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@ -31,7 +31,7 @@
<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="">2.1.1.9154</small>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9156</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">
@ -90,7 +90,7 @@
website update since they are based on randomly created values and the
page was written in <a href="https://rmarkdown.rstudio.com/" class="external-link">R
Markdown</a>. However, the methodology remains unchanged. This page was
generated on 22 February 2025.</p>
generated on 23 February 2025.</p>
<div class="section level2">
<h2 id="introduction">Introduction<a class="anchor" aria-label="anchor" href="#introduction"></a>
</h2>
@ -146,21 +146,21 @@ make the structure of your data generally look like this:</p>
</tr></thead>
<tbody>
<tr class="odd">
<td align="center">2025-02-22</td>
<td align="center">2025-02-23</td>
<td align="center">abcd</td>
<td align="center">Escherichia coli</td>
<td align="center">S</td>
<td align="center">S</td>
</tr>
<tr class="even">
<td align="center">2025-02-22</td>
<td align="center">2025-02-23</td>
<td align="center">abcd</td>
<td align="center">Escherichia coli</td>
<td align="center">S</td>
<td align="center">R</td>
</tr>
<tr class="odd">
<td align="center">2025-02-22</td>
<td align="center">2025-02-23</td>
<td align="center">efgh</td>
<td align="center">Escherichia coli</td>
<td align="center">R</td>
@ -858,20 +858,16 @@ language to be Spanish using the <code>language</code> argument:</p>
<p>To create a combined antibiogram, use antibiotic codes or names with
a plus <code>+</code> character like this:</p>
<div class="sourceCode" id="cb18"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="fu"><a href="../reference/antibiogram.html">antibiogram</a></span><span class="op">(</span><span class="va">example_isolates</span>,</span>
<span> antibiotics <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"TZP"</span>, <span class="st">"TZP+TOB"</span>, <span class="st">"TZP+GEN"</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
<code class="sourceCode R"><span><span class="va">combined_ab</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/antibiogram.html">antibiogram</a></span><span class="op">(</span><span class="va">example_isolates</span>,</span>
<span> antibiotics <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"TZP"</span>, <span class="st">"TZP+TOB"</span>, <span class="st">"TZP+GEN"</span><span class="op">)</span>,</span>
<span> ab_transform <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span></span>
<span><span class="va">combined_ab</span></span></code></pre></div>
<table class="table">
<colgroup>
<col width="14%">
<col width="20%">
<col width="32%">
<col width="32%">
</colgroup>
<thead><tr class="header">
<th align="left">Pathogen</th>
<th align="left">Piperacillin/tazobactam</th>
<th align="left">Piperacillin/tazobactam + Gentamicin</th>
<th align="left">Piperacillin/tazobactam + Tobramycin</th>
<th align="left">TZP</th>
<th align="left">TZP + GEN</th>
<th align="left">TZP + TOB</th>
</tr></thead>
<tbody>
<tr class="odd">
@ -1112,72 +1108,109 @@ argument must be used. This can be any column in the data, or e.g. an
<div class="section level4">
<h4 id="weighted-incidence-syndromic-combination-antibiogram-wisca">Weighted-Incidence Syndromic Combination Antibiogram (WISCA)<a class="anchor" aria-label="anchor" href="#weighted-incidence-syndromic-combination-antibiogram-wisca"></a>
</h4>
<p>To create a WISCA, you must state combination therapy in the
<code>antibiotics</code> argument (similar to the Combination
Antibiogram), define a syndromic group with the
<code>syndromic_group</code> argument (similar to the Syndromic
Antibiogram) in which cases are predefined based on clinical or
demographic characteristics (e.g., endocarditis in 75+ females). This
next example is a simplification without clinical characteristics, but
just gives an idea of how a WISCA can be created:</p>
<p>To create a <strong>Weighted-Incidence Syndromic Combination
Antibiogram (WISCA)</strong>, simply set <code>wisca = TRUE</code> in
the <code><a href="../reference/antibiogram.html">antibiogram()</a></code> function, or use the dedicated
