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@ -6,12 +6,11 @@
<meta http-equiv="X-UA-Compatible" content="IE=edge">
<meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
<title>How to conduct AMR data analysis • AMR (for R)</title>
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@ -31,7 +30,7 @@
<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="">2.1.1.9259</small>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9263</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">
@ -92,7 +91,7 @@
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 27 April 2025.</p>
generated on 29 April 2025.</p>
<div class="section level2">
<h2 id="introduction">Introduction<a class="anchor" aria-label="anchor" href="#introduction"></a>
</h2>
@ -148,21 +147,21 @@ make the structure of your data generally look like this:</p>
</tr></thead>
<tbody>
<tr class="odd">
<td align="center">2025-04-27</td>
<td align="center">2025-04-29</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">2025-04-27</td>
<td align="center">2025-04-29</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">2025-04-27</td>
<td align="center">2025-04-29</td>
<td align="center">efgh</td>
<td align="center">Escherichia coli</td>
<td align="center">R</td>
@ -703,7 +702,7 @@ previously mentioned antibiotic class selectors:</p>
<span><span class="co">#&gt; <span style="color: #0000BB;">ℹ For </span><span style="color: #0000BB; background-color: #EEEEEE;">carbapenems()</span><span style="color: #0000BB;"> using columns '</span><span style="color: #0000BB; font-weight: bold;">IPM</span><span style="color: #0000BB;">' (imipenem) and '</span><span style="color: #0000BB; font-weight: bold;">MEM</span><span style="color: #0000BB;">' (meropenem)</span></span></span></code></pre></div>
<table class="table">
<colgroup>
<col width="11%">
<col width="12%">
<col width="15%">
<col width="14%">
<col width="15%">
@ -731,84 +730,84 @@ previously mentioned antibiotic class selectors:</p>
<td align="left">22% (12-35%,N=55)</td>
</tr>
<tr class="even">
<td align="left">E. coli</td>
<td align="left"><em>E. coli</em></td>
<td align="left">100% (98-100%,N=171)</td>
<td align="left">98% (96-99%,N=460)</td>
<td align="left">100% (99-100%,N=422)</td>
<td align="left"></td>
<td align="left">NA</td>
<td align="left">100% (99-100%,N=418)</td>
<td align="left">97% (96-99%,N=462)</td>
</tr>
<tr class="odd">
<td align="left">E. faecalis</td>
<td align="left"><em>E. faecalis</em></td>
<td align="left">0% (0-9%,N=39)</td>
<td align="left">0% (0-9%,N=39)</td>
<td align="left">100% (91-100%,N=38)</td>
<td align="left">0% (0-9%,N=39)</td>
<td align="left"></td>
<td align="left">NA</td>
<td align="left">0% (0-9%,N=39)</td>
</tr>
<tr class="even">
<td align="left">K. pneumoniae</td>
<td align="left"></td>
<td align="left"><em>K. pneumoniae</em></td>
<td align="left">NA</td>
<td align="left">90% (79-96%,N=58)</td>
<td align="left">100% (93-100%,N=51)</td>
<td align="left"></td>
<td align="left">NA</td>
<td align="left">100% (93-100%,N=53)</td>
<td align="left">90% (79-96%,N=58)</td>
</tr>
<tr class="odd">
<td align="left">P. aeruginosa</td>
<td align="left"></td>
<td align="left"><em>P. aeruginosa</em></td>
<td align="left">NA</td>
<td align="left">100% (88-100%,N=30)</td>
<td align="left"></td>
<td align="left">NA</td>
<td align="left">0% (0-12%,N=30)</td>
<td align="left"></td>
<td align="left">NA</td>
<td align="left">100% (88-100%,N=30)</td>
</tr>
<tr class="even">
<td align="left">P. mirabilis</td>
<td align="left"></td>
<td align="left"><em>P. mirabilis</em></td>
