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@ -30,7 +30,7 @@
<a class="navbar-brand me-2" href="https://msberends.github.io/AMR/index.html">AMR (for R)</a>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9095</small>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9099</small>
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@ -7,7 +7,7 @@
<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="">2.1.1.9095</small>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9099</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">

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@ -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="">2.1.1.9095</small>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9099</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">
@ -87,7 +87,7 @@
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 15 October 2024.</p>
generated on 17 October 2024.</p>
<div class="section level2">
<h2 id="introduction">Introduction<a class="anchor" aria-label="anchor" href="#introduction"></a>
</h2>
@ -143,21 +143,21 @@ make the structure of your data generally look like this:</p>
</tr></thead>
<tbody>
<tr class="odd">
<td align="center">2024-10-15</td>
<td align="center">2024-10-17</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">2024-10-15</td>
<td align="center">2024-10-17</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">2024-10-15</td>
<td align="center">2024-10-17</td>
<td align="center">efgh</td>
<td align="center">Escherichia coli</td>
<td align="center">R</td>

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@ -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="">2.1.1.9095</small>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9099</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">
@ -90,53 +90,64 @@
antimicrobial resistance (AMR) analysis. It provides extensive features
for handling microbial and antimicrobial data. However, for those who
work primarily in Python, we now have a more intuitive option available:
the <code>AMR</code> Python package, which uses <code>rpy2</code>
internally. This package allows Python users to access all the functions
from the R <code>AMR</code> package without the need to set up
<code>rpy2</code> themselves. Since this Python package is not a true
‘port’ (which would require all R functions to be rewritten into
Python), R and the AMR R package are still required to be installed.
Yet, Python users can now easily work with AMR data directly through
Python code.</p>
<p>In this document, we explain how this works and provide simple
examples of using the <code>AMR</code> Python package.</p>
<div class="section level3">
<h3 id="how-it-works">How It Works<a class="anchor" aria-label="anchor" href="#how-it-works"></a>
</h3>
<p>The <code>AMR</code> Python package acts as a wrapper around the
functions in the <code>AMR</code> R package. The package simplifies the
process of calling R functions in Python, eliminating the need to
manually manage the <code>rpy2</code> setup, which Python uses
internally to be able to work with the R package. By just using
<code>import AMR</code>, Python users can directly use the functions
from the <code>AMR</code> R package as if they were native Python
functions.</p>
<p>Internally, <code>rpy2</code> is still being used, but all complexity
is hidden from the user. This approach keeps the Python code clean and
Pythonic, while still leveraging the full power of the R
<code>AMR</code> package.</p>
the <a href="https://pypi.org/project/AMR/" class="external-link"><code>AMR</code> Python
Package Index</a>.</p>
<p>This Python package is a wrapper round the <code>AMR</code> R
package. It uses the <code>rpy2</code> package internally. Despite the
need to have R installed, Python users can now easily work with AMR data
directly through Python code.</p>
</div>
<div class="section level2">
<h2 id="install">Install<a class="anchor" aria-label="anchor" href="#install"></a>
</h2>
<ol style="list-style-type: decimal">
<li>
<p>First make sure you have R installed. There is <strong>no need to
install the <code>AMR</code> R package</strong>, as it will be installed
automatically.</p>
<p>For Linux:</p>
<div class="sourceCode" id="cb1"><pre class="sourceCode bash"><code class="sourceCode bash"><span id="cb1-1"><a href="#cb1-1" tabindex="-1"></a><span class="co"># Ubuntu / Debian</span></span>
<span id="cb1-2"><a href="#cb1-2" tabindex="-1"></a><span class="fu">sudo</span> apt install r-base</span>
<span id="cb1-3"><a href="#cb1-3" tabindex="-1"></a><span class="co"># Fedora:</span></span>
<span id="cb1-4"><a href="#cb1-4" tabindex="-1"></a><span class="fu">sudo</span> dnf install R</span>
<span id="cb1-5"><a href="#cb1-5" tabindex="-1"></a><span class="co"># CentOS/RHEL</span></span>
<span id="cb1-6"><a href="#cb1-6" tabindex="-1"></a><span class="fu">sudo</span> yum install R</span></code></pre></div>
<p>For macOS (using <a href="https://brew.sh" class="external-link">Homebrew</a>):</p>
