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articles/EUCAST.html
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@ -38,7 +38,7 @@
<a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9033</small>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9034</small>
<button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@ -171,18 +171,39 @@
<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><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></span>
<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>
@ -200,7 +221,10 @@
<span><span class="co"># mo ampicillin</span></span>
<span><span class="co"># 1 Klebsiella R</span></span>
<span><span class="co"># 2 Escherichia S</span></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><span class="fu"><a href="../reference/mo_property.html">mo_is_intrinsic_resistant</a></span><span class="op">(</span></span>
<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>
@ -213,7 +237,11 @@
<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>
<span><span class="op">)</span></span>
<span><span class="co"># [1] TRUE FALSE</span></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><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></span>
<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>

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articles/MDR.html
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@ -38,7 +38,7 @@
<a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9033</small>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9034</small>
<button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@ -168,56 +168,89 @@
<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><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>
<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>
<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>
<span><span class="op">)</span></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><span class="va">custom</span></span>
<span><span class="co"># A set of custom MDRO rules:</span></span>
@ -227,38 +260,59 @@
<span><span class="co"># </span></span>
<span><span class="co"># Unmatched rows will return NA.</span></span>
<span><span class="co"># Results will be of class 'factor', with ordered levels: Negative &lt; Elderly Type A &lt; Elderly Type B</span></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><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>
<span><span class="fu"><a href="https://rdrr.io/r/base/table.html" class="external-link">table</a></span><span class="op">(</span><span class="va">x</span><span class="op">)</span></span>
<span><span class="co"># x</span></span>
<span><span class="co"># Negative Elderly Type A Elderly Type B </span></span>
<span><span class="co"># 1070 198 732</span></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><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>
<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></span></code></pre></div>
<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/cleaner/" class="external-link">cleaner</a></span><span class="op">)</span> <span class="co"># to create frequency tables</span></span></code></pre></div>
<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
<code class="sourceCode R"><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>
<span> <span class="fu"><a href="../reference/mdro.html">mdro</a></span><span class="op">(</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
<span> <span class="fu"><a href="https://rdrr.io/pkg/cleaner/man/freq.html" class="external-link">freq</a></span><span class="op">(</span><span class="op">)</span> <span class="co"># show frequency table of the result</span></span>
<span> <span class="fu"><a href="https://msberends.github.io/cleaner/reference/freq.html" class="external-link">freq</a></span><span class="op">(</span><span class="op">)</span> <span class="co"># show frequency table of the result</span></span>
<span><span class="co"># Warning: in `mdro()`: NA introduced for isolates where the available percentage of</span></span>
<span><span class="co"># antimicrobial classes was below 50% (set with `pct_required_classes`)</span></span></code></pre></div>
<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 class="table">
<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>
<thead><tr class="header">
<th align="left"></th>
<th align="left">Item</th>
@ -272,21 +326,22 @@ Unique: 2</p>
<td align="left">1</td>
<td align="left">Negative</td>
<td align="right">1601</td>
<td align="right">92.60%</td>
<td align="right">92.6%</td>
<td align="right">1601</td>
<td align="right">92.60%</td>
<td align="right">92.6%</td>
</tr>
<tr class="even">
<td align="left">2</td>
<td align="left">Multi-drug-resistant (MDR)</td>
<td align="right">128</td>
<td align="right">7.40%</td>
<td align="right">7.4%</td>
<td align="right">1729</td>
<td align="right">100.00%</td>
<td align="right">100.0%</td>
</tr>
</tbody>
</table>
<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><span class="co"># random_rsi() is a helper function to generate</span></span>
<span><span class="co"># a random vector with values S, I and R</span></span>
@ -299,7 +354,8 @@ Unique: 2</p>
<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>,</span>
<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>
<span><span class="op">)</span></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><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></span>
<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>,</span>
@ -314,20 +370,21 @@ Unique: 2</p>
<div class="sourceCode" id="cb8"><pre class="downlit sourceCode r">
<code class="sourceCode R"><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">my_TB_data</span><span class="op">)</span></span>
<span><span class="co"># rifampicin isoniazid gatifloxacin ethambutol pyrazinamide moxifloxacin</span></span>
<span><span class="co"># 1 I S S R S R</span></span>
<span><span class="co"># 2 I R R R S S</span></span>
<span><span class="co"># 3 I I S S R R</span></span>
<span><span class="co"># 4 R R I I R I</span></span>
<span><span class="co"># 5 R I R S S R</span></span>
<span><span class="co"># 6 I I I S I I</span></span>
<span><span class="co"># 1 R S I R S S</span></span>
<span><span class="co"># 2 R S I I R I</span></span>
<span><span class="co"># 3 R S S R S R</span></span>
<span><span class="co"># 4 R S R S R I</span></span>
<span><span class="co"># 5 R I S R I I</span></span>
<span><span class="co"># 6 S S R S I I</span></span>
<span><span class="co"># kanamycin</span></span>
<span><span class="co"># 1 S</span></span>
<span><span class="co"># 1 I</span></span>
<span><span class="co"># 2 I</span></span>
<span><span class="co"># 3 I</span></span>
<span><span class="co"># 4 S</span></span>
<span><span class="co"># 5 I</span></span>
<span><span class="co"># 6 S</span></span></code></pre></div>
<p>We can now add the interpretation of MDR-TB to our data set. You can use:</p>
<span><span class="co"># 4 R</span></span>
<span><span class="co"># 5 R</span></span>
<span><span class="co"># 6 R</span></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><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></span></code></pre></div>
<p>or its shortcut <code><a href="../reference/mdro.html">mdr_tb()</a></code>:</p>
@ -337,14 +394,23 @@ Unique: 2</p>
<span><span class="co"># Mycobacterium tuberculosis.</span></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><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">$</span><span class="va">mdr</span><span class="op">)</span></span></code></pre></div>
<code class="sourceCode R"><span><span class="fu"><a href="https://msberends.github.io/cleaner/reference/freq.html" class="external-link">freq</a></span><span class="op">(</span><span class="va">my_TB_data</span><span class="op">$</span><span class="va">mdr</span><span class="op">)</span></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>
Available: 5,000 (100.0%, NA: 0 = 0.0%)<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 class="table">
<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>
<thead><tr class="header">
<th align="left"></th>
<th align="left">Item</th>
@ -357,40 +423,40 @@ Unique: 5</p>
<tr class="odd">
<td align="left">1</td>
<td align="left">Mono-resistant</td>
<td align="right">3193</td>
<td align="right">63.86%</td>
<td align="right">3193</td>
<td align="right">63.86%</td>
<td align="right">3211</td>
<td align="right">64.22%</td>
<td align="right">3211</td>
<td align="right">64.22%</td>
</tr>
<tr class="even">
<td align="left">2</td>
<td align="left">Negative</td>
<td align="right">988</td>
<td align="right">19.76%</td>
<td align="right">4181</td>
<td align="right">83.62%</td>
<td align="right">1025</td>
<td align="right">20.50%</td>
<td align="right">4236</td>
<td align="right">84.72%</td>
</tr>
<tr class="odd">
<td align="left">3</td>
<td align="left">Multi-drug-resistant</td>
<td align="right">429</td>
<td align="right">8.58%</td>
<td align="right">4610</td>
<td align="right">92.20%</td>
<td align="right">425</td>
<td align="right">8.50%</td>
<td align="right">4661</td>
<td align="right">93.22%</td>
</tr>
<tr class="even">
<td align="left">4</td>
<td align="left">Poly-resistant</td>
<td align="right">282</td>
<td align="right">5.64%</td>
<td align="right">4892</td>
<td align="right">97.84%</td>
<td align="right">233</td>
<td align="right">4.66%</td>
<td align="right">4894</td>
<td align="right">97.88%</td>
</tr>
<tr class="odd">
<td align="left">5</td>
<td align="left">Extensively drug-resistant</td>
<td align="right">108</td>
<td align="right">2.16%</td>
<td align="right">106</td>
<td align="right">2.12%</td>
<td align="right">5000</td>
<td align="right">100.00%</td>
</tr>