<td align="left">NA</td>
<td align="left">94% (80-99%,N=34)</td>
<td align="left">94% (79-99%,N=32)</td>
<td align="left"></td>
<td align="left"></td>
<td align="left">NA</td>
<td align="left">NA</td>
<td align="left">94% (80-99%,N=34)</td>
</tr>
<tr class="odd">
<td align="left">S. aureus</td>
<td align="left"></td>
<td align="left"><em>S. aureus</em></td>
<td align="left">NA</td>
<td align="left">99% (97-100%,N=233)</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left">NA</td>
<td align="left">NA</td>
<td align="left">NA</td>
<td align="left">98% (92-100%,N=86)</td>
</tr>
<tr class="even">
<td align="left">S. epidermidis</td>
<td align="left"><em>S. epidermidis</em></td>
<td align="left">0% (0-8%,N=44)</td>
<td align="left">79% (71-85%,N=163)</td>
<td align="left"></td>
<td align="left">NA</td>
<td align="left">0% (0-8%,N=44)</td>
<td align="left"></td>
<td align="left">NA</td>
<td align="left">51% (40-61%,N=89)</td>
</tr>
<tr class="odd">
<td align="left">S. hominis</td>
<td align="left"></td>
<td align="left"><em>S. hominis</em></td>
<td align="left">NA</td>
<td align="left">92% (84-97%,N=80)</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left">NA</td>
<td align="left">NA</td>
<td align="left">NA</td>
<td align="left">85% (74-93%,N=62)</td>
</tr>
<tr class="even">
<td align="left">S. pneumoniae</td>
<td align="left"><em>S. pneumoniae</em></td>
<td align="left">0% (0-3%,N=117)</td>
<td align="left">0% (0-3%,N=117)</td>
<td align="left"></td>
<td align="left">NA</td>
<td align="left">0% (0-3%,N=117)</td>
<td align="left"></td>
<td align="left">NA</td>
<td align="left">0% (0-3%,N=117)</td>
</tr>
</tbody>
@ -875,7 +874,7 @@ a plus <code>+</code> character like this:</p>
<span><span class="va">combined_ab</span></span></code></pre></div>
<table class="table">
<colgroup>
<col width="19%">
<col width="21%">
<col width="26%">
<col width="26%">
<col width="26%">
@ -891,52 +890,52 @@ a plus <code>+</code> character like this:</p>
<td align="left">CoNS</td>
<td align="left">30% (16-49%,N=33)</td>
<td align="left">97% (95-99%,N=274)</td>
<td align="left"></td>
<td align="left">NA</td>
</tr>
<tr class="even">
<td align="left">E. coli</td>
<td align="left"><em>E. coli</em></td>
<td align="left">94% (92-96%,N=416)</td>
<td align="left">100% (98-100%,N=459)</td>
<td align="left">99% (97-100%,N=461)</td>
</tr>
<tr class="odd">
<td align="left">K. pneumoniae</td>
<td align="left"><em>K. pneumoniae</em></td>
<td align="left">89% (77-96%,N=53)</td>
<td align="left">93% (83-98%,N=58)</td>
<td align="left">93% (83-98%,N=58)</td>
</tr>
<tr class="even">
<td align="left">P. aeruginosa</td>
<td align="left"></td>
<td align="left"><em>P. aeruginosa</em></td>
<td align="left">NA</td>
<td align="left">100% (88-100%,N=30)</td>
<td align="left">100% (88-100%,N=30)</td>
</tr>
<tr class="odd">
<td align="left">P. mirabilis</td>
<td align="left"></td>
<td align="left"><em>P. mirabilis</em></td>
<td align="left">NA</td>
<td align="left">100% (90-100%,N=34)</td>
<td align="left">100% (90-100%,N=34)</td>
</tr>
<tr class="even">
<td align="left">S. aureus</td>
<td align="left"></td>
<td align="left"><em>S. aureus</em></td>
<td align="left">NA</td>
<td align="left">100% (98-100%,N=231)</td>
<td align="left">100% (96-100%,N=91)</td>
</tr>
<tr class="odd">
<td align="left">S. epidermidis</td>
<td align="left"></td>
<td align="left"><em>S. epidermidis</em></td>
<td align="left">NA</td>
<td align="left">100% (97-100%,N=128)</td>
<td align="left">100% (92-100%,N=46)</td>
</tr>
<tr class="even">
