Built site for AMR@3.0.1.9075: 5f63723

Built site for AMR@3.0.1.9074: 3c17679
Built site for AMR@3.0.1.9073: 4ca7fdf
2026-09-16 18:29:38 +02:00 · 2026-06-26 10:15:27 +00:00 · 2026-06-26 10:07:44 +00:00 · 2026-06-26 10:00:02 +00:00 · 2026-06-26 09:45:33 +00:00 · 2026-06-26 07:36:21 +00:00
780 changed files with 77480 additions and 3902 deletions
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-.Rproj.user
-.Rhistory
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-.Ruserdata
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+
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-language: r
-cache: packages
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+    <a class="navbar-brand me-2" href="https://amr-for-r.org/index.html">AMR (for R)</a>
+
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9075</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">
+      <span class="navbar-toggler-icon"></span>
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+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto">
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-how-to" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true"><span class="fa fa-question-circle"></span> How to</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-how-to">
+<li><a class="dropdown-item" href="https://amr-for-r.org/articles/AMR.html"><span class="fa fa-directions"></span> Conduct AMR Analysis</a></li>
+    <li><a class="dropdown-item" href="https://amr-for-r.org/reference/antibiogram.html"><span class="fa fa-file-prescription"></span> Generate Antibiogram (Trad./Syndromic/WISCA)</a></li>
+    <li><a class="dropdown-item" href="https://amr-for-r.org/articles/AMR_with_tidymodels.html"><span class="fa fa-square-root-variable"></span> Use AMR for Predictive Modelling (tidymodels)</a></li>
+    <li><a class="dropdown-item" href="https://amr-for-r.org/articles/datasets.html"><span class="fa fa-database"></span> Download Data Sets for Own Use</a></li>
+    <li><a class="dropdown-item" href="https://amr-for-r.org/reference/AMR-options.html"><span class="fa fa-gear"></span> Set User- Or Team-specific Package Settings</a></li>
+    <li><a class="dropdown-item" href="https://amr-for-r.org/articles/PCA.html"><span class="fa fa-compress"></span> Conduct Principal Component Analysis for AMR</a></li>
+    <li><a class="dropdown-item" href="https://amr-for-r.org/reference/mdro.html"><span class="fa fa-skull-crossbones"></span> Determine Multi-Drug Resistance (MDR)</a></li>
+    <li><a class="dropdown-item" href="https://amr-for-r.org/articles/WHONET.html"><span class="fa fa-globe-americas"></span> Work with WHONET Data</a></li>
+    <li><a class="dropdown-item" href="https://amr-for-r.org/articles/EUCAST.html"><span class="fa fa-exchange-alt"></span> Apply EUCAST Rules</a></li>
+    <li><a class="dropdown-item" href="https://amr-for-r.org/reference/mo_property.html"><span class="fa fa-bug"></span> Get Taxonomy of a Microorganism</a></li>
+    <li><a class="dropdown-item" href="https://amr-for-r.org/reference/ab_property.html"><span class="fa fa-capsules"></span> Get Properties of an Antibiotic Drug</a></li>
+    <li><a class="dropdown-item" href="https://amr-for-r.org/reference/av_property.html"><span class="fa fa-capsules"></span> Get Properties of an Antiviral Drug</a></li>
+  </ul>
+</li>
+<li class="nav-item"><a class="nav-link" href="https://amr-for-r.org/articles/AMR_for_Python.html"><span class="fa fab fa-python"></span> AMR for Python</a></li>
+<li class="nav-item"><a class="nav-link" href="https://amr-for-r.org/reference/index.html"><span class="fa fa-book-open"></span> Manual</a></li>
+<li class="nav-item"><a class="nav-link" href="https://amr-for-r.org/authors.html"><span class="fa fa-users"></span> Authors</a></li>
+      </ul>
+<ul class="navbar-nav">
+<li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="search.json">
+</form></li>
+<li class="nav-item"><a class="nav-link" href="https://amr-for-r.org/news/index.html"><span class="fa fa-newspaper"></span> Changelog</a></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/msberends/AMR"><span class="fa fa-github"></span> Source Code</a></li>
+      </ul>
+</div>
+
+
+  </div>
+</nav><div class="container template-title-body">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="https://amr-for-r.org/logo.svg" class="logo" alt=""><h1>Page not found (404)</h1>
+
+    </div>
+
+Content not found. Please use links in the navbar.
+
+  </main>
+</div>
+
+
+    <footer><div class="pkgdown-footer-left">
+  <p><code>AMR</code> (for R). Free and open-source, licenced under the <a target="_blank" href="https://github.com/msberends/AMR/blob/main/LICENSE" class="external-link">GNU GPL 2.0</a>. Developed at the <a target="_blank" href="https://www.rug.nl" class="external-link">University of Groningen</a> and <a target="_blank" href="https://www.umcg.nl" class="external-link">University Medical Center Groningen</a> in The Netherlands, in collaboration with <a href="https://amr-for-r.org/authors.html">many colleagues from around the world</a>.</p>
+</div>
+
+<div class="pkgdown-footer-right">
+  <p><a target="_blank" href="https://www.rug.nl" class="external-link"><img src="https://amr-for-r.org/logo_rug.svg" style="max-width: 150px;"></a><a target="_blank" href="https://www.umcg.nl" class="external-link"><img src="https://amr-for-r.org/logo_umcg.svg" style="max-width: 150px;"></a></p>
+</div>
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+    </footer>
+</div>
+
+
+
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+
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+</html>
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+Content not found. Please use links in the navbar.
+
+# Page not found (404)
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+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>CLAUDE.md — AMR R Package • AMR (for R)</title><!-- favicons --><link rel="icon" type="image/png" sizes="96x96" href="favicon-96x96.png"><link rel="icon" type="”image/svg+xml”" href="favicon.svg"><link rel="apple-touch-icon" sizes="180x180" href="apple-touch-icon.png"><link rel="icon" sizes="any" href="favicon.ico"><link rel="manifest" href="site.webmanifest"><script src="deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="deps/bootstrap-5.3.8/bootstrap.min.css" rel="stylesheet"><script src="deps/bootstrap-5.3.8/bootstrap.bundle.min.js"></script><link href="deps/Lato-0.4.10/font.css" rel="stylesheet"><link href="deps/Fira_Code-0.4.10/font.css" rel="stylesheet"><link href="deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="deps/headroom-0.11.0/headroom.min.js"></script><script src="deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="deps/search-1.0.0/fuse.min.js"></script><script src="deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="pkgdown.js"></script><link href="extra.css" rel="stylesheet"><script src="extra.js"></script><meta property="og:title" content="CLAUDE.md — AMR R Package"><meta property="og:image" content="https://amr-for-r.org/logo.svg"><link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/katex@0.16.11/dist/katex.min.css" integrity="sha384-nB0miv6/jRmo5UMMR1wu3Gz6NLsoTkbqJghGIsx//Rlm+ZU03BU6SQNC66uf4l5+" crossorigin="anonymous"><script defer src="https://cdn.jsdelivr.net/npm/katex@0.16.11/dist/katex.min.js" integrity="sha384-7zkQWkzuo3B5mTepMUcHkMB5jZaolc2xDwL6VFqjFALcbeS9Ggm/Yr2r3Dy4lfFg" crossorigin="anonymous"></script><script defer src="https://cdn.jsdelivr.net/npm/katex@0.16.11/dist/contrib/auto-render.min.js" integrity="sha384-43gviWU0YVjaDtb/GhzOouOXtZMP/7XUzwPTstBeZFe/+rCMvRwr4yROQP43s0Xk" crossorigin="anonymous" onload="renderMathInElement(document.body);"></script></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-primary" data-bs-theme="dark" aria-label="Site navigation"><div class="container">
+
+    <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="">3.0.1.9075</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">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-how-to" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true"><span class="fa fa-question-circle"></span> How to</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-how-to"><li><a class="dropdown-item" href="articles/AMR.html"><span class="fa fa-directions"></span> Conduct AMR Analysis</a></li>
+    <li><a class="dropdown-item" href="reference/antibiogram.html"><span class="fa fa-file-prescription"></span> Generate Antibiogram (Trad./Syndromic/WISCA)</a></li>
+    <li><a class="dropdown-item" href="articles/AMR_with_tidymodels.html"><span class="fa fa-square-root-variable"></span> Use AMR for Predictive Modelling (tidymodels)</a></li>
+    <li><a class="dropdown-item" href="articles/datasets.html"><span class="fa fa-database"></span> Download Data Sets for Own Use</a></li>
+    <li><a class="dropdown-item" href="reference/AMR-options.html"><span class="fa fa-gear"></span> Set User- Or Team-specific Package Settings</a></li>
+    <li><a class="dropdown-item" href="articles/PCA.html"><span class="fa fa-compress"></span> Conduct Principal Component Analysis for AMR</a></li>
+    <li><a class="dropdown-item" href="reference/mdro.html"><span class="fa fa-skull-crossbones"></span> Determine Multi-Drug Resistance (MDR)</a></li>
+    <li><a class="dropdown-item" href="articles/WHONET.html"><span class="fa fa-globe-americas"></span> Work with WHONET Data</a></li>
+    <li><a class="dropdown-item" href="articles/EUCAST.html"><span class="fa fa-exchange-alt"></span> Apply EUCAST Rules</a></li>
+    <li><a class="dropdown-item" href="reference/mo_property.html"><span class="fa fa-bug"></span> Get Taxonomy of a Microorganism</a></li>
+    <li><a class="dropdown-item" href="reference/ab_property.html"><span class="fa fa-capsules"></span> Get Properties of an Antibiotic Drug</a></li>
+    <li><a class="dropdown-item" href="reference/av_property.html"><span class="fa fa-capsules"></span> Get Properties of an Antiviral Drug</a></li>
+  </ul></li>
+<li class="nav-item"><a class="nav-link" href="articles/AMR_for_Python.html"><span class="fa fab fa-python"></span> AMR for Python</a></li>
+<li class="nav-item"><a class="nav-link" href="reference/index.html"><span class="fa fa-book-open"></span> Manual</a></li>
+<li class="nav-item"><a class="nav-link" href="authors.html"><span class="fa fa-users"></span> Authors</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="search.json"></form></li>
+<li class="nav-item"><a class="nav-link" href="news/index.html"><span class="fa fa-newspaper"></span> Changelog</a></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/msberends/AMR"><span class="fa fa-github"></span> Source Code</a></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-title-body">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="logo.svg" class="logo" alt=""><h1>CLAUDE.md — AMR R Package</h1>
+      <small class="dont-index">Source: <a href="https://github.com/msberends/AMR/blob/main/CLAUDE.md" class="external-link"><code>CLAUDE.md</code></a></small>
+    </div>
+
+<div id="claudemd--amr-r-package" class="section level1">
+
+<p>This file provides context for Claude Code when working in this repository.</p>
+<div class="section level2">
+<h2 id="project-overview">Project Overview<a class="anchor" aria-label="anchor" href="#project-overview"></a></h2>
+<p><strong>AMR</strong> is a zero-dependency R package for antimicrobial resistance (AMR) data analysis using a One Health approach. It is peer-reviewed, used in 175+ countries, and supports 28 languages.</p>
+<p>Key capabilities: - SIR (Susceptible/Intermediate/Resistant) classification using EUCAST 2011–2025 and CLSI 2011–2025 breakpoints - Antibiogram generation: traditional, combined, syndromic, and WISCA - Microorganism taxonomy database (~79,000 species) - Antimicrobial drug database (~620 drugs) - Multi-drug resistant organism (MDRO) classification - First-isolate identification - Minimum Inhibitory Concentration (MIC) and disk diffusion handling - Multilingual output (28 languages)</p>
+</div>
+<div class="section level2">
+<h2 id="common-commands">Common Commands<a class="anchor" aria-label="anchor" href="#common-commands"></a></h2>
+<p>All commands run inside an R session:</p>
+<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="co"># Rebuild documentation (roxygen2 → .Rd files + NAMESPACE)</span></span>
+<span><span class="fu">devtools</span><span class="fu">::</span><span class="fu">document</span><span class="op">(</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Run all tests</span></span>
+<span><span class="fu">devtools</span><span class="fu">::</span><span class="fu">test</span><span class="op">(</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Full package check (CRAN-level: docs + tests + checks)</span></span>
+<span><span class="fu">devtools</span><span class="fu">::</span><span class="fu">check</span><span class="op">(</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Build pkgdown website locally</span></span>
+<span><span class="fu">pkgdown</span><span class="fu">::</span><span class="fu"><a href="https://pkgdown.r-lib.org/reference/build_site.html" class="external-link">build_site</a></span><span class="op">(</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Code coverage report</span></span>
+<span><span class="fu">covr</span><span class="fu">::</span><span class="fu"><a href="http://covr.r-lib.org/reference/package_coverage.html" class="external-link">package_coverage</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
+<p>From the shell:</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="co"># CRAN check from parent directory</span></span>
+<span id="cb2-2"><a href="#cb2-2" tabindex="-1"></a><span class="ex">R</span> CMD check AMR</span></code></pre></div>
+</div>
+<div class="section level2">
+<h2 id="repository-structure">Repository Structure<a class="anchor" aria-label="anchor" href="#repository-structure"></a></h2>
+<pre><code>R/              # All R source files (62 files, ~28,000 lines)
+man/            # Auto-generated .Rd documentation (do not edit manually)
+tests/testthat/ # testthat test files (test-*.R) and helper-functions.R
+data/           # Pre-compiled .rda datasets
+data-raw/       # Scripts used to generate data/ files
+vignettes/      # Rmd vignette articles
+inst/           # Installed files (translations, etc.)
+_pkgdown.yml    # pkgdown website configuration</code></pre>
+</div>
+<div class="section level2">
+<h2 id="r-source-file-conventions">R Source File Conventions<a class="anchor" aria-label="anchor" href="#r-source-file-conventions"></a></h2>
+<p><strong>Naming conventions in <code>R/</code>:</strong></p>
+<table class="table"><thead><tr><th>Prefix/Name</th>
+<th>Purpose</th>
+</tr></thead><tbody><tr><td><code>aa_*.R</code></td>
+<td>Loaded first (helpers, globals, options, package docs)</td>
+</tr><tr><td><code>zz_deprecated.R</code></td>
+<td>Deprecated function wrappers</td>
+</tr><tr><td><code>zzz.R</code></td>
+<td>
+<code>.onLoad</code> / <code>.onAttach</code> initialization</td>
+</tr></tbody></table><p><strong>Key source files:</strong></p>
+<ul><li>
+<code>aa_helper_functions.R</code> / <code>aa_helper_pm_functions.R</code> — internal utility functions (large; ~63 KB and ~37 KB)</li>
+<li>
+<code>aa_globals.R</code> — global constants and breakpoint lookup structures</li>
+<li>
+<code>aa_options.R</code> — <code>amr_options()</code> / <code>get_AMR_option()</code> system</li>
+<li>
+<code>mo.R</code> / <code>mo_property.R</code> — microorganism lookup and properties</li>
+<li>
+<code>ab.R</code> / <code>ab_property.R</code> — antimicrobial drug functions</li>
+<li>
+<code>av.R</code> / <code>av_property.R</code> — antiviral drug functions</li>
+<li>
+<code>sir.R</code> / <code>sir_calc.R</code> / <code>sir_df.R</code> — SIR classification engine</li>
+<li>
+<code>mic.R</code> / <code>disk.R</code> — MIC and disk diffusion classes</li>
+<li>
+<code>antibiogram.R</code> — antibiogram generation (traditional, combined, syndromic, WISCA)</li>
+<li>
+<code>first_isolate.R</code> — first-isolate identification algorithms</li>
+<li>
+<code>mdro.R</code> — MDRO classification (EUCAST, CLSI, CDC, custom guidelines)</li>
+<li>
+<code>amr_selectors.R</code> — tidyselect helpers for selecting AMR columns</li>
+<li>
+<code>interpretive_rules.R</code> / <code>custom_eucast_rules.R</code> — clinical interpretation rules</li>
+<li>
+<code>translate.R</code> — 28-language translation system</li>
+<li>
+<code>ggplot_sir.R</code> / <code>ggplot_pca.R</code> / <code>plotting.R</code> — visualisation functions</li>
+</ul></div>
+<div class="section level2">
+<h2 id="custom-s3-classes">Custom S3 Classes<a class="anchor" aria-label="anchor" href="#custom-s3-classes"></a></h2>
+<p>The package defines five S3 classes with full print/format/plot/vctrs support:</p>
+<table class="table"><thead><tr><th>Class</th>
+<th>Created by</th>
+<th>Represents</th>
+</tr></thead><tbody><tr><td><code>&lt;mo&gt;</code></td>
+<td><code><a href="reference/as.mo.html">as.mo()</a></code></td>
+<td>Microorganism code</td>
+</tr><tr><td><code>&lt;ab&gt;</code></td>
+<td><code><a href="reference/as.ab.html">as.ab()</a></code></td>
+<td>Antimicrobial drug code</td>
+</tr><tr><td><code>&lt;av&gt;</code></td>
+<td><code><a href="reference/as.av.html">as.av()</a></code></td>
+<td>Antiviral drug code</td>
+</tr><tr><td><code>&lt;sir&gt;</code></td>
+<td><code><a href="reference/as.sir.html">as.sir()</a></code></td>
+<td>SIR value (S/I/R/SDD)</td>
+</tr><tr><td><code>&lt;mic&gt;</code></td>
+<td><code><a href="reference/as.mic.html">as.mic()</a></code></td>
+<td>Minimum inhibitory concentration</td>
+</tr><tr><td><code>&lt;disk&gt;</code></td>
+<td><code><a href="reference/as.disk.html">as.disk()</a></code></td>
+<td>Disk diffusion diameter</td>
+</tr></tbody></table></div>
+<div class="section level2">
+<h2 id="data-files">Data Files<a class="anchor" aria-label="anchor" href="#data-files"></a></h2>
+<p>Pre-compiled in <code>data/</code> (do not edit directly; regenerate via <code>data-raw/</code> scripts):</p>
+<table class="table"><colgroup><col width="50%"><col width="50%"></colgroup><thead><tr><th>File</th>
+<th>Contents</th>
+</tr></thead><tbody><tr><td><code>microorganisms.rda</code></td>
+<td>~79,000 microbial species with full taxonomy</td>
+</tr><tr><td><code>antimicrobials.rda</code></td>
+<td>~620 antimicrobial drugs with ATC codes</td>
+</tr><tr><td><code>antivirals.rda</code></td>
+<td>Antiviral drugs</td>
+</tr><tr><td><code>clinical_breakpoints.rda</code></td>
+<td>EUCAST + CLSI breakpoints (2011–2025)</td>
+</tr><tr><td><code>intrinsic_resistant.rda</code></td>
+<td>Intrinsic resistance patterns</td>
+</tr><tr><td><code>example_isolates.rda</code></td>
+<td>Example AMR dataset for documentation/testing</td>
+</tr><tr><td><code>WHONET.rda</code></td>
+<td>Example WHONET-format dataset</td>
+</tr></tbody></table></div>
+<div class="section level2">
+<h2 id="zero-dependency-design">Zero-Dependency Design<a class="anchor" aria-label="anchor" href="#zero-dependency-design"></a></h2>
+<p>The package has <strong>no <code>Imports</code></strong> in <code>DESCRIPTION</code>. All optional integrations (ggplot2, dplyr, data.table, tidymodels, cli, crayon, etc.) are listed in <code>Suggests</code> and guarded with:</p>
+<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/ns-load.html" class="external-link">requireNamespace</a></span><span class="op">(</span><span class="st">"pkg"</span>, quietly <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span> <span class="va">...</span> <span class="op">}</span></span></code></pre></div>
+<p>Never add packages to <code>Imports</code>. If new functionality requires an external package, add it to <code>Suggests</code> and guard usage appropriately.</p>
+</div>
+<div class="section level2">
+<h2 id="testing">Testing<a class="anchor" aria-label="anchor" href="#testing"></a></h2>
+<ul><li>
+<strong>Framework:</strong> <code>testthat</code> (R ≥ 3.1); legacy <code>tinytest</code> used for R 3.0–3.6 CI</li>
+<li>
+<strong>Test files:</strong> <code>tests/testthat/test-*.R</code>
+</li>
+<li>
+<strong>Helpers:</strong> <code>tests/testthat/helper-functions.R</code>
+</li>
+<li>
+<strong>CI matrix:</strong> GitHub Actions across Windows / macOS / Linux × R devel / release / oldrel-1 through oldrel-4</li>
+<li>
+<strong>Coverage:</strong> <code>covr</code> (some files excluded: <code>atc_online.R</code>, <code>mo_source.R</code>, <code>translate.R</code>, <code>resistance_predict.R</code>, <code>zz_deprecated.R</code>, helper files, <code>zzz.R</code>)</li>
+</ul></div>
+<div class="section level2">
+<h2 id="documentation">Documentation<a class="anchor" aria-label="anchor" href="#documentation"></a></h2>
+<ul><li>All exported functions use <strong>roxygen2</strong> blocks (<code>RoxygenNote: 7.3.3</code>, markdown enabled)</li>
+<li>Run <code>devtools::document()</code> after any change to roxygen comments</li>
+<li>Never edit files in <code>man/</code> directly — they are auto-generated</li>
+<li>Vignettes live in <code>vignettes/</code> as <code>.Rmd</code> files</li>
+<li>The pkgdown website is configured in <code>_pkgdown.yml</code>
+</li>
+</ul></div>
+<div class="section level2">
+<h2 id="versioning">Versioning<a class="anchor" aria-label="anchor" href="#versioning"></a></h2>
+<p>Version format: <code>major.minor.patch.dev</code> (e.g., <code>3.0.1.9021</code>)</p>
+<ul><li>Development versions use a <code>.9xxx</code> suffix</li>
+<li>Stable CRAN releases drop the dev suffix (e.g., <code>3.0.1</code>)</li>
+<li>
+<code>NEWS.md</code> uses sections <strong>New</strong>, <strong>Fixes</strong>, <strong>Updates</strong> with GitHub issue references (<code>#NNN</code>)</li>
+</ul><div class="section level3">
+<h3 id="version-and-date-bump-required-for-every-pr">Version and date bump required for every PR<a class="anchor" aria-label="anchor" href="#version-and-date-bump-required-for-every-pr"></a></h3>
+<p>All PRs are <strong>squash-merged</strong>, so each PR lands as exactly <strong>one commit</strong> on the default branch. Version numbers are kept in sync with the cumulative commit count since the last released tag. Therefore <strong>exactly one version bump is allowed per PR</strong>, regardless of how many intermediate commits are made on the branch.</p>
+<div class="section level4">
+<h4 id="computing-the-correct-version-number">Computing the correct version number<a class="anchor" aria-label="anchor" href="#computing-the-correct-version-number"></a></h4>
+<p><strong>First, ensure <code>git</code> and <code>gh</code> are installed</strong> — both are required for the version computation and for pushing changes. Install them if missing before doing anything else:</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="fu">which</span> git <span class="kw">||</span> <span class="ex">apt-get</span> install <span class="at">-y</span> git</span>
+<span id="cb5-2"><a href="#cb5-2" tabindex="-1"></a><span class="fu">which</span> gh   <span class="kw">||</span> <span class="ex">apt-get</span> install <span class="at">-y</span> gh</span>
+<span id="cb5-3"><a href="#cb5-3" tabindex="-1"></a><span class="co"># Also ensure all tags are fetched so git describe works</span></span>
+<span id="cb5-4"><a href="#cb5-4" tabindex="-1"></a><span class="fu">git</span> fetch <span class="at">--tags</span></span></code></pre></div>
+<p>Then run the following from the repo root to determine the version string to use:</p>
+<div class="sourceCode" id="cb6"><pre class="sourceCode bash"><code class="sourceCode bash"><span id="cb6-1"><a href="#cb6-1" tabindex="-1"></a><span class="va">currenttag</span><span class="op">=</span><span class="va">$(</span><span class="fu">git</span> describe <span class="at">--tags</span> <span class="at">--abbrev</span><span class="op">=</span>0 <span class="kw">|</span> <span class="fu">sed</span> <span class="st">'s/v//'</span><span class="va">)</span></span>
+<span id="cb6-2"><a href="#cb6-2" tabindex="-1"></a><span class="va">currenttagfull</span><span class="op">=</span><span class="va">$(</span><span class="fu">git</span> describe <span class="at">--tags</span> <span class="at">--abbrev</span><span class="op">=</span>0<span class="va">)</span></span>
+<span id="cb6-3"><a href="#cb6-3" tabindex="-1"></a><span class="va">defaultbranch</span><span class="op">=</span><span class="va">$(</span><span class="fu">git</span> branch <span class="kw">|</span> <span class="fu">cut</span> <span class="at">-c</span> 3- <span class="kw">|</span> <span class="fu">grep</span> <span class="at">-E</span> <span class="st">'^master$|^main$'</span><span class="va">)</span></span>
+<span id="cb6-4"><a href="#cb6-4" tabindex="-1"></a><span class="fu">git</span> fetch origin <span class="va">${defaultbranch}</span> <span class="at">--quiet</span></span>
+<span id="cb6-5"><a href="#cb6-5" tabindex="-1"></a><span class="va">currentcommit</span><span class="op">=</span><span class="va">$(</span><span class="fu">git</span> rev-list <span class="at">--count</span> <span class="va">${currenttagfull}</span>..origin/<span class="va">${defaultbranch})</span></span>
+<span id="cb6-6"><a href="#cb6-6" tabindex="-1"></a><span class="va">currentversion</span><span class="op">=</span><span class="st">"</span><span class="va">${currenttag}</span><span class="st">.</span><span class="va">$((currentcommit</span> <span class="op">+</span> <span class="dv">9001</span> <span class="op">+</span> <span class="dv">1</span><span class="va">))</span><span class="st">"</span></span>
+<span id="cb6-7"><a href="#cb6-7" tabindex="-1"></a><span class="bu">echo</span> <span class="st">"</span><span class="va">$currentversion</span><span class="st">"</span></span></code></pre></div>
+<p>The <code>+ 1</code> accounts for the fact that this PR’s squash commit is not yet on the default branch. Set <strong>both</strong> of these files to the resulting version string (and only once per PR, even across multiple commits):</p>
+<ol style="list-style-type: decimal"><li><p><strong><code>DESCRIPTION</code></strong> — the <code>Version:</code> field</p></li>
+<li>
+<p><strong><code>NEWS.md</code></strong> — <strong>only replace line 1</strong> (the <code># AMR &lt;version&gt;</code> heading) with the new version number; do <strong>not</strong> create a new section. <code>NEWS.md</code> is a <strong>continuous log</strong> for the entire current <code>x.y.z.9nnn</code> development series: all changes since the last stable release accumulate under that single heading. After updating line 1, append the new change as a bullet under the appropriate sub-heading (<code>### New</code>, <code>### Fixes</code>, or <code>### Updates</code>).</p>
+<p>Style rules for <code>NEWS.md</code> entries:</p>
+<ul><li>Be <strong>extremely concise</strong> — one short line per item</li>
+<li>Do <strong>not</strong> end with a full stop (period)</li>
+<li>No verbose explanations; just the essential fact</li>
+</ul></li>
+</ol><p>If <code>git describe</code> fails (e.g. no tags exist in the environment), fall back to reading the current version from <code>DESCRIPTION</code> and adding 1 to the last numeric component — but only if no bump has already been made in this PR.</p>
+</div>
+<div class="section level4">
+<h4 id="date-field">Date field<a class="anchor" aria-label="anchor" href="#date-field"></a></h4>
+<p>The <code>Date:</code> field in <code>DESCRIPTION</code> must reflect the date of the <strong>last commit to the PR</strong> (not the first), in ISO format. Update it with every commit so it is always current:</p>
+<pre><code><span><span class="va">Date</span><span class="op">:</span> <span class="fl">2026</span><span class="op">-</span><span class="fl">03</span><span class="op">-</span><span class="fl">07</span></span></code></pre>
+</div>
+</div>
+</div>
+<div class="section level2">
+<h2 id="internal-state">Internal State<a class="anchor" aria-label="anchor" href="#internal-state"></a></h2>
+<p>The package uses a private <code>AMR_env</code> environment (created in <code>aa_globals.R</code>) for caching expensive lookups (e.g., microorganism matching scores, breakpoint tables). This avoids re-computation within a session.</p>
+</div>
+</div>
+
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
+
+
+    <footer><div class="pkgdown-footer-left">
+  <p><code>AMR</code> (for R). Free and open-source, licenced under the <a target="_blank" href="https://github.com/msberends/AMR/blob/main/LICENSE" class="external-link">GNU GPL 2.0</a>. Developed at the <a target="_blank" href="https://www.rug.nl" class="external-link">University of Groningen</a> and <a target="_blank" href="https://www.umcg.nl" class="external-link">University Medical Center Groningen</a> in The Netherlands, in collaboration with <a href="https://amr-for-r.org/authors.html">many colleagues from around the world</a>.</p>
+</div>
+
+<div class="pkgdown-footer-right">
+  <p><a target="_blank" href="https://www.rug.nl" class="external-link"><img src="https://amr-for-r.org/logo_rug.svg" style="max-width: 150px;"></a><a target="_blank" href="https://www.umcg.nl" class="external-link"><img src="https://amr-for-r.org/logo_umcg.svg" style="max-width: 150px;"></a></p>
+</div>
+
+    </footer></div>
+
+
+
+
+
+  </body></html>
+
@@ -0,0 +1,238 @@
+# CLAUDE.md — AMR R Package
+
+This file provides context for Claude Code when working in this
+repository.
+
+## Project Overview
+
+**AMR** is a zero-dependency R package for antimicrobial resistance
+(AMR) data analysis using a One Health approach. It is peer-reviewed,
+used in 175+ countries, and supports 28 languages.
+
+Key capabilities: - SIR (Susceptible/Intermediate/Resistant)
+classification using EUCAST 2011–2025 and CLSI 2011–2025 breakpoints -
+Antibiogram generation: traditional, combined, syndromic, and WISCA -
+Microorganism taxonomy database (~79,000 species) - Antimicrobial drug
+database (~620 drugs) - Multi-drug resistant organism (MDRO)
+classification - First-isolate identification - Minimum Inhibitory
+Concentration (MIC) and disk diffusion handling - Multilingual output
+(28 languages)
+
+## Common Commands
+
+All commands run inside an R session:
+
+``` r
+
+# Rebuild documentation (roxygen2 → .Rd files + NAMESPACE)
+devtools::document()
+
+# Run all tests
+devtools::test()
+
+# Full package check (CRAN-level: docs + tests + checks)
+devtools::check()
+
+# Build pkgdown website locally
+pkgdown::build_site()
+
+# Code coverage report
+covr::package_coverage()
+```
+
+From the shell:
+
+``` bash
+# CRAN check from parent directory
+R CMD check AMR
+```
+
+## Repository Structure
+
+    R/              # All R source files (62 files, ~28,000 lines)
+    man/            # Auto-generated .Rd documentation (do not edit manually)
+    tests/testthat/ # testthat test files (test-*.R) and helper-functions.R
+    data/           # Pre-compiled .rda datasets
+    data-raw/       # Scripts used to generate data/ files
+    vignettes/      # Rmd vignette articles
+    inst/           # Installed files (translations, etc.)
+    _pkgdown.yml    # pkgdown website configuration
+
+## R Source File Conventions
+
+**Naming conventions in `R/`:**
+
+| Prefix/Name       | Purpose                                                |
+|-------------------|--------------------------------------------------------|
+| `aa_*.R`          | Loaded first (helpers, globals, options, package docs) |
+| `zz_deprecated.R` | Deprecated function wrappers                           |
+| `zzz.R`           | `.onLoad` / `.onAttach` initialization                 |
+
+**Key source files:**
+
+- `aa_helper_functions.R` / `aa_helper_pm_functions.R` — internal
+  utility functions (large; ~63 KB and ~37 KB)
+- `aa_globals.R` — global constants and breakpoint lookup structures
+- `aa_options.R` — `amr_options()` / `get_AMR_option()` system
+- `mo.R` / `mo_property.R` — microorganism lookup and properties
+- `ab.R` / `ab_property.R` — antimicrobial drug functions
+- `av.R` / `av_property.R` — antiviral drug functions
+- `sir.R` / `sir_calc.R` / `sir_df.R` — SIR classification engine
+- `mic.R` / `disk.R` — MIC and disk diffusion classes
+- `antibiogram.R` — antibiogram generation (traditional, combined,
+  syndromic, WISCA)
+- `first_isolate.R` — first-isolate identification algorithms
+- `mdro.R` — MDRO classification (EUCAST, CLSI, CDC, custom guidelines)
+- `amr_selectors.R` — tidyselect helpers for selecting AMR columns
+- `interpretive_rules.R` / `custom_eucast_rules.R` — clinical
+  interpretation rules
+- `translate.R` — 28-language translation system
+- `ggplot_sir.R` / `ggplot_pca.R` / `plotting.R` — visualisation
+  functions
+
+## Custom S3 Classes
+
+The package defines five S3 classes with full print/format/plot/vctrs
+support:
+
+| Class | Created by | Represents |
+|----|----|----|
+| `<mo>` | [`as.mo()`](https://amr-for-r.org/reference/as.mo.md) | Microorganism code |
+| `<ab>` | [`as.ab()`](https://amr-for-r.org/reference/as.ab.md) | Antimicrobial drug code |
+| `<av>` | [`as.av()`](https://amr-for-r.org/reference/as.av.md) | Antiviral drug code |
+| `<sir>` | [`as.sir()`](https://amr-for-r.org/reference/as.sir.md) | SIR value (S/I/R/SDD) |
+| `<mic>` | [`as.mic()`](https://amr-for-r.org/reference/as.mic.md) | Minimum inhibitory concentration |
+| `<disk>` | [`as.disk()`](https://amr-for-r.org/reference/as.disk.md) | Disk diffusion diameter |
+
+## Data Files
+
+Pre-compiled in `data/` (do not edit directly; regenerate via
+`data-raw/` scripts):
+
+| File                       | Contents                                      |
+|----------------------------|-----------------------------------------------|
+| `microorganisms.rda`       | ~79,000 microbial species with full taxonomy  |
+| `antimicrobials.rda`       | ~620 antimicrobial drugs with ATC codes       |
+| `antivirals.rda`           | Antiviral drugs                               |
+| `clinical_breakpoints.rda` | EUCAST + CLSI breakpoints (2011–2025)         |
+| `intrinsic_resistant.rda`  | Intrinsic resistance patterns                 |
+| `example_isolates.rda`     | Example AMR dataset for documentation/testing |
+| `WHONET.rda`               | Example WHONET-format dataset                 |
+
+## Zero-Dependency Design
+
+The package has **no `Imports`** in `DESCRIPTION`. All optional
+integrations (ggplot2, dplyr, data.table, tidymodels, cli, crayon, etc.)
+are listed in `Suggests` and guarded with:
+
+``` r
+
+if (requireNamespace("pkg", quietly = TRUE)) { ... }
+```
+
+Never add packages to `Imports`. If new functionality requires an
+external package, add it to `Suggests` and guard usage appropriately.
+
+## Testing
+
+- **Framework:** `testthat` (R ≥ 3.1); legacy `tinytest` used for R
+  3.0–3.6 CI
+- **Test files:** `tests/testthat/test-*.R`
+- **Helpers:** `tests/testthat/helper-functions.R`
+- **CI matrix:** GitHub Actions across Windows / macOS / Linux × R devel
+  / release / oldrel-1 through oldrel-4
+- **Coverage:** `covr` (some files excluded: `atc_online.R`,
+  `mo_source.R`, `translate.R`, `resistance_predict.R`,
+  `zz_deprecated.R`, helper files, `zzz.R`)
+
+## Documentation
+
+- All exported functions use **roxygen2** blocks (`RoxygenNote: 7.3.3`,
+  markdown enabled)
+- Run `devtools::document()` after any change to roxygen comments
+- Never edit files in `man/` directly — they are auto-generated
+- Vignettes live in `vignettes/` as `.Rmd` files
+- The pkgdown website is configured in `_pkgdown.yml`
+
+## Versioning
+
+Version format: `major.minor.patch.dev` (e.g., `3.0.1.9021`)
+
+- Development versions use a `.9xxx` suffix
+- Stable CRAN releases drop the dev suffix (e.g., `3.0.1`)
+- `NEWS.md` uses sections **New**, **Fixes**, **Updates** with GitHub
+  issue references (`#NNN`)
+
+### Version and date bump required for every PR
+
+All PRs are **squash-merged**, so each PR lands as exactly **one
+commit** on the default branch. Version numbers are kept in sync with
+the cumulative commit count since the last released tag. Therefore
+**exactly one version bump is allowed per PR**, regardless of how many
+intermediate commits are made on the branch.
+
+#### Computing the correct version number
+
+**First, ensure `git` and `gh` are installed** — both are required for
+the version computation and for pushing changes. Install them if missing
+before doing anything else:
+
+``` bash
+which git || apt-get install -y git
+which gh   || apt-get install -y gh
+# Also ensure all tags are fetched so git describe works
+git fetch --tags
+```
+
+Then run the following from the repo root to determine the version
+string to use:
+
+``` bash
+currenttag=$(git describe --tags --abbrev=0 | sed 's/v//')
+currenttagfull=$(git describe --tags --abbrev=0)
+defaultbranch=$(git branch | cut -c 3- | grep -E '^master$|^main$')
+git fetch origin ${defaultbranch} --quiet
+currentcommit=$(git rev-list --count ${currenttagfull}..origin/${defaultbranch})
+currentversion="${currenttag}.$((currentcommit + 9001 + 1))"
+echo "$currentversion"
+```
+
+The `+ 1` accounts for the fact that this PR’s squash commit is not yet
+on the default branch. Set **both** of these files to the resulting
+version string (and only once per PR, even across multiple commits):
+
+1.  **`DESCRIPTION`** — the `Version:` field
+
+2.  **`NEWS.md`** — **only replace line 1** (the `# AMR <version>`
+    heading) with the new version number; do **not** create a new
+    section. `NEWS.md` is a **continuous log** for the entire current
+    `x.y.z.9nnn` development series: all changes since the last stable
+    release accumulate under that single heading. After updating line 1,
+    append the new change as a bullet under the appropriate sub-heading
+    (`### New`, `### Fixes`, or `### Updates`).
+
+    Style rules for `NEWS.md` entries:
+
+    - Be **extremely concise** — one short line per item
+    - Do **not** end with a full stop (period)
+    - No verbose explanations; just the essential fact
+
+If `git describe` fails (e.g. no tags exist in the environment), fall
+back to reading the current version from `DESCRIPTION` and adding 1 to
+the last numeric component — but only if no bump has already been made
+in this PR.
+
+#### Date field
+
+The `Date:` field in `DESCRIPTION` must reflect the date of the **last
+commit to the PR** (not the first), in ISO format. Update it with every
+commit so it is always current:
+
+    Date: 2026-03-07
+
+## Internal State
+
+The package uses a private `AMR_env` environment (created in
+`aa_globals.R`) for caching expensive lookups (e.g., microorganism
+matching scores, breakpoint tables). This avoids re-computation within a
+session.
@@ -0,0 +1 @@
+amr-for-r.org
@@ -1,32 +0,0 @@
-Package: AMR
-Version: 0.1.1
-Date: 2018-03-13
-Title: Antimicrobial Resistance Analysis
-Authors@R: c(
-    person(
-        given = c("Matthijs", "S."),
-        family = "Berends",
-        email = "m.s.berends@umcg.nl",
-        role = c("aut", "cre")),
-    person(
-        given = c("Christian", "F."),
-        family = "Luz",
-        email = "c.f.luz@umcg.nl",
-        role = c("aut", "ctb")),
-    person(
-        given = c("Erwin", "E.A."),
-        family = "Hassing",
-        email = "e.hassing@certe.nl",
-        role = "ctb"))
-Description: Functions to simplify the analysis of Antimicrobial Resistance (AMR)
-    of microbial isolates, by using new S3 classes and applying EUCAST expert rules
-    on antibiograms according to Leclercq (2013)
-    <doi:10.1111/j.1469-0691.2011.03703.x>.
