span class="st">"ms"</span>, signif <span class="op">=</span> <span class="fl">3</span><span class="op">)</span>
<span class="co"># Unit: milliseconds</span>
<span class="co"># expr min lq mean median uq max neval</span>
<span class="co"># A 1.89 1.93 2.09 2.05 2.17 2.40 10</span>
<span class="co"># B 1.89 1.93 2.08 2.00 2.19 2.63 10</span>
<span class="co"># C 1.91 1.92 2.10 1.97 2.30 2.43 10</span>
<span class="co"># D 1.90 1.94 2.21 2.02 2.53 2.88 10</span>
<span class="co"># E 1.87 1.95 2.09 2.04 2.22 2.33 10</span>
<span class="co"># F 1.84 1.91 1.97 1.92 2.04 2.14 10</span>
<span class="co"># G 1.87 1.92 2.10 1.96 2.12 2.96 10</span>
<span class="co"># H 1.90 1.96 2.12 2.06 2.21 2.47 10</span></code></pre></div>
<p>Of course, when running <code>mo_phylum("Firmicutes")</code> the
function has zero knowledge about the actual microorganism, namely
<em>S. aureus</em>. But since the result would be
<code>"Firmicutes"</code> anyway, there is no point in calculating the
result. And because this package contains all phyla of all known
bacteria, it can just return the initial value immediately.</p>
<span class="co"># A 2.92 2.93 3.02 2.94 3.02 3.40 10</span>
<span class="co"># B 2.87 2.90 3.14 3.09 3.32 3.71 10</span>
<span class="co"># C 2.91 2.94 3.15 3.12 3.33 3.46 10</span>
<span class="co"># D 2.86 2.90 3.05 2.96 3.27 3.30 10</span>
<span class="co"># E 2.87 2.88 3.03 2.96 3.16 3.29 10</span>
<span class="co"># F 2.92 2.95 3.08 2.98 3.29 3.35 10</span>
<span class="co"># G 2.89 2.96 3.04 2.99 3.11 3.29 10</span>
<span class="co"># H 2.85 2.95 3.11 3.08 3.31 3.38 10</span></code></pre></div>
<p>Of course, when running <code>mo_phylum("Firmicutes")</code> the function has zero knowledge about the actual microorganism, namely <em>S. aureus</em>. But since the result would be <code>"Firmicutes"</code> anyway, there is no point in calculating the result. And because this package contains all phyla of all known bacteria, it can just return the initial value immediately.</p>
</div>
<div class="section level3">
<h3 id="results-in-other-languages">Results in other languages<a class="anchor" aria-label="anchor" href="#results-in-other-languages"></a>
</h3>
<p>When the system language is non-English and supported by this
<code>AMR</code> package, some functions will have a translated result.
This almost does’t take extra time (compare “en” from the table below
with the other languages):</p>
<p>When the system language is non-English and supported by this <code>AMR</code> package, some functions will have a translated result. This almost does’t take extra time (compare “en” from the table below with the other languages):</p>
<div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="va">CoNS</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/as.mo.html">as.mo</a></span><span class="op">(</span><span class="st">"CoNS"</span><span class="op">)</span>
<span class="va">CoNS</span>
@ -394,21 +347,20 @@ with the other languages):</p>
ru <span class="op">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_name</a></span><span class="op">(</span><span class="va">CoNS</span>, language <span class="op">=</span> <span class="st">"ru"</span><span class="op">)</span>,
sv <span class="op">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_name</a></span><span class="op">(</span><span class="va">CoNS</span>, language <span class="op">=</span> <span class="st">"sv"</span><span class="op">)</span>,
times <span class="op">=</span> <span class="fl">100</span><span class="op">)</span>
<span class="fu"><a href="https://docs.ropensci.org/skimr/reference/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">run_it</span>, unit <span class="op">=</span> <span class="st">"ms"</span>, signif <span class="op">=</span> <span class="fl">4</span><span class="op">)</span>
<span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">run_it</span>, unit <span class="op">=</span> <span class="st">"ms"</span>, signif <span class="op">=</span> <span class="fl">4</span><span class="op">)</span>
<span class="co"># Unit: milliseconds</span>
<span class="co"># expr min lq mean median uq max neval</span>
<span class="co"># da 2.133 2.304 3.442 2.494 2.816 46.020 100</span>
<span class="co"># de 2.128 2.312 3.068 2.520 2.699 53.220 100</span>
<span class="co"># en 1.014 1.115 1.262 1.227 1.362 2.424 100</span>
<span class="co"># es 2.133 2.338 2.981 2.570 2.737 43.770 100</span>
<span class="co"># fr 1.986 2.149 3.139 2.377 2.567 41.610 100</span>
<span class="co"># it 2.072 2.268 2.911 2.468 2.656 44.560 100</span>
<span class="co"># nl 2.115 2.286 2.962 2.521 2.723 43.240 100</span>
<span class="co"># pt 2.055 2.205 2.912 2.520 2.687 39.520 100</span>
<span class="co"># ru 1.998 2.210 2.866 2.474 2.631 39.820 100</span>
<span class="co"># sv 2.022 2.187 2.759 2.357 2.536 38.560 100</span></code></pre></div>
<p>Currently supported languages are Danish, Dutch, English, French,
German, Italian, Portuguese, Russian, Spanish and Swedish.</p>
<span class="co"># da 3.546 3.643 4.072 3.704 3.832 35.930 100</span>
<span class="co"># de 3.597 3.659 4.422 3.734 3.839 36.400 100</span>
<span class="co"># en 1.672 1.726 1.804 1.767 1.794 2.259 100</span>
<span class="co"># es 3.609 3.685 4.496 3.760 3.843 36.540 100</span>
<span class="co"># fr 3.484 3.567 3.725 3.654 3.713 6.281 100</span>
<span class="co"># it 3.523 3.615 4.419 3.720 3.787 36.720 100</span>
<span class="co"># nl 3.614 3.676 3.805 3.732 3.838 4.703 100</span>
<span class="co"># pt 3.512 3.595 4.077 3.659 3.789 37.310 100</span>
<span class="co"># ru 3.556 3.647 4.057 3.680 3.812 35.230 100</span>
<span class="co"># sv 3.540 3.642 4.093 3.732 3.803 36.340 100</span></code></pre></div>
<p>Currently supported languages are Danish, Dutch, English, French, German, Italian, Portuguese, Russian, Spanish and Swedish.</p>
</div>
</div>
@ -422,14 +374,12 @@ German, Italian, Portuguese, Russian, Spanish and Swedish.</p>
<footer><div class="copyright">
<p></p>
<p>Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein,
Erwin E. A. Hassing.</p>
<p>Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein, Erwin E. A. Hassing.</p>
</div>
<div class="pkgdown">
<p></p>
<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a>
2.0.2.</p>
<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.2.</p>
</div>
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@ -44,7 +44,7 @@
</button>
<span class="navbar-brand">
<a class="navbar-link" href="../index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.8.0.9010</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.8.1</span>
</span>
</div>
@ -185,12 +185,12 @@
</header><div class="row">
</header><script src="datasets_files/accessible-code-block-0.0.1/empty-anchor.js"></script><div class="row">
<div class="col-md-9 contents">
<div class="page-header toc-ignore">
<h1 data-toc-skip>Data sets for download / own use</h1>
<h4 data-toc-skip class="date">15 March 2022</h4>
<h4 data-toc-skip class="date">27 March 2022</h4>
<small class="dont-index">Source: <a href="https://github.com/msberends/AMR/blob/HEAD/vignettes/datasets.Rmd" class="external-link"><code>vignettes/datasets.Rmd</code></a></small>
<div class="hidden name"><code>datasets.Rmd</code></div>
@ -199,71 +199,42 @@
<p>All reference data (about microorganisms, antibiotics, R/SI
interpretation, EUCAST rules, etc.) in this <code>AMR</code> package are
reliable, up-to-date and freely available. We continually export our
data sets to formats for use in R, SPSS, SAS, Stata and Excel. We also
supply tab separated files that are machine-readable and suitable for
input in any software program, such as laboratory information
systems.</p>
<p>On this page, we explain how to download them and how the structure
of the data sets look like.</p>
<p>All reference data (about microorganisms, antibiotics, R/SI interpretation, EUCAST rules, etc.) in this <code>AMR</code> package are reliable, up-to-date and freely available. We continually export our data sets to formats for use in R, SPSS, SAS, Stata and Excel. We also supply tab separated files that are machine-readable and suitable for input in any software program, such as laboratory information systems.</p>
<p>On this page, we explain how to download them and how the structure of the data sets look like.</p>
<p class="dataset-within-r">
If you are reading this page from within R, please
<a href="https://msberends.github.io/AMR/articles/datasets.html">visit
our website</a>, which is automatically updated with every code change.
If you are reading this page from within R, please <a href="https://msberends.github.io/AMR/articles/datasets.html">visit our website</a>, which is automatically updated with every code change.
</p>
<div class="section level2">
<h2 id="microorganisms-currently-accepted-names">Microorganisms (currently accepted names)<a class="anchor" aria-label="anchor" href="#microorganisms-currently-accepted-names"></a>
</h2>
<p>A data set with 70,760 rows and 16 columns, containing the following
column names:<br><em>mo</em>, <em>fullname</em>, <em>kingdom</em>, <em>phylum</em>,
<em>class</em>, <em>order</em>, <em>family</em>, <em>genus</em>,
<em>species</em>, <em>subspecies</em>, <em>rank</em>, <em>ref</em>,
<em>species_id</em>, <em>source</em>, <em>prevalence</em> and
<em>snomed</em>.</p>
<p>This data set is in R available as <code>microorganisms</code>, after
you load the <code>AMR</code> package.</p>
<p>It was last updated on 29 November 2021 11:38:23 UTC. Find more info
about the structure of this data set <a href="https://msberends.github.io/AMR/reference/microorganisms.html">here</a>.</p>
<p>A data set with 70,760 rows and 16 columns, containing the following column names:<br><em>mo</em>, <em>fullname</em>, <em>kingdom</em>, <em>phylum</em>, <em>class</em>, <em>order</em>, <em>family</em>, <em>genus</em>, <em>species</em>, <em>subspecies</em>, <em>rank</em>, <em>ref</em>, <em>species_id</em>, <em>source</em>, <em>prevalence</em> and <em>snomed</em>.</p>
<p>This data set is in R available as <code>microorganisms</code>, after you load the <code>AMR</code> package.</p>
<p>It was last updated on 1 February 2022 22:08:20 UTC. Find more info about the structure of this data set <a href="https://msberends.github.io/AMR/reference/microorganisms.html">here</a>.</p>
<p><strong>Direct download links:</strong></p>
<ul>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/microorganisms.rds" class="external-link">R
file</a> (1.3 MB)<br>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/microorganisms.rds" class="external-link">R file</a> (1.3 MB)<br>
</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/microorganisms.xlsx" class="external-link">Excel
file</a> (6.4 MB)<br>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/microorganisms.xlsx" class="external-link">Excel file</a> (6.4 MB)<br>
</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/microorganisms.txt" class="external-link">plain
text file</a> (13.1 MB)<br>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/microorganisms.txt" class="external-link">plain text file</a> (13.1 MB)<br>
</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/microorganisms.sas" class="external-link">SAS
file</a> (30.7 MB)<br>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/microorganisms.sas" class="external-link">SAS file</a> (30.7 MB)<br>
</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/microorganisms.sav" class="external-link">SPSS
file</a> (16.3 MB)<br>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/microorganisms.sav" class="external-link">SPSS file</a> (16.3 MB)<br>
</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/microorganisms.dta" class="external-link">Stata
file</a> (27.5 MB)</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/microorganisms.dta" class="external-link">Stata file</a> (27.5 MB)</li>
</ul>
<p><strong>NOTE: The exported files for SAS, SPSS and Stata do not
contain SNOMED codes, as their file size would exceed 100 MB; the file
size limit of GitHub.</strong> Advice? Use R instead.</p>
<p><strong>NOTE: The exported files for SAS, SPSS and Stata do not contain SNOMED codes, as their file size would exceed 100 MB; the file size limit of GitHub.</strong> Advice? Use R instead.</p>
<div class="section level3">
<h3 id="source">Source<a class="anchor" aria-label="anchor" href="#source"></a>
</h3>
<p>Our full taxonomy of microorganisms is based on the authoritative and
comprehensive:</p>
<p>Our full taxonomy of microorganisms is based on the authoritative and comprehensive:</p>
<ul>
<li>
<a href="http://www.catalogueoflife.org" class="external-link">Catalogue of Life</a>
(included version: 2019)</li>
<a href="http://www.catalogueoflife.org" class="external-link">Catalogue of Life</a> (included version: 2019)</li>
<li>
<a href="https://lpsn.dsmz.de" class="external-link">List of Prokaryotic names with
Standing in Nomenclature</a> (LPSN, last updated: 5 October 2021)</li>
<li>US Edition of SNOMED CT from 1 September 2020, retrieved from the <a href="https://phinvads.cdc.gov/vads/ViewValueSet.action?oid=2.16.840.1.114222.4.11.1009" class="external-link">Public
Health Information Network Vocabulary Access and Distribution System
(PHIN VADS)</a>, OID 2.16.840.1.114222.4.11.1009, version 12</li>
<a href="https://lpsn.dsmz.de" class="external-link">List of Prokaryotic names with Standing in Nomenclature</a> (LPSN, last updated: 5 October 2021)</li>
<li>US Edition of SNOMED CT from 1 September 2020, retrieved from the <a href="https://phinvads.cdc.gov/vads/ViewValueSet.action?oid=2.16.840.1.114222.4.11.1009" class="external-link">Public Health Information Network Vocabulary Access and Distribution System (PHIN VADS)</a>, OID 2.16.840.1.114222.4.11.1009, version 12</li>
</ul>
</div>
<div class="section level3">
@ -456,64 +427,40 @@ Health Information Network Vocabulary Access and Distribution System
<div class="section level2">
<h2 id="microorganisms-previously-accepted-names">Microorganisms (previously accepted names)<a class="anchor" aria-label="anchor" href="#microorganisms-previously-accepted-names"></a>
</h2>
<p>A data set with 14,338 rows and 4 columns, containing the following
column names:<br><em>fullname</em>, <em>fullname_new</em>, <em>ref</em> and
<em>prevalence</em>.</p>
<p><strong>Note:</strong> remember that the ‘ref’ columns contains the
scientific reference to the old taxonomic entries, i.e. of column
<em>‘fullname’</em>. For the scientific reference of the new names,
i.e. of column <em>‘fullname_new’</em>, see the
<code>microorganisms</code> data set.</p>
<p>This data set is in R available as <code>microorganisms.old</code>,
after you load the <code>AMR</code> package.</p>
<p>It was last updated on 6 October 2021 14:38:29 UTC. Find more info
about the structure of this data set <a href="https://msberends.github.io/AMR/reference/microorganisms.old.html">here</a>.</p>
<p>A data set with 14,338 rows and 4 columns, containing the following column names:<br><em>fullname</em>, <em>fullname_new</em>, <em>ref</em> and <em>prevalence</em>.</p>
<p><strong>Note:</strong> remember that the ‘ref’ columns contains the scientific reference to the old taxonomic entries, i.e. of column <em>‘fullname’</em>. For the scientific reference of the new names, i.e. of column <em>‘fullname_new’</em>, see the <code>microorganisms</code> data set.</p>
<p>This data set is in R available as <code>microorganisms.old</code>, after you load the <code>AMR</code> package.</p>
<p>It was last updated on 1 February 2022 22:08:19 UTC. Find more info about the structure of this data set <a href="https://msberends.github.io/AMR/reference/microorganisms.old.html">here</a>.</p>
<p><strong>Direct download links:</strong></p>
<ul>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/microorganisms.old.rds" class="external-link">R
file</a> (0.2 MB)<br>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/microorganisms.old.rds" class="external-link">R file</a> (0.2 MB)<br>
</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/microorganisms.old.xlsx" class="external-link">Excel
file</a> (0.5 MB)<br>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/microorganisms.old.xlsx" class="external-link">Excel file</a> (0.5 MB)<br>
</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/microorganisms.old.txt" class="external-link">plain
text file</a> (1 MB)<br>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/microorganisms.old.txt" class="external-link">plain text file</a> (1 MB)<br>
</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/microorganisms.old.sas" class="external-link">SAS
file</a> (2.1 MB)<br>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/microorganisms.old.sas" class="external-link">SAS file</a> (2.1 MB)<br>
</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/microorganisms.old.sav" class="external-link">SPSS
file</a> (1.3 MB)<br>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/microorganisms.old.sav" class="external-link">SPSS file</a> (1.3 MB)<br>
</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/microorganisms.old.dta" class="external-link">Stata
file</a> (2 MB)</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/microorganisms.old.dta" class="external-link">Stata file</a> (2 MB)</li>
</ul>
<div class="section level3">
<h3 id="source-1">Source<a class="anchor" aria-label="anchor" href="#source-1"></a>
</h3>
<p>This data set contains old, previously accepted taxonomic names. The
data sources are the same as the <code>microorganisms</code> data
set:</p>
<p>This data set contains old, previously accepted taxonomic names. The data sources are the same as the <code>microorganisms</code> data set:</p>
<ul>
<li>
<a href="http://www.catalogueoflife.org" class="external-link">Catalogue of Life</a>
(included version: 2019)</li>
<a href="http://www.catalogueoflife.org" class="external-link">Catalogue of Life</a> (included version: 2019)</li>
<li>
<a href="https://lpsn.dsmz.de" class="external-link">List of Prokaryotic names with
Standing in Nomenclature</a> (LPSN, last updated: 5 October 2021)</li>
<a href="https://lpsn.dsmz.de" class="external-link">List of Prokaryotic names with Standing in Nomenclature</a> (LPSN, last updated: 5 October 2021)</li>
</ul>
</div>
<div class="section level3">
<h3 id="example-content-1">Example content<a class="anchor" aria-label="anchor" href="#example-content-1"></a>
</h3>
<p>Example rows when filtering on <em>Escherichia</em>:</p>
<table style="width:100%;" class="table">
<colgroup>
<col width="31%">
<col width="30%">
<col width="24%">
<col width="13%">
</colgroup>
<table class="table">
<thead><tr class="header">
<th align="center">fullname</th>
<th align="center">fullname_new</th>
@ -546,50 +493,31 @@ Standing in Nomenclature</a> (LPSN, last updated: 5 October 2021)</li>
<div class="section level2">
<h2 id="antibiotic-agents">Antibiotic agents<a class="anchor" aria-label="anchor" href="#antibiotic-agents"></a>
</h2>
<p>A data set with 464 rows and 14 columns, containing the following
column names:<br><em>ab</em>, <em>cid</em>, <em>name</em>, <em>group</em>, <em>atc</em>,
<em>atc_group1</em>, <em>atc_group2</em>, <em>abbreviations</em>,
<em>synonyms</em>, <em>oral_ddd</em>, <em>oral_units</em>,
<em>iv_ddd</em>, <em>iv_units</em> and <em>loinc</em>.</p>
<p>This data set is in R available as <code>antibiotics</code>, after
you load the <code>AMR</code> package.</p>
<p>It was last updated on 14 December 2021 21:59:33 UTC. Find more info
about the structure of this data set <a href="https://msberends.github.io/AMR/reference/antibiotics.html">here</a>.</p>
<p>A data set with 464 rows and 14 columns, containing the following column names:<br><em>ab</em>, <em>cid</em>, <em>name</em>, <em>group</em>, <em>atc</em>, <em>atc_group1</em>, <em>atc_group2</em>, <em>abbreviations</em>, <em>synonyms</em>, <em>oral_ddd</em>, <em>oral_units</em>, <em>iv_ddd</em>, <em>iv_units</em> and <em>loinc</em>.</p>
<p>This data set is in R available as <code>antibiotics</code>, after you load the <code>AMR</code> package.</p>
<p>It was last updated on 1 February 2022 22:08:19 UTC. Find more info about the structure of this data set <a href="https://msberends.github.io/AMR/reference/antibiotics.html">here</a>.</p>
<p><strong>Direct download links:</strong></p>
<ul>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/antibiotics.rds" class="external-link">R
file</a> (33 kB)<br>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/antibiotics.rds" class="external-link">R file</a> (33 kB)<br>