<code><a href="../reference/antibiogram.html">wisca()</a></code> function. Unlike traditional antibiograms, WISCA
provides syndrome-based susceptibility estimates, weighted by pathogen
incidence and antimicrobial susceptibility patterns.</p>
<div class="sourceCode" id="cb20"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="va">wisca</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/antibiogram.html">antibiogram</a></span><span class="op">(</span><span class="va">example_isolates</span>,</span>
<span> antibiotics <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"AMC"</span>, <span class="st">"AMC+CIP"</span>, <span class="st">"TZP"</span>, <span class="st">"TZP+TOB"</span><span class="op">)</span>,</span>
<span> mo_transform <span class="op">=</span> <span class="st">"gramstain"</span>,</span>
<span> minimum <span class="op">=</span> <span class="fl">10</span>, <span class="co"># this should be &gt;= 30, but now just as example</span></span>
<span> syndromic_group <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/ifelse.html" class="external-link">ifelse</a></span><span class="op">(</span><span class="va">example_isolates</span><span class="op">$</span><span class="va">age</span> <span class="op">&gt;=</span> <span class="fl">65</span> <span class="op">&amp;</span></span>
<span> <span class="va">example_isolates</span><span class="op">$</span><span class="va">gender</span> <span class="op">==</span> <span class="st">"M"</span>,</span>
<span> <span class="st">"WISCA Group 1"</span>, <span class="st">"WISCA Group 2"</span><span class="op">)</span><span class="op">)</span></span>
<span><span class="va">wisca</span></span></code></pre></div>
<table class="table">
<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/antibiogram.html">wisca</a></span><span class="op">(</span>antibiotics <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"TZP"</span>, <span class="st">"TZP+TOB"</span>, <span class="st">"TZP+GEN"</span><span class="op">)</span>,</span>
<span> minimum <span class="op">=</span> <span class="fl">10</span><span class="op">)</span> <span class="co"># Recommended threshold: ≥30</span></span></code></pre></div>
<table style="width:100%;" class="table">
<colgroup>
<col width="9%">
<col width="8%">
<col width="17%">
<col width="26%">
<col width="14%">
<col width="22%">
<col width="24%">
<col width="37%">
<col width="37%">
</colgroup>
<thead><tr class="header">
<th align="left">Syndromic Group</th>
<th align="left">Pathogen</th>
<th align="left">Amoxicillin/clavulanic acid</th>
<th align="left">Amoxicillin/clavulanic acid + Ciprofloxacin</th>
<th align="left">Piperacillin/tazobactam</th>
<th align="left">Piperacillin/tazobactam + Gentamicin</th>
<th align="left">Piperacillin/tazobactam + Tobramycin</th>
</tr></thead>
<tbody><tr class="odd">
<td align="left">73.7% (65.8-81.1%)</td>
<td align="left">97% (94.2-98.7%)</td>
<td align="left">92.4% (85.9-96.6%)</td>
</tr></tbody>
</table>
<p>WISCA uses a <strong>Bayesian decision model</strong> to integrate
data from multiple pathogens, improving empirical therapy guidance,
especially for low-incidence infections. It is
<strong>pathogen-agnostic</strong>, meaning results are syndrome-based
rather than stratified by microorganism.</p>
<p>For reliable results, ensure your data includes <strong>only first
isolates</strong> (use <code><a href="../reference/first_isolate.html">first_isolate()</a></code>) and consider
filtering for <strong>the top <em>n</em> species</strong> (use
<code><a href="../reference/top_n_microorganisms.html">top_n_microorganisms()</a></code>), as WISCA outcomes are most
meaningful when based on robust incidence estimates.</p>
<p>For <strong>patient- or syndrome-specific WISCA</strong>, run the
function on a grouped <code>tibble</code>, i.e., using
<code><a href="https://dplyr.tidyverse.org/reference/group_by.html" class="external-link">group_by()</a></code> first:</p>