<div class="sourceCode" id="cb2"><pre class="sourceCode bash"><code class="sourceCode bash"><span id="cb2-1"><a href="#cb2-1" tabindex="-1"></a><span class="ex">brew</span> install r</span></code></pre></div>
<p>For Windows, visit the <a href="https://cran.r-project.org" class="external-link">CRAN
download page</a> to download and install R.</p>
</li>
<li>
<p>Since the Python package is available on the official <a href="https://pypi.org/project/AMR/" class="external-link">Python Package Index</a>, you can
just run:</p>
<div class="sourceCode" id="cb3"><pre class="sourceCode bash"><code class="sourceCode bash"><span id="cb3-1"><a href="#cb3-1" tabindex="-1"></a><span class="ex">pip</span> install AMR</span></code></pre></div>
</li>
</ol>
</div>
<div class="section level2">
<h2 id="examples-of-usage">Examples of Usage<a class="anchor" aria-label="anchor" href="#examples-of-usage"></a>
</h2>
<div class="section level3">
<h3 id="example-of-usage">Example of Usage<a class="anchor" aria-label="anchor" href="#example-of-usage"></a>
<h3 id="cleaning-taxonomy">Cleaning Taxonomy<a class="anchor" aria-label="anchor" href="#cleaning-taxonomy"></a>
</h3>
<p>Here’s an example that demonstrates how to clean microorganism and
drug names using the <code>AMR</code> Python package:</p>
<div class="sourceCode" id="cb1"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb1-1"><a href="#cb1-1" tabindex="-1"></a><span class="im">import</span> pandas <span class="im">as</span> pd</span>
<span id="cb1-2"><a href="#cb1-2" tabindex="-1"></a><span class="im">import</span> AMR</span>
<span id="cb1-3"><a href="#cb1-3" tabindex="-1"></a></span>
<span id="cb1-4"><a href="#cb1-4" tabindex="-1"></a><span class="co"># Sample data</span></span>
<span id="cb1-5"><a href="#cb1-5" tabindex="-1"></a>data <span class="op">=</span> {</span>
<span id="cb1-6"><a href="#cb1-6" tabindex="-1"></a> <span class="st">"MOs"</span>: [<span class="st">'E. coli'</span>, <span class="st">'ESCCOL'</span>, <span class="st">'esco'</span>, <span class="st">'Esche coli'</span>],</span>
<span id="cb1-7"><a href="#cb1-7" tabindex="-1"></a> <span class="st">"Drug"</span>: [<span class="st">'Cipro'</span>, <span class="st">'CIP'</span>, <span class="st">'J01MA02'</span>, <span class="st">'Ciproxin'</span>]</span>
<span id="cb1-8"><a href="#cb1-8" tabindex="-1"></a>}</span>
<span id="cb1-9"><a href="#cb1-9" tabindex="-1"></a>df <span class="op">=</span> pd.DataFrame(data)</span>
<span id="cb1-10"><a href="#cb1-10" tabindex="-1"></a></span>
<span id="cb1-11"><a href="#cb1-11" tabindex="-1"></a><span class="co"># Use AMR functions to clean microorganism and drug names</span></span>
<span id="cb1-12"><a href="#cb1-12" tabindex="-1"></a>df[<span class="st">'MO_clean'</span>] <span class="op">=</span> AMR.mo_name(df[<span class="st">'MOs'</span>])</span>
<span id="cb1-13"><a href="#cb1-13" tabindex="-1"></a>df[<span class="st">'Drug_clean'</span>] <span class="op">=</span> AMR.ab_name(df[<span class="st">'Drug'</span>])</span>
<span id="cb1-14"><a href="#cb1-14" tabindex="-1"></a></span>
<span id="cb1-15"><a href="#cb1-15" tabindex="-1"></a><span class="co"># Display the results</span></span>
<span id="cb1-16"><a href="#cb1-16" tabindex="-1"></a><span class="bu">print</span>(df)</span></code></pre></div>
<div class="sourceCode" id="cb4"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb4-1"><a href="#cb4-1" tabindex="-1"></a><span class="im">import</span> pandas <span class="im">as</span> pd</span>
<span id="cb4-2"><a href="#cb4-2" tabindex="-1"></a><span class="im">import</span> AMR</span>
<span id="cb4-3"><a href="#cb4-3" tabindex="-1"></a></span>
<span id="cb4-4"><a href="#cb4-4" tabindex="-1"></a><span class="co"># Sample data</span></span>
<span id="cb4-5"><a href="#cb4-5" tabindex="-1"></a>data <span class="op">=</span> {</span>
<span id="cb4-6"><a href="#cb4-6" tabindex="-1"></a> <span class="st">"MOs"</span>: [<span class="st">'E. coli'</span>, <span class="st">'ESCCOL'</span>, <span class="st">'esco'</span>, <span class="st">'Esche coli'</span>],</span>
<span id="cb4-7"><a href="#cb4-7" tabindex="-1"></a> <span class="st">"Drug"</span>: [<span class="st">'Cipro'</span>, <span class="st">'CIP'</span>, <span class="st">'J01MA02'</span>, <span class="st">'Ciproxin'</span>]</span>
<span id="cb4-8"><a href="#cb4-8" tabindex="-1"></a>}</span>
<span id="cb4-9"><a href="#cb4-9" tabindex="-1"></a>df <span class="op">=</span> pd.DataFrame(data)</span>
<span id="cb4-10"><a href="#cb4-10" tabindex="-1"></a></span>
<span id="cb4-11"><a href="#cb4-11" tabindex="-1"></a><span class="co"># Use AMR functions to clean microorganism and drug names</span></span>
<span id="cb4-12"><a href="#cb4-12" tabindex="-1"></a>df[<span class="st">'MO_clean'</span>] <span class="op">=</span> AMR.mo_name(df[<span class="st">'MOs'</span>])</span>
<span id="cb4-13"><a href="#cb4-13" tabindex="-1"></a>df[<span class="st">'Drug_clean'</span>] <span class="op">=</span> AMR.ab_name(df[<span class="st">'Drug'</span>])</span>