-Depends: R (>= 3.0)
-Imports: dplyr (>= 0.7.0), reshape2 (>= 1.4.0), xml2, rvest
-URL: https://github.com/msberends/AMR
-BugReports: https://github.com/msberends/AMR/issues
-License: GPL-2 | file LICENSE
-Encoding: UTF-8
-LazyData: true
-RoxygenNote: 6.0.1.9000
@@ -1,339 +0,0 @@
-                    GNU GENERAL PUBLIC LICENSE
-                       Version 2, June 1991
-
- Copyright (C) 1989, 1991 Free Software Foundation, Inc., <http://fsf.org/>
- 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
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@@ -0,0 +1,319 @@
+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>License • AMR (for R)</title><!-- favicons --><link rel="icon" type="image/png" sizes="96x96" href="favicon-96x96.png"><link rel="icon" type="”image/svg+xml”" href="favicon.svg"><link rel="apple-touch-icon" sizes="180x180" href="apple-touch-icon.png"><link rel="icon" sizes="any" href="favicon.ico"><link rel="manifest" href="site.webmanifest"><script src="deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="deps/bootstrap-5.3.8/bootstrap.min.css" rel="stylesheet"><script src="deps/bootstrap-5.3.8/bootstrap.bundle.min.js"></script><link href="deps/Lato-0.4.10/font.css" rel="stylesheet"><link href="deps/Fira_Code-0.4.10/font.css" rel="stylesheet"><link href="deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="deps/headroom-0.11.0/headroom.min.js"></script><script src="deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="deps/search-1.0.0/fuse.min.js"></script><script src="deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="pkgdown.js"></script><link href="extra.css" rel="stylesheet"><script src="extra.js"></script><meta property="og:title" content="License"><meta property="og:image" content="https://amr-for-r.org/logo.svg"><link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/katex@0.16.11/dist/katex.min.css" integrity="sha384-nB0miv6/jRmo5UMMR1wu3Gz6NLsoTkbqJghGIsx//Rlm+ZU03BU6SQNC66uf4l5+" crossorigin="anonymous"><script defer src="https://cdn.jsdelivr.net/npm/katex@0.16.11/dist/katex.min.js" integrity="sha384-7zkQWkzuo3B5mTepMUcHkMB5jZaolc2xDwL6VFqjFALcbeS9Ggm/Yr2r3Dy4lfFg" crossorigin="anonymous"></script><script defer src="https://cdn.jsdelivr.net/npm/katex@0.16.11/dist/contrib/auto-render.min.js" integrity="sha384-43gviWU0YVjaDtb/GhzOouOXtZMP/7XUzwPTstBeZFe/+rCMvRwr4yROQP43s0Xk" crossorigin="anonymous" onload="renderMathInElement(document.body);"></script></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-primary" data-bs-theme="dark" aria-label="Site navigation"><div class="container">
+
+    <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="">3.0.1.9075</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">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
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+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-how-to" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true"><span class="fa fa-question-circle"></span> How to</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-how-to"><li><a class="dropdown-item" href="articles/AMR.html"><span class="fa fa-directions"></span> Conduct AMR Analysis</a></li>
+    <li><a class="dropdown-item" href="reference/antibiogram.html"><span class="fa fa-file-prescription"></span> Generate Antibiogram (Trad./Syndromic/WISCA)</a></li>
+    <li><a class="dropdown-item" href="articles/AMR_with_tidymodels.html"><span class="fa fa-square-root-variable"></span> Use AMR for Predictive Modelling (tidymodels)</a></li>
+    <li><a class="dropdown-item" href="articles/datasets.html"><span class="fa fa-database"></span> Download Data Sets for Own Use</a></li>
+    <li><a class="dropdown-item" href="reference/AMR-options.html"><span class="fa fa-gear"></span> Set User- Or Team-specific Package Settings</a></li>
+    <li><a class="dropdown-item" href="articles/PCA.html"><span class="fa fa-compress"></span> Conduct Principal Component Analysis for AMR</a></li>
+    <li><a class="dropdown-item" href="reference/mdro.html"><span class="fa fa-skull-crossbones"></span> Determine Multi-Drug Resistance (MDR)</a></li>
+    <li><a class="dropdown-item" href="articles/WHONET.html"><span class="fa fa-globe-americas"></span> Work with WHONET Data</a></li>
+    <li><a class="dropdown-item" href="articles/EUCAST.html"><span class="fa fa-exchange-alt"></span> Apply EUCAST Rules</a></li>
+    <li><a class="dropdown-item" href="reference/mo_property.html"><span class="fa fa-bug"></span> Get Taxonomy of a Microorganism</a></li>
+    <li><a class="dropdown-item" href="reference/ab_property.html"><span class="fa fa-capsules"></span> Get Properties of an Antibiotic Drug</a></li>
+    <li><a class="dropdown-item" href="reference/av_property.html"><span class="fa fa-capsules"></span> Get Properties of an Antiviral Drug</a></li>
+  </ul></li>
+<li class="nav-item"><a class="nav-link" href="articles/AMR_for_Python.html"><span class="fa fab fa-python"></span> AMR for Python</a></li>
+<li class="nav-item"><a class="nav-link" href="reference/index.html"><span class="fa fa-book-open"></span> Manual</a></li>
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+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="search.json"></form></li>
+<li class="nav-item"><a class="nav-link" href="news/index.html"><span class="fa fa-newspaper"></span> Changelog</a></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/msberends/AMR"><span class="fa fa-github"></span> Source Code</a></li>
+      </ul></div>
+
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+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="logo.svg" class="logo" alt=""><h1>License</h1>
+
+    </div>
+
+<pre>GNU GENERAL PUBLIC LICENSE
+Version 2, June 1991
+
+Copyright (C) 1989, 1991 Free Software Foundation, Inc., &lt;http://fsf.org/&gt;
+  51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
+Everyone is permitted to copy and distribute verbatim copies
+of this license document, but changing it is not allowed.
+
+A SUMMARY OF THIS LICENSE BY THE ORIGINAL AUTHORS OF THE AMR R PACKAGE
+
+This R package, with package name 'AMR':
+- May be used for commercial purposes
+- May be used for private purposes
+- May NOT be used for patent purposes
+- May be modified, although:
+  - Modifications MUST be released under the same license when distributing the package
+  - Changes made to the code MUST be documented
+- May be distributed, although:
+  - Source code MUST be made available when the package is distributed
+  - A copy of the license and copyright notice MUST be included with the package.
+- Comes with a LIMITATION of liability
+- Comes with NO warranty
+
+END OF THE SUMMARY
+
+
+GNU GENERAL PUBLIC LICENSE
+TERMS AND CONDITIONS FOR COPYING, DISTRIBUTION AND MODIFICATION
+
+0. This License applies to any program or other work which contains
+a notice placed by the copyright holder saying it may be distributed
+under the terms of this General Public License. The "Program", below,
+refers to any such program or work, and a "work based on the Program"
+means either the Program or any derivative work under copyright law:
+that is to say, a work containing the Program or a portion of it,
+either verbatim or with modifications and/or translated into another
+language. (Hereinafter, translation is included without limitation in
+the term "modification".) Each licensee is addressed as "you".
+
+Activities other than copying, distribution and modification are not
+covered by this License; they are outside its scope. The act of
+running the Program is not restricted, and the output from the Program
+is covered only if its contents constitute a work based on the
+Program (independent of having been made by running the Program).
+Whether that is true depends on what the Program does.
+
+1. You may copy and distribute verbatim copies of the Program's
+source code as you receive it, in any medium, provided that you
+conspicuously and appropriately publish on each copy an appropriate
+copyright notice and disclaimer of warranty; keep intact all the
+notices that refer to this License and to the absence of any warranty;
+and give any other recipients of the Program a copy of this License
+along with the Program.
+
+You may charge a fee for the physical act of transferring a copy, and
+you may at your option offer warranty protection in exchange for a fee.
+
+2. You may modify your copy or copies of the Program or any portion
+of it, thus forming a work based on the Program, and copy and
+distribute such modifications or work under the terms of Section 1
+above, provided that you also meet all of these conditions:
+
+    a) You must cause the modified files to carry prominent notices
+    stating that you changed the files and the date of any change.
+
+    b) You must cause any work that you distribute or publish, that in
+    whole or in part contains or is derived from the Program or any
+    part thereof, to be licensed as a whole at no charge to all third
+    parties under the terms of this License.
+
+    c) If the modified program normally reads commands interactively
+    when run, you must cause it, when started running for such
+    interactive use in the most ordinary way, to print or display an
+    announcement including an appropriate copyright notice and a
+    notice that there is no warranty (or else, saying that you provide
+    a warranty) and that users may redistribute the program under
+    these conditions, and telling the user how to view a copy of this
+    License.  (Exception: if the Program itself is interactive but
+    does not normally print such an announcement, your work based on
+    the Program is not required to print an announcement.)
+
+These requirements apply to the modified work as a whole. If
+identifiable sections of that work are not derived from the Program,
+and can be reasonably considered independent and separate works in
+themselves, then this License, and its terms, do not apply to those
+sections when you distribute them as separate works. But when you
+distribute the same sections as part of a whole which is a work based
+on the Program, the distribution of the whole must be on the terms of
+this License, whose permissions for other licensees extend to the
+entire whole, and thus to each and every part regardless of who wrote it.
+
+Thus, it is not the intent of this section to claim rights or contest
+your rights to work written entirely by you; rather, the intent is to
+exercise the right to control the distribution of derivative or
+collective works based on the Program.
+
+In addition, mere aggregation of another work not based on the Program
+with the Program (or with a work based on the Program) on a volume of
+a storage or distribution medium does not bring the other work under
+the scope of this License.
+
+3. You may copy and distribute the Program (or a work based on it,
+under Section 2) in object code or executable form under the terms of
+Sections 1 and 2 above provided that you also do one of the following:
+
+    a) Accompany it with the complete corresponding machine-readable
+    source code, which must be distributed under the terms of Sections
+    1 and 2 above on a medium customarily used for software interchange; or,
+
+    b) Accompany it with a written offer, valid for at least three
+    years, to give any third party, for a charge no more than your
+    cost of physically performing source distribution, a complete
+    machine-readable copy of the corresponding source code, to be
+    distributed under the terms of Sections 1 and 2 above on a medium
+    customarily used for software interchange; or,
+
+    c) Accompany it with the information you received as to the offer
+    to distribute corresponding source code.  (This alternative is
+    allowed only for noncommercial distribution and only if you
+    received the program in object code or executable form with such
+    an offer, in accord with Subsection b above.)
+
+The source code for a work means the preferred form of the work for
+making modifications to it. For an executable work, complete source
+code means all the source code for all modules it contains, plus any
+associated interface definition files, plus the scripts used to
+control compilation and installation of the executable. However, as a
+special exception, the source code distributed need not include
+anything that is normally distributed (in either source or binary
+form) with the major components (compiler, kernel, and so on) of the
+operating system on which the executable runs, unless that component
+itself accompanies the executable.
+
+If distribution of executable or object code is made by offering
+access to copy from a designated place, then offering equivalent
+access to copy the source code from the same place counts as
+distribution of the source code, even though third parties are not
+compelled to copy the source along with the object code.
+
+4. You may not copy, modify, sublicense, or distribute the Program
+except as expressly provided under this License. Any attempt
+otherwise to copy, modify, sublicense or distribute the Program is
+void, and will automatically terminate your rights under this License.
+However, parties who have received copies, or rights, from you under
+this License will not have their licenses terminated so long as such
+parties remain in full compliance.
+
+5. You are not required to accept this License, since you have not
+signed it. However, nothing else grants you permission to modify or
+distribute the Program or its derivative works. These actions are
+prohibited by law if you do not accept this License. Therefore, by
+modifying or distributing the Program (or any work based on the
+Program), you indicate your acceptance of this License to do so, and
+all its terms and conditions for copying, distributing or modifying
+the Program or works based on it.
+
+6. Each time you redistribute the Program (or any work based on the
+Program), the recipient automatically receives a license from the
+original licensor to copy, distribute or modify the Program subject to
+these terms and conditions. You may not impose any further
+restrictions on the recipients' exercise of the rights granted herein.
+You are not responsible for enforcing compliance by third parties to
+this License.
+
+7. If, as a consequence of a court judgment or allegation of patent
+infringement or for any other reason (not limited to patent issues),
+conditions are imposed on you (whether by court order, agreement or
+otherwise) that contradict the conditions of this License, they do not
+excuse you from the conditions of this License. If you cannot
+distribute so as to satisfy simultaneously your obligations under this
+License and any other pertinent obligations, then as a consequence you
+may not distribute the Program at all. For example, if a patent
+license would not permit royalty-free redistribution of the Program by
+all those who receive copies directly or indirectly through you, then
+the only way you could satisfy both it and this License would be to
+refrain entirely from distribution of the Program.
+
+If any portion of this section is held invalid or unenforceable under
+any particular circumstance, the balance of the section is intended to
+apply and the section as a whole is intended to apply in other
+circumstances.
+
+It is not the purpose of this section to induce you to infringe any
+patents or other property right claims or to contest validity of any
+such claims; this section has the sole purpose of protecting the
+integrity of the free software distribution system, which is
+implemented by public license practices. Many people have made
+generous contributions to the wide range of software distributed
+through that system in reliance on consistent application of that
+system; it is up to the author/donor to decide if he or she is willing
+to distribute software through any other system and a licensee cannot
+impose that choice.
+
+This section is intended to make thoroughly clear what is believed to
+be a consequence of the rest of this License.
+
+8. If the distribution and/or use of the Program is restricted in
+certain countries either by patents or by copyrighted interfaces, the
+original copyright holder who places the Program under this License
+may add an explicit geographical distribution limitation excluding
+those countries, so that distribution is permitted only in or among
+countries not thus excluded. In such case, this License incorporates
+the limitation as if written in the body of this License.
+
+9. The Free Software Foundation may publish revised and/or new versions
+of the General Public License from time to time. Such new versions will
+be similar in spirit to the present version, but may differ in detail to
+address new problems or concerns.
+
+Each version is given a distinguishing version number. If the Program
+specifies a version number of this License which applies to it and "any
+later version", you have the option of following the terms and conditions
+either of that version or of any later version published by the Free
+Software Foundation. If the Program does not specify a version number of
+this License, you may choose any version ever published by the Free Software
+Foundation.
+
+10. If you wish to incorporate parts of the Program into other free
+programs whose distribution conditions are different, write to the author
+to ask for permission. For software which is copyrighted by the Free
+Software Foundation, write to the Free Software Foundation; we sometimes
+make exceptions for this. Our decision will be guided by the two goals
+of preserving the free status of all derivatives of our free software and
+of promoting the sharing and reuse of software generally.
+
+NO WARRANTY
+
+11. BECAUSE THE PROGRAM IS LICENSED FREE OF CHARGE, THERE IS NO WARRANTY
+FOR THE PROGRAM, TO THE EXTENT PERMITTED BY APPLICABLE LAW. EXCEPT WHEN
+OTHERWISE STATED IN WRITING THE COPYRIGHT HOLDERS AND/OR OTHER PARTIES
+PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY OF ANY KIND, EITHER EXPRESSED
+OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF
+MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE ENTIRE RISK AS
+TO THE QUALITY AND PERFORMANCE OF THE PROGRAM IS WITH YOU. SHOULD THE
+PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF ALL NECESSARY SERVICING,
+REPAIR OR CORRECTION.
+
+12. IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
+WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MAY MODIFY AND/OR
+REDISTRIBUTE THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES,
+INCLUDING ANY GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING
+OUT OF THE USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED
+TO LOSS OF DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY
+YOU OR THIRD PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER
+PROGRAMS), EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE
+POSSIBILITY OF SUCH DAMAGES.
+
+END OF TERMS AND CONDITIONS
+</pre>
+
+  </main></div>
+
+
+    <footer><div class="pkgdown-footer-left">
+  <p><code>AMR</code> (for R). Free and open-source, licenced under the <a target="_blank" href="https://github.com/msberends/AMR/blob/main/LICENSE" class="external-link">GNU GPL 2.0</a>. Developed at the <a target="_blank" href="https://www.rug.nl" class="external-link">University of Groningen</a> and <a target="_blank" href="https://www.umcg.nl" class="external-link">University Medical Center Groningen</a> in The Netherlands, in collaboration with <a href="https://amr-for-r.org/authors.html">many colleagues from around the world</a>.</p>
+</div>
+
+<div class="pkgdown-footer-right">
+  <p><a target="_blank" href="https://www.rug.nl" class="external-link"><img src="https://amr-for-r.org/logo_rug.svg" style="max-width: 150px;"></a><a target="_blank" href="https://www.umcg.nl" class="external-link"><img src="https://amr-for-r.org/logo_umcg.svg" style="max-width: 150px;"></a></p>
+</div>
+
+    </footer></div>
+
+
+
+
+
+  </body></html>
+
@@ -0,0 +1,250 @@
+# License
+
+    GNU GENERAL PUBLIC LICENSE
+    Version 2, June 1991
+
+    Copyright (C) 1989, 1991 Free Software Foundation, Inc., <http://fsf.org/>
+      51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
+    Everyone is permitted to copy and distribute verbatim copies
+    of this license document, but changing it is not allowed.
+
+    A SUMMARY OF THIS LICENSE BY THE ORIGINAL AUTHORS OF THE AMR R PACKAGE
+
+    This R package, with package name 'AMR':
+    - May be used for commercial purposes
+    - May be used for private purposes
+    - May NOT be used for patent purposes
+    - May be modified, although:
+      - Modifications MUST be released under the same license when distributing the package
+      - Changes made to the code MUST be documented
+    - May be distributed, although:
+      - Source code MUST be made available when the package is distributed
+      - A copy of the license and copyright notice MUST be included with the package.
+    - Comes with a LIMITATION of liability
+    - Comes with NO warranty
+
+    END OF THE SUMMARY
+
+
+    GNU GENERAL PUBLIC LICENSE
+    TERMS AND CONDITIONS FOR COPYING, DISTRIBUTION AND MODIFICATION
+
+    0. This License applies to any program or other work which contains
+    a notice placed by the copyright holder saying it may be distributed
+    under the terms of this General Public License. The "Program", below,
+    refers to any such program or work, and a "work based on the Program"
+    means either the Program or any derivative work under copyright law:
+    that is to say, a work containing the Program or a portion of it,
+    either verbatim or with modifications and/or translated into another
+    language. (Hereinafter, translation is included without limitation in
+    the term "modification".) Each licensee is addressed as "you".
+
+    Activities other than copying, distribution and modification are not
+    covered by this License; they are outside its scope. The act of
+    running the Program is not restricted, and the output from the Program
+    is covered only if its contents constitute a work based on the
+    Program (independent of having been made by running the Program).
+    Whether that is true depends on what the Program does.
+
+    1. You may copy and distribute verbatim copies of the Program's
+    source code as you receive it, in any medium, provided that you
+    conspicuously and appropriately publish on each copy an appropriate
+    copyright notice and disclaimer of warranty; keep intact all the
+    notices that refer to this License and to the absence of any warranty;
+    and give any other recipients of the Program a copy of this License
+    along with the Program.
+
+    You may charge a fee for the physical act of transferring a copy, and
+    you may at your option offer warranty protection in exchange for a fee.
+
+    2. You may modify your copy or copies of the Program or any portion
+    of it, thus forming a work based on the Program, and copy and
+    distribute such modifications or work under the terms of Section 1
+    above, provided that you also meet all of these conditions:
+
+        a) You must cause the modified files to carry prominent notices
+        stating that you changed the files and the date of any change.
+
+        b) You must cause any work that you distribute or publish, that in
+        whole or in part contains or is derived from the Program or any
+        part thereof, to be licensed as a whole at no charge to all third
+        parties under the terms of this License.
+
+        c) If the modified program normally reads commands interactively
+        when run, you must cause it, when started running for such
+        interactive use in the most ordinary way, to print or display an
+        announcement including an appropriate copyright notice and a
+        notice that there is no warranty (or else, saying that you provide
+        a warranty) and that users may redistribute the program under
+        these conditions, and telling the user how to view a copy of this
+        License.  (Exception: if the Program itself is interactive but
+        does not normally print such an announcement, your work based on
+        the Program is not required to print an announcement.)
+
+    These requirements apply to the modified work as a whole. If
+    identifiable sections of that work are not derived from the Program,
+    and can be reasonably considered independent and separate works in
+    themselves, then this License, and its terms, do not apply to those
+    sections when you distribute them as separate works. But when you
+    distribute the same sections as part of a whole which is a work based
+    on the Program, the distribution of the whole must be on the terms of
+    this License, whose permissions for other licensees extend to the
+    entire whole, and thus to each and every part regardless of who wrote it.
+
+    Thus, it is not the intent of this section to claim rights or contest
+    your rights to work written entirely by you; rather, the intent is to
+    exercise the right to control the distribution of derivative or
+    collective works based on the Program.
+
+    In addition, mere aggregation of another work not based on the Program
+    with the Program (or with a work based on the Program) on a volume of
+    a storage or distribution medium does not bring the other work under
+    the scope of this License.
+
+    3. You may copy and distribute the Program (or a work based on it,
+    under Section 2) in object code or executable form under the terms of
+    Sections 1 and 2 above provided that you also do one of the following:
+
+        a) Accompany it with the complete corresponding machine-readable
+        source code, which must be distributed under the terms of Sections
+        1 and 2 above on a medium customarily used for software interchange; or,
+
+        b) Accompany it with a written offer, valid for at least three
+        years, to give any third party, for a charge no more than your
+        cost of physically performing source distribution, a complete
+        machine-readable copy of the corresponding source code, to be
+        distributed under the terms of Sections 1 and 2 above on a medium
+        customarily used for software interchange; or,
+
+        c) Accompany it with the information you received as to the offer
+        to distribute corresponding source code.  (This alternative is
+        allowed only for noncommercial distribution and only if you
+        received the program in object code or executable form with such
+        an offer, in accord with Subsection b above.)
+
+    The source code for a work means the preferred form of the work for
+    making modifications to it. For an executable work, complete source
+    code means all the source code for all modules it contains, plus any
+    associated interface definition files, plus the scripts used to
+    control compilation and installation of the executable. However, as a
+    special exception, the source code distributed need not include
+    anything that is normally distributed (in either source or binary
+    form) with the major components (compiler, kernel, and so on) of the
+    operating system on which the executable runs, unless that component
+    itself accompanies the executable.
+
+    If distribution of executable or object code is made by offering
+    access to copy from a designated place, then offering equivalent
+    access to copy the source code from the same place counts as
+    distribution of the source code, even though third parties are not
+    compelled to copy the source along with the object code.
+
+    4. You may not copy, modify, sublicense, or distribute the Program
+    except as expressly provided under this License. Any attempt
+    otherwise to copy, modify, sublicense or distribute the Program is
+    void, and will automatically terminate your rights under this License.
+    However, parties who have received copies, or rights, from you under
+    this License will not have their licenses terminated so long as such
+    parties remain in full compliance.
+
+    5. You are not required to accept this License, since you have not
+    signed it. However, nothing else grants you permission to modify or
+    distribute the Program or its derivative works. These actions are
+    prohibited by law if you do not accept this License. Therefore, by
+    modifying or distributing the Program (or any work based on the
+    Program), you indicate your acceptance of this License to do so, and
+    all its terms and conditions for copying, distributing or modifying
+    the Program or works based on it.
+
+    6. Each time you redistribute the Program (or any work based on the
+    Program), the recipient automatically receives a license from the
+    original licensor to copy, distribute or modify the Program subject to
+    these terms and conditions. You may not impose any further
+    restrictions on the recipients' exercise of the rights granted herein.
+    You are not responsible for enforcing compliance by third parties to
+    this License.
+
+    7. If, as a consequence of a court judgment or allegation of patent
+    infringement or for any other reason (not limited to patent issues),
+    conditions are imposed on you (whether by court order, agreement or
+    otherwise) that contradict the conditions of this License, they do not
+    excuse you from the conditions of this License. If you cannot
+    distribute so as to satisfy simultaneously your obligations under this
+    License and any other pertinent obligations, then as a consequence you
+    may not distribute the Program at all. For example, if a patent
+    license would not permit royalty-free redistribution of the Program by
+    all those who receive copies directly or indirectly through you, then
+    the only way you could satisfy both it and this License would be to
+    refrain entirely from distribution of the Program.
+
+    If any portion of this section is held invalid or unenforceable under
+    any particular circumstance, the balance of the section is intended to
+    apply and the section as a whole is intended to apply in other
+    circumstances.
+
+    It is not the purpose of this section to induce you to infringe any
+    patents or other property right claims or to contest validity of any
+    such claims; this section has the sole purpose of protecting the
+    integrity of the free software distribution system, which is
+    implemented by public license practices. Many people have made
+    generous contributions to the wide range of software distributed
+    through that system in reliance on consistent application of that
+    system; it is up to the author/donor to decide if he or she is willing
+    to distribute software through any other system and a licensee cannot
+    impose that choice.
+
+    This section is intended to make thoroughly clear what is believed to
+    be a consequence of the rest of this License.
+
+    8. If the distribution and/or use of the Program is restricted in
+    certain countries either by patents or by copyrighted interfaces, the
+    original copyright holder who places the Program under this License
+    may add an explicit geographical distribution limitation excluding
+    those countries, so that distribution is permitted only in or among
+    countries not thus excluded. In such case, this License incorporates
+    the limitation as if written in the body of this License.
+
+    9. The Free Software Foundation may publish revised and/or new versions
+    of the General Public License from time to time. Such new versions will
+    be similar in spirit to the present version, but may differ in detail to
+    address new problems or concerns.
+
+    Each version is given a distinguishing version number. If the Program
+    specifies a version number of this License which applies to it and "any
+    later version", you have the option of following the terms and conditions
+    either of that version or of any later version published by the Free
+    Software Foundation. If the Program does not specify a version number of
+    this License, you may choose any version ever published by the Free Software
+    Foundation.
+
+    10. If you wish to incorporate parts of the Program into other free
+    programs whose distribution conditions are different, write to the author
+    to ask for permission. For software which is copyrighted by the Free
+    Software Foundation, write to the Free Software Foundation; we sometimes
+    make exceptions for this. Our decision will be guided by the two goals
+    of preserving the free status of all derivatives of our free software and
+    of promoting the sharing and reuse of software generally.
+
+    NO WARRANTY
+
+    11. BECAUSE THE PROGRAM IS LICENSED FREE OF CHARGE, THERE IS NO WARRANTY
+    FOR THE PROGRAM, TO THE EXTENT PERMITTED BY APPLICABLE LAW. EXCEPT WHEN
+    OTHERWISE STATED IN WRITING THE COPYRIGHT HOLDERS AND/OR OTHER PARTIES
+    PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY OF ANY KIND, EITHER EXPRESSED
+    OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF
+    MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE ENTIRE RISK AS
+    TO THE QUALITY AND PERFORMANCE OF THE PROGRAM IS WITH YOU. SHOULD THE
+    PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF ALL NECESSARY SERVICING,
+    REPAIR OR CORRECTION.
+
+    12. IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
+    WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MAY MODIFY AND/OR
+    REDISTRIBUTE THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES,
+    INCLUDING ANY GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING
+    OUT OF THE USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED
+    TO LOSS OF DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY
+    YOU OR THIRD PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER
+    PROGRAMS), EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE
+    POSSIBILITY OF SUCH DAMAGES.
+
+    END OF TERMS AND CONDITIONS
@@ -1,75 +0,0 @@
-# Generated by roxygen2: do not edit by hand
-
-S3method(as.double,mic)
-S3method(as.integer,mic)
-S3method(as.numeric,mic)
-S3method(barplot,mic)
-S3method(barplot,rsi)
-S3method(plot,mic)
-S3method(plot,rsi)
-S3method(print,mic)
-S3method(print,rsi)
-S3method(summary,mic)
-S3method(summary,rsi)
-export(EUCAST_rules)
-export(abname)
-export(anti_join_bactlist)
-export(as.mic)
-export(as.rsi)
-export(atc_property)
-export(first_isolate)
-export(full_join_bactlist)
-export(inner_join_bactlist)
-export(interpretive_reading)
-export(is.mic)
-export(is.rsi)
-export(key_antibiotics)
-export(left_join_bactlist)
-export(mo_property)
-export(right_join_bactlist)
-export(rsi)
-export(rsi_df)
-export(rsi_predict)
-export(semi_join_bactlist)
-exportMethods(as.double.mic)
-exportMethods(as.integer.mic)
-exportMethods(as.numeric.mic)
-exportMethods(barplot.mic)
-exportMethods(barplot.rsi)
-exportMethods(plot.mic)
-exportMethods(plot.rsi)
-exportMethods(print.mic)
-exportMethods(print.rsi)
-exportMethods(summary.mic)
-exportMethods(summary.rsi)
-importFrom(dplyr,"%>%")
-importFrom(dplyr,all_vars)
-importFrom(dplyr,any_vars)
-importFrom(dplyr,arrange)
-importFrom(dplyr,arrange_at)
-importFrom(dplyr,between)
-importFrom(dplyr,filter)
-importFrom(dplyr,filter_at)
-importFrom(dplyr,group_by)
-importFrom(dplyr,group_by_at)
-importFrom(dplyr,if_else)
-importFrom(dplyr,lag)
-importFrom(dplyr,left_join)
-importFrom(dplyr,mutate)
-importFrom(dplyr,n_distinct)
-importFrom(dplyr,progress_estimated)
-importFrom(dplyr,pull)
-importFrom(dplyr,row_number)
-importFrom(dplyr,select)
-importFrom(dplyr,slice)
-importFrom(dplyr,summarise)
-importFrom(dplyr,tibble)
-importFrom(dplyr,vars)
-importFrom(graphics,axis)
-importFrom(graphics,barplot)
-importFrom(graphics,plot)
-importFrom(graphics,text)
-importFrom(reshape2,dcast)
-importFrom(rvest,html_nodes)
-importFrom(rvest,html_table)
-importFrom(xml2,read_html)
@@ -1,9 +0,0 @@
-## 0.1.1
- `EUCAST_rules` applies for amoxicillin even if ampicillin is missing
- Edited column names to comply with GLIMS, the laboratory information system
- Added more valid MIC values
- Renamed 'Daily Defined Dose' to 'Defined Daily Dose'
- Added barplots for `rsi` and `mic` classes
-
-## 0.1.0
- First submission to CRAN.
@@ -1,660 +0,0 @@
-# ==================================================================== #
-# TITLE                                                                #
-# Antimicrobial Resistance (AMR) Analysis                              #
-#                                                                      #
-# AUTHORS                                                              #
-# Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl)           #
-#                                                                      #
-# LICENCE                                                              #
-# This program is free software; you can redistribute it and/or modify #
-# it under the terms of the GNU General Public License version 2.0,    #
-# as published by the Free Software Foundation.                        #
-#                                                                      #
-# This program is distributed in the hope that it will be useful,      #
-# but WITHOUT ANY WARRANTY; without even the implied warranty of       #
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the        #
-# GNU General Public License for more details.                         #
-# ==================================================================== #
-
-#' EUCAST expert rules
-#'
-#' Apply expert rules (like intrinsic resistance), as defined by the European Committee on Antimicrobial Susceptibility Testing (EUCAST, \url{http://eucast.org}), see \emph{Source}.
-#' @param tbl table with antibiotic columns, like e.g. \code{amox} and \code{amcl}
-#' @param col_bactcode column name of the bacteria ID in \code{tbl} - values of this column should be present in \code{bactlist$bactid}, see \code{\link{bactlist}}
-#' @param info print progress
-#' @param amcl,amik,amox,ampi,azit,aztr,cefa,cfra,cfep,cfot,cfox,cfta,cftr,cfur,chlo,cipr,clar,clin,clox,coli,czol,dapt,doxy,erta,eryt,fosf,fusi,gent,imip,kana,levo,linc,line,mero,mino,moxi,nali,neom,neti,nitr,novo,norf,oflo,peni,pita,poly,qida,rifa,roxi,siso,teic,tetr,tica,tige,tobr,trim,trsu,vanc column names of antibiotics. Use \code{NA} to skip a column, like \code{tica = NA}. Non-existing column will be skipped.
-#' @param ... parameters that are passed on to \code{EUCAST_rules}
-#' @rdname EUCAST
-#' @export
-#' @importFrom dplyr %>% left_join select
-#' @return table with edited variables of antibiotics.
-#' @source
-#'   EUCAST Expert Rules Version 2.0: \cr
-#'   Leclercq et al. \strong{EUCAST expert rules in antimicrobial susceptibility testing.} \emph{Clin Microbiol Infect.} 2013;19(2):141-60. \cr
-#'   \url{https://doi.org/10.1111/j.1469-0691.2011.03703.x} \cr
-#'   \cr
-#'   EUCAST Expert Rules Version 3.1: \cr
-#'   \url{http://www.eucast.org/expert_rules_and_intrinsic_resistance}
-#' @examples
-#' a <- data.frame(bactid = c("STAAUR",  # Staphylococcus aureus
-#'                            "ENCFAE",  # Enterococcus faecalis
-#'                            "ESCCOL",  # Escherichia coli
-#'                            "KLEPNE",  # Klebsiella pneumoniae
-#'                            "PSEAER"), # Pseudomonas aeruginosa
-#'                 vanc = "-",           # Vancomycin
-#'                 amox = "-",           # Amoxicillin
-#'                 coli = "-",           # Colistin
-#'                 cfta = "-",           # Ceftazidime
-#'                 cfur = "-",           # Cefuroxime
-#'                 stringsAsFactors = FALSE)
-#' a
-#' 
-#' b <- EUCAST_rules(a)
-#' b
-EUCAST_rules <- function(tbl,
-                         col_bactcode = 'bactid',
-                         info = TRUE,
-                         amcl = 'amcl',
-                         amik = 'amik',
-                         amox = 'amox',
-                         ampi = 'ampi',
-                         azit = 'azit',
-                         aztr = 'aztr',
-                         cefa = 'cefa',
-                         cfra = 'cfra',
-                         cfep = 'cfep',
-                         cfot = 'cfot',
-                         cfox = 'cfox',
-                         cfta = 'cfta',
-                         cftr = 'cftr',
-                         cfur = 'cfur',
-                         chlo = 'chlo',
-                         cipr = 'cipr',
-                         clar = 'clar',
-                         clin = 'clin',
-                         clox = 'clox',
-                         coli = 'coli',
-                         czol = 'czol',
-                         dapt = 'dapt',
-                         doxy = 'doxy',
-                         erta = 'erta',
-                         eryt = 'eryt',
-                         fosf = 'fosf',
-                         fusi = 'fusi',
-                         gent = 'gent',
-                         imip = 'imip',
-                         kana = 'kana',
-                         levo = 'levo',
-                         linc = 'linc',
-                         line = 'line',
-                         mero = 'mero',
-                         mino = 'mino',
-                         moxi = 'moxi',
-                         nali = 'nali',
-                         neom = 'neom',
-                         neti = 'neti',
-                         nitr = 'nitr',
-                         novo = 'novo',
-                         norf = 'norf',
-                         oflo = 'oflo',
-                         peni = 'peni',
-                         pita = 'pita',
-                         poly = 'poly',
-                         qida = 'qida',
-                         rifa = 'rifa',
-                         roxi = 'roxi',
-                         siso = 'siso',
-                         teic = 'teic',
-                         tetr = 'tetr',
-                         tica = 'tica',
-                         tige = 'tige',
-                         tobr = 'tobr',
-                         trim = 'trim',
-                         trsu = 'trsu',
-                         vanc = 'vanc') {
-  
-  if (!col_bactcode %in% colnames(tbl)) {
-    stop('Column ', col_bactcode, ' not found.')
-  }
-  
-  # kolommen controleren
-  col.list <- c(amcl, amik, amox, ampi, azit, aztr, cefa, cfra, cfep,
-                cfot, cfox, cfta, cftr, cfur, cipr, clar, clin, clox, coli, czol,
-                dapt, doxy, erta, eryt, fusi, gent, imip, kana, levo, linc, line,
-                mero, mino, moxi, nali, neom, neti, nitr, novo, norf, oflo, peni,
-                pita, poly, qida, rifa, roxi, siso, teic, tetr, tica, tige, tobr,
-                trim, trsu, vanc)
-  col.list <- col.list[!is.na(col.list)]
-  if (!all(col.list %in% colnames(tbl))) {
-    if (info == TRUE) {
-      cat('\n')
-    }
-    if (info == TRUE) {
-      warning('These columns do not exist and will be ignored:\n',
-              col.list[!(col.list %in% colnames(tbl))] %>% toString(),
-              immediate. = TRUE,
-              call. = FALSE)
-    }
-    if (!amcl %in% colnames(tbl)) { amcl <- NA }
-    if (!amik %in% colnames(tbl)) { amik <- NA }
-    if (!amox %in% colnames(tbl)) { amox <- NA }
-    if (!ampi %in% colnames(tbl)) { ampi <- NA }
-    if (!azit %in% colnames(tbl)) { azit <- NA }
-    if (!aztr %in% colnames(tbl)) { aztr <- NA }
-    if (!cefa %in% colnames(tbl)) { cefa <- NA }
-    if (!cfra %in% colnames(tbl)) { cfra <- NA }
-    if (!cfep %in% colnames(tbl)) { cfep <- NA }
-    if (!cfot %in% colnames(tbl)) { cfot <- NA }
-    if (!cfox %in% colnames(tbl)) { cfox <- NA }
-    if (!cfta %in% colnames(tbl)) { cfta <- NA }
-    if (!cftr %in% colnames(tbl)) { cftr <- NA }
-    if (!cfur %in% colnames(tbl)) { cfur <- NA }
-    if (!chlo %in% colnames(tbl)) { chlo <- NA }
-    if (!cipr %in% colnames(tbl)) { cipr <- NA }
-    if (!clar %in% colnames(tbl)) { clar <- NA }
-    if (!clin %in% colnames(tbl)) { clin <- NA }
-    if (!clox %in% colnames(tbl)) { clox <- NA }
-    if (!coli %in% colnames(tbl)) { coli <- NA }
-    if (!czol %in% colnames(tbl)) { czol <- NA }
-    if (!dapt %in% colnames(tbl)) { dapt <- NA }
-    if (!doxy %in% colnames(tbl)) { doxy <- NA }
-    if (!erta %in% colnames(tbl)) { erta <- NA }
-    if (!eryt %in% colnames(tbl)) { eryt <- NA }
-    if (!fosf %in% colnames(tbl)) { fosf <- NA }
-    if (!fusi %in% colnames(tbl)) { fusi <- NA }
-    if (!gent %in% colnames(tbl)) { gent <- NA }
-    if (!imip %in% colnames(tbl)) { imip <- NA }
-    if (!kana %in% colnames(tbl)) { kana <- NA }
-    if (!levo %in% colnames(tbl)) { levo <- NA }
-    if (!linc %in% colnames(tbl)) { linc <- NA }
-    if (!line %in% colnames(tbl)) { line <- NA }
-    if (!mero %in% colnames(tbl)) { mero <- NA }
-    if (!mino %in% colnames(tbl)) { mino <- NA }
-    if (!moxi %in% colnames(tbl)) { moxi <- NA }
-    if (!nali %in% colnames(tbl)) { nali <- NA }
-    if (!neom %in% colnames(tbl)) { neom <- NA }
-    if (!neti %in% colnames(tbl)) { neti <- NA }
-    if (!nitr %in% colnames(tbl)) { nitr <- NA }
-    if (!novo %in% colnames(tbl)) { novo <- NA }
-    if (!norf %in% colnames(tbl)) { norf <- NA }
-    if (!oflo %in% colnames(tbl)) { oflo <- NA }
-    if (!peni %in% colnames(tbl)) { peni <- NA }
-    if (!pita %in% colnames(tbl)) { pita <- NA }
-    if (!poly %in% colnames(tbl)) { poly <- NA }
-    if (!qida %in% colnames(tbl)) { qida <- NA }
-    if (!rifa %in% colnames(tbl)) { rifa <- NA }
-    if (!roxi %in% colnames(tbl)) { roxi <- NA }
-    if (!siso %in% colnames(tbl)) { siso <- NA }
-    if (!teic %in% colnames(tbl)) { teic <- NA }
-    if (!tetr %in% colnames(tbl)) { tetr <- NA }
-    if (!tica %in% colnames(tbl)) { tica <- NA }
-    if (!tige %in% colnames(tbl)) { tige <- NA }
-    if (!tobr %in% colnames(tbl)) { tobr <- NA }
-    if (!trim %in% colnames(tbl)) { trim <- NA }
-    if (!trsu %in% colnames(tbl)) { trsu <- NA }
-    if (!vanc %in% colnames(tbl)) { vanc <- NA }
-  }
-  
-  total <- 0
-  total_rows <- integer(0)
-  
-  # functie voor uitvoeren
-  edit_rsi <- function(to, rows, cols) {
-    #voortgang$tick()$print()
-    cols <- cols[!is.na(cols)]
-    if (length(rows) > 0 & length(cols) > 0) {
-      tbl[rows, cols] <<- to
-      total <<- total + (length(rows) * length(cols))
-      total_rows <<- c(total_rows, rows)
-    }
-  }
-  
-  # bactlist aan vastknopen (bestaande kolommen krijgen extra suffix)
-  joinby <- colnames(AMR::bactlist)[1]
-  names(joinby) <- col_bactcode
-  tbl <- tbl %>% left_join(y = AMR::bactlist, by = joinby, suffix = c("_tempbactlist", ""))
-  
-  # antibioticagroepen
-  aminoglycosiden <- c(tobr, gent, kana, neom, neti, siso)
-  tetracyclines <- c(doxy, mino, tetr) # sinds EUCAST v3.1 is tige(cycline) apart
-  polymyxines <- c(poly, coli)
-  macroliden <- c(eryt, azit, roxi, clar) # sinds EUCAST v3.1 is clinda apart
-  glycopeptiden <- c(vanc, teic)
-  streptogramines <- qida # eigenlijk pristinamycine en quinupristine/dalfopristine
-  cefalosporines <- c(cfep, cfot, cfox, cfra, cfta, cftr, cfur, czol)
-  carbapenems <- c(erta, imip, mero)
-  aminopenicillines <- c(ampi, amox)
-  ureidopenicillines <- pita # eigenlijk ook azlo en mezlo
-  fluorochinolonen <- c(oflo, cipr, norf, levo, moxi)
-  
-  if (info == TRUE) {
-    cat('\nApplying rules to',
-        tbl[!is.na(tbl$genus),] %>% nrow() %>% format(big.mark = ","),
-        'rows according to "EUCAST Expert Rules Version 3.1"\n\n')
-  }
-  
-  # Table 1: Intrinsic resistance in Enterobacteriaceae ----
-  if (info == TRUE) {
-    cat('...Table 1: Intrinsic resistance in Enterobacteriaceae\n')
-  }
-  #voortgang <- progress_estimated(17)
-  # Intrisiek R voor groep
-  edit_rsi(to = 'R',
-           rows = which(tbl$family == 'Enterobacteriaceae'),
-           cols = c(peni, glycopeptiden, fusi, macroliden, linc, streptogramines, rifa, dapt, line))
-  # Citrobacter
-  edit_rsi(to = 'R',
-           rows = which(tbl$fullname %like% '^Citrobacter (koseri|amalonaticus|sedlakii|farmeri|rodentium)'),
-           cols = c(aminopenicillines, tica))
-  edit_rsi(to = 'R',
-           rows = which(tbl$fullname %like% '^Citrobacter (freundii|braakii|murliniae|werkmanii|youngae)'),
-           cols = c(aminopenicillines, amcl, czol, cfox))
-  # Enterobacter
-  edit_rsi(to = 'R',
-           rows = which(tbl$fullname %like% '^Enterobacter cloacae'),
-           cols = c(aminopenicillines, amcl, czol, cfox))
-  edit_rsi(to = 'R',
-           rows = which(tbl$fullname %like% '^Enterobacter aerogenes'),
-           cols = c(aminopenicillines, amcl, czol, cfox))
-  # Escherichia
-  edit_rsi(to = 'R',
-           rows = which(tbl$fullname %like% '^Escherichia hermanni'),
-           cols = c(aminopenicillines, tica))
-  # Hafnia
-  edit_rsi(to = 'R',
-           rows = which(tbl$fullname %like% '^Hafnia alvei'),
-           cols = c(aminopenicillines, amcl, czol, cfox))
-  # Klebsiella
-  edit_rsi(to = 'R',
-           rows = which(tbl$fullname %like% '^Klebsiella'),
-           cols = c(aminopenicillines, tica))
-  # Morganella / Proteus
-  edit_rsi(to = 'R',
-           rows = which(tbl$fullname %like% '^Morganella morganii'),
-           cols = c(aminopenicillines, amcl, czol, tetracyclines, polymyxines, nitr))
-  edit_rsi(to = 'R',
-           rows = which(tbl$fullname %like% '^Proteus mirabilis'),
-           cols = c(tetracyclines, tige, polymyxines, nitr))
-  edit_rsi(to = 'R',
-           rows = which(tbl$fullname %like% '^Proteus penneri'),
-           cols = c(aminopenicillines, czol, cfur, tetracyclines, tige, polymyxines, nitr))
-  edit_rsi(to = 'R',
-           rows = which(tbl$fullname %like% '^Proteus vulgaris'),
-           cols = c(aminopenicillines, czol, cfur, tetracyclines, tige, polymyxines, nitr))
-  # Providencia
-  edit_rsi(to = 'R',
-           rows = which(tbl$fullname %like% '^Providencia rettgeri'),
-           cols = c(aminopenicillines, amcl, czol, cfur, tetracyclines, tige, polymyxines, nitr))
-  edit_rsi(to = 'R',
-           rows = which(tbl$fullname %like% '^Providencia stuartii'),
-           cols = c(aminopenicillines, amcl, czol, cfur, tetracyclines, tige, polymyxines, nitr))
-  # Raoultella
-  edit_rsi(to = 'R',
-           rows = which(tbl$fullname %like% '^Raoultella'),
-           cols = c(aminopenicillines, tica))
-  # Serratia
-  edit_rsi(to = 'R',
-           rows = which(tbl$fullname %like% '^Serratia marcescens'),
-           cols = c(aminopenicillines, amcl, czol, cfox, cfur, tetracyclines[tetracyclines != 'mino'], polymyxines, nitr))
-  # Yersinia
-  edit_rsi(to = 'R',
-           rows = which(tbl$fullname %like% '^Yersinia enterocolitica'),
-           cols = c(aminopenicillines, amcl, tica, czol, cfox))
-  edit_rsi(to = 'R',
-           rows = which(tbl$fullname %like% '^Yersinia pseudotuberculosis'),
-           cols = c(poly, coli))
-  
-  
-  # Table 2: Intrinsic resistance in non-fermentative Gram-negative bacteria ----
-  if (info == TRUE) {
-    cat('...Table 2: Intrinsic resistance in non-fermentative Gram-negative bacteria\n')
-  }
-  #voortgang <- progress_estimated(8)
-  # Intrisiek R voor groep
-  edit_rsi(to = 'R',
-           rows = which(tbl$genus %in% c('Achromobacter',
-                                         'Acinetobacter',
-                                         'Alcaligenes',
-                                         'Bordatella',
-                                         'Burkholderia',
-                                         'Elizabethkingia',
-                                         'Flavobacterium',
-                                         'Ochrobactrum',
-                                         'Pseudomonas',
-                                         'Stenotrophomonas')),
-           cols = c(peni, cfox, cfur, glycopeptiden, fusi, macroliden, linc, streptogramines, rifa, dapt, line))
-  # Acinetobacter
-  edit_rsi(to = 'R',
-           rows = which(tbl$fullname %like% '^Acinetobacter (baumannii|pittii|nosocomialis|calcoaceticus)'),
-           cols = c(aminopenicillines, amcl, czol, cfot, cftr, aztr, erta, trim, fosf, tetracyclines[tetracyclines != 'mino']))
-  # Achromobacter
-  edit_rsi(to = 'R',
-           rows = which(tbl$fullname %like% '^Achromobacter (xylosoxydans|xylosoxidans)'),
-           cols = c(aminopenicillines, czol, cfot, cftr, erta))
-  # Burkholderia
-  edit_rsi(to = 'R',
-           # onder 'Burkholderia cepacia complex' vallen deze species allemaal: PMID 16217180.