28
articles/PCA.html
View File

@ -38,7 +38,7 @@
<a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9033</small>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9034</small>
<button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@ -168,7 +168,8 @@
<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>
@ -176,7 +177,8 @@
<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><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>
<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>
@ -229,7 +231,8 @@
<span><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, R, R, …</span></span>
<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, NA, NA…</span></span>
<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, R, R, N…</span></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><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>
<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>
@ -257,12 +260,15 @@
<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><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>
<span><span class="co"># ℹ Columns selected for PCA: "AMC", "CAZ", "CTX", "CXM", "GEN", "SXT", "TMP"</span></span>
<span><span class="co"># and "TOB". Total observations available: 7.</span></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><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>
<span><span class="co"># Groups (n=4, named as 'order'):</span></span>
@ -274,7 +280,11 @@
<span><span class="co"># Cumulative Proportion 0.5799 0.9330 0.9801 0.99446 0.99988 1.00000 1.000e+00</span></span></code></pre></div>
<pre><code><span><span class="co"># Groups (n=4, named as 'order'):</span></span>
<span><span class="co"># [1] "Caryophanales" "Enterobacterales" "Lactobacillales" "Pseudomonadales"</span></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>
@ -282,7 +292,9 @@
<div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><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></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><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></span></code></pre></div>
<p><img src="PCA_files/figure-html/unnamed-chunk-6-1.png" width="750"></p>

152
articles/SPSS.html
View File

@ -38,7 +38,7 @@
<a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9033</small>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9034</small>
<button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@ -160,9 +160,10 @@
<div class="row">
<main id="main" class="col-md-9"><div class="page-header">
<img src="../logo.svg" class="logo" alt=""><h1>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">22 October 2022</h4>
<h4 data-toc-skip class="date">29 October 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="d-none name"><code>SPSS.Rmd</code></div>
@ -173,45 +174,135 @@
<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">466,988 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">467,961
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>
@ -257,13 +348,23 @@
<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>as_factor()</code>.</p>
<p>If you want named variables to be imported as factors so it resembles
SPSS more, use <code>as_factor()</code>.</p>
<p>The difference is this:</p>
<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="va">SPSS_data</span></span>
@ -301,7 +402,8 @@
<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><span class="co"># download and install the latest version:</span></span>
<span><span class="fu"><a href="https://rdrr.io/r/utils/install.packages.html" class="external-link">install.packages</a></span><span class="op">(</span><span class="st">"haven"</span><span class="op">)</span></span>