<span id="cb4-14"><a href="#cb4-14" tabindex="-1"></a></span>
<span id="cb4-15"><a href="#cb4-15" tabindex="-1"></a><span class="co"># Display the results</span></span>
<span id="cb4-16"><a href="#cb4-16" tabindex="-1"></a><span class="bu">print</span>(df)</span></code></pre></div>
<table class="table">
<thead><tr class="header">
<th>MOs</th>
@ -186,15 +197,16 @@ antimicrobial names. The different representations of ciprofloxacin
the standard name, “Ciprofloxacin”.</p></li>
</ul>
</div>
<div class="section level4">
<h4 id="taxonomic-data-sets-now-in-python">Taxonomic Data Sets Now in Python!<a class="anchor" aria-label="anchor" href="#taxonomic-data-sets-now-in-python"></a>
</h4>
</div>
<div class="section level3">
<h3 id="taxonomic-data-sets-now-in-python">Taxonomic Data Sets Now in Python!<a class="anchor" aria-label="anchor" href="#taxonomic-data-sets-now-in-python"></a>
</h3>
<p>As a Python user, you might like that the most important data sets of
the <code>AMR</code> R package, <code>microorganisms</code>,
<code>antibiotics</code>, <code>clinical_breakpoints</code>, and
<code>example_isolates</code>, are now available as regular Python data
frames:</p>
<div class="sourceCode" id="cb2"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb2-1"><a href="#cb2-1" tabindex="-1"></a>AMR.microorganisms</span></code></pre></div>
<div class="sourceCode" id="cb5"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb5-1"><a href="#cb5-1" tabindex="-1"></a>AMR.microorganisms</span></code></pre></div>
<table class="table">
<colgroup>
<col width="11%">
@ -329,7 +341,7 @@ frames:</p>
</tr>
</tbody>
</table>
<div class="sourceCode" id="cb3"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb3-1"><a href="#cb3-1" tabindex="-1"></a>AMR.antibiotics</span></code></pre></div>
<div class="sourceCode" id="cb6"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb6-1"><a href="#cb6-1" tabindex="-1"></a>AMR.antibiotics</span></code></pre></div>
<table style="width:100%;" class="table">
<colgroup>
<col width="4%">
@ -465,64 +477,26 @@ frames:</p>
</tbody>
</table>
</div>
</div>
</div>
<div class="section level2">
<h2 id="installation">Installation<a class="anchor" aria-label="anchor" href="#installation"></a>
</h2>
<p>To be able to use the <code>AMR</code> Python package, it is required
to install both R and the <code>AMR</code> R package.</p>
<div class="section level4">
<h4 id="preparation-install-r-and-amr-r-package">Preparation: Install R and <code>AMR</code> R package<a class="anchor" aria-label="anchor" href="#preparation-install-r-and-amr-r-package"></a>
</h4>
<p>For Linux and macOS, this is just:</p>
<div class="sourceCode" id="cb4"><pre class="sourceCode bash"><code class="sourceCode bash"><span id="cb4-1"><a href="#cb4-1" tabindex="-1"></a><span class="co"># Ubuntu / Debian</span></span>
<span id="cb4-2"><a href="#cb4-2" tabindex="-1"></a><span class="fu">sudo</span> apt install r-base <span class="kw">&amp;&amp;</span> <span class="ex">Rscript</span> <span class="at">-e</span> <span class="st">'install.packages("AMR")'</span></span>
<span id="cb4-3"><a href="#cb4-3" tabindex="-1"></a><span class="co"># Fedora:</span></span>
<span id="cb4-4"><a href="#cb4-4" tabindex="-1"></a><span class="fu">sudo</span> dnf install R <span class="kw">&amp;&amp;</span> <span class="ex">Rscript</span> <span class="at">-e</span> <span class="st">'install.packages("AMR")'</span></span>
<span id="cb4-5"><a href="#cb4-5" tabindex="-1"></a><span class="co"># CentOS/RHEL</span></span>
<span id="cb4-6"><a href="#cb4-6" tabindex="-1"></a><span class="fu">sudo</span> yum install R <span class="kw">&amp;&amp;</span> <span class="ex">Rscript</span> <span class="at">-e</span> <span class="st">'install.packages("AMR")'</span></span>
<span id="cb4-7"><a href="#cb4-7" tabindex="-1"></a><span class="co"># Arch Linux</span></span>
<span id="cb4-8"><a href="#cb4-8" tabindex="-1"></a><span class="fu">sudo</span> pacman <span class="at">-S</span> r <span class="kw">&amp;&amp;</span> <span class="ex">Rscript</span> <span class="at">-e</span> <span class="st">'install.packages("AMR")'</span></span>
<span id="cb4-9"><a href="#cb4-9" tabindex="-1"></a><span class="co"># macOS</span></span>
<span id="cb4-10"><a href="#cb4-10" tabindex="-1"></a><span class="ex">brew</span> install r <span class="kw">&amp;&amp;</span> <span class="ex">Rscript</span> <span class="at">-e</span> <span class="st">'install.packages("AMR")'</span></span></code></pre></div>
<p>For Windows, visit the <a href="https://cran.r-project.org" class="external-link">CRAN
download page</a> in install R, then afterwards install the ‘AMR’
package manually.</p>
</div>
<div class="section level4">
<h4 id="install-amr-python-package">Install <code>AMR</code> Python Package<a class="anchor" aria-label="anchor" href="#install-amr-python-package"></a>
</h4>
<p>Since the Python package is available on the official <a href="https://pypi.org/project/AMR/" class="external-link">Python Package Index</a>, you can
just run:</p>
