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articles/AMR.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="">2.1.1.9125</small>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9133</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 17 January 2025.</p>
generated on 27 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">2025-01-17</td>
<td align="center">2025-01-27</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">2025-01-17</td>
<td align="center">2025-01-27</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">2025-01-17</td>
<td align="center">2025-01-27</td>
<td align="center">efgh</td>
<td align="center">Escherichia coli</td>
<td align="center">R</td>
@ -691,26 +691,9 @@ previously mentioned antibiotic class selectors:</p>
<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/antimicrobial_class_selectors.html">aminoglycosides</a></span><span class="op">(</span><span class="op">)</span>, <span class="fu"><a href="../reference/antimicrobial_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>
<span><span class="co">#&gt; data.frame call, e.g.:</span></span>
<span><span class="co">#&gt; • your_data %&gt;% select(aminoglycosides())</span></span>
<span><span class="co">#&gt; • your_data %&gt;% select(column_a, column_b, aminoglycosides())</span></span>
<span><span class="co">#&gt; • your_data %&gt;% filter(any(aminoglycosides() == "R"))</span></span>
<span><span class="co">#&gt; • your_data[, aminoglycosides()]</span></span>
<span><span class="co">#&gt; • your_data[, c("column_a", "column_b", aminoglycosides())]</span></span>
<span><span class="co">#&gt; </span></span>
<span><span class="co">#&gt; Now returning a vector of all possible antimicrobials that</span></span>
<span><span class="co">#&gt; aminoglycosides() can select.</span></span>
<span><span class="co">#&gt; ℹ The function carbapenems() should be used inside a dplyr verb or</span></span>
<span><span class="co">#&gt; data.frame call, e.g.:</span></span>
<span><span class="co">#&gt; • your_data %&gt;% select(carbapenems())</span></span>
<span><span class="co">#&gt; • your_data %&gt;% select(column_a, column_b, carbapenems())</span></span>
<span><span class="co">#&gt; • your_data %&gt;% filter(any(carbapenems() == "R"))</span></span>
<span><span class="co">#&gt; • your_data[, carbapenems()]</span></span>
<span><span class="co">#&gt; • your_data[, c("column_a", "column_b", carbapenems())]</span></span>
<span><span class="co">#&gt; </span></span>
<span><span class="co">#&gt; Now returning a vector of all possible antimicrobials that carbapenems()</span></span>
<span><span class="co">#&gt; can select.</span></span></code></pre></div>
<span><span class="co">#&gt; ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB'</span></span>
<span><span class="co">#&gt; (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin)</span></span>
<span><span class="co">#&gt; ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem)</span></span></code></pre></div>
<table class="table">
<colgroup>
<col width="16%">
@ -838,16 +821,8 @@ language to be Spanish using the <code>language</code> argument:</p>
<span> antibiotics <span class="op">=</span> <span class="fu"><a href="../reference/antimicrobial_class_selectors.html">aminoglycosides</a></span><span class="op">(</span><span class="op">)</span>,</span>
<span> ab_transform <span class="op">=</span> <span class="st">"name"</span>,</span>
<span> language <span class="op">=</span> <span class="st">"es"</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>
<span><span class="co">#&gt; data.frame call, e.g.:</span></span>
<span><span class="co">#&gt; • your_data %&gt;% select(aminoglycosides())</span></span>
<span><span class="co">#&gt; • your_data %&gt;% select(column_a, column_b, aminoglycosides())</span></span>
<span><span class="co">#&gt; • your_data %&gt;% filter(any(aminoglycosides() == "R"))</span></span>
<span><span class="co">#&gt; • your_data[, aminoglycosides()]</span></span>
<span><span class="co">#&gt; • your_data[, c("column_a", "column_b", aminoglycosides())]</span></span>
<span><span class="co">#&gt; </span></span>
<span><span class="co">#&gt; Now returning a vector of all possible antimicrobials that</span></span>
<span><span class="co">#&gt; aminoglycosides() can select.</span></span></code></pre></div>
<span><span class="co">#&gt; ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB'</span></span>
<span><span class="co">#&gt; (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin)</span></span></code></pre></div>
<table class="table">
<colgroup>
<col width="20%">