-           rows = which(tbl$fullname %like% '^Burkholderia (cepacia|multivorans|cenocepacia|stabilis|vietnamiensis|dolosa|ambifaria|anthina|pyrrocinia|ubonensis)'),
-           cols = c(aminopenicillines, amcl, tica, pita, czol, cfot, cftr, aztr, erta, cipr, chlo, aminoglycosiden, trim, fosf, polymyxines))
-  # Elizabethkingia
-  edit_rsi(to = 'R',
-           rows = which(tbl$fullname %like% '^Elizabethkingia meningoseptic(a|um)'),
-           cols = c(aminopenicillines, amcl, tica, czol, cfot, cftr, cfta, cfep, aztr, erta, imip, mero, polymyxines))
-  # Ochrobactrum
-  edit_rsi(to = 'R',
-           rows = which(tbl$fullname %like% '^Ochrobactrum anthropi'),
-           cols = c(aminopenicillines, amcl, tica, pita, czol, cfot, cftr, cfta, cfep, aztr, erta))
-  # Pseudomonas
-  edit_rsi(to = 'R',
-           rows = which(tbl$fullname %like% '^Pseudomonas aeruginosa'),
-           cols = c(aminopenicillines, amcl, czol, cfot, cftr, erta, chlo, kana, neom, trim, trsu, tetracyclines, tige))
-  # Stenotrophomonas
-  edit_rsi(to = 'R',
-           rows = which(tbl$fullname %like% '^Stenotrophomonas maltophilia'),
-           cols = c(aminopenicillines, amcl, tica, pita, czol, cfot, cftr, cfta, aztr, erta, imip, mero, aminoglycosiden, trim, fosf, tetr))
-  
-  
-  # Table 3: Intrinsic resistance in other Gram-negative bacteria ----
-  if (info == TRUE) {
-    cat('...Table 3: Intrinsic resistance in other Gram-negative bacteria\n')
-  }
-  #voortgang <- progress_estimated(7)
-  # Intrisiek R voor groep
-  edit_rsi(to = 'R',
-           rows = which(tbl$genus %in% c('Haemophilus',
-                                         'Moraxella',
-                                         'Neisseria',
-                                         'Campylobacter')),
-           cols = c(glycopeptiden, linc, dapt, line))
-  # Haemophilus
-  edit_rsi(to = 'R',
-           rows = which(tbl$fullname %like% '^Haemophilus influenzae'),
-           cols = c(fusi, streptogramines))
-  # Moraxella
-  edit_rsi(to = 'R',
-           rows = which(tbl$fullname %like% '^Moraxella catarrhalis'),
-           cols = trim)
-  # Neisseria
-  edit_rsi(to = 'R',
-           rows = which(tbl$genus == 'Neisseria'),
-           cols = trim)
-  # Campylobacter
-  edit_rsi(to = 'R',
-           rows = which(tbl$fullname %like% '^Campylobacter fetus'),
-           cols = c(fusi, streptogramines, trim, nali))
-  edit_rsi(to = 'R',
-           rows = which(tbl$fullname %like% '^Campylobacter (jejuni|coli)'),
-           cols = c(fusi, streptogramines, trim))
-  
-  
-  # Table 4: Intrinsic resistance in Gram-positive bacteria ----
-  if (info == TRUE) {
-    cat('...Table 4: Intrinsic resistance in Gram-positive bacteria\n')
-  }
-  #voortgang <- progress_estimated(14)
-  # Intrisiek R voor groep
-  edit_rsi(to = 'R',
-           rows = which(tbl$gramstain %like% 'Positi(e|)(v|f)'),
-           cols = c(aztr, polymyxines, nali))
-  # Staphylococcus
-  edit_rsi(to = 'R',
-           rows = which(tbl$fullname %like% '^Staphylococcus saprophyticus'),
-           cols = c(fusi, cfta, fosf, novo))
-  edit_rsi(to = 'R',
-           rows = which(tbl$fullname %like% '^Staphylococcus (cohnii|xylosus)'),
-           cols = c(cfta, novo))
-  edit_rsi(to = 'R',
-           rows = which(tbl$fullname %like% '^Staphylococcus capitis'),
-           cols = c(cfta, fosf))
-  edit_rsi(to = 'R',
-           rows = which(tbl$fullname %like% '^Staphylococcus (aureus|epidermidis|coagulase negatief|hominis|haemolyticus|intermedius|pseudointermedius)'),
-           cols = cfta)
-  # Streptococcus
-  edit_rsi(to = 'R',
-           rows = which(tbl$genus == 'Streptococcus'),
-           cols = c(fusi, cfta, aminoglycosiden))
-  # Enterococcus
-  edit_rsi(to = 'R',
-           rows = which(tbl$fullname %like% '^Enterococcus faecalis'),
-           cols = c(fusi, cfta, cefalosporines[cefalosporines != cfta], aminoglycosiden, macroliden, clin, qida, trim, trsu))
-  edit_rsi(to = 'R',
-           rows = which(tbl$fullname %like% '^Enterococcus (gallinarum|casseliflavus)'),
-           cols = c(fusi, cfta, cefalosporines[cefalosporines != cfta], aminoglycosiden, macroliden, clin, qida, vanc, trim, trsu))
-  edit_rsi(to = 'R',
-           rows = which(tbl$fullname %like% '^Enterococcus faecium'),
-           cols = c(fusi, cfta, cefalosporines[cefalosporines != cfta], aminoglycosiden, macroliden, trim, trsu))
-  # Corynebacterium
-  edit_rsi(to = 'R',
-           rows = which(tbl$genus == 'Corynebacterium'),
-           cols = fosf)
-  # Listeria
-  edit_rsi(to = 'R',
-           rows = which(tbl$fullname %like% '^Listeria monocytogenes'),
-           cols = c(cfta, cefalosporines[cefalosporines != cfta]))
-  # overig
-  edit_rsi(to = 'R',
-           rows = which(tbl$genus %in% c('Leuconostoc', 'Pediococcus')),
-           cols = c(vanc, teic))
-  edit_rsi(to = 'R',
-           rows = which(tbl$genus == 'Lactobacillus'),
-           cols = c(vanc, teic))
-  edit_rsi(to = 'R',
-           rows = which(tbl$fullname %like% '^Clostridium (ramosum|innocuum)'),
-           cols = vanc)
-  
-  # Table 8: Interpretive rules for B-lactam agents and Gram-positive cocci ----
-  if (info == TRUE) {
-    cat('...Table 8: Interpretive rules for B-lactam agents and Gram-positive cocci\n')
-  }
-  #voortgang <- progress_estimated(2)
-  # regel 8.3
-  if (!is.na(peni)) {
-    edit_rsi(to = 'S',
-             rows = which(tbl$fullname %like% '^Streptococcus (pyogenes|agalactiae|dysgalactiae|groep A|groep B|groep C|groep G)'
-                          & tbl[, peni] == 'S'),
-             cols = c(aminopenicillines, cefalosporines, carbapenems))
-  }
-  # regel 8.6
-  if (!is.na(ampi)) {
-    edit_rsi(to = 'R',
-             rows = which(tbl$genus == 'Enterococcus'
-                          & tbl[, ampi] == 'R'),
-             cols = c(ureidopenicillines, carbapenems))
-  }
-  if (!is.na(amox)) {
-    edit_rsi(to = 'R',
-             rows = which(tbl$genus == 'Enterococcus'
-                          & tbl[, amox] == 'R'),
-             cols = c(ureidopenicillines, carbapenems))
-  }
-  
-  # Table 9: Interpretive rules for B-lactam agents and Gram-negative rods ----
-  if (info == TRUE) {
-    cat('...Table 9: Interpretive rules for B-lactam agents and Gram-negative rods\n')
-  }
-  #voortgang <- progress_estimated(1)
-  # regel 9.3
-  if (!is.na(tica) & !is.na(pita)) {
-    edit_rsi(to = 'R',
-             rows = which(tbl$family == 'Enterobacteriaceae'
-                          & tbl[, tica] == 'R'
-                          & tbl[, pita] == 'S'),
-             cols = pita)
-  }
-  
-  # Table 10: Interpretive rules for B-lactam agents and other Gram-negative bacteria ----
-  if (info == TRUE) {
-    cat('...Table 10: Interpretive rules for B-lactam agents and other Gram-negative bacteria\n')
-  }
-  #voortgang <- progress_estimated(1)
-  # regel 10.2
-  if (!is.na(ampi)) {
-    # hiervoor moeten we eerst weten of ze B-lactamase-positief zijn
-    # edit_rsi(to = 'R',
-    #          rows = which(tbl$fullname %like% '^Haemophilus influenza'
-    #                       & tbl[, ampi] == 'R'),
-    #          cols = c(ampi, amox, amcl, pita, cfur))
-  }
-  
-  # Table 11: Interpretive rules for macrolides, lincosamides, and streptogramins ----
-  if (info == TRUE) {
-    cat('...Table 11: Interpretive rules for macrolides, lincosamides, and streptogramins\n')
-  }
-  # regel 11.1
-  if (!is.na(eryt)) {
-    if (!is.na(azit)) {
-      tbl[, azit] <- tbl[, eryt]
-    }
-    if (!is.na(clar)) {
-      tbl[, clar] <- tbl[, eryt]
-    }
-  }
-  
-  # Table 12: Interpretive rules for aminoglycosides ----
-  if (info == TRUE) {
-    cat('...Table 12: Interpretive rules for aminoglycosides\n')
-  }
-  #voortgang <- progress_estimated(4)
-  # regel 12.2
-  if (!is.na(tobr)) {
-    edit_rsi(to = 'R',
-             rows = which(tbl$genus == 'Staphylococcus'
-                          & tbl[, tobr] == 'R'),
-             cols = c(kana, amik))
-  }
-  # regel 12.3
-  if (!is.na(gent)) {
-    edit_rsi(to = 'R',
-             rows = which(tbl$genus == 'Staphylococcus'
-                          & tbl[, gent] == 'R'),
-             cols = aminoglycosiden)
-  }
-  # regel 12.8
-  if (!is.na(gent) & !is.na(tobr)) {
-    edit_rsi(to = 'R',
-             rows = which(tbl$family == 'Enterobacteriaceae'
-                          & tbl[, gent] == 'I'
-                          & tbl[, tobr] == 'S'),
-             cols = gent)
-  }
-  # regel 12.9
-  if (!is.na(gent) & !is.na(tobr)) {
-    edit_rsi(to = 'R',
-             rows = which(tbl$family == 'Enterobacteriaceae'
-                          & tbl[, tobr] == 'I'
-                          & tbl[, gent] == 'R'),
-             cols = tobr)
-  }
-  
-  
-  # Table 13: Interpretive rules for quinolones ----
-  if (info == TRUE) {
-    cat('...Table 13: Interpretive rules for quinolones\n')
-  }
-  #voortgang <- progress_estimated(4)
-  # regel 13.2
-  if (!is.na(moxi)) {
-    edit_rsi(to = 'R',
-             rows = which(tbl$genus == 'Staphylococcus'
-                          & tbl[, moxi] == 'R'),
-             cols = fluorochinolonen)
-  }
-  # regel 13.4
-  if (!is.na(moxi)) {
-    edit_rsi(to = 'R',
-             rows = which(tbl$fullname %like% '^Streptococcus pneumoniae'
-                          & tbl[, moxi] == 'R'),
-             cols = fluorochinolonen)
-  }
-  # regel 13.5
-  if (!is.na(cipr)) {
-    edit_rsi(to = 'R',
-             rows = which(tbl$family == 'Enterobacteriaceae'
-                          & tbl[, cipr] == 'R'),
-             cols = fluorochinolonen)
-  }
-  # regel 13.8
-  if (!is.na(cipr)) {
-    edit_rsi(to = 'R',
-             rows = which(tbl$fullname %like% '^Neisseria gonorrhoeae'
-                          & tbl[, cipr] == 'R'),
-             cols = fluorochinolonen)
-  }
-  
-  
-  # Other ----
-  if (info == TRUE) {
-    cat('...Other\n')
-  }
-  #voortgang <- progress_estimated(2)
-  if (!is.na(amcl)) {
-    edit_rsi(to = 'R',
-             rows = which(tbl[, amcl] == 'R'),
-             cols = ampi)
-  }
-  if (!is.na(trsu)) {
-    edit_rsi(to = 'R',
-             rows = which(tbl[, trsu] == 'R'),
-             cols = trim)
-  }
-  if (!is.na(ampi) & !is.na(amox)) {
-    tbl[, amox] <- tbl %>% pull(ampi)
-  }
-  
-  # Toegevoegde kolommen weer verwijderen
-  bactlist.ncol <- ncol(AMR::bactlist) - 2
-  tbl.ncol <- ncol(tbl)
-  tbl <- tbl %>% select(-c((tbl.ncol - bactlist.ncol):tbl.ncol))
-  # en eventueel toegevoegde suffix aan bestaande kolommen weer verwijderen
-  colnames(tbl) <- gsub("_tempbactlist", "", colnames(tbl))
-  
-  if (info == TRUE) {
-    cat('\nDone.\nEUCAST Expert rules applied to',
-        total_rows %>% unique() %>% length() %>% format(big.mark = ","),
-        'different rows, to a total of',
-        total %>% format(big.mark = ","), 'test results.\n\n')
-  }
-  
-  tbl
-}
-
-#' @rdname EUCAST
-#' @export
-interpretive_reading <- function(...) {
-  EUCAST_rules(...)
-}
-
-#' Poperties of a microorganism
-#'
-#' @param bactcode ID of a microorganisme, like \code{"STAAUR} and \code{"ESCCOL}
-#' @param property One of the values \code{bactid}, \code{bactsys}, \code{family}, \code{genus}, \code{species}, \code{subspecies}, \code{fullname}, \code{type}, \code{gramstain}, \code{aerobic}
-#' @export
-#' @importFrom dplyr %>% filter select
-#' @seealso \code{\link{bactlist}}
-mo_property <- function(bactcode, property = 'fullname') {
-  
-  mocode <- as.character(bactcode)
-  
-  for (i in 1:length(mocode)) {
-    bug <- mocode[i]
-    
-    if (!is.na(bug)) {
-      result = tryCatch({
-        mocode[i] <-
-          AMR::bactlist %>%
-          filter(bactid == bactcode) %>%
-          select(property) %>%
-          unlist() %>%
-          as.character()
-      }, error = function(error_condition) {
-        warning('Code ', bug, ' not found in bacteria list.')
-      }, finally = {
-        if (mocode[i] == bug & !property %in% c('bactid', 'bactsys')) {
-          mocode[i] <- NA
-        }
-      })
-    }
-    
-  }
-  mocode
-}
@@ -1,226 +0,0 @@
-# ==================================================================== #
-# TITLE                                                                #
-# Antimicrobial Resistance (AMR) Analysis                              #
-#                                                                      #
-# AUTHORS                                                              #
-# Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl)           #
-#                                                                      #
-# LICENCE                                                              #
-# This program is free software; you can redistribute it and/or modify #
-# it under the terms of the GNU General Public License version 2.0,    #
-# as published by the Free Software Foundation.                        #
-#                                                                      #
-# This program is distributed in the hope that it will be useful,      #
-# but WITHOUT ANY WARRANTY; without even the implied warranty of       #
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the        #
-# GNU General Public License for more details.                         #
-# ==================================================================== #
-
-#' Properties of an ATC code
-#'
-#' Gets data from the WHO to determine properties of an ATC (e.g. an antibiotic) like name, defined daily dose (DDD) or standard unit. \strong{This function requires an internet connection.}
-#' @param atc_code a character or character vector with ATC code(s) of antibiotic(s)
-#' @param property property of an ATC code. Valid values are \code{"ATC code"}, \code{"Name"}, \code{"DDD"}, \code{"U"} (\code{"unit"}), \code{"Adm.R"} en \code{"Note"}.
-#' @param administration type of administration, see \emph{Details}
-#' @param url url of website of the WHO. The sign \code{\%s} can be used as a placeholder for ATC codes.
-#' @details
-#' Abbreviations for the property \code{"Adm.R"} (parameter \code{administration}):
-#' \itemize{
-#'   \item{\code{"Implant"}}{ = Implant}
-#'   \item{\code{"Inhal"}}{ = Inhalation}
-#'   \item{\code{"Instill"}}{ = Instillation}
-#'   \item{\code{"N"}}{ = nasal}
-#'   \item{\code{"O"}}{ = oral}
-#'   \item{\code{"P"}}{ = parenteral}
-#'   \item{\code{"R"}}{ = rectal}
-#'   \item{\code{"SL"}}{ = sublingual/buccal}
-#'   \item{\code{"TD"}}{ = transdermal}
-#'   \item{\code{"V"}}{ = vaginal}
-#' }
-#'
-#' Abbreviations for the property \code{"U"} (unit):
-#' \itemize{
-#'   \item{\code{"g"}}{ = gram}
-#'   \item{\code{"mg"}}{ = milligram}
-#'   \item{\code{"mcg"}}{ = microgram}
-#'   \item{\code{"U"}}{ = unit}
-#'   \item{\code{"TU"}}{ = thousand units}
-#'   \item{\code{"MU"}}{ = million units}
-#'   \item{\code{"mmol"}}{ = millimole}
-#'   \item{\code{"ml"}}{ = milliliter (e.g. eyedrops)}
-#' }
-#' @export
-#' @importFrom dplyr %>% progress_estimated
-#' @importFrom xml2 read_html
-#' @importFrom rvest html_nodes html_table
-#' @source \url{https://www.whocc.no/atc_ddd_alterations__cumulative/ddd_alterations/abbrevations/}
-#' @examples 
-#' \donttest{
-#' atc_property("J01CA04", "DDD", "O") # oral DDD (Defined Daily Dose) of amoxicillin
-#' atc_property("J01CA04", "DDD", "P") # parenteral DDD (Defined Daily Dose) of amoxicillin
-#' }
-atc_property <- function(atc_code,
-                         property,
-                         administration = 'O',
-                         url = 'https://www.whocc.no/atc_ddd_index/?code=%s&showdescription=no') {
-  
-  # property <- property %>% tolower()
-  #
-  if (property %like% 'unit') {
-    property <- 'U'
-  }
-  
-  # validation of properties
-  valid_properties.bak <- c("ATC code", "Name", "DDD", "U", "Adm.R", "Note")
-  valid_properties <- valid_properties.bak #%>% tolower()
-  if (!property %in% valid_properties) {
-    stop('Invalid `property`, use one of ', paste(valid_properties, collapse = ", "), '.')
-  }
-  
-  returnvalue <- rep(NA_character_, length(atc_code))
-  if (property == 'DDD') {
-    returnvalue <- rep(NA_real_, length(atc_code))
-  }
-  
-  progress <- progress_estimated(n = length(atc_code))
-  
-  for (i in 1:length(atc_code)) {
-    
-    progress$tick()$print()
-    
-    atc_url <- sub('%s', atc_code[i], url, fixed = TRUE)
-    tbl <- xml2::read_html(atc_url) %>%
-      rvest::html_nodes('table') %>%
-      rvest::html_table(header = TRUE)
-    
-    if (length(tbl) == 0) {
-      warning('ATC not found: ', atc_code[i], '. Please check ', atc_url, '.', call. = FALSE)
-      returnvalue[i] <- NA
-      next
-    }
-    
-    tbl <- tbl[[1]]
-    
-    if (property == 'Name') {
-      returnvalue[i] <- tbl[1, 2]
-    } else {
-      
-      names(returnvalue)[i] <- tbl[1, 2] %>% as.character()
-      
-      if (!'Adm.R' %in% colnames(tbl) | is.na(tbl[1, 'Adm.R'])) {
-        returnvalue[i] <- NA
-        next
-      } else {
-        for (j in 1:nrow(tbl)) {
-          if (tbl[j, 'Adm.R'] == administration) {
-            returnvalue[i] <- tbl[j, property]
-          }
-        }
-      }
-    }
-  }
-  
-  cat('\n')
-  returnvalue
-  
-}
-
-#' Name of an antibiotic
-#'
-#' Convert antibiotic codes (from a laboratory information system like MOLIS or GLIMS) to a (trivial) antibiotic name or ATC code, or vice versa. This uses the data from \code{\link{ablist}}.
-#' @param abcode a code or name, like \code{"AMOX"}, \code{"AMCL"} or \code{"J01CA04"}
-#' @param from,to type to transform from and to. See \code{\link{ablist}} for its column names.
-#' @param textbetween text to put between multiple returned texts
-#' @param tolower return output as lower case with function \code{\link{tolower}}.
-#' @keywords ab antibiotics
-#' @source \code{\link{ablist}}
-#' @export
-#' @importFrom dplyr %>% filter select slice 
-#' @examples
-#' abname("AMCL")
-#' # "amoxicillin and enzyme inhibitor"
-#'
-#' abname("AMCL+GENT")
-#' # "amoxicillin and enzyme inhibitor + gentamicin"
-#'
-#' abname(c("AMCL", "GENT"))
-#' # "amoxicillin and enzyme inhibitor" "gentamicin" 
-#'
-#' abname("AMCL", to = "trivial_nl")
-#' # "Amoxicilline/clavulaanzuur"
-#'
-#' abname("AMCL", to = "atc")
-#' # "J01CR02"
-#'
-#' abname("J01CR02", from = "atc", to = "umcg")
-#' # "AMCL"
-abname <- function(abcode, from = 'umcg', to = 'official', textbetween = ' + ', tolower = FALSE) {
-  
-  ablist <- AMR::ablist
-  colnames(ablist) <- colnames(ablist) %>% tolower()
-  from <- from %>% tolower()
-  to <- to %>% tolower()
-  
-  if (!from %in% colnames(ablist) |
-      !to %in% colnames(ablist)) {
-    stop(paste0('Invalid `from` or `to`. Choose one of ', 
-                colnames(ablist) %>% paste(collapse = ","), '.'), call. = FALSE)
-  }
-  
-  abcode <- as.character(abcode)
-  
-  for (i in 1:length(abcode)) {
-    drug <- abcode[i]
-    if (!grepl('+', drug, fixed = TRUE) & !grepl(' en ', drug, fixed = TRUE)) {
-      # bestaat maar uit 1 middel
-      if (any(ablist[, from] == drug)) {
-        abcode[i] <-
-          ablist %>%
-          filter(.[, from] == drug) %>%
-          select(to) %>%
-          slice(1) %>%
-          as.character()
-      } else {
-        # niet gevonden
-        warning('Code "', drug, '" not found in antibiotics list.', call. = FALSE)
-        abcode[i] <- NA
-      }
-    } else {
-      # meerdere middelen
-      if (grepl('+', drug, fixed = TRUE)) {
-        drug.group <-
-          strsplit(drug, '+', fixed = TRUE) %>%
-          unlist() %>%
-          trimws('both')
-      } else if (grepl(' en ', drug, fixed = TRUE)) {
-        drug.group <-
-          strsplit(drug, ' en ', fixed = TRUE) %>%
-          unlist() %>%
-          trimws('both')
-      } else {
-        warning('Invalid concat.')
-        abcode[i] <- NA
-        next
-      }
-      
-      for (j in 1:length(drug.group)) {
-        drug.group[j] <-
-          ablist %>%
-          filter(.[, from] == drug.group[j]) %>%
-          select(to) %>%
-          slice(1) %>%
-          as.character()
-        if (j > 1 & to %in% c('official', 'trivial_nl')) {
-          drug.group[j] <- drug.group[j] %>% tolower()
-        }
-      }
-      abcode[i] <- paste(drug.group, collapse = textbetween)
-    }
-  }
-  
-  if (tolower == TRUE) {
-    abcode <- abcode %>% tolower()
-  }
-  
-  abcode
-}
@@ -1,408 +0,0 @@
-# ==================================================================== #
-# TITLE                                                                #
-# Antimicrobial Resistance (AMR) Analysis                              #
-#                                                                      #
-# AUTHORS                                                              #
-# Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl)           #
-#                                                                      #
-# LICENCE                                                              #
-# This program is free software; you can redistribute it and/or modify #
-# it under the terms of the GNU General Public License version 2.0,    #
-# as published by the Free Software Foundation.                        #
-#                                                                      #
-# This program is distributed in the hope that it will be useful,      #
-# but WITHOUT ANY WARRANTY; without even the implied warranty of       #
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the        #
-# GNU General Public License for more details.                         #
-# ==================================================================== #
-
-#' Class 'rsi'
-#'
-#' This transforms a vector to a new class \code{rsi}, which is an ordered factor with levels \code{S < I < R}. Invalid antimicrobial interpretations will be translated as \code{NA} with a warning.
-#' @rdname as.rsi
-#' @param x vector
-#' @return New class \code{rsi}
-#' @export
-#' @importFrom dplyr %>%
-#' @examples
-#' rsi_data <- as.rsi(c(rep("S", 474), rep("I", 36), rep("R", 370)))
-#' rsi_data <- as.rsi(c(rep("S", 474), rep("I", 36), rep("R", 370), "A", "B", "C"))
-#' is.rsi(rsi_data)
-#' 
-#' plot(rsi_data)    # for percentages
-#' barplot(rsi_data) # for frequencies
-as.rsi <- function(x) {
-  if (is.rsi(x)) {
-    x
-  } else {
-    
-    x <- x %>% unlist()
-    x.bak <- x
-    
-    na_before <- x[is.na(x) | x == ''] %>% length()
-    x <- gsub('[^RSI]+', '', x %>% toupper())
-    # needed for UMCG in cases of "S;S" but also "S;I"; the latter will be NA:
-    x <- gsub('^S+$', 'S', x)
-    x <- gsub('^I+$', 'I', x)
-    x <- gsub('^R+$', 'R', x)
-    x[!x %in% c('S', 'I', 'R')] <- NA
-    na_after <- x[is.na(x) | x == ''] %>% length()
-    
-    if (na_before != na_after) {
-      list_missing <- x.bak[is.na(x) & !is.na(x.bak) & x.bak != ''] %>%
-        unique() %>%
-        sort()
-      list_missing <- paste0('"', list_missing , '"', collapse = ", ")
-      warning(na_after - na_before, ' results truncated (',
-              round(((na_after - na_before) / length(x)) / 100),
-              '%) that were invalid antimicrobial interpretations: ',
-              list_missing, call. = FALSE)
-    }
-    
-    x <- x %>% toupper() %>% factor(levels = c("S", "I", "R"), ordered = TRUE)
-    class(x) <- c('rsi', 'ordered', 'factor')
-    x
-  }
-}
-
-#' @rdname as.rsi
-#' @export
-#' @importFrom dplyr %>%
-is.rsi <- function(x) {
-  class(x) %>% identical(c('rsi', 'ordered', 'factor'))
-}
-
-#' @exportMethod print.rsi
-#' @export
-#' @importFrom dplyr %>%
-#' @noRd
-print.rsi <- function(x, ...) {
-  n_total <- x %>% length()
-  x <- x[!is.na(x)]
-  n <- x %>% length()
-  S <- x[x == 'S'] %>% length()
-  I <- x[x == 'I'] %>% length()
-  R <- x[x == 'R'] %>% length()
-  IR <- x[x %in% c('I', 'R')] %>% length()
-  cat("Class 'rsi': ", n, " isolates\n", sep = '')
-  cat('\n')
-  cat('<NA>:      ', n_total - n, '\n')
-  cat('Sum of S:  ', S, '\n')
-  cat('Sum of IR: ', IR, '\n')
-  cat('- Sum of R:', R, '\n')
-  cat('- Sum of I:', I, '\n')
-  cat('\n')
-  print(c(
-    `%S` = round((S / n) * 100, 1),
-    `%IR` = round((IR / n) * 100, 1),
-    `%I` = round((I / n) * 100, 1),
-    `%R` = round((R / n) * 100, 1)
-  ))
-}
-
-#' @exportMethod summary.rsi
-#' @export
-#' @importFrom dplyr %>%
-#' @noRd
-summary.rsi <- function(object, ...) {
-  x <- object
-  n_total <- x %>% length()
-  x <- x[!is.na(x)]
-  n <- x %>% length()
-  S <- x[x == 'S'] %>% length()
-  I <- x[x == 'I'] %>% length()
-  R <- x[x == 'R'] %>% length()
-  IR <- x[x %in% c('I', 'R')] %>% length()
-  lst <- c('rsi', n_total - n, S, IR, R, I)
-  names(lst) <- c("Mode", "<NA>", "Sum S", "Sum IR", "Sum R", "Sum I")
-  lst
-}
-
-#' @exportMethod plot.rsi
-#' @export
-#' @importFrom dplyr %>% group_by summarise filter mutate if_else n_distinct
-#' @importFrom graphics plot text
-#' @noRd
-plot.rsi <- function(x, ...) {
-  x_name <- deparse(substitute(x))
-  
-  data <- data.frame(x = x,
-                     y = 1,
-                     stringsAsFactors = TRUE) %>%
-    group_by(x) %>%
-    summarise(n = sum(y)) %>%
-    filter(!is.na(x)) %>%
-    mutate(s = round((n / sum(n)) * 100, 1))
-  data$x <- factor(data$x, levels = c('S', 'I', 'R'), ordered = TRUE)
-  
-  ymax <- if_else(max(data$s) > 95, 105, 100)
-
-  plot(x = data$x,
-       y = data$s,
-       lwd = 2,
-       col = c('green', 'orange', 'red'),
-       ylim = c(0, ymax),
-       ylab = 'Percentage',
-       xlab = 'Antimicrobial Interpretation',
-       main = paste('Susceptibilty Analysis of', x_name),
-       axes = FALSE,
-       ...)
-  # x axis
-  axis(side = 1, at = 1:n_distinct(data$x), labels = levels(data$x), lwd = 0)
-  # y axis, 0-100%
-  axis(side = 2, at = seq(0, 100, 5))
-  
-  text(x = data$x,
-       y = data$s + 4,
-       labels = paste0(data$s, '% (n = ', data$n, ')'))
-}
-
-
-#' @exportMethod barplot.rsi
-#' @export
-#' @importFrom dplyr %>% group_by summarise filter mutate if_else n_distinct
-#' @importFrom graphics barplot axis
-#' @noRd
-barplot.rsi <- function(height, ...) {
-  x <- height
-  x_name <- deparse(substitute(height))
-  
-  data <- data.frame(rsi = x, cnt = 1) %>%
-    group_by(rsi) %>%
-    summarise(cnt = sum(cnt)) %>%
-    droplevels()
-
-  barplot(table(x),
-          col = c('green3', 'orange2', 'red3'),
-          xlab = 'Antimicrobial Interpretation',
-          main = paste('Susceptibilty Analysis of', x_name),
-          ylab = 'Frequency',
-          axes = FALSE,
-          ...)
-  # y axis, 0-100%
-  axis(side = 2, at = seq(0, max(data$cnt) + max(data$cnt) * 1.1, by = 25))
-}
-
-#' Class 'mic'
-#'
-#' This transforms a vector to a new class\code{mic}, which is an ordered factor with valid MIC values as levels. Invalid MIC values will be translated as \code{NA} with a warning.