<div class="sourceCode" id="cb5"><pre class="sourceCode bash"><code class="sourceCode bash"><span id="cb5-1"><a href="#cb5-1" tabindex="-1"></a><span class="ex">pip</span> install AMR</span></code></pre></div>
</div>
</div>
<div class="section level2">
<h2 id="working-with-amr-in-python">Working with <code>AMR</code> in Python<a class="anchor" aria-label="anchor" href="#working-with-amr-in-python"></a>
</h2>
<p>Now that we have everything set up, let’s walk through some practical
examples of using the <code>AMR</code> package within Python.</p>
<div class="section level3">
<h3 id="example-1-calculating-amr">Example 1: Calculating AMR<a class="anchor" aria-label="anchor" href="#example-1-calculating-amr"></a>
<h3 id="calculating-amr">Calculating AMR<a class="anchor" aria-label="anchor" href="#calculating-amr"></a>
</h3>
<div class="sourceCode" id="cb6"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb6-1"><a href="#cb6-1" tabindex="-1"></a><span class="im">import</span> AMR</span>
<span id="cb6-2"><a href="#cb6-2" tabindex="-1"></a><span class="im">import</span> pandas <span class="im">as</span> pd</span>
<span id="cb6-3"><a href="#cb6-3" tabindex="-1"></a></span>
<span id="cb6-4"><a href="#cb6-4" tabindex="-1"></a>df <span class="op">=</span> AMR.example_isolates</span>
<span id="cb6-5"><a href="#cb6-5" tabindex="-1"></a>result <span class="op">=</span> AMR.resistance(df[<span class="st">"AMX"</span>])</span>
<span id="cb6-6"><a href="#cb6-6" tabindex="-1"></a><span class="bu">print</span>(result)</span></code></pre></div>
<div class="sourceCode" id="cb7"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb7-1"><a href="#cb7-1" tabindex="-1"></a><span class="im">import</span> AMR</span>
<span id="cb7-2"><a href="#cb7-2" tabindex="-1"></a><span class="im">import</span> pandas <span class="im">as</span> pd</span>
<span id="cb7-3"><a href="#cb7-3" tabindex="-1"></a></span>
<span id="cb7-4"><a href="#cb7-4" tabindex="-1"></a>df <span class="op">=</span> AMR.example_isolates</span>
<span id="cb7-5"><a href="#cb7-5" tabindex="-1"></a>result <span class="op">=</span> AMR.resistance(df[<span class="st">"AMX"</span>])</span>
<span id="cb7-6"><a href="#cb7-6" tabindex="-1"></a><span class="bu">print</span>(result)</span></code></pre></div>
<pre><code>[0.59555556]</code></pre>
</div>
<div class="section level3">
<h3 id="example-2-generating-antibiograms">Example 2: Generating Antibiograms<a class="anchor" aria-label="anchor" href="#example-2-generating-antibiograms"></a>
<h3 id="generating-antibiograms">Generating Antibiograms<a class="anchor" aria-label="anchor" href="#generating-antibiograms"></a>
</h3>
<p>One of the core functions of the <code>AMR</code> package is
generating an antibiogram, a table that summarises the antimicrobial
susceptibility of bacterial isolates. Here’s how you can generate an
antibiogram from Python:</p>
<div class="sourceCode" id="cb8"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb8-1"><a href="#cb8-1" tabindex="-1"></a>result2a <span class="op">=</span> AMR.antibiogram(df[[<span class="st">"mo"</span>, <span class="st">"AMX"</span>, <span class="st">"CIP"</span>, <span class="st">"TZP"</span>]])</span>
<span id="cb8-2"><a href="#cb8-2" tabindex="-1"></a><span class="bu">print</span>(result2a)</span></code></pre></div>
<div class="sourceCode" id="cb9"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb9-1"><a href="#cb9-1" tabindex="-1"></a>result2a <span class="op">=</span> AMR.antibiogram(df[[<span class="st">"mo"</span>, <span class="st">"AMX"</span>, <span class="st">"CIP"</span>, <span class="st">"TZP"</span>]])</span>
<span id="cb9-2"><a href="#cb9-2" tabindex="-1"></a><span class="bu">print</span>(result2a)</span></code></pre></div>
<table class="table">
<colgroup>
<col width="22%">
@ -593,8 +567,8 @@ antibiogram from Python:</p>
</tr>
</tbody>
</table>
<div class="sourceCode" id="cb9"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb9-1"><a href="#cb9-1" tabindex="-1"></a>result2b <span class="op">=</span> AMR.antibiogram(df[[<span class="st">"mo"</span>, <span class="st">"AMX"</span>, <span class="st">"CIP"</span>, <span class="st">"TZP"</span>]], mo_transform <span class="op">=</span> <span class="st">"gramstain"</span>)</span>
<span id="cb9-2"><a href="#cb9-2" tabindex="-1"></a><span class="bu">print</span>(result2b)</span></code></pre></div>
<div class="sourceCode" id="cb10"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb10-1"><a href="#cb10-1" tabindex="-1"></a>result2b <span class="op">=</span> AMR.antibiogram(df[[<span class="st">"mo"</span>, <span class="st">"AMX"</span>, <span class="st">"CIP"</span>, <span class="st">"TZP"</span>]], mo_transform <span class="op">=</span> <span class="st">"gramstain"</span>)</span>
<span id="cb10-2"><a href="#cb10-2" tabindex="-1"></a><span class="bu">print</span>(result2b)</span></code></pre></div>
<table class="table">
<colgroup>
<col width="20%">
@ -637,11 +611,12 @@ provides a clean, easy-to-use interface for antimicrobial resistance