<td align="left">S. hominis</td>
<td align="left"></td>
<td align="left"><em>S. hominis</em></td>
<td align="left">NA</td>
<td align="left">100% (95-100%,N=74)</td>
<td align="left">100% (93-100%,N=53)</td>
</tr>
<tr class="odd">
<td align="left">S. pneumoniae</td>
<td align="left"><em>S. pneumoniae</em></td>
<td align="left">100% (97-100%,N=112)</td>
<td align="left">100% (97-100%,N=112)</td>
<td align="left">100% (97-100%,N=112)</td>
@ -960,12 +959,12 @@ argument must be used. This can be any column in the data, or e.g. an
<table class="table">
<colgroup>
<col width="10%">
<col width="10%">
<col width="14%">
<col width="11%">
<col width="13%">
<col width="13%">
<col width="13%">
<col width="14%">
<col width="10%">
<col width="14%">
<col width="13%">
<col width="13%">
</colgroup>
<thead><tr class="header">
@ -982,141 +981,141 @@ argument must be used. This can be any column in the data, or e.g. an
<tr class="odd">
<td align="left">Clinical</td>
<td align="left">CoNS</td>
<td align="left"></td>
<td align="left">NA</td>
<td align="left">89% (84-93%,N=205)</td>
<td align="left">57% (39-74%,N=35)</td>
<td align="left"></td>
<td align="left">NA</td>
<td align="left">57% (39-74%,N=35)</td>
<td align="left">26% (12-45%,N=31)</td>
</tr>
<tr class="even">
<td align="left">ICU</td>
<td align="left">CoNS</td>
<td align="left"></td>
<td align="left">NA</td>
<td align="left">79% (68-88%,N=73)</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left">NA</td>
<td align="left">NA</td>
<td align="left">NA</td>
<td align="left">NA</td>
</tr>
<tr class="odd">
<td align="left">Outpatient</td>
<td align="left">CoNS</td>
<td align="left"></td>
<td align="left">NA</td>
<td align="left">84% (66-95%,N=31)</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left">NA</td>
<td align="left">NA</td>
<td align="left">NA</td>
<td align="left">NA</td>
</tr>
<tr class="even">
<td align="left">Clinical</td>
<td align="left">E. coli</td>
<td align="left"><em>E. coli</em></td>
<td align="left">100% (97-100%,N=104)</td>
<td align="left">98% (96-99%,N=297)</td>
<td align="left">100% (99-100%,N=266)</td>
<td align="left"></td>
<td align="left">NA</td>
<td align="left">100% (99-100%,N=276)</td>
<td align="left">98% (96-99%,N=299)</td>
</tr>
<tr class="odd">
<td align="left">ICU</td>
<td align="left">E. coli</td>
<td align="left"><em>E. coli</em></td>
<td align="left">100% (93-100%,N=52)</td>
<td align="left">99% (95-100%,N=137)</td>
<td align="left">100% (97-100%,N=133)</td>
<td align="left"></td>
<td align="left">NA</td>
<td align="left">100% (97-100%,N=118)</td>
<td align="left">96% (92-99%,N=137)</td>
</tr>
<tr class="even">
<td align="left">Clinical</td>
<td align="left">K. pneumoniae</td>
<td align="left"></td>
<td align="left"><em>K. pneumoniae</em></td>
<td align="left">NA</td>
<td align="left">92% (81-98%,N=51)</td>
<td align="left">100% (92-100%,N=44)</td>
<td align="left"></td>
<td align="left">NA</td>
<td align="left">100% (92-100%,N=46)</td>
<td align="left">92% (81-98%,N=51)</td>
</tr>
<tr class="odd">
<td align="left">Clinical</td>
<td align="left">P. mirabilis</td>
<td align="left"></td>
<td align="left"><em>P. mirabilis</em></td>
<td align="left">NA</td>
<td align="left">100% (88-100%,N=30)</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left">NA</td>
<td align="left">NA</td>
<td align="left">NA</td>
<td align="left">100% (88-100%,N=30)</td>
</tr>
<tr class="even">
<td align="left">Clinical</td>
<td align="left">S. aureus</td>
<td align="left"></td>
<td align="left"><em>S. aureus</em></td>
<td align="left">NA</td>