-#' @rdname as.mic
-#' @param x vector
-#' @param na.rm a logical indicating whether missing values should be removed
-#' @return New class \code{mic}
-#' @export
-#' @importFrom dplyr %>%
-#' @examples
-#' mic_data <- as.mic(c(">=32", "1.0", "1", "1.00", 8, "<=0.128", "8", "16", "16"))
-#' is.mic(mic_data)
-#' 
-#' plot(mic_data)
-#' barplot(mic_data)
-as.mic <- function(x, na.rm = FALSE) {
-  if (is.mic(x)) {
-    x
-  } else {
-    x <- x %>% unlist()
-    if (na.rm == TRUE) {
-      x <- x[!is.na(x)]
-    }
-    x.bak <- x
-    
-    # comma to dot
-    x <- gsub(',', '.', x, fixed = TRUE)
-    # starting dots must start with 0
-    x <- gsub('^[.]', '0.', x)
-    # <=0.2560.512 should be 0.512
-    x <- gsub('.*[.].*[.]', '0.', x)
-    # remove ending .0
-    x <- gsub('[.]0$', '', x)
-    # remove all after last digit
-    x <- gsub('[^0-9]$', '', x)
-    # remove last zeroes
-    x <- gsub('[.]?0+$', '', x)
-    
-    lvls <- c("<0.002", "<=0.002", "0.002", ">=0.002", ">0.002",
-              "<0.003", "<=0.003", "0.003", ">=0.003", ">0.003",
-              "<0.004", "<=0.004", "0.004", ">=0.004", ">0.004",
-              "<0.006", "<=0.006", "0.006", ">=0.006", ">0.006",
-              "<0.008", "<=0.008", "0.008", ">=0.008", ">0.008",
-              "<0.012", "<=0.012", "0.012", ">=0.012", ">0.012",
-              "<0.016", "<=0.016", "0.016", ">=0.016", ">0.016",
-              "<0.023", "<=0.023", "0.023", ">=0.023", ">0.023",
-              "<0.025", "<=0.025", "0.025", ">=0.025", ">0.025",
-              "<0.03", "<=0.03", "0.03", ">=0.03", ">0.03",
-              "<0.032", "<=0.032", "0.032", ">=0.032", ">0.032",
-              "<0.047", "<=0.047", "0.047", ">=0.047", ">0.047",
-              "<0.05", "<=0.05", "0.05", ">=0.05", ">0.05",
-              "<0.06", "<=0.06", "0.06", ">=0.06", ">0.06",
-              "<0.0625", "<=0.0625", "0.0625", ">=0.0625", ">0.0625",
-              "<0.063", "<=0.063", "0.063", ">=0.063", ">0.063",
-              "<0.064", "<=0.064", "0.064", ">=0.064", ">0.064",
-              "<0.09", "<=0.09", "0.09", ">=0.09", ">0.09",
-              "<0.094", "<=0.094", "0.094", ">=0.094", ">0.094",
-              "<0.12", "<=0.12", "0.12", ">=0.12", ">0.12",
-              "<0.125", "<=0.125", "0.125", ">=0.125", ">0.125",
-              "<0.128", "<=0.128", "0.128", ">=0.128", ">0.128",
-              "<0.16", "<=0.16", "0.16", ">=0.16", ">0.16",
-              "<0.19", "<=0.19", "0.19", ">=0.19", ">0.19",
-              "<0.25", "<=0.25", "0.25", ">=0.25", ">0.25",
-              "<0.256", "<=0.256", "0.256", ">=0.256", ">0.256",
-              "<0.32", "<=0.32", "0.32", ">=0.32", ">0.32",
-              "<0.38", "<=0.38", "0.38", ">=0.38", ">0.38",
-              "<0.5", "<=0.5", "0.5", ">=0.5", ">0.5",
-              "<0.512", "<=0.512", "0.512", ">=0.512", ">0.512",
-              "<0.64", "<=0.64", "0.64", ">=0.64", ">0.64",
-              "<0.75", "<=0.75", "0.75", ">=0.75", ">0.75",
-              "<1", "<=1", "1", ">=1", ">1",
-              "<1.5", "<=1.5", "1.5", ">=1.5", ">1.5",
-              "<2", "<=2", "2", ">=2", ">2",
-              "<3", "<=3", "3", ">=3", ">3",
-              "<4", "<=4", "4", ">=4", ">4",
-              "<6", "<=6", "6", ">=6", ">6",
-              "<8", "<=8", "8", ">=8", ">8",
-              "<10", "<=10", "10", ">=10", ">10",
-              "<12", "<=12", "12", ">=12", ">12",
-              "<16", "<=16", "16", ">=16", ">16",
-              "<20", "<=20", "20", ">=20", ">20",
-              "<24", "<=24", "24", ">=24", ">24",
-              "<32", "<=32", "32", ">=32", ">32",
-              "<40", "<=40", "40", ">=40", ">40",
-              "<48", "<=48", "48", ">=48", ">48",
-              "<64", "<=64", "64", ">=64", ">64",
-              "<80", "<=80", "80", ">=80", ">80",
-              "<96", "<=96", "96", ">=96", ">96",
-              "<128", "<=128", "128", ">=128", ">128",
-              "<160", "<=160", "160", ">=160", ">160",
-              "<256", "<=256", "256", ">=256", ">256",
-              "<320", "<=320", "320", ">=320", ">320",
-              "<512", "<=512", "512", ">=512", ">512",
-              "<1024", "<=1024", "1024", ">=1024", ">1024")
-    x <- x %>% as.character()
-    
-    na_before <- x[is.na(x) | x == ''] %>% length()
-    x[!x %in% lvls] <- NA
-    na_after <- x[is.na(x) | x == ''] %>% length()
-    
-    if (na_before != na_after) {
-      list_missing <- x.bak[is.na(x) & !is.na(x.bak) & x.bak != ''] %>%
-        unique() %>%
-        sort()
-      list_missing <- paste0('"', list_missing , '"', collapse = ", ")
-      warning(na_after - na_before, ' results truncated (',
-              round(((na_after - na_before) / length(x)) / 100),
-              '%) that were invalid MICs: ',
-              list_missing, call. = FALSE)
-    }
-    
-    x <- factor(x = x,
-                levels = lvls,
-                ordered = TRUE)
-    class(x) <- c('mic', 'ordered', 'factor')
-    x
-  }
-}
-
-#' @rdname as.mic
-#' @export
-#' @importFrom dplyr %>%
-is.mic <- function(x) {
-  class(x) %>% identical(c('mic', 'ordered', 'factor'))
-}
-
-#' @exportMethod as.double.mic
-#' @export
-#' @noRd
-as.double.mic <- function(x, ...) {
-  as.double(gsub('(<|=|>)+', '', as.character(x)))
-}
-
-#' @exportMethod as.integer.mic
-#' @export
-#' @noRd
-as.integer.mic <- function(x, ...) {
-  as.integer(gsub('(<|=|>)+', '', as.character(x)))
-}
-
-#' @exportMethod as.numeric.mic
-#' @export
-#' @noRd
-as.numeric.mic <- function(x, ...) {
-  as.numeric(gsub('(<|=|>)+', '', as.character(x)))
-}
-
-#' @exportMethod print.mic
-#' @export
-#' @importFrom dplyr %>% tibble group_by summarise pull
-#' @noRd
-print.mic <- function(x, ...) {
-  n_total <- x %>% length()
-  x <- x[!is.na(x)]
-  n <- x %>% length()
-  cat("Class 'mic': ", n, " isolates\n", sep = '')
-  cat('\n')
-  cat('<NA> ', n_total - n, '\n')
-  cat('\n')
-  tbl <- tibble(x = x, y = 1) %>% group_by(x) %>% summarise(y = sum(y))
-  cnt <- tbl %>% pull(y)
-  names(cnt) <- tbl %>% pull(x)
-  print(cnt)
-}
-
-#' @exportMethod summary.mic
-#' @export
-#' @importFrom dplyr %>% tibble group_by summarise pull
-#' @noRd
-summary.mic <- function(object, ...) {
-  x <- object
-  n_total <- x %>% length()
-  x <- x[!is.na(x)]
-  n <- x %>% length()
-  return(c("Mode" = 'mic',
-           "NA" = n_total - n,
-           "Min." = sort(x)[1] %>% as.character(),
-           "Max." = sort(x)[n] %>% as.character()
-  ))
-  cat("Class 'mic': ", n, " isolates\n", sep = '')
-  cat('\n')
-  cat('<NA> ', n_total - n, '\n')
-  cat('\n')
-  tbl <- tibble(x = x, y = 1) %>% group_by(x) %>% summarise(y = sum(y))
-  cnt <- tbl %>% pull(y)
-  names(cnt) <- tbl %>% pull(x)
-  print(cnt)
-}
-
-#' @exportMethod plot.mic
-#' @export
-#' @importFrom dplyr %>% group_by summarise
-#' @importFrom graphics plot text
-#' @noRd
-plot.mic <- function(x, ...) {
-  x_name <- deparse(substitute(x))
-  create_barplot_mic(x, x_name, ...)
-}
-
-#' @exportMethod barplot.mic
-#' @export
-#' @importFrom dplyr %>% group_by summarise
-#' @importFrom graphics barplot axis
-#' @noRd
-barplot.mic <- function(height, ...) {
-  x_name <- deparse(substitute(height))
-  create_barplot_mic(height, x_name, ...)
-}
-
-#' @importFrom graphics barplot axis
-create_barplot_mic <- function(x, x_name, ...) {
-  data <- data.frame(mic = x, cnt = 1) %>%
-    group_by(mic) %>%
-    summarise(cnt = sum(cnt)) %>%
-    droplevels()
-  barplot(table(droplevels(x)),
-          ylab = 'Frequency',
-          xlab = 'MIC value',
-          main = paste('MIC values of', x_name), 
-          axes = FALSE,
-          ...)
-  axis(2, seq(0, max(data$cnt)))
-}
@@ -1,96 +0,0 @@
-# ==================================================================== #
-# TITLE                                                                #
-# Antimicrobial Resistance (AMR) Analysis                              #
-#                                                                      #
-# AUTHORS                                                              #
-# Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl)           #
-#                                                                      #
-# LICENCE                                                              #
-# This program is free software; you can redistribute it and/or modify #
-# it under the terms of the GNU General Public License version 2.0,    #
-# as published by the Free Software Foundation.                        #
-#                                                                      #
-# This program is distributed in the hope that it will be useful,      #
-# but WITHOUT ANY WARRANTY; without even the implied warranty of       #
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the        #
-# GNU General Public License for more details.                         #
-# ==================================================================== #
-
-#' Dataset with 420 antibiotics
-#'
-#' A dataset containing all antibiotics with a J0 code, with their DDD's.
-#' @format A data.frame with 420 observations and 12 variables:
-#' \describe{
-#'   \item{\code{atc}}{ATC code, like \code{J01CR02}}
-#'   \item{\code{molis}}{MOLIS code, like \code{amcl}}
-#'   \item{\code{umcg}}{UMCG code, like \code{AMCL}}
-#'   \item{\code{official}}{Official name by the WHO, like \code{"amoxicillin and enzyme inhibitor"}}
-#'   \item{\code{official_nl}}{Official name in the Netherlands, like \code{"Amoxicilline met enzymremmer"}}
-#'   \item{\code{trivial_nl}}{Trivial name in Dutch, like \code{"Amoxicilline/clavulaanzuur"}}
-#'   \item{\code{oral_ddd}}{Defined Daily Dose (DDD) according to the WHO, oral treatment}
-#'   \item{\code{oral_units}}{Units of \code{ddd_units}}
-#'   \item{\code{iv_ddd}}{Defined Daily Dose (DDD) according to the WHO, parenteral treatment}
-#'   \item{\code{iv_units}}{Units of \code{iv_ddd}}
-#'   \item{\code{atc_group1}}{ATC group in Dutch, like \code{"Macroliden, lincosamiden en streptograminen"}}
-#'   \item{\code{atc_group2}}{Subgroup of \code{atc_group1} in Dutch, like \code{"Macroliden"}}
-#' }
-#' @source MOLIS (LIS of Certe) - \url{https://www.certe.nl} \cr \cr GLIMS (LIS of UMCG) - \url{https://www.umcg.nl} \cr \cr World Health Organization - \url{https://www.whocc.no/atc_ddd_index/}
-#' @seealso \code{\link{bactlist}}
-# todo:
-# ablist <- ablist %>% mutate(useful_gramnegative = if_else(atc_group2 == 'Tetracyclines', FALSE, TRUE))
-# ablist <- ablist %>% mutate(useful_gramnegative = if_else(atc_group2 %like% 'Glycopept', FALSE, useful_gramnegative))
-# Tbl1 Enterobacteriaceae are also intrinsically resistant to benzylpenicillin, glycopeptides, fusidic acid, macrolides (with some exceptions1), lincosamides, streptogramins, rifampicin, daptomycin and linezolid.
-# Tbl2 Non-fermentative Gram-negative bacteria are also generally intrinsically resistant to benzylpenicillin, first and second generation cephalosporins, glycopeptides, fusidic acid, macrolides, lincosamides, streptogramins, rifampicin, daptomycin and linezolid
-# Tbl3 Gram-negative bacteria other than Enterobacteriaceae and non-fermentative Gram-negative bacteria listed are also intrinsically resistant to glycopeptides, lincosamides, daptomycin and linezolid.
-"ablist"
-
-#' Dataset with ~2500 microorganisms
-#'
-#' A dataset containing all microorganisms of MOLIS. MO codes of the UMCG can be looked up using \code{\link{bactlist.umcg}}.
-#' @format A data.frame with 2507 observations and 10 variables:
-#' \describe{
-#'   \item{\code{bactid}}{ID of microorganism}
-#'   \item{\code{bactsys}}{Bactsyscode of microorganism}
-#'   \item{\code{family}}{Family name of microorganism}
-#'   \item{\code{genus}}{Genus name of microorganism, like \code{"Echerichia"}}
-#'   \item{\code{species}}{Species name of microorganism, like \code{"coli"}}
-#'   \item{\code{subspecies}}{Subspecies name of bio-/serovar of microorganism, like \code{"EHEC"}}
-#'   \item{\code{fullname}}{Full name, like \code{"Echerichia coli (EHEC)"}}
-#'   \item{\code{type}}{Type of microorganism in Dutch, like \code{"Bacterie"} and \code{"Schimmel/gist"}}
-#'   \item{\code{gramstain}}{Gram of microorganism in Dutch, like \code{"Negatieve staven"}}
-#'   \item{\code{aerobic}}{Type aerobe/anaerobe of bacteria}
-#' }
-#' @source MOLIS (LIS of Certe) - \url{https://www.certe.nl}
-#' @seealso \code{\link{ablist}} \code{\link{bactlist.umcg}}
-"bactlist"
-
-#' Translation table for UMCG with ~1100 microorganisms
-#'
-#' A dataset containing all bacteria codes of UMCG MMB. These codes can be joined to data with an ID from \code{\link{bactlist}$bactid}, using \code{\link{left_join_bactlist}}.
-#' @format A data.frame with 1090 observations and 2 variables:
-#' \describe{
-#'   \item{\code{mocode}}{Code of microorganism according to UMCG MMB}
-#'   \item{\code{bactid}}{Code of microorganism in \code{\link{bactlist}}}
-#' }
-#' @source MOLIS (LIS of Certe) - \url{https://www.certe.nl} \cr \cr GLIMS (LIS of UMCG) - \url{https://www.umcg.nl}
-#' @seealso \code{\link{bactlist}}
-"bactlist.umcg"
-
-#' Dataset with 2000 blood culture isolates of septic patients
-#'
-#' An anonymised dataset containing 2000 microbial blood culture isolates with their antibiogram of septic patients found in 5 different hospitals in the Netherlands, between 2001 and 2017. This data.frame can be used to practice AMR analysis e.g. with \code{\link{rsi}} or \code{\link{rsi_predict}}, or it can be used to practice other statistics.
-#' @format A data.frame with 2000 observations and 47 variables:
-#' \describe{
-#'   \item{\code{date}}{date of receipt at the laboratory}
-#'   \item{\code{hospital_id}}{ID of the hospital}
-#'   \item{\code{ward_icu}}{logical to determine if ward is an intensive care unit}
-#'   \item{\code{ward_clinical}}{logical to determine if ward is a regular clinical ward}
-#'   \item{\code{ward_outpatient}}{logical to determine if ward is an outpatient clinic}
-#'   \item{\code{age}}{age of the patient}
-#'   \item{\code{sex}}{sex of the patient}
-#'   \item{\code{patient_id}}{ID of the patient, first 10 characters of an SHA hash containing irretrievable information}
-#'   \item{\code{bactid}}{ID of microorganism, see \code{\link{bactlist}}}
-#'   \item{\code{peni:mupi}}{38 different antibiotics with class \code{rsi} (see \code{\link{as.rsi}}), these column names occur in \code{\link{ablist}} and can be translated with \code{\link{abname}}}
-#' }
-#' @source MOLIS (LIS of Certe) - \url{https://www.certe.nl}
-"septic_patients"
@@ -1,504 +0,0 @@
-# ==================================================================== #
-# TITLE                                                                #
-# Antimicrobial Resistance (AMR) Analysis                              #
-#                                                                      #
-# AUTHORS                                                              #
-# Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl)           #
-#                                                                      #
-# LICENCE                                                              #
-# This program is free software; you can redistribute it and/or modify #
-# it under the terms of the GNU General Public License version 2.0,    #
-# as published by the Free Software Foundation.                        #
-#                                                                      #
-# This program is distributed in the hope that it will be useful,      #
-# but WITHOUT ANY WARRANTY; without even the implied warranty of       #
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the        #
-# GNU General Public License for more details.                         #
-# ==================================================================== #
-
-#' Determine first (weighted) isolates
-#'
-#' Determine first (weighted) isolates of all microorganisms of every patient per episode and (if needed) per specimen type.
-#' @param tbl a \code{data.frame} containing isolates.
-#' @param col_date column name of the result date (or date that is was received on the lab)
-#' @param col_patient_id column name of the unique IDs of the patients
-#' @param col_genus column name of the genus of the microorganisms
-#' @param col_species column name of the species of the microorganisms
-#' @param col_testcode column name of the test codes. Use \code{col_testcode = NA} to \strong{not} exclude certain test codes (like test codes for screening). In that case \code{testcodes_exclude} will be ignored.
-#' @param col_specimen column name of the specimen type or group
-#' @param col_icu column name of the logicals (\code{TRUE}/\code{FALSE}) whether a ward or department is an Intensive Care Unit (ICU)
-#' @param col_keyantibiotics column name of the key antibiotics to determine first \emph{weighted} isolates, see \code{\link{key_antibiotics}}.
-#' @param episode_days episode in days after which a genus/species combination will be determined as 'first isolate' again
-#' @param testcodes_exclude character vector with test codes that should be excluded (caseINsensitive)
-#' @param icu_exclude logical whether ICU isolates should be excluded
-#' @param filter_specimen specimen group or type that should be excluded
-#' @param output_logical return output as \code{logical} (will else the values \code{0} or \code{1})
-#' @param points_threshold points until the comparison of key antibiotics will lead to inclusion of an isolate, see Details
-#' @param info print progress
-#' @details \strong{Why this is so important} \cr
-#'     To conduct an analysis of antimicrobial resistance, you should only include the first isolate of every patient per episode \href{https://www.ncbi.nlm.nih.gov/pubmed/17304462}{[1]}. If you would not do this, you could easily get an overestimate or underestimate of the resistance of an antibiotic. Imagine that a patient was admitted with an MRSA and that it was found in 5 different blood cultures the following week. The resistance percentage of oxacillin of all \emph{S. aureus} isolates would be overestimated, because you included this MRSA more than once. It would be \href{https://en.wikipedia.org/wiki/Selection_bias}{selection bias}.
-#'
-#'     \strong{Using parameter \code{points_threshold}} \cr
-#'     To compare key antibiotics, the difference between antimicrobial interpretations will be measured. A difference from I to S|R (or vice versa) means 0.5 points. A difference from S to R (or vice versa) means 1 point. When the sum of points exceeds \code{points_threshold}, an isolate will be (re)selected as a first weighted isolate.
-#' @keywords isolate isolates first
-#' @export
-#' @importFrom dplyr arrange_at lag between row_number filter mutate arrange
-#' @return A vector to add to table, see Examples.
-#' @examples
-#' \dontrun{
-#'
-#' # set key antibiotics to a new variable
-#' tbl$keyab <- key_antibiotics(tbl)
-#'
-#' tbl$first_isolate <-
-#'   first_isolate(tbl)
-#'
-#' tbl$first_isolate_weighed <-
-#'   first_isolate(tbl,
-#'                 col_keyantibiotics = 'keyab')
-#'
-#' tbl$first_blood_isolate <-
-#'   first_isolate(tbl,
-#'                 filter_specimen = 'Blood')
-#'
-#' tbl$first_blood_isolate_weighed <-
-#'   first_isolate(tbl,
-#'                 filter_specimen = 'Blood',
-#'                 col_keyantibiotics = 'keyab')
-#'
-#' tbl$first_urine_isolate <-
-#'   first_isolate(tbl,
-#'                 filter_specimen = 'Urine')
-#'
-#' tbl$first_urine_isolate_weighed <-
-#'   first_isolate(tbl,
-#'                 filter_specimen = 'Urine',
-#'                 col_keyantibiotics = 'keyab')
-#'
-#' tbl$first_resp_isolate <-
-#'   first_isolate(tbl,
-#'                 filter_specimen = 'Respiratory')
-#'
-#' tbl$first_resp_isolate_weighed <-
-#'   first_isolate(tbl,
-#'                 filter_specimen = 'Respiratory',
-#'                 col_keyantibiotics = 'keyab')
-#' }
-first_isolate <- function(tbl,
-                          col_date,
-                          col_patient_id,
-                          col_genus,
-                          col_species,
-                          col_testcode = NA,
-                          col_specimen,
-                          col_icu,
-                          col_keyantibiotics = NA,
-                          episode_days = 365,
-                          testcodes_exclude = '',
-                          icu_exclude = FALSE,
-                          filter_specimen = NA,
-                          output_logical = TRUE,
-                          points_threshold = 2,
-                          info = TRUE) {
-  
-  # controleren of kolommen wel bestaan
-  check_columns_existance <- function(column, tblname = tbl) {
-    if (NROW(tblname) <= 1 | NCOL(tblname) <= 1) {
-      stop('Please check tbl for existance.')
-    }
-    
-    if (!is.na(column)) {
-      if (!(column %in% colnames(tblname))) {
-        stop('Column ', column, ' not found.')
-      }
-    }
-  }
-  
-  check_columns_existance(col_date)
-  check_columns_existance(col_patient_id)
-  check_columns_existance(col_genus)
-  check_columns_existance(col_species)
-  check_columns_existance(col_testcode)
-  check_columns_existance(col_icu)
-  check_columns_existance(col_keyantibiotics)
-  
-  if (is.na(col_testcode)) {
-    testcodes_exclude <- NA
-  }
-  # testcodes verwijderen die ingevuld zijn
-  if (!is.na(testcodes_exclude[1]) & testcodes_exclude[1] != '' & info == TRUE) {
-    cat('Isolates from these test codes will be ignored:\n', toString(testcodes_exclude), '\n')
-  }
-  
-  if (is.na(col_icu)) {
-    icu_exclude <- FALSE
-  } else {
-    tbl <- tbl %>%
-      mutate(col_icu = tbl %>% pull(col_icu) %>% as.logical())
-  }
-  
-  specgroup.notice <- ''
-  weighted.notice <- ''
-  # filteren op materiaalgroep en sleutelantibiotica gebruiken wanneer deze ingevuld zijn
-  if (!is.na(filter_specimen) & filter_specimen != '') {
-    check_columns_existance(col_specimen, tbl)
-    if (info == TRUE) {
-      cat('Isolates other than of specimen group \'', filter_specimen, '\' will be ignored. ', sep = '')
-    }
-  } else {
-    filter_specimen <- ''
-  }
-  if (col_keyantibiotics %in% c(NA, '')) {
-    col_keyantibiotics <- ''
-  } else {
-    tbl <- tbl %>% mutate(key_ab = tbl %>% pull(col_keyantibiotics))
-  }
-  
-  if (is.na(testcodes_exclude[1])) {
-    testcodes_exclude <- ''
-  }
-  
-  # nieuwe dataframe maken met de oorspronkelijke rij-index, 0-bepaling en juiste sortering
-  #cat('Sorting table...')
-  tbl <- tbl %>%
-    mutate(first_isolate_row_index = 1:nrow(tbl),
-           eersteisolaatbepaling = 0,
-           date_lab = tbl %>% pull(col_date),
-           patient_id = tbl %>% pull(col_patient_id),
-           species = tbl %>% pull(col_species),
-           genus = tbl %>% pull(col_genus)) %>%
-    mutate(species = if_else(is.na(species), '', species),
-           genus = if_else(is.na(genus), '', genus))
-  
-  if (filter_specimen == '') {
-    
-    if (icu_exclude == FALSE) {
-      if (info == TRUE) {
-        cat('Isolates from ICU will *NOT* be ignored.\n')
-      }
-      tbl <- tbl %>%
-        arrange_at(c(col_patient_id,
-                     col_genus,
-                     col_species,
-                     col_date))
-      row.start <- 1
-      row.end <- nrow(tbl)
-    } else {
-      if (info == TRUE) {
-        cat('Isolates from ICU will be ignored.\n')
-      }
-      tbl <- tbl %>%
-        arrange_at(c(col_icu,
-                     col_patient_id,
-                     col_genus,
-                     col_species,
-                     col_date))
-      
-      suppressWarnings(
-        row.start <- which(tbl %>% pull(col_icu) == FALSE) %>% min(na.rm = TRUE)
-      )
-      suppressWarnings(
-        row.end <- which(tbl %>% pull(col_icu) == FALSE) %>% max(na.rm = TRUE)
-      )
-    }
-    
-  } else {
-    # sorteren op materiaal en alleen die rijen analyseren om tijd te besparen
-    if (icu_exclude == FALSE) {
-      if (info == TRUE) {
-        cat('Isolates from ICU will *NOT* be ignored.\n')
-      }
-      tbl <- tbl %>%
-        arrange_at(c(col_specimen,
-                     col_patient_id,
-                     col_genus,
-                     col_species,
-                     col_date))
-      suppressWarnings(
-        row.start <- which(tbl %>% pull(col_specimen) == filter_specimen) %>% min(na.rm = TRUE)
-      )
-      suppressWarnings(
-        row.end <- which(tbl %>% pull(col_specimen) == filter_specimen) %>% max(na.rm = TRUE)
-      )
-    } else {
-      if (info == TRUE) {
-        cat('Isolates from ICU will be ignored.\n')
-      }
-      tbl <- tbl %>%
-        arrange_at(c(col_icu,
-                     col_specimen,
-                     col_patient_id,
-                     col_genus,
-                     col_species,
-                     col_date))
-      suppressWarnings(
-        row.start <- which(tbl %>% pull(col_specimen) == filter_specimen
-                           & tbl %>% pull(col_icu) == FALSE) %>% min(na.rm = TRUE)
-      )
-      suppressWarnings(
-        row.end <- which(tbl %>% pull(col_specimen) == filter_specimen
-                         & tbl %>% pull(col_icu) == FALSE) %>% max(na.rm = TRUE)
-      )
-    }
-    
-  }
-  
-  if (abs(row.start) == Inf | abs(row.end) == Inf) {
-    if (info == TRUE) {
-      cat('No isolates found.\n')
-    }
-    # NA's maken waar genus niet beschikbaar is
-    tbl <- tbl %>%
-      mutate(real_first_isolate = if_else(genus == '', NA, FALSE))
-    if (output_logical == FALSE) {
-      tbl$real_first_isolate <- tbl %>% pull(real_first_isolate) %>% as.integer()
-    }
-    return(tbl %>% pull(real_first_isolate))
-  }
-  
-  scope.size <- tbl %>%
-    filter(row_number() %>%
-             between(row.start,
-                     row.end),
-           genus != '') %>%
-    nrow()
-  
-  # Analyse van eerste isolaat ----
-  all_first <- tbl %>%
-    mutate(other_pat_or_mo = if_else(patient_id == lag(patient_id)
-                                     & genus == lag(genus)
-                                     & species == lag(species),
-                                     FALSE,
-                                     TRUE),
-           days_diff = 0) %>%
-    mutate(days_diff = if_else(other_pat_or_mo == FALSE,
-                               (date_lab - lag(date_lab)) + lag(days_diff),
-                               0))
-  
-  if (col_keyantibiotics != '') {
-    if (info == TRUE) {
-      cat(paste0('Comparing key antibiotics for first weighted isolates (using points threshold of '
-                 , points_threshold, ')...\n'))
-    }
-    all_first <- all_first %>%
-      mutate(key_ab_lag = lag(key_ab)) %>%
-      mutate(key_ab_other = !key_antibiotics_equal(x = key_ab,
-                                                   y = key_ab_lag,
-                                                   points_threshold = points_threshold,
-                                                   info = info)) %>%
-      mutate(
-        real_first_isolate =
-          if_else(
-            between(row_number(), row.start, row.end)
-            & genus != ''
-            & (other_pat_or_mo
-               | days_diff >= episode_days
-               | key_ab_other),
-            TRUE,
-            FALSE))
-    if (info == TRUE) {
-      cat('\n')
-    }
-  } else {
-    all_first <- all_first %>%
-      mutate(
-        real_first_isolate =
-          if_else(
-            between(row_number(), row.start, row.end)
-            & genus != ''
-            & (other_pat_or_mo
-               | days_diff >= episode_days),
-            TRUE,
-            FALSE))
-  }
-  
-  # allereerst isolaat als TRUE
-  all_first[row.start, 'real_first_isolate'] <- TRUE
-  # geen testen die uitgesloten moeten worden, of ICU
-  if (!is.na(col_testcode)) {
-    all_first[which(all_first[, col_testcode] %in% tolower(testcodes_exclude)), 'real_first_isolate'] <- FALSE
-  }
-  if (icu_exclude == TRUE) {
-    all_first[which(all_first[, col_icu] == TRUE), 'real_first_isolate'] <- FALSE
-  }
-  
-  # NA's maken waar genus niet beschikbaar is
-  all_first <- all_first %>%
-    mutate(real_first_isolate = if_else(genus == '', NA, real_first_isolate))
-  
-  all_first <- all_first %>%
-    arrange(first_isolate_row_index) %>%
-    pull(real_first_isolate)
-  
-  if (info == TRUE) {
-    cat(paste0('\nFound ',
-               all_first %>% sum(na.rm = TRUE),
-               ' first ', weighted.notice, 'isolates (',
-               (all_first %>% sum(na.rm = TRUE) / scope.size) %>% percent(),
-               ' of isolates in scope [where genus was not empty] and ',
-               (all_first %>% sum(na.rm = TRUE) / tbl %>% nrow()) %>% percent(),
-               ' of total)\n'))
-  }
-  
-  if (output_logical == FALSE) {
-    all_first <- all_first %>% as.integer()
-  }
-  
-  all_first
-  
-}
-
-#' Key antibiotics based on bacteria ID
-#'
-#' @param tbl table with antibiotics coloms, like \code{amox} and \code{amcl}.
-#' @param col_bactcode column of bacteria IDs in \code{tbl}; these should occur in \code{bactlist$bactid}, see \code{\link{bactlist}}
-#' @param info print warnings
-#' @param amcl,amox,cfot,cfta,cftr,cfur,cipr,clar,clin,clox,doxy,gent,line,mero,peni,pita,rifa,teic,trsu,vanc column names of antibiotics.
-#' @export
-#' @importFrom dplyr %>% mutate if_else 
-#' @return Character of length 1.
-#' @seealso \code{\link{mo_property}} \code{\link{ablist}}
-#' @examples 
-#' \donttest{
-#' #' # set key antibiotics to a new variable
-#' tbl$keyab <- key_antibiotics(tbl)
-#' }
-key_antibiotics <- function(tbl,
-                            col_bactcode = 'bactid',
-                            info = TRUE,
-                            amcl = 'amcl',
-                            amox = 'amox',
-                            cfot = 'cfot',
-                            cfta = 'cfta',
-                            cftr = 'cftr',
-                            cfur = 'cfur',
-                            cipr = 'cipr',
-                            clar = 'clar',
-                            clin = 'clin',
-                            clox = 'clox',
-                            doxy = 'doxy',
-                            gent = 'gent',
-                            line = 'line',
-                            mero = 'mero',
-                            peni = 'peni',
-                            pita = 'pita',
-                            rifa = 'rifa',
-                            teic = 'teic',
-                            trsu = 'trsu',
-                            vanc = 'vanc') {
-  
-  keylist <- character(length = nrow(tbl))
-  
-  # check columns
-  col.list <- c(amox, cfot, cfta, cftr, cfur, cipr, clar,
-                clin, clox, doxy, gent, line, mero, peni,
-                pita, rifa, teic, trsu, vanc)
-  col.list <- col.list[!is.na(col.list)]
-  if (!all(col.list %in% colnames(tbl))) {
-    if (info == TRUE) {
-      warning('These columns do not exist and will be ignored:\n',
-              col.list[!(col.list %in% colnames(tbl))] %>% toString(),
-              immediate. = TRUE,
-              call. = FALSE)
-    }
-  }
-  
-  # bactlist aan vastknopen
-  tbl <- tbl %>% left_join_bactlist(col_bactcode)
-  
-  tbl$key_ab <- NA_character_
-  
-  # Staphylococcus
-  list_ab <- c(clox, trsu, teic, vanc, doxy, line, clar, rifa)
-  list_ab <- list_ab[list_ab %in% colnames(tbl)]
-  tbl <- tbl %>% mutate(key_ab =
-                          if_else(genus == 'Staphylococcus',
-                                  apply(X = tbl[, list_ab],
-                                        MARGIN = 1,
-                                        FUN = function(x) paste(x, collapse = "")),
-                                  key_ab))
-  
-  # Rest of Gram +
-  list_ab <- c(peni, amox, teic, vanc, clin, line, clar, trsu)
-  list_ab <- list_ab[list_ab %in% colnames(tbl)]
-  tbl <- tbl %>% mutate(key_ab =
-                          if_else(gramstain %like% '^Positi[e]?ve',
-                                  apply(X = tbl[, list_ab],
-                                        MARGIN = 1,
-                                        FUN = function(x) paste(x, collapse = "")),
-                                  key_ab))
-  
-  # Gram -
-  list_ab <- c(amox, amcl, pita, cfur, cfot, cfta, cftr, mero, cipr, trsu, gent)
-  list_ab <- list_ab[list_ab %in% colnames(tbl)]
-  tbl <- tbl %>% mutate(key_ab =
-                          if_else(gramstain %like% '^Negati[e]?ve',
-                                  apply(X = tbl[, list_ab],
-                                        MARGIN = 1,
-                                        FUN = function(x) paste(x, collapse = "")),
-                                  key_ab))
-  
-  # format
-  tbl <- tbl %>%
-    mutate(key_ab = gsub('(NA|NULL)', '-', key_ab) %>% toupper())
-  
-  tbl$key_ab
-  
-}
-
-#' @importFrom dplyr progress_estimated %>%
-#' @noRd
-key_antibiotics_equal <- function(x, y, points_threshold = 2, info = FALSE) {
-  # x is active row, y is lag
-
-  if (length(x) != length(y)) {
-    stop('Length of `x` and `y` must be equal.')
-  }
-  
-  result <- logical(length(x))
-  
-  if (info == TRUE) {
-    p <- dplyr::progress_estimated(length(x))
-  }
-  
-  for (i in 1:length(x)) {
-    
-    if (info == TRUE) {
-      p$tick()$print()
-    }
-    
-    if (is.na(x[i])) {
-      x[i] <- ''
-    }
-    if (is.na(y[i])) {
-      y[i] <- ''
-    }
-    
-    if (nchar(x[i]) != nchar(y[i])) {
-      
-      result[i] <- FALSE
-      
-    } else if (x[i] == '' & y[i] == '') {
-      
-      result[i] <- TRUE
-      
-    } else {
-      
-      # count points for every single character:
-      # - no change is 0 points
-      # - I <-> S|R is 0.5 point
-      # - S|R <-> R|S is 1 point
-      # use the levels of as.rsi (S = 1, I = 2, R = 3)
-
-      x2 <- strsplit(x[i], "")[[1]] %>% as.rsi() %>% as.double()
-      y2 <- strsplit(y[i], "")[[1]] %>% as.rsi() %>% as.double()
-      
-      points <- (x2 - y2) %>% abs() %>% sum(na.rm = TRUE)
-      result[i] <- ((points / 2) >= points_threshold)
-    }
-  }
-  if (info == TRUE) {
-    cat('\n')
-  }
-  result
-}
@@ -1,37 +0,0 @@
-# ==================================================================== #
-# TITLE                                                                #
-# Antimicrobial Resistance (AMR) Analysis                              #
-#                                                                      #
-# AUTHORS                                                              #
-# Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl)           #
-#                                                                      #
-# LICENCE                                                              #
-# This program is free software; you can redistribute it and/or modify #
-# it under the terms of the GNU General Public License version 2.0,    #
-# as published by the Free Software Foundation.                        #
-#                                                                      #
-# This program is distributed in the hope that it will be useful,      #
-# but WITHOUT ANY WARRANTY; without even the implied warranty of       #
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the        #
-# GNU General Public License for more details.                         #
-# ==================================================================== #
-
-globalVariables(c('.',
-                  'abname',
-                  'bactid',
-                  'cnt',
-                  'date_lab',
-                  'days_diff',
-                  'first_isolate_row_index',
-                  'genus',
-                  'gramstain',
-                  'key_ab',
-                  'key_ab_lag',
-                  'key_ab_other',
-                  'mic',
-                  'n',
-                  'other_pat_or_mo',
-                  'patient_id',
-                  'real_first_isolate',
-                  'species',
-                  'y'))
@@ -1,109 +0,0 @@
-#' Join a table with \code{bactlist}
-#'
-#' Join the list of microorganisms \code{\link{bactlist}} easily to an existing table.
-#' @rdname join
-#' @name join
-#' @aliases join inner_join
-#' @param x existing table to join
-#' @param by a variable to join by - could be a column name of \code{x} with values that exist in \code{bactlist$bactid} (like \code{by = "bacteria_id"}), or another column in \code{\link{bactlist}} (but then it should be named, like \code{by = c("my_genus_species" = "fullname")})
-#' @param ... other parameters to pass on to \code{dplyr::\link[dplyr]{join}}.
-#' @details As opposed to the \code{\link[dplyr]{join}} functions of \code{dplyr}, at default existing columns will get a suffix \code{"2"} and the newly joined columns will not get a suffix. See \code{\link[dplyr]{join}} for more information.
-#' @export
-#' @examples 
-#' df <- data.frame(date = seq(from = as.Date("2018-01-01"),
-#'                             to = as.Date("2018-01-07"),
-#'                             by = 1),
-#'                  bacteria_id = c("STAAUR", "STAAUR", "STAAUR", "STAAUR",
-#'                                  "ESCCOL", "ESCCOL", "ESCCOL"),
-#'                  stringsAsFactors = FALSE)
-#'                  
-#' colnames(df)
-#' df2 <- left_join_bactlist(df, "bacteria_id")
-#' colnames(df2)
-inner_join_bactlist <- function(x, by = 'bactid', ...) {
-  # no name set to `by` parameter
-  if (is.null(names(by))) {
-    joinby <- colnames(AMR::bactlist)[1]
-    names(joinby) <- by
-  } else {
-    joinby <- by
-  }
-  join <- dplyr::inner_join(x = x, y = AMR::bactlist, by = joinby, suffix = c("2", ""), ...)
-  if (nrow(join) > nrow(x)) {
-    warning('the newly joined tbl contains ', nrow(join) - nrow(x), ' rows more that its original')
-  }
-  join
-}
-
-#' @rdname join
-#' @export
-left_join_bactlist <- function(x, by = 'bactid', ...) {
-  # no name set to `by` parameter
-  if (is.null(names(by))) {
-    joinby <- colnames(AMR::bactlist)[1]
-    names(joinby) <- by
-  } else {
-    joinby <- by
-  }
-  join <- dplyr::left_join(x = x, y = AMR::bactlist, by = joinby, suffix = c("2", ""), ...)
-  if (nrow(join) > nrow(x)) {
-    warning('the newly joined tbl contains ', nrow(join) - nrow(x), ' rows more that its original')
-  }
-  join
-}
-
-#' @rdname join
-#' @export
-right_join_bactlist <- function(x, by = 'bactid', ...) {
-  # no name set to `by` parameter
-  if (is.null(names(by))) {
-    joinby <- colnames(AMR::bactlist)[1]
-    names(joinby) <- by
-  } else {
-    joinby <- by
-  }
-  join <- dplyr::right_join(x = x, y = AMR::bactlist, by = joinby, suffix = c("2", ""), ...)
-  if (nrow(join) > nrow(x)) {
-    warning('the newly joined tbl contains ', nrow(join) - nrow(x), ' rows more that its original')
-  }
-  join
-}
-
-#' @rdname join
-#' @export
-full_join_bactlist <- function(x, by = 'bactid', ...) {
-  # no name set to `by` parameter
-  if (is.null(names(by))) {
-    joinby <- colnames(AMR::bactlist)[1]
-    names(joinby) <- by
-  } else {
-    joinby <- by
-  }
-  dplyr::full_join(x = x, y = AMR::bactlist, by = joinby, suffix = c("2", ""), ...)
-}
-
-#' @rdname join
-#' @export
-semi_join_bactlist <- function(x, by = 'bactid', ...) {
-  # no name set to `by` parameter
-  if (is.null(names(by))) {
-    joinby <- colnames(AMR::bactlist)[1]
-    names(joinby) <- by
-  } else {
-    joinby <- by
-  }
-  dplyr::semi_join(x = x, y = AMR::bactlist, by = joinby, ...)
-}
-
-#' @rdname join
-#' @export
-anti_join_bactlist <- function(x, by = 'bactid', ...) {
-  # no name set to `by` parameter
-  if (is.null(names(by))) {
-    joinby <- colnames(AMR::bactlist)[1]
-    names(joinby) <- by
-  } else {
-    joinby <- by
-  }
-  dplyr::anti_join(x = x, y = AMR::bactlist, by = joinby, ...)
-}
@@ -1,49 +0,0 @@
-# ==================================================================== #
-# TITLE                                                                #
-# Antimicrobial Resistance (AMR) Analysis                              #
-#                                                                      #
-# AUTHORS                                                              #
-# Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl)           #
-#                                                                      #
-# LICENCE                                                              #
-# This program is free software; you can redistribute it and/or modify #
-# it under the terms of the GNU General Public License version 2.0,    #
-# as published by the Free Software Foundation.                        #
-#                                                                      #
-# This program is distributed in the hope that it will be useful,      #
-# but WITHOUT ANY WARRANTY; without even the implied warranty of       #
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the        #
-# GNU General Public License for more details.                         #
-# ==================================================================== #
-
-# No export, no Rd
-"%like%" <- function(vector, pattern) {
-  # Source: https://github.com/Rdatatable/data.table/blob/master/R/like.R
-  if (is.factor(vector)) {
-    as.integer(vector) %in% grep(pattern, levels(vector))
-  } else {
-    grepl(pattern, vector)
-  }
-}
-
-percent <- function(x, round = 1, ...) {
-  base::paste0(base::round(x * 100, digits = round), "%")
-}
-
-quasiquotate <- function(deparsed, parsed) {
-  # when text: remove first and last "
-  if (any(deparsed %like% '^".+"$' | deparsed %like% "^'.+'$")) {
-    deparsed <- deparsed %>% substr(2, nchar(.) - 1)
-  }
-  # apply if needed
-  if (any(!deparsed %like% '[[$:()]'
-      & !deparsed %in% c('""', "''", "", # empty text
-                         ".", ".data", # dplyr references
-                         "TRUE", "FALSE", # logicals
-                         "NA", "NaN", "NULL", # empty values
-                         ls(.GlobalEnv)))) {
-    deparsed
-  } else {
-    parsed
-  }
-}
@@ -1,426 +0,0 @@
-# ==================================================================== #
-# TITLE                                                                #
-# Antimicrobial Resistance (AMR) Analysis                              #
-#                                                                      #
-# AUTHORS                                                              #
-# Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl)           #
-#                                                                      #
-# LICENCE                                                              #
-# This program is free software; you can redistribute it and/or modify #
-# it under the terms of the GNU General Public License version 2.0,    #
-# as published by the Free Software Foundation.                        #
-#                                                                      #
-# This program is distributed in the hope that it will be useful,      #
-# but WITHOUT ANY WARRANTY; without even the implied warranty of       #
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the        #
-# GNU General Public License for more details.                         #
-# ==================================================================== #
-
-#' Resistance of isolates in data.frame
-#'
-#' \strong{NOTE: use \code{\link{rsi}} in dplyr functions like \code{\link[dplyr]{summarise}}.} \cr Calculate the percentage of S, SI, I, IR or R of a \code{data.frame} containing isolates.