analysis. The examples provided above demonstrate how this can be
applied to typical workflows, such as standardising microorganism and
antimicrobial names or calculating resistance.</p>
<p>By using <code>import AMR</code>, you can seamlessly integrate the
robust features of the R <code>AMR</code> package into your Python
workflows. Whether you’re cleaning data or analysing resistance
patterns, the <code>AMR</code> Python package makes it easy to work with
AMR data in Python.</p>
<p>By just running <code>import AMR</code>, users can seamlessly
integrate the robust features of the R <code>AMR</code> package into
Python workflows.</p>
<p>Whether you’re cleaning data or analysing resistance patterns, the
<code>AMR</code> Python package makes it easy to work with AMR data in
Python.</p>
</div>
</main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
</nav></aside>

2
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articles/MDR.html
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articles/WHONET.html
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@ -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="">2.1.1.9095</small>
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4
articles/datasets.html
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@ -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="">2.1.1.9095</small>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9099</small>
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@ -76,7 +76,7 @@
<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">15 October 2024</h4>
<h4 data-toc-skip class="date">17 October 2024</h4>
<small class="dont-index">Source: <a href="https://github.com/msberends/AMR/blob/main/vignettes/datasets.Rmd" class="external-link"><code>vignettes/datasets.Rmd</code></a></small>
<div class="d-none name"><code>datasets.Rmd</code></div>

2
articles/index.html
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@ -7,7 +7,7 @@
<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="">2.1.1.9095</small>
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2
articles/resistance_predict.html
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@ -29,7 +29,7 @@
<a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
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@ -29,7 +29,7 @@
<a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
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2
authors.html
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@ -7,7 +7,7 @@
<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="">2.1.1.9095</small>
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6
deps/Fira_Code-0.4.9/font.css vendored
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@ -5,7 +5,7 @@
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}

4
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font-display: swap;
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2
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@ -32,7 +32,7 @@
<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="">2.1.1.9095</small>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9099</small>
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16
news/index.html
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@ -7,7 +7,7 @@
<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="">2.1.1.9095</small>
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<button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@ -47,18 +47,18 @@
</div>
<div class="section level2">
<h2 class="pkg-version" data-toc-text="2.1.1.9095" id="amr-2119095">AMR 2.1.1.9095<a class="anchor" aria-label="anchor" href="#amr-2119095"></a></h2>
<h2 class="pkg-version" data-toc-text="2.1.1.9099" id="amr-2119099">AMR 2.1.1.9099<a class="anchor" aria-label="anchor" href="#amr-2119099"></a></h2>
<p><em>(this beta version will eventually become v3.0. We’re happy to reach a new major milestone soon, which will be all about the new One Health support! Install this beta using <a href="https://msberends.github.io/AMR/#latest-development-version">the instructions here</a>.)</em></p>
<div class="section level5">
<h5 id="a-new-milestone-amr-v30-with-one-health-support--human--veterinary--environmental-2-1-1-9095">A New Milestone: AMR v3.0 with One Health Support (= Human + Veterinary + Environmental)<a class="anchor" aria-label="anchor" href="#a-new-milestone-amr-v30-with-one-health-support--human--veterinary--environmental-2-1-1-9095"></a></h5>
<h5 id="a-new-milestone-amr-v30-with-one-health-support--human--veterinary--environmental-2-1-1-9099">A New Milestone: AMR v3.0 with One Health Support (= Human + Veterinary + Environmental)<a class="anchor" aria-label="anchor" href="#a-new-milestone-amr-v30-with-one-health-support--human--veterinary--environmental-2-1-1-9099"></a></h5>