<div class="sourceCode" id="cb21"><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/top_n_microorganisms.html">top_n_microorganisms</a></span><span class="op">(</span>n <span class="op">=</span> <span class="fl">10</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://dplyr.tidyverse.org/reference/group_by.html" class="external-link">group_by</a></span><span class="op">(</span>age_group <span class="op">=</span> <span class="fu"><a href="../reference/age_groups.html">age_groups</a></span><span class="op">(</span><span class="va">age</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="fl">25</span>, <span class="fl">50</span>, <span class="fl">75</span><span class="op">)</span><span class="op">)</span>,</span>
<span> <span class="va">gender</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="../reference/antibiogram.html">wisca</a></span><span class="op">(</span>antibiotics <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"TZP"</span>, <span class="st">"TZP+TOB"</span>, <span class="st">"TZP+GEN"</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
<table style="width:100%;" class="table">
<colgroup>
<col width="8%">
<col width="6%">
<col width="32%">
<col width="32%">
<col width="20%">
</colgroup>
<thead><tr class="header">
<th align="left">age_group</th>
<th align="left">gender</th>
<th align="left">Piperacillin/tazobactam + Gentamicin</th>
<th align="left">Piperacillin/tazobactam + Tobramycin</th>
<th align="left">Piperacillin/tazobactam</th>
</tr></thead>
<tbody>
<tr class="odd">
<td align="left">WISCA Group 1</td>
<td align="left">Gram-negative</td>
<td align="left">76% (70-81%)</td>
<td align="left">95% (92-97%)</td>
<td align="left">89% (84-92%)</td>
<td align="left">99% (96-100%)</td>
<td align="left">25-49</td>
<td align="left">F</td>
<td align="left">94.4% (79.6-99.9%)</td>
<td align="left">94.5% (79.6-99.8%)</td>
<td align="left"></td>
</tr>
<tr class="even">
<td align="left">WISCA Group 2</td>
<td align="left">Gram-negative</td>
<td align="left">76% (72-80%)</td>
<td align="left">98% (96-99%)</td>
<td align="left">88% (84-91%)</td>
<td align="left">98% (96-99%)</td>
<td align="left">25-49</td>
<td align="left">M</td>
<td align="left">96.1% (85.4-99.9%)</td>
<td align="left"></td>
<td align="left"></td>
</tr>
<tr class="odd">
<td align="left">WISCA Group 1</td>
<td align="left">Gram-positive</td>
<td align="left">76% (72-80%)</td>
<td align="left">89% (85-92%)</td>
<td align="left">81% (73-88%)</td>
<td align="left">95% (91-98%)</td>
<td align="left">50-74</td>
<td align="left">F</td>
<td align="left">97.1% (91.2-99.7%)</td>
<td align="left">97.7% (91.9-100%)</td>
<td align="left">93.6% (87.4-97.5%)</td>
</tr>
<tr class="even">
<td align="left">WISCA Group 2</td>
<td align="left">Gram-positive</td>
<td align="left">76% (73-79%)</td>
<td align="left">89% (86-91%)</td>
<td align="left">88% (83-92%)</td>
<td align="left">95% (92-97%)</td>
<td align="left">50-74</td>
<td align="left">M</td>
<td align="left">96.7% (90.7-99.5%)</td>
<td align="left">97% (91-99.8%)</td>
<td align="left">92.5% (84.3-97.4%)</td>
</tr>
<tr class="odd">
<td align="left">75+</td>
<td align="left">F</td>
<td align="left">97.6% (91.7-99.9%)</td>
<td align="left">95.7% (88.2-99.2%)</td>
<td align="left">92.1% (85.8-96.4%)</td>
</tr>
<tr class="even">
<td align="left">75+</td>
<td align="left">M</td>
<td align="left">97.2% (91.5-99.7%)</td>
<td align="left">97.1% (89.6-99.9%)</td>
<td align="left">96.5% (91.3-99.5%)</td>
</tr>
</tbody>
</table>
@ -1188,8 +1221,8 @@ just gives an idea of how a WISCA can be created:</p>
<p>Antibiograms can be plotted using <code><a href="https://ggplot2.tidyverse.org/reference/autoplot.html" class="external-link">autoplot()</a></code> from the
<code>ggplot2</code> packages, since this <code>AMR</code> package
provides an extension to that function:</p>
<div class="sourceCode" id="cb21"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="fu"><a href="https://ggplot2.tidyverse.org/reference/autoplot.html" class="external-link">autoplot</a></span><span class="op">(</span><span class="va">wisca</span><span class="op">)</span></span></code></pre></div>
<div class="sourceCode" id="cb22"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="fu"><a href="https://ggplot2.tidyverse.org/reference/autoplot.html" class="external-link">autoplot</a></span><span class="op">(</span><span class="va">combined_ab</span><span class="op">)</span></span></code></pre></div>