-#' @param tbl \code{data.frame} containing columns with antibiotic interpretations.
-#' @param antibiotics character vector with 1, 2 or 3 antibiotics that occur as column names in \code{tbl}, like \code{antibiotics = c("amox", "amcl")}
-#' @param interpretation antimicrobial interpretation of which the portion must be calculated. Valid values are \code{"S"}, \code{"SI"}, \code{"I"}, \code{"IR"} or \code{"R"}.
-#' @param minimum minimal amount of available isolates. Any number lower than \code{minimum} will return \code{NA} with a warning (when \code{warning = TRUE}).
-#' @param percent return output as percent (text), will else (at default) be a double
-#' @param info calculate the amount of available isolates and print it, like \code{n = 423}
-#' @param warning show a warning when the available amount of isolates is below \code{minimum}
-#' @details Remember that you should filter your table to let it contain \strong{only first isolates}!
-#' @keywords rsi antibiotics isolate isolates
-#' @return Double or, when \code{percent = TRUE}, a character.
-#' @export
-#' @importFrom dplyr %>% n_distinct filter filter_at pull vars all_vars any_vars
-#' @seealso \code{\link{rsi}} for the function that can be used with \code{\link[dplyr]{summarise}} directly.
-#' @examples
-#' \dontrun{
-#' rsi_df(tbl_with_bloodcultures, 'amcl')
-#'
-#' rsi_df(tbl_with_bloodcultures, c('amcl', 'gent'), interpretation = 'IR')
-#'
-#' library(dplyr)
-#' # calculate current empiric therapy of Helicobacter gastritis:
-#' my_table %>%
-#'   filter(first_isolate == TRUE, 
-#'          genus == "Helicobacter") %>%
-#'   rsi_df(antibiotics = c("amox", "metr"))
-#' }
-rsi_df <- function(tbl,
-                   antibiotics,
-                   interpretation = 'IR',
-                   minimum = 30,
-                   percent = FALSE,
-                   info = TRUE,
-                   warning = TRUE) {
-  
-  # we willen niet dat tbl$interpretation toevallig ook bestaat, dus:
-  te_testen_uitslag_ab <- interpretation
-  
-  # validatie:
-  if (min(grepl('^[a-z]{3,4}$', antibiotics)) == 0 &
-      min(grepl('^rsi[1-2]$', antibiotics)) == 0) {
-    for (i in 1:length(antibiotics)) {
-      antibiotics[i] <- paste0('rsi', i)
-    }
-  }
-  if (!grepl('^(S|SI|IS|I|IR|RI|R){1}$', te_testen_uitslag_ab)) {
-    stop('Invalid `interpretation`; must be "S", "SI", "I", "IR", or "R".')
-  }
-  if ('is_ic' %in% colnames(tbl)) {
-    if (n_distinct(tbl$is_ic) > 1) {
-      warning('Dataset contains isolates from the Intensive Care. Exclude them from proper epidemiological analysis.')
-    }
-  }
-  
-  # transformeren wanneer gezocht wordt op verschillende uitslagen
-  if (te_testen_uitslag_ab %in% c('SI', 'IS')) {
-    for (i in 1:length(antibiotics)) {
-      lijst <- tbl[, antibiotics[i]]
-      if ('I' %in% lijst) {
-        tbl[which(tbl[antibiotics[i]] == 'I'), ][antibiotics[i]] <- 'S'
-      }
-    }
-    te_testen_uitslag_ab <- 'S'
-  }
-  if (te_testen_uitslag_ab %in% c('RI', 'IR')) {
-    for (i in 1:length(antibiotics)) {
-      lijst <- tbl[, antibiotics[i]]
-      if ('I' %in% lijst) {
-        tbl[which(tbl[antibiotics[i]] == 'I'), ][antibiotics[i]] <- 'R'
-      }
-    }
-    te_testen_uitslag_ab <- 'R'
-  }
-  
-  # breuk samenstellen
-  if (length(antibiotics) == 1) {
-    numerator <- tbl %>%
-      filter(pull(., antibiotics[1]) == te_testen_uitslag_ab) %>%
-      nrow()
-    
-    denominator <- tbl %>%
-      filter(pull(., antibiotics[1]) %in% c("S", "I", "R")) %>%
-      nrow()
-    
-  } else if (length(antibiotics) == 2) {
-    numerator <- tbl %>%
-      filter_at(vars(antibiotics[1], antibiotics[2]),
-                any_vars(. == te_testen_uitslag_ab)) %>%
-      filter_at(vars(antibiotics[1], antibiotics[2]),
-                all_vars(. %in% c("S", "R", "I"))) %>%
-      nrow()
-    
-    denominator <- tbl %>%
-      filter_at(vars(antibiotics[1], antibiotics[2]),
-                all_vars(. %in% c("S", "R", "I"))) %>%
-      nrow()
-    
-  } else if (length(antibiotics) == 3) {
-    numerator <- tbl %>%
-      filter_at(vars(antibiotics[1], antibiotics[2], antibiotics[3]),
-                any_vars(. == te_testen_uitslag_ab)) %>%
-      filter_at(vars(antibiotics[1], antibiotics[2], antibiotics[3]),
-                all_vars(. %in% c("S", "R", "I"))) %>%
-      nrow()
-    
-    denominator <- tbl %>%
-      filter_at(vars(antibiotics[1], antibiotics[2], antibiotics[3]),
-                all_vars(. %in% c("S", "R", "I"))) %>%
-      nrow()
-    
-  } else {
-    stop('Maximum of 3 drugs allowed.')
-  }
-  
-  # tekstdeel opbouwen
-  if (info == TRUE) {
-    cat('n =', denominator)
-    info.txt1 <- percent(denominator / nrow(tbl))
-    if (denominator == 0) {
-      info.txt1 <- 'none'
-    }
-    info.txt2 <- gsub(',', ' and',
-                      antibiotics %>%
-                        abname(to = 'trivial',
-                               tolower = TRUE) %>%
-                        toString(), fixed = TRUE)
-    info.txt2 <- gsub('rsi1 and rsi2', 'these two drugs', info.txt2, fixed = TRUE)
-    info.txt2 <- gsub('rsi1', 'this drug', info.txt2, fixed = TRUE)
-    cat(paste0(' (of ', nrow(tbl), ' in total; ', info.txt1, ' tested on ', info.txt2, ')\n'))
-  }
-  
-  # rekenen en opmaken
-  y <- numerator / denominator
-  if (percent == TRUE) {
-    y <- percent(y)
-  }
-  if (denominator < minimum) {
-    if (warning == TRUE) {
-      warning(paste0('TOO FEW ISOLATES OF ', toString(antibiotics), ' (n = ', denominator, ', n < ', minimum, '); NO RESULT.'))
-    }
-    y <- NA
-  }
-  
-  # output
-  y
-}
-
-#' Resistance of isolates
-#'
-#' This function can be used in \code{dplyr}s \code{\link[dplyr]{summarise}}, see \emph{Examples}. Calculate the percentage S, SI, I, IR or R of a vector of isolates.
-#' @param ab1,ab2 list with interpretations of an antibiotic
-#' @inheritParams rsi_df
-#' @details This function uses the \code{\link{rsi_df}} function internally.
-#' @keywords rsi antibiotics isolate isolates
-#' @return Double or, when \code{percent = TRUE}, a character.
-#' @export
-#' @examples
-#' \dontrun{
-#' tbl %>%
-#'   group_by(hospital) %>%
-#'   summarise(cipr = rsi(cipr))
-#'   
-#' tbl %>%
-#'   group_by(year, hospital) %>%
-#'   summarise(
-#'     isolates = n(),
-#'     cipro = rsi(cipr %>% as.rsi(), percent = TRUE),
-#'     amoxi = rsi(amox %>% as.rsi(), percent = TRUE))
-#'     
-#' rsi(as.rsi(isolates$amox))
-#'
-#' rsi(as.rsi(isolates$amcl), interpretation = "S")
-#' }
-rsi <- function(ab1, ab2 = NA, interpretation = 'IR', minimum = 30, percent = FALSE, info = FALSE, warning = FALSE) {
-  function_text <- as.character(match.call())
-  # param 1 = functienaam
-  # param 2 = ab1
-  # param 3 = ab2
-  ab1.naam <- function_text[2]
-  if (!grepl('^[a-z]{3,4}$', ab1.naam)) {
-    ab1.naam <- 'rsi1'
-  }
-  ab2.naam <- function_text[3]
-  if (!grepl('^[a-z]{3,4}$', ab2.naam)) {
-    ab2.naam <- 'rsi2'
-  }
-  
-  tbl <- tibble(rsi1 = ab1, rsi2 = ab2)
-  
-  colnames(tbl) <- c(ab1.naam, ab2.naam)
-  
-  if (length(ab2) == 1) {
-    return(rsi_df(tbl = tbl,
-                  antibiotics = ab1.naam,
-                  interpretation = interpretation,
-                  minimum = minimum,
-                  percent = percent,
-                  info = info,
-                  warning = warning))
-  } else {
-    if (length(ab1) != length(ab2)) {
-      stop('`ab1` (n = ', length(ab1), ') and `ab2` (n = ', length(ab2), ') must be of same length.', call. = FALSE)
-    }
-    if (interpretation != 'S') {
-      warning('`interpretation` is not set to S, albeit analysing a combination therapy.')
-    }
-    return(rsi_df(tbl = tbl,
-                  antibiotics = c(ab1.naam, ab2.naam),
-                  interpretation = interpretation,
-                  minimum = minimum,
-                  percent = percent,
-                  info = info,
-                  warning = warning))
-  }
-}
-
-#' Predict antimicrobial resistance
-#'
-#' Create a prediction model to predict antimicrobial resistance for the next years on statistical solid ground. Standard errors (SE) will be returned as columns \code{se_min} and \code{se_max}. See Examples for a real live example.
-#' @param tbl table that contains columns \code{col_ab} and \code{col_date}
-#' @param col_ab column name of \code{tbl} with antimicrobial interpretations (\code{R}, \code{I} and \code{S}), supports tidyverse-like quotation
-#' @param col_date column name of the date, will be used to calculate years if this column doesn't consist of years already, supports tidyverse-like quotation
-#' @param year_max highest year to use in the prediction model, deafults to 15 years after today
-#' @param year_every unit of sequence between lowest year found in the data and \code{year_max}
-#' @param model the statistical model of choice. Valid values are \code{"binomial"} (or \code{"binom"} or \code{"logit"}) or \code{"loglin"} or \code{"linear"} (or \code{"lin"}).
-#' @param I_as_R treat \code{I} as \code{R}
-#' @param preserve_measurements overwrite predictions of years that are actually available in the data, with the original data. The standard errors of those years will be \code{NA}.
-#' @param info print textual analysis with the name and \code{\link{summary}} of the model.
-#' @return \code{data.frame} with columns \code{year}, \code{probR}, \code{se_min} and \code{se_max}.
-#' @seealso \code{\link{lm}} \cr \code{\link{glm}}
-#' @export
-#' @importFrom dplyr %>% pull mutate group_by_at summarise filter
-#' @importFrom reshape2 dcast
-#' @examples
-#' \dontrun{
-#' # use it directly:
-#' rsi_predict(tbl = tbl[which(first_isolate == TRUE & genus == "Haemophilus"),],
-#'             col_ab = "amcl", col_date = "date")
-#'   
-#' # or with dplyr so you can actually read it:
-#' library(dplyr)
-#' tbl %>%
-#'   filter(first_isolate == TRUE,
-#'          genus == "Haemophilus") %>%
-#'   rsi_predict(amcl, date)
-#' }
-#'
-#'
-#' # real live example:
-#' library(dplyr)
-#' septic_patients %>%
-#'   # get bacteria properties like genus and species
-#'   left_join_bactlist("bactid") %>% 
-#'   # calculate first isolates
-#'   mutate(first_isolate = 
-#'            first_isolate(.,
-#'                          "date",
-#'                          "patient_id",
-#'                          "genus",
-#'                          "species",
-#'                          col_specimen = NA,
-#'                          col_icu = NA)) %>% 
-#'   # filter on first E. coli isolates
-#'   filter(genus == "Escherichia", 
-#'          species == "coli", 
-#'          first_isolate == TRUE) %>%
-#'   # predict resistance of cefotaxime for next years
-#'   rsi_predict(col_ab = cfot,
-#'               col_date = date,
-#'               year_max = 2025,
-#'               preserve_measurements = FALSE)
-#'
-rsi_predict <- function(tbl,
-                        col_ab,
-                        col_date,
-                        year_max = as.integer(format(as.Date(Sys.Date()), '%Y')) + 15,
-                        year_every = 1,
-                        model = 'binomial',
-                        I_as_R = TRUE,
-                        preserve_measurements = TRUE,
-                        info = TRUE) {
-  
-  col_ab <- quasiquotate(deparse(substitute(col_ab)), col_ab)
-  if (!col_ab %in% colnames(tbl)) {
-    stop('Column ', col_ab, ' not found.')
-  }
-  col_date <- quasiquotate(deparse(substitute(col_date)), col_date)
-  if (!col_date %in% colnames(tbl)) {
-    stop('Column ', col_date, ' not found.')
-  }
-  if ('grouped_df' %in% class(tbl)) {
-    # no grouped tibbles please, mutate will throw errors
-    tbl <- base::as.data.frame(tbl, stringsAsFactors = FALSE)
-  }
-
-  if (I_as_R == TRUE) {
-    tbl[, col_ab] <- gsub('I', 'R', tbl %>% pull(col_ab))
-  }
-
-  if (!all(tbl %>% pull(col_ab) %>% as.rsi() %in% c(NA, 'S', 'I', 'R'))) {
-    stop('Column ', col_ab, ' must contain antimicrobial interpretations (S, I, R).')
-  }
-  
-  year <- function(x) {
-    if (all(grepl('^[0-9]{4}$', x))) {
-      x
-    } else {
-      as.integer(format(as.Date(x), '%Y'))
-    }
-  }
-  
-  years_predict <- seq(from = min(year(tbl %>% pull(col_date))), to = year_max, by = year_every)
-  
-  df <- tbl %>%
-    mutate(year = year(tbl %>% pull(col_date))) %>%
-    group_by_at(c('year', col_ab)) %>%
-    summarise(n())
-  colnames(df) <- c('year', 'antibiotic', 'count')
-  df <- df %>%
-    reshape2::dcast(year ~ antibiotic, value.var = 'count')
-  
-  if (model %in% c('binomial', 'binom', 'logit')) {
-    logitmodel <- with(df, glm(cbind(R, S) ~ year, family = binomial))
-    if (info == TRUE) {
-      cat('\nLogistic regression model (logit) with binomial distribution')
-      cat('\n------------------------------------------------------------\n')
-      print(summary(logitmodel))
-    }
-    
-    predictmodel <- stats::predict(logitmodel, newdata = with(df, list(year = years_predict)), type = "response", se.fit = TRUE)
-    prediction <- predictmodel$fit
-    se <- predictmodel$se.fit
-    
-  } else if (model == 'loglin') {
-    loglinmodel <- with(df, glm(R ~ year, family = poisson))
-    if (info == TRUE) {
-      cat('\nLog-linear regression model (loglin) with poisson distribution')
-      cat('\n--------------------------------------------------------------\n')
-      print(summary(loglinmodel))
-    }
-    
-    predictmodel <- stats::predict(loglinmodel, newdata = with(df, list(year = years_predict)), type = "response", se.fit = TRUE)
-    prediction <- predictmodel$fit
-    se <- predictmodel$se.fit
-    
-  } else if (model %in% c('lin', 'linear')) {
-    linmodel <- with(df, lm((R / (R + S)) ~ year))
-    if (info == TRUE) {
-      cat('\nLinear regression model')
-      cat('\n-----------------------\n')
-      print(summary(linmodel))
-    }
-    
-    predictmodel <- stats::predict(linmodel, newdata = with(df, list(year = years_predict)), se.fit = TRUE)
-    prediction <- predictmodel$fit
-    se <- predictmodel$se.fit
-    
-  } else {
-    stop('No valid model selected.')
-  }
-  
-  # prepare the output dataframe
-  prediction <- data.frame(year = years_predict, probR = prediction, stringsAsFactors = FALSE)
-  
-  prediction$se_min <- prediction$probR - se
-  prediction$se_max <- prediction$probR + se
-  
-  if (model == 'loglin') {
-    prediction$probR <- prediction$probR %>%
-      format(scientific = FALSE) %>%
-      as.integer()
-    prediction$se_min <- prediction$se_min %>% as.integer()
-    prediction$se_max <- prediction$se_max %>% as.integer()
-    
-    colnames(prediction) <- c('year', 'amountR', 'se_max', 'se_min')
-  } else {
-    prediction$se_max[which(prediction$se_max > 1)] <- 1
-  }
-  prediction$se_min[which(prediction$se_min < 0)] <- 0
-  
-  total <- prediction
-  
-  if (preserve_measurements == TRUE) {
-    # geschatte data vervangen door gemeten data
-    if (I_as_R == TRUE) {
-      if (!'I' %in% colnames(df)) {
-        df$I <- 0
-      }
-      df$probR <- df$R / rowSums(df[, c('R', 'S', 'I')])
-    } else {
-      df$probR <- df$R / rowSums(df[, c('R', 'S')])
-    }
-    measurements <- data.frame(year = df$year,
-                           probR = df$probR,
-                           se_min = NA,
-                           se_max = NA,
-                           stringsAsFactors = FALSE)
-    colnames(measurements) <- colnames(prediction)
-    prediction <- prediction %>% filter(!year %in% df$year)
-    
-    total <- rbind(measurements, prediction)
-  }
-  
-  total
-  
-}
@@ -1,182 +0,0 @@
-# `AMR`
-This is an [R package](https://www.r-project.org) to simplify the analysis and prediction of Antimicrobial Resistance (AMR).
-
-![logo_uni](man/figures/logo_en.png)![logo_umcg](man/figures/logo_umcg.png)
-
-This R package was created for academic research by PhD students of the Faculty of Medical Sciences of the [University of Groningen](https://www.rug.nl/) and the Medical Microbiology & Infection Prevention department of the University Medical Center Groningen (UMCG). They also maintain this package, see [Authors](#authors).
-
-## Why this package?
-This R package contains functions to make microbiological, epidemiological data analysis easier. It allows the use of some new S3 classes to work with MIC values and antimicrobial interpretations (i.e. values S, I and R).
-
-AMR can also be predicted for the forthcoming years with the `rsi_predict` function. For use with the `dplyr` package, the `rsi` function can be used in conjunction with `summarise` to calculate the resistance percentages of different antibiotic columns of a table.
-
-It also contains functions to translate antibiotic codes from the lab (like `"AMOX"`) or the [WHO](https://www.whocc.no/atc_ddd_index/?code=J01CA04&showdescription=no) (like `"J01CA04"`) to trivial names (like `"amoxicillin"`) and vice versa.
-
-## How to get it?
-This package is available on CRAN and also here on GitHub.
-
-### From CRAN (recommended, latest stable version)
-[![CRAN_Badge](https://img.shields.io/cran/v/AMR.svg?label=CRAN&colorB=3679BC)](http://cran.r-project.org/package=AMR)
-[![CRAN_Downloads](https://cranlogs.r-pkg.org/badges/grand-total/AMR)](http://cran.r-project.org/package=AMR)
-
- RStudio:
-  - Click on `Tools` and then `Install Packages...`
-  -  Type in `AMR` and press <kbd>Install</kbd>
-
- R console:
-  - `install.packages("AMR")`
-
-### From GitHub (latest development version)
-[![Travis_Build](https://travis-ci.org/msberends/AMR.svg?branch=master)](https://travis-ci.org/msberends/AMR)
-[![Since_Release](https://img.shields.io/github/commits-since/msberends/AMR/latest.svg?colorB=3679BC)](https://github.com/msberends/AMR/releases)
-[![Last_Commit](https://img.shields.io/github/last-commit/msberends/AMR.svg?colorB=3679BC)](https://github.com/msberends/AMR/commits/master)
-
-```r
-install.packages("devtools")
-devtools::install_github("msberends/AMR")
-```
-
-## How to use it?
-```r
-# Call it with:
-library(AMR)
-
-# For a list of functions:
-help(package = "AMR")
-```
-### Overwrite/force resistance based on EUCAST rules
-This is also called *interpretive reading*.
-```r
-before <- data.frame(bactid = c("STAAUR",  # Staphylococcus aureus
-                                "ENCFAE",  # Enterococcus faecalis
-                                "ESCCOL",  # Escherichia coli
-                                "KLEPNE",  # Klebsiella pneumoniae
-                                "PSEAER"), # Pseudomonas aeruginosa
-                     vanc = "-",           # Vancomycin
-                     amox = "-",           # Amoxicillin
-                     coli = "-",           # Colistin
-                     cfta = "-",           # Ceftazidime
-                     cfur = "-",           # Cefuroxime
-                     stringsAsFactors = FALSE)
-before
-#   bactid vanc amox coli cfta cfur
-# 1 STAAUR    -    -    -    -    -
-# 2 ENCFAE    -    -    -    -    -
-# 3 ESCCOL    -    -    -    -    -
-# 4 KLEPNE    -    -    -    -    -
-# 5 PSEAER    -    -    -    -    -
-
-# Now apply those rules; just need a column with bacteria ID's and antibiotic results:
-after <- EUCAST_rules(before)
-after
-#   bactid vanc amox coli cfta cfur
-# 1 STAAUR    -    -    R    R    -
-# 2 ENCFAE    -    -    R    R    R
-# 3 ESCCOL    R    -    -    -    -
-# 4 KLEPNE    R    R    -    -    -
-# 5 PSEAER    R    R    -    -    R
-```
-
-### New classes
-This package contains two new S3 classes: `mic` for MIC values (e.g. from Vitek or Phoenix) and `rsi` for antimicrobial drug interpretations (i.e. S, I and R). Both are actually ordered factors under the hood (an MIC of `2` being higher than `<=1` but lower than `>=32`, and for class `rsi` factors are ordered as `S < I < R`). 
-Both classes have extensions for existing generic functions like `print`, `summary` and `plot`.
-
-```r
-# Transform values to new classes
-mic_data <- as.mic(c(">=32", "1.0", "8", "<=0.128", "8", "16", "16"))
-rsi_data <- as.rsi(c(rep("S", 474), rep("I", 36), rep("R", 370)))
-```
-These functions also try to coerce valid values.
-
-Quick overviews when just printing objects:
-```r
-mic_data
-# Class 'mic': 7 isolates
-# 
-# <NA>  0
-# 
-# <=0.128       1       8      16    >=32
-#       1       1       2       2       1
-
-rsi_data
-# Class 'rsi': 880 isolates
-# 
-# <NA>:       0 
-# Sum of S:   474 
-# Sum of IR:  406 
-# - Sum of R: 370 
-# - Sum of I: 36 
-# 
-#   %S  %IR   %I   %R 
-# 53.9 46.1  4.1 42.0 
-```
-
-A plot of `rsi_data`:
-```r
-plot(rsi_data)
-```
-
-![example](man/figures/rsi_example.png)
-
-Other epidemiological functions:
-
-```r
-# Determine key antibiotic based on bacteria ID
-key_antibiotics(...)
-
-# Selection of first isolates of any patient
-first_isolate(...)
-
-# Calculate resistance levels of antibiotics, can be used with `summarise` (dplyr)
-rsi(...)
-# Predict resistance levels of antibiotics
-rsi_predict(...)
-
-# Get name of antibiotic by ATC code
-abname(...)
-abname("J01CR02", from = "atc", to = "umcg") # "AMCL"
-```
-
-### Databases included in package
-Datasets to work with antibiotics and bacteria properties.
-```r
-# Dataset with ATC antibiotics codes, official names and DDD's (oral and parenteral)
-ablist        # A tibble: 420 x 12
-
-# Dataset with bacteria codes and properties like gram stain and aerobic/anaerobic
-bactlist      # A tibble: 2,507 x 10
-```
-
-
-## Authors
-
-  - [Berends MS](https://github.com/msberends)<sup>1,2</sup>, PhD Student
-  - [Luz CF](https://github.com/ceefluz)<sup>1</sup>, PhD Student
-  - [Hassing EEA](https://github.com/erwinhassing)<sup>2</sup>, Data Analyst (contributor)
-  
-<sup>1</sup> Department of Medical Microbiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
-
-<sup>2</sup> Department of Medical, Market and Innovation (MMI), Certe Medische diagnostiek & advies, Groningen, the Netherlands
-
-## Copyright
-[![License](https://img.shields.io/github/license/msberends/AMR.svg?colorB=3679BC)](https://github.com/msberends/AMR/blob/master/LICENSE)
-
-This R package is licensed under the [GNU General Public License (GPL) v2.0](https://github.com/msberends/AMR/blob/master/LICENSE). In a nutshell, this means that this package:
-
- May be used for commercial purposes
-
- May be used for private purposes
-
- May be modified, although:
-
-  - Modifications **must** be released under the same license when distributing the package
-  - Changes made to the code **must** be documented
-
- May be distributed, although:
-
-  - Source code **must** be made available when the package is distributed
-  - A copy of the license and copyright notice **must** be included with the package.
-
- Comes with a LIMITATION of liability
-
- Comes with NO warranty
@@ -0,0 +1,660 @@
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+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="../logo.svg" class="logo" alt=""><h1>AMR for Python</h1>
+            
+      
+      <small class="dont-index">Source: <a href="https://github.com/msberends/AMR/blob/main/vignettes/AMR_for_Python.Rmd" class="external-link"><code>vignettes/AMR_for_Python.Rmd</code></a></small>
+      <div class="d-none name"><code>AMR_for_Python.Rmd</code></div>
+    </div>
+
+    
+    
+<div class="section level2">
+<h2 id="introduction">Introduction<a class="anchor" aria-label="anchor" href="#introduction"></a>
+</h2>
+<p>The <code>AMR</code> package for R is a powerful tool for
+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 <a href="https://pypi.org/project/AMR/" class="external-link"><code>AMR</code> Python
+package</a>.</p>
+<p>This Python package is a wrapper around 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="prerequisites">Prerequisites<a class="anchor" aria-label="anchor" href="#prerequisites"></a>
+</h2>
+<p>This package was only tested with a <a href="https://docs.python.org/3/library/venv.html" class="external-link">virtual environment
+(venv)</a>. You can set up such an environment by running:</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="co"># linux and macOS:</span></span>
+<span id="cb1-2"><a href="#cb1-2" tabindex="-1"></a>python <span class="op">-</span>m venv <span class="op">/</span>path<span class="op">/</span>to<span class="op">/</span>new<span class="op">/</span>virtual<span class="op">/</span>environment</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"># Windows:</span></span>
+<span id="cb1-5"><a href="#cb1-5" tabindex="-1"></a>python <span class="op">-</span>m venv C:\path\to\new\virtual\environment</span></code></pre></div>
+<p>Then you can <a href="https://docs.python.org/3/library/venv.html#how-venvs-work" class="external-link">activate
+the environment</a>, after which the venv is ready to work with.</p>
+</div>
+<div class="section level2">
+<h2 id="install-amr">Install AMR<a class="anchor" aria-label="anchor" href="#install-amr"></a>
+</h2>
+<ol style="list-style-type: decimal">
+<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="cb2"><pre class="sourceCode bash"><code class="sourceCode bash"><span id="cb2-1"><a href="#cb2-1" tabindex="-1"></a><span class="ex">pip</span> install AMR</span></code></pre></div>
+</li>
+<li>
+<p>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="cb3"><pre class="sourceCode bash"><code class="sourceCode bash"><span id="cb3-1"><a href="#cb3-1" tabindex="-1"></a><span class="co"># Ubuntu / Debian</span></span>
+<span id="cb3-2"><a href="#cb3-2" tabindex="-1"></a><span class="fu">sudo</span> apt install r-base</span>
+<span id="cb3-3"><a href="#cb3-3" tabindex="-1"></a><span class="co"># Fedora:</span></span>
+<span id="cb3-4"><a href="#cb3-4" tabindex="-1"></a><span class="fu">sudo</span> dnf install R</span>
+<span id="cb3-5"><a href="#cb3-5" tabindex="-1"></a><span class="co"># CentOS/RHEL</span></span>
+<span id="cb3-6"><a href="#cb3-6" 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="cb4"><pre class="sourceCode bash"><code class="sourceCode bash"><span id="cb4-1"><a href="#cb4-1" 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>
+</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="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="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> pandas <span class="im">as</span> pd</span>
+<span id="cb5-2"><a href="#cb5-2" tabindex="-1"></a><span class="im">import</span> AMR</span>
+<span id="cb5-3"><a href="#cb5-3" tabindex="-1"></a></span>
+<span id="cb5-4"><a href="#cb5-4" tabindex="-1"></a><span class="co"># Sample data</span></span>
+<span id="cb5-5"><a href="#cb5-5" tabindex="-1"></a>data <span class="op">=</span> {</span>
+<span id="cb5-6"><a href="#cb5-6" 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="cb5-7"><a href="#cb5-7" 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="cb5-8"><a href="#cb5-8" tabindex="-1"></a>}</span>
+<span id="cb5-9"><a href="#cb5-9" tabindex="-1"></a>df <span class="op">=</span> pd.DataFrame(data)</span>
+<span id="cb5-10"><a href="#cb5-10" tabindex="-1"></a></span>
+<span id="cb5-11"><a href="#cb5-11" tabindex="-1"></a><span class="co"># Use AMR functions to clean microorganism and drug names</span></span>
+<span id="cb5-12"><a href="#cb5-12" 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="cb5-13"><a href="#cb5-13" 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="cb5-14"><a href="#cb5-14" tabindex="-1"></a></span>
+<span id="cb5-15"><a href="#cb5-15" tabindex="-1"></a><span class="co"># Display the results</span></span>
+<span id="cb5-16"><a href="#cb5-16" tabindex="-1"></a><span class="bu">print</span>(df)</span></code></pre></div>
+<table class="table">
+<thead><tr class="header">
+<th>MOs</th>
+<th>Drug</th>
+<th>MO_clean</th>
+<th>Drug_clean</th>
+</tr></thead>
+<tbody>
+<tr class="odd">
+<td>E. coli</td>
+<td>Cipro</td>
+<td>Escherichia coli</td>
+<td>Ciprofloxacin</td>
+</tr>
+<tr class="even">
+<td>ESCCOL</td>
+<td>CIP</td>
+<td>Escherichia coli</td>
+<td>Ciprofloxacin</td>
+</tr>
+<tr class="odd">
+<td>esco</td>
+<td>J01MA02</td>
+<td>Escherichia coli</td>
+<td>Ciprofloxacin</td>
+</tr>
+<tr class="even">
+<td>Esche coli</td>
+<td>Ciproxin</td>
+<td>Escherichia coli</td>
+<td>Ciprofloxacin</td>
+</tr>
+</tbody>
+</table>
+<div class="section level4">
+<h4 id="explanation">Explanation<a class="anchor" aria-label="anchor" href="#explanation"></a>
+</h4>
+<ul>
+<li><p><strong>mo_name:</strong> This function standardises
+microorganism names. Here, different variations of <em>Escherichia
+coli</em> (such as “E. coli”, “ESCCOL”, “esco”, and “Esche coli”) are
+all converted into the correct, standardised form, “Escherichia
+coli”.</p></li>
+<li><p><strong>ab_name</strong>: Similarly, this function standardises
+antimicrobial names. The different representations of ciprofloxacin
+(e.g., “Cipro”, “CIP”, “J01MA02”, and “Ciproxin”) are all converted to
+the standard name, “Ciprofloxacin”.</p></li>
+</ul>
+</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="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>
+<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="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>
+<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="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>
+<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
+the <code>AMR</code> R package, <code>microorganisms</code>,
+<code>antimicrobials</code>, <code>clinical_breakpoints</code>, and
+<code>example_isolates</code>, are now available as regular Python data
+frames:</p>
+<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.microorganisms</span></code></pre></div>
+<table class="table">
+<colgroup>
+<col width="11%">
+<col width="29%">
+<col width="8%">
+<col width="8%">
+<col width="8%">
+<col width="10%">
+<col width="13%">
+<col width="9%">
+</colgroup>
+<thead><tr class="header">
+<th>mo</th>
+<th>fullname</th>
+<th>status</th>
+<th>kingdom</th>
+<th>gbif</th>
+<th>gbif_parent</th>
+<th>gbif_renamed_to</th>
+<th>prevalence</th>
+</tr></thead>
+<tbody>
+<tr class="odd">
+<td>B_GRAMN</td>
+<td>(unknown Gram-negatives)</td>
+<td>unknown</td>
+<td>Bacteria</td>
+<td>None</td>
+<td>None</td>
+<td>None</td>
+<td>2.0</td>
+</tr>
+<tr class="even">
+<td>B_GRAMP</td>
+<td>(unknown Gram-positives)</td>
+<td>unknown</td>
+<td>Bacteria</td>
+<td>None</td>
+<td>None</td>
+<td>None</td>
+<td>2.0</td>
+</tr>
+<tr class="odd">
+<td>B_ANAER-NEG</td>
+<td>(unknown anaerobic Gram-negatives)</td>
+<td>unknown</td>
+<td>Bacteria</td>
+<td>None</td>
+<td>None</td>
+<td>None</td>
+<td>2.0</td>
+</tr>
+<tr class="even">
+<td>B_ANAER-POS</td>
+<td>(unknown anaerobic Gram-positives)</td>
+<td>unknown</td>
+<td>Bacteria</td>
+<td>None</td>
+<td>None</td>
+<td>None</td>
+<td>2.0</td>
+</tr>
+<tr class="odd">
+<td>B_ANAER</td>
+<td>(unknown anaerobic bacteria)</td>
+<td>unknown</td>
+<td>Bacteria</td>
+<td>None</td>
+<td>None</td>
+<td>None</td>
+<td>2.0</td>
+</tr>
+<tr class="even">
+<td>…</td>
+<td>…</td>
+<td>…</td>
+<td>…</td>
+<td>…</td>
+<td>…</td>
+<td>…</td>
+<td>…</td>
+</tr>
+<tr class="odd">
+<td>B_ZYMMN_POMC</td>
+<td>Zymomonas pomaceae</td>
+<td>accepted</td>
+<td>Bacteria</td>
+<td>10744418</td>
+<td>3221412</td>
+<td>None</td>
+<td>2.0</td>
+</tr>
+<tr class="even">
+<td>B_ZYMPH</td>
+<td>Zymophilus</td>
+<td>synonym</td>
+<td>Bacteria</td>
+<td>None</td>
+<td>9475166</td>
+<td>None</td>
+<td>2.0</td>
+</tr>
+<tr class="odd">
+<td>B_ZYMPH_PCVR</td>
+<td>Zymophilus paucivorans</td>
+<td>synonym</td>
+<td>Bacteria</td>
+<td>None</td>
+<td>None</td>
+<td>None</td>
+<td>2.0</td>
+</tr>
+<tr class="even">
+<td>B_ZYMPH_RFFN</td>
+<td>Zymophilus raffinosivorans</td>
+<td>synonym</td>
+<td>Bacteria</td>
+<td>None</td>
+<td>None</td>
+<td>None</td>
+<td>2.0</td>
+</tr>
+<tr class="odd">
+<td>F_ZYZYG</td>
+<td>Zyzygomyces</td>
+<td>unknown</td>
+<td>Fungi</td>
+<td>None</td>
+<td>7581</td>
+<td>None</td>
+<td>2.0</td>
+</tr>
+</tbody>
+</table>
+<div class="sourceCode" id="cb11"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb11-1"><a href="#cb11-1" tabindex="-1"></a>AMR.antimicrobials</span></code></pre></div>
+<table style="width:100%;" class="table">
+<colgroup>
+<col width="4%">
+<col width="12%">
+<col width="20%">
+<col width="25%">
+<col width="9%">
+<col width="11%">
+<col width="7%">
+<col width="9%">
+</colgroup>
+<thead><tr class="header">
+<th>ab</th>
+<th>cid</th>
+<th>name</th>
+<th>group</th>
+<th>oral_ddd</th>
+<th>oral_units</th>
+<th>iv_ddd</th>
+<th>iv_units</th>
+</tr></thead>
+<tbody>
+<tr class="odd">
+<td>AMA</td>
+<td>4649.0</td>
+<td>4-aminosalicylic acid</td>
+<td>Antimycobacterials</td>
+<td>12.00</td>
+<td>g</td>
+<td>NaN</td>
+<td>None</td>
+</tr>
+<tr class="even">
+<td>ACM</td>
+<td>6450012.0</td>
+<td>Acetylmidecamycin</td>
+<td>Macrolides/lincosamides</td>
+<td>NaN</td>
+<td>None</td>
+<td>NaN</td>
+<td>None</td>
+</tr>
+<tr class="odd">
+<td>ASP</td>
+<td>49787020.0</td>
+<td>Acetylspiramycin</td>
+<td>Macrolides/lincosamides</td>
+<td>NaN</td>
+<td>None</td>
+<td>NaN</td>
+<td>None</td>
+</tr>
+<tr class="even">
+<td>ALS</td>
+<td>8954.0</td>
+<td>Aldesulfone sodium</td>
+<td>Other antibacterials</td>
+<td>0.33</td>
+<td>g</td>
+<td>NaN</td>
+<td>None</td>
+</tr>
+<tr class="odd">
+<td>AMK</td>
+<td>37768.0</td>
+<td>Amikacin</td>
+<td>Aminoglycosides</td>
+<td>NaN</td>
+<td>None</td>
+<td>1.0</td>
+<td>g</td>
+</tr>
+<tr class="even">
+<td>…</td>
+<td>…</td>
+<td>…</td>
+<td>…</td>
+<td>…</td>
+<td>…</td>
+<td>…</td>
+<td>…</td>
+</tr>
+<tr class="odd">
+<td>VIR</td>
+<td>11979535.0</td>
+<td>Virginiamycine</td>
+<td>Other antibacterials</td>
+<td>NaN</td>
+<td>None</td>
+<td>NaN</td>
+<td>None</td>
+</tr>
+<tr class="even">
+<td>VOR</td>
+<td>71616.0</td>
+<td>Voriconazole</td>
+<td>Antifungals/antimycotics</td>
+<td>0.40</td>
+<td>g</td>
+<td>0.4</td>
+<td>g</td>
+</tr>
+<tr class="odd">
+<td>XBR</td>
+<td>72144.0</td>
+<td>Xibornol</td>
+<td>Other antibacterials</td>
+<td>NaN</td>
+<td>None</td>
+<td>NaN</td>
+<td>None</td>
+</tr>
+<tr class="even">
+<td>ZID</td>
+<td>77846445.0</td>
+<td>Zidebactam</td>
+<td>Other antibacterials</td>
+<td>NaN</td>
+<td>None</td>
+<td>NaN</td>
+<td>None</td>
+</tr>
+<tr class="odd">
+<td>ZFD</td>
+<td>NaN</td>
+<td>Zoliflodacin</td>
+<td>None</td>
+<td>NaN</td>
+<td>None</td>
+<td>NaN</td>
+<td>None</td>
+</tr>
+</tbody>
+</table>
+</div>
+</div>
+<div class="section level2">
+<h2 id="conclusion">Conclusion<a class="anchor" aria-label="anchor" href="#conclusion"></a>
+</h2>
+<p>With the <code>AMR</code> Python package, Python users can now
+effortlessly call R functions from the <code>AMR</code> R package. This
+eliminates the need for complex <code>rpy2</code> configurations and
+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 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>
+</div>
+
+
+
+    <footer><div class="pkgdown-footer-left">
+  <p><code>AMR</code> (for R). Free and open-source, licenced under the <a target="_blank" href="https://github.com/msberends/AMR/blob/main/LICENSE" class="external-link">GNU GPL 2.0</a>. Developed at the <a target="_blank" href="https://www.rug.nl" class="external-link">University of Groningen</a> and <a target="_blank" href="https://www.umcg.nl" class="external-link">University Medical Center Groningen</a> in The Netherlands, in collaboration with <a href="https://amr-for-r.org/authors.html">many colleagues from around the world</a>.</p>
+</div>
+
+<div class="pkgdown-footer-right">
+  <p><a target="_blank" href="https://www.rug.nl" class="external-link"><img src="https://amr-for-r.org/logo_rug.svg" style="max-width: 150px;"></a><a target="_blank" href="https://www.umcg.nl" class="external-link"><img src="https://amr-for-r.org/logo_umcg.svg" style="max-width: 150px;"></a></p>
+</div>
+
+    </footer>
+</div>
+
+
+
+
+
+  </body>
+</html>
@@ -0,0 +1,217 @@
+# AMR for Python
+
+## Introduction
+
+The `AMR` package for R is a powerful tool for 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 [`AMR` Python
+package](https://pypi.org/project/AMR/).
+
+This Python package is a wrapper around the `AMR` R package. It uses the
+`rpy2` package internally. Despite the need to have R installed, Python
+users can now easily work with AMR data directly through Python code.