@ -970,26 +945,9 @@ argument must be used. This can be any column in the data, or e.g. an
<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/antimicrobial_class_selectors.html">aminoglycosides</a></span><span class="op">(</span><span class="op">)</span>, <span class="fu"><a href="../reference/antimicrobial_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>
<span><span class="co">#&gt; ℹ The function aminoglycosides() should be used inside a dplyr verb or</span></span>
<span><span class="co">#&gt; data.frame call, e.g.:</span></span>
<span><span class="co">#&gt; • your_data %&gt;% select(aminoglycosides())</span></span>
<span><span class="co">#&gt; • your_data %&gt;% select(column_a, column_b, aminoglycosides())</span></span>
<span><span class="co">#&gt; • your_data %&gt;% filter(any(aminoglycosides() == "R"))</span></span>
<span><span class="co">#&gt; • your_data[, aminoglycosides()]</span></span>
<span><span class="co">#&gt; • your_data[, c("column_a", "column_b", aminoglycosides())]</span></span>
<span><span class="co">#&gt; </span></span>
<span><span class="co">#&gt; Now returning a vector of all possible antimicrobials that</span></span>
<span><span class="co">#&gt; aminoglycosides() can select.</span></span>
<span><span class="co">#&gt; ℹ The function carbapenems() should be used inside a dplyr verb or</span></span>
<span><span class="co">#&gt; data.frame call, e.g.:</span></span>
<span><span class="co">#&gt; • your_data %&gt;% select(carbapenems())</span></span>
<span><span class="co">#&gt; • your_data %&gt;% select(column_a, column_b, carbapenems())</span></span>
<span><span class="co">#&gt; • your_data %&gt;% filter(any(carbapenems() == "R"))</span></span>
<span><span class="co">#&gt; • your_data[, carbapenems()]</span></span>
<span><span class="co">#&gt; • your_data[, c("column_a", "column_b", carbapenems())]</span></span>
<span><span class="co">#&gt; </span></span>
<span><span class="co">#&gt; Now returning a vector of all possible antimicrobials that carbapenems()</span></span>
<span><span class="co">#&gt; can select.</span></span></code></pre></div>
<span><span class="co">#&gt; ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB'</span></span>
<span><span class="co">#&gt; (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin)</span></span>
<span><span class="co">#&gt; ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem)</span></span></code></pre></div>
<table class="table">
<colgroup>
<col width="13%">

252
articles/AMR_for_Python.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="">2.1.1.9125</small>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9133</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">
@ -200,6 +200,129 @@ the standard name, “Ciprofloxacin”.</p></li>
</div>
</div>
<div class="section level3">
<h3 id="calculating-amr">Calculating AMR<a class="anchor" aria-label="anchor" href="#calculating-amr"></a>
</h3>
<div class="sourceCode" id="cb5"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb5-1"><a href="#cb5-1" tabindex="-1"></a><span class="im">import</span> AMR</span>
<span id="cb5-2"><a href="#cb5-2" tabindex="-1"></a><span class="im">import</span> pandas <span class="im">as</span> pd</span>
<span id="cb5-3"><a href="#cb5-3" tabindex="-1"></a></span>
<span id="cb5-4"><a href="#cb5-4" tabindex="-1"></a>df <span class="op">=</span> AMR.example_isolates</span>
<span id="cb5-5"><a href="#cb5-5" tabindex="-1"></a>result <span class="op">=</span> AMR.resistance(df[<span class="st">"AMX"</span>])</span>
<span id="cb5-6"><a href="#cb5-6" 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="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="cb7"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb7-1"><a href="#cb7-1" 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="cb7-2"><a href="#cb7-2" tabindex="-1"></a><span class="bu">print</span>(result2a)</span></code></pre></div>
<table class="table">
<colgroup>
<col width="22%">
<col width="22%">
<col width="22%">
<col width="33%">
</colgroup>
<thead><tr class="header">
<th>Pathogen</th>
<th>Amoxicillin</th>
<th>Ciprofloxacin</th>
<th>Piperacillin/tazobactam</th>
</tr></thead>
<tbody>
<tr class="odd">
<td>CoNS</td>
<td>7% (10/142)</td>
<td>73% (183/252)</td>
<td>30% (10/33)</td>
</tr>
<tr class="even">
<td>E. coli</td>
<td>50% (196/392)</td>
<td>88% (399/456)</td>
<td>94% (393/416)</td>
</tr>
<tr class="odd">
<td>K. pneumoniae</td>
<td>0% (0/58)</td>
<td>96% (53/55)</td>
<td>89% (47/53)</td>
</tr>
<tr class="even">
<td>P. aeruginosa</td>
<td>0% (0/30)</td>
<td>100% (30/30)</td>
<td>None</td>
</tr>
<tr class="odd">
<td>P. mirabilis</td>
<td>None</td>