56
articles/WHONET.html
View File

@ -38,7 +38,7 @@
<a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9033</small>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9034</small>
<button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@ -171,26 +171,43 @@
<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><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>
<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></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><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>
<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>
<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>
<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></span></code></pre></div>
<p>We will have to transform some variables to simplify and automate the analysis:</p>
<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/cleaner/" class="external-link">cleaner</a></span><span class="op">)</span> <span class="co"># to create frequency tables</span></span></code></pre></div>
<p>We will have to transform some variables to simplify and automate the
analysis:</p>
<ul>
<li>Microorganisms should be transformed to our own microorganism codes (called an <code>mo</code>) using <a href="https://msberends.github.io/AMR/reference/catalogue_of_life">our Catalogue of Life reference data set</a>, which contains all ~70,000 microorganisms from the taxonomic kingdoms Bacteria, Fungi and Protozoa. We do the tranformation with <code><a href="../reference/as.mo.html">as.mo()</a></code>. This function also recognises almost all WHONET abbreviations of microorganisms.</li>
<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><span class="co"># transform variables</span></span>
@ -200,19 +217,29 @@
<span> <span class="co"># transform everything from "AMP_ND10" to "CIP_EE" to the new `rsi` class</span></span>
<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></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://msberends.github.io/cleaner/reference/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><span class="co"># our newly created `mo` variable, put in the mo_name() function</span></span>
<span><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://rdrr.io/pkg/cleaner/man/freq.html" class="external-link">freq</a></span><span class="op">(</span><span class="fu"><a href="../reference/mo_property.html">mo_name</a></span><span class="op">(</span><span class="va">mo</span><span class="op">)</span>, nmax <span class="op">=</span> <span class="fl">10</span><span class="op">)</span></span></code></pre></div>
<span><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://msberends.github.io/cleaner/reference/freq.html" class="external-link">freq</a></span><span class="op">(</span><span class="fu"><a href="../reference/mo_property.html">mo_name</a></span><span class="op">(</span><span class="va">mo</span><span class="op">)</span>, nmax <span class="op">=</span> <span class="fl">10</span><span class="op">)</span></span></code></pre></div>
<p><strong>Frequency table</strong></p>
<p>Class: character<br>
Length: 500<br>
Available: 500 (100.0%, NA: 0 = 0.0%)<br>
Available: 500 (100%, NA: 0 = 0%)<br>
Unique: 38</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>
@ -304,11 +331,11 @@ Longest: 40</p>
</tr>
</tbody>
</table>
<p>(omitted 28 entries, n = 57 [11.40%])</p>
<p>(omitted 28 entries, n = 57 [11.4%])</p>
<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="co"># our transformed antibiotic columns</span></span>
<span><span class="co"># amoxicillin/clavulanic acid (J01CR02) as an example</span></span>
<span><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://rdrr.io/pkg/cleaner/man/freq.html" class="external-link">freq</a></span><span class="op">(</span><span class="va">AMC_ND2</span><span class="op">)</span></span></code></pre></div>
<span><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://msberends.github.io/cleaner/reference/freq.html" class="external-link">freq</a></span><span class="op">(</span><span class="va">AMC_ND2</span><span class="op">)</span></span></code></pre></div>
<p><strong>Frequency table</strong></p>
<p>Class: factor &gt; ordered &gt; rsi (numeric)<br>
Length: 500<br>
@ -358,7 +385,8 @@ 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><span class="va">data</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
<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></span>