</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/antibiotics.xlsx" class="external-link">Excel
file</a> (65 kB)<br>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/antibiotics.xlsx" class="external-link">Excel file</a> (65 kB)<br>
</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/antibiotics.txt" class="external-link">plain
text file</a> (0.1 MB)<br>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/antibiotics.txt" class="external-link">plain text file</a> (0.1 MB)<br>
</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/antibiotics.sas" class="external-link">SAS
file</a> (1.8 MB)<br>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/antibiotics.sas" class="external-link">SAS file</a> (1.8 MB)<br>
</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/antibiotics.sav" class="external-link">SPSS
file</a> (0.3 MB)<br>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/antibiotics.sav" class="external-link">SPSS file</a> (0.3 MB)<br>
</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/antibiotics.dta" class="external-link">Stata
file</a> (0.3 MB)</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/antibiotics.dta" class="external-link">Stata file</a> (0.3 MB)</li>
</ul>
<div class="section level3">
<h3 id="source-2">Source<a class="anchor" aria-label="anchor" href="#source-2"></a>
</h3>
<p>This data set contains all EARS-Net and ATC codes gathered from WHO
and WHONET, and all compound IDs from PubChem. It also contains all
brand names (synonyms) as found on PubChem and Defined Daily Doses
(DDDs) for oral and parenteral administration.</p>
<p>This data set contains all EARS-Net and ATC codes gathered from WHO and WHONET, and all compound IDs from PubChem. It also contains all brand names (synonyms) as found on PubChem and Defined Daily Doses (DDDs) for oral and parenteral administration.</p>
<ul>
<li>
<a href="https://www.whocc.no/atc_ddd_index/" class="external-link">ATC/DDD index from WHO
Collaborating Centre for Drug Statistics Methodology</a> (note: this may
not be used for commercial purposes, but is freely available from the
WHO CC website for personal use)</li>
<li><a href="https://pubchem.ncbi.nlm.nih.gov" class="external-link">PubChem by the US
National Library of Medicine</a></li>
<a href="https://www.whocc.no/atc_ddd_index/" class="external-link">ATC/DDD index from WHO Collaborating Centre for Drug Statistics Methodology</a> (note: this may not be used for commercial purposes, but is freely available from the WHO CC website for personal use)</li>
<li><a href="https://pubchem.ncbi.nlm.nih.gov" class="external-link">PubChem by the US National Library of Medicine</a></li>
<li><a href="https://whonet.org" class="external-link">WHONET software 2019</a></li>
</ul>
</div>
@ -669,8 +597,7 @@ National Library of Medicine</a></li>
<td align="center">Beta-lactams/penicillins</td>
<td align="center">J01CR02</td>
<td align="center">Beta-lactam antibacterials, penicillins</td>
<td align="center">Combinations of penicillins, incl. beta-lactamase
inhibitors</td>
<td align="center">Combinations of penicillins, incl. beta-lactamase inhibitors</td>
<td align="center">a/c, amcl, aml, …</td>
<td align="center">amocla, amoclan, amoclav, …</td>
<td align="center">1.5</td>
@ -734,49 +661,31 @@ inhibitors</td>
<div class="section level2">
<h2 id="antiviral-agents">Antiviral agents<a class="anchor" aria-label="anchor" href="#antiviral-agents"></a>
</h2>
<p>A data set with 102 rows and 9 columns, containing the following
column names:<br><em>atc</em>, <em>cid</em>, <em>name</em>, <em>atc_group</em>,
<em>synonyms</em>, <em>oral_ddd</em>, <em>oral_units</em>,
<em>iv_ddd</em> and <em>iv_units</em>.</p>
<p>This data set is in R available as <code>antivirals</code>, after you
load the <code>AMR</code> package.</p>
<p>It was last updated on 29 August 2020 19:53:07 UTC. Find more info
about the structure of this data set <a href="https://msberends.github.io/AMR/reference/antibiotics.html">here</a>.</p>
<p>A data set with 102 rows and 9 columns, containing the following column names:<br><em>atc</em>, <em>cid</em>, <em>name</em>, <em>atc_group</em>, <em>synonyms</em>, <em>oral_ddd</em>, <em>oral_units</em>, <em>iv_ddd</em> and <em>iv_units</em>.</p>
<p>This data set is in R available as <code>antivirals</code>, after you load the <code>AMR</code> package.</p>
<p>It was last updated on 23 July 2021 20:35:47 UTC. Find more info about the structure of this data set <a href="https://msberends.github.io/AMR/reference/antibiotics.html">here</a>.</p>
<p><strong>Direct download links:</strong></p>
<ul>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/antivirals.rds" class="external-link">R
file</a> (5 kB)<br>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/antivirals.rds" class="external-link">R file</a> (5 kB)<br>
</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/antivirals.xlsx" class="external-link">Excel
file</a> (14 kB)<br>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/antivirals.xlsx" class="external-link">Excel file</a> (14 kB)<br>
</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/antivirals.txt" class="external-link">plain
text file</a> (16 kB)<br>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/antivirals.txt" class="external-link">plain text file</a> (16 kB)<br>
</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/antivirals.sas" class="external-link">SAS
file</a> (80 kB)<br>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/antivirals.sas" class="external-link">SAS file</a> (80 kB)<br>
</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/antivirals.sav" class="external-link">SPSS
file</a> (68 kB)<br>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/antivirals.sav" class="external-link">SPSS file</a> (68 kB)<br>
</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/antivirals.dta" class="external-link">Stata
file</a> (67 kB)</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/antivirals.dta" class="external-link">Stata file</a> (67 kB)</li>
</ul>
<div class="section level3">
<h3 id="source-3">Source<a class="anchor" aria-label="anchor" href="#source-3"></a>
</h3>
<p>This data set contains all ATC codes gathered from WHO and all
compound IDs from PubChem. It also contains all brand names (synonyms)
as found on PubChem and Defined Daily Doses (DDDs) for oral and
parenteral administration.</p>
<p>This data set contains all ATC codes gathered from WHO and all compound IDs from PubChem. It also contains all brand names (synonyms) as found on PubChem and Defined Daily Doses (DDDs) for oral and parenteral administration.</p>
<ul>
<li>
<a href="https://www.whocc.no/atc_ddd_index/" class="external-link">ATC/DDD index from WHO
Collaborating Centre for Drug Statistics Methodology</a> (note: this may
not be used for commercial purposes, but is freely available from the
WHO CC website for personal use)</li>
<li><a href="https://pubchem.ncbi.nlm.nih.gov" class="external-link">PubChem by the US
National Library of Medicine</a></li>
<a href="https://www.whocc.no/atc_ddd_index/" class="external-link">ATC/DDD index from WHO Collaborating Centre for Drug Statistics Methodology</a> (note: this may not be used for commercial purposes, but is freely available from the WHO CC website for personal use)</li>
<li><a href="https://pubchem.ncbi.nlm.nih.gov" class="external-link">PubChem by the US National Library of Medicine</a></li>
</ul>
</div>
<div class="section level3">
@ -810,8 +719,7 @@ National Library of Medicine</a></li>
<td align="center">J05AF06</td>
<td align="center">441300</td>
<td align="center">Abacavir</td>
<td align="center">Nucleoside and nucleotide reverse transcriptase
inhibitors</td>
<td align="center">Nucleoside and nucleotide reverse transcriptase inhibitors</td>
<td align="center">Abacavir, Abacavir sulfate, Ziagen</td>
<td align="center">0.6</td>
<td align="center">g</td>
@ -822,8 +730,7 @@ inhibitors</td>
<td align="center">J05AB01</td>
<td align="center">135398513</td>
<td align="center">Aciclovir</td>
<td align="center">Nucleosides and nucleotides excl. reverse
transcriptase inhibitors</td>
<td align="center">Nucleosides and nucleotides excl. reverse transcriptase inhibitors</td>
<td align="center">Acicloftal, Aciclovier, Aciclovir, …</td>
<td align="center">4.0</td>
<td align="center">g</td>
@ -834,10 +741,8 @@ transcriptase inhibitors</td>
<td align="center">J05AF08</td>
<td align="center">60871</td>
<td align="center">Adefovir dipivoxil</td>
<td align="center">Nucleoside and nucleotide reverse transcriptase
inhibitors</td>
<td align="center">Adefovir di ester, Adefovir dipivoxil, Adefovir
Dipivoxil, …</td>
<td align="center">Nucleoside and nucleotide reverse transcriptase inhibitors</td>
<td align="center">Adefovir di ester, Adefovir dipivoxil, Adefovir Dipivoxil, …</td>
<td align="center">10.0</td>
<td align="center">mg</td>
<td align="center"></td>
@ -883,39 +788,27 @@ Dipivoxil, …</td>
<div class="section level2">
<h2 id="intrinsic-bacterial-resistance">Intrinsic bacterial resistance<a class="anchor" aria-label="anchor" href="#intrinsic-bacterial-resistance"></a>
</h2>
<p>A data set with 134,956 rows and 2 columns, containing the following
column names:<br><em>mo</em> and <em>ab</em>.</p>
<p>This data set is in R available as <code>intrinsic_resistant</code>,
after you load the <code>AMR</code> package.</p>
<p>It was last updated on 14 December 2021 21:59:33 UTC. Find more info
about the structure of this data set <a href="https://msberends.github.io/AMR/reference/intrinsic_resistant.html">here</a>.</p>
<p>A data set with 134,956 rows and 2 columns, containing the following column names:<br><em>mo</em> and <em>ab</em>.</p>
<p>This data set is in R available as <code>intrinsic_resistant</code>, after you load the <code>AMR</code> package.</p>
<p>It was last updated on 1 February 2022 22:08:19 UTC. Find more info about the structure of this data set <a href="https://msberends.github.io/AMR/reference/intrinsic_resistant.html">here</a>.</p>
<p><strong>Direct download links:</strong></p>
<ul>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/intrinsic_resistant.rds" class="external-link">R
file</a> (78 kB)<br>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/intrinsic_resistant.rds" class="external-link">R file</a> (78 kB)<br>
</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/intrinsic_resistant.xlsx" class="external-link">Excel
file</a> (0.9 MB)<br>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/intrinsic_resistant.xlsx" class="external-link">Excel file</a> (0.9 MB)<br>
</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/intrinsic_resistant.txt" class="external-link">plain
text file</a> (5.1 MB)<br>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/intrinsic_resistant.txt" class="external-link">plain text file</a> (5.1 MB)<br>
</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/intrinsic_resistant.sas" class="external-link">SAS
file</a> (10.4 MB)<br>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/intrinsic_resistant.sas" class="external-link">SAS file</a> (10.4 MB)<br>
</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/intrinsic_resistant.sav" class="external-link">SPSS
file</a> (7.4 MB)<br>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/intrinsic_resistant.sav" class="external-link">SPSS file</a> (7.4 MB)<br>
</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/intrinsic_resistant.dta" class="external-link">Stata
file</a> (10.2 MB)</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/intrinsic_resistant.dta" class="external-link">Stata file</a> (10.2 MB)</li>
</ul>
<div class="section level3">
<h3 id="source-4">Source<a class="anchor" aria-label="anchor" href="#source-4"></a>
</h3>
<p>This data set contains all defined intrinsic resistance by EUCAST of
all bug-drug combinations, and is based on <a href="https://www.eucast.org/expert_rules_and_intrinsic_resistance/" class="external-link">‘EUCAST
Expert Rules’ and ‘EUCAST Intrinsic Resistance and Unusual Phenotypes’
v3.3</a> (2021).</p>
<p>This data set contains all defined intrinsic resistance by EUCAST of all bug-drug combinations, and is based on <a href="https://www.eucast.org/expert_rules_and_expected_phenotypes/" class="external-link">‘EUCAST Expert Rules’ and ‘EUCAST Intrinsic Resistance and Unusual Phenotypes’ v3.3</a> (2021).</p>
</div>
<div class="section level3">
<h3 id="example-content-4">Example content<a class="anchor" aria-label="anchor" href="#example-content-4"></a>
@ -1162,40 +1055,27 @@ v3.3</a> (2021).</p>
<div class="section level2">
<h2 id="interpretation-from-mic-values-disk-diameters-to-rsi">Interpretation from MIC values / disk diameters to R/SI<a class="anchor" aria-label="anchor" href="#interpretation-from-mic-values-disk-diameters-to-rsi"></a>
</h2>
<p>A data set with 20,318 rows and 11 columns, containing the following
column names:<br><em>guideline</em>, <em>method</em>, <em>site</em>, <em>mo</em>,
<em>rank_index</em>, <em>ab</em>, <em>ref_tbl</em>, <em>disk_dose</em>,
<em>breakpoint_S</em>, <em>breakpoint_R</em> and <em>uti</em>.</p>
<p>This data set is in R available as <code>rsi_translation</code>,
after you load the <code>AMR</code> package.</p>
<p>It was last updated on 14 December 2021 21:59:33 UTC. Find more info
about the structure of this data set <a href="https://msberends.github.io/AMR/reference/rsi_translation.html">here</a>.</p>
<p>A data set with 20,318 rows and 11 columns, containing the following column names:<br><em>guideline</em>, <em>method</em>, <em>site</em>, <em>mo</em>, <em>rank_index</em>, <em>ab</em>, <em>ref_tbl</em>, <em>disk_dose</em>, <em>breakpoint_S</em>, <em>breakpoint_R</em> and <em>uti</em>.</p>
<p>This data set is in R available as <code>rsi_translation</code>, after you load the <code>AMR</code> package.</p>
<p>It was last updated on 1 February 2022 22:08:20 UTC. Find more info about the structure of this data set <a href="https://msberends.github.io/AMR/reference/rsi_translation.html">here</a>.</p>
<p><strong>Direct download links:</strong></p>
<ul>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/rsi_translation.rds" class="external-link">R
file</a> (39 kB)<br>
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</ul>
<div class="section level3">
<h3 id="source-5">Source<a class="anchor" aria-label="anchor" href="#source-5"></a>
</h3>
<p>This data set contains interpretation rules for MIC values and disk
diffusion diameters. Included guidelines are CLSI (2010-2021) and EUCAST
(2011-2021).</p>
<p>This data set contains interpretation rules for MIC values and disk diffusion diameters. Included guidelines are CLSI (2010-2021) and EUCAST (2011-2021).</p>
</div>
<div class="section level3">
<h3 id="example-content-5">Example content<a class="anchor" aria-label="anchor" href="#example-content-5"></a>
@ -1313,57 +1193,33 @@ diffusion diameters. Included guidelines are CLSI (2010-2021) and EUCAST
<div class="section level2">
<h2 id="dosage-guidelines-from-eucast">Dosage guidelines from EUCAST<a class="anchor" aria-label="anchor" href="#dosage-guidelines-from-eucast"></a>
</h2>
<p>A data set with 169 rows and 9 columns, containing the following
column names:<br><em>ab</em>, <em>name</em>, <em>type</em>, <em>dose</em>,
<em>dose_times</em>, <em>administration</em>, <em>notes</em>,
<em>original_txt</em> and <em>eucast_version</em>.</p>
<p>This data set is in R available as <code>dosage</code>, after you
load the <code>AMR</code> package.</p>
<p>It was last updated on 25 January 2021 20:58:20 UTC. Find more info
about the structure of this data set <a href="https://msberends.github.io/AMR/reference/dosage.html">here</a>.</p>
<p>A data set with 169 rows and 9 columns, containing the following column names:<br><em>ab</em>, <em>name</em>, <em>type</em>, <em>dose</em>, <em>dose_times</em>, <em>administration</em>, <em>notes</em>, <em>original_txt</em> and <em>eucast_version</em>.</p>
<p>This data set is in R available as <code>dosage</code>, after you load the <code>AMR</code> package.</p>
<p>It was last updated on 23 July 2021 20:35:47 UTC. Find more info about the structure of this data set <a href="https://msberends.github.io/AMR/reference/dosage.html">here</a>.</p>
<p><strong>Direct download links:</strong></p>
<ul>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/dosage.rds" class="external-link">R
file</a> (3 kB)<br>
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</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/dosage.txt" class="external-link">plain
text file</a> (15 kB)<br>
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</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/dosage.sas" class="external-link">SAS
file</a> (52 kB)<br>
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</li>
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file</a> (45 kB)<br>
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</li>
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<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/dosage.dta" class="external-link">Stata file</a> (44 kB)</li>
</ul>
<div class="section level3">
<h3 id="source-6">Source<a class="anchor" aria-label="anchor" href="#source-6"></a>
</h3>
<p>EUCAST breakpoints used in this package are based on the dosages in
this data set.</p>
<p>Currently included dosages in the data set are meant for: <a href="https://www.eucast.org/clinical_breakpoints/" class="external-link">‘EUCAST Clinical
Breakpoint Tables’ v11.0</a> (2021).</p>
<p>EUCAST breakpoints used in this package are based on the dosages in this data set.</p>
<p>Currently included dosages in the data set are meant for: <a href="https://www.eucast.org/clinical_breakpoints/" class="external-link">‘EUCAST Clinical Breakpoint Tables’ v11.0</a> (2021).</p>
</div>
<div class="section level3">
<h3 id="example-content-6">Example content<a class="anchor" aria-label="anchor" href="#example-content-6"></a>
</h3>
<table class="table">
<colgroup>
<col width="4%">
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<col width="10%">
<col width="9%">
<col width="13%">
<col width="5%">
<col width="16%">
<col width="13%">
</colgroup>
<thead><tr class="header">
<th align="center">ab</th>
<th align="center">name</th>
@ -1460,14 +1316,12 @@ Breakpoint Tables’ v11.0</a> (2021).</p>
<footer><div class="copyright">
<p></p>
<p>Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein,
Erwin E. A. Hassing.</p>
<p>Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein, Erwin E. A. Hassing.</p>
</div>
<div class="pkgdown">
<p></p>
<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a>
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<p></p><p>Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein,
Erwin E. A. Hassing.</p>
<p></p><p>Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein, Erwin E. A. Hassing.</p>
</div>
<div class="pkgdown">
<p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a>
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@ -185,7 +185,7 @@
</header><div class="row">
</header><script src="resistance_predict_files/accessible-code-block-0.0.1/empty-anchor.js"></script><div class="row">
<div class="col-md-9 contents">
<div class="page-header toc-ignore">
<h1 data-toc-skip>How to predict antimicrobial resistance</h1>
@ -201,13 +201,8 @@
<div class="section level2">
<h2 id="needed-r-packages">Needed R packages<a class="anchor" aria-label="anchor" href="#needed-r-packages"></a>
</h2>
<p>As with many uses in R, we need some additional packages for AMR data
analysis. Our package works closely together with the <a href="https://www.tidyverse.org" class="external-link">tidyverse packages</a> <a href="https://dplyr.tidyverse.org/" class="external-link"><code>dplyr</code></a> and <a href="https://ggplot2.tidyverse.org" class="external-link"><code>ggplot2</code></a> by Dr
Hadley Wickham. The tidyverse tremendously improves the way we conduct
data science - it allows for a very natural way of writing syntaxes and
creating beautiful plots in R.</p>
<p>Our <code>AMR</code> package depends on these packages and even
extends their use and functions.</p>
<p>As with many uses in R, we need some additional packages for AMR data analysis. Our package works closely together with the <a href="https://www.tidyverse.org" class="external-link">tidyverse packages</a> <a href="https://dplyr.tidyverse.org/" class="external-link"><code>dplyr</code></a> and <a href="https://ggplot2.tidyverse.org" class="external-link"><code>ggplot2</code></a> by Dr Hadley Wickham. The tidyverse tremendously improves the way we conduct data science - it allows for a very natural way of writing syntaxes and creating beautiful plots in R.</p>