+
+## Prerequisites
+
+This package was only tested with a [virtual environment
+(venv)](https://docs.python.org/3/library/venv.html). You can set up
+such an environment by running:
+
+``` python
+# linux and macOS:
+python -m venv /path/to/new/virtual/environment
+
+# Windows:
+python -m venv C:\path\to\new\virtual\environment
+```
+
+Then you can [activate the
+environment](https://docs.python.org/3/library/venv.html#how-venvs-work),
+after which the venv is ready to work with.
+
+## Install AMR
+
+1.  Since the Python package is available on the official [Python
+    Package Index](https://pypi.org/project/AMR/), you can just run:
+
+    ``` bash
+    pip install AMR
+    ```
+
+2.  Make sure you have R installed. There is **no need to install the
+    `AMR` R package**, as it will be installed automatically.
+
+    For Linux:
+
+    ``` bash
+    # Ubuntu / Debian
+    sudo apt install r-base
+    # Fedora:
+    sudo dnf install R
+    # CentOS/RHEL
+    sudo yum install R
+    ```
+
+    For macOS (using [Homebrew](https://brew.sh)):
+
+    ``` bash
+    brew install r
+    ```
+
+    For Windows, visit the [CRAN download
+    page](https://cran.r-project.org) to download and install R.
+
+## Examples of Usage
+
+### Cleaning Taxonomy
+
+Here’s an example that demonstrates how to clean microorganism and drug
+names using the `AMR` Python package:
+
+``` python
+import pandas as pd
+import AMR
+
+# Sample data
+data = {
+    "MOs": ['E. coli', 'ESCCOL', 'esco', 'Esche coli'],
+    "Drug": ['Cipro', 'CIP', 'J01MA02', 'Ciproxin']
+}
+df = pd.DataFrame(data)
+
+# Use AMR functions to clean microorganism and drug names
+df['MO_clean'] = AMR.mo_name(df['MOs'])
+df['Drug_clean'] = AMR.ab_name(df['Drug'])
+
+# Display the results
+print(df)
+```
+
+| MOs        | Drug     | MO_clean         | Drug_clean    |
+|------------|----------|------------------|---------------|
+| E. coli    | Cipro    | Escherichia coli | Ciprofloxacin |
+| ESCCOL     | CIP      | Escherichia coli | Ciprofloxacin |
+| esco       | J01MA02  | Escherichia coli | Ciprofloxacin |
+| Esche coli | Ciproxin | Escherichia coli | Ciprofloxacin |
+
+#### Explanation
+
+- **mo_name:** This function standardises microorganism names. Here,
+  different variations of *Escherichia coli* (such as “E. coli”,
+  “ESCCOL”, “esco”, and “Esche coli”) are all converted into the
+  correct, standardised form, “Escherichia coli”.
+
+- **ab_name**: Similarly, this function standardises antimicrobial
+  names. The different representations of ciprofloxacin (e.g., “Cipro”,
+  “CIP”, “J01MA02”, and “Ciproxin”) are all converted to the standard
+  name, “Ciprofloxacin”.
+
+### Calculating AMR
+
+``` python
+import AMR
+import pandas as pd
+
+df = AMR.example_isolates
+result = AMR.resistance(df["AMX"])
+print(result)
+```
+
+    [0.59555556]
+
+### Generating Antibiograms
+
+One of the core functions of the `AMR` 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:
+
+``` python
+result2a = AMR.antibiogram(df[["mo", "AMX", "CIP", "TZP"]])
+print(result2a)
+```
+
+| Pathogen       | Amoxicillin    | Ciprofloxacin | Piperacillin/tazobactam |
+|----------------|----------------|---------------|-------------------------|
+| CoNS           | 7% (10/142)    | 73% (183/252) | 30% (10/33)             |
+| E. coli        | 50% (196/392)  | 88% (399/456) | 94% (393/416)           |
+| K. pneumoniae  | 0% (0/58)      | 96% (53/55)   | 89% (47/53)             |
+| P. aeruginosa  | 0% (0/30)      | 100% (30/30)  | None                    |
+| P. mirabilis   | None           | 94% (34/36)   | None                    |
+| S. aureus      | 6% (8/131)     | 90% (171/191) | None                    |
+| S. epidermidis | 1% (1/91)      | 64% (87/136)  | None                    |
+| S. hominis     | None           | 80% (56/70)   | None                    |
+| S. pneumoniae  | 100% (112/112) | None          | 100% (112/112)          |
+
+``` python
+result2b = AMR.antibiogram(df[["mo", "AMX", "CIP", "TZP"]], mo_transform = "gramstain")
+print(result2b)
+```
+
+| Pathogen      | Amoxicillin   | Ciprofloxacin | Piperacillin/tazobactam |
+|---------------|---------------|---------------|-------------------------|
+| Gram-negative | 36% (226/631) | 91% (621/684) | 88% (565/641)           |
+| Gram-positive | 43% (305/703) | 77% (560/724) | 86% (296/345)           |
+
+In this example, we generate an antibiogram by selecting various
+antibiotics.
+
+### Taxonomic Data Sets Now in Python!
+
+As a Python user, you might like that the most important data sets of
+the `AMR` R package, `microorganisms`, `antimicrobials`,
+`clinical_breakpoints`, and `example_isolates`, are now available as
+regular Python data frames:
+
+``` python
+AMR.microorganisms
+```
+
+| mo | fullname | status | kingdom | gbif | gbif_parent | gbif_renamed_to | prevalence |
+|----|----|----|----|----|----|----|----|
+| B_GRAMN | (unknown Gram-negatives) | unknown | Bacteria | None | None | None | 2.0 |
+| B_GRAMP | (unknown Gram-positives) | unknown | Bacteria | None | None | None | 2.0 |
+| B_ANAER-NEG | (unknown anaerobic Gram-negatives) | unknown | Bacteria | None | None | None | 2.0 |
+| B_ANAER-POS | (unknown anaerobic Gram-positives) | unknown | Bacteria | None | None | None | 2.0 |
+| B_ANAER | (unknown anaerobic bacteria) | unknown | Bacteria | None | None | None | 2.0 |
+| … | … | … | … | … | … | … | … |
+| B_ZYMMN_POMC | Zymomonas pomaceae | accepted | Bacteria | 10744418 | 3221412 | None | 2.0 |
+| B_ZYMPH | Zymophilus | synonym | Bacteria | None | 9475166 | None | 2.0 |
+| B_ZYMPH_PCVR | Zymophilus paucivorans | synonym | Bacteria | None | None | None | 2.0 |
+| B_ZYMPH_RFFN | Zymophilus raffinosivorans | synonym | Bacteria | None | None | None | 2.0 |
+| F_ZYZYG | Zyzygomyces | unknown | Fungi | None | 7581 | None | 2.0 |
+
+``` python
+AMR.antimicrobials
+```
+
+| ab | cid | name | group | oral_ddd | oral_units | iv_ddd | iv_units |
+|----|----|----|----|----|----|----|----|
+| AMA | 4649.0 | 4-aminosalicylic acid | Antimycobacterials | 12.00 | g | NaN | None |
+| ACM | 6450012.0 | Acetylmidecamycin | Macrolides/lincosamides | NaN | None | NaN | None |
+| ASP | 49787020.0 | Acetylspiramycin | Macrolides/lincosamides | NaN | None | NaN | None |
+| ALS | 8954.0 | Aldesulfone sodium | Other antibacterials | 0.33 | g | NaN | None |
+| AMK | 37768.0 | Amikacin | Aminoglycosides | NaN | None | 1.0 | g |
+| … | … | … | … | … | … | … | … |
+| VIR | 11979535.0 | Virginiamycine | Other antibacterials | NaN | None | NaN | None |
+| VOR | 71616.0 | Voriconazole | Antifungals/antimycotics | 0.40 | g | 0.4 | g |
+| XBR | 72144.0 | Xibornol | Other antibacterials | NaN | None | NaN | None |
+| ZID | 77846445.0 | Zidebactam | Other antibacterials | NaN | None | NaN | None |
+| ZFD | NaN | Zoliflodacin | None | NaN | None | NaN | None |
+
+## Conclusion
+
+With the `AMR` Python package, Python users can now effortlessly call R
+functions from the `AMR` R package. This eliminates the need for complex
+`rpy2` configurations and 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.
+
+By just running `import AMR`, users can seamlessly integrate the robust
+features of the R `AMR` package into Python workflows.
+
+Whether you’re cleaning data or analysing resistance patterns, the `AMR`
+Python package makes it easy to work with AMR data in Python.
@@ -0,0 +1,903 @@
+# AMR with tidymodels
+
+> This page was almost entirely written by our [AMR for R
+> Assistant](https://chat.amr-for-r.org), a ChatGPT manually-trained
+> model able to answer any question about the `AMR` package.
+
+Antimicrobial resistance (AMR) is a global health crisis, and
+understanding resistance patterns is crucial for managing effective
+treatments. The `AMR` R package provides robust tools for analysing AMR
+data, including convenient antimicrobial selector functions like
+[`aminoglycosides()`](https://amr-for-r.org/reference/antimicrobial_selectors.md)
+and
+[`betalactams()`](https://amr-for-r.org/reference/antimicrobial_selectors.md).
+
+In this post, we will explore how to use the `tidymodels` framework to
+predict resistance patterns in the `example_isolates` dataset in two
+examples.
+
+This post contains the following examples:
+
+1.  Using Antimicrobial Selectors
+2.  Predicting ESBL Presence Using Raw MICs
+3.  Predicting AMR Over Time
+
+## Example 1: Using Antimicrobial Selectors
+
+By leveraging the power of `tidymodels` and the `AMR` package, we’ll
+build a reproducible machine learning workflow to predict the Gramstain
+of the microorganism to two important antibiotic classes:
+aminoglycosides and beta-lactams.
+
+### **Objective**
+
+Our goal is to build a predictive model using the `tidymodels` framework
+to determine the Gramstain of the microorganism based on microbial data.
+We will:
+
+1.  Preprocess data using the selector functions
+    [`aminoglycosides()`](https://amr-for-r.org/reference/antimicrobial_selectors.md)
+    and
+    [`betalactams()`](https://amr-for-r.org/reference/antimicrobial_selectors.md).
+2.  Define a logistic regression model for prediction.
+3.  Use a structured `tidymodels` workflow to preprocess, train, and
+    evaluate the model.
+
+### **Data Preparation**
+
+We begin by loading the required libraries and preparing the
+`example_isolates` dataset from the `AMR` package.
+
+``` r
+
+# Load required libraries
+library(AMR) # For AMR data analysis
+library(tidymodels) # For machine learning workflows, and data manipulation (dplyr, tidyr, ...)
+```
+
+Prepare the data:
+
+``` r
+
+# Your data could look like this:
+example_isolates
+#> # A tibble: 2,000 × 46
+#>    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX  
+#>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir>
+#>  1 2002-01-02 A77334     65 F      Clinical B_ESCHR_COLI   R     NA    NA    NA 
+#>  2 2002-01-03 A77334     65 F      Clinical B_ESCHR_COLI   R     NA    NA    NA 
+#>  3 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA 
+#>  4 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA 
+#>  5 2002-01-13 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA 
+#>  6 2002-01-13 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA 
+#>  7 2002-01-14 462729     78 M      Clinical B_STPHY_AURS   R     NA    S     R  
+#>  8 2002-01-14 462729     78 M      Clinical B_STPHY_AURS   R     NA    S     R  
+#>  9 2002-01-16 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA 
+#> 10 2002-01-17 858515     79 F      ICU      B_STPHY_EPDR   R     NA    S     NA 
+#> # ℹ 1,990 more rows
+#> # ℹ 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, CZO <sir>, FEP <sir>,
+#> #   CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, CRO <sir>, GEN <sir>,
+#> #   TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, SXT <sir>, NIT <sir>,
+#> #   FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, VAN <sir>, TEC <sir>,
+#> #   TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, CLI <sir>, AZM <sir>,
+#> #   IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, COL <sir>, MUP <sir>, …
+
+# Select relevant columns for prediction
+data <- example_isolates %>%
+  # select AB results dynamically
+  select(mo, aminoglycosides(), betalactams()) %>%
+  # replace NAs with NI (not-interpretable)
+  mutate(
+    across(
+      where(is.sir),
+      ~ replace_na(.x, "NI")
+    ),
+    # make factors of SIR columns
+    across(
+      where(is.sir),
+      as.integer
+    ),
+    # get Gramstain of microorganisms
+    mo = as.factor(mo_gramstain(mo))
+  ) %>%
+  # drop NAs - the ones without a Gramstain (fungi, etc.)
+  drop_na()
+#> ℹ For `aminoglycosides()` using columns GEN (gentamicin), TOB (tobramycin), AMK
+#>   (amikacin), and KAN (kanamycin)
+#> ℹ For `betalactams()` using columns PEN (benzylpenicillin), OXA (oxacillin),
+#>   FLC (flucloxacillin), AMX (amoxicillin), AMC (amoxicillin/clavulanic acid),
+#>   AMP (ampicillin), TZP (piperacillin/tazobactam), CZO (cefazolin), FEP
+#>   (cefepime), CXM (cefuroxime), FOX (cefoxitin), CTX (cefotaxime), CAZ
+#>   (ceftazidime), CRO (ceftriaxone), IPM (imipenem), and MEM (meropenem)
+```
+
+**Explanation:**
+
+- [`aminoglycosides()`](https://amr-for-r.org/reference/antimicrobial_selectors.md)
+  and
+  [`betalactams()`](https://amr-for-r.org/reference/antimicrobial_selectors.md)
+  dynamically select columns for antimicrobials in these classes.
+- `drop_na()` ensures the model receives complete cases for training.
+
+### **Defining the Workflow**
+
+We now define the `tidymodels` workflow, which consists of three steps:
+preprocessing, model specification, and fitting.
+
+#### 1. Preprocessing with a Recipe
+
+We create a recipe to preprocess the data for modelling.
+
+``` r
+
+# Define the recipe for data preprocessing
+resistance_recipe <- recipe(mo ~ ., data = data) %>%
+  step_corr(c(aminoglycosides(), betalactams()), threshold = 0.9)
+resistance_recipe
+#> 
+#> ── Recipe ──────────────────────────────────────────────────────────────────────
+#> 
+#> ── Inputs
+#> Number of variables by role
+#> outcome:    1
+#> predictor: 20
+#> 
+#> ── Operations
+#> • Correlation filter on: c(aminoglycosides(), betalactams())
+```
+
+For a recipe that includes at least one preprocessing operation, like we
+have with `step_corr()`, the necessary parameters can be estimated from
+a training set using `prep()`:
+
+``` r
+
+prep(resistance_recipe)
+#> ℹ For `aminoglycosides()` using columns GEN (gentamicin), TOB (tobramycin), AMK
+#>   (amikacin), and KAN (kanamycin)
+#> ℹ For `betalactams()` using columns PEN (benzylpenicillin), OXA (oxacillin),
+#>   FLC (flucloxacillin), AMX (amoxicillin), AMC (amoxicillin/clavulanic acid),
+#>   AMP (ampicillin), TZP (piperacillin/tazobactam), CZO (cefazolin), FEP
+#>   (cefepime), CXM (cefuroxime), FOX (cefoxitin), CTX (cefotaxime), CAZ
+#>   (ceftazidime), CRO (ceftriaxone), IPM (imipenem), and MEM (meropenem)
+#> 
+#> 
+#> ── Recipe ──────────────────────────────────────────────────────────────────────
+#> 
+#> 
+#> 
+#> ── Inputs 
+#> 
+#> Number of variables by role
+#> 
+#> outcome:    1
+#> predictor: 20
+#> 
+#> 
+#> 
+#> ── Training information 
+#> 
+#> Training data contained 1968 data points and no incomplete rows.
+#> 
+#> 
+#> 
+#> ── Operations 
+#> 
+#> • Correlation filter on: AMX CTX | Trained
+```
+
+**Explanation:**
+
+- `recipe(mo ~ ., data = data)` will take the `mo` column as outcome and
+  all other columns as predictors.
+- `step_corr()` removes predictors (i.e., antibiotic columns) that have
+  a higher correlation than 90%.
+
+Notice how the recipe contains just the antimicrobial selector
+functions - no need to define the columns specifically. In the
+preparation (retrieved with `prep()`) we can see that the columns or
+variables ‘AMX’ and ‘CTX’ were removed as they correlate too much with
+existing, other variables.
+
+#### 2. Specifying the Model
+
+We define a logistic regression model since resistance prediction is a
+binary classification task.
+
+``` r
+
+# Specify a logistic regression model
+logistic_model <- logistic_reg() %>%
+  set_engine("glm") # Use the Generalised Linear Model engine
+logistic_model
+#> Logistic Regression Model Specification (classification)
+#> 
+#> Computational engine: glm
+```
+
+**Explanation:**
+
+- `logistic_reg()` sets up a logistic regression model.
+- `set_engine("glm")` specifies the use of R’s built-in GLM engine.
+
+#### 3. Building the Workflow
+
+We bundle the recipe and model together into a `workflow`, which
+organises the entire modelling process.
+
+``` r
+
+# Combine the recipe and model into a workflow
+resistance_workflow <- workflow() %>%
+  add_recipe(resistance_recipe) %>% # Add the preprocessing recipe
+  add_model(logistic_model) # Add the logistic regression model
+resistance_workflow
+#> ══ Workflow ════════════════════════════════════════════════════════════════════
+#> Preprocessor: Recipe
+#> Model: logistic_reg()
+#> 
+#> ── Preprocessor ────────────────────────────────────────────────────────────────
+#> 1 Recipe Step
+#> 
+#> • step_corr()
+#> 
+#> ── Model ───────────────────────────────────────────────────────────────────────
+#> Logistic Regression Model Specification (classification)
+#> 
+#> Computational engine: glm
+```
+
+### **Training and Evaluating the Model**
+
+To train the model, we split the data into training and testing sets.
+Then, we fit the workflow on the training set and evaluate its
+performance.
+
+``` r
+
+# Split data into training and testing sets
+set.seed(123) # For reproducibility
+data_split <- initial_split(data, prop = 0.8) # 80% training, 20% testing
+training_data <- training(data_split) # Training set
+testing_data <- testing(data_split) # Testing set
+
+# Fit the workflow to the training data
+fitted_workflow <- resistance_workflow %>%
+  fit(training_data) # Train the model
+```
+
+**Explanation:**
+
+- `initial_split()` splits the data into training and testing sets.
+- `fit()` trains the workflow on the training set.
+
+Notice how in `fit()`, the antimicrobial selector functions are
+internally called again. For training, these functions are called since
+they are stored in the recipe.
+
+Next, we evaluate the model on the testing data.
+
+``` r
+
+# Make predictions on the testing set
+predictions <- fitted_workflow %>%
+  predict(testing_data) # Generate predictions
+probabilities <- fitted_workflow %>%
+  predict(testing_data, type = "prob") # Generate probabilities
+
+predictions <- predictions %>%
+  bind_cols(probabilities) %>%
+  bind_cols(testing_data) # Combine with true labels
+
+predictions
+#> # A tibble: 394 × 24
+#>    .pred_class   `.pred_Gram-negative` `.pred_Gram-positive` mo        GEN   TOB
+#>    <fct>                         <dbl>                 <dbl> <fct>   <int> <int>
+#>  1 Gram-positive              1.07e- 1             8.93 e- 1 Gram-p…     5     5
+#>  2 Gram-positive              3.17e- 8             1.000e+ 0 Gram-p…     5     1
+#>  3 Gram-negative              9.99e- 1             1.42 e- 3 Gram-n…     5     5
+#>  4 Gram-positive              2.22e-16             1    e+ 0 Gram-p…     5     5
+#>  5 Gram-negative              9.46e- 1             5.42 e- 2 Gram-n…     5     5
+#>  6 Gram-positive              1.07e- 1             8.93 e- 1 Gram-p…     5     5
+#>  7 Gram-positive              2.22e-16             1    e+ 0 Gram-p…     1     5
+#>  8 Gram-positive              2.22e-16             1    e+ 0 Gram-p…     4     4
+#>  9 Gram-negative              1   e+ 0             2.22 e-16 Gram-n…     1     1
+#> 10 Gram-positive              6.05e-11             1.000e+ 0 Gram-p…     4     4
+#> # ℹ 384 more rows
+#> # ℹ 18 more variables: AMK <int>, KAN <int>, PEN <int>, OXA <int>, FLC <int>,
+#> #   AMX <int>, AMC <int>, AMP <int>, TZP <int>, CZO <int>, FEP <int>,
+#> #   CXM <int>, FOX <int>, CTX <int>, CAZ <int>, CRO <int>, IPM <int>, MEM <int>
+
+# Evaluate model performance
+metrics <- predictions %>%
+  metrics(truth = mo, estimate = .pred_class) # Calculate performance metrics
+
+metrics
+#> # A tibble: 2 × 3
+#>   .metric  .estimator .estimate
+#>   <chr>    <chr>          <dbl>
+#> 1 accuracy binary         0.995
+#> 2 kap      binary         0.989
+
+
+# To assess some other model properties, you can make our own `metrics()` function
+our_metrics <- metric_set(accuracy, kap, ppv, npv) # add Positive Predictive Value and Negative Predictive Value
+metrics2 <- predictions %>%
+  our_metrics(truth = mo, estimate = .pred_class) # run again on our `our_metrics()` function
+
+metrics2
+#> # A tibble: 4 × 3
+#>   .metric  .estimator .estimate
+#>   <chr>    <chr>          <dbl>
+#> 1 accuracy binary         0.995
+#> 2 kap      binary         0.989
+#> 3 ppv      binary         0.987
+#> 4 npv      binary         1
+```
+
+**Explanation:**
+
+- [`predict()`](https://rdrr.io/r/stats/predict.html) generates
+  predictions on the testing set.
+- `metrics()` computes evaluation metrics like accuracy and kappa.
+
+It appears we can predict the Gram stain with a 99.5% accuracy based on
+AMR results of only aminoglycosides and beta-lactam antibiotics. The ROC
+curve looks like this:
+
+``` r
+
+predictions %>%
+  roc_curve(mo, `.pred_Gram-negative`) %>%
+  autoplot()
+```
+
+![](AMR_with_tidymodels_files/figure-html/unnamed-chunk-8-1.png)
+
+### **Conclusion**
+
+In this example, we demonstrated how to build a machine learning
+pipeline with the `tidymodels` framework and the `AMR` package. By
+combining selector functions like
+[`aminoglycosides()`](https://amr-for-r.org/reference/antimicrobial_selectors.md)
+and
+[`betalactams()`](https://amr-for-r.org/reference/antimicrobial_selectors.md)
+with `tidymodels`, we efficiently prepared data, trained a model, and
+evaluated its performance.
+
+This workflow is extensible to other antimicrobial classes and
+resistance patterns, empowering users to analyse AMR data systematically
+and reproducibly.
+
+------------------------------------------------------------------------
+
+## Example 2: Predicting ESBL Presence Using Raw MICs
+
+In this second example, we demonstrate how to use `<mic>` columns
+directly in `tidymodels` workflows using AMR-specific recipe steps. This
+includes a transformation to `log2` scale using
+[`step_mic_log2()`](https://amr-for-r.org/reference/amr-tidymodels.md),
+which prepares MIC values for use in classification models.
+
+This approach and idea formed the basis for the publication [DOI:
+10.3389/fmicb.2025.1582703](https://doi.org/10.3389/fmicb.2025.1582703)
+to model the presence of extended-spectrum beta-lactamases (ESBL) based
+on MIC values.
+
+### **Objective**
+
+Our goal is to:
+
+1.  Use raw MIC values to predict whether a bacterial isolate produces
+    ESBL.
+2.  Apply AMR-aware preprocessing in a `tidymodels` recipe.
+3.  Train a classification model and evaluate its predictive
+    performance.
+
+### **Data Preparation**
+
+We use the `esbl_isolates` dataset that comes with the AMR package.
+
+``` r
+
+# Load required libraries
+library(AMR)
+library(tidymodels)
+
+# View the esbl_isolates data set
+esbl_isolates
+#> # A tibble: 500 × 19
+#>    esbl  genus   AMC   AMP   TZP   CXM   FOX   CTX   CAZ   GEN   TOB   TMP   SXT
+#>    <lgl> <chr> <mic> <mic> <mic> <mic> <mic> <mic> <mic> <mic> <mic> <mic> <mic>
+#>  1 FALSE Esch…    32    32     4    64    64  8.00  8.00     1     1  16.0    20
+#>  2 FALSE Esch…    32    32     4    64    64  4.00  8.00     1     1  16.0   320
+#>  3 FALSE Esch…     4     2    64     8     4  8.00  0.12    16    16   0.5    20
+#>  4 FALSE Kleb…    32    32    16    64    64  8.00  8.00     1     1   0.5    20
+#>  5 FALSE Esch…    32    32     4     4     4  0.25  2.00     1     1  16.0   320
+#>  6 FALSE Citr…    32    32    16    64    64 64.00 32.00     1     1   0.5    20
+#>  7 FALSE Morg…    32    32     4    64    64 16.00  2.00     1     1   0.5    20
+#>  8 FALSE Prot…    16    32     4     1     4  8.00  0.12     1     1  16.0   320
+#>  9 FALSE Ente…    32    32     8    64    64 32.00  4.00     1     1   0.5    20
+#> 10 FALSE Citr…    32    32    32    64    64  8.00 64.00     1     1  16.0   320
+#> # ℹ 490 more rows
+#> # ℹ 6 more variables: NIT <mic>, FOS <mic>, CIP <mic>, IPM <mic>, MEM <mic>,
+#> #   COL <mic>
+
+# Prepare a binary outcome and convert to ordered factor
+data <- esbl_isolates %>%
+  mutate(esbl = factor(esbl, levels = c(FALSE, TRUE), ordered = TRUE))
+```
+
+**Explanation:**
+
+- `esbl_isolates`: Contains MIC test results and ESBL status for each
+  isolate.
+- `mutate(esbl = ...)`: Converts the target column to an ordered factor
+  for classification.
+
+### **Defining the Workflow**
+
+#### 1. Preprocessing with a Recipe
+
+We use our
+[`step_mic_log2()`](https://amr-for-r.org/reference/amr-tidymodels.md)
+function to log2-transform MIC values, ensuring that MICs are numeric
+and properly scaled. All MIC predictors can easily and agnostically
+selected using the new
+[`all_mic_predictors()`](https://amr-for-r.org/reference/amr-tidymodels.md):
+
+``` r
+
+# Split into training and testing sets
+set.seed(123)
+split <- initial_split(data)
+training_data <- training(split)
+testing_data <- testing(split)
+
+# Define the recipe
+mic_recipe <- recipe(esbl ~ ., data = training_data) %>%
+  remove_role(genus, old_role = "predictor") %>% # Remove non-informative variable
+  step_mic_log2(all_mic_predictors()) # Log2 transform all MIC predictors
+
+prep(mic_recipe)
+#> 
+#> ── Recipe ──────────────────────────────────────────────────────────────────────
+#> 
+#> ── Inputs
+#> Number of variables by role
+#> outcome:          1
+#> predictor:       17
+#> undeclared role:  1
+#> 
+#> ── Training information
+#> Training data contained 375 data points and no incomplete rows.
+#> 
+#> ── Operations
+#> • Log2 transformation of MIC columns: AMC, AMP, TZP, CXM, FOX, ... | Trained
+```
+
+**Explanation:**
+
+- `remove_role()`: Removes irrelevant variables like genus.
+- [`step_mic_log2()`](https://amr-for-r.org/reference/amr-tidymodels.md):
+  Applies `log2(as.numeric(...))` to all MIC predictors in one go.
+- `prep()`: Finalises the recipe based on training data.
+
+#### 2. Specifying the Model
+
+We use a simple logistic regression to model ESBL presence, though
+recent models such as xgboost ([link to `parsnip`
+manual](https://parsnip.tidymodels.org/reference/details_boost_tree_xgboost.html))
+could be much more precise.
+
+``` r
+
+# Define the model
+model <- logistic_reg(mode = "classification") %>%
+  set_engine("glm")
+
+model
+#> Logistic Regression Model Specification (classification)
+#> 
+#> Computational engine: glm
+```
+
+**Explanation:**
+
+- `logistic_reg()`: Specifies a binary classification model.
+- `set_engine("glm")`: Uses the base R GLM engine.
+
+#### 3. Building the Workflow
+
+``` r
+
+# Create workflow
+workflow_model <- workflow() %>%
+  add_recipe(mic_recipe) %>%
+  add_model(model)
+
+workflow_model
+#> ══ Workflow ════════════════════════════════════════════════════════════════════
+#> Preprocessor: Recipe
+#> Model: logistic_reg()
+#> 
+#> ── Preprocessor ────────────────────────────────────────────────────────────────
+#> 1 Recipe Step
+#> 
+#> • step_mic_log2()
+#> 
+#> ── Model ───────────────────────────────────────────────────────────────────────
+#> Logistic Regression Model Specification (classification)
+#> 
+#> Computational engine: glm
+```
+
+### **Training and Evaluating the Model**
+
+``` r
+
+# Fit the model
+fitted <- fit(workflow_model, training_data)
+
+# Generate predictions
+predictions <- predict(fitted, testing_data) %>%
+  bind_cols(predict(fitted, testing_data, type = "prob")) %>% # add probabilities
+  bind_cols(testing_data)
+
+# Evaluate model performance
+our_metrics <- metric_set(accuracy, recall, precision, sensitivity, specificity, ppv, npv)
+metrics <- our_metrics(predictions, truth = esbl, estimate = .pred_class)
+
+metrics
+#> # A tibble: 7 × 3
+#>   .metric     .estimator .estimate
+#>   <chr>       <chr>          <dbl>
+#> 1 accuracy    binary         0.92 
+#> 2 recall      binary         0.921
+#> 3 precision   binary         0.921
+#> 4 sensitivity binary         0.921
+#> 5 specificity binary         0.919
+#> 6 ppv         binary         0.921
+#> 7 npv         binary         0.919
+```
+
+**Explanation:**
+
+- `fit()`: Trains the model on the processed training data.
+- [`predict()`](https://rdrr.io/r/stats/predict.html): Produces
+  predictions for unseen test data.
+- `metric_set()`: Allows evaluating multiple classification metrics.
+  This will make `our_metrics` to become a function that we can use to
+  check the predictions with.
+
+It appears we can predict ESBL gene presence with a positive predictive
+value (PPV) of 92.1% and a negative predictive value (NPV) of 91.9%
+using a simplistic logistic regression model.
+
+### **Visualising Predictions**
+
+We can visualise predictions by comparing predicted and actual ESBL
+status.
+
+``` r
+
+library(ggplot2)
+
+ggplot(predictions, aes(x = esbl, fill = .pred_class)) +
+  geom_bar(position = "stack") +
+  labs(
+    title = "Predicted vs Actual ESBL Status",
+    x = "Actual ESBL",
+    y = "Count"
+  ) +
+  theme_minimal()
+```
+
+![](AMR_with_tidymodels_files/figure-html/unnamed-chunk-14-1.png)
+
+And plot the certainties too - how certain were the actual predictions?
+
+``` r
+
+predictions %>%
+  mutate(
+    certainty = ifelse(.pred_class == "FALSE",
+      .pred_FALSE,
+      .pred_TRUE
+    ),
+    correct = ifelse(esbl == .pred_class, "Right", "Wrong")
+  ) %>%
+  ggplot(aes(
+    x = seq_len(nrow(predictions)),
+    y = certainty,
+    colour = correct
+  )) +
+  scale_colour_manual(
+    values = c(Right = "green3", Wrong = "red2"),
+    name = "Correct?"
+  ) +
+  geom_point() +
+  scale_y_continuous(
+    labels = function(x) paste0(x * 100, "%"),
+    limits = c(0.5, 1)
+  ) +
+  theme_minimal()
+```
+
+![](AMR_with_tidymodels_files/figure-html/unnamed-chunk-15-1.png)
+
+### **Conclusion**
+
+In this example, we showcased how the new `AMR`-specific recipe steps
+simplify working with `<mic>` columns in `tidymodels`. The
+[`step_mic_log2()`](https://amr-for-r.org/reference/amr-tidymodels.md)
+transformation converts MICs (with or without operators) to
+log2-transformed numerics, improving compatibility with classification
+models.
+
+This pipeline enables realistic, reproducible, and interpretable
+modelling of antimicrobial resistance data.
+
+------------------------------------------------------------------------
+
+## Example 3: Predicting AMR Over Time
+
+In this third example, we aim to predict antimicrobial resistance (AMR)
+trends over time using `tidymodels`. We will model resistance to three
+antibiotics (amoxicillin `AMX`, amoxicillin-clavulanic acid `AMC`, and
+ciprofloxacin `CIP`), based on historical data grouped by year and
+hospital ward.
+
+### **Objective**
+
+Our goal is to:
+
+1.  Prepare the dataset by aggregating resistance data over time.
+2.  Define a regression model to predict AMR trends.
+3.  Use `tidymodels` to preprocess, train, and evaluate the model.
+
+### **Data Preparation**
+
+We start by transforming the `example_isolates` dataset into a
+structured time-series format.
+
+``` r
+
+# Load required libraries
+library(AMR)
+library(tidymodels)
+
+# Transform dataset
+data_time <- example_isolates %>%
+  top_n_microorganisms(n = 10) %>% # Filter on the top #10 species
+  mutate(
+    year = as.integer(format(date, "%Y")), # Extract year from date
+    gramstain = mo_gramstain(mo)
+  ) %>% # Get taxonomic names
+  group_by(year, gramstain) %>%
+  summarise(
+    across(c(AMX, AMC, CIP),
+      function(x) resistance(x, minimum = 0),
+      .names = "res_{.col}"
+    ),
+    .groups = "drop"
+  ) %>%
+  filter(!is.na(res_AMX) & !is.na(res_AMC) & !is.na(res_CIP)) # Drop missing values
+#> ℹ Using column mo as input for `col_mo`.
+#> ℹ `resistance()` assumes the EUCAST guideline and thus considers the 'I'
+#>   category susceptible. Set the `guideline` argument or the `AMR_guideline`
+#>   option to either "CLSI" or "EUCAST", see `?AMR-options`.
+#> ℹ This message will be shown once per session.
+
+data_time
+#> # A tibble: 32 × 5
+#>     year gramstain     res_AMX res_AMC res_CIP
+#>    <int> <chr>           <dbl>   <dbl>   <dbl>
+#>  1  2002 Gram-negative   1      0.105   0.0606
+#>  2  2002 Gram-positive   0.838  0.182   0.162 
+#>  3  2003 Gram-negative   1      0.0714  0     
+#>  4  2003 Gram-positive   0.714  0.244   0.154 
+#>  5  2004 Gram-negative   0.464  0.0938  0     
+#>  6  2004 Gram-positive   0.849  0.299   0.244 
+#>  7  2005 Gram-negative   0.412  0.132   0.0588
+#>  8  2005 Gram-positive   0.882  0.382   0.154 
+#>  9  2006 Gram-negative   0.379  0       0.1   
+#> 10  2006 Gram-positive   0.778  0.333   0.353 
+#> # ℹ 22 more rows
+```
+
+**Explanation:**
+
+- `mo_name(mo)`: Converts microbial codes into proper species names.
+- [`resistance()`](https://amr-for-r.org/reference/proportion.md):
+  Converts AMR results into numeric values (proportion of resistant
+  isolates).
+- `group_by(year, ward, species)`: Aggregates resistance rates by year
+  and ward.
+
+### **Defining the Workflow**
+
+We now define the modelling workflow, which consists of a preprocessing
+step, a model specification, and the fitting process.
+
+#### 1. Preprocessing with a Recipe
+
+``` r
+
+# Define the recipe
+resistance_recipe_time <- recipe(res_AMX ~ year + gramstain, data = data_time) %>%
+  step_dummy(gramstain, one_hot = TRUE) %>% # Convert categorical to numerical
+  step_normalize(year) %>% # Normalise year for better model performance
+  step_nzv(all_predictors()) # Remove near-zero variance predictors
+
+resistance_recipe_time
+#> 
+#> ── Recipe ──────────────────────────────────────────────────────────────────────
+#> 
+#> ── Inputs
+#> Number of variables by role
+#> outcome:   1
+#> predictor: 2
+#> 
+#> ── Operations
+#> • Dummy variables from: gramstain
+#> • Centering and scaling for: year
+#> • Sparse, unbalanced variable filter on: all_predictors()
+```
+
+**Explanation:**
+
+- `step_dummy()`: Encodes categorical variables (`ward`, `species`) as
+  numerical indicators.
+- `step_normalize()`: Normalises the `year` variable.
+- `step_nzv()`: Removes near-zero variance predictors.
+
+#### 2. Specifying the Model
+
+We use a linear regression model to predict resistance trends.
+
+``` r
+
+# Define the linear regression model
+lm_model <- linear_reg() %>%
+  set_engine("lm") # Use linear regression
+
+lm_model
+#> Linear Regression Model Specification (regression)
+#> 
+#> Computational engine: lm
+```
+
+**Explanation:**
+
+- `linear_reg()`: Defines a linear regression model.
+- `set_engine("lm")`: Uses R’s built-in linear regression engine.
+
+#### 3. Building the Workflow
+
+We combine the preprocessing recipe and model into a workflow.
+
+``` r
+
+# Create workflow
+resistance_workflow_time <- workflow() %>%
+  add_recipe(resistance_recipe_time) %>%
+  add_model(lm_model)
+
+resistance_workflow_time
+#> ══ Workflow ════════════════════════════════════════════════════════════════════
+#> Preprocessor: Recipe
+#> Model: linear_reg()
+#> 
+#> ── Preprocessor ────────────────────────────────────────────────────────────────
+#> 3 Recipe Steps
+#> 
+#> • step_dummy()
+#> • step_normalize()
+#> • step_nzv()
+#> 
+#> ── Model ───────────────────────────────────────────────────────────────────────
+#> Linear Regression Model Specification (regression)
+#> 
+#> Computational engine: lm
+```
+
+### **Training and Evaluating the Model**
+
+We split the data into training and testing sets, fit the model, and
+evaluate performance.
+
+``` r
+
+# Split the data
+set.seed(123)
+data_split_time <- initial_split(data_time, prop = 0.8)
+train_time <- training(data_split_time)
+test_time <- testing(data_split_time)
+
+# Train the model
+fitted_workflow_time <- resistance_workflow_time %>%
+  fit(train_time)
+
+# Make predictions
+predictions_time <- fitted_workflow_time %>%
+  predict(test_time) %>%
+  bind_cols(test_time)
+
+# Evaluate model
+metrics_time <- predictions_time %>%
+  metrics(truth = res_AMX, estimate = .pred)
+
+metrics_time
+#> # A tibble: 3 × 3
+#>   .metric .estimator .estimate
+#>   <chr>   <chr>          <dbl>
+#> 1 rmse    standard      0.0774
+#> 2 rsq     standard      0.711 
+#> 3 mae     standard      0.0704
+```
+
+**Explanation:**
+
+- `initial_split()`: Splits data into training and testing sets.
+- `fit()`: Trains the workflow.
+- [`predict()`](https://rdrr.io/r/stats/predict.html): Generates
+  resistance predictions.
+- `metrics()`: Evaluates model performance.
+
+### **Visualising Predictions**
+
+We plot resistance trends over time for amoxicillin.
+
+``` r
+
+library(ggplot2)
+
+# Plot actual vs predicted resistance over time
+ggplot(predictions_time, aes(x = year)) +
+  geom_point(aes(y = res_AMX, color = "Actual")) +
+  geom_line(aes(y = .pred, color = "Predicted")) +
+  labs(
+    title = "Predicted vs Actual AMX Resistance Over Time",
+    x = "Year",
+    y = "Resistance Proportion"
+  ) +
+  theme_minimal()
+```
+
+![](AMR_with_tidymodels_files/figure-html/unnamed-chunk-21-1.png)
+
+Additionally, we can visualise resistance trends in `ggplot2` and
+directly add linear models there:
+
+``` r
+
+ggplot(data_time, aes(x = year, y = res_AMX, color = gramstain)) +
+  geom_line() +
+  labs(
+    title = "AMX Resistance Trends",
+    x = "Year",
+    y = "Resistance Proportion"
+  ) +
+  # add a linear model directly in ggplot2:
+  geom_smooth(
+    method = "lm",
+    formula = y ~ x,
+    alpha = 0.25
+  ) +
+  theme_minimal()
+```
+
+![](AMR_with_tidymodels_files/figure-html/unnamed-chunk-22-1.png)
+
+### **Conclusion**
+
+In this example, we demonstrated how to analyze AMR trends over time
+using `tidymodels`. By aggregating resistance rates by year and hospital
+ward, we built a predictive model to track changes in resistance to
+amoxicillin (`AMX`), amoxicillin-clavulanic acid (`AMC`), and
+ciprofloxacin (`CIP`).
+
+This method can be extended to other antibiotics and resistance
+patterns, providing valuable insights into AMR dynamics in healthcare
+settings.