382
articles/datasets.html
View File

@ -38,7 +38,7 @@
<a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9033</small>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9034</small>
<button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@ -161,7 +161,7 @@
<main id="main" class="col-md-9"><div class="page-header">
<img src="../logo.svg" class="logo" alt=""><h1>Data sets for download / own use</h1>
<h4 data-toc-skip class="date">22 October 2022</h4>
<h4 data-toc-skip class="date">29 October 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="d-none name"><code>datasets.Rmd</code></div>
@ -169,42 +169,76 @@
<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, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, and Stata. We also provide tab-separated text 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, MS Excel, Apache Feather, Apache
Parquet, SPSS, SAS, and Stata. We also provide tab-separated text 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>
<div class="section level2">
<h2 id="microorganisms-full-microbial-taxonomy">
<code>microorganisms</code>: Full Microbial Taxonomy<a class="anchor" aria-label="anchor" href="#microorganisms-full-microbial-taxonomy"></a>
</h2>
<p>A data set with 48,788 rows and 22 columns, containing the following column names:<br><em>mo</em>, <em>fullname</em>, <em>status</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>source</em>, <em>lpsn</em>, <em>lpsn_parent</em>, <em>lpsn_renamed_to</em>, <em>gbif</em>, <em>gbif_parent</em>, <em>gbif_renamed_to</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 22 October 2022 20:03:38 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 48,883 rows and 22 columns, containing the following
column names:<br><em>mo</em>, <em>fullname</em>, <em>status</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>source</em>, <em>lpsn</em>, <em>lpsn_parent</em>,
<em>lpsn_renamed_to</em>, <em>gbif</em>, <em>gbif_parent</em>,
<em>gbif_renamed_to</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 October 2022 12:16:58 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">original R Data Structure (RDS) file</a> (1.1 MB)<br>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/microorganisms.rds" class="external-link">original
R Data Structure (RDS) file</a> (1.1 MB)<br>
</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/microorganisms.txt" class="external-link">tab-separated text file</a> (0.4 kB)<br>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/microorganisms.txt" class="external-link">tab-separated
text file</a> (0.4 kB)<br>
</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/microorganisms.xlsx" class="external-link">Microsoft Excel workbook</a> (4.8 MB)<br>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/microorganisms.xlsx" class="external-link">Microsoft
Excel workbook</a> (4.8 MB)<br>
</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/microorganisms.feather" class="external-link">Apache Feather file</a> (5.1 MB)<br>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/microorganisms.feather" class="external-link">Apache
Feather file</a> (5.1 MB)<br>
</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/microorganisms.parquet" class="external-link">Apache Parquet file</a> (2.5 MB)<br>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/microorganisms.parquet" class="external-link">Apache
Parquet file</a> (2.5 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 data file</a> (47.7 MB)<br>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/microorganisms.sas" class="external-link">SAS
data file</a> (47.8 MB)<br>
</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/microorganisms.sav" class="external-link">IBM SPSS Statistics data file</a> (15.8 MB)<br>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/microorganisms.sav" class="external-link">IBM
SPSS Statistics data file</a> (15.8 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 DTA file</a> (44.4 MB)</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/microorganisms.dta" class="external-link">Stata
DTA file</a> (43.8 MB)</li>
</ul>
<p><strong>NOTE: The exported files for Excel, SAS, SPSS and Stata contain only the first 50 SNOMED codes per record, as their file size would otherwise exceed 100 MB; the file size limit of GitHub.</strong> Advice? Use R instead.</p>
<p><strong>NOTE: The exported files for Excel, SAS, SPSS and Stata
contain only the first 50 SNOMED codes per record, as their file size
would otherwise 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>This data set contains the full microbial taxonomy of five kingdoms from the List of Prokaryotic names with Standing in Nomenclature (LPSN) and the Global Biodiversity Information Facility (GBIF):</p>
<p>This data set contains the full microbial taxonomy of five kingdoms
from the List of Prokaryotic names with Standing in Nomenclature (LPSN)
and the Global Biodiversity Information Facility (GBIF):</p>
<ul>
<li>Parte, AC <em>et al.</em> (2020). <strong>List of Prokaryotic names with Standing in Nomenclature (LPSN) moves to the DSMZ.</strong> International Journal of Systematic and Evolutionary Microbiology, 70, 5607-5612; . Accessed from <a href="https://lpsn.dsmz.de" class="external-link uri">https://lpsn.dsmz.de</a> on 12 September, 2022.</li>
<li>GBIF Secretariat (November 26, 2021). GBIF Backbone Taxonomy. Checklist dataset . Accessed from <a href="https://www.gbif.org" class="external-link uri">https://www.gbif.org</a> on 12 September, 2022.</li>
<li>Public Health Information Network Vocabulary Access and Distribution System (PHIN VADS). US Edition of SNOMED CT from 1 September 2020. Value Set Name ‘Microoganism’, OID 2.16.840.1.114222.4.11.1009 (v12). URL: <a href="https://phinvads.cdc.gov" class="external-link uri">https://phinvads.cdc.gov</a>
<li>Parte, AC <em>et al.</em> (2020). <strong>List of Prokaryotic names
with Standing in Nomenclature (LPSN) moves to the DSMZ.</strong>
International Journal of Systematic and Evolutionary Microbiology, 70,
5607-5612; . Accessed from <a href="https://lpsn.dsmz.de" class="external-link uri">https://lpsn.dsmz.de</a> on 12 September, 2022.</li>
<li>GBIF Secretariat (November 26, 2021). GBIF Backbone Taxonomy.
Checklist dataset . Accessed from <a href="https://www.gbif.org" class="external-link uri">https://www.gbif.org</a> on 12 September, 2022.</li>
<li>Public Health Information Network Vocabulary Access and Distribution
System (PHIN VADS). US Edition of SNOMED CT from 1 September 2020. Value