<td>94% (34/36)</td>
<td>None</td>
</tr>
<tr class="even">
<td>S. aureus</td>
<td>6% (8/131)</td>
<td>90% (171/191)</td>
<td>None</td>
</tr>
<tr class="odd">
<td>S. epidermidis</td>
<td>1% (1/91)</td>
<td>64% (87/136)</td>
<td>None</td>
</tr>
<tr class="even">
<td>S. hominis</td>
<td>None</td>
<td>80% (56/70)</td>
<td>None</td>
</tr>
<tr class="odd">
<td>S. pneumoniae</td>
<td>100% (112/112)</td>
<td>None</td>
<td>100% (112/112)</td>
</tr>
</tbody>
</table>
<div class="sourceCode" id="cb8"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb8-1"><a href="#cb8-1" 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="cb8-2"><a href="#cb8-2" tabindex="-1"></a><span class="bu">print</span>(result2b)</span></code></pre></div>
<table class="table">
<colgroup>
<col width="20%">
<col width="22%">
<col width="23%">
<col width="33%">
</colgroup>
<thead><tr class="header">
<th>Pathogen</th>
<th>Amoxicillin</th>
<th>Ciprofloxacin</th>
<th>Piperacillin/tazobactam</th>
</tr></thead>
<tbody>
<tr class="odd">
<td>Gram-negative</td>
<td>36% (226/631)</td>
<td>91% (621/684)</td>
<td>88% (565/641)</td>
</tr>
<tr class="even">
<td>Gram-positive</td>
<td>43% (305/703)</td>
<td>77% (560/724)</td>
<td>86% (296/345)</td>
</tr>
</tbody>
</table>
<p>In this example, we generate an antibiogram by selecting various
antibiotics.</p>
</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
@ -207,7 +330,7 @@ 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="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>
<div class="sourceCode" id="cb9"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb9-1"><a href="#cb9-1" tabindex="-1"></a>AMR.microorganisms</span></code></pre></div>
<table class="table">
<colgroup>
<col width="11%">
@ -342,7 +465,7 @@ frames:</p>
</tr>
</tbody>
</table>
<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>
<div class="sourceCode" id="cb10"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb10-1"><a href="#cb10-1" tabindex="-1"></a>AMR.antibiotics</span></code></pre></div>
<table style="width:100%;" class="table">
<colgroup>
<col width="4%">
@ -478,129 +601,6 @@ frames:</p>
</tbody>
</table>
</div>
<div class="section level3">
<h3 id="calculating-amr">Calculating AMR<a class="anchor" aria-label="anchor" href="#calculating-amr"></a>
</h3>
<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="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="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%">
<col width="22%">
<col width="22%">
<col width="33%">
</colgroup>
<thead><tr class="header">
<th>Pathogen</th>
<th>Amoxicillin</th>
<th>Ciprofloxacin</th>
<th>Piperacillin/tazobactam</th>
</tr></thead>
<tbody>
<tr class="odd">
<td>CoNS</td>
<td>7% (10/142)</td>
<td>73% (183/252)</td>
<td>30% (10/33)</td>
</tr>
<tr class="even">
<td>E. coli</td>
<td>50% (196/392)</td>
<td>88% (399/456)</td>
<td>94% (393/416)</td>
</tr>
<tr class="odd">
<td>K. pneumoniae</td>
<td>0% (0/58)</td>
<td>96% (53/55)</td>
<td>89% (47/53)</td>
</tr>
<tr class="even">
<td>P. aeruginosa</td>
<td>0% (0/30)</td>
<td>100% (30/30)</td>
<td>None</td>
</tr>
<tr class="odd">
<td>P. mirabilis</td>
<td>None</td>
<td>94% (34/36)</td>
<td>None</td>
</tr>
<tr class="even">
<td>S. aureus</td>
<td>6% (8/131)</td>
<td>90% (171/191)</td>
<td>None</td>
</tr>
<tr class="odd">
<td>S. epidermidis</td>
<td>1% (1/91)</td>
<td>64% (87/136)</td>
<td>None</td>
</tr>
<tr class="even">
<td>S. hominis</td>
<td>None</td>
<td>80% (56/70)</td>
<td>None</td>
</tr>
<tr class="odd">
<td>S. pneumoniae</td>
<td>100% (112/112)</td>
<td>None</td>
<td>100% (112/112)</td>
</tr>
</tbody>
</table>
<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%">
<col width="22%">
<col width="23%">
<col width="33%">
</colgroup>
<thead><tr class="header">
<th>Pathogen</th>
<th>Amoxicillin</th>
<th>Ciprofloxacin</th>
<th>Piperacillin/tazobactam</th>
</tr></thead>
<tbody>
<tr class="odd">
<td>Gram-negative</td>
<td>36% (226/631)</td>
<td>91% (621/684)</td>
<td>88% (565/641)</td>
</tr>
<tr class="even">
<td>Gram-positive</td>
<td>43% (305/703)</td>
<td>77% (560/724)</td>
<td>86% (296/345)</td>
</tr>
</tbody>
</table>
<p>In this example, we generate an antibiogram by selecting various
antibiotics.</p>
</div>
</div>
<div class="section level2">
<h2 id="conclusion">Conclusion<a class="anchor" aria-label="anchor" href="#conclusion"></a>

6
articles/AMR_with_tidymodels.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="">2.1.1.9125</small>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9133</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">
@ -134,13 +134,13 @@ package.</p>