<p>Our <code>AMR</code> package depends on these packages and even extends their use and functions.</p>
<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://dplyr.tidyverse.org" class="external-link">dplyr</a></span><span class="op">)</span>
<span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://ggplot2.tidyverse.org" class="external-link">ggplot2</a></span><span class="op">)</span>
@ -219,34 +214,24 @@ extends their use and functions.</p>
<div class="section level2">
<h2 id="prediction-analysis">Prediction analysis<a class="anchor" aria-label="anchor" href="#prediction-analysis"></a>
</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>Our package contains a function <code><a href="../reference/resistance_predict.html">resistance_predict()</a></code>, which takes the same input as functions for <a href="./AMR.html">other AMR data analysis</a>. Based on a date column, it calculates cases per year and uses a regression model to predict antimicrobial resistance.</p>
<p>It is basically as easy as:</p>
<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a><span class="co"># resistance prediction of piperacillin/tazobactam (TZP):</span></span>
<span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a><span class="fu">resistance_predict</span>(<span class="at">tbl =</span> example_isolates, <span class="at">col_date =</span> <span class="st">"date"</span>, <span class="at">col_ab =</span> <span class="st">"TZP"</span>, <span class="at">model =</span> <span class="st">"binomial"</span>)</span>
<span id="cb2-3"><a href="#cb2-3" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb2-4"><a href="#cb2-4" aria-hidden="true" tabindex="-1"></a><span class="co"># or:</span></span>
<span id="cb2-5"><a href="#cb2-5" aria-hidden="true" tabindex="-1"></a>example_isolates <span class="sc">%&gt;%</span> </span>
<span id="cb2-6"><a href="#cb2-6" aria-hidden="true" tabindex="-1"></a> <span class="fu">resistance_predict</span>(<span class="at">col_ab =</span> <span class="st">"TZP"</span>,</span>
<span id="cb2-7"><a href="#cb2-7" aria-hidden="true" tabindex="-1"></a> model <span class="st">"binomial"</span>)</span>
<span id="cb2-8"><a href="#cb2-8" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb2-9"><a href="#cb2-9" aria-hidden="true" tabindex="-1"></a><span class="co"># to bind it to object 'predict_TZP' for example:</span></span>
<span id="cb2-10"><a href="#cb2-10" aria-hidden="true" tabindex="-1"></a>predict_TZP <span class="ot">&lt;-</span> example_isolates <span class="sc">%&gt;%</span> </span>
<span id="cb2-11"><a href="#cb2-11" aria-hidden="true" tabindex="-1"></a> <span class="fu">resistance_predict</span>(<span class="at">col_ab =</span> <span class="st">"TZP"</span>,</span>
<span id="cb2-12"><a href="#cb2-12" aria-hidden="true" tabindex="-1"></a> <span class="at">model =</span> <span class="st">"binomial"</span>)</span></code></pre></div>
<p>The function will look for a date column itself if
<code>col_date</code> is not set.</p>
<p>When running any of these commands, a summary of the regression model
will be printed unless using
<code>resistance_predict(..., info = FALSE)</code>.</p>
<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true"></a><span class="co"># resistance prediction of piperacillin/tazobactam (TZP):</span></span>
<span id="cb2-2"><a href="#cb2-2" aria-hidden="true"></a><span class="kw">resistance_predict</span>(<span class="dt">tbl =</span> example_isolates, <span class="dt">col_date =</span> <span class="st">"date"</span>, <span class="dt">col_ab =</span> <span class="st">"TZP"</span>, <span class="dt">model =</span> <span class="st">"binomial"</span>)</span>
<span id="cb2-3"><a href="#cb2-3" aria-hidden="true"></a></span>
<span id="cb2-4"><a href="#cb2-4" aria-hidden="true"></a><span class="co"># or:</span></span>
<span id="cb2-5"><a href="#cb2-5" aria-hidden="true"></a>example_isolates <span class="op">%&gt;%</span><span class="st"> </span></span>
<span id="cb2-6"><a href="#cb2-6" aria-hidden="true"></a><span class="st"> </span><span class="kw">resistance_predict</span>(<span class="dt">col_ab =</span> <span class="st">"TZP"</span>,</span>
<span id="cb2-7"><a href="#cb2-7" aria-hidden="true"></a> model <span class="st">"binomial"</span>)</span>
<span id="cb2-8"><a href="#cb2-8" aria-hidden="true"></a></span>
<span id="cb2-9"><a href="#cb2-9" aria-hidden="true"></a><span class="co"># to bind it to object 'predict_TZP' for example:</span></span>
<span id="cb2-10"><a href="#cb2-10" aria-hidden="true"></a>predict_TZP &lt;-<span class="st"> </span>example_isolates <span class="op">%&gt;%</span><span class="st"> </span></span>
<span id="cb2-11"><a href="#cb2-11" aria-hidden="true"></a><span class="st"> </span><span class="kw">resistance_predict</span>(<span class="dt">col_ab =</span> <span class="st">"TZP"</span>,</span>
<span id="cb2-12"><a href="#cb2-12" aria-hidden="true"></a> <span class="dt">model =</span> <span class="st">"binomial"</span>)</span></code></pre></div>
<p>The function will look for a date column itself if <code>col_date</code> is not set.</p>
<p>When running any of these commands, a summary of the regression model will be printed unless using <code>resistance_predict(..., info = FALSE)</code>.</p>
<pre><code><span class="co"># ℹ Using column 'date' as input for `col_date`.</span></code></pre>
<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>
<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 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>
@ -281,18 +266,12 @@ resistance and the standard error below and above the estimation:</p>
<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>
<span class="co"># 31 2032 0.53574417 0.4169574 0.6545309 NA NA 0.53574417</span></code></pre></div>
<p>The function <code>plot</code> is available in base R, and can be
extended by other packages to depend the output based on the type of
input. We extended its function to cope with resistance predictions:</p>
<p>The function <code>plot</code> is available in base R, and can be extended by other packages to depend the output based on the type of input. We extended its function to cope with resistance predictions:</p>
<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="fu"><a href="../reference/plot.html">plot</a></span><span class="op">(</span><span class="va">predict_TZP</span><span class="op">)</span></code></pre></div>
<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>
<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 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>
@ -304,9 +283,7 @@ plots:</p>
<div class="section level3">
<h3 id="choosing-the-right-model">Choosing the right model<a class="anchor" aria-label="anchor" href="#choosing-the-right-model"></a>
</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>
<p>Resistance is not easily predicted; if we look at vancomycin resistance in Gram-positive bacteria, the spread (i.e. standard error) is enormous:</p>
<div class="sourceCode" id="cb8"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="va">example_isolates</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span>
<span class="fu"><a href="https://dplyr.tidyverse.org/reference/filter.html" class="external-link">filter</a></span><span class="op">(</span><span class="fu"><a href="../reference/mo_property.html">mo_gramstain</a></span><span class="op">(</span><span class="va">mo</span>, language <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span> <span class="op">==</span> <span class="st">"Gram-positive"</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span>
@ -314,13 +291,8 @@ is enormous:</p>
<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>
<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>
<p>Valid values are:</p>
<table class="table">
<colgroup>
@ -336,8 +308,7 @@ more resistance.</p>
<tbody>
<tr class="odd">
<td>
<code>"binomial"</code> or <code>"binom"</code> or
<code>"logit"</code>
<code>"binomial"</code> or <code>"binom"</code> or <code>"logit"</code>
</td>
<td><code>glm(..., family = binomial)</code></td>
<td>Generalised linear model with binomial distribution</td>
@ -358,9 +329,7 @@ more resistance.</p>
</tr>
</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>
<p>For the vancomycin resistance in Gram-positive bacteria, a linear model might be more appropriate since no binomial distribution is to be expected based on the observed years:</p>
<div class="sourceCode" id="cb9"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="va">example_isolates</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span>
<span class="fu"><a href="https://dplyr.tidyverse.org/reference/filter.html" class="external-link">filter</a></span><span class="op">(</span><span class="fu"><a href="../reference/mo_property.html">mo_gramstain</a></span><span class="op">(</span><span class="va">mo</span>, language <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span> <span class="op">==</span> <span class="st">"Gram-positive"</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span>
@ -369,8 +338,7 @@ expected based on the observed years:</p>
<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>
<p>The model itself is also available from the object, as an <code>attribute</code>:</p>
<div class="sourceCode" id="cb10"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="va">model</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/attributes.html" class="external-link">attributes</a></span><span class="op">(</span><span class="va">predict_TZP</span><span class="op">)</span><span class="op">$</span><span class="va">model</span>
@ -399,14 +367,12 @@ expected based on the observed years:</p>
<footer><div class="copyright">
<p></p>
<p>Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein,
Erwin E. A. Hassing.</p>
<p>Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein, Erwin E. A. Hassing.</p>
</div>
<div class="pkgdown">
<p></p>
<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a>
2.0.2.</p>
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@ -185,7 +185,7 @@
</header><div class="row">
</header><script src="welcome_to_AMR_files/accessible-code-block-0.0.1/empty-anchor.js"></script><div class="row">
<div class="col-md-9 contents">
<div class="page-header toc-ignore">
<h1 data-toc-skip>Welcome to the <code>AMR</code> package</h1>
@ -198,83 +198,33 @@
<p>Note: to keep the package size as small as possible, we only included
this vignette on CRAN. You can read more vignettes on our website about
how to conduct AMR data analysis, determine MDRO’s, find explanation of
EUCAST rules, and much more: <a href="https://msberends.github.io/AMR/articles/" class="uri">https://msberends.github.io/AMR/articles/</a>.</p>
<p>Note: to keep the package size as small as possible, we only included this vignette on CRAN. You can read more vignettes on our website about how to conduct AMR data analysis, determine MDRO’s, find explanation of EUCAST rules, and much more: <a href="https://msberends.github.io/AMR/articles/" class="uri">https://msberends.github.io/AMR/articles/</a>.</p>
<hr>
<p><code>AMR</code> is a free, open-source and independent R package
(see <a href="https://msberends.github.io/AMR/#copyright">Copyright</a>)
to simplify the analysis and prediction of Antimicrobial Resistance
(AMR) and to work with microbial and antimicrobial data and properties,
by using evidence-based methods. <strong>Our aim is to provide a
standard</strong> for clean and reproducible antimicrobial resistance
data analysis, that can therefore empower epidemiological analyses to
continuously enable surveillance and treatment evaluation in any
setting.</p>
<p>After installing this package, R knows ~71,000 distinct microbial
species and all ~570 antibiotic, antimycotic and antiviral drugs by name
and code (including ATC, EARS-Net, PubChem, LOINC and SNOMED CT), and
knows all about valid R/SI and MIC values. It supports any data format,
including WHONET/EARS-Net data.</p>
<p>The <code>AMR</code> package is available in Danish, Dutch, English,
French, German, Italian, Portuguese, Russian, Spanish and Swedish.
Antimicrobial drug (group) names and colloquial microorganism names are
provided in these languages.</p>
<p>This package is fully independent of any other R package and works on
Windows, macOS and Linux with all versions of R since R-3.0 (April
2013). <strong>It was designed to work in any setting, including those
with very limited resources</strong>. Since its first public release in
early 2018, this package has been downloaded from more than 175
countries.</p>
<p><code>AMR</code> is a free, open-source and independent R package (see <a href="https://msberends.github.io/AMR/#copyright">Copyright</a>) to simplify the analysis and prediction of Antimicrobial Resistance (AMR) and to work with microbial and antimicrobial data and properties, by using evidence-based methods. <strong>Our aim is to provide a standard</strong> for clean and reproducible antimicrobial resistance data analysis, that can therefore empower epidemiological analyses to continuously enable surveillance and treatment evaluation in any setting.</p>
<p>After installing this package, R knows ~71,000 distinct microbial species and all ~570 antibiotic, antimycotic and antiviral drugs by name and code (including ATC, EARS-Net, PubChem, LOINC and SNOMED CT), and knows all about valid R/SI and MIC values. It supports any data format, including WHONET/EARS-Net data.</p>
<p>The <code>AMR</code> package is available in Danish, Dutch, English, French, German, Italian, Portuguese, Russian, Spanish and Swedish. Antimicrobial drug (group) names and colloquial microorganism names are provided in these languages.</p>
<p>This package is fully independent of any other R package and works on Windows, macOS and Linux with all versions of R since R-3.0 (April 2013). <strong>It was designed to work in any setting, including those with very limited resources</strong>. Since its first public release in early 2018, this package has been downloaded from more than 175 countries.</p>
<p>This package can be used for:</p>
<ul>
<li>Reference for the taxonomy of microorganisms, since the package
contains all microbial (sub)species from the Catalogue of Life and List
of Prokaryotic names with Standing in Nomenclature</li>
<li>Interpreting raw MIC and disk diffusion values, based on the latest
CLSI or EUCAST guidelines</li>
<li>Retrieving antimicrobial drug names, doses and forms of
administration from clinical health care records</li>
<li>Reference for the taxonomy of microorganisms, since the package contains all microbial (sub)species from the Catalogue of Life and List of Prokaryotic names with Standing in Nomenclature</li>
<li>Interpreting raw MIC and disk diffusion values, based on the latest CLSI or EUCAST guidelines</li>
<li>Retrieving antimicrobial drug names, doses and forms of administration from clinical health care records</li>
<li>Determining first isolates to be used for AMR data analysis</li>
<li>Calculating antimicrobial resistance</li>
<li>Determining multi-drug resistance (MDR) / multi-drug resistant
organisms (MDRO)</li>
<li>Calculating (empirical) susceptibility of both mono therapy and
combination therapies</li>
<li>Predicting future antimicrobial resistance using regression
models</li>
<li>Getting properties for any microorganism (like Gram stain, species,
genus or family)</li>
<li>Getting properties for any antibiotic (like name, code of
EARS-Net/ATC/LOINC/PubChem, defined daily dose or trade name)</li>
<li>Determining multi-drug resistance (MDR) / multi-drug resistant organisms (MDRO)</li>
<li>Calculating (empirical) susceptibility of both mono therapy and combination therapies</li>
<li>Predicting future antimicrobial resistance using regression models</li>
<li>Getting properties for any microorganism (like Gram stain, species, genus or family)</li>
<li>Getting properties for any antibiotic (like name, code of EARS-Net/ATC/LOINC/PubChem, defined daily dose or trade name)</li>
<li>Plotting antimicrobial resistance</li>
<li>Applying EUCAST expert rules</li>
<li>Getting SNOMED codes of a microorganism, or getting properties of a
microorganism based on a SNOMED code</li>
<li>Getting LOINC codes of an antibiotic, or getting properties of an
antibiotic based on a LOINC code</li>
<li>Machine reading the EUCAST and CLSI guidelines from 2011-2020 to
translate MIC values and disk diffusion diameters to R/SI</li>
<li>Getting SNOMED codes of a microorganism, or getting properties of a microorganism based on a SNOMED code</li>
<li>Getting LOINC codes of an antibiotic, or getting properties of an antibiotic based on a LOINC code</li>
<li>Machine reading the EUCAST and CLSI guidelines from 2011-2020 to translate MIC values and disk diffusion diameters to R/SI</li>
<li>Principal component analysis for AMR</li>
</ul>
<p>All reference data sets (about microorganisms, antibiotics, R/SI
interpretation, EUCAST rules, etc.) in this <code>AMR</code> package are
publicly and freely available. We continually export our data sets to
formats for use in R, SPSS, SAS, Stata and Excel. We also supply flat
files that are machine-readable and suitable for input in any software
program, such as laboratory information systems. Please find <a href="https://msberends.github.io/AMR/articles/datasets.html">all
download links on our website</a>, which is automatically updated with
every code change.</p>
<p>This R package was created for both routine data analysis and
academic research at the Faculty of Medical Sciences of the <a href="https://www.rug.nl" class="external-link">University of Groningen</a>, in collaboration
with non-profit organisations <a href="https://www.certe.nl" class="external-link">Certe
Medical Diagnostics and Advice Foundation</a> and <a href="https://www.umcg.nl" class="external-link">University Medical Center Groningen</a>. This
R package formed the basis of two PhD theses (<a href="https://doi.org/10.33612/diss.177417131" class="external-link">DOI
10.33612/diss.177417131</a> and <a href="https://doi.org/10.33612/diss.192486375" class="external-link">DOI
10.33612/diss.192486375</a>) but is actively and durably maintained (see
<a href="https://msberends.github.io/AMR/news/index.html">changelog)</a>)
by two public healthcare organisations in the Netherlands.</p>
<p>All reference data sets (about microorganisms, antibiotics, R/SI interpretation, EUCAST rules, etc.) in this <code>AMR</code> package are publicly and freely available. We continually export our data sets to formats for use in R, SPSS, SAS, Stata and Excel. We also supply flat files that are machine-readable and suitable for input in any software program, such as laboratory information systems. Please find <a href="https://msberends.github.io/AMR/articles/datasets.html">all download links on our website</a>, which is automatically updated with every code change.</p>
<p>This R package was created for both routine data analysis and academic research at the Faculty of Medical Sciences of the <a href="https://www.rug.nl" class="external-link">University of Groningen</a>, in collaboration with non-profit organisations <a href="https://www.certe.nl" class="external-link">Certe Medical Diagnostics and Advice Foundation</a> and <a href="https://www.umcg.nl" class="external-link">University Medical Center Groningen</a>. This R package formed the basis of two PhD theses (<a href="https://doi.org/10.33612/diss.177417131" class="external-link">DOI 10.33612/diss.177417131</a> and <a href="https://doi.org/10.33612/diss.192486375" class="external-link">DOI 10.33612/diss.192486375</a>) but is actively and durably maintained (see <a href="https://msberends.github.io/AMR/news/index.html">changelog)</a>) by two public healthcare organisations in the Netherlands.</p>
</div>
<div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
@ -287,14 +237,12 @@ by two public healthcare organisations in the Netherlands.</p>
<footer><div class="copyright">
<p></p>
<p>Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein,
Erwin E. A. Hassing.</p>
<p>Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein, Erwin E. A. Hassing.</p>
</div>
<div class="pkgdown">
<p></p>
<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a>
2.0.2.</p>
<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.2.</p>
</div>
</footer>