Set Name ‘Microoganism’, OID 2.16.840.1.114222.4.11.1009 (v12). URL: <a href="https://phinvads.cdc.gov" class="external-link uri">https://phinvads.cdc.gov</a>
</li>
</ul>
</div>
@ -220,11 +254,11 @@
<tbody>
<tr class="odd">
<td align="center">(unknown kingdom)</td>
<td align="center">3</td>
<td align="center">5</td>
</tr>
<tr class="even">
<td align="center">Animalia</td>
<td align="center">1,523</td>
<td align="center">1,524</td>
</tr>
<tr class="odd">
<td align="center">Archaea</td>
@ -232,15 +266,15 @@
</tr>
<tr class="even">
<td align="center">Bacteria</td>
<td align="center">33,714</td>
<td align="center">33,716</td>
</tr>
<tr class="odd">
<td align="center">Fungi</td>
<td align="center">7,365</td>
<td align="center">7,450</td>
</tr>
<tr class="even">
<td align="center">Protozoa</td>
<td align="center">4,946</td>
<td align="center">4,951</td>
</tr>
</tbody>
</table>
@ -447,35 +481,56 @@
<h2 id="antibiotics-antibiotic-agents">
<code>antibiotics</code>: Antibiotic Agents<a class="anchor" aria-label="anchor" href="#antibiotics-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 22 October 2022 20:03:38 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 29 October 2022 12:16:58 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">original R Data Structure (RDS) file</a> (36 kB)<br>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/antibiotics.rds" class="external-link">original
R Data Structure (RDS) file</a> (36 kB)<br>
</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/antibiotics.txt" class="external-link">tab-separated text file</a> (0.2 kB)<br>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/antibiotics.txt" class="external-link">tab-separated
text file</a> (0.2 kB)<br>
</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/antibiotics.xlsx" class="external-link">Microsoft Excel workbook</a> (66 kB)<br>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/antibiotics.xlsx" class="external-link">Microsoft
Excel workbook</a> (66 kB)<br>
</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/antibiotics.feather" class="external-link">Apache Feather file</a> (97 kB)<br>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/antibiotics.feather" class="external-link">Apache
Feather file</a> (97 kB)<br>
</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/antibiotics.parquet" class="external-link">Apache Parquet file</a> (74 kB)<br>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/antibiotics.parquet" class="external-link">Apache
Parquet file</a> (74 kB)<br>
</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/antibiotics.sas" class="external-link">SAS data 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
data 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">IBM SPSS Statistics data 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">IBM
SPSS Statistics data 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 DTA 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
DTA file</a> (0.3 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 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>
@ -555,7 +610,8 @@
<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>
@ -620,35 +676,55 @@
<h2 id="antivirals-antiviral-agents">
<code>antivirals</code>: Antiviral Agents<a class="anchor" aria-label="anchor" href="#antivirals-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 22 October 2022 20:03:38 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 29 October 2022 12:16:58 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">original R Data Structure (RDS) file</a> (4 kB)<br>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/antivirals.rds" class="external-link">original
R Data Structure (RDS) file</a> (4 kB)<br>
</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/antivirals.txt" class="external-link">tab-separated 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">tab-separated
text file</a> (16 kB)<br>
</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/antivirals.xlsx" class="external-link">Microsoft Excel workbook</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">Microsoft
Excel workbook</a> (14 kB)<br>
</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/antivirals.feather" class="external-link">Apache Feather file</a> (12 kB)<br>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/antivirals.feather" class="external-link">Apache
Feather file</a> (12 kB)<br>
</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/antivirals.parquet" class="external-link">Apache Parquet file</a> (10 kB)<br>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/antivirals.parquet" class="external-link">Apache
Parquet file</a> (10 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 data 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
data 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">IBM SPSS Statistics data file</a> (27 kB)<br>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/antivirals.sav" class="external-link">IBM
SPSS Statistics data file</a> (27 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 DTA 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
DTA file</a> (67 kB)</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 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">
@ -682,7 +758,8 @@
<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>
@ -693,7 +770,8 @@
<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>
@ -704,8 +782,10 @@
<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>
@ -750,33 +830,49 @@
</div>
<div class="section level2">
<h2 id="rsi_translation-interpretation-from-mic-values-disk-diameters-to-rsi">
<code>rsi_translation</code>: Interpretation from MIC values / disk diameters to R/SI<a class="anchor" aria-label="anchor" href="#rsi_translation-interpretation-from-mic-values-disk-diameters-to-rsi"></a>
<code>rsi_translation</code>: Interpretation from MIC values / disk
diameters to R/SI<a class="anchor" aria-label="anchor" href="#rsi_translation-interpretation-from-mic-values-disk-diameters-to-rsi"></a>
</h2>
<p>A data set with 18,308 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 22 October 2022 20:03:38 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 18,308 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 29 October 2022 12:16:58 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">original R Data Structure (RDS) file</a> (42 kB)<br>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/rsi_translation.rds" class="external-link">original
R Data Structure (RDS) file</a> (42 kB)<br>
