P281424/AMR
1
0
Fork 0
mirror of https://github.com/msberends/AMR.git synced 2025-09-11 04:09:39 +02:00
Code Releases Activity

Built site for AMR@2.1.1.9123: 08ddbaa

Browse Source
This commit is contained in:
github-actions
2025-01-15 15:25:08 +00:00
parent a49e633c9c
commit 71ff130527
88 changed files with 236 additions and 237 deletions
Download Patch File Download Diff File Expand all files Collapse all files

63
articles/AMR.html
View File

@@ -29,7 +29,7 @@
<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="">2.1.1.9122</small>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9123</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">
@@ -88,7 +88,7 @@
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 20 December 2024.</p>
generated on 15 January 2025.</p>
<div class="section level2">
<h2 id="introduction">Introduction<a class="anchor" aria-label="anchor" href="#introduction"></a>
</h2>
@@ -144,21 +144,21 @@ make the structure of your data generally look like this:</p>
</tr></thead>
<tbody>
<tr class="odd">
<td align="center">2024-12-20</td>
<td align="center">2025-01-15</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">2024-12-20</td>
<td align="center">2025-01-15</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">2024-12-20</td>
<td align="center">2025-01-15</td>
<td align="center">efgh</td>
<td align="center">Escherichia coli</td>
<td align="center">R</td>
@@ -177,11 +177,8 @@ creating beautiful plots in R.</p>
<p>We will also use the <code>cleaner</code> package, that can be used
for cleaning data and creating frequency tables.</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></code></pre></div>
<p>Error in get(paste0(generic, “.”, class), envir = get_method_env()) :
object ‘type_sum.accel’ not found</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://ggplot2.tidyverse.org" class="external-link">ggplot2</a></span><span class="op">)</span></span>
<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>
<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>
<span><span class="co"># (if not yet installed, install with:)</span></span>
@@ -189,7 +186,7 @@ object ‘type_sum.accel’ not found</p>
<p>The <code>AMR</code> package contains a data set
<code>example_isolates_unclean</code>, which might look data that users
have extracted from their laboratory systems:</p>
<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="va">example_isolates_unclean</span></span>
<span><span class="co">#&gt; <span style="color: #949494;"># A tibble: 3,000 × 8</span></span></span>
<span><span class="co">#&gt; patient_id hospital date bacteria AMX AMC CIP GEN </span></span>
@@ -222,7 +219,7 @@ included data were retrieved on 24 Jun 2024.</p>
<p>The codes of the AMR packages that come from <code><a href="../reference/as.mo.html">as.mo()</a></code> are
short, but still human readable. More importantly, <code><a href="../reference/as.mo.html">as.mo()</a></code>
supports all kinds of input:</p>
<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="fu"><a href="../reference/as.mo.html">as.mo</a></span><span class="op">(</span><span class="st">"Klebsiella pneumoniae"</span><span class="op">)</span></span>
<span><span class="co">#&gt; Class 'mo'</span></span>
<span><span class="co">#&gt; [1] B_KLBSL_PNMN</span></span>
@@ -242,7 +239,7 @@ retrieve taxonomic properties, such as the name, genus, species, family,
order, and even Gram-stain. They all start with <code>mo_</code> and
they use <code><a href="../reference/as.mo.html">as.mo()</a></code> internally, so that still any arbitrary
user input can be used:</p>
<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="fu"><a href="../reference/mo_property.html">mo_family</a></span><span class="op">(</span><span class="st">"K. pneumoniae"</span><span class="op">)</span></span>
<span><span class="co">#&gt; [1] "Enterobacteriaceae"</span></span>
<span><span class="fu"><a href="../reference/mo_property.html">mo_genus</a></span><span class="op">(</span><span class="st">"K. pneumoniae"</span><span class="op">)</span></span>
@@ -261,14 +258,14 @@ user input can be used:</p>
<span><span class="co">#&gt; [1] "1098101000112102" "446870005" "1098201000112108" "409801009" </span></span>
<span><span class="co">#&gt; [5] "56415008" "714315002" "713926009"</span></span></code></pre></div>
<p>Now we can thus clean our data:</p>
<div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="va">our_data</span><span class="op">$</span><span class="va">bacteria</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/as.mo.html">as.mo</a></span><span class="op">(</span><span class="va">our_data</span><span class="op">$</span><span class="va">bacteria</span>, info <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span>
<span><span class="co">#&gt; ℹ Microorganism translation was uncertain for four microorganisms. Run</span></span>
<span><span class="co">#&gt; mo_uncertainties() to review these uncertainties, or use</span></span>
<span><span class="co">#&gt; add_custom_microorganisms() to add custom entries.</span></span></code></pre></div>
<p>Apparently, there was some uncertainty about the translation to
taxonomic codes. Let’s check this:</p>
<div class="sourceCode" id="cb7"><pre class="downlit sourceCode r">
<div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="fu"><a href="../reference/as.mo.html">mo_uncertainties</a></span><span class="op">(</span><span class="op">)</span></span>