15
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@ -0,0 +1,15 @@
// Hide empty <a> tag within highlighted CodeBlock for screen reader accessibility (see https://github.com/jgm/pandoc/issues/6352#issuecomment-626106786) -->
// v0.0.1
// Written by JooYoung Seo (jooyoung@psu.edu) and Atsushi Yasumoto on June 1st, 2020.
document.addEventListener('DOMContentLoaded', function() {
const codeList = document.getElementsByClassName("sourceCode");
for (var i = 0; i < codeList.length; i++) {
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@ -17,7 +17,7 @@
</button>
<span class="navbar-brand">
<a class="navbar-link" href="index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.8.0.9010</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.8.1</span>
</span>
</div>
@ -269,13 +269,11 @@ Antimicrobial Resistance Data. Journal of Statistical Software (accepted for pub
<footer><div class="copyright">
<p></p><p>Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein,
Erwin E. A. Hassing.</p>
<p></p><p>Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein, Erwin E. A. Hassing.</p>
</div>
<div class="pkgdown">
<p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a>
2.0.2.</p>
<p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.2.</p>
</div>
</footer></div>

332
docs/index.html
View File

@ -47,7 +47,7 @@
</button>
<span class="navbar-brand">
<a class="navbar-link" href="index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.8.0.9010</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.8.1</span>
</span>
</div>
@ -195,75 +195,22 @@
<code>AMR</code> (for R) <img src="./logo.svg" align="right"><a class="anchor" aria-label="anchor" href="#amr-for-r-"></a>
</h1></div>
<blockquote>
<p>Update: The latest <a href="https://www.eucast.org/expert_rules_and_intrinsic_resistance/" class="external-link">EUCAST
guideline for intrinsic resistance</a> (v3.3, October 2021) is now
supported, the CLSI 2021 interpretation guideline is now supported, and
our taxonomy tables have been updated as well (LPSN, 5 October
2021).</p>
<p>Update: The latest <a href="https://www.eucast.org/expert_rules_and_intrinsic_resistance/" class="external-link">EUCAST guideline for intrinsic resistance</a> (v3.3, October 2021) is now supported, the CLSI 2021 interpretation guideline is now supported, and our taxonomy tables have been updated as well (LPSN, 5 October 2021).</p>
</blockquote>
<div class="section level3">
<h3 id="what-is-amr-for-r">What is <code>AMR</code> (for R)?<a class="anchor" aria-label="anchor" href="#what-is-amr-for-r"></a>
</h3>
<p><code>AMR</code> is a free, open-source and independent <a href="https://www.r-project.org" class="external-link">R package</a> (see <a href="#copyright">Copyright</a>) to simplify the analysis and prediction
of Antimicrobial Resistance (AMR) and to work with microbial and
antimicrobial data and properties, by using evidence-based methods.
<strong>Our aim is to provide a standard</strong> for clean and
reproducible AMR data analysis, that can therefore empower
epidemiological analyses to continuously enable surveillance and
treatment evaluation in any setting.</p>
<p>After installing this package, R knows <a href="./reference/microorganisms.html"><strong>~71,000 distinct
microbial species</strong></a> and all <a href="./reference/antibiotics.html"><strong>~570 antibiotic, antimycotic
and antiviral drugs</strong></a> by name and code (including ATC,
WHONET/EARS-Net, PubChem, LOINC and SNOMED CT), and knows all about
valid R/SI and MIC values. It supports any data format, including
WHONET/EARS-Net data.</p>
<p>The <code>AMR</code> package is available in
<img src="lang_da.svg" style="height: 11px !important; vertical-align: initial !important;">
Danish,
<img src="lang_nl.svg" style="height: 11px !important; vertical-align: initial !important;">
Dutch,
<img src="lang_en.svg" style="height: 11px !important; vertical-align: initial !important;">
English,
<img src="lang_fr.svg" style="height: 11px !important; vertical-align: initial !important;">
French,
<img src="lang_de.svg" style="height: 11px !important; vertical-align: initial !important;">
German,
<img src="lang_it.svg" style="height: 11px !important; vertical-align: initial !important;">
Italian,
<img src="lang_pt.svg" style="height: 11px !important; vertical-align: initial !important;">
Portuguese,
<img src="lang_ru.svg" style="height: 11px !important; vertical-align: initial !important;">
Russian,
<img src="lang_es.svg" style="height: 11px !important; vertical-align: initial !important;">
Spanish and
<img src="lang_sv.svg" style="height: 11px !important; vertical-align: initial !important;">
Swedish. Antimicrobial drug (group) names and colloquial microorganism
names are provided in these languages.</p>
<p>This package is <a href="https://en.wikipedia.org/wiki/Dependency_hell" class="external-link">fully independent
of any other R package</a> and works on Windows, macOS and Linux with
all versions of R since R-3.0 (April 2013). <strong>It was designed to
work in any setting, including those with very limited
resources</strong>. It was created for both routine data analysis and
academic research at the Faculty of Medical Sciences of the <a href="https://www.rug.nl" class="external-link">University of Groningen</a>, in collaboration
with non-profit organisations <a href="https://www.certe.nl" class="external-link">Certe
Medical Diagnostics and Advice Foundation</a> and <a href="https://www.umcg.nl" class="external-link">University Medical Center Groningen</a>. This
R package formed the basis of two PhD theses (<a href="https://doi.org/10.33612/diss.177417131" class="external-link">DOI
10.33612/diss.177417131</a> and <a href="https://doi.org/10.33612/diss.192486375" class="external-link">DOI
10.33612/diss.192486375</a>) but is <a href="./news">actively and
durably maintained</a> by two public healthcare organisations in the
Netherlands.</p>
<p><code>AMR</code> is a free, open-source and independent <a href="https://www.r-project.org" class="external-link">R package</a> (see <a href="#copyright">Copyright</a>) to simplify the analysis and prediction of Antimicrobial Resistance (AMR) and to work with microbial and antimicrobial data and properties, by using evidence-based methods. <strong>Our aim is to provide a standard</strong> for clean and reproducible AMR data analysis, that can therefore empower epidemiological analyses to continuously enable surveillance and treatment evaluation in any setting.</p>
<p>After installing this package, R knows <a href="./reference/microorganisms.html"><strong>~71,000 distinct microbial species</strong></a> and all <a href="./reference/antibiotics.html"><strong>~570 antibiotic, antimycotic and antiviral drugs</strong></a> by name and code (including ATC, WHONET/EARS-Net, PubChem, LOINC and SNOMED CT), and knows all about valid R/SI and MIC values. It supports any data format, including WHONET/EARS-Net data.</p>
<p>The <code>AMR</code> package is available in <img src="lang_da.svg" style="height: 11px !important; vertical-align: initial !important;"> Danish, <img src="lang_nl.svg" style="height: 11px !important; vertical-align: initial !important;"> Dutch, <img src="lang_en.svg" style="height: 11px !important; vertical-align: initial !important;"> English, <img src="lang_fr.svg" style="height: 11px !important; vertical-align: initial !important;"> French, <img src="lang_de.svg" style="height: 11px !important; vertical-align: initial !important;"> German, <img src="lang_it.svg" style="height: 11px !important; vertical-align: initial !important;"> Italian, <img src="lang_pt.svg" style="height: 11px !important; vertical-align: initial !important;"> Portuguese, <img src="lang_ru.svg" style="height: 11px !important; vertical-align: initial !important;"> Russian, <img src="lang_es.svg" style="height: 11px !important; vertical-align: initial !important;"> Spanish and <img src="lang_sv.svg" style="height: 11px !important; vertical-align: initial !important;"> Swedish. Antimicrobial drug (group) names and colloquial microorganism names are provided in these languages.</p>
<p>This package is <a href="https://en.wikipedia.org/wiki/Dependency_hell" class="external-link">fully independent of any other R package</a> and works on Windows, macOS and Linux with all versions of R since R-3.0 (April 2013). <strong>It was designed to work in any setting, including those with very limited resources</strong>. It was created for both routine data analysis and academic research at the Faculty of Medical Sciences of the <a href="https://www.rug.nl" class="external-link">University of Groningen</a>, in collaboration with non-profit organisations <a href="https://www.certe.nl" class="external-link">Certe Medical Diagnostics and Advice Foundation</a> and <a href="https://www.umcg.nl" class="external-link">University Medical Center Groningen</a>. This R package formed the basis of two PhD theses (<a href="https://doi.org/10.33612/diss.177417131" class="external-link">DOI 10.33612/diss.177417131</a> and <a href="https://doi.org/10.33612/diss.192486375" class="external-link">DOI 10.33612/diss.192486375</a>) but is <a href="./news">actively and durably maintained</a> by two public healthcare organisations in the Netherlands.</p>
<div class="main-content" style="display: inline-block;">
<p>
<a href="./countries_large.png" target="_blank"><img src="./countries.png" class="countries_map"></a>
<strong>Used in 175 countries</strong><br> Since its first public
release in early 2018, this R package has been used in almost all
countries in the world. Click the map to enlarge and to see the country
names.
<a href="./countries_large.png" target="_blank"><img src="./countries.png" class="countries_map"></a> <strong>Used in 175 countries</strong><br> Since its first public release in early 2018, this R package has been used in almost all countries in the world. Click the map to enlarge and to see the country names.
</p>
</div>
<div class="section level5">
<h5 id="with-amr-for-r-theres-always-a-knowledgeable-microbiologist-by-your-side">With <code>AMR</code> (for R), there’s always a knowledgeable
microbiologist by your side!<a class="anchor" aria-label="anchor" href="#with-amr-for-r-theres-always-a-knowledgeable-microbiologist-by-your-side"></a>
<h5 id="with-amr-for-r-theres-always-a-knowledgeable-microbiologist-by-your-side">With <code>AMR</code> (for R), there’s always a knowledgeable microbiologist by your side!<a class="anchor" aria-label="anchor" href="#with-amr-for-r-theres-always-a-knowledgeable-microbiologist-by-your-side"></a>
</h5>
<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="co"># AMR works great with dplyr, but it's not required or neccesary</span>
@ -277,12 +224,7 @@ microbiologist by your side!<a class="anchor" aria-label="anchor" href="#with-am
<span class="fu"><a href="https://dplyr.tidyverse.org/reference/select.html" class="external-link">select</a></span><span class="op">(</span><span class="va">bacteria</span>,
<span class="fu"><a href="reference/antibiotic_class_selectors.html">aminoglycosides</a></span><span class="op">(</span><span class="op">)</span>,
<span class="fu"><a href="reference/antibiotic_class_selectors.html">carbapenems</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span></code></pre></div>
<p>With only having defined a row filter on Gram-negative bacteria with
intrinsic resistance to cefotaxime (<code><a href="reference/mo_property.html">mo_is_gram_negative()</a></code>
and <code><a href="reference/mo_property.html">mo_is_intrinsic_resistant()</a></code>) and a column selection on
two antibiotic groups (<code><a href="reference/antibiotic_class_selectors.html">aminoglycosides()</a></code> and
<code><a href="reference/antibiotic_class_selectors.html">carbapenems()</a></code>), the reference data about <a href="./reference/microorganisms.html">all microorganisms</a> and <a href="./reference/antibiotics.html">all antibiotics</a> in the
<code>AMR</code> package make sure you get what you meant:</p>
<p>With only having defined a row filter on Gram-negative bacteria with intrinsic resistance to cefotaxime (<code><a href="reference/mo_property.html">mo_is_gram_negative()</a></code> and <code><a href="reference/mo_property.html">mo_is_intrinsic_resistant()</a></code>) and a column selection on two antibiotic groups (<code><a href="reference/antibiotic_class_selectors.html">aminoglycosides()</a></code> and <code><a href="reference/antibiotic_class_selectors.html">carbapenems()</a></code>), the reference data about <a href="./reference/microorganisms.html">all microorganisms</a> and <a href="./reference/antibiotics.html">all antibiotics</a> in the <code>AMR</code> package make sure you get what you meant:</p>
<table class="table">
<thead><tr class="header">
<th align="left">bacteria</th>
@ -396,14 +338,9 @@ two antibiotic groups (<code><a href="reference/antibiotic_class_selectors.html"
<div class="section level4">
<h4 id="partners">Partners<a class="anchor" aria-label="anchor" href="#partners"></a>
</h4>
<p>The development of this package is part of, related to, or made
possible by:</p>
<p>The development of this package is part of, related to, or made possible by:</p>
<div align="center">
<p><a href="https://www.rug.nl" title="University of Groningen" class="external-link"><img src="./logo_rug.png" class="partner_logo"></a>
<a href="https://www.umcg.nl" title="University Medical Center Groningen" class="external-link"><img src="./logo_umcg.png" class="partner_logo"></a>
<a href="https://www.certe.nl" title="Certe Medical Diagnostics and Advice Foundation" class="external-link"><img src="./logo_certe.png" class="partner_logo"></a>
<a href="https://www.deutschland-nederland.eu" title="EurHealth-1-Health" class="external-link"><img src="./logo_eh1h.png" class="partner_logo"></a>
<a href="https://www.deutschland-nederland.eu" title="INTERREG" class="external-link"><img src="./logo_interreg.png" class="partner_logo"></a></p>
<p><a href="https://www.rug.nl" title="University of Groningen" class="external-link"><img src="./logo_rug.png" class="partner_logo"></a> <a href="https://www.umcg.nl" title="University Medical Center Groningen" class="external-link"><img src="./logo_umcg.png" class="partner_logo"></a> <a href="https://www.certe.nl" title="Certe Medical Diagnostics and Advice Foundation" class="external-link"><img src="./logo_certe.png" class="partner_logo"></a> <a href="https://www.deutschland-nederland.eu" title="EurHealth-1-Health" class="external-link"><img src="./logo_eh1h.png" class="partner_logo"></a> <a href="https://www.deutschland-nederland.eu" title="INTERREG" class="external-link"><img src="./logo_interreg.png" class="partner_logo"></a></p>
</div>
</div>
</div>
@ -412,33 +349,21 @@ possible by:</p>
</h3>
<p>This package can be used for:</p>
<ul>
<li>Reference for the taxonomy of microorganisms, since the package
contains all microbial (sub)species from the <a href="http://www.catalogueoflife.org" class="external-link">Catalogue of Life</a> and <a href="https://lpsn.dsmz.de" class="external-link">List of Prokaryotic names with Standing in
Nomenclature</a> (<a href="./reference/mo_property.html">manual</a>)</li>
<li>Interpreting raw MIC and disk diffusion values, based on the latest
CLSI or EUCAST guidelines (<a href="./reference/as.rsi.html">manual</a>)</li>
<li>Retrieving antimicrobial drug names, doses and forms of
administration from clinical health care records (<a href="./reference/ab_from_text.html">manual</a>)</li>
<li>Reference for the taxonomy of microorganisms, since the package contains all microbial (sub)species from the <a href="http://www.catalogueoflife.org" class="external-link">Catalogue of Life</a> and <a href="https://lpsn.dsmz.de" class="external-link">List of Prokaryotic names with Standing in Nomenclature</a> (<a href="./reference/mo_property.html">manual</a>)</li>
<li>Interpreting raw MIC and disk diffusion values, based on the latest CLSI or EUCAST guidelines (<a href="./reference/as.rsi.html">manual</a>)</li>
<li>Retrieving antimicrobial drug names, doses and forms of administration from clinical health care records (<a href="./reference/ab_from_text.html">manual</a>)</li>
<li>Determining first isolates to be used for AMR data analysis (<a href="./reference/first_isolate.html">manual</a>)</li>
<li>Calculating antimicrobial resistance (<a href="./articles/AMR.html">tutorial</a>)</li>
<li>Determining multi-drug resistance (MDR) / multi-drug resistant
organisms (MDRO) (<a href="./articles/MDR.html">tutorial</a>)</li>
<li>Calculating (empirical) susceptibility of both mono therapy and
combination therapies (<a href="./articles/AMR.html">tutorial</a>)</li>
<li>Predicting future antimicrobial resistance using regression models
(<a href="./articles/resistance_predict.html">tutorial</a>)</li>
<li>Getting properties for any microorganism (like Gram stain, species,
genus or family) (<a href="./reference/mo_property.html">manual</a>)</li>
<li>Getting properties for any antibiotic (like name, code of
EARS-Net/ATC/LOINC/PubChem, defined daily dose or trade name) (<a href="./reference/ab_property.html">manual</a>)</li>
<li>Determining multi-drug resistance (MDR) / multi-drug resistant organisms (MDRO) (<a href="./articles/MDR.html">tutorial</a>)</li>
<li>Calculating (empirical) susceptibility of both mono therapy and combination therapies (<a href="./articles/AMR.html">tutorial</a>)</li>
<li>Predicting future antimicrobial resistance using regression models (<a href="./articles/resistance_predict.html">tutorial</a>)</li>
<li>Getting properties for any microorganism (like Gram stain, species, genus or family) (<a href="./reference/mo_property.html">manual</a>)</li>
<li>Getting properties for any antibiotic (like name, code of EARS-Net/ATC/LOINC/PubChem, defined daily dose or trade name) (<a href="./reference/ab_property.html">manual</a>)</li>
<li>Plotting antimicrobial resistance (<a href="./articles/AMR.html">tutorial</a>)</li>
<li>Applying EUCAST expert rules (<a href="./reference/eucast_rules.html">manual</a>)</li>
<li>Getting SNOMED codes of a microorganism, or getting properties of a
microorganism based on a SNOMED code (<a href="./reference/mo_property.html">manual</a>)</li>
<li>Getting LOINC codes of an antibiotic, or getting properties of an
antibiotic based on a LOINC code (<a href="./reference/ab_property.html">manual</a>)</li>
<li>Machine reading the EUCAST and CLSI guidelines from 2011-2021 to
translate MIC values and disk diffusion diameters to R/SI (<a href="./articles/datasets.html">link</a>)</li>
<li>Getting SNOMED codes of a microorganism, or getting properties of a microorganism based on a SNOMED code (<a href="./reference/mo_property.html">manual</a>)</li>
<li>Getting LOINC codes of an antibiotic, or getting properties of an antibiotic based on a LOINC code (<a href="./reference/ab_property.html">manual</a>)</li>
<li>Machine reading the EUCAST and CLSI guidelines from 2011-2021 to translate MIC values and disk diffusion diameters to R/SI (<a href="./articles/datasets.html">link</a>)</li>
<li>Principal component analysis for AMR (<a href="./articles/PCA.html">tutorial</a>)</li>
</ul>
</div>
@ -449,24 +374,17 @@ translate MIC values and disk diffusion diameters to R/SI (<a href="./articles/d
<h4 id="latest-released-version">Latest released version<a class="anchor" aria-label="anchor" href="#latest-released-version"></a>
</h4>
<p><a href="https://cran.r-project.org/package=AMR" class="external-link"><img src="https://www.r-pkg.org/badges/version-ago/AMR" alt="CRAN"></a> <a href="https://cran.r-project.org/package=AMR" class="external-link"><img src="https://cranlogs.r-pkg.org/badges/grand-total/AMR" alt="CRANlogs"></a></p>
<p>This package is available <a href="https://cran.r-project.org/package=AMR" class="external-link">here on the official R
network (CRAN)</a>. Install this package in R from CRAN by using the
command:</p>
<p>This package is available <a href="https://cran.r-project.org/package=AMR" class="external-link">here on the official R network (CRAN)</a>. Install this package in R from CRAN by using the command:</p>
<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/utils/install.packages.html" class="external-link">install.packages</a></span><span class="op">(</span><span class="st">"AMR"</span><span class="op">)</span></code></pre></div>
<p>It will be downloaded and installed automatically. For RStudio, click
on the menu <em>Tools</em> &gt; <em>Install Packages…</em> and then type
in “AMR” and press <kbd>Install</kbd>.</p>
<p><strong>Note:</strong> Not all functions on this website may be
available in this latest release. To use all functions and data sets
mentioned on this website, install the latest development version.</p>
<p>It will be downloaded and installed automatically. For RStudio, click on the menu <em>Tools</em> &gt; <em>Install Packages…</em> and then type in “AMR” and press <kbd>Install</kbd>.</p>
<p><strong>Note:</strong> Not all functions on this website may be available in this latest release. To use all functions and data sets mentioned on this website, install the latest development version.</p>
</div>
<div class="section level4">
<h4 id="latest-development-version">Latest development version<a class="anchor" aria-label="anchor" href="#latest-development-version"></a>
</h4>
<p><a href="https://codecov.io/gh/msberends/AMR?branch=main" class="external-link"><img src="https://github.com/msberends/AMR/workflows/R-code-check/badge.svg?branch=main" alt="R-code-check"></a> <a href="https://www.codefactor.io/repository/github/msberends/amr" class="external-link"><img src="https://www.codefactor.io/repository/github/msberends/amr/badge" alt="CodeFactor"></a> <a href="https://codecov.io/gh/msberends/AMR?branch=main" class="external-link"><img src="https://codecov.io/gh/msberends/AMR/branch/main/graph/badge.svg" alt="Codecov"></a></p>
<p>The latest and unpublished development version can be installed from
GitHub in two ways:</p>
<p>The latest and unpublished development version can be installed from GitHub in two ways:</p>
<ol style="list-style-type: decimal">
<li>
<p>Manually, using:</p>
@ -475,16 +393,11 @@ GitHub in two ways:</p>
<span class="fu">remotes</span><span class="fu">::</span><span class="fu"><a href="https://remotes.r-lib.org/reference/install_github.html" class="external-link">install_github</a></span><span class="op">(</span><span class="st">"msberends/AMR"</span><span class="op">)</span></code></pre></div>
</li>
<li>
<p>Automatically, using the <a href="https://ropensci.org/r-universe/" class="external-link">rOpenSci R-universe
platform</a>, by adding <a href="https://msberends.r-universe.dev" class="external-link">our
R-universe address</a> to your list of repositories (‘repos’):</p>
<p>Automatically, using the <a href="https://ropensci.org/r-universe/" class="external-link">rOpenSci R-universe platform</a>, by adding <a href="https://msberends.r-universe.dev" class="external-link">our R-universe address</a> to your list of repositories (‘repos’):</p>
<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/base/options.html" class="external-link">options</a></span><span class="op">(</span>repos <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/options.html" class="external-link">getOption</a></span><span class="op">(</span><span class="st">"repos"</span><span class="op">)</span>,
msberends <span class="op">=</span> <span class="st">"https://msberends.r-universe.dev"</span><span class="op">)</span><span class="op">)</span></code></pre></div>
<p>After this, you can install and update this <code>AMR</code> package
like any official release (e.g., using
<code>install.packages("AMR")</code> or in RStudio via <em>Tools</em>
&gt; <em>Check for Package Updates…</em>).</p>
<p>After this, you can install and update this <code>AMR</code> package like any official release (e.g., using <code>install.packages("AMR")</code> or in RStudio via <em>Tools</em> &gt; <em>Check for Package Updates…</em>).</p>
</li>
</ol>
<p>You can also download the latest build from our repository: <a href="https://github.com/msberends/AMR/raw/main/data-raw/AMR_latest.tar.gz" class="external-link uri">https://github.com/msberends/AMR/raw/main/data-raw/AMR_latest.tar.gz</a></p>
@ -493,8 +406,7 @@ like any official release (e.g., using
<div class="section level3">
<h3 id="get-started">Get started<a class="anchor" aria-label="anchor" href="#get-started"></a>
</h3>
<p>To find out how to conduct AMR data analysis, please <a href="./articles/AMR.html">continue reading here to get started</a> or
click a link in the <a href="https://msberends.github.io/AMR/articles/">‘How to’ menu</a>.</p>
<p>To find out how to conduct AMR data analysis, please <a href="./articles/AMR.html">continue reading here to get started</a> or click a link in the <a href="https://msberends.github.io/AMR/articles/">‘How to’ menu</a>.</p>
</div>
<div class="section level3">
<h3 id="short-introduction">Short introduction<a class="anchor" aria-label="anchor" href="#short-introduction"></a>
@ -502,181 +414,66 @@ click a link in the <a href="https://msberends.github.io/AMR/articles/">‘How t
<div class="section level4">
<h4 id="microbial-taxonomic-reference-data">Microbial (taxonomic) reference data<a class="anchor" aria-label="anchor" href="#microbial-taxonomic-reference-data"></a>
</h4>
<p>This package contains the complete taxonomic tree of almost all