<td align="left">99% (95-100%,N=150)</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left">NA</td>
<td align="left">NA</td>
<td align="left">NA</td>
<td align="left">97% (89-100%,N=63)</td>
</tr>
<tr class="odd">
<td align="left">ICU</td>
<td align="left">S. aureus</td>
<td align="left"></td>
<td align="left"><em>S. aureus</em></td>
<td align="left">NA</td>
<td align="left">100% (95-100%,N=66)</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left">NA</td>
<td align="left">NA</td>
<td align="left">NA</td>
<td align="left">NA</td>
</tr>
<tr class="even">
<td align="left">Clinical</td>
<td align="left">S. epidermidis</td>
<td align="left"></td>
<td align="left"><em>S. epidermidis</em></td>
<td align="left">NA</td>
<td align="left">82% (72-90%,N=79)</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left">NA</td>
<td align="left">NA</td>
<td align="left">NA</td>
<td align="left">55% (39-70%,N=44)</td>
</tr>
<tr class="odd">
<td align="left">ICU</td>
<td align="left">S. epidermidis</td>
<td align="left"></td>
<td align="left"><em>S. epidermidis</em></td>
<td align="left">NA</td>
<td align="left">72% (60-82%,N=75)</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left">NA</td>
<td align="left">NA</td>
<td align="left">NA</td>
<td align="left">41% (26-58%,N=41)</td>
</tr>
<tr class="even">
<td align="left">Clinical</td>
<td align="left">S. hominis</td>
<td align="left"></td>
<td align="left"><em>S. hominis</em></td>
<td align="left">NA</td>
<td align="left">96% (85-99%,N=45)</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left">NA</td>
<td align="left">NA</td>
<td align="left">NA</td>
<td align="left">94% (79-99%,N=31)</td>
</tr>
<tr class="odd">
<td align="left">Clinical</td>
<td align="left">S. pneumoniae</td>
<td align="left"><em>S. pneumoniae</em></td>
<td align="left">0% (0-5%,N=78)</td>
<td align="left">0% (0-5%,N=78)</td>
<td align="left"></td>
<td align="left">NA</td>
<td align="left">0% (0-5%,N=78)</td>
<td align="left"></td>
<td align="left">NA</td>
<td align="left">0% (0-5%,N=78)</td>
</tr>
<tr class="even">
<td align="left">ICU</td>
<td align="left">S. pneumoniae</td>
<td align="left"><em>S. pneumoniae</em></td>
<td align="left">0% (0-12%,N=30)</td>
<td align="left">0% (0-12%,N=30)</td>
<td align="left"></td>
<td align="left">NA</td>
<td align="left">0% (0-12%,N=30)</td>
<td align="left"></td>
<td align="left">NA</td>
<td align="left">0% (0-12%,N=30)</td>
</tr>
</tbody>
@ -1147,9 +1146,9 @@ incidence and antimicrobial susceptibility patterns.</p>
<th align="left">Piperacillin/tazobactam + Tobramycin</th>
</tr></thead>
<tbody><tr class="odd">
<td align="left">69.2% (51-85.2%)</td>
<td align="left">92.9% (84-98.5%)</td>
<td align="left">89% (76.4-96.9%)</td>
<td align="left">69.4% (64.3-74.3%)</td>
<td align="left">92.6% (91.2-93.8%)</td>
<td align="left">88.7% (85.9-91.2%)</td>
</tr></tbody>
</table>
<p>WISCA uses a <strong>Bayesian decision model</strong> to integrate
@ -1190,58 +1189,58 @@ function on a grouped <code>tibble</code>, i.e., using
<tr class="odd">
<td align="left">0-24</td>
<td align="left">F</td>
<td align="left">57.5% (7-98.4%)</td>
<td align="left">74.6% (30.6-98.9%)</td>
<td align="left">69.7% (24.5-98.5%)</td>
<td align="left">56.6% (25.2-83.9%)</td>
<td align="left">74% (50.5-91.9%)</td>
<td align="left">68% (39.7-88.9%)</td>
</tr>
<tr class="even">
<td align="left">0-24</td>
<td align="left">M</td>
<td align="left">59% (14.8-97.9%)</td>
<td align="left">80% (40.6-99.2%)</td>
<td align="left">60.1% (15-98.2%)</td>
<td align="left">60.3% (28.4-87.1%)</td>