<p><img src="AMR_files/figure-html/unnamed-chunk-10-1.png" width="720"></p>
<p>To calculate antimicrobial resistance in a more sensible way, also by
correcting for too few results, we use the <code><a href="../reference/proportion.html">resistance()</a></code> and
@ -1217,12 +1250,12 @@ proportion of R (<code><a href="../reference/proportion.html">proportion_R()</a>
I (<code><a href="../reference/proportion.html">proportion_SI()</a></code>, equal to
<code><a href="../reference/proportion.html">susceptibility()</a></code>). These functions can be used on their
own:</p>
<div class="sourceCode" id="cb22"><pre class="downlit sourceCode r">
<div class="sourceCode" id="cb23"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="va">our_data_1st</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span> <span class="fu"><a href="../reference/proportion.html">resistance</a></span><span class="op">(</span><span class="va">AMX</span><span class="op">)</span></span>
<span><span class="co">#&gt; [1] 0.4203377</span></span></code></pre></div>
<p>Or can be used in conjunction with <code><a href="https://dplyr.tidyverse.org/reference/group_by.html" class="external-link">group_by()</a></code> and
<code><a href="https://dplyr.tidyverse.org/reference/summarise.html" class="external-link">summarise()</a></code>, both from the <code>dplyr</code> package:</p>
<div class="sourceCode" id="cb23"><pre class="downlit sourceCode r">
<div class="sourceCode" id="cb24"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="va">our_data_1st</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">hospital</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://dplyr.tidyverse.org/reference/summarise.html" class="external-link">summarise</a></span><span class="op">(</span>amoxicillin <span class="op">=</span> <span class="fu"><a href="../reference/proportion.html">resistance</a></span><span class="op">(</span><span class="va">AMX</span><span class="op">)</span><span class="op">)</span></span>
@ -1232,8 +1265,81 @@ own:</p>
<span><span class="co">#&gt; <span style="color: #BCBCBC;">1</span> A 0.340</span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;">2</span> B 0.551</span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;">3</span> C 0.370</span></span></code></pre></div>
</div>
<div class="section level3">
<h3 id="interpreting-mic-and-disk-diffusion-values">Interpreting MIC and Disk Diffusion Values<a class="anchor" aria-label="anchor" href="#interpreting-mic-and-disk-diffusion-values"></a>
</h3>
<p>Minimal inhibitory concentration (MIC) values and disk diffusion
diameters can be interpreted into clinical breakpoints (SIR) using
<code><a href="../reference/as.sir.html">as.sir()</a></code>. Here’s an example with randomly generated MIC
values for <em>Klebsiella pneumoniae</em> and ciprofloxacin:</p>
<div class="sourceCode" id="cb25"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/Random.html" class="external-link">set.seed</a></span><span class="op">(</span><span class="fl">123</span><span class="op">)</span></span>
<span><span class="va">mic_values</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/random.html">random_mic</a></span><span class="op">(</span><span class="fl">100</span><span class="op">)</span></span>
<span><span class="va">sir_values</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/as.sir.html">as.sir</a></span><span class="op">(</span><span class="va">mic_values</span>, mo <span class="op">=</span> <span class="st">"K. pneumoniae"</span>, ab <span class="op">=</span> <span class="st">"cipro"</span>, guideline <span class="op">=</span> <span class="st">"EUCAST 2024"</span><span class="op">)</span></span>
<span><span class="co">#&gt; </span></span>
<span><span class="co">#&gt; <span style="color: #00BB00;">ℹ Run </span><span style="color: #00BB00; background-color: #EEEEEE;">sir_interpretation_history()</span><span style="color: #00BB00;"> afterwards to retrieve a logbook with</span></span></span>
<span><span class="co"><span style="color: #00BB00;">#&gt; all the details of the breakpoint interpretations.</span></span></span>
<span><span class="co">#&gt; </span></span>