~71,000 microorganisms from the authoritative and comprehensive
Catalogue of Life (CoL, <a href="http://www.catalogueoflife.org" class="external-link">www.catalogueoflife.org</a>),
supplemented by data from the List of Prokaryotic names with Standing in
Nomenclature (LPSN, <a href="https://lpsn.dsmz.de" class="external-link">lpsn.dsmz.de</a>).
This supplementation is needed until the <a href="https://github.com/Sp2000/colplus" class="external-link">CoL+ project</a> is finished,
which we await. With <code><a href="reference/catalogue_of_life_version.html">catalogue_of_life_version()</a></code> can be
checked which version of the CoL is included in this package.</p>
<p>This package contains the complete taxonomic tree of almost all ~71,000 microorganisms from the authoritative and comprehensive Catalogue of Life (CoL, <a href="http://www.catalogueoflife.org" class="external-link">www.catalogueoflife.org</a>), supplemented by data from the List of Prokaryotic names with Standing in Nomenclature (LPSN, <a href="https://lpsn.dsmz.de" class="external-link">lpsn.dsmz.de</a>). This supplementation is needed until the <a href="https://github.com/Sp2000/colplus" class="external-link">CoL+ project</a> is finished, which we await. With <code><a href="reference/catalogue_of_life_version.html">catalogue_of_life_version()</a></code> can be checked which version of the CoL is included in this package.</p>
<p>Read more about which data from the Catalogue of Life <a href="./reference/catalogue_of_life.html">in our manual</a>.</p>
</div>
<div class="section level4">
<h4 id="antimicrobial-reference-data">Antimicrobial reference data<a class="anchor" aria-label="anchor" href="#antimicrobial-reference-data"></a>
</h4>
<p>This package contains <strong>all ~570 antibiotic, antimycotic and
antiviral drugs</strong> and their Anatomical Therapeutic Chemical (ATC)
codes, ATC groups and Defined Daily Dose (DDD, oral and IV) from the
World Health Organization Collaborating Centre for Drug Statistics
Methodology (WHOCC, <a href="https://www.whocc.no" class="external-link uri">https://www.whocc.no</a>) and the <a href="https://ec.europa.eu/health/documents/community-register/html/reg_hum_atc.htm" class="external-link">Pharmaceuticals
Community Register of the European Commission</a>.</p>
<p><strong>NOTE: The WHOCC copyright does not allow use for commercial
purposes, unlike any other info from this package. See <a href="https://www.whocc.no/copyright_disclaimer/" class="external-link uri">https://www.whocc.no/copyright_disclaimer/</a>.</strong></p>
<p>This package contains <strong>all ~570 antibiotic, antimycotic and antiviral drugs</strong> and their Anatomical Therapeutic Chemical (ATC) codes, ATC groups and Defined Daily Dose (DDD, oral and IV) from the World Health Organization Collaborating Centre for Drug Statistics Methodology (WHOCC, <a href="https://www.whocc.no" class="external-link uri">https://www.whocc.no</a>) and the <a href="https://ec.europa.eu/health/documents/community-register/html/reg_hum_atc.htm" class="external-link">Pharmaceuticals Community Register of the European Commission</a>.</p>
<p><strong>NOTE: The WHOCC copyright does not allow use for commercial purposes, unlike any other info from this package. See <a href="https://www.whocc.no/copyright_disclaimer/" class="external-link uri">https://www.whocc.no/copyright_disclaimer/</a>.</strong></p>
<p>Read more about the data from WHOCC <a href="./reference/WHOCC.html">in our manual</a>.</p>
</div>
<div class="section level4">
<h4 id="whonet--ears-net">WHONET / EARS-Net<a class="anchor" aria-label="anchor" href="#whonet--ears-net"></a>
</h4>
<p>We support WHONET and EARS-Net data. Exported files from WHONET can
be imported into R and can be analysed easily using this package. For
education purposes, we created an <a href="./reference/WHONET.html">example data set <code>WHONET</code></a>
with the exact same structure as a WHONET export file. Furthermore, this
package also contains a <a href="./reference/antibiotics.html">data set
antibiotics</a> with all EARS-Net antibiotic abbreviations, and knows
almost all WHONET abbreviations for microorganisms. When using WHONET
data as input for analysis, all input parameters will be set
automatically.</p>
<p>Read our tutorial about <a href="./articles/WHONET.html">how to work
with WHONET data here</a>.</p>
<p>We support WHONET and EARS-Net data. Exported files from WHONET can be imported into R and can be analysed easily using this package. For education purposes, we created an <a href="./reference/WHONET.html">example data set <code>WHONET</code></a> with the exact same structure as a WHONET export file. Furthermore, this package also contains a <a href="./reference/antibiotics.html">data set antibiotics</a> with all EARS-Net antibiotic abbreviations, and knows almost all WHONET abbreviations for microorganisms. When using WHONET data as input for analysis, all input parameters will be set automatically.</p>
<p>Read our tutorial about <a href="./articles/WHONET.html">how to work with WHONET data here</a>.</p>
</div>
<div class="section level4">
<h4 id="overview-of-functions">Overview of functions<a class="anchor" aria-label="anchor" href="#overview-of-functions"></a>
</h4>
<p>The <code>AMR</code> package basically does four important
things:</p>
<p>The <code>AMR</code> package basically does four important things:</p>
<ol style="list-style-type: decimal">
<li>
<p>It <strong>cleanses existing data</strong> by providing new
<em>classes</em> for microoganisms, antibiotics and antimicrobial
results (both S/I/R and MIC). By installing this package, you teach R
everything about microbiology that is needed for analysis. These
functions all use intelligent rules to guess results that you would
expect:</p>
<p>It <strong>cleanses existing data</strong> by providing new <em>classes</em> for microoganisms, antibiotics and antimicrobial results (both S/I/R and MIC). By installing this package, you teach R everything about microbiology that is needed for analysis. These functions all use intelligent rules to guess results that you would expect:</p>
<ul>
<li>Use <code><a href="reference/as.mo.html">as.mo()</a></code> to get a microbial ID. The IDs are human
readable for the trained eye - the ID of <em>Klebsiella pneumoniae</em>
is “B_KLBSL_PNMN” (B stands for Bacteria) and the ID of <em>S.
aureus</em> is “B_STPHY_AURS”. The function takes almost any text as
input that looks like the name or code of a microorganism like “E.
coli”, “esco” or “esccol” and tries to find expected results using
intelligent rules combined with the included Catalogue of Life data set.
It only takes milliseconds to find results, please see our <a href="./articles/benchmarks.html">benchmarks</a>. Moreover, it can group
<em>Staphylococci</em> into coagulase negative and positive (CoNS and
CoPS, see <a href="./reference/as.mo.html#source">source</a>) and can
categorise <em>Streptococci</em> into Lancefield groups (like
beta-haemolytic <em>Streptococcus</em> Group B, <a href="./reference/as.mo.html#source">source</a>).</li>
<li>Use <code><a href="reference/as.ab.html">as.ab()</a></code> to get an antibiotic ID. Like microbial
IDs, these IDs are also human readable based on those used by EARS-Net.
For example, the ID of amoxicillin is <code>AMX</code> and the ID of
gentamicin is <code>GEN</code>. The <code><a href="reference/as.ab.html">as.ab()</a></code> function also
uses intelligent rules to find results like accepting misspelling, trade
names and abbrevations used in many laboratory systems. For instance,
the values “Furabid”, “Furadantin”, “nitro” all return the ID of
Nitrofurantoine. To accomplish this, the package contains a database
with most LIS codes, official names, trade names, ATC codes, defined
daily doses (DDD) and drug categories of antibiotics.</li>
<li>Use <code><a href="reference/as.rsi.html">as.rsi()</a></code> to get antibiotic interpretations based on
raw MIC values (in mg/L) or disk diffusion values (in mm), or transform
existing values to valid antimicrobial results. It produces just S, I or
R based on your input and warns about invalid values. Even values like
“&lt;=0.002; S” (combined MIC/RSI) will result in “S”.</li>
<li>Use <code><a href="reference/as.mic.html">as.mic()</a></code> to cleanse your MIC values. It produces a
so-called factor (called <em>ordinal</em> in SPSS) with valid MIC values
as levels. A value like “&lt;=0.002; S” (combined MIC/RSI) will result
in “&lt;=0.002”.</li>
<li>Use <code><a href="reference/as.mo.html">as.mo()</a></code> to get a microbial ID. The IDs are human readable for the trained eye - the ID of <em>Klebsiella pneumoniae</em> is “B_KLBSL_PNMN” (B stands for Bacteria) and the ID of <em>S. aureus</em> is “B_STPHY_AURS”. The function takes almost any text as input that looks like the name or code of a microorganism like “E. coli”, “esco” or “esccol” and tries to find expected results using intelligent rules combined with the included Catalogue of Life data set. It only takes milliseconds to find results, please see our <a href="./articles/benchmarks.html">benchmarks</a>. Moreover, it can group <em>Staphylococci</em> into coagulase negative and positive (CoNS and CoPS, see <a href="./reference/as.mo.html#source">source</a>) and can categorise <em>Streptococci</em> into Lancefield groups (like beta-haemolytic <em>Streptococcus</em> Group B, <a href="./reference/as.mo.html#source">source</a>).</li>
<li>Use <code><a href="reference/as.ab.html">as.ab()</a></code> to get an antibiotic ID. Like microbial IDs, these IDs are also human readable based on those used by EARS-Net. For example, the ID of amoxicillin is <code>AMX</code> and the ID of gentamicin is <code>GEN</code>. The <code><a href="reference/as.ab.html">as.ab()</a></code> function also uses intelligent rules to find results like accepting misspelling, trade names and abbrevations used in many laboratory systems. For instance, the values “Furabid”, “Furadantin”, “nitro” all return the ID of Nitrofurantoine. To accomplish this, the package contains a database with most LIS codes, official names, trade names, ATC codes, defined daily doses (DDD) and drug categories of antibiotics.</li>
<li>Use <code><a href="reference/as.rsi.html">as.rsi()</a></code> to get antibiotic interpretations based on raw MIC values (in mg/L) or disk diffusion values (in mm), or transform existing values to valid antimicrobial results. It produces just S, I or R based on your input and warns about invalid values. Even values like “&lt;=0.002; S” (combined MIC/RSI) will result in “S”.</li>
<li>Use <code><a href="reference/as.mic.html">as.mic()</a></code> to cleanse your MIC values. It produces a so-called factor (called <em>ordinal</em> in SPSS) with valid MIC values as levels. A value like “&lt;=0.002; S” (combined MIC/RSI) will result in “&lt;=0.002”.</li>
</ul>
</li>
<li>
<p>It <strong>enhances existing data</strong> and <strong>adds new
data</strong> from data sets included in this package.</p>
<p>It <strong>enhances existing data</strong> and <strong>adds new data</strong> from data sets included in this package.</p>
<ul>
<li>Use <code><a href="reference/eucast_rules.html">eucast_rules()</a></code> to apply <a href="https://www.eucast.org/expert_rules_and_intrinsic_resistance/" class="external-link">EUCAST
expert rules to isolates</a> (not the translation from MIC to R/SI
values, use <code><a href="reference/as.rsi.html">as.rsi()</a></code> for that).</li>
<li>Use <code><a href="reference/first_isolate.html">first_isolate()</a></code> to identify the first isolates of
every patient <a href="https://clsi.org/standards/products/microbiology/documents/m39/" class="external-link">using
guidelines from the CLSI</a> (Clinical and Laboratory Standards
Institute).
<li>Use <code><a href="reference/eucast_rules.html">eucast_rules()</a></code> to apply <a href="https://www.eucast.org/expert_rules_and_intrinsic_resistance/" class="external-link">EUCAST expert rules to isolates</a> (not the translation from MIC to R/SI values, use <code><a href="reference/as.rsi.html">as.rsi()</a></code> for that).</li>
<li>Use <code><a href="reference/first_isolate.html">first_isolate()</a></code> to identify the first isolates of every patient <a href="https://clsi.org/standards/products/microbiology/documents/m39/" class="external-link">using guidelines from the CLSI</a> (Clinical and Laboratory Standards Institute).
<ul>
<li>You can also identify first <em>weighted</em> isolates of every
patient, an adjusted version of the CLSI guideline. This takes into
account key antibiotics of every strain and compares them.</li>
<li>You can also identify first <em>weighted</em> isolates of every patient, an adjusted version of the CLSI guideline. This takes into account key antibiotics of every strain and compares them.</li>
</ul>
</li>
<li>Use <code><a href="reference/mdro.html">mdro()</a></code> to determine which micro-organisms are
multi-drug resistant organisms (MDRO). It supports a variety of
international guidelines, such as the MDR-paper by Magiorakos <em>et
al.</em> (2012, <a href="https://www.ncbi.nlm.nih.gov/pubmed/?term=21793988" class="external-link">PMID
21793988</a>), the exceptional phenotype definitions of EUCAST and the
WHO guideline on multi-drug resistant TB. It also supports the national
guidelines of the Netherlands and Germany.</li>
<li>The <a href="./reference/microorganisms.html">data set
microorganisms</a> contains the complete taxonomic tree of ~70,000
microorganisms. Furthermore, some colloquial names and all Gram stains
are available, which enables resistance analysis of e.g. different
antibiotics per Gram stain. The package also contains functions to look
up values in this data set like <code><a href="reference/mo_property.html">mo_genus()</a></code>,
<code><a href="reference/mo_property.html">mo_family()</a></code>, <code><a href="reference/mo_property.html">mo_gramstain()</a></code> or even
<code><a href="reference/mo_property.html">mo_phylum()</a></code>. Use <code><a href="reference/mo_property.html">mo_snomed()</a></code> to look up any
SNOMED CT code associated with a microorganism. As all these function
use <code><a href="reference/as.mo.html">as.mo()</a></code> internally, they also use the same intelligent
rules for determination. For example, <code>mo_genus("MRSA")</code> and
<code>mo_genus("S. aureus")</code> will both return
<code>"Staphylococcus"</code>. They also come with support for German,
Danish, Dutch, Spanish, Italian, French and Portuguese. These functions
can be used to add new variables to your data.</li>
<li>The <a href="./reference/antibiotics.html">data set antibiotics</a>
contains ~450 antimicrobial drugs with their EARS-Net code, ATC code,
PubChem compound ID, LOINC code, official name, common LIS codes and
DDDs of both oral and parenteral administration. It also contains all
(thousands of) trade names found in PubChem. Use functions like
<code><a href="reference/ab_property.html">ab_name()</a></code>, <code><a href="reference/ab_property.html">ab_group()</a></code>, <code><a href="reference/ab_property.html">ab_atc()</a></code>,
<code><a href="reference/ab_property.html">ab_loinc()</a></code> and <code><a href="reference/ab_property.html">ab_tradenames()</a></code> to look up
values. The <code>ab_*</code> functions use <code><a href="reference/as.ab.html">as.ab()</a></code>
internally so they support the same intelligent rules to guess the most
probable result. For example, <code>ab_name("Fluclox")</code>,
<code>ab_name("Floxapen")</code> and <code>ab_name("J01CF05")</code>
will all return <code>"Flucloxacillin"</code>. These functions can again
be used to add new variables to your data.</li>
<li>Use <code><a href="reference/mdro.html">mdro()</a></code> to determine which micro-organisms are multi-drug resistant organisms (MDRO). It supports a variety of international guidelines, such as the MDR-paper by Magiorakos <em>et al.</em> (2012, <a href="https://www.ncbi.nlm.nih.gov/pubmed/?term=21793988" class="external-link">PMID 21793988</a>), the exceptional phenotype definitions of EUCAST and the WHO guideline on multi-drug resistant TB. It also supports the national guidelines of the Netherlands and Germany.</li>
<li>The <a href="./reference/microorganisms.html">data set microorganisms</a> contains the complete taxonomic tree of ~70,000 microorganisms. Furthermore, some colloquial names and all Gram stains are available, which enables resistance analysis of e.g. different antibiotics per Gram stain. The package also contains functions to look up values in this data set like <code><a href="reference/mo_property.html">mo_genus()</a></code>, <code><a href="reference/mo_property.html">mo_family()</a></code>, <code><a href="reference/mo_property.html">mo_gramstain()</a></code> or even <code><a href="reference/mo_property.html">mo_phylum()</a></code>. Use <code><a href="reference/mo_property.html">mo_snomed()</a></code> to look up any SNOMED CT code associated with a microorganism. As all these function use <code><a href="reference/as.mo.html">as.mo()</a></code> internally, they also use the same intelligent rules for determination. For example, <code>mo_genus("MRSA")</code> and <code>mo_genus("S. aureus")</code> will both return <code>"Staphylococcus"</code>. They also come with support for German, Danish, Dutch, Spanish, Italian, French and Portuguese. These functions can be used to add new variables to your data.</li>
<li>The <a href="./reference/antibiotics.html">data set antibiotics</a> contains ~450 antimicrobial drugs with their EARS-Net code, ATC code, PubChem compound ID, LOINC code, official name, common LIS codes and DDDs of both oral and parenteral administration. It also contains all (thousands of) trade names found in PubChem. Use functions like <code><a href="reference/ab_property.html">ab_name()</a></code>, <code><a href="reference/ab_property.html">ab_group()</a></code>, <code><a href="reference/ab_property.html">ab_atc()</a></code>, <code><a href="reference/ab_property.html">ab_loinc()</a></code> and <code><a href="reference/ab_property.html">ab_tradenames()</a></code> to look up values. The <code>ab_*</code> functions use <code><a href="reference/as.ab.html">as.ab()</a></code> internally so they support the same intelligent rules to guess the most probable result. For example, <code>ab_name("Fluclox")</code>, <code>ab_name("Floxapen")</code> and <code>ab_name("J01CF05")</code> will all return <code>"Flucloxacillin"</code>. These functions can again be used to add new variables to your data.</li>
</ul>
</li>
<li>
<p>It <strong>analyses the data</strong> with convenient functions
that use well-known methods.</p>
<p>It <strong>analyses the data</strong> with convenient functions that use well-known methods.</p>
<ul>
<li>Calculate the microbial susceptibility or resistance (and even
co-resistance) with the <code><a href="reference/proportion.html">susceptibility()</a></code> and
<code><a href="reference/proportion.html">resistance()</a></code> functions, or be even more specific with the
<code><a href="reference/proportion.html">proportion_R()</a></code>, <code><a href="reference/proportion.html">proportion_IR()</a></code>,
<code><a href="reference/proportion.html">proportion_I()</a></code>, <code><a href="reference/proportion.html">proportion_SI()</a></code> and
<code><a href="reference/proportion.html">proportion_S()</a></code> functions. Similarly, the <em>number</em> of
isolates can be determined with the <code><a href="reference/count.html">count_resistant()</a></code>,
<code><a href="reference/count.html">count_susceptible()</a></code> and <code><a href="reference/count.html">count_all()</a></code> functions.
All these functions can be used with the <code>dplyr</code> package
(e.g. in conjunction with <code><a href="https://dplyr.tidyverse.org/reference/summarise.html" class="external-link">summarise()</a></code>)</li>
<li>Plot AMR results with <code><a href="reference/ggplot_rsi.html">geom_rsi()</a></code>, a function made for
the <code>ggplot2</code> package</li>
<li>Predict antimicrobial resistance for the nextcoming years using
logistic regression models with the <code><a href="reference/resistance_predict.html">resistance_predict()</a></code>
function</li>
<li>Calculate the microbial susceptibility or resistance (and even co-resistance) with the <code><a href="reference/proportion.html">susceptibility()</a></code> and <code><a href="reference/proportion.html">resistance()</a></code> functions, or be even more specific with the <code><a href="reference/proportion.html">proportion_R()</a></code>, <code><a href="reference/proportion.html">proportion_IR()</a></code>, <code><a href="reference/proportion.html">proportion_I()</a></code>, <code><a href="reference/proportion.html">proportion_SI()</a></code> and <code><a href="reference/proportion.html">proportion_S()</a></code> functions. Similarly, the <em>number</em> of isolates can be determined with the <code><a href="reference/count.html">count_resistant()</a></code>, <code><a href="reference/count.html">count_susceptible()</a></code> and <code><a href="reference/count.html">count_all()</a></code> functions. All these functions can be used with the <code>dplyr</code> package (e.g. in conjunction with <code><a href="https://dplyr.tidyverse.org/reference/summarise.html" class="external-link">summarise()</a></code>)</li>
<li>Plot AMR results with <code><a href="reference/ggplot_rsi.html">geom_rsi()</a></code>, a function made for the <code>ggplot2</code> package</li>
<li>Predict antimicrobial resistance for the nextcoming years using logistic regression models with the <code><a href="reference/resistance_predict.html">resistance_predict()</a></code> function</li>
</ul>
</li>
<li>
<p>It <strong>teaches the user</strong> how to use all the above
actions.</p>
<p>It <strong>teaches the user</strong> how to use all the above actions.</p>
<ul>
<li>Aside from this website with many tutorials, the package itself
contains extensive help pages with many examples for all functions.</li>
<li>Aside from this website with many tutorials, the package itself contains extensive help pages with many examples for all functions.</li>
<li>The package also contains example data sets:
<ul>
<li>The <a href="./reference/example_isolates.html"><code>example_isolates</code>
data set</a>. This data set contains 2,000 microbial isolates with their
full antibiograms. It reflects reality and can be used to practice AMR
data analysis.</li>
<li>The <a href="./reference/WHONET.html"><code>WHONET</code> data
set</a>. This data set only contains fake data, but with the exact same
structure as files exported by WHONET. Read more about WHONET <a href="./articles/WHONET.html">on its tutorial page</a>.</li>
<li>The <a href="./reference/example_isolates.html"><code>example_isolates</code> data set</a>. This data set contains 2,000 microbial isolates with their full antibiograms. It reflects reality and can be used to practice AMR data analysis.</li>
<li>The <a href="./reference/WHONET.html"><code>WHONET</code> data set</a>. This data set only contains fake data, but with the exact same structure as files exported by WHONET. Read more about WHONET <a href="./articles/WHONET.html">on its tutorial page</a>.</li>
</ul>
</li>
</ul>
@ -687,9 +484,7 @@ structure as files exported by WHONET. Read more about WHONET <a href="./article
<div class="section level3">
<h3 id="copyright">Copyright<a class="anchor" aria-label="anchor" href="#copyright"></a>
</h3>
<p>This R package is free, open-source software and licensed under the
<a href="./LICENSE-text.html">GNU General Public License v2.0
(GPL-2)</a>. In a nutshell, this means that this package:</p>
<p>This R package is free, open-source software and licensed under the <a href="./LICENSE-text.html">GNU General Public License v2.0 (GPL-2)</a>. In a nutshell, this means that this package:</p>
<ul>
<li><p>May be used for commercial purposes</p></li>
<li><p>May be used for private purposes</p></li>
@ -697,18 +492,15 @@ structure as files exported by WHONET. Read more about WHONET <a href="./article
<li>
<p>May be modified, although:</p>
<ul>
<li>Modifications <strong>must</strong> be released under the same
license when distributing the package</li>
<li>Modifications <strong>must</strong> be released under the same license when distributing the package</li>
<li>Changes made to the code <strong>must</strong> be documented</li>
</ul>
</li>
<li>
<p>May be distributed, although:</p>
<ul>
<li>Source code <strong>must</strong> be made available when the package
is distributed</li>
<li>A copy of the license and copyright notice <strong>must</strong> be
included with the package.</li>
<li>Source code <strong>must</strong> be made available when the package is distributed</li>
<li>A copy of the license and copyright notice <strong>must</strong> be included with the package.</li>
</ul>
</li>
<li><p>Comes with a LIMITATION of liability</p></li>
@ -764,14 +556,12 @@ included with the package.</li>
<footer><div class="copyright">
<p></p>
<p>Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein,
Erwin E. A. Hassing.</p>
<p>Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein, Erwin E. A. Hassing.</p>
</div>
<div class="pkgdown">
<p></p>
<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a>
2.0.2.</p>
<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.2.</p>
</div>
</footer>