</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/rsi_translation.txt" class="external-link">tab-separated text file</a> (1.9 MB)<br>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/rsi_translation.txt" class="external-link">tab-separated
text file</a> (1.9 MB)<br>
</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/rsi_translation.xlsx" class="external-link">Microsoft Excel workbook</a> (0.8 MB)<br>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/rsi_translation.xlsx" class="external-link">Microsoft
Excel workbook</a> (0.8 MB)<br>
</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/rsi_translation.feather" class="external-link">Apache Feather file</a> (0.7 MB)<br>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/rsi_translation.feather" class="external-link">Apache
Feather file</a> (0.7 MB)<br>
</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/rsi_translation.parquet" class="external-link">Apache Parquet file</a> (87 kB)<br>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/rsi_translation.parquet" class="external-link">Apache
Parquet file</a> (87 kB)<br>
</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/rsi_translation.sas" class="external-link">SAS data file</a> (3.6 MB)<br>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/rsi_translation.sas" class="external-link">SAS
data file</a> (3.6 MB)<br>
</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/rsi_translation.sav" class="external-link">IBM SPSS Statistics data file</a> (2.3 MB)<br>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/rsi_translation.sav" class="external-link">IBM
SPSS Statistics data file</a> (2.3 MB)<br>
</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/rsi_translation.dta" class="external-link">Stata DTA file</a> (3.4 MB)</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/rsi_translation.dta" class="external-link">Stata
DTA file</a> (3.4 MB)</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 interpretation rules for MIC values and disk diffusion diameters. Included guidelines are CLSI (2013-2022) and EUCAST (2013-2022).</p>
<p>This data set contains interpretation rules for MIC values and disk
diffusion diameters. Included guidelines are CLSI (2013-2022) and EUCAST
(2013-2022).</p>
</div>
<div class="section level3">
<h3 id="example-content-3">Example content<a class="anchor" aria-label="anchor" href="#example-content-3"></a>
@ -909,33 +1005,48 @@
</div>
<div class="section level2">
<h2 id="intrinsic_resistant-intrinsic-bacterial-resistance">
<code>intrinsic_resistant</code>: Intrinsic Bacterial Resistance<a class="anchor" aria-label="anchor" href="#intrinsic_resistant-intrinsic-bacterial-resistance"></a>
<code>intrinsic_resistant</code>: Intrinsic Bacterial
Resistance<a class="anchor" aria-label="anchor" href="#intrinsic_resistant-intrinsic-bacterial-resistance"></a>
</h2>
<p>A data set with 134,659 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 22 October 2022 20:03:38 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 134,659 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 29 October 2022 12:16:58 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">original R Data Structure (RDS) 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">original
R Data Structure (RDS) file</a> (78 kB)<br>
</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/intrinsic_resistant.txt" class="external-link">tab-separated 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">tab-separated
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.xlsx" class="external-link">Microsoft Excel workbook</a> (1.3 MB)<br>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/intrinsic_resistant.xlsx" class="external-link">Microsoft
Excel workbook</a> (1.3 MB)<br>
</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/intrinsic_resistant.feather" class="external-link">Apache Feather file</a> (1.2 MB)<br>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/intrinsic_resistant.feather" class="external-link">Apache
Feather file</a> (1.2 MB)<br>
</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/intrinsic_resistant.parquet" class="external-link">Apache Parquet file</a> (0.2 MB)<br>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/intrinsic_resistant.parquet" class="external-link">Apache
Parquet file</a> (0.2 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 data file</a> (9.8 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
data file</a> (9.8 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">IBM SPSS Statistics data 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">IBM
SPSS Statistics data 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 DTA file</a> (9.6 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
DTA file</a> (9.6 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_expected_phenotypes/" 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>
@ -1183,37 +1294,63 @@
<h2 id="dosage-dosage-guidelines-from-eucast">
<code>dosage</code>: Dosage Guidelines from EUCAST<a class="anchor" aria-label="anchor" href="#dosage-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 22 October 2022 20:03:38 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 29 October 2022 12:16:58 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">original R Data Structure (RDS) file</a> (3 kB)<br>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/dosage.rds" class="external-link">original
R Data Structure (RDS) file</a> (3 kB)<br>
</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/dosage.txt" class="external-link">tab-separated text file</a> (15 kB)<br>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/dosage.txt" class="external-link">tab-separated
text file</a> (15 kB)<br>
</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/dosage.xlsx" class="external-link">Microsoft Excel workbook</a> (14 kB)<br>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/dosage.xlsx" class="external-link">Microsoft
Excel workbook</a> (14 kB)<br>
</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/dosage.feather" class="external-link">Apache Feather file</a> (11 kB)<br>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/dosage.feather" class="external-link">Apache
Feather file</a> (11 kB)<br>
</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/dosage.parquet" class="external-link">Apache Parquet file</a> (7 kB)<br>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/dosage.parquet" class="external-link">Apache
Parquet file</a> (7 kB)<br>