<span><span class="co">#&gt; Matching scores are based on the resemblance between the input and the full</span></span>
<span><span class="co">#&gt; taxonomic name, and the pathogenicity in humans. See ?mo_matching_score.</span></span>
@@ -325,10 +322,10 @@ diffusion values, read more about that on the <code><a href="../reference/as.sir
page.</p>
<p>For now, we will just clean the SIR columns in our data using
dplyr:</p>
<div class="sourceCode" id="cb8"><pre class="downlit sourceCode r">
<div class="sourceCode" id="cb7"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="co"># method 1, be explicit about the columns:</span></span>
<span><span class="va">our_data</span> <span class="op">&lt;-</span> <span class="va">our_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/mutate_all.html" class="external-link">mutate_at</a></span><span class="op">(</span><span class="fu"><a href="https://ggplot2.tidyverse.org/reference/vars.html" class="external-link">vars</a></span><span class="op">(</span><span class="va">AMX</span><span class="op">:</span><span class="va">GEN</span><span class="op">)</span>, <span class="va">as.sir</span><span class="op">)</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">AMX</span><span class="op">:</span><span class="va">GEN</span><span class="op">)</span>, <span class="va">as.sir</span><span class="op">)</span></span>
<span></span>
<span><span class="co"># method 2, let the AMR package determine the eligible columns</span></span>
<span><span class="va">our_data</span> <span class="op">&lt;-</span> <span class="va">our_data</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
@@ -393,7 +390,7 @@ analysis, but the default phenotype-based method is in any case the
method to properly correct for most duplicate isolates. Read more about
the methods on the <code><a href="../reference/first_isolate.html">first_isolate()</a></code> page.</p>
<p>The outcome of the function can easily be added to our data:</p>
<div class="sourceCode" id="cb9"><pre class="downlit sourceCode r">
<div class="sourceCode" id="cb8"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="va">our_data</span> <span class="op">&lt;-</span> <span class="va">our_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/mutate.html" class="external-link">mutate</a></span><span class="op">(</span>first <span class="op">=</span> <span class="fu"><a href="../reference/first_isolate.html">first_isolate</a></span><span class="op">(</span>info <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span><span class="op">)</span></span>
<span><span class="co">#&gt; ℹ Determining first isolates using an episode length of 365 days</span></span>
@@ -407,16 +404,16 @@ the methods on the <code><a href="../reference/first_isolate.html">first_isolate
<p>So only 91% is suitable for resistance analysis! We can now filter on
it with the <code><a href="https://dplyr.tidyverse.org/reference/filter.html" class="external-link">filter()</a></code> function, also from the
<code>dplyr</code> package:</p>
<div class="sourceCode" id="cb10"><pre class="downlit sourceCode r">
<div class="sourceCode" id="cb9"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="va">our_data_1st</span> <span class="op">&lt;-</span> <span class="va">our_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/filter.html" class="external-link">filter</a></span><span class="op">(</span><span class="va">first</span> <span class="op">==</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div>
<p>For future use, the above two syntaxes can be shortened:</p>
<div class="sourceCode" id="cb11"><pre class="downlit sourceCode r">
<div class="sourceCode" id="cb10"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="va">our_data_1st</span> <span class="op">&lt;-</span> <span class="va">our_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="../reference/first_isolate.html">filter_first_isolate</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
<p>So we end up with 2 724 isolates for analysis. Now our data looks
like:</p>
<div class="sourceCode" id="cb12"><pre class="downlit sourceCode r">
<div class="sourceCode" id="cb11"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="va">our_data_1st</span></span>
<span><span class="co">#&gt; <span style="color: #949494;"># A tibble: 2,724 × 9</span></span></span>
<span><span class="co">#&gt; patient_id hospital date bacteria AMX AMC CIP GEN first</span></span>
@@ -441,7 +438,7 @@ like:</p>
<p>The base R <code><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary()</a></code> function gives a good first
impression, as it comes with support for the new <code>mo</code> and
<code>sir</code> classes that we now have in our data set:</p>
<div class="sourceCode" id="cb13"><pre class="downlit sourceCode r">
<div class="sourceCode" id="cb12"><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">our_data_1st</span><span class="op">)</span></span>
<span><span class="co">#&gt; patient_id hospital date </span></span>
<span><span class="co">#&gt; Length:2724 Length:2724 Min. :2011-01-01 </span></span>
@@ -490,7 +487,7 @@ impression, as it comes with support for the new <code>mo</code> and
<p>To just get an idea how the species are distributed, create a
frequency table with <code><a href="../reference/count.html">count()</a></code> based on the name of the