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pandoc: 2.17.1.1
pandoc: 2.9.2.1
pkgdown: 2.0.2
pkgdown_sha: ~
articles:
@ -12,7 +12,7 @@ articles:
datasets: datasets.html
resistance_predict: resistance_predict.html
welcome_to_AMR: welcome_to_AMR.html
last_built: 2022-03-14T15:38Z
last_built: 2022-03-27T07:33Z
urls:
reference: https://msberends.github.io/AMR/reference
article: https://msberends.github.io/AMR/articles

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<footer><div class="copyright">
<p></p><p>Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein,
Erwin E. A. Hassing.</p>
<p></p><p>Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein, Erwin E. A. Hassing.</p>
</div>
<div class="pkgdown">
<p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a>
2.0.2.</p>
<p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.2.</p>
</div>
</footer></div>

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@ -221,13 +221,11 @@ The Netherlands
<footer><div class="copyright">
<p></p><p>Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein,
Erwin E. A. Hassing.</p>
<p></p><p>Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein, Erwin E. A. Hassing.</p>
</div>
<div class="pkgdown">
<p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a>
2.0.2.</p>
<p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.2.</p>
</div>
</footer></div>

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@ -195,13 +195,11 @@ This package contains <strong>all ~550 antibiotic, antimycotic and antiviral dru
<footer><div class="copyright">
<p></p><p>Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein,
Erwin E. A. Hassing.</p>
<p></p><p>Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein, Erwin E. A. Hassing.</p>
</div>
<div class="pkgdown">
<p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a>
2.0.2.</p>
<p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.2.</p>
</div>
</footer></div>

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@ -215,13 +215,11 @@
<footer><div class="copyright">
<p></p><p>Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein,
Erwin E. A. Hassing.</p>
<p></p><p>Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein, Erwin E. A. Hassing.</p>
</div>
<div class="pkgdown">
<p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a>
2.0.2.</p>
<p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.2.</p>
</div>
</footer></div>

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<footer><div class="copyright">
<p></p><p>Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein,
Erwin E. A. Hassing.</p>
<p></p><p>Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein, Erwin E. A. Hassing.</p>
</div>
<div class="pkgdown">
<p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a>
2.0.2.</p>
<p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.2.</p>
</div>
</footer></div>

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@ -344,13 +344,11 @@ The <a href="lifecycle.html">lifecycle</a> of this function is <strong>stable</s
<footer><div class="copyright">
<p></p><p>Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein,
Erwin E. A. Hassing.</p>
<p></p><p>Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein, Erwin E. A. Hassing.</p>
</div>
<div class="pkgdown">
<p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a>
2.0.2.</p>
<p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.2.</p>
</div>
</footer></div>