<p>This package now supports not only tools for AMR data analysis in clinical settings, but also for veterinary and environmental microbiology. This was made possible through a collaboration with the <a href="https://www.upei.ca/avc" class="external-link">University of Prince Edward Island’s Atlantic Veterinary College</a>, Canada. To celebrate this great improvement of the package, we also updated the package logo to reflect this change.</p>
</div>
<div class="section level3">
<h3 id="breaking-2-1-1-9095">Breaking<a class="anchor" aria-label="anchor" href="#breaking-2-1-1-9095"></a></h3>
<h3 id="breaking-2-1-1-9099">Breaking<a class="anchor" aria-label="anchor" href="#breaking-2-1-1-9099"></a></h3>
<ul><li>Removed all functions and references that used the deprecated <code>rsi</code> class, which were all replaced with their <code>sir</code> equivalents over a year ago</li>
</ul></div>
<div class="section level3">
<h3 id="new-2-1-1-9095">New<a class="anchor" aria-label="anchor" href="#new-2-1-1-9095"></a></h3>
<h3 id="new-2-1-1-9099">New<a class="anchor" aria-label="anchor" href="#new-2-1-1-9099"></a></h3>
<ul><li>
<strong>One Health implementation</strong>
<ul><li>Function <code><a href="../reference/as.sir.html">as.sir()</a></code> now has extensive support for veterinary breakpoints from CLSI. Use <code>breakpoint_type = "animal"</code> and set the <code>host</code> argument to a variable that contains animal species names.</li>
@ -99,7 +99,7 @@
</ul></li>
</ul></div>
<div class="section level3">
<h3 id="changed-2-1-1-9095">Changed<a class="anchor" aria-label="anchor" href="#changed-2-1-1-9095"></a></h3>
<h3 id="changed-2-1-1-9099">Changed<a class="anchor" aria-label="anchor" href="#changed-2-1-1-9099"></a></h3>
<ul><li>SIR interpretation
<ul><li>It is now possible to use column names for argument <code>ab</code>, <code>mo</code>, and <code>uti</code>: <code>as.sir(..., ab = "column1", mo = "column2", uti = "column3")</code>. This greatly improves the flexibility for users.</li>
<li>Users can now set their own criteria (using regular expressions) as to what should be considered S, I, R, SDD, and NI.</li>
@ -152,14 +152,14 @@
<li>Improved algorithm of <code><a href="../reference/first_isolate.html">first_isolate()</a></code> when using the phenotype-based method, to prioritise records with the highest availability of SIR values</li>
</ul></div>
<div class="section level3">
<h3 id="other-2-1-1-9095">Other<a class="anchor" aria-label="anchor" href="#other-2-1-1-9095"></a></h3>
<h3 id="other-2-1-1-9099">Other<a class="anchor" aria-label="anchor" href="#other-2-1-1-9099"></a></h3>
<ul><li>Greatly improved <code>vctrs</code> integration, a Tidyverse package working in the background for many Tidyverse functions. For users, this means that functions such as <code>dplyr</code>’s <code><a href="https://dplyr.tidyverse.org/reference/bind_rows.html" class="external-link">bind_rows()</a></code>, <code><a href="https://dplyr.tidyverse.org/reference/rowwise.html" class="external-link">rowwise()</a></code> and <code><a href="https://dplyr.tidyverse.org/reference/c_across.html" class="external-link">c_across()</a></code> are now supported for e.g. columns of class <code>mic</code>. Despite this, this <code>AMR</code> package is still zero-dependent on any other package, including <code>dplyr</code> and <code>vctrs</code>.</li>
<li>Greatly updated and expanded documentation</li>
<li>Added Larisse Bolton, Jordan Stull, Matthew Saab, and Javier Sanchez as contributors, to thank them for their valuable input</li>
<li>Stopped support for SAS (<code>.xpt</code>) files, since their file structure and extremely inefficient and requires more disk space than GitHub allows in a single commit.</li>
</ul></div>
<div class="section level3">
<h3 id="older-versions-2-1-1-9095">Older Versions<a class="anchor" aria-label="anchor" href="#older-versions-2-1-1-9095"></a></h3>
<h3 id="older-versions-2-1-1-9099">Older Versions<a class="anchor" aria-label="anchor" href="#older-versions-2-1-1-9099"></a></h3>
<p>This changelog only contains changes from AMR v3.0 (October 2024) and later.</p>
<ul><li>For prior v2 versions, please see <a href="https://github.com/msberends/AMR/blob/v2.1.1/NEWS.md" class="external-link">our v2 archive</a>.</li>
<li>For prior v1 versions, please see <a href="https://github.com/msberends/AMR/blob/v1.8.2/NEWS.md" class="external-link">our v1 archive</a>.</li>

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@ -11,7 +11,7 @@ articles:
resistance_predict: resistance_predict.html
welcome_to_AMR: welcome_to_AMR.html
WHONET: WHONET.html
last_built: 2024-10-15T15:34Z
last_built: 2024-10-17T09:59Z
urls:
reference: https://msberends.github.io/AMR/reference
article: https://msberends.github.io/AMR/articles

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@ -7,7 +7,7 @@
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@ -21,7 +21,7 @@ The AMR package is available in English, Chinese, Czech, Danish, Dutch, Finnish,
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@ -7,7 +7,7 @@
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reference/age.html
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@ -7,7 +7,7 @@