microorganisms:</p>
<div class="sourceCode" id="cb14"><pre class="downlit sourceCode r">
<div class="sourceCode" id="cb13"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="va">our_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="../reference/count.html">count</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">bacteria</span><span class="op">)</span>, sort <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span>
<span><span class="co">#&gt; <span style="color: #949494;"># A tibble: 4 × 2</span></span></span>
@@ -517,7 +514,7 @@ microorganisms:</p>
<p>Using so-called antibiotic class selectors, you can select or filter
columns based on the antibiotic class that your antibiotic results are
in:</p>
<div class="sourceCode" id="cb15"><pre class="downlit sourceCode r">
<div class="sourceCode" id="cb14"><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/select.html" class="external-link">select</a></span><span class="op">(</span><span class="va">date</span>, <span class="fu"><a href="../reference/antibiotic_class_selectors.html">aminoglycosides</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span>
<span><span class="co">#&gt; ℹ For aminoglycosides() using column 'GEN' (gentamicin)</span></span>
@@ -662,7 +659,7 @@ failure</li>
function to create any of the above antibiogram types. For starters,
this is what the included <code>example_isolates</code> data set looks
like:</p>
<div class="sourceCode" id="cb16"><pre class="downlit sourceCode r">
<div class="sourceCode" id="cb15"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="va">example_isolates</span></span>
<span><span class="co">#&gt; <span style="color: #949494;"># A tibble: 2,000 × 46</span></span></span>
<span><span class="co">#&gt; date patient age gender ward mo PEN OXA FLC AMX </span></span>
@@ -691,7 +688,7 @@ like:</p>
should be used. The <code>antibiotics</code> argument in the
<code><a href="../reference/antibiogram.html">antibiogram()</a></code> function supports any (combination) of the
previously mentioned antibiotic class selectors:</p>
<div class="sourceCode" id="cb17"><pre class="downlit sourceCode r">
<div class="sourceCode" id="cb16"><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="fu"><a href="../reference/antibiotic_class_selectors.html">aminoglycosides</a></span><span class="op">(</span><span class="op">)</span>, <span class="fu"><a href="../reference/antibiotic_class_selectors.html">carbapenems</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
<span><span class="co">#&gt; ℹ The function aminoglycosides() should be used inside a dplyr verb or</span></span>
@@ -835,7 +832,7 @@ Chinese, Czech, Danish, Dutch, Finnish, French, German, Greek, Italian,
Japanese, Norwegian, Polish, Portuguese, Romanian, Russian, Spanish,
Swedish, Turkish, or Ukrainian. In this next example, we force the
language to be Spanish using the <code>language</code> argument:</p>
<div class="sourceCode" id="cb18"><pre class="downlit sourceCode r">
<div class="sourceCode" id="cb17"><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> mo_transform <span class="op">=</span> <span class="st">"gramstain"</span>,</span>
<span> antibiotics <span class="op">=</span> <span class="fu"><a href="../reference/antibiotic_class_selectors.html">aminoglycosides</a></span><span class="op">(</span><span class="op">)</span>,</span>
@@ -889,7 +886,7 @@ language to be Spanish using the <code>language</code> argument:</p>
</h4>
<p>To create a combined antibiogram, use antibiotic codes or names with
a plus <code>+</code> character like this:</p>
<div class="sourceCode" id="cb19"><pre class="downlit sourceCode r">
<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>
<table class="table">
@@ -969,7 +966,7 @@ a plus <code>+</code> character like this:</p>
<p>To create a syndromic antibiogram, the <code>syndromic_group</code>
argument must be used. This can be any column in the data, or e.g. an
<code><a href="https://rdrr.io/r/base/ifelse.html" class="external-link">ifelse()</a></code> with calculations based on certain columns:</p>
<div class="sourceCode" id="cb20"><pre class="downlit sourceCode r">
<div class="sourceCode" id="cb19"><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="fu"><a href="../reference/antibiotic_class_selectors.html">aminoglycosides</a></span><span class="op">(</span><span class="op">)</span>, <span class="fu"><a href="../reference/antibiotic_class_selectors.html">carbapenems</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span>,</span>
<span> syndromic_group <span class="op">=</span> <span class="st">"ward"</span><span class="op">)</span></span>
@@ -1169,7 +1166,7 @@ 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>
<div class="sourceCode" id="cb21"><pre class="downlit sourceCode r">
<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>
@@ -1237,7 +1234,7 @@ 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="cb22"><pre class="downlit sourceCode r">
<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>
<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
@@ -1266,12 +1263,12 @@ 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="cb23"><pre class="downlit sourceCode r">
<div class="sourceCode" id="cb22"><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="cb24"><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>
<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>