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@ -226,13 +226,11 @@ The <a href="lifecycle.html">lifecycle</a> of this function is <strong>stable</s
<footer><div class="copyright">
<p></p><p>Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein,
Erwin E. A. Hassing.</p>
<p></p><p>Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein, Erwin E. A. Hassing.</p>
</div>
<div class="pkgdown">
<p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a>
2.0.2.</p>
<p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.2.</p>
</div>
</footer></div>

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@ -250,13 +250,11 @@ The <a href="lifecycle.html">lifecycle</a> of this function is <strong>stable</s
<footer><div class="copyright">
<p></p><p>Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein,
Erwin E. A. Hassing.</p>
<p></p><p>Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein, Erwin E. A. Hassing.</p>
</div>
<div class="pkgdown">
<p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a>
2.0.2.</p>
<p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.2.</p>
</div>
</footer></div>

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<p>The <code>ab_class()</code> function can be used to filter/select on a manually defined antibiotic class. It searches for results in the <a href="antibiotics.html">antibiotics</a> data set within the columns <code>group</code>, <code>atc_group1</code> and <code>atc_group2</code>.</p>
<p>The <code>ab_selector()</code> function can be used to internally filter the <a href="antibiotics.html">antibiotics</a> data set on any results, see <em>Examples</em>. It allows for filtering on a (part of) a certain name, and/or a group name or even a minimum of DDDs for oral treatment. This function yields the highest flexibility, but is also the least user-friendly, since it requires a hard-coded filter to set.</p>
<p>The <code>administrable_per_os()</code> and <code>administrable_iv()</code> functions also rely on the <a href="antibiotics.html">antibiotics</a> data set - antibiotic columns will be matched where a DDD (defined daily dose) for resp. oral and IV treatment is available in the <a href="antibiotics.html">antibiotics</a> data set.</p>
<p>The <code>not_intrinsic_resistant()</code> function can be used to only select antibiotic columns that pose no intrinsic resistance for the microorganisms in the data set. For example, if a data set contains only microorganism codes or names of <em>E. coli</em> and <em>K. pneumoniae</em> and contains a column "vancomycin", this column will be removed (or rather, unselected) using this function. It currently applies <a href="https://www.eucast.org/expert_rules_and_intrinsic_resistance/" class="external-link">'EUCAST Expert Rules' and 'EUCAST Intrinsic Resistance and Unusual Phenotypes' v3.3</a> (2021) to determine intrinsic resistance, using the <code><a href="eucast_rules.html">eucast_rules()</a></code> function internally. Because of this determination, this function is quite slow in terms of performance.</p>
<p>The <code>not_intrinsic_resistant()</code> function can be used to only select antibiotic columns that pose no intrinsic resistance for the microorganisms in the data set. For example, if a data set contains only microorganism codes or names of <em>E. coli</em> and <em>K. pneumoniae</em> and contains a column "vancomycin", this column will be removed (or rather, unselected) using this function. It currently applies <a href="https://www.eucast.org/expert_rules_and_expected_phenotypes/" class="external-link">'EUCAST Expert Rules' and 'EUCAST Intrinsic Resistance and Unusual Phenotypes' v3.3</a> (2021) to determine intrinsic resistance, using the <code><a href="eucast_rules.html">eucast_rules()</a></code> function internally. Because of this determination, this function is quite slow in terms of performance.</p>
</div>
<div id="full-list-of-supported-antibiotic-classes">
<h2>Full list of supported (antibiotic) classes</h2>
@ -430,13 +430,11 @@ The <a href="lifecycle.html">lifecycle</a> of this function is <strong>stable</s
<footer><div class="copyright">
<p></p><p>Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein,
Erwin E. A. Hassing.</p>
<p></p><p>Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein, Erwin E. A. Hassing.</p>
</div>
<div class="pkgdown">
<p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a>
2.0.2.</p>
<p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.2.</p>
</div>
</footer></div>

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@ -259,13 +259,11 @@ This package contains <strong>all ~550 antibiotic, antimycotic and antiviral dru
<footer><div class="copyright">
<p></p><p>Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein,
Erwin E. A. Hassing.</p>
<p></p><p>Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein, Erwin E. A. Hassing.</p>
</div>
<div class="pkgdown">
<p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a>
2.0.2.</p>
<p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.2.</p>
</div>
</footer></div>

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@ -280,13 +280,11 @@ This package contains <strong>all ~550 antibiotic, antimycotic and antiviral dru
<footer><div class="copyright">
<p></p><p>Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein,
Erwin E. A. Hassing.</p>
<p></p><p>Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein, Erwin E. A. Hassing.</p>
</div>
<div class="pkgdown">
<p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a>
2.0.2.</p>
<p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.2.</p>
</div>
</footer></div>

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@ -239,13 +239,11 @@ The <a href="lifecycle.html">lifecycle</a> of this function is <strong>stable</s
<footer><div class="copyright">
<p></p><p>Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein,
Erwin E. A. Hassing.</p>
<p></p><p>Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein, Erwin E. A. Hassing.</p>
</div>
<div class="pkgdown">
<p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a>
2.0.2.</p>
<p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.2.</p>
</div>
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@ -273,13 +273,11 @@ The <a href="lifecycle.html">lifecycle</a> of this function is <strong>stable</s
<footer><div class="copyright">
<p></p><p>Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein,
Erwin E. A. Hassing.</p>
<p></p><p>Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein, Erwin E. A. Hassing.</p>
</div>
<div class="pkgdown">
<p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a>
2.0.2.</p>
<p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.2.</p>
</div>
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@ -377,13 +377,11 @@ This package contains the complete taxonomic tree of almost all microorganisms (
<footer><div class="copyright">
<p></p><p>Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein,
Erwin E. A. Hassing.</p>
<p></p><p>Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein, Erwin E. A. Hassing.</p>
</div>
<div class="pkgdown">
<p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a>
2.0.2.</p>
<p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.2.</p>
</div>
</footer></div>

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@ -231,7 +231,7 @@
<dt>conserve_capped_values</dt>
<dd><p>a <a href="https://rdrr.io/r/base/logical.html" class="external-link">logical</a> to indicate that MIC values starting with <code>"&gt;"</code> (but not <code>"&gt;="</code>) must always return "R" , and that MIC values starting with <code>"&lt;"</code> (but not <code>"&lt;="</code>) must always return "S"</p></dd>
<dt>add_intrinsic_resistance</dt>
<dd><p><em>(only useful when using a EUCAST guideline)</em> a <a href="https://rdrr.io/r/base/logical.html" class="external-link">logical</a> to indicate whether intrinsic antibiotic resistance must also be considered for applicable bug-drug combinations, meaning that e.g. ampicillin will always return "R" in <em>Klebsiella</em> species. Determination is based on the <a href="intrinsic_resistant.html">intrinsic_resistant</a> data set, that itself is based on <a href="https://www.eucast.org/expert_rules_and_intrinsic_resistance/" class="external-link">'EUCAST Expert Rules' and 'EUCAST Intrinsic Resistance and Unusual Phenotypes' v3.3</a> (2021).</p></dd>
<dd><p><em>(only useful when using a EUCAST guideline)</em> a <a href="https://rdrr.io/r/base/logical.html" class="external-link">logical</a> to indicate whether intrinsic antibiotic resistance must also be considered for applicable bug-drug combinations, meaning that e.g. ampicillin will always return "R" in <em>Klebsiella</em> species. Determination is based on the <a href="intrinsic_resistant.html">intrinsic_resistant</a> data set, that itself is based on <a href="https://www.eucast.org/expert_rules_and_expected_phenotypes/" class="external-link">'EUCAST Expert Rules' and 'EUCAST Intrinsic Resistance and Unusual Phenotypes' v3.3</a> (2021).</p></dd>
<dt>reference_data</dt>
<dd><p>a <a href="https://rdrr.io/r/base/data.frame.html" class="external-link">data.frame</a> to be used for interpretation, which defaults to the <a href="rsi_translation.html">rsi_translation</a> data set. Changing this argument allows for using own interpretation guidelines. This argument must contain a data set that is equal in structure to the <a href="rsi_translation.html">rsi_translation</a> data set (same column names and column types). Please note that the <code>guideline</code> argument will be ignored when <code>reference_data</code> is manually set.</p></dd>
<dt>col_mo</dt>
@ -334,7 +334,7 @@ The <a href="lifecycle.html">lifecycle</a> of this function is <strong>stable</s
<span class="co"># \donttest{</span>
<span class="kw">if</span> <span class="op">(</span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">require</a></span><span class="op">(</span><span class="st"><a href="https://docs.ropensci.org/skimr/" class="external-link">"skimr"</a></span><span class="op">)</span><span class="op">)</span> <span class="op">{</span>
<span class="co"># class &lt;rsi&gt; supported in skim() too:</span>
<span class="fu"><a href="https://docs.ropensci.org/skimr/reference/skim.html" class="external-link">skim</a></span><span class="op">(</span><span class="va">example_isolates</span><span class="op">)</span>
<span class="fu">skim</span><span class="op">(</span><span class="va">example_isolates</span><span class="op">)</span>
<span class="op">}</span>
<span class="co"># }</span>
<span class="co"># For INTERPRETING disk diffusion and MIC values -----------------------</span>
@ -423,13 +423,11 @@ The <a href="lifecycle.html">lifecycle</a> of this function is <strong>stable</s
<footer><div class="copyright">
<p></p><p>Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein,
Erwin E. A. Hassing.</p>
<p></p><p>Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein, Erwin E. A. Hassing.</p>
</div>
<div class="pkgdown">
<p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a>
2.0.2.</p>
<p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.2.</p>
</div>
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6
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@ -257,13 +257,11 @@ The <a href="lifecycle.html">lifecycle</a> of this function is <strong>stable</s
<footer><div class="copyright">
<p></p><p>Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein,
Erwin E. A. Hassing.</p>
<p></p><p>Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein, Erwin E. A. Hassing.</p>
</div>
<div class="pkgdown">
<p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a>
2.0.2.</p>
<p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.2.</p>
</div>
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6
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@ -216,13 +216,11 @@ The <a href="lifecycle.html">lifecycle</a> of this function is <strong>stable</s
<footer><div class="copyright">
<p></p><p>Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein,
Erwin E. A. Hassing.</p>
<p></p><p>Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein, Erwin E. A. Hassing.</p>
</div>
<div class="pkgdown">
<p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a>
2.0.2.</p>
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</div>
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6
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@ -265,13 +265,11 @@ The <a href="lifecycle.html">lifecycle</a> of this function is <strong>stable</s
<footer><div class="copyright">
<p></p><p>Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein,
Erwin E. A. Hassing.</p>
<p></p><p>Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein, Erwin E. A. Hassing.</p>
</div>
<div class="pkgdown">
<p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a>
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6
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@ -235,13 +235,11 @@ Function <code><a href="as.mo.html">as.mo()</a></code> to use the data for intel
<footer><div class="copyright">
<p></p><p>Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein,
Erwin E. A. Hassing.</p>
<p></p><p>Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein, Erwin E. A. Hassing.</p>
</div>
<div class="pkgdown">
<p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a>
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6
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@ -200,13 +200,11 @@ This package contains the complete taxonomic tree of almost all microorganisms (
<footer><div class="copyright">
<p></p><p>Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein,
Erwin E. A. Hassing.</p>
<p></p><p>Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein, Erwin E. A. Hassing.</p>
</div>
<div class="pkgdown">
<p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a>
2.0.2.</p>
<p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.2.</p>
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6
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View File

@ -358,13 +358,11 @@ A microorganism is categorised as <em>Susceptible, Increased exposure</em> when
<footer><div class="copyright">
<p></p><p>Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein,
Erwin E. A. Hassing.</p>
<p></p><p>Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein, Erwin E. A. Hassing.</p>
</div>
<div class="pkgdown">
<p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a>
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</div>
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6
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@ -301,13 +301,11 @@ The <a href="lifecycle.html">lifecycle</a> of this function is <strong>stable</s
<footer><div class="copyright">
<p></p><p>Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein,
Erwin E. A. Hassing.</p>
<p></p><p>Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein, Erwin E. A. Hassing.</p>
</div>
<div class="pkgdown">
<p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a>
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docs/reference/dosage.html
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@ -202,13 +202,11 @@
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<p></p><p>Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein,
Erwin E. A. Hassing.</p>
<p></p><p>Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein, Erwin E. A. Hassing.</p>
</div>
<div class="pkgdown">
<p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a>
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6
docs/reference/eucast_rules.html
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@ -334,13 +334,11 @@ The <a href="lifecycle.html">lifecycle</a> of this function is <strong>stable</s
<footer><div class="copyright">
<p></p><p>Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein,
Erwin E. A. Hassing.</p>
<p></p><p>Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein, Erwin E. A. Hassing.</p>
</div>
<div class="pkgdown">
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@ -199,13 +199,11 @@
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<p></p><p>Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein,
Erwin E. A. Hassing.</p>
<p></p><p>Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein, Erwin E. A. Hassing.</p>
</div>
<div class="pkgdown">
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@ -194,13 +194,11 @@
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<p></p><p>Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein, Erwin E. A. Hassing.</p>
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6
docs/reference/first_isolate.html
View File

@ -371,13 +371,11 @@ The <a href="lifecycle.html">lifecycle</a> of this function is <strong>stable</s
<footer><div class="copyright">
<p></p><p>Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein,
Erwin E. A. Hassing.</p>
<p></p><p>Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein, Erwin E. A. Hassing.</p>
</div>
<div class="pkgdown">
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6
docs/reference/g.test.html
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@ -323,13 +323,11 @@ The <a href="lifecycle.html">lifecycle</a> of this function is <strong>questioni
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<p></p><p>Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein,
Erwin E. A. Hassing.</p>
<p></p><p>Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein, Erwin E. A. Hassing.</p>
</div>
<div class="pkgdown">
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6
docs/reference/get_episode.html
View File

@ -278,13 +278,11 @@ The <a href="lifecycle.html">lifecycle</a> of this function is <strong>stable</s
<footer><div class="copyright">
<p></p><p>Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein,
Erwin E. A. Hassing.</p>
<p></p><p>Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein, Erwin E. A. Hassing.</p>
</div>
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6
docs/reference/ggplot_pca.html
View File

@ -304,13 +304,11 @@ The <a href="lifecycle.html">lifecycle</a> of this function is <strong>stable</s
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<p></p><p>Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein,
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6
docs/reference/ggplot_rsi.html
View File

@ -395,13 +395,11 @@ The <a href="lifecycle.html">lifecycle</a> of this function is <strong>stable</s
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6
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View File