<td align="left">79.3% (59.5-94.2%)</td>
<td align="left">60.1% (29.3-87.5%)</td>
</tr>
<tr class="odd">
<td align="left">25-49</td>
<td align="left">F</td>
<td align="left">65.4% (30.2-97.9%)</td>
<td align="left">91.6% (72.9-99.7%)</td>
<td align="left">82.6% (49.7-99.3%)</td>
<td align="left">66.6% (45.6-85.5%)</td>
<td align="left">91.8% (85.1-96.5%)</td>
<td align="left">82.6% (66.7-93.9%)</td>
</tr>
<tr class="even">
<td align="left">25-49</td>
<td align="left">M</td>
<td align="left">54.4% (10.9-97.3%)</td>
<td align="left">89.4% (66.7-99.7%)</td>
<td align="left">70.7% (26.9-98.6%)</td>
<td align="left">56.4% (29.1-81.7%)</td>
<td align="left">89.5% (80-95.6%)</td>
<td align="left">72.2% (47.1-90%)</td>
</tr>
<tr class="odd">
<td align="left">50-74</td>
<td align="left">F</td>
<td align="left">68.2% (44.6-92.6%)</td>
<td align="left">95.8% (87.6-99.5%)</td>
<td align="left">88.3% (71-97.7%)</td>
<td align="left">67.8% (55.8-80.1%)</td>
<td align="left">95.6% (93.2-97.7%)</td>
<td align="left">88% (80.2-94.2%)</td>
</tr>
<tr class="even">
<td align="left">50-74</td>
<td align="left">M</td>
<td align="left">65.8% (43.7-86.2%)</td>
<td align="left">95.3% (86-99.6%)</td>
<td align="left">83.7% (62.9-97%)</td>
<td align="left">66.2% (54.8-75.8%)</td>
<td align="left">95.3% (92.5-97.4%)</td>
<td align="left">84.2% (75.2-92.4%)</td>
</tr>
<tr class="odd">
<td align="left">75+</td>
<td align="left">F</td>
<td align="left">71.8% (48-92%)</td>
<td align="left">96.6% (90-99.7%)</td>
<td align="left">90.8% (76.5-98.8%)</td>
<td align="left">71.7% (61-81.7%)</td>
<td align="left">96.5% (94.4-98.2%)</td>
<td align="left">90.7% (84.4-95.7%)</td>
</tr>
<tr class="even">
<td align="left">75+</td>
<td align="left">M</td>
<td align="left">72.9% (53.7-89.7%)</td>
<td align="left">96.7% (89.9-99.7%)</td>
<td align="left">92.5% (80.3-98.9%)</td>
<td align="left">72.9% (63.8-82%)</td>
<td align="left">96.6% (94.5-98.1%)</td>
<td align="left">92.8% (87.6-96.3%)</td>
</tr>
</tbody>
</table>

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<img src="../logo.svg" class="logo" alt=""><h1>Data sets for download / own use</h1>
<h4 data-toc-skip class="date">27 April 2025</h4>
<h4 data-toc-skip class="date">29 April 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>
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<title>Welcome to the `AMR` package • AMR (for R)</title>
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<p>Note: to keep the package size as small as possible, we only include
this vignette on CRAN. You can read more vignettes on our website about
how to conduct AMR data analysis, determine MDROs, find explanation of
EUCAST and CLSI breakpoints, and much more: <a href="https://amr-for-r.org/articles/" class="uri">https://amr-for-r.org/articles/</a>.</p>
EUCAST and CLSI breakpoints, and much more: <a href="https://amr-for-r.org" class="uri">https://amr-for-r.org</a>.</p>
<hr>
<p>The <code>AMR</code> package is a <a href="https://amr-for-r.org/">free and open-source</a> R package with <a href="https://en.wikipedia.org/wiki/Dependency_hell" class="external-link">zero
<p>The <code>AMR</code> package is a peer-reviewed, <a href="https://amr-for-r.org/#copyright">free and open-source</a> R
package with <a href="https://en.wikipedia.org/wiki/Dependency_hell" class="external-link">zero
dependencies</a> to simplify the analysis and prediction of
Antimicrobial Resistance (AMR) and to work with microbial and
antimicrobial data and properties, by using evidence-based methods.