</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/dosage.sas" class="external-link">SAS data file</a> (52 kB)<br>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/dosage.sas" class="external-link">SAS
data file</a> (52 kB)<br>
</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/dosage.sav" class="external-link">IBM SPSS Statistics data file</a> (23 kB)<br>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/dosage.sav" class="external-link">IBM
SPSS Statistics data file</a> (23 kB)<br>
</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/dosage.dta" class="external-link">Stata DTA file</a> (44 kB)</li>
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/dosage.dta" class="external-link">Stata
DTA file</a> (44 kB)</li>
</ul>
<div class="section level3">
<h3 id="source-5">Source<a class="anchor" aria-label="anchor" href="#source-5"></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-5">Example content<a class="anchor" aria-label="anchor" href="#example-content-5"></a>
</h3>
<table class="table">
<colgroup>
<col width="4%">
<col width="10%">
<col width="15%">
<col width="10%">
<col width="9%">
<col width="13%">
<col width="5%">
<col width="16%">
<col width="13%">
</colgroup>
<thead><tr class="header">
<th align="center">ab</th>
<th align="center">name</th>
@ -1300,13 +1437,26 @@
<h2 id="example_isolates-example-data-for-practice">
<code>example_isolates</code>: Example Data for Practice<a class="anchor" aria-label="anchor" href="#example_isolates-example-data-for-practice"></a>
</h2>
<p>A data set with 2,000 rows and 46 columns, containing the following column names:<br><em>date</em>, <em>patient</em>, <em>age</em>, <em>gender</em>, <em>ward</em>, <em>mo</em>, <em>PEN</em>, <em>OXA</em>, <em>FLC</em>, <em>AMX</em>, <em>AMC</em>, <em>AMP</em>, <em>TZP</em>, <em>CZO</em>, <em>FEP</em>, <em>CXM</em>, <em>FOX</em>, <em>CTX</em>, <em>CAZ</em>, <em>CRO</em>, <em>GEN</em>, <em>TOB</em>, <em>AMK</em>, <em>KAN</em>, <em>TMP</em>, <em>SXT</em>, <em>NIT</em>, <em>FOS</em>, <em>LNZ</em>, <em>CIP</em>, <em>MFX</em>, <em>VAN</em>, <em>TEC</em>, <em>TCY</em>, <em>TGC</em>, <em>DOX</em>, <em>ERY</em>, <em>CLI</em>, <em>AZM</em>, <em>IPM</em>, <em>MEM</em>, <em>MTR</em>, <em>CHL</em>, <em>COL</em>, <em>MUP</em> and <em>RIF</em>.</p>
<p>This data set is in R available as <code>example_isolates</code>, after you load the <code>AMR</code> package.</p>
<p>It was last updated on 22 October 2022 20:03:38 UTC. Find more info about the structure of this data set <a href="https://msberends.github.io/AMR/reference/example_isolates.html">here</a>.</p>
<p>A data set with 2,000 rows and 46 columns, containing the following
column names:<br><em>date</em>, <em>patient</em>, <em>age</em>, <em>gender</em>,
<em>ward</em>, <em>mo</em>, <em>PEN</em>, <em>OXA</em>, <em>FLC</em>,
<em>AMX</em>, <em>AMC</em>, <em>AMP</em>, <em>TZP</em>, <em>CZO</em>,
<em>FEP</em>, <em>CXM</em>, <em>FOX</em>, <em>CTX</em>, <em>CAZ</em>,
<em>CRO</em>, <em>GEN</em>, <em>TOB</em>, <em>AMK</em>, <em>KAN</em>,
<em>TMP</em>, <em>SXT</em>, <em>NIT</em>, <em>FOS</em>, <em>LNZ</em>,
<em>CIP</em>, <em>MFX</em>, <em>VAN</em>, <em>TEC</em>, <em>TCY</em>,
<em>TGC</em>, <em>DOX</em>, <em>ERY</em>, <em>CLI</em>, <em>AZM</em>,
<em>IPM</em>, <em>MEM</em>, <em>MTR</em>, <em>CHL</em>, <em>COL</em>,
<em>MUP</em> and <em>RIF</em>.</p>
<p>This data set is in R available as <code>example_isolates</code>,
after you load the <code>AMR</code> package.</p>
<p>It was last updated on 29 October 2022 12:16:58 UTC. Find more info
about the structure of this data set <a href="https://msberends.github.io/AMR/reference/example_isolates.html">here</a>.</p>
<div class="section level3">
<h3 id="source-6">Source<a class="anchor" aria-label="anchor" href="#source-6"></a>
</h3>
<p>This data set contains randomised fictitious data, but reflects reality and can be used to practise AMR data analysis.</p>
<p>This data set contains randomised fictitious data, but reflects
reality and can be used to practise AMR data analysis.</p>
</div>
<div class="section level3">
<h3 id="example-content-6">Example content<a class="anchor" aria-label="anchor" href="#example-content-6"></a>
@ -1703,20 +1853,38 @@
</div>
<div class="section level2">
<h2 id="example_isolates_unclean-example-data-for-practice">
<code>example_isolates_unclean</code>: Example Data for Practice<a class="anchor" aria-label="anchor" href="#example_isolates_unclean-example-data-for-practice"></a>
<code>example_isolates_unclean</code>: Example Data for
Practice<a class="anchor" aria-label="anchor" href="#example_isolates_unclean-example-data-for-practice"></a>
</h2>
<p>A data set with 3,000 rows and 8 columns, containing the following column names:<br><em>patient_id</em>, <em>hospital</em>, <em>date</em>, <em>bacteria</em>, <em>AMX</em>, <em>AMC</em>, <em>CIP</em> and <em>GEN</em>.</p>
<p>This data set is in R available as <code>example_isolates_unclean</code>, after you load the <code>AMR</code> package.</p>
<p>It was last updated on 22 October 2022 20:03:38 UTC. Find more info about the structure of this data set <a href="https://msberends.github.io/AMR/reference/example_isolates_unclean.html">here</a>.</p>
<p>A data set with 3,000 rows and 8 columns, containing the following
column names:<br><em>patient_id</em>, <em>hospital</em>, <em>date</em>,
<em>bacteria</em>, <em>AMX</em>, <em>AMC</em>, <em>CIP</em> and
<em>GEN</em>.</p>
<p>This data set is in R available as
<code>example_isolates_unclean</code>, after you load the
<code>AMR</code> package.</p>
<p>It was last updated on 29 October 2022 12:16:58 UTC. Find more info
about the structure of this data set <a href="https://msberends.github.io/AMR/reference/example_isolates_unclean.html">here</a>.</p>
<div class="section level3">
<h3 id="source-7">Source<a class="anchor" aria-label="anchor" href="#source-7"></a>
</h3>
<p>This data set contains randomised fictitious data, but reflects reality and can be used to practise AMR data analysis.</p>
<p>This data set contains randomised fictitious data, but reflects
reality and can be used to practise AMR data analysis.</p>
</div>
<div class="section level3">
<h3 id="example-content-7">Example content<a class="anchor" aria-label="anchor" href="#example-content-7"></a>
</h3>
<table class="table">
<table style="width:100%;" class="table">
<colgroup>
<col width="17%">
<col width="14%">
<col width="17%">
<col width="21%">
<col width="7%">
<col width="7%">
<col width="7%">
<col width="7%">
</colgroup>
<thead><tr class="header">
<th align="center">patient_id</th>
<th align="center">hospital</th>