@ -243,13 +243,11 @@ The <a href="lifecycle.html">lifecycle</a> of this function is <strong>stable</s
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47
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@ -157,10 +157,7 @@
<table class="ref-index"><colgroup><col class="alias"><col class="title"></colgroup><tbody><tr><th colspan="2">
<h2 id="preparing-data-microorganisms">Preparing data: microorganisms <a href="#preparing-data-microorganisms" class="anchor" aria-hidden="true"></a></h2>
<p class="section-desc"></p><p>These functions are meant to get taxonomically valid properties of
microorganisms from any input. Use <code><a href="../reference/mo_source.html">mo_source()</a></code> to teach
this package how to translate your own codes to valid microorganism
codes.</p>
<p class="section-desc"></p><p>These functions are meant to get taxonomically valid properties of microorganisms from any input. Use <code><a href="../reference/mo_source.html">mo_source()</a></code> to teach this package how to translate your own codes to valid microorganism codes.</p>
</th>
</tr></tbody><tbody><tr><td>
<p><code><a href="as.mo.html">as.mo()</a></code> <code><a href="as.mo.html">is.mo()</a></code> <code><a href="as.mo.html">mo_failures()</a></code> <code><a href="as.mo.html">mo_uncertainties()</a></code> <code><a href="as.mo.html">mo_renamed()</a></code> </p>
@ -176,9 +173,7 @@ codes.</p>
<td><p>User-Defined Reference Data Set for Microorganisms</p></td>
</tr></tbody><tbody><tr><th colspan="2">
<h2 id="preparing-data-antibiotics">Preparing data: antibiotics <a href="#preparing-data-antibiotics" class="anchor" aria-hidden="true"></a></h2>
<p class="section-desc"></p><p>Use these functions to get valid properties of antibiotics from any
input or to clean your input. You can even retrieve drug names and doses
from clinical text records, using <code><a href="../reference/ab_from_text.html">ab_from_text()</a></code>.</p>
<p class="section-desc"></p><p>Use these functions to get valid properties of antibiotics from any input or to clean your input. You can even retrieve drug names and doses from clinical text records, using <code><a href="../reference/ab_from_text.html">ab_from_text()</a></code>.</p>
</th>
</tr></tbody><tbody><tr><td>
<p><code><a href="as.ab.html">as.ab()</a></code> <code><a href="as.ab.html">is.ab()</a></code> </p>
@ -198,13 +193,7 @@ from clinical text records, using <code><a href="../reference/ab_from_text.html"
<td><p>Get ATC Properties from WHOCC Website</p></td>
</tr></tbody><tbody><tr><th colspan="2">
<h2 id="preparing-data-antimicrobial-resistance">Preparing data: antimicrobial resistance <a href="#preparing-data-antimicrobial-resistance" class="anchor" aria-hidden="true"></a></h2>
<p class="section-desc"></p><p>With <code><a href="../reference/as.mic.html">as.mic()</a></code> and <code><a href="../reference/as.disk.html">as.disk()</a></code> you can
transform your raw input to valid MIC or disk diffusion values. Use
<code><a href="../reference/as.rsi.html">as.rsi()</a></code> for cleaning raw data to let it only contain “R”,
“I” and “S”, or to interpret MIC or disk diffusion values as R/SI based
on the lastest EUCAST and CLSI guidelines. Afterwards, you can extend
antibiotic interpretations by applying <a href="https://www.eucast.org/expert_rules_and_intrinsic_resistance/" class="external-link">EUCAST
rules</a> with <code><a href="../reference/eucast_rules.html">eucast_rules()</a></code>.</p>
<p class="section-desc"></p><p>With <code><a href="../reference/as.mic.html">as.mic()</a></code> and <code><a href="../reference/as.disk.html">as.disk()</a></code> you can transform your raw input to valid MIC or disk diffusion values. Use <code><a href="../reference/as.rsi.html">as.rsi()</a></code> for cleaning raw data to let it only contain “R”, “I” and “S”, or to interpret MIC or disk diffusion values as R/SI based on the lastest EUCAST and CLSI guidelines. Afterwards, you can extend antibiotic interpretations by applying <a href="https://www.eucast.org/expert_rules_and_intrinsic_resistance/" class="external-link">EUCAST rules</a> with <code><a href="../reference/eucast_rules.html">eucast_rules()</a></code>.</p>
</th>
</tr></tbody><tbody><tr><td>
<p><code><a href="as.rsi.html">as.rsi()</a></code> <code><a href="as.rsi.html">NA_rsi_</a></code> <code><a href="as.rsi.html">is.rsi()</a></code> <code><a href="as.rsi.html">is.rsi.eligible()</a></code> </p>
@ -228,14 +217,7 @@ rules</a> with <code><a href="../reference/eucast_rules.html">eucast_rules()</a>
<td><p>Define Custom EUCAST Rules</p></td>
</tr></tbody><tbody><tr><th colspan="2">
<h2 id="analysing-data-antimicrobial-resistance">Analysing data: antimicrobial resistance <a href="#analysing-data-antimicrobial-resistance" class="anchor" aria-hidden="true"></a></h2>
<p class="section-desc"></p><p>Use these function for the analysis part. You can use
<code><a href="../reference/proportion.html">susceptibility()</a></code> or <code><a href="../reference/proportion.html">resistance()</a></code> on any
antibiotic column. Be sure to first select the isolates that are
appropiate for analysis, by using <code><a href="../reference/first_isolate.html">first_isolate()</a></code> or
<code><a href="../reference/get_episode.html">is_new_episode()</a></code>. You can also filter your data on certain
resistance in certain antibiotic classes (<code><a href="../reference/antibiotic_class_selectors.html">carbapenems()</a></code>,
<code><a href="../reference/antibiotic_class_selectors.html">aminoglycosides()</a></code>), or determine multi-drug resistant
microorganisms (MDRO, <code><a href="../reference/mdro.html">mdro()</a></code>).</p>
<p class="section-desc"></p><p>Use these function for the analysis part. You can use <code><a href="../reference/proportion.html">susceptibility()</a></code> or <code><a href="../reference/proportion.html">resistance()</a></code> on any antibiotic column. Be sure to first select the isolates that are appropiate for analysis, by using <code><a href="../reference/first_isolate.html">first_isolate()</a></code> or <code><a href="../reference/get_episode.html">is_new_episode()</a></code>. You can also filter your data on certain resistance in certain antibiotic classes (<code><a href="../reference/antibiotic_class_selectors.html">carbapenems()</a></code>, <code><a href="../reference/antibiotic_class_selectors.html">aminoglycosides()</a></code>), or determine multi-drug resistant microorganisms (MDRO, <code><a href="../reference/mdro.html">mdro()</a></code>).</p>
</th>
</tr></tbody><tbody><tr><td>
<p><code><a href="proportion.html">resistance()</a></code> <code><a href="proportion.html">susceptibility()</a></code> <code><a href="proportion.html">proportion_R()</a></code> <code><a href="proportion.html">proportion_IR()</a></code> <code><a href="proportion.html">proportion_I()</a></code> <code><a href="proportion.html">proportion_SI()</a></code> <code><a href="proportion.html">proportion_S()</a></code> <code><a href="proportion.html">proportion_df()</a></code> <code><a href="proportion.html">rsi_df()</a></code> </p>
@ -287,9 +269,7 @@ microorganisms (MDRO, <code><a href="../reference/mdro.html">mdro()</a></code>).
<td><p>Guess Antibiotic Column</p></td>
</tr></tbody><tbody><tr><th colspan="2">
<h2 id="background-information-on-included-data">Background information on included data <a href="#background-information-on-included-data" class="anchor" aria-hidden="true"></a></h2>
<p class="section-desc"></p><p>Some pages about our package and its external sources. Be sure to
read our <a href="./../articles/index.html">How To’s</a> for more
information about how to work with functions in this package.</p>
<p class="section-desc"></p><p>Some pages about our package and its external sources. Be sure to read our <a href="./../articles/index.html">How To’s</a> for more information about how to work with functions in this package.</p>
</th>
</tr></tbody><tbody><tr><td>
<p><code><a href="AMR.html">AMR</a></code> </p>
@ -353,9 +333,7 @@ information about how to work with functions in this package.</p>
<td><p>Data Set with 500 Isolates - WHONET Example</p></td>
</tr></tbody><tbody><tr><th colspan="2">
<h2 id="other-miscellaneous-functions">Other: miscellaneous functions <a href="#other-miscellaneous-functions" class="anchor" aria-hidden="true"></a></h2>
<p class="section-desc"></p><p>These functions are mostly for internal use, but some of them may
also be suitable for your analysis. Especially the ‘like’ function can
be useful: <code>if (x %like% y) {...}</code>.</p>
<p class="section-desc"></p><p>These functions are mostly for internal use, but some of them may also be suitable for your analysis. Especially the ‘like’ function can be useful: <code>if (x %like% y) {...}</code>.</p>
</th>
</tr></tbody><tbody><tr><td>
<p><code><a href="age_groups.html">age_groups()</a></code> </p>
@ -403,8 +381,7 @@ be useful: <code>if (x %like% y) {...}</code>.</p>
<td><p>Random MIC Values/Disk Zones/RSI Generation</p></td>
</tr></tbody><tbody><tr><th colspan="2">
<h2 id="other-statistical-tests">Other: statistical tests <a href="#other-statistical-tests" class="anchor" aria-hidden="true"></a></h2>
<p class="section-desc"></p><p>Some statistical tests or methods are not part of base R and were
added to this package for convenience.</p>
<p class="section-desc"></p><p>Some statistical tests or methods are not part of base R and were added to this package for convenience.</p>
</th>
</tr></tbody><tbody><tr><td>
<p><code><a href="g.test.html">g.test()</a></code> </p>
@ -420,9 +397,7 @@ added to this package for convenience.</p>
<td><p>Skewness of the Sample</p></td>
</tr></tbody><tbody><tr><th colspan="2">
<h2 id="other-deprecated-functions">Other: deprecated functions <a href="#other-deprecated-functions" class="anchor" aria-hidden="true"></a></h2>
<p class="section-desc"></p><p>These functions are deprecated, meaning that they will still work but
show a warning with every use and will be removed in a future
version.</p>
<p class="section-desc"></p><p>These functions are deprecated, meaning that they will still work but show a warning with every use and will be removed in a future version.</p>
</th>
</tr></tbody><tbody><tr><td>
<p><code><a href="AMR-deprecated.html">AMR-deprecated</a></code> </p>
@ -437,13 +412,11 @@ version.</p>
<footer><div class="copyright">
<p></p><p>Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein,
Erwin E. A. Hassing.</p>
<p></p><p>Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein, Erwin E. A. Hassing.</p>
</div>
<div class="pkgdown">
<p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a>
2.0.2.</p>
<p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.2.</p>
</div>
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8
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@ -173,7 +173,7 @@
<div id="details">
<h2>Details</h2>
<p>The repository of this <code>AMR</code> package contains a file comprising this data set with full taxonomic and antibiotic names: <a href="https://github.com/msberends/AMR/blob/main/data-raw/intrinsic_resistant.txt" class="external-link">https://github.com/msberends/AMR/blob/main/data-raw/intrinsic_resistant.txt</a>. This file <strong>allows for machine reading EUCAST guidelines about intrinsic resistance</strong>, which is almost impossible with the Excel and PDF files distributed by EUCAST. The file is updated automatically.</p>
<p>This data set is based on <a href="https://www.eucast.org/expert_rules_and_intrinsic_resistance/" class="external-link">'EUCAST Expert Rules' and 'EUCAST Intrinsic Resistance and Unusual Phenotypes' v3.3</a> (2021).</p>
<p>This data set is based on <a href="https://www.eucast.org/expert_rules_and_expected_phenotypes/" class="external-link">'EUCAST Expert Rules' and 'EUCAST Intrinsic Resistance and Unusual Phenotypes' v3.3</a> (2021).</p>
</div>
<div id="reference-data-publicly-available">
<h2>Reference Data Publicly Available</h2>
@ -210,13 +210,11 @@
<footer><div class="copyright">
<p></p><p>Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein,
Erwin E. A. Hassing.</p>
<p></p><p>Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein, Erwin E. A. Hassing.</p>
</div>
<div class="pkgdown">
<p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a>
2.0.2.</p>
<p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.2.</p>
</div>
</footer></div>

6
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@ -221,13 +221,11 @@ The <a href="lifecycle.html">lifecycle</a> of this function is <strong>stable</s
<footer><div class="copyright">
<p></p><p>Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein,
Erwin E. A. Hassing.</p>
<p></p><p>Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein, Erwin E. A. Hassing.</p>
</div>
<div class="pkgdown">
<p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a>
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6
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@ -242,13 +242,11 @@ The <a href="lifecycle.html">lifecycle</a> of this function is <strong>stable</s
<footer><div class="copyright">
<p></p><p>Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein,
Erwin E. A. Hassing.</p>
<p></p><p>Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein, Erwin E. A. Hassing.</p>
</div>
<div class="pkgdown">
<p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a>
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<p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.2.</p>
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6
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@ -308,13 +308,11 @@ The <a href="lifecycle.html">lifecycle</a> of this function is <strong>stable</s
<footer><div class="copyright">
<p></p><p>Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein,
Erwin E. A. Hassing.</p>
<p></p><p>Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein, Erwin E. A. Hassing.</p>
</div>
<div class="pkgdown">
<p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a>
2.0.2.</p>
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6
docs/reference/kurtosis.html
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@ -210,13 +210,11 @@ The <a href="lifecycle.html">lifecycle</a> of this function is <strong>stable</s
<footer><div class="copyright">
<p></p><p>Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein,
Erwin E. A. Hassing.</p>
<p></p><p>Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein, Erwin E. A. Hassing.</p>
</div>
<div class="pkgdown">
<p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a>
2.0.2.</p>
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6
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@ -211,13 +211,11 @@ The lifecycle of this function is <strong>questioning</strong>. This function mi
<footer><div class="copyright">
<p></p><p>Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein,
Erwin E. A. Hassing.</p>
<p></p><p>Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein, Erwin E. A. Hassing.</p>
</div>
<div class="pkgdown">
<p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a>
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6
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@ -260,13 +260,11 @@ The <a href="lifecycle.html">lifecycle</a> of this function is <strong>stable</s
<footer><div class="copyright">
<p></p><p>Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein,
Erwin E. A. Hassing.</p>
<p></p><p>Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein, Erwin E. A. Hassing.</p>
</div>
<div class="pkgdown">
<p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a>
2.0.2.</p>
<p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.2.</p>
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8
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@ -245,7 +245,7 @@ Ordered <a href="https://rdrr.io/r/base/factor.html" class="external-link">facto
<li><p><code>guideline = "EUCAST3.1"</code></p>
<p>The European international guideline - EUCAST Expert Rules Version 3.1 "Intrinsic Resistance and Exceptional Phenotypes Tables" (<a href="https://www.eucast.org/fileadmin/src/media/PDFs/EUCAST_files/Expert_Rules/Expert_rules_intrinsic_exceptional_V3.1.pdf" class="external-link">link</a>)</p></li>
<li><p><code>guideline = "TB"</code></p>
<p>The international guideline for multi-drug resistant tuberculosis - World Health Organization "Companion handbook to the WHO guidelines for the programmatic management of drug-resistant tuberculosis" (<a href="https://www.who.int/tb/publications/pmdt_companionhandbook/en/" class="external-link">link</a>)</p></li>
<p>The international guideline for multi-drug resistant tuberculosis - World Health Organization "Companion handbook to the WHO guidelines for the programmatic management of drug-resistant tuberculosis" (<a href="https://www.who.int/publications/i/item/9789241548809" class="external-link">link</a>)</p></li>
<li><p><code>guideline = "MRGN"</code></p>
<p>The German national guideline - Mueller et al. (2015) Antimicrobial Resistance and Infection Control 4:7; doi: <a href="https://doi.org/10.1186/s13756-015-0047-6" class="external-link">10.1186/s13756-015-0047-6</a></p></li>
<li><p><code>guideline = "BRMO"</code></p>
@ -352,13 +352,11 @@ A microorganism is categorised as <em>Susceptible, Increased exposure</em> when
<footer><div class="copyright">
<p></p><p>Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein,
Erwin E. A. Hassing.</p>
<p></p><p>Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein, Erwin E. A. Hassing.</p>
</div>
<div class="pkgdown">
<p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a>
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6
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@ -203,13 +203,11 @@ This package contains the complete taxonomic tree of almost all microorganisms (
<footer><div class="copyright">
<p></p><p>Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein,
Erwin E. A. Hassing.</p>
<p></p><p>Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein, Erwin E. A. Hassing.</p>
</div>
<div class="pkgdown">
<p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a>
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6
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@ -254,13 +254,11 @@ This package contains the complete taxonomic tree of almost all microorganisms (
<footer><div class="copyright">
<p></p><p>Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein,
Erwin E. A. Hassing.</p>
<p></p><p>Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein, Erwin E. A. Hassing.</p>
</div>
<div class="pkgdown">
<p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a>
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6
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@ -210,13 +210,11 @@ This package contains the complete taxonomic tree of almost all microorganisms (
<footer><div class="copyright">
<p></p><p>Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein,
Erwin E. A. Hassing.</p>
<p></p><p>Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein, Erwin E. A. Hassing.</p>
</div>
<div class="pkgdown">
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6
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@ -231,13 +231,11 @@ The <a href="lifecycle.html">lifecycle</a> of this function is <strong>stable</s
<footer><div class="copyright">
<p></p><p>Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein,
Erwin E. A. Hassing.</p>
<p></p><p>Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein, Erwin E. A. Hassing.</p>
</div>
<div class="pkgdown">
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8
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@ -253,7 +253,7 @@
<p>Since the top-level of the taxonomy is sometimes referred to as 'kingdom' and sometimes as 'domain', the functions <code>mo_kingdom()</code> and <code>mo_domain()</code> return the exact same results.</p>
<p>The Gram stain - <code>mo_gramstain()</code> - will be determined based on the taxonomic kingdom and phylum. According to Cavalier-Smith (2002, <a href="https://pubmed.ncbi.nlm.nih.gov/11837318" class="external-link">PMID 11837318</a>), who defined subkingdoms Negibacteria and Posibacteria, only these phyla are Posibacteria: Actinobacteria, Chloroflexi, Firmicutes and Tenericutes. These bacteria are considered Gram-positive, except for members of the class Negativicutes which are Gram-negative. Members of other bacterial phyla are all considered Gram-negative. Species outside the kingdom of Bacteria will return a value <code>NA</code>. Functions <code>mo_is_gram_negative()</code> and <code>mo_is_gram_positive()</code> always return <code>TRUE</code> or <code>FALSE</code> (except when the input is <code>NA</code> or the MO code is <code>UNKNOWN</code>), thus always return <code>FALSE</code> for species outside the taxonomic kingdom of Bacteria.</p>
<p>Determination of yeasts - <code>mo_is_yeast()</code> - will be based on the taxonomic kingdom and class. <em>Budding yeasts</em> are fungi of the phylum Ascomycetes, class Saccharomycetes (also called Hemiascomycetes). <em>True yeasts</em> are aggregated into the underlying order Saccharomycetales. Thus, for all microorganisms that are fungi and member of the taxonomic class Saccharomycetes, the function will return <code>TRUE</code>. It returns <code>FALSE</code> otherwise (except when the input is <code>NA</code> or the MO code is <code>UNKNOWN</code>).</p>
<p>Intrinsic resistance - <code>mo_is_intrinsic_resistant()</code> - will be determined based on the <a href="intrinsic_resistant.html">intrinsic_resistant</a> data set, which is based on <a href="https://www.eucast.org/expert_rules_and_intrinsic_resistance/" class="external-link">'EUCAST Expert Rules' and 'EUCAST Intrinsic Resistance and Unusual Phenotypes' v3.3</a> (2021). The <code>mo_is_intrinsic_resistant()</code> functions can be vectorised over arguments <code>x</code> (input for microorganisms) and over <code>ab</code> (input for antibiotics).</p>
<p>Intrinsic resistance - <code>mo_is_intrinsic_resistant()</code> - will be determined based on the <a href="intrinsic_resistant.html">intrinsic_resistant</a> data set, which is based on <a href="https://www.eucast.org/expert_rules_and_expected_phenotypes/" class="external-link">'EUCAST Expert Rules' and 'EUCAST Intrinsic Resistance and Unusual Phenotypes' v3.3</a> (2021). The <code>mo_is_intrinsic_resistant()</code> functions can be vectorised over arguments <code>x</code> (input for microorganisms) and over <code>ab</code> (input for antibiotics).</p>
<p>All output <a href="translate.html">will be translated</a> where possible.</p>
<p>The function <code>mo_url()</code> will return the direct URL to the online database entry, which also shows the scientific reference of the concerned species.</p>
<p>SNOMED codes - <code>mo_snomed()</code> - are from the US Edition of SNOMED CT from 1 September 2020. See <em>Source</em> and the <a href="microorganisms.html">microorganisms</a> data set for more info.</p>
@ -433,13 +433,11 @@ This package contains the complete taxonomic tree of almost all microorganisms (
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@ -267,13 +267,11 @@ The <a href="lifecycle.html">lifecycle</a> of this function is <strong>stable</s
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@ -332,13 +332,11 @@ The <a href="lifecycle.html">lifecycle</a> of this function is <strong>stable</s
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@ -381,13 +381,11 @@ A microorganism is categorised as <em>Susceptible, Increased exposure</em> when
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@ -232,13 +232,11 @@ The <a href="lifecycle.html">lifecycle</a> of this function is <strong>stable</s
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@ -358,13 +358,11 @@ A microorganism is categorised as <em>Susceptible, Increased exposure</em> when
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