<strong>Our aim is to provide a standard</strong> for clean and
reproducible AMR data analysis, that can therefore empower
epidemiological analyses to continuously enable surveillance and
treatment evaluation in any setting. <a href="https://amr-for-r.org/authors.html">Many different researchers</a>
from around the globe are continually helping us to make this a
successful and durable project!</p>
treatment evaluation in any setting. We are a team of <a href="https://amr-for-r.org/authors.html">many different researchers</a>
from around the globe to make this a successful and durable project!</p>
<p>This work was published in the Journal of Statistical Software
(Volume 104(3); <a href="https://doi.org/10.18637/jss.v104.i03" class="external-link">DOI
10.18637/jss.v104.i03</a>) and formed the basis of two PhD theses (<a href="https://doi.org/10.33612/diss.177417131" class="external-link">DOI
10.33612/diss.177417131</a> and <a href="https://doi.org/10.33612/diss.192486375" class="external-link">DOI
10.33612/diss.192486375</a>).</p>
<p>After installing this package, R knows ~79 000 distinct microbial
species and all ~620 antibiotic, antimycotic and antiviral drugs by name
and code (including ATC, EARS-Net, ASIARS-Net, PubChem, LOINC and SNOMED
CT), and knows all about valid SIR and MIC values. The integral
breakpoint guidelines from CLSI and EUCAST are included from the last 10
years. It supports and can read any data format, including WHONET
data.</p>
<p>With the help of contributors from all corners of the world, the
<code>AMR</code> package is available in English, Czech, Chinese,
(Volume 104(3); ) and formed the basis of two PhD theses ( and ).</p>
<p>After installing this package, R knows <a href="https://amr-for-r.org/reference/microorganisms.html"><strong>~79
000 distinct microbial species</strong></a> (updated June 2024) and all
<a href="https://amr-for-r.org/reference/antimicrobials.html"><strong>~620
antimicrobial and antiviral drugs</strong></a> by name and code
(including ATC, EARS-Net, ASIARS-Net, PubChem, LOINC and SNOMED CT), and
knows all about valid SIR and MIC values. The integral clinical
breakpoint guidelines from CLSI 2011-2025 and EUCAST 2011-2025 are
included, even with epidemiological cut-off (ECOFF) values. It supports
and can read any data format, including WHONET data. This package works
on Windows, macOS and Linux with all versions of R since R-3.0 (April
2013). <strong>It was designed to work in any setting, including those
with very limited resources</strong>. It was created for both routine
data analysis and academic research at the Faculty of Medical Sciences
of the <a href="https://www.rug.nl" class="external-link">University of Groningen</a> and the
<a href="https://www.umcg.nl" class="external-link">University Medical Center
Groningen</a>.</p>
<p>The <code>AMR</code> package is available in English, Chinese, Czech,
Danish, Dutch, Finnish, French, German, Greek, Italian, Japanese,
Norwegian, Polish, Portuguese, Romanian, Russian, Spanish, Swedish,
Turkish, and Ukrainian. Antimicrobial drug (group) names and colloquial
microorganism names are provided in these languages.</p>
<p>This package is fully independent of any other R package and works on
Windows, macOS and Linux with all versions of R since R-3.0 (April
2013). <strong>It was designed to work in any setting, including those
with very limited resources</strong>. Since its first public release in
early 2018, this package has been downloaded from more than 175
countries.</p>
<p>This package can be used for:</p>
<p>This package was intended as a comprehensive toolbox for integrated
AMR data analysis. This package can be used for:</p>
<ul>
<li>Reference for the taxonomy of microorganisms, since the package
contains all microbial (sub)species from the List of Prokaryotic names
with Standing in Nomenclature (LPSN) and the Global Biodiversity
Information Facility (GBIF)</li>
<li>Interpreting raw MIC and disk diffusion values, based on the latest
CLSI or EUCAST guidelines</li>
with Standing in Nomenclature (<a href="(https://lpsn.dsmz.de)" class="external-link">LPSN</a>) and the Global Biodiversity
Information Facility (<a href="https://www.gbif.org" class="external-link">GBIF</a>) (<a href="https://amr-for-r.org/reference/mo_property.html">manual</a>)</li>
<li>Interpreting raw MIC and disk diffusion values, based on any CLSI or
EUCAST guideline (<a href="https://amr-for-r.org/reference/as.sir.html">manual</a>)</li>
<li>Retrieving antimicrobial drug names, doses and forms of
administration from clinical health care records</li>
<li>Determining first isolates to be used for AMR data analysis</li>
<li>Calculating antimicrobial resistance</li>