<span><span class="co">#&gt; <span style="color: #0000BB;">Interpreting MIC values: '</span><span style="color: #0000BB; font-weight: bold;">cipro</span><span style="color: #0000BB;">' (CIP, ciprofloxacin), </span><span style="color: #0000BB; font-weight: bold;">EUCAST 2024</span><span style="color: #0000BB;">...</span><span style="color: #080808; background-color: #FFFF87;"> NOTE </span></span></span>
<span><span class="co">#&gt; <span style="color: #080808;"> • Multiple breakpoints available for </span><span style="color: #080808; font-weight: bold;">ciprofloxacin (CIP)</span><span style="color: #080808;"> in </span><span style="color: #080808; font-style: italic;">Klebsiella pneumoniae</span><span style="color: #080808;"> - assuming body site 'Non-meningitis'.</span></span></span>
<span></span>
<span><span class="va">my_data</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://tibble.tidyverse.org/reference/tibble.html" class="external-link">tibble</a></span><span class="op">(</span>MIC <span class="op">=</span> <span class="va">mic_values</span>, SIR <span class="op">=</span> <span class="va">sir_values</span><span class="op">)</span></span>
<span><span class="va">my_data</span></span>
<span><span class="co">#&gt; <span style="color: #949494;"># A tibble: 100 × 2</span></span></span>
<span><span class="co">#&gt; MIC SIR </span></span>
<span><span class="co">#&gt; <span style="color: #949494; font-style: italic;">&lt;mic&gt;</span> <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span></span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 1</span> 16<span style="color: #555555;">.000</span> <span style="color: #080808; background-color: #FFAFAF;"> R </span></span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 2</span> 0.005 <span style="color: #080808; background-color: #5FD7AF;"> S </span></span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 3</span> 1<span style="color: #555555;">.000</span> <span style="color: #080808; background-color: #FFAFAF;"> R </span></span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 4</span> <span style="color: #555555;">&gt;=</span>256<span style="color: #555555;">.000</span> <span style="color: #080808; background-color: #FFAFAF;"> R </span></span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 5</span> 2<span style="color: #555555;">.000</span> <span style="color: #080808; background-color: #FFAFAF;"> R </span></span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 6</span> 0.025 <span style="color: #080808; background-color: #5FD7AF;"> S </span></span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 7</span> 16<span style="color: #555555;">.000</span> <span style="color: #080808; background-color: #FFAFAF;"> R </span></span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 8</span> 0.025 <span style="color: #080808; background-color: #5FD7AF;"> S </span></span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 9</span> 0.5<span style="color: #555555;">00</span> <span style="color: #080808; background-color: #FFD787;"> I </span></span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;">10</span> 0.005 <span style="color: #080808; background-color: #5FD7AF;"> S </span></span></span>
<span><span class="co">#&gt; <span style="color: #949494;"># ℹ 90 more rows</span></span></span></code></pre></div>
<p>This allows direct interpretation according to EUCAST or CLSI
breakpoints, facilitating automated AMR data processing.</p>
</div>
<div class="section level3">
<h3 id="plotting-mic-and-sir-interpretations">Plotting MIC and SIR Interpretations<a class="anchor" aria-label="anchor" href="#plotting-mic-and-sir-interpretations"></a>
</h3>
<p>We can visualise MIC distributions and their SIR interpretations
using <code>ggplot2</code>, using the new <code><a href="../reference/plot.html">scale_y_mic()</a></code> for
the y-axis and <code><a href="../reference/plot.html">scale_colour_sir()</a></code> to colour-code SIR
categories.</p>
<div class="sourceCode" id="cb26"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="co"># add a group</span></span>