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@ -110,16 +110,16 @@
<span class="r-in"><span></span></span>
<span class="r-in"><span><span class="va">df</span></span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> birth_date age age_exact age_at_y2k</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 1 1965-12-05 58 58.86066 34</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 2 1980-03-01 44 44.62295 19</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 3 1949-11-01 74 74.95355 50</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 4 1947-02-14 77 77.66667 52</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 5 1940-02-19 84 84.65301 59</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 6 1988-01-10 36 36.76230 11</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 7 1997-08-27 27 27.13388 2</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 8 1978-01-26 46 46.71858 21</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 9 1972-06-17 52 52.32787 27</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 10 1986-08-10 38 38.18033 13</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 1 1965-12-05 58 58.86612 34</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 2 1980-03-01 44 44.62842 19</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 3 1949-11-01 74 74.95902 50</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 4 1947-02-14 77 77.67213 52</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 5 1940-02-19 84 84.65847 59</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 6 1988-01-10 36 36.76776 11</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 7 1997-08-27 27 27.13934 2</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 8 1978-01-26 46 46.72404 21</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 9 1972-06-17 52 52.33333 27</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 10 1986-08-10 38 38.18579 13</span>
</code></pre></div>
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@ -9,7 +9,7 @@ In short, if you have a column name that resembles an antimicrobial drug, it wil
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@ -21,7 +21,7 @@ All breakpoints used for interpretation are available in our clinical_breakpoint
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@ -785,16 +785,16 @@ A microorganism is categorised as "Resistant" when there is a high likelihood of
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># A tibble: 57 × 16</span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> datetime index ab_given mo_given host_given ab mo </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">*</span> <span style="color: #949494; font-style: italic;">&lt;dttm&gt;</span> <span style="color: #949494; font-style: italic;">&lt;int&gt;</span> <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span> <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span> <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span> <span style="color: #949494; font-style: italic;">&lt;ab&gt;</span> <span style="color: #949494; font-style: italic;">&lt;mo&gt;</span> </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 1</span> 2024-10-15 <span style="color: #949494;">15:35:01</span> 4 AMX B_STRPT… human AMX B_STRPT_PNMN </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 2</span> 2024-10-15 <span style="color: #949494;">15:35:08</span> 4 genta Escheri… human GEN B_[ORD]_ENTRBCTR</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 3</span> 2024-10-15 <span style="color: #949494;">15:35:09</span> 4 genta Escheri… human GEN B_[ORD]_ENTRBCTR</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 4</span> 2024-10-15 <span style="color: #949494;">15:35:09</span> 4 genta Escheri… cattle GEN B_ESCHR_COLI </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 5</span> 2024-10-15 <span style="color: #949494;">15:35:10</span> 4 genta Escheri… cattle GEN B_ESCHR_COLI </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 6</span> 2024-10-15 <span style="color: #949494;">15:35:01</span> 3 AMX B_STRPT… human AMX B_STRPT_PNMN </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 7</span> 2024-10-15 <span style="color: #949494;">15:35:08</span> 3 tobra Escheri… human TOB B_[ORD]_ENTRBCTR</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 8</span> 2024-10-15 <span style="color: #949494;">15:35:08</span> 3 tobra Escheri… human TOB B_[ORD]_ENTRBCTR</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 9</span> 2024-10-15 <span style="color: #949494;">15:35:09</span> 3 tobra Escheri… horses TOB B_ESCHR_COLI </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">10</span> 2024-10-15 <span style="color: #949494;">15:35:10</span> 3 tobra Escheri… horses TOB B_ESCHR_COLI </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 1</span> 2024-10-17 <span style="color: #949494;">10:00:40</span> 4 AMX B_STRPT… human AMX B_STRPT_PNMN </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 2</span> 2024-10-17 <span style="color: #949494;">10:00:47</span> 4 genta Escheri… human GEN B_[ORD]_ENTRBCTR</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 3</span> 2024-10-17 <span style="color: #949494;">10:00:47</span> 4 genta Escheri… human GEN B_[ORD]_ENTRBCTR</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 4</span> 2024-10-17 <span style="color: #949494;">10:00:48</span> 4 genta Escheri… cattle GEN B_ESCHR_COLI </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 5</span> 2024-10-17 <span style="color: #949494;">10:00:48</span> 4 genta Escheri… cattle GEN B_ESCHR_COLI </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 6</span> 2024-10-17 <span style="color: #949494;">10:00:40</span> 3 AMX B_STRPT… human AMX B_STRPT_PNMN </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 7</span> 2024-10-17 <span style="color: #949494;">10:00:47</span> 3 tobra Escheri… human TOB B_[ORD]_ENTRBCTR</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 8</span> 2024-10-17 <span style="color: #949494;">10:00:47</span> 3 tobra Escheri… human TOB B_[ORD]_ENTRBCTR</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 9</span> 2024-10-17 <span style="color: #949494;">10:00:48</span> 3 tobra Escheri… horses TOB B_ESCHR_COLI </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">10</span> 2024-10-17 <span style="color: #949494;">10:00:48</span> 3 tobra Escheri… horses TOB B_ESCHR_COLI </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># ℹ 47 more rows</span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># ℹ 9 more variables: host &lt;chr&gt;, method &lt;chr&gt;, input &lt;dbl&gt;, outcome &lt;sir&gt;,</span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># notes &lt;chr&gt;, guideline &lt;chr&gt;, ref_table &lt;chr&gt;, uti &lt;lgl&gt;,</span></span>

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@ -21,7 +21,7 @@ Use as.sir() to transform MICs or disks measurements to SIR values."><meta prope
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reference/export_ncbi_biosample.html
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@ -7,7 +7,7 @@
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reference/get_episode.html
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@ -7,7 +7,7 @@
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reference/ggplot_pca.html
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@ -7,7 +7,7 @@
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reference/ggplot_sir.html
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reference/guess_ab_col.html
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reference/index.html
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reference/intrinsic_resistant.html
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reference/italicise_taxonomy.html
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reference/join.html
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reference/key_antimicrobials.html
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reference/like.html
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reference/mean_amr_distance.html
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reference/microorganisms.codes.html
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reference/microorganisms.groups.html
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reference/microorganisms.html
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@ -9,7 +9,7 @@ This data set is carefully crafted, yet made 100% reproducible from public and a
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reference/mo_matching_score.html
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reference/mo_property.html
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reference/mo_source.html
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@ -9,7 +9,7 @@ This is the fastest way to have your organisation (or analysis) specific codes p
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reference/pca.html
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reference/plot.html
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@ -9,7 +9,7 @@ Especially the scale_*_mic() functions are relevant wrappers to plot MIC values
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reference/proportion.html
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@ -9,7 +9,7 @@ resistance() should be used to calculate resistance, susceptibility() should be
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reference/random.html
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reference/resistance_predict.html
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reference/skewness.html
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@ -9,7 +9,7 @@ When negative ('left-skewed'): the left tail is longer; the mass of the distribu
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reference/translate.html
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search.json
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