administration from clinical health care records (<a href="https://amr-for-r.org/reference/ab_from_text.html">manual</a>)</li>
<li>Determining first isolates to be used for AMR data analysis (<a href="https://amr-for-r.org/reference/first_isolate.html">manual</a>)</li>
<li>Calculating antimicrobial resistance (<a href="https://amr-for-r.org/articles/AMR.html">tutorial</a>)</li>
<li>Determining multi-drug resistance (MDR) / multi-drug resistant
organisms (MDRO)</li>
organisms (MDRO) (<a href="https://amr-for-r.org/articles/MDR.html">tutorial</a>)</li>
<li>Calculating (empirical) susceptibility of both mono therapy and
combination therapies</li>
<li>Predicting future antimicrobial resistance using regression
models</li>
combination therapies (<a href="https://amr-for-r.org/articles/AMR.html">tutorial</a>)</li>
<li>Apply AMR function in predictive modelling (<a href="https://amr-for-r.org/articles/AMR_with_tidymodels.html">tutorial</a>)</li>
<li>Getting properties for any microorganism (like Gram stain, species,
genus or family)</li>
<li>Getting properties for any antibiotic (like name, code of
EARS-Net/ATC/LOINC/PubChem, defined daily dose or trade name)</li>
<li>Plotting antimicrobial resistance</li>
<li>Applying EUCAST expert rules</li>
genus or family) (<a href="https://amr-for-r.org/reference/mo_property.html">manual</a>)</li>
<li>Getting properties for any antimicrobial (like name, code of
EARS-Net/ATC/LOINC/PubChem, defined daily dose or trade name) (<a href="https://amr-for-r.org/reference/ab_property.html">manual</a>)</li>
<li>Plotting antimicrobial resistance (<a href="https://amr-for-r.org/articles/AMR.html">tutorial</a>)</li>
<li>Applying EUCAST expert rules (<a href="https://amr-for-r.org/reference/eucast_rules.html">manual</a>)</li>
<li>Getting SNOMED codes of a microorganism, or getting properties of a
microorganism based on a SNOMED code</li>
microorganism based on a SNOMED code (<a href="https://amr-for-r.org/reference/mo_property.html">manual</a>)</li>
<li>Getting LOINC codes of an antibiotic, or getting properties of an
antibiotic based on a LOINC code</li>
<li>Machine reading the EUCAST and CLSI guidelines from 2011-2020 to
translate MIC values and disk diffusion diameters to SIR</li>
<li>Principal component analysis for AMR</li>
antibiotic based on a LOINC code (<a href="https://amr-for-r.org/reference/ab_property.html">manual</a>)</li>
<li>Machine reading the EUCAST and CLSI guidelines from 2011-2021 to
translate MIC values and disk diffusion diameters to SIR (<a href="https://amr-for-r.org/articles/datasets.html">link</a>)</li>
<li>Principal component analysis for AMR (<a href="https://amr-for-r.org/articles/PCA.html">tutorial</a>)</li>
</ul>
<p>All reference data sets (about microorganisms, antimicrobials, SIR
interpretation, EUCAST rules, etc.) in this <code>AMR</code> package are
publicly and freely available. We continually export our data sets to
formats for use in R, SPSS, Stata and Excel. We also supply flat files
that are machine-readable and suitable for input in any software
program, such as laboratory information systems. Please find <a href="https://amr-for-r.org/articles/datasets.html">all download links
on our website</a>, which is automatically updated with every code
change.</p>
<p>This R package was created for both routine data analysis and
academic research at the Faculty of Medical Sciences of the <a href="https://www.rug.nl" class="external-link">University of Groningen</a>, in collaboration
with non-profit organisations <a href="https://www.certe.nl" class="external-link">Certe
Medical Diagnostics and Advice Foundation</a> and <a href="https://www.umcg.nl" class="external-link">University Medical Center Groningen</a>, and
is being <a href="https://amr-for-r.org/news/">actively and durably
maintained</a> by two public healthcare organisations in the
Netherlands.</p>
<p>All reference data sets in the AMR package - including information on
microorganisms, antimicrobials, and clinical breakpoints - are freely
available for download in multiple formats: R, MS Excel, Apache Feather,
Apache Parquet, SPSS, and Stata.</p>
<p>For maximum compatibility, we also provide machine-readable,
tab-separated plain text files suitable for use in any software,
including laboratory information systems.</p>
<p>Visit <a href="https://amr-for-r.org/articles/datasets.html">our
website for direct download links</a>, or explore the actual files in <a href="https://github.com/msberends/AMR/tree/main/data-raw/datasets" class="external-link">our
GitHub repository</a>.</p>
<hr>
<p><small> This AMR package for R is free, open-source software and
licensed under the <a href="https://amr-for-r.org/LICENSE-text.html">GNU