<span><span class="co">#&gt; <span style="color: #00BB00;">✔</span> <span style="color: #0000BB;">infer </span> 1.0.7 <span style="color: #00BB00;">✔</span> <span style="color: #0000BB;">tune </span> 1.2.1</span></span>
<span><span class="co">#&gt; <span style="color: #00BB00;">✔</span> <span style="color: #0000BB;">modeldata </span> 1.4.0 <span style="color: #00BB00;">✔</span> <span style="color: #0000BB;">workflows </span> 1.1.4</span></span>
<span><span class="co">#&gt; <span style="color: #00BB00;">✔</span> <span style="color: #0000BB;">parsnip </span> 1.2.1 <span style="color: #00BB00;">✔</span> <span style="color: #0000BB;">workflowsets</span> 1.1.0</span></span>
<span><span class="co">#&gt; <span style="color: #00BB00;">✔</span> <span style="color: #0000BB;">purrr </span> 1.0.2 <span style="color: #00BB00;">✔</span> <span style="color: #0000BB;">yardstick </span> 1.3.1</span></span>
<span><span class="co">#&gt; <span style="color: #00BB00;">✔</span> <span style="color: #0000BB;">purrr </span> 1.0.2 <span style="color: #00BB00;">✔</span> <span style="color: #0000BB;">yardstick </span> 1.3.2</span></span>
<span><span class="co">#&gt; ── <span style="font-weight: bold;">Conflicts</span> ───────────────────────────────────────── tidymodels_conflicts() ──</span></span>
<span><span class="co">#&gt; <span style="color: #BB0000;">✖</span> <span style="color: #0000BB;">purrr</span>::<span style="color: #00BB00;">discard()</span> masks <span style="color: #0000BB;">scales</span>::discard()</span></span>
<span><span class="co">#&gt; <span style="color: #BB0000;">✖</span> <span style="color: #0000BB;">dplyr</span>::<span style="color: #00BB00;">filter()</span> masks <span style="color: #0000BB;">stats</span>::filter()</span></span>
<span><span class="co">#&gt; <span style="color: #BB0000;">✖</span> <span style="color: #0000BB;">dplyr</span>::<span style="color: #00BB00;">lag()</span> masks <span style="color: #0000BB;">stats</span>::lag()</span></span>
<span><span class="co">#&gt; <span style="color: #BB0000;">✖</span> <span style="color: #0000BB;">recipes</span>::<span style="color: #00BB00;">step()</span> masks <span style="color: #0000BB;">stats</span>::step()</span></span>
<span><span class="co">#&gt; <span style="color: #0000BB;">•</span> Dig deeper into tidy modeling with R at <span style="color: #00BB00;">https://www.tmwr.org</span></span></span>
<span><span class="co">#&gt; <span style="color: #0000BB;">•</span> Use <span style="color: #00BB00;">tidymodels_prefer()</span> to resolve common conflicts.</span></span>
<span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://msberends.github.io/AMR/">AMR</a></span><span class="op">)</span> <span class="co"># For AMR data analysis</span></span>
<span></span>
<span><span class="co"># Load the example_isolates dataset</span></span>

2
articles/EUCAST.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="">2.1.1.9125</small>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9133</small>
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2
articles/MDR.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="">2.1.1.9125</small>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9133</small>
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2
articles/PCA.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="">2.1.1.9125</small>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9133</small>
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2
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="">2.1.1.9125</small>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9133</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="">2.1.1.9125</small>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9133</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">
@ -77,7 +77,7 @@
<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">17 January 2025</h4>
<h4 data-toc-skip class="date">27 January 2025</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>

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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="">2.1.1.9125</small>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9133</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>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9125</small>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9133</small>
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2
articles/welcome_to_AMR.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="">2.1.1.9125</small>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9133</small>
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