2
articles/index.html
View File

@ -10,7 +10,7 @@
<a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9033</small>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9034</small>
<button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">

63
articles/resistance_predict.html
View File

@ -38,7 +38,7 @@
<a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9033</small>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9034</small>
<button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@ -171,8 +171,13 @@
<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>. 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>. 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><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>
<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>
@ -184,7 +189,11 @@
<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="downlit sourceCode r">
<code class="sourceCode R"><span><span class="co"># resistance prediction of piperacillin/tazobactam (TZP):</span></span>
@ -203,9 +212,15 @@
<span> col_ab <span class="op">=</span> <span class="st">"TZP"</span>,</span>
<span> model <span class="op">=</span> <span class="st">"binomial"</span></span>
<span> <span class="op">)</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>
<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>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>
<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="cb3"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="va">predict_TZP</span></span>
<span><span class="co"># <span style="color: #949494;"># A tibble: 31 × 7</span></span></span>
@ -222,12 +237,18 @@
<span><span class="co"># <span style="color: #BCBCBC;"> 9</span> <span style="text-decoration: underline;">2</span>010 0.056<span style="text-decoration: underline;">6</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> 53 0.056<span style="text-decoration: underline;">6</span> 0.116 </span></span>
<span><span class="co"># <span style="color: #BCBCBC;">10</span> <span style="text-decoration: underline;">2</span>011 0.183 <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> 93 0.183 0.127 </span></span>
<span><span class="co"># <span style="color: #949494;"># … with 21 more rows</span></span></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="cb4"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><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></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="cb5"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="fu"><a href="../reference/resistance_predict.html">ggplot_rsi_predict</a></span><span class="op">(</span><span class="va">predict_TZP</span><span class="op">)</span></span></code></pre></div>
<p><img src="resistance_predict_files/figure-html/unnamed-chunk-5-1.png" width="720"></p>
@ -239,7 +260,9 @@
<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="cb7"><pre class="downlit sourceCode r">
<code class="sourceCode R"><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>
<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></span>
@ -247,8 +270,13 @@
<span> <span class="fu"><a href="../reference/resistance_predict.html">ggplot_rsi_predict</a></span><span class="op">(</span><span class="op">)</span></span>
<span><span class="co"># ℹ Using column 'date' as input for `col_date`.</span></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 remain very low.</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 remain
very low.</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>
@ -264,7 +292,8 @@
<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>
@ -285,7 +314,8 @@
</tr>
</tbody>
</table>
<p>For the vancomycin resistance in Gram-positive bacteria, a linear model might be more appropriate:</p>
<p>For the vancomycin resistance in Gram-positive bacteria, a linear
model might be more appropriate:</p>
<div class="sourceCode" id="cb8"><pre class="downlit sourceCode r">
<code class="sourceCode R"><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>
<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></span>
@ -293,7 +323,8 @@
<span> <span class="fu"><a href="../reference/resistance_predict.html">ggplot_rsi_predict</a></span><span class="op">(</span><span class="op">)</span></span>
<span><span class="co"># ℹ Using column 'date' as input for `col_date`.</span></span></code></pre></div>
<p><img src="resistance_predict_files/figure-html/unnamed-chunk-7-1.png" width="720"></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="cb9"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="va">model</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/attributes.html" class="external-link">attributes</a></span><span class="op">(</span><span class="va">predict_TZP</span><span class="op">)</span><span class="op">$</span><span class="va">model</span></span>
<span></span>

104
articles/welcome_to_AMR.html
View File

@ -38,7 +38,7 @@
<a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9033</small>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9034</small>
<button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@ -168,36 +168,98 @@
<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 MDROs, 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 MDROs, 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>The <code>AMR</code> package is a <a href="https://msberends.github.io/AMR/#copyright">free and open-source</a> R package with <a href="https://en.wikipedia.org/wiki/Dependency_hell" class="external-link">zero dependencies</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>This work was published in the Journal of Statistical Software (Volume 104(3); <a href="https://doi.org/10.18637/jss.v104.i03" class="external-link">DOI 10.18637/jss.v104.i03</a>) and 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>).</p>
<p>After installing this package, R knows ~49,000 distinct microbial species and all ~570 antibiotic, antimycotic and antiviral drugs by name and code (including ATC, EARS-Net, ASIARS-Net, PubChem, LOINC and SNOMED CT), and knows all about valid R/SI and MIC values. The integral breakpoint guidelines from CLSI and EUCAST are included from the last 10 years. It supports and can read any data format, including WHONET data.</p>
<p>The <code>AMR</code> package is available in English, Chinese, Danish, Dutch, French, German, Greek, Italian, Japanese, Polish, Portuguese, Russian, Spanish, Swedish, Turkish and Ukrainian. 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>The <code>AMR</code> package is a <a href="https://msberends.github.io/AMR/#copyright">free and
open-source</a> R package with <a href="https://en.wikipedia.org/wiki/Dependency_hell" class="external-link">zero
dependencies</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>This work was published in the Journal of Statistical Software
(Volume 104(3); <a href="https://doi.org/10.18637/jss.v104.i03" class="external-link">DOI
10.18637/jss.v104.i03</a>) and 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>).</p>
<p>After installing this package, R knows ~49,000 distinct microbial
species and all ~570 antibiotic, antimycotic and antiviral drugs by name
and code (including ATC, EARS-Net, ASIARS-Net, PubChem, LOINC and SNOMED
CT), and knows all about valid R/SI and MIC values. The integral
breakpoint guidelines from CLSI and EUCAST are included from the last 10
years. It supports and can read any data format, including WHONET
data.</p>
<p>The <code>AMR</code> package is available in English, Chinese,
Danish, Dutch, French, German, Greek, Italian, Japanese, Polish,
Portuguese, Russian, Spanish, Swedish, Turkish and Ukrainian.
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 List of Prokaryotic names with Standing in Nomenclature (LPSN) and the Global Biodiversity Information Facility (GBIF)</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 List of Prokaryotic names
with Standing in Nomenclature (LPSN) and the Global Biodiversity
Information Facility (GBIF)</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>, and is being <a href="./news">actively and durably maintained</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>, and
is being <a href="./news">actively and durably maintained</a> by two
public healthcare organisations in the Netherlands.</p>
<hr>
<p><small> This AMR package for R is free, open-source software and licensed under the <a href="https://msberends.github.io/AMR/LICENSE-text.html">GNU General Public License v2.0 (GPL-2)</a>. These requirements are consequently legally binding: modifications must be released under the same license when distributing the package, changes made to the code must be documented, source code must be made available when the package is distributed, and a copy of the license and copyright notice must be included with the package. </small></p>
<p><small> This AMR package for R is free, open-source software and
licensed under the <a href="https://msberends.github.io/AMR/LICENSE-text.html">GNU General
Public License v2.0 (GPL-2)</a>. These requirements are consequently
legally binding: modifications must be released under the same license
when distributing the package, changes made to the code must be
documented, source code must be made available when the package is
distributed, and a copy of the license and copyright notice must be
included with the package. </small></p>
</main>
</div>