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+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="../logo.svg" class="logo" alt=""><h1>Apply EUCAST rules</h1>
+            
+      
+      <small class="dont-index">Source: <a href="https://github.com/msberends/AMR/blob/main/vignettes/EUCAST.Rmd" class="external-link"><code>vignettes/EUCAST.Rmd</code></a></small>
+      <div class="d-none name"><code>EUCAST.Rmd</code></div>
+    </div>
+
+    
+    
+<div class="section level2">
+<h2 id="introduction">Introduction<a class="anchor" aria-label="anchor" href="#introduction"></a>
+</h2>
+<p>What are EUCAST rules? The European Committee on Antimicrobial
+Susceptibility Testing (EUCAST) states <a href="https://www.eucast.org/expert_rules_and_expected_phenotypes" class="external-link">on
+their website</a>:</p>
+<blockquote>
+<p><em>EUCAST expert rules (see below) are a tabulated collection of
+expert knowledge on interpretive rules, expected resistant phenotypes
+and expected susceptible phenotypes which should be applied to
+antimicrobial susceptibility testing in order to reduce testing, reduce
+errors and make appropriate recommendations for reporting particular
+resistances.</em></p>
+</blockquote>
+<p>In Europe, a lot of medical microbiological laboratories already
+apply these rules (<a href="https://www.eurosurveillance.org/content/10.2807/1560-7917.ES2015.20.2.21008" class="external-link">Brown
+<em>et al.</em>, 2015</a>). Our package features their latest insights
+on expected resistant phenotypes (v1.2, 2023).</p>
+</div>
+<div class="section level2">
+<h2 id="examples">Examples<a class="anchor" aria-label="anchor" href="#examples"></a>
+</h2>
+<p>These rules can be used to discard improbable bug-drug combinations
+in your data. For example, <em>Klebsiella</em> produces beta-lactamase
+that prevents ampicillin (or amoxicillin) from working against it. In
+other words, practically every strain of <em>Klebsiella</em> is
+resistant to ampicillin.</p>
+<p>Sometimes, laboratory data can still contain such strains with
+<em>Klebsiella</em> being susceptible to ampicillin. This could be
+because an antibiogram is available before an identification is
+available, and the antibiogram is then not re-interpreted based on the
+identification. The <code><a href="../reference/interpretive_rules.html">eucast_rules()</a></code> function resolves this,
+by applying the latest ‘EUCAST Expected Resistant Phenotypes’
+guideline:</p>
+<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">oops</span> <span class="op">&lt;-</span> <span class="fu">tibble</span><span class="fu">::</span><span class="fu"><a href="https://tibble.tidyverse.org/reference/tibble.html" class="external-link">tibble</a></span><span class="op">(</span></span>
+<span>  mo <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>
+<span>    <span class="st">"Klebsiella pneumoniae"</span>,</span>
+<span>    <span class="st">"Escherichia coli"</span></span>
+<span>  <span class="op">)</span>,</span>
+<span>  ampicillin <span class="op">=</span> <span class="fu"><a href="../reference/as.sir.html">as.sir</a></span><span class="op">(</span><span class="st">"S"</span><span class="op">)</span></span>
+<span><span class="op">)</span></span>
+<span><span class="va">oops</span></span>
+<span><span class="co">#&gt; <span style="color: #949494;"># A tibble: 2 × 2</span></span></span>
+<span><span class="co">#&gt;   mo                    ampicillin</span></span>
+<span><span class="co">#&gt;   <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>                 <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span>     </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;">1</span> Klebsiella pneumoniae <span style="color: #080808; background-color: #5FD7AF;">  S  </span>     </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;">2</span> Escherichia coli      <span style="color: #080808; background-color: #5FD7AF;">  S  </span></span></span>
+<span></span>
+<span><span class="fu"><a href="../reference/interpretive_rules.html">eucast_rules</a></span><span class="op">(</span><span class="va">oops</span>, info <span class="op">=</span> <span class="cn">FALSE</span>, overwrite <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span>
+<span><span class="co">#&gt; <span style="color: #949494;"># A tibble: 2 × 2</span></span></span>
+<span><span class="co">#&gt;   mo                    ampicillin</span></span>
+<span><span class="co">#&gt;   <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>                 <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span>     </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;">1</span> Klebsiella pneumoniae <span style="color: #080808; background-color: #FF5F5F;">  R  </span>     </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;">2</span> Escherichia coli      <span style="color: #080808; background-color: #5FD7AF;">  S  </span></span></span></code></pre></div>
+<p>A more convenient function is
+<code><a href="../reference/mo_property.html">mo_is_intrinsic_resistant()</a></code> that uses the same guideline,
+but allows to check for one or more specific microorganisms or
+antimicrobials:</p>
+<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="../reference/mo_property.html">mo_is_intrinsic_resistant</a></span><span class="op">(</span></span>
+<span>  <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"Klebsiella pneumoniae"</span>, <span class="st">"Escherichia coli"</span><span class="op">)</span>,</span>
+<span>  <span class="st">"ampicillin"</span></span>
+<span><span class="op">)</span></span>
+<span><span class="co">#&gt; [1]  TRUE FALSE</span></span>
+<span></span>
+<span><span class="fu"><a href="../reference/mo_property.html">mo_is_intrinsic_resistant</a></span><span class="op">(</span></span>
+<span>  <span class="st">"Klebsiella pneumoniae"</span>,</span>
+<span>  <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"ampicillin"</span>, <span class="st">"kanamycin"</span><span class="op">)</span></span>
+<span><span class="op">)</span></span>
+<span><span class="co">#&gt; [1]  TRUE FALSE</span></span></code></pre></div>
+<p>EUCAST rules can not only be used for correction, they can also be
+used for filling in known resistance and susceptibility based on results
+of other antimicrobials drugs. This process is called <em>interpretive
+reading</em>, and is basically a form of imputation:</p>
+<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">data</span> <span class="op">&lt;-</span> <span class="fu">tibble</span><span class="fu">::</span><span class="fu"><a href="https://tibble.tidyverse.org/reference/tibble.html" class="external-link">tibble</a></span><span class="op">(</span></span>
+<span>  mo <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>
+<span>    <span class="st">"Staphylococcus aureus"</span>,</span>
+<span>    <span class="st">"Enterococcus faecalis"</span>,</span>
+<span>    <span class="st">"Escherichia coli"</span>,</span>
+<span>    <span class="st">"Klebsiella pneumoniae"</span>,</span>
+<span>    <span class="st">"Pseudomonas aeruginosa"</span></span>
+<span>  <span class="op">)</span>,</span>
+<span>  VAN <span class="op">=</span> <span class="st">"-"</span>, <span class="co"># Vancomycin</span></span>
+<span>  AMX <span class="op">=</span> <span class="st">"-"</span>, <span class="co"># Amoxicillin</span></span>
+<span>  COL <span class="op">=</span> <span class="st">"-"</span>, <span class="co"># Colistin</span></span>
+<span>  CAZ <span class="op">=</span> <span class="st">"-"</span>, <span class="co"># Ceftazidime</span></span>
+<span>  CXM <span class="op">=</span> <span class="st">"-"</span>, <span class="co"># Cefuroxime</span></span>
+<span>  PEN <span class="op">=</span> <span class="st">"S"</span>, <span class="co"># Benzylenicillin</span></span>
+<span>  FOX <span class="op">=</span> <span class="st">"S"</span> <span class="co"># Cefoxitin</span></span>
+<span><span class="op">)</span></span></code></pre></div>
+<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">data</span></span></code></pre></div>
+<table class="table">
+<thead><tr class="header">
+<th align="left">mo</th>
+<th align="center">VAN</th>
+<th align="center">AMX</th>
+<th align="center">COL</th>
+<th align="center">CAZ</th>
+<th align="center">CXM</th>
+<th align="center">PEN</th>
+<th align="center">FOX</th>
+</tr></thead>
+<tbody>
+<tr class="odd">
+<td align="left">Staphylococcus aureus</td>
+<td align="center">-</td>
+<td align="center">-</td>
+<td align="center">-</td>
+<td align="center">-</td>
+<td align="center">-</td>
+<td align="center">S</td>
+<td align="center">S</td>
+</tr>
+<tr class="even">
+<td align="left">Enterococcus faecalis</td>
+<td align="center">-</td>
+<td align="center">-</td>
+<td align="center">-</td>
+<td align="center">-</td>
+<td align="center">-</td>
+<td align="center">S</td>
+<td align="center">S</td>
+</tr>
+<tr class="odd">
+<td align="left">Escherichia coli</td>
+<td align="center">-</td>
+<td align="center">-</td>
+<td align="center">-</td>
+<td align="center">-</td>
+<td align="center">-</td>
+<td align="center">S</td>
+<td align="center">S</td>
+</tr>
+<tr class="even">
+<td align="left">Klebsiella pneumoniae</td>
+<td align="center">-</td>
+<td align="center">-</td>
+<td align="center">-</td>
+<td align="center">-</td>
+<td align="center">-</td>
+<td align="center">S</td>
+<td align="center">S</td>
+</tr>
+<tr class="odd">
+<td align="left">Pseudomonas aeruginosa</td>
+<td align="center">-</td>
+<td align="center">-</td>
+<td align="center">-</td>
+<td align="center">-</td>
+<td align="center">-</td>
+<td align="center">S</td>
+<td align="center">S</td>
+</tr>
+</tbody>
+</table>
+<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="../reference/interpretive_rules.html">eucast_rules</a></span><span class="op">(</span><span class="va">data</span>, overwrite <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div>
+<table class="table">
+<thead><tr class="header">
+<th align="left">mo</th>
+<th align="center">VAN</th>
+<th align="center">AMX</th>
+<th align="center">COL</th>
+<th align="center">CAZ</th>
+<th align="center">CXM</th>
+<th align="center">PEN</th>
+<th align="center">FOX</th>
+</tr></thead>
+<tbody>
+<tr class="odd">
+<td align="left">Staphylococcus aureus</td>
+<td align="center">-</td>
+<td align="center">S</td>
+<td align="center">R</td>
+<td align="center">R</td>
+<td align="center">S</td>
+<td align="center">S</td>
+<td align="center">S</td>
+</tr>
+<tr class="even">
+<td align="left">Enterococcus faecalis</td>
+<td align="center">-</td>
+<td align="center">-</td>
+<td align="center">R</td>
+<td align="center">R</td>
+<td align="center">R</td>
+<td align="center">S</td>
+<td align="center">R</td>
+</tr>
+<tr class="odd">
+<td align="left">Escherichia coli</td>
+<td align="center">R</td>
+<td align="center">-</td>
+<td align="center">-</td>
+<td align="center">-</td>
+<td align="center">-</td>
+<td align="center">R</td>
+<td align="center">S</td>
+</tr>
+<tr class="even">
+<td align="left">Klebsiella pneumoniae</td>
+<td align="center">R</td>
+<td align="center">R</td>
+<td align="center">-</td>
+<td align="center">-</td>
+<td align="center">-</td>
+<td align="center">R</td>
+<td align="center">S</td>
+</tr>
+<tr class="odd">
+<td align="left">Pseudomonas aeruginosa</td>
+<td align="center">R</td>
+<td align="center">R</td>
+<td align="center">-</td>
+<td align="center">-</td>
+<td align="center">R</td>
+<td align="center">R</td>
+<td align="center">R</td>
+</tr>
+</tbody>
+</table>
+</div>
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+  <p><code>AMR</code> (for R). Free and open-source, licenced under the <a target="_blank" href="https://github.com/msberends/AMR/blob/main/LICENSE" class="external-link">GNU GPL 2.0</a>. Developed at the <a target="_blank" href="https://www.rug.nl" class="external-link">University of Groningen</a> and <a target="_blank" href="https://www.umcg.nl" class="external-link">University Medical Center Groningen</a> in The Netherlands, in collaboration with <a href="https://amr-for-r.org/authors.html">many colleagues from around the world</a>.</p>
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+</html>
@@ -0,0 +1,131 @@
+# Apply EUCAST rules
+
+## Introduction
+
+What are EUCAST rules? The European Committee on Antimicrobial
+Susceptibility Testing (EUCAST) states [on their
+website](https://www.eucast.org/expert_rules_and_expected_phenotypes):
+
+> *EUCAST expert rules (see below) are a tabulated collection of expert
+> knowledge on interpretive rules, expected resistant phenotypes and
+> expected susceptible phenotypes which should be applied to
+> antimicrobial susceptibility testing in order to reduce testing,
+> reduce errors and make appropriate recommendations for reporting
+> particular resistances.*
+
+In Europe, a lot of medical microbiological laboratories already apply
+these rules ([Brown *et al.*,
+2015](https://www.eurosurveillance.org/content/10.2807/1560-7917.ES2015.20.2.21008)).
+Our package features their latest insights on expected resistant
+phenotypes (v1.2, 2023).
+
+## Examples
+
+These rules can be used to discard improbable bug-drug combinations in
+your data. For example, *Klebsiella* produces beta-lactamase that
+prevents ampicillin (or amoxicillin) from working against it. In other
+words, practically every strain of *Klebsiella* is resistant to
+ampicillin.
+
+Sometimes, laboratory data can still contain such strains with
+*Klebsiella* being susceptible to ampicillin. This could be because an
+antibiogram is available before an identification is available, and the
+antibiogram is then not re-interpreted based on the identification. The
+[`eucast_rules()`](https://amr-for-r.org/reference/interpretive_rules.md)
+function resolves this, by applying the latest ‘EUCAST Expected
+Resistant Phenotypes’ guideline:
+
+``` r
+
+oops <- tibble::tibble(
+  mo = c(
+    "Klebsiella pneumoniae",
+    "Escherichia coli"
+  ),
+  ampicillin = as.sir("S")
+)
+oops
+#> # A tibble: 2 × 2
+#>   mo                    ampicillin
+#>   <chr>                 <sir>     
+#> 1 Klebsiella pneumoniae   S       
+#> 2 Escherichia coli        S  
+
+eucast_rules(oops, info = FALSE, overwrite = TRUE)
+#> # A tibble: 2 × 2
+#>   mo                    ampicillin
+#>   <chr>                 <sir>     
+#> 1 Klebsiella pneumoniae   R       
+#> 2 Escherichia coli        S  
+```
+
+A more convenient function is
+[`mo_is_intrinsic_resistant()`](https://amr-for-r.org/reference/mo_property.md)
+that uses the same guideline, but allows to check for one or more
+specific microorganisms or antimicrobials:
+
+``` r
+
+mo_is_intrinsic_resistant(
+  c("Klebsiella pneumoniae", "Escherichia coli"),
+  "ampicillin"
+)
+#> [1]  TRUE FALSE
+
+mo_is_intrinsic_resistant(
+  "Klebsiella pneumoniae",
+  c("ampicillin", "kanamycin")
+)
+#> [1]  TRUE FALSE
+```
+
+EUCAST rules can not only be used for correction, they can also be used
+for filling in known resistance and susceptibility based on results of
+other antimicrobials drugs. This process is called *interpretive
+reading*, and is basically a form of imputation:
+
+``` r
+
+data <- tibble::tibble(
+  mo = c(
+    "Staphylococcus aureus",
+    "Enterococcus faecalis",
+    "Escherichia coli",
+    "Klebsiella pneumoniae",
+    "Pseudomonas aeruginosa"
+  ),
+  VAN = "-", # Vancomycin
+  AMX = "-", # Amoxicillin
+  COL = "-", # Colistin
+  CAZ = "-", # Ceftazidime
+  CXM = "-", # Cefuroxime
+  PEN = "S", # Benzylenicillin
+  FOX = "S" # Cefoxitin
+)
+```
+
+``` r
+
+data
+```
+
+| mo                     | VAN | AMX | COL | CAZ | CXM | PEN | FOX |
+|:-----------------------|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
+| Staphylococcus aureus  | \-  | \-  | \-  | \-  | \-  |  S  |  S  |
+| Enterococcus faecalis  | \-  | \-  | \-  | \-  | \-  |  S  |  S  |
+| Escherichia coli       | \-  | \-  | \-  | \-  | \-  |  S  |  S  |
+| Klebsiella pneumoniae  | \-  | \-  | \-  | \-  | \-  |  S  |  S  |
+| Pseudomonas aeruginosa | \-  | \-  | \-  | \-  | \-  |  S  |  S  |
+
+``` r
+
+eucast_rules(data, overwrite = TRUE)
+```
+
+| mo                     | VAN | AMX | COL | CAZ | CXM | PEN | FOX |
+|:-----------------------|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
+| Staphylococcus aureus  | \-  |  S  |  R  |  R  |  S  |  S  |  S  |
+| Enterococcus faecalis  | \-  | \-  |  R  |  R  |  R  |  S  |  R  |
+| Escherichia coli       |  R  | \-  | \-  | \-  | \-  |  R  |  S  |
+| Klebsiella pneumoniae  |  R  |  R  | \-  | \-  | \-  |  R  |  S  |
+| Pseudomonas aeruginosa |  R  |  R  | \-  | \-  |  R  |  R  |  R  |
@@ -0,0 +1,254 @@
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+    <li><a class="dropdown-item" href="../articles/PCA.html"><span class="fa fa-compress"></span> Conduct Principal Component Analysis for AMR</a></li>
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+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="../logo.svg" class="logo" alt=""><h1>Conduct principal component analysis (PCA) for AMR</h1>
+            
+      
+      <small class="dont-index">Source: <a href="https://github.com/msberends/AMR/blob/main/vignettes/PCA.Rmd" class="external-link"><code>vignettes/PCA.Rmd</code></a></small>
+      <div class="d-none name"><code>PCA.Rmd</code></div>
+    </div>
+
+    
+    
+<p><strong>NOTE: This page will be updated soon, as the pca() function
+is currently being developed.</strong></p>
+<div class="section level2">
+<h2 id="introduction">Introduction<a class="anchor" aria-label="anchor" href="#introduction"></a>
+</h2>
+</div>
+<div class="section level2">
+<h2 id="transforming">Transforming<a class="anchor" aria-label="anchor" href="#transforming"></a>
+</h2>
+<p>For PCA, we need to transform our AMR data first. This is what the
+<code>example_isolates</code> data set in this package looks like:</p>
+<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://amr-for-r.org">AMR</a></span><span class="op">)</span></span>
+<span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://dplyr.tidyverse.org" class="external-link">dplyr</a></span><span class="op">)</span></span>
+<span><span class="fu"><a href="https://pillar.r-lib.org/reference/glimpse.html" class="external-link">glimpse</a></span><span class="op">(</span><span class="va">example_isolates</span><span class="op">)</span></span>
+<span><span class="co">#&gt; Rows: 2,000</span></span>
+<span><span class="co">#&gt; Columns: 46</span></span>
+<span><span class="co">#&gt; $ date    <span style="color: #949494; font-style: italic;">&lt;date&gt;</span> 2002-01-02<span style="color: #949494;">, </span>2002-01-03<span style="color: #949494;">, </span>2002-01-07<span style="color: #949494;">, </span>2002-01-07<span style="color: #949494;">, </span>2002-01-13<span style="color: #949494;">, </span>2…</span></span>
+<span><span class="co">#&gt; $ patient <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span> "A77334"<span style="color: #949494;">, </span>"A77334"<span style="color: #949494;">, </span>"067927"<span style="color: #949494;">, </span>"067927"<span style="color: #949494;">, </span>"067927"<span style="color: #949494;">, </span>"067927"<span style="color: #949494;">, </span>"4…</span></span>
+<span><span class="co">#&gt; $ age     <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> 65<span style="color: #949494;">, </span>65<span style="color: #949494;">, </span>45<span style="color: #949494;">, </span>45<span style="color: #949494;">, </span>45<span style="color: #949494;">, </span>45<span style="color: #949494;">, </span>78<span style="color: #949494;">, </span>78<span style="color: #949494;">, </span>45<span style="color: #949494;">, </span>79<span style="color: #949494;">, </span>67<span style="color: #949494;">, </span>67<span style="color: #949494;">, </span>71<span style="color: #949494;">, </span>71<span style="color: #949494;">, </span>75<span style="color: #949494;">, </span>50…</span></span>
+<span><span class="co">#&gt; $ gender  <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span> "F"<span style="color: #949494;">, </span>"F"<span style="color: #949494;">, </span>"F"<span style="color: #949494;">, </span>"F"<span style="color: #949494;">, </span>"F"<span style="color: #949494;">, </span>"F"<span style="color: #949494;">, </span>"M"<span style="color: #949494;">, </span>"M"<span style="color: #949494;">, </span>"F"<span style="color: #949494;">, </span>"F"<span style="color: #949494;">, </span>"M"<span style="color: #949494;">, </span>"M"<span style="color: #949494;">, </span>"M…</span></span>
+<span><span class="co">#&gt; $ ward    <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span> "Clinical"<span style="color: #949494;">, </span>"Clinical"<span style="color: #949494;">, </span>"ICU"<span style="color: #949494;">, </span>"ICU"<span style="color: #949494;">, </span>"ICU"<span style="color: #949494;">, </span>"ICU"<span style="color: #949494;">, </span>"Clinical"…</span></span>
+<span><span class="co">#&gt; $ mo      <span style="color: #949494; font-style: italic;">&lt;mo&gt;</span> "B_ESCHR_COLI"<span style="color: #949494;">, </span>"B_ESCHR_COLI"<span style="color: #949494;">, </span>"B_STPHY_EPDR"<span style="color: #949494;">, </span>"B_STPHY_EPDR"<span style="color: #949494;">,</span>…</span></span>
+<span><span class="co">#&gt; $ PEN     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">,</span>…</span></span>
+<span><span class="co">#&gt; $ OXA     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span>…</span></span>
+<span><span class="co">#&gt; $ FLC     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R…</span></span>
+<span><span class="co">#&gt; $ AMX     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>R<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">N</span>…</span></span>
+<span><span class="co">#&gt; $ AMC     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> I<span style="color: #949494;">, </span>I<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>I<span style="color: #949494;">, </span>I<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>I<span style="color: #949494;">, </span>I<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">N</span>…</span></span>
+<span><span class="co">#&gt; $ AMP     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>R<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">N</span>…</span></span>
+<span><span class="co">#&gt; $ TZP     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span>…</span></span>
+<span><span class="co">#&gt; $ CZO     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>R<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">,</span>…</span></span>
+<span><span class="co">#&gt; $ FEP     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span>…</span></span>
+<span><span class="co">#&gt; $ CXM     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> I<span style="color: #949494;">, </span>I<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S…</span></span>
+<span><span class="co">#&gt; $ FOX     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>R<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">,</span>…</span></span>
+<span><span class="co">#&gt; $ CTX     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S…</span></span>
+<span><span class="co">#&gt; $ CAZ     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>…</span></span>
+<span><span class="co">#&gt; $ CRO     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S…</span></span>
+<span><span class="co">#&gt; $ GEN     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span>…</span></span>
+<span><span class="co">#&gt; $ TOB     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span>…</span></span>
+<span><span class="co">#&gt; $ AMK     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span>…</span></span>
+<span><span class="co">#&gt; $ KAN     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span>…</span></span>
+<span><span class="co">#&gt; $ TMP     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>…</span></span>
+<span><span class="co">#&gt; $ SXT     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">N</span>…</span></span>
+<span><span class="co">#&gt; $ NIT     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>R<span style="color: #949494;">,</span>…</span></span>
+<span><span class="co">#&gt; $ FOS     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span>…</span></span>
+<span><span class="co">#&gt; $ LNZ     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span><span style="color: #BB0000;">N</span>…</span></span>
+<span><span class="co">#&gt; $ CIP     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S…</span></span>
+<span><span class="co">#&gt; $ MFX     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span>…</span></span>
+<span><span class="co">#&gt; $ VAN     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>…</span></span>
+<span><span class="co">#&gt; $ TEC     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span><span style="color: #BB0000;">N</span>…</span></span>
+<span><span class="co">#&gt; $ TCY     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>I<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>I<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>I<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>…</span></span>
+<span><span class="co">#&gt; $ TGC     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span>…</span></span>
+<span><span class="co">#&gt; $ DOX     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span>…</span></span>
+<span><span class="co">#&gt; $ ERY     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">,</span>…</span></span>
+<span><span class="co">#&gt; $ CLI     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>R<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span>…</span></span>
+<span><span class="co">#&gt; $ AZM     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">,</span>…</span></span>
+<span><span class="co">#&gt; $ IPM     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S…</span></span>
+<span><span class="co">#&gt; $ MEM     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span>…</span></span>
+<span><span class="co">#&gt; $ MTR     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span>…</span></span>
+<span><span class="co">#&gt; $ CHL     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span>…</span></span>
+<span><span class="co">#&gt; $ COL     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>…</span></span>
+<span><span class="co">#&gt; $ MUP     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span>…</span></span>
+<span><span class="co">#&gt; $ RIF     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span><span style="color: #BB0000;">N</span>…</span></span></code></pre></div>
+<p>Now to transform this to a data set with only resistance percentages
+per taxonomic order and genus:</p>
+<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">resistance_data</span> <span class="op">&lt;-</span> <span class="va">example_isolates</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
+<span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/group_by.html" class="external-link">group_by</a></span><span class="op">(</span></span>
+<span>    order <span class="op">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_order</a></span><span class="op">(</span><span class="va">mo</span><span class="op">)</span>, <span class="co"># group on anything, like order</span></span>
+<span>    genus <span class="op">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_genus</a></span><span class="op">(</span><span class="va">mo</span><span class="op">)</span></span>
+<span>  <span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span> <span class="co">#  and genus as we do here</span></span>
+<span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/summarise_all.html" class="external-link">summarise_if</a></span><span class="op">(</span><span class="va">is.sir</span>, <span class="va">resistance</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span> <span class="co"># then get resistance of all drugs</span></span>
+<span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/select.html" class="external-link">select</a></span><span class="op">(</span></span>
+<span>    <span class="va">order</span>, <span class="va">genus</span>, <span class="va">AMC</span>, <span class="va">CXM</span>, <span class="va">CTX</span>,</span>
+<span>    <span class="va">CAZ</span>, <span class="va">GEN</span>, <span class="va">TOB</span>, <span class="va">TMP</span>, <span class="va">SXT</span></span>
+<span>  <span class="op">)</span> <span class="co"># and select only relevant columns</span></span>
+<span><span class="co">#&gt; <span style="color: #00BBBB;">ℹ</span> `resistance()` assumes the EUCAST guideline and thus considers the 'I'</span></span>
+<span><span class="co">#&gt;   category susceptible. Set the `guideline` argument or the `AMR_guideline`</span></span>
+<span><span class="co">#&gt;   option to either "CLSI" or "EUCAST", see `?AMR-options`.</span></span>
+<span><span class="co">#&gt; <span style="color: #00BBBB;">ℹ</span> This message will be shown once per session.</span></span>
+<span></span>
+<span><span class="fu"><a href="https://rdrr.io/r/utils/head.html" class="external-link">head</a></span><span class="op">(</span><span class="va">resistance_data</span><span class="op">)</span></span>
+<span><span class="co">#&gt; <span style="color: #949494;"># A tibble: 6 × 10</span></span></span>
+<span><span class="co">#&gt; <span style="color: #949494;"># Groups:   order [5]</span></span></span>
+<span><span class="co">#&gt;   order             genus          AMC   CXM   CTX   CAZ   GEN   TOB   TMP   SXT</span></span>
+<span><span class="co">#&gt;   <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;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span></span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;">1</span> (unknown order)   (unknown ge…    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span></span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;">2</span> Actinomycetales   Schaalia        <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span></span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;">3</span> Bacteroidales     Bacteroides     <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span></span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;">4</span> Campylobacterales Campylobact…    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span></span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;">5</span> Caryophanales     Gemella         <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span></span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;">6</span> Caryophanales     Listeria        <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span></span></span></code></pre></div>
+</div>
+<div class="section level2">
+<h2 id="perform-principal-component-analysis">Perform principal component analysis<a class="anchor" aria-label="anchor" href="#perform-principal-component-analysis"></a>
+</h2>
+<p>The new <code><a href="../reference/pca.html">pca()</a></code> function will automatically filter on rows
+that contain numeric values in all selected variables, so we now only
+need to do:</p>
+<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">pca_result</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/pca.html">pca</a></span><span class="op">(</span><span class="va">resistance_data</span><span class="op">)</span></span>
+<span><span class="co">#&gt; <span style="color: #00BBBB;">ℹ</span> Columns selected for PCA: <span style="color: #0000BB;">"\033[1mAMC\033[22m"</span>, <span style="color: #0000BB;">"\033[1mCAZ\033[22m"</span>,</span></span>
+<span><span class="co">#&gt;   <span style="color: #0000BB;">"\033[1mCTX\033[22m"</span>, <span style="color: #0000BB;">"\033[1mCXM\033[22m"</span>, <span style="color: #0000BB;">"\033[1mGEN\033[22m"</span>,</span></span>
+<span><span class="co">#&gt;   <span style="color: #0000BB;">"\033[1mSXT\033[22m"</span>, <span style="color: #0000BB;">"\033[1mTMP\033[22m"</span>, and <span style="color: #0000BB;">"\033[1mTOB\033[22m"</span>. Total</span></span>
+<span><span class="co">#&gt;   observations available: 7.</span></span></code></pre></div>
+<p>The result can be reviewed with the good old <code><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary()</a></code>
+function:</p>
+<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">pca_result</span><span class="op">)</span></span>
+<span><span class="co">#&gt; Groups (n=4, named as 'order'):</span></span>
+<span><span class="co">#&gt; [1] "Caryophanales"    "Enterobacterales" "Lactobacillales"  "Pseudomonadales"</span></span>
+<span><span class="co">#&gt; Importance of components:</span></span>
+<span><span class="co">#&gt;                           PC1    PC2    PC3     PC4     PC5     PC6       PC7</span></span>
+<span><span class="co">#&gt; Standard deviation     2.1539 1.6807 0.6138 0.33879 0.20808 0.03140 1.232e-16</span></span>
+<span><span class="co">#&gt; Proportion of Variance 0.5799 0.3531 0.0471 0.01435 0.00541 0.00012 0.000e+00</span></span>
+<span><span class="co">#&gt; Cumulative Proportion  0.5799 0.9330 0.9801 0.99446 0.99988 1.00000 1.000e+00</span></span></code></pre></div>
+<pre><code><span><span class="co">#&gt; Groups (n=4, named as 'order'):</span></span>
+<span><span class="co">#&gt; [1] "Caryophanales"    "Enterobacterales" "Lactobacillales"  "Pseudomonadales"</span></span></code></pre>
+<p>Good news. The first two components explain a total of 93.3% of the
+variance (see the PC1 and PC2 values of the <em>Proportion of
+Variance</em>. We can create a so-called biplot with the base R
+<code><a href="https://rdrr.io/r/stats/biplot.html" class="external-link">biplot()</a></code> function, to see which antimicrobial resistance
+per drug explain the difference per microorganism.</p>
+</div>
+<div class="section level2">
+<h2 id="plotting-the-results">Plotting the results<a class="anchor" aria-label="anchor" href="#plotting-the-results"></a>
+</h2>
+<div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/stats/biplot.html" class="external-link">biplot</a></span><span class="op">(</span><span class="va">pca_result</span><span class="op">)</span></span></code></pre></div>
+<p><img src="PCA_files/figure-html/unnamed-chunk-5-1.png" class="r-plt" alt="" width="750"></p>
+<p>But we can’t see the explanation of the points. Perhaps this works
+better with our new <code><a href="../reference/ggplot_pca.html">ggplot_pca()</a></code> function, that
+automatically adds the right labels and even groups:</p>
+<div class="sourceCode" id="cb7"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="../reference/ggplot_pca.html">ggplot_pca</a></span><span class="op">(</span><span class="va">pca_result</span><span class="op">)</span></span></code></pre></div>
+<p><img src="PCA_files/figure-html/unnamed-chunk-6-1.png" class="r-plt" alt="" width="750"></p>
+<p>You can also print an ellipse per group, and edit the appearance:</p>
+<div class="sourceCode" id="cb8"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="../reference/ggplot_pca.html">ggplot_pca</a></span><span class="op">(</span><span class="va">pca_result</span>, ellipse <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">ggplot2</span><span class="fu">::</span><span class="fu"><a href="https://ggplot2.tidyverse.org/reference/labs.html" class="external-link">labs</a></span><span class="op">(</span>title <span class="op">=</span> <span class="st">"An AMR/PCA biplot!"</span><span class="op">)</span></span></code></pre></div>
+<p><img src="PCA_files/figure-html/unnamed-chunk-7-1.png" class="r-plt" alt="" width="750"></p>
+</div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside>
+</div>
+
+
+
+    <footer><div class="pkgdown-footer-left">
+  <p><code>AMR</code> (for R). Free and open-source, licenced under the <a target="_blank" href="https://github.com/msberends/AMR/blob/main/LICENSE" class="external-link">GNU GPL 2.0</a>. Developed at the <a target="_blank" href="https://www.rug.nl" class="external-link">University of Groningen</a> and <a target="_blank" href="https://www.umcg.nl" class="external-link">University Medical Center Groningen</a> in The Netherlands, in collaboration with <a href="https://amr-for-r.org/authors.html">many colleagues from around the world</a>.</p>
+</div>
+
+<div class="pkgdown-footer-right">
+  <p><a target="_blank" href="https://www.rug.nl" class="external-link"><img src="https://amr-for-r.org/logo_rug.svg" style="max-width: 150px;"></a><a target="_blank" href="https://www.umcg.nl" class="external-link"><img src="https://amr-for-r.org/logo_umcg.svg" style="max-width: 150px;"></a></p>
+</div>
+
+    </footer>
+</div>
+
+
+
+
+
+  </body>
+</html>
@@ -0,0 +1,170 @@
+# Conduct principal component analysis (PCA) for AMR
+
+**NOTE: This page will be updated soon, as the pca() function is
+currently being developed.**
+
+## Introduction
+
+## Transforming
+
+For PCA, we need to transform our AMR data first. This is what the
+`example_isolates` data set in this package looks like:
+
+``` r
+
+library(AMR)
+library(dplyr)
+glimpse(example_isolates)
+#> Rows: 2,000
+#> Columns: 46
+#> $ date    <date> 2002-01-02, 2002-01-03, 2002-01-07, 2002-01-07, 2002-01-13, 2…
+#> $ patient <chr> "A77334", "A77334", "067927", "067927", "067927", "067927", "4…
+#> $ age     <dbl> 65, 65, 45, 45, 45, 45, 78, 78, 45, 79, 67, 67, 71, 71, 75, 50…
+#> $ gender  <chr> "F", "F", "F", "F", "F", "F", "M", "M", "F", "F", "M", "M", "M…
+#> $ ward    <chr> "Clinical", "Clinical", "ICU", "ICU", "ICU", "ICU", "Clinical"…
+#> $ mo      <mo> "B_ESCHR_COLI", "B_ESCHR_COLI", "B_STPHY_EPDR", "B_STPHY_EPDR",…
+#> $ PEN     <sir> R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, S,…
+#> $ OXA     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
+#> $ FLC     <sir> NA, NA, R, R, R, R, S, S, R, S, S, S, NA, NA, NA, NA, NA, R, R…
+#> $ AMX     <sir> NA, NA, NA, NA, NA, NA, R, R, NA, NA, NA, NA, NA, NA, R, NA, N…
+#> $ AMC     <sir> I, I, NA, NA, NA, NA, S, S, NA, NA, S, S, I, I, R, I, I, NA, N…
+#> $ AMP     <sir> NA, NA, NA, NA, NA, NA, R, R, NA, NA, NA, NA, NA, NA, R, NA, N…
+#> $ TZP     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
+#> $ CZO     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, NA,…
+#> $ FEP     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
+#> $ CXM     <sir> I, I, R, R, R, R, S, S, R, S, S, S, S, S, NA, S, S, R, R, S, S…
+#> $ FOX     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, NA,…
+#> $ CTX     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, S, S, NA, S, S…
+#> $ CAZ     <sir> NA, NA, R, R, R, R, R, R, R, R, R, R, NA, NA, NA, S, S, R, R, …
+#> $ CRO     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, S, S, NA, S, S…
+#> $ GEN     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
+#> $ TOB     <sir> NA, NA, NA, NA, NA, NA, S, S, NA, NA, NA, NA, S, S, NA, NA, NA…
+#> $ AMK     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
+#> $ KAN     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
+#> $ TMP     <sir> R, R, S, S, R, R, R, R, S, S, NA, NA, S, S, S, S, S, R, R, R, …
+#> $ SXT     <sir> R, R, S, S, NA, NA, NA, NA, S, S, NA, NA, S, S, S, S, S, NA, N…
+#> $ NIT     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R,…
+#> $ FOS     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
+#> $ LNZ     <sir> R, R, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, R, R, R, R, N…
+#> $ CIP     <sir> NA, NA, NA, NA, NA, NA, NA, NA, S, S, NA, NA, NA, NA, NA, S, S…
+#> $ MFX     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
+#> $ VAN     <sir> R, R, S, S, S, S, S, S, S, S, NA, NA, R, R, R, R, R, S, S, S, …
+#> $ TEC     <sir> R, R, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, R, R, R, R, N…
+#> $ TCY     <sir> R, R, S, S, S, S, S, S, S, I, S, S, NA, NA, I, R, R, S, I, R, …
+#> $ TGC     <sir> NA, NA, S, S, S, S, S, S, S, NA, S, S, NA, NA, NA, R, R, S, NA…
+#> $ DOX     <sir> NA, NA, S, S, S, S, S, S, S, NA, S, S, NA, NA, NA, R, R, S, NA…
+#> $ ERY     <sir> R, R, R, R, R, R, S, S, R, S, S, S, R, R, R, R, R, R, R, R, S,…
+#> $ CLI     <sir> R, R, NA, NA, NA, R, NA, NA, NA, NA, NA, NA, R, R, R, R, R, NA…
+#> $ AZM     <sir> R, R, R, R, R, R, S, S, R, S, S, S, R, R, R, R, R, R, R, R, S,…
+#> $ IPM     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, S, S, NA, S, S…
+#> $ MEM     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
+#> $ MTR     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
+#> $ CHL     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
+#> $ COL     <sir> NA, NA, R, R, R, R, R, R, R, R, R, R, NA, NA, NA, R, R, R, R, …
+#> $ MUP     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
+#> $ RIF     <sir> R, R, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, R, R, R, R, N…
+```
+
+Now to transform this to a data set with only resistance percentages per
+taxonomic order and genus:
+
+``` r
+
+resistance_data <- example_isolates %>%
+  group_by(
+    order = mo_order(mo), # group on anything, like order
+    genus = mo_genus(mo)
+  ) %>% #  and genus as we do here
+  summarise_if(is.sir, resistance) %>% # then get resistance of all drugs
+  select(
+    order, genus, AMC, CXM, CTX,
+    CAZ, GEN, TOB, TMP, SXT
+  ) # and select only relevant columns
+#> ℹ `resistance()` assumes the EUCAST guideline and thus considers the 'I'
+#>   category susceptible. Set the `guideline` argument or the `AMR_guideline`
+#>   option to either "CLSI" or "EUCAST", see `?AMR-options`.
+#> ℹ This message will be shown once per session.
+
+head(resistance_data)
+#> # A tibble: 6 × 10
+#> # Groups:   order [5]
+#>   order             genus          AMC   CXM   CTX   CAZ   GEN   TOB   TMP   SXT
+#>   <chr>             <chr>        <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
+#> 1 (unknown order)   (unknown ge…    NA    NA    NA    NA    NA    NA    NA    NA
+#> 2 Actinomycetales   Schaalia        NA    NA    NA    NA    NA    NA    NA    NA
+#> 3 Bacteroidales     Bacteroides     NA    NA    NA    NA    NA    NA    NA    NA
+#> 4 Campylobacterales Campylobact…    NA    NA    NA    NA    NA    NA    NA    NA
+#> 5 Caryophanales     Gemella         NA    NA    NA    NA    NA    NA    NA    NA
+#> 6 Caryophanales     Listeria        NA    NA    NA    NA    NA    NA    NA    NA
+```
+
+## Perform principal component analysis
+
+The new [`pca()`](https://amr-for-r.org/reference/pca.md) function will
+automatically filter on rows that contain numeric values in all selected
+variables, so we now only need to do:
+
+``` r
+
+pca_result <- pca(resistance_data)
+#> ℹ Columns selected for PCA: "\033[1mAMC\033[22m", "\033[1mCAZ\033[22m",
+#>   "\033[1mCTX\033[22m", "\033[1mCXM\033[22m", "\033[1mGEN\033[22m",
+#>   "\033[1mSXT\033[22m", "\033[1mTMP\033[22m", and "\033[1mTOB\033[22m". Total
+#>   observations available: 7.