<span><span class="va">my_data</span><span class="op">$</span><span class="va">group</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/rep.html" class="external-link">rep</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"A"</span>, <span class="st">"B"</span>, <span class="st">"C"</span>, <span class="st">"D"</span><span class="op">)</span>, each <span class="op">=</span> <span class="fl">25</span><span class="op">)</span> </span>
<span></span>
<span><span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggplot.html" class="external-link">ggplot</a></span><span class="op">(</span><span class="va">my_data</span>,</span>
<span> <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/aes.html" class="external-link">aes</a></span><span class="op">(</span>x <span class="op">=</span> <span class="va">group</span>, y <span class="op">=</span> <span class="va">MIC</span>, colour <span class="op">=</span> <span class="va">SIR</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
<span> <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/geom_jitter.html" class="external-link">geom_jitter</a></span><span class="op">(</span>width <span class="op">=</span> <span class="fl">0.2</span>, size <span class="op">=</span> <span class="fl">2</span><span class="op">)</span> <span class="op">+</span></span>
<span> <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/geom_boxplot.html" class="external-link">geom_boxplot</a></span><span class="op">(</span>fill <span class="op">=</span> <span class="cn">NA</span>, colour <span class="op">=</span> <span class="st">"grey40"</span><span class="op">)</span> <span class="op">+</span></span>
<span> <span class="fu"><a href="../reference/plot.html">scale_y_mic</a></span><span class="op">(</span><span class="op">)</span> <span class="op">+</span></span>
<span> <span class="fu"><a href="../reference/plot.html">scale_colour_sir</a></span><span class="op">(</span><span class="op">)</span> <span class="op">+</span></span>
<span> <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/labs.html" class="external-link">labs</a></span><span class="op">(</span>title <span class="op">=</span> <span class="st">"MIC Distribution and SIR Interpretation"</span>,</span>
<span> x <span class="op">=</span> <span class="st">"Sample Groups"</span>,</span>
<span> y <span class="op">=</span> <span class="st">"MIC (mg/L)"</span><span class="op">)</span></span></code></pre></div>
<p><img src="AMR_files/figure-html/mic_plot-1.png" width="720"></p>
<p>This plot provides an intuitive way to assess susceptibility patterns
across different groups while incorporating clinical breakpoints.</p>
<p>For a more straightforward and less manual approach,
<code>ggplot2</code>’s function <code><a href="https://ggplot2.tidyverse.org/reference/autoplot.html" class="external-link">autoplot()</a></code> has been
extended by this package to directly plot MIC and disk diffusion
values:</p>
<div class="sourceCode" id="cb27"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="fu"><a href="https://ggplot2.tidyverse.org/reference/autoplot.html" class="external-link">autoplot</a></span><span class="op">(</span><span class="va">mic_values</span><span class="op">)</span></span></code></pre></div>
<p><img src="AMR_files/figure-html/autoplot-1.png" width="720"></p>
<div class="sourceCode" id="cb28"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span></span>
<span><span class="co"># by providing `mo` and `ab`, colours will indicate the SIR interpretation:</span></span>
<span><span class="fu"><a href="https://ggplot2.tidyverse.org/reference/autoplot.html" class="external-link">autoplot</a></span><span class="op">(</span><span class="va">mic_values</span>, mo <span class="op">=</span> <span class="st">"K. pneumoniae"</span>, ab <span class="op">=</span> <span class="st">"cipro"</span>, guideline <span class="op">=</span> <span class="st">"EUCAST 2024"</span><span class="op">)</span></span></code></pre></div>
<p><img src="AMR_files/figure-html/autoplot-2.png" width="720"></p>
<hr>
<p><em>Author: Dr. Matthijs Berends, 26th Feb 2023</em></p>
<p><em>Author: Dr. Matthijs Berends, 23rd Feb 2025</em></p>
</div>
</div>
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<h4 data-toc-skip class="date">22 February 2025</h4>
<h4 data-toc-skip class="date">23 February 2025</h4>
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