+```
+
+The result can be reviewed with the good old
+[`summary()`](https://rdrr.io/r/base/summary.html) function:
+
+``` r
+
+summary(pca_result)
+#> Groups (n=4, named as 'order'):
+#> [1] "Caryophanales"    "Enterobacterales" "Lactobacillales"  "Pseudomonadales"
+#> Importance of components:
+#>                           PC1    PC2    PC3     PC4     PC5     PC6       PC7
+#> Standard deviation     2.1539 1.6807 0.6138 0.33879 0.20808 0.03140 1.232e-16
+#> Proportion of Variance 0.5799 0.3531 0.0471 0.01435 0.00541 0.00012 0.000e+00
+#> Cumulative Proportion  0.5799 0.9330 0.9801 0.99446 0.99988 1.00000 1.000e+00
+```
+
+    #> Groups (n=4, named as 'order'):
+    #> [1] "Caryophanales"    "Enterobacterales" "Lactobacillales"  "Pseudomonadales"
+
+Good news. The first two components explain a total of 93.3% of the
+variance (see the PC1 and PC2 values of the *Proportion of Variance*. We
+can create a so-called biplot with the base R
+[`biplot()`](https://rdrr.io/r/stats/biplot.html) function, to see which
+antimicrobial resistance per drug explain the difference per
+microorganism.
+
+## Plotting the results
+
+``` r
+
+biplot(pca_result)
+```
+
+![](PCA_files/figure-html/unnamed-chunk-5-1.png)
+
+But we can’t see the explanation of the points. Perhaps this works
+better with our new
+[`ggplot_pca()`](https://amr-for-r.org/reference/ggplot_pca.md)
+function, that automatically adds the right labels and even groups:
+
+``` r
+
+ggplot_pca(pca_result)
+```
+
+![](PCA_files/figure-html/unnamed-chunk-6-1.png)
+
+You can also print an ellipse per group, and edit the appearance:
+
+``` r
+
+ggplot_pca(pca_result, ellipse = TRUE) +
+  ggplot2::labs(title = "An AMR/PCA biplot!")
+```
+
+![](PCA_files/figure-html/unnamed-chunk-7-1.png)
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+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="../logo.svg" class="logo" alt=""><h1>Work with WHONET data</h1>
+            
+      
+      <small class="dont-index">Source: <a href="https://github.com/msberends/AMR/blob/main/vignettes/WHONET.Rmd" class="external-link"><code>vignettes/WHONET.Rmd</code></a></small>
+      <div class="d-none name"><code>WHONET.Rmd</code></div>
+    </div>
+
+    
+    
+<div class="section level3">
+<h3 id="import-of-data">Import of data<a class="anchor" aria-label="anchor" href="#import-of-data"></a>
+</h3>
+<p>This tutorial assumes you already imported the WHONET data with
+e.g. the <a href="https://readxl.tidyverse.org/" class="external-link"><code>readxl</code>
+package</a>. In RStudio, this can be done using the menu button ‘Import
+Dataset’ in the tab ‘Environment’. Choose the option ‘From Excel’ and
+select your exported file. Make sure date fields are imported
+correctly.</p>
+<p>An example syntax could look like this:</p>
+<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://readxl.tidyverse.org" class="external-link">readxl</a></span><span class="op">)</span></span>
+<span><span class="va">data</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://readxl.tidyverse.org/reference/read_excel.html" class="external-link">read_excel</a></span><span class="op">(</span>path <span class="op">=</span> <span class="st">"path/to/your/file.xlsx"</span><span class="op">)</span></span></code></pre></div>
+<p>This package comes with an <a href="https://amr-for-r.org/reference/WHONET.html">example data set
+<code>WHONET</code></a>. We will use it for this analysis.</p>
+</div>
+<div class="section level3">
+<h3 id="preparation">Preparation<a class="anchor" aria-label="anchor" href="#preparation"></a>
+</h3>
+<p>First, load the relevant packages if you did not yet did this. I use
+the tidyverse for all of my analyses. All of them. If you don’t know it
+yet, I suggest you read about it on their website: <a href="https://www.tidyverse.org/" class="external-link uri">https://www.tidyverse.org/</a>.</p>
+<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://dplyr.tidyverse.org" class="external-link">dplyr</a></span><span class="op">)</span> <span class="co"># part of tidyverse</span></span>
+<span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://ggplot2.tidyverse.org" class="external-link">ggplot2</a></span><span class="op">)</span> <span class="co"># part of tidyverse</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://amr-for-r.org">AMR</a></span><span class="op">)</span> <span class="co"># this package</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/cleaner/" class="external-link">cleaner</a></span><span class="op">)</span> <span class="co"># to create frequency tables</span></span></code></pre></div>
+<p>We will have to transform some variables to simplify and automate the
+analysis:</p>
+<ul>
+<li>Microorganisms should be transformed to our own microorganism codes
+(called an <code>mo</code>) using <a href="https://amr-for-r.org/reference/catalogue_of_life">our Catalogue
+of Life reference data set</a>, which contains all ~70,000
+microorganisms from the taxonomic kingdoms Bacteria, Fungi and Protozoa.
+We do the tranformation with <code><a href="../reference/as.mo.html">as.mo()</a></code>. This function also
+recognises almost all WHONET abbreviations of microorganisms.</li>
+<li>Antimicrobial results or interpretations have to be clean and valid.
+In other words, they should only contain values <code>"S"</code>,
+<code>"I"</code> or <code>"R"</code>. That is exactly where the
+<code><a href="../reference/as.sir.html">as.sir()</a></code> function is for.</li>
+</ul>
+<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="co"># transform variables</span></span>
+<span><span class="va">data</span> <span class="op">&lt;-</span> <span class="va">WHONET</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
+<span>  <span class="co"># get microbial ID based on given organism</span></span>
+<span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/mutate.html" class="external-link">mutate</a></span><span class="op">(</span>mo <span class="op">=</span> <span class="fu"><a href="../reference/as.mo.html">as.mo</a></span><span class="op">(</span><span class="va">Organism</span><span class="op">)</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
+<span>  <span class="co"># transform everything from "AMP_ND10" to "CIP_EE" to the new `sir` class</span></span>
+<span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/mutate_all.html" class="external-link">mutate_at</a></span><span class="op">(</span><span class="fu"><a href="https://dplyr.tidyverse.org/reference/vars.html" class="external-link">vars</a></span><span class="op">(</span><span class="va">AMP_ND10</span><span class="op">:</span><span class="va">CIP_EE</span><span class="op">)</span>, <span class="va">as.sir</span><span class="op">)</span></span></code></pre></div>
+<p>No errors or warnings, so all values are transformed succesfully.</p>
+<p>We also created a package dedicated to data cleaning and checking,
+called the <code>cleaner</code> package. Its <code><a href="https://msberends.github.io/cleaner/reference/freq.html" class="external-link">freq()</a></code>
+function can be used to create frequency tables.</p>
+<p>So let’s check our data, with a couple of frequency tables:</p>
+<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="co"># our newly created `mo` variable, put in the mo_name() function</span></span>
+<span><span class="va">data</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span> <span class="fu"><a href="https://msberends.github.io/cleaner/reference/freq.html" class="external-link">freq</a></span><span class="op">(</span><span class="fu"><a href="../reference/mo_property.html">mo_name</a></span><span class="op">(</span><span class="va">mo</span><span class="op">)</span>, nmax <span class="op">=</span> <span class="fl">10</span><span class="op">)</span></span></code></pre></div>
+<p><strong>Frequency table</strong></p>
+<p>Class: character<br>
+Length: 500<br>
+Available: 500 (100%, NA: 0 = 0%)<br>
+Unique: 38</p>
+<p>Shortest: 11<br>
+Longest: 40</p>
+<table class="table">
+<colgroup>
+<col width="4%">
+<col width="47%">
+<col width="7%">
+<col width="10%">
+<col width="13%">
+<col width="15%">
+</colgroup>
+<thead><tr class="header">
+<th align="left"></th>
+<th align="left">Item</th>
+<th align="right">Count</th>
+<th align="right">Percent</th>
+<th align="right">Cum. Count</th>
+<th align="right">Cum. Percent</th>
+</tr></thead>
+<tbody>
+<tr class="odd">
+<td align="left">1</td>
+<td align="left">Escherichia coli</td>
+<td align="right">245</td>
+<td align="right">49.0%</td>
+<td align="right">245</td>
+<td align="right">49.0%</td>
+</tr>
+<tr class="even">
+<td align="left">2</td>
+<td align="left">Coagulase-negative Staphylococcus (CoNS)</td>
+<td align="right">74</td>
+<td align="right">14.8%</td>
+<td align="right">319</td>
+<td align="right">63.8%</td>
+</tr>
+<tr class="odd">
+<td align="left">3</td>
+<td align="left">Staphylococcus epidermidis</td>
+<td align="right">38</td>
+<td align="right">7.6%</td>
+<td align="right">357</td>
+<td align="right">71.4%</td>
+</tr>
+<tr class="even">
+<td align="left">4</td>
+<td align="left">Streptococcus pneumoniae</td>
+<td align="right">31</td>
+<td align="right">6.2%</td>
+<td align="right">388</td>
+<td align="right">77.6%</td>
+</tr>
+<tr class="odd">
+<td align="left">5</td>
+<td align="left">Staphylococcus hominis</td>
+<td align="right">21</td>
+<td align="right">4.2%</td>
+<td align="right">409</td>
+<td align="right">81.8%</td>
+</tr>
+<tr class="even">
+<td align="left">6</td>
+<td align="left">Proteus mirabilis</td>
+<td align="right">9</td>
+<td align="right">1.8%</td>
+<td align="right">418</td>
+<td align="right">83.6%</td>
+</tr>
+<tr class="odd">
+<td align="left">7</td>
+<td align="left">Enterococcus faecium</td>
+<td align="right">8</td>
+<td align="right">1.6%</td>
+<td align="right">426</td>
+<td align="right">85.2%</td>
+</tr>
+<tr class="even">
+<td align="left">8</td>
+<td align="left">Staphylococcus capitis urealyticus</td>
+<td align="right">8</td>
+<td align="right">1.6%</td>
+<td align="right">434</td>
+<td align="right">86.8%</td>
+</tr>
+<tr class="odd">
+<td align="left">9</td>
+<td align="left">Enterobacter cloacae</td>
+<td align="right">5</td>
+<td align="right">1.0%</td>
+<td align="right">439</td>
+<td align="right">87.8%</td>
+</tr>
+<tr class="even">
+<td align="left">10</td>
+<td align="left">Enterococcus columbae</td>
+<td align="right">4</td>
+<td align="right">0.8%</td>
+<td align="right">443</td>
+<td align="right">88.6%</td>
+</tr>
+</tbody>
+</table>
+<p>(omitted 28 entries, n = 57 [11.4%])</p>
+<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="co"># our transformed antibiotic columns</span></span>
+<span><span class="co"># amoxicillin/clavulanic acid (J01CR02) as an example</span></span>
+<span><span class="va">data</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span> <span class="fu"><a href="https://msberends.github.io/cleaner/reference/freq.html" class="external-link">freq</a></span><span class="op">(</span><span class="va">AMC_ND2</span><span class="op">)</span></span>
+<span><span class="co">#&gt; <span style="color: #00BBBB;">ℹ</span> `susceptibility()` assumes the EUCAST guideline and thus considers the 'I'</span></span>
+<span><span class="co">#&gt;   category susceptible. Set the `guideline` argument or the `AMR_guideline`</span></span>
+<span><span class="co">#&gt;   option to either "CLSI" or "EUCAST", see `?AMR-options`.</span></span>
+<span><span class="co">#&gt; <span style="color: #00BBBB;">ℹ</span> This message will be shown once per session.</span></span></code></pre></div>
+<p><strong>Frequency table</strong></p>
+<p>Class: factor &gt; ordered &gt; sir (numeric)<br>
+Length: 500<br>
+Levels: 8: S &lt; SDD &lt; I &lt; R &lt; NI &lt; WT &lt; NWT &lt;
+NS<br>
+Available: 481 (96.2%, NA: 19 = 3.8%)<br>
+Unique: 3</p>
+<p>Drug: Amoxicillin/clavulanic acid (AMC, J01CR02/QJ01CR02)<br>
+Drug group: Aminopenicillins<br>
+%SI: 78.59%</p>
+<table class="table">
+<thead><tr class="header">
+<th align="left"></th>
+<th align="left">Item</th>
+<th align="right">Count</th>
+<th align="right">Percent</th>
+<th align="right">Cum. Count</th>
+<th align="right">Cum. Percent</th>
+</tr></thead>
+<tbody>
+<tr class="odd">
+<td align="left">1</td>
+<td align="left">S</td>
+<td align="right">356</td>
+<td align="right">74.01%</td>
+<td align="right">356</td>
+<td align="right">74.01%</td>
+</tr>
+<tr class="even">
+<td align="left">2</td>
+<td align="left">R</td>
+<td align="right">103</td>
+<td align="right">21.41%</td>
+<td align="right">459</td>
+<td align="right">95.43%</td>
+</tr>
+<tr class="odd">
+<td align="left">3</td>
+<td align="left">I</td>
+<td align="right">22</td>
+<td align="right">4.57%</td>
+<td align="right">481</td>
+<td align="right">100.00%</td>
+</tr>
+</tbody>
+</table>
+</div>
+<div class="section level3">
+<h3 id="a-first-glimpse-at-results">A first glimpse at results<a class="anchor" aria-label="anchor" href="#a-first-glimpse-at-results"></a>
+</h3>
+<p>An easy <code>ggplot</code> will already give a lot of information,
+using the included <code><a href="../reference/ggplot_sir.html">ggplot_sir()</a></code> function:</p>
+<div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">data</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
+<span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/group_by.html" class="external-link">group_by</a></span><span class="op">(</span><span class="va">Country</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
+<span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/select.html" class="external-link">select</a></span><span class="op">(</span><span class="va">Country</span>, <span class="va">AMP_ND2</span>, <span class="va">AMC_ED20</span>, <span class="va">CAZ_ED10</span>, <span class="va">CIP_ED5</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
+<span>  <span class="fu"><a href="../reference/ggplot_sir.html">ggplot_sir</a></span><span class="op">(</span>translate_ab <span class="op">=</span> <span class="st">"ab"</span>, facet <span class="op">=</span> <span class="st">"Country"</span>, datalabels <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span></code></pre></div>
+<p><img src="WHONET_files/figure-html/unnamed-chunk-7-1.png" class="r-plt" alt="" width="720"></p>
+</div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside>
+</div>
+
+
+
+    <footer><div class="pkgdown-footer-left">
+  <p><code>AMR</code> (for R). Free and open-source, licenced under the <a target="_blank" href="https://github.com/msberends/AMR/blob/main/LICENSE" class="external-link">GNU GPL 2.0</a>. Developed at the <a target="_blank" href="https://www.rug.nl" class="external-link">University of Groningen</a> and <a target="_blank" href="https://www.umcg.nl" class="external-link">University Medical Center Groningen</a> in The Netherlands, in collaboration with <a href="https://amr-for-r.org/authors.html">many colleagues from around the world</a>.</p>
+</div>
+
+<div class="pkgdown-footer-right">
+  <p><a target="_blank" href="https://www.rug.nl" class="external-link"><img src="https://amr-for-r.org/logo_rug.svg" style="max-width: 150px;"></a><a target="_blank" href="https://www.umcg.nl" class="external-link"><img src="https://amr-for-r.org/logo_umcg.svg" style="max-width: 150px;"></a></p>
+</div>
+
+    </footer>
+</div>
+
+
+
+
+
+  </body>
+</html>
@@ -0,0 +1,147 @@
+# Work with WHONET data
+
+### Import of data
+
+This tutorial assumes you already imported the WHONET data with e.g. the
+[`readxl` package](https://readxl.tidyverse.org/). In RStudio, this can
+be done using the menu button ‘Import Dataset’ in the tab ‘Environment’.
+Choose the option ‘From Excel’ and select your exported file. Make sure
+date fields are imported correctly.
+
+An example syntax could look like this:
+
+``` r
+
+library(readxl)
+data <- read_excel(path = "path/to/your/file.xlsx")
+```
+
+This package comes with an [example data set
+`WHONET`](https://amr-for-r.org/reference/WHONET.html). We will use it
+for this analysis.
+
+### Preparation
+
+First, load the relevant packages if you did not yet did this. I use the
+tidyverse for all of my analyses. All of them. If you don’t know it yet,
+I suggest you read about it on their website:
+<https://www.tidyverse.org/>.
+
+``` r
+
+library(dplyr) # part of tidyverse
+library(ggplot2) # part of tidyverse
+library(AMR) # this package
+library(cleaner) # to create frequency tables
+```
+
+We will have to transform some variables to simplify and automate the
+analysis:
+
+- Microorganisms should be transformed to our own microorganism codes
+  (called an `mo`) using [our Catalogue of Life reference data
+  set](https://amr-for-r.org/reference/catalogue_of_life), which
+  contains all ~70,000 microorganisms from the taxonomic kingdoms
+  Bacteria, Fungi and Protozoa. We do the tranformation with
+  [`as.mo()`](https://amr-for-r.org/reference/as.mo.md). This function
+  also recognises almost all WHONET abbreviations of microorganisms.
+- Antimicrobial results or interpretations have to be clean and valid.
+  In other words, they should only contain values `"S"`, `"I"` or `"R"`.
+  That is exactly where the
+  [`as.sir()`](https://amr-for-r.org/reference/as.sir.md) function is
+  for.
+
+``` r
+
+# transform variables
+data <- WHONET %>%
+  # get microbial ID based on given organism
+  mutate(mo = as.mo(Organism)) %>%
+  # transform everything from "AMP_ND10" to "CIP_EE" to the new `sir` class
+  mutate_at(vars(AMP_ND10:CIP_EE), as.sir)
+```
+
+No errors or warnings, so all values are transformed succesfully.
+
+We also created a package dedicated to data cleaning and checking,
+called the `cleaner` package. Its
+[`freq()`](https://msberends.github.io/cleaner/reference/freq.html)
+function can be used to create frequency tables.
+
+So let’s check our data, with a couple of frequency tables:
+
+``` r
+
+# our newly created `mo` variable, put in the mo_name() function
+data %>% freq(mo_name(mo), nmax = 10)
+```
+
+**Frequency table**
+
+Class: character  
+Length: 500  
+Available: 500 (100%, NA: 0 = 0%)  
+Unique: 38
+
+Shortest: 11  
+Longest: 40
+
+|  | Item | Count | Percent | Cum. Count | Cum. Percent |
+|:---|:---|---:|---:|---:|---:|
+| 1 | Escherichia coli | 245 | 49.0% | 245 | 49.0% |
+| 2 | Coagulase-negative Staphylococcus (CoNS) | 74 | 14.8% | 319 | 63.8% |
+| 3 | Staphylococcus epidermidis | 38 | 7.6% | 357 | 71.4% |
+| 4 | Streptococcus pneumoniae | 31 | 6.2% | 388 | 77.6% |
+| 5 | Staphylococcus hominis | 21 | 4.2% | 409 | 81.8% |
+| 6 | Proteus mirabilis | 9 | 1.8% | 418 | 83.6% |
+| 7 | Enterococcus faecium | 8 | 1.6% | 426 | 85.2% |
+| 8 | Staphylococcus capitis urealyticus | 8 | 1.6% | 434 | 86.8% |
+| 9 | Enterobacter cloacae | 5 | 1.0% | 439 | 87.8% |
+| 10 | Enterococcus columbae | 4 | 0.8% | 443 | 88.6% |
+
+(omitted 28 entries, n = 57 \[11.4%\])
+
+``` r
+
+# our transformed antibiotic columns
+# amoxicillin/clavulanic acid (J01CR02) as an example
+data %>% freq(AMC_ND2)
+#> ℹ `susceptibility()` assumes the EUCAST guideline and thus considers the 'I'
+#>   category susceptible. Set the `guideline` argument or the `AMR_guideline`
+#>   option to either "CLSI" or "EUCAST", see `?AMR-options`.
+#> ℹ This message will be shown once per session.
+```
+
+**Frequency table**
+
+Class: factor \> ordered \> sir (numeric)  
+Length: 500  
+Levels: 8: S \< SDD \< I \< R \< NI \< WT \< NWT \< NS  
+Available: 481 (96.2%, NA: 19 = 3.8%)  
+Unique: 3
+
+Drug: Amoxicillin/clavulanic acid (AMC, J01CR02/QJ01CR02)  
+Drug group: Aminopenicillins  
+%SI: 78.59%
+
+|     | Item | Count | Percent | Cum. Count | Cum. Percent |
+|:----|:-----|------:|--------:|-----------:|-------------:|
+| 1   | S    |   356 |  74.01% |        356 |       74.01% |
+| 2   | R    |   103 |  21.41% |        459 |       95.43% |
+| 3   | I    |    22 |   4.57% |        481 |      100.00% |
+
+### A first glimpse at results
+
+An easy `ggplot` will already give a lot of information, using the
+included [`ggplot_sir()`](https://amr-for-r.org/reference/ggplot_sir.md)
+function:
+
+``` r
+
+data %>%
+  group_by(Country) %>%
+  select(Country, AMP_ND2, AMC_ED20, CAZ_ED10, CIP_ED5) %>%
+  ggplot_sir(translate_ab = "ab", facet = "Country", datalabels = FALSE)
+```
+
+![](WHONET_files/figure-html/unnamed-chunk-7-1.png)
@@ -0,0 +1,672 @@
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+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/msberends/AMR"><span class="fa fa-github"></span> Source Code</a></li>
+      </ul>
+</div>
+
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+  </div>
+</nav><div class="container template-article">
+
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+
+
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="../logo.svg" class="logo" alt=""><h1>Estimating Empirical Coverage with WISCA</h1>
+            
+      
+      <small class="dont-index">Source: <a href="https://github.com/msberends/AMR/blob/main/vignettes/WISCA.Rmd" class="external-link"><code>vignettes/WISCA.Rmd</code></a></small>
+      <div class="d-none name"><code>WISCA.Rmd</code></div>
+    </div>
+
+    
+    
+<div class="section level2">
+<h2 id="why-wisca">Why WISCA?<a class="anchor" aria-label="anchor" href="#why-wisca"></a>
+</h2>
+<p>When a clinician starts empirical antimicrobial therapy, the
+causative pathogen is unknown. The question they need answered is not
+<em>“what proportion of</em> E. coli <em>is susceptible to
+ciprofloxacin?“</em> but rather <em>“what is the probability that this
+regimen will adequately cover whatever pathogen turns out to be causing
+my patient’s infection?”</em></p>
+<p>The traditional cumulative antibiogram, as standardised by CLSI M39,
+cannot answer that question. It presents susceptibility percentages per
+species per antibiotic, but:</p>
+<ul>
+<li>
+<strong>It fragments information by organism.</strong> The clinician
+must mentally combine susceptibility rates across multiple species,
+weighting by how often each species causes the syndrome, a calculation
+nobody does at the bedside.</li>
+<li>
+<strong>It ignores pathogen incidence.</strong> A species that
+causes 2% of infections is given the same visual weight as one that
+causes 60%.</li>
+<li>
+<strong>It does not evaluate combination regimens.</strong> Much
+empirical therapy consists of two or more agents, but the traditional
+antibiogram only shows monotherapy per organism.</li>
+<li>
+<strong>It provides no measure of uncertainty.</strong> A reported
+“90% susceptible” based on 50 isolates has a 95% confidence interval of
+roughly 78-97% (Clopper-Pearson), yet the antibiogram presents it as a
+point estimate without context.</li>
+</ul>
+<p><strong>WISCA</strong> (Weighted-Incidence Syndromic Combination
+Antibiogram) resolves all four limitations. It estimates the probability
+that a regimen will provide adequate empirical coverage for a given
+infection syndrome, weighted by local pathogen incidence, with full
+uncertainty quantification via Bayesian inference.</p>
+<p>The concept was introduced by Hebert <em>et al.</em> (2012), who
+demonstrated that traditional antibiogram susceptibility rates could be
+misleading: ciprofloxacin appeared 84% effective against <em>E.
+coli</em> in the traditional antibiogram, but WISCA revealed only 62%
+coverage for UTI and 37% for abdominal infections, because enterococci
+(intrinsically resistant) and other species contribute substantially to
+these syndromes. Randhawa <em>et al.</em> (2014) showed that
+WISCA-guided regimen selection could improve time-to-adequate-coverage
+on the ICU by over 40%. Bielicki <em>et al.</em> (2016) introduced the
+Bayesian framework now used in this package, enabling credible intervals
+and multi-centre pooling. Cook <em>et al.</em> (2022) applied it
+globally across 52 hospitals in 23 countries.</p>
+</div>
+<div class="section level2">
+<h2 id="the-idea">The idea<a class="anchor" aria-label="anchor" href="#the-idea"></a>
+</h2>
+<p>WISCA asks:</p>
+<blockquote>
+<p>“What is the <strong>probability</strong> that this regimen
+<strong>will cover</strong> the pathogen, given the syndrome?”</p>
+</blockquote>
+<p>This means combining two quantities:</p>
+<ul>
+<li>
+<strong>Pathogen incidence</strong> in the syndrome (how often each
+species causes it),</li>
+<li>
+<strong>Susceptibility</strong> of each pathogen to the
+regimen.</li>
+</ul>
+<p>We can write this as:</p>
+<p><math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext mathvariant="normal">Coverage</mtext><mo>=</mo><munder><mo>∑</mo><mi>i</mi></munder><mo stretchy="false" form="prefix">(</mo><msub><mtext mathvariant="normal">Incidence</mtext><mi>i</mi></msub><mo>×</mo><msub><mtext mathvariant="normal">Susceptibility</mtext><mi>i</mi></msub><mo stretchy="false" form="postfix">)</mo></mrow><annotation encoding="application/x-tex">\text{Coverage} = \sum_i (\text{Incidence}_i \times \text{Susceptibility}_i)</annotation></semantics></math></p>
+<p>For example, suppose in your hospital:</p>
+<ul>
+<li>
+<em>E. coli</em> causes 60% of UTIs, and 90% of <em>E. coli</em> are
+susceptible to a drug.</li>
+<li>
+<em>Klebsiella</em> causes 40% of UTIs, and 70% of
+<em>Klebsiella</em> are susceptible.</li>
+</ul>
+<p>Then:</p>
+<p><math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext mathvariant="normal">Coverage</mtext><mo>=</mo><mo stretchy="false" form="prefix">(</mo><mn>0.6</mn><mo>×</mo><mn>0.9</mn><mo stretchy="false" form="postfix">)</mo><mo>+</mo><mo stretchy="false" form="prefix">(</mo><mn>0.4</mn><mo>×</mo><mn>0.7</mn><mo stretchy="false" form="postfix">)</mo><mo>=</mo><mn>0.82</mn></mrow><annotation encoding="application/x-tex">\text{Coverage} = (0.6 \times 0.9) + (0.4 \times 0.7) = 0.82</annotation></semantics></math></p>
+<p>That 82% is a far more clinically meaningful number than the
+species-level “90% of <em>E. coli</em>” and “70% of <em>Klebsiella</em>”
+reported separately in a traditional antibiogram, because it directly
+answers the question the clinician actually faces.</p>
+<p>But in real data, both incidence and susceptibility are
+<strong>estimated from finite samples</strong>, so they carry
+uncertainty. A sample of 50 isolates is not a census. WISCA models this
+uncertainty <strong>probabilistically</strong>, using conjugate Bayesian
+distributions.</p>
+</div>
+<div class="section level2">
+<h2 id="the-bayesian-engine">The Bayesian engine<a class="anchor" aria-label="anchor" href="#the-bayesian-engine"></a>
+</h2>
+<div class="section level3">
+<h3 id="pathogen-incidence">Pathogen incidence<a class="anchor" aria-label="anchor" href="#pathogen-incidence"></a>
+</h3>
+<p>Let:</p>
+<ul>
+<li>
+<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mi>K</mi><annotation encoding="application/x-tex">K</annotation></semantics></math>
+be the number of pathogens,</li>
+<li>
+<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>𝛂</mi><mo>=</mo><mo stretchy="false" form="prefix">(</mo><mn>1</mn><mo>,</mo><mn>1</mn><mo>,</mo><mi>…</mi><mo>,</mo><mn>1</mn><mo stretchy="false" form="postfix">)</mo></mrow><annotation encoding="application/x-tex">\boldsymbol{\alpha} = (1, 1, \ldots, 1)</annotation></semantics></math>
+be a
+<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mtext mathvariant="normal">Dirichlet</mtext><annotation encoding="application/x-tex">\text{Dirichlet}</annotation></semantics></math>
+prior (uniform, non-informative),</li>
+<li>
+<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>𝐧</mi><mo>=</mo><mo stretchy="false" form="prefix">(</mo><msub><mi>n</mi><mn>1</mn></msub><mo>,</mo><mi>…</mi><mo>,</mo><msub><mi>n</mi><mi>K</mi></msub><mo stretchy="false" form="postfix">)</mo></mrow><annotation encoding="application/x-tex">\boldsymbol{n} = (n_1, \ldots, n_K)</annotation></semantics></math>
+be the observed isolate counts per species.</li>
+</ul>
+<p>Then the posterior incidence is:</p>
+<p><math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>𝐩</mi><mo>∼</mo><mtext mathvariant="normal">Dirichlet</mtext><mo stretchy="false" form="prefix">(</mo><msub><mi>α</mi><mn>1</mn></msub><mo>+</mo><msub><mi>n</mi><mn>1</mn></msub><mo>,</mo><mi>…</mi><mo>,</mo><msub><mi>α</mi><mi>K</mi></msub><mo>+</mo><msub><mi>n</mi><mi>K</mi></msub><mo stretchy="false" form="postfix">)</mo></mrow><annotation encoding="application/x-tex">\boldsymbol{p} \sim \text{Dirichlet}(\alpha_1 + n_1, \ldots, \alpha_K + n_K)</annotation></semantics></math></p>
+<p>To simulate from this, we use:</p>
+<p><math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mi>x</mi><mi>i</mi></msub><mo>∼</mo><mtext mathvariant="normal">Gamma</mtext><mo stretchy="false" form="prefix">(</mo><msub><mi>α</mi><mi>i</mi></msub><mo>+</mo><msub><mi>n</mi><mi>i</mi></msub><mo>,</mo><mspace width="0.222em"></mspace><mn>1</mn><mo stretchy="false" form="postfix">)</mo><mo>,</mo><mspace width="1.0em"></mspace><msub><mi>p</mi><mi>i</mi></msub><mo>=</mo><mfrac><msub><mi>x</mi><mi>i</mi></msub><mrow><munderover><mo>∑</mo><mrow><mi>j</mi><mo>=</mo><mn>1</mn></mrow><mi>K</mi></munderover><msub><mi>x</mi><mi>j</mi></msub></mrow></mfrac></mrow><annotation encoding="application/x-tex">x_i \sim \text{Gamma}(\alpha_i + n_i,\ 1), \quad p_i = \frac{x_i}{\sum_{j=1}^{K} x_j}</annotation></semantics></math></p>
+<p>The Dirichlet is the conjugate prior for multinomial data. With the
+non-informative prior
+<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext mathvariant="normal">Dirichlet</mtext><mo stretchy="false" form="prefix">(</mo><mn>1</mn><mo>,</mo><mn>1</mn><mo>,</mo><mi>…</mi><mo>,</mo><mn>1</mn><mo stretchy="false" form="postfix">)</mo></mrow><annotation encoding="application/x-tex">\text{Dirichlet}(1, 1, \ldots, 1)</annotation></semantics></math>,
+the posterior is dominated by the data once sample sizes are reasonable.
+With small samples, the posterior is appropriately more diffuse,
+reflecting genuine uncertainty, and the resulting credible intervals
+will be wider.</p>
+</div>
+<div class="section level3">
+<h3 id="susceptibility">Susceptibility<a class="anchor" aria-label="anchor" href="#susceptibility"></a>
+</h3>
+<p>Each pathogen-regimen pair has a prior and observed data:</p>
+<ul>
+<li>Default prior:
+<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext mathvariant="normal">Beta</mtext><mo stretchy="false" form="prefix">(</mo><mn>0.5</mn><mo>,</mo><mn>0.5</mn><mo stretchy="false" form="postfix">)</mo></mrow><annotation encoding="application/x-tex">\text{Beta}(0.5, 0.5)</annotation></semantics></math>
+(Jeffreys prior)</li>
+<li>Intrinsically resistant pairs:
+<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext mathvariant="normal">Beta</mtext><mo stretchy="false" form="prefix">(</mo><mn>1</mn><mo>,</mo><mn>9999</mn><mo stretchy="false" form="postfix">)</mo></mrow><annotation encoding="application/x-tex">\text{Beta}(1, 9999)</annotation></semantics></math>,
+forcing near-zero susceptibility regardless of observed data (based on
+EUCAST Expected Resistant Phenotypes)</li>
+<li>Data:
+<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mi>S</mi><annotation encoding="application/x-tex">S</annotation></semantics></math>
+susceptible out of
+<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mi>N</mi><annotation encoding="application/x-tex">N</annotation></semantics></math>
+tested</li>
+</ul>
+<p>The
+<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mi>S</mi><annotation encoding="application/x-tex">S</annotation></semantics></math>
+category could also include values SDD (susceptible, dose-dependent) and
+I (intermediate [CLSI], or susceptible, increased exposure
+[EUCAST]).</p>
+<p>Then the posterior is:</p>
+<p><math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>θ</mi><mo>∼</mo><mtext mathvariant="normal">Beta</mtext><mo stretchy="false" form="prefix">(</mo><msub><mi>α</mi><mn>0</mn></msub><mo>+</mo><mi>S</mi><mo>,</mo><mspace width="0.222em"></mspace><msub><mi>β</mi><mn>0</mn></msub><mo>+</mo><mi>N</mi><mo>−</mo><mi>S</mi><mo stretchy="false" form="postfix">)</mo></mrow><annotation encoding="application/x-tex">\theta \sim \text{Beta}(\alpha_0 + S,\ \beta_0 + N - S)</annotation></semantics></math></p>
+</div>
+<div class="section level3">
+<h3 id="final-coverage-estimate">Final coverage estimate<a class="anchor" aria-label="anchor" href="#final-coverage-estimate"></a>
+</h3>
+<p>Putting it together:</p>
+<ol style="list-style-type: decimal">
+<li>Simulate pathogen incidence:
+<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>𝐩</mi><mo>∼</mo><mtext mathvariant="normal">Dirichlet</mtext></mrow><annotation encoding="application/x-tex">\boldsymbol{p} \sim \text{Dirichlet}</annotation></semantics></math>
+</li>
+<li>Simulate susceptibility:
+<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mi>θ</mi><mi>i</mi></msub><mo>∼</mo><mtext mathvariant="normal">Beta</mtext><mo stretchy="false" form="prefix">(</mo><msub><mi>α</mi><mn>0</mn></msub><mo>+</mo><msub><mi>S</mi><mi>i</mi></msub><mo>,</mo><mspace width="0.222em"></mspace><msub><mi>β</mi><mn>0</mn></msub><mo>+</mo><msub><mi>N</mi><mi>i</mi></msub><mo>−</mo><msub><mi>S</mi><mi>i</mi></msub><mo stretchy="false" form="postfix">)</mo></mrow><annotation encoding="application/x-tex">\theta_i \sim \text{Beta}(\alpha_0 + S_i,\ \beta_0 + N_i - S_i)</annotation></semantics></math>
+</li>
+<li>Combine:</li>
+</ol>
+<p><math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext mathvariant="normal">Coverage</mtext><mo>=</mo><munderover><mo>∑</mo><mrow><mi>i</mi><mo>=</mo><mn>1</mn></mrow><mi>K</mi></munderover><msub><mi>p</mi><mi>i</mi></msub><mo>⋅</mo><msub><mi>θ</mi><mi>i</mi></msub></mrow><annotation encoding="application/x-tex">\text{Coverage} = \sum_{i=1}^{K} p_i \cdot \theta_i</annotation></semantics></math></p>
+<p>Repeat this simulation (e.g., 1000 times) and summarise:</p>
+<ul>
+<li>
+<strong>Mean</strong> = expected coverage</li>
+<li>
+<strong>Quantiles</strong> = credible interval (95% by default)</li>
+</ul>
+<p>Because each simulation draws from the full posterior, the resulting
+distribution of coverage estimates naturally captures the joint
+uncertainty in both pathogen incidence and susceptibility. The credible
+interval tells you how confident you can be in the coverage estimate,
+something a traditional antibiogram never provides.</p>
+</div>
+</div>
+<div class="section level2">
+<h2 id="when-to-use-wisca-vs--traditional-antibiograms">When to use WISCA vs. traditional antibiograms<a class="anchor" aria-label="anchor" href="#when-to-use-wisca-vs--traditional-antibiograms"></a>
+</h2>
+<table class="table">
+<thead><tr class="header">
+<th>Goal</th>
+<th>Recommended approach</th>
+</tr></thead>
+<tbody>
+<tr class="odd">
+<td>Guide empirical therapy decisions</td>
+<td><strong>WISCA</strong></td>
+</tr>
+<tr class="even">
+<td>Compare regimens for a syndrome</td>
+<td><strong>WISCA</strong></td>
+</tr>
+<tr class="odd">
+<td>Evaluate combination regimens</td>
+<td><strong>WISCA</strong></td>
+</tr>
+<tr class="even">
+<td>Antimicrobial stewardship (A-team)</td>
+<td><strong>WISCA</strong></td>
+</tr>
+<tr class="odd">
+<td>Track resistance trends per species</td>
+<td>Traditional / Combination</td>
+</tr>
+<tr class="even">
+<td>AMR surveillance reporting</td>
+<td>Traditional / Syndromic</td>
+</tr>
+<tr class="odd">
+<td>Understand species-level epidemiology</td>
+<td>Traditional</td>
+</tr>
+</tbody>
+</table>
+<p>In short: if the end goal involves a <em>patient</em> who does not
+yet have a culture result, WISCA is the appropriate tool. If the end
+goal is <em>surveillance</em> of resistance at the species level, the
+traditional antibiogram remains fit for purpose.</p>
+</div>
+<div class="section level2">
+<h2 id="practical-use-in-the-amr-package">Practical use in the <code>AMR</code> package<a class="anchor" aria-label="anchor" href="#practical-use-in-the-amr-package"></a>
+</h2>
+<div class="section level3">
+<h3 id="prepare-data">Prepare data<a class="anchor" aria-label="anchor" href="#prepare-data"></a>
+</h3>
+<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://amr-for-r.org">AMR</a></span><span class="op">)</span></span>
+<span><span class="va">data</span> <span class="op">&lt;-</span> <span class="va">example_isolates</span></span>
+<span></span>
+<span><span class="co"># Structure of our data</span></span>
+<span><span class="va">data</span></span>
+<span><span class="co">#&gt; <span style="color: #949494;"># A tibble: 2,000 × 46</span></span></span>
+<span><span class="co">#&gt;    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX  </span></span>
+<span><span class="co">#&gt;    <span style="color: #949494; font-style: italic;">&lt;date&gt;</span>     <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>   <span style="color: #949494; font-style: italic;">&lt;dbl&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;mo&gt;</span>         <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span></span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 1</span> 2002-01-02 A77334     65 F      Clinical <span style="color: #949494;">B_</span>ESCHR<span style="color: #949494;">_</span>COLI <span style="color: #080808; background-color: #FF5F5F;">  R  </span> <span style="color: #949494;">  NA</span>  <span style="color: #949494;">  NA</span>  <span style="color: #949494;">  NA</span> </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 2</span> 2002-01-03 A77334     65 F      Clinical <span style="color: #949494;">B_</span>ESCHR<span style="color: #949494;">_</span>COLI <span style="color: #080808; background-color: #FF5F5F;">  R  </span> <span style="color: #949494;">  NA</span>  <span style="color: #949494;">  NA</span>  <span style="color: #949494;">  NA</span> </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 3</span> 2002-01-07 067927     45 F      ICU      <span style="color: #949494;">B_</span>STPHY<span style="color: #949494;">_</span>EPDR <span style="color: #080808; background-color: #FF5F5F;">  R  </span> <span style="color: #949494;">  NA</span>  <span style="color: #080808; background-color: #FF5F5F;">  R  </span> <span style="color: #949494;">  NA</span> </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 4</span> 2002-01-07 067927     45 F      ICU      <span style="color: #949494;">B_</span>STPHY<span style="color: #949494;">_</span>EPDR <span style="color: #080808; background-color: #FF5F5F;">  R  </span> <span style="color: #949494;">  NA</span>  <span style="color: #080808; background-color: #FF5F5F;">  R  </span> <span style="color: #949494;">  NA</span> </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 5</span> 2002-01-13 067927     45 F      ICU      <span style="color: #949494;">B_</span>STPHY<span style="color: #949494;">_</span>EPDR <span style="color: #080808; background-color: #FF5F5F;">  R  </span> <span style="color: #949494;">  NA</span>  <span style="color: #080808; background-color: #FF5F5F;">  R  </span> <span style="color: #949494;">  NA</span> </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 6</span> 2002-01-13 067927     45 F      ICU      <span style="color: #949494;">B_</span>STPHY<span style="color: #949494;">_</span>EPDR <span style="color: #080808; background-color: #FF5F5F;">  R  </span> <span style="color: #949494;">  NA</span>  <span style="color: #080808; background-color: #FF5F5F;">  R  </span> <span style="color: #949494;">  NA</span> </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 7</span> 2002-01-14 462729     78 M      Clinical <span style="color: #949494;">B_</span>STPHY<span style="color: #949494;">_</span>AURS <span style="color: #080808; background-color: #FF5F5F;">  R  </span> <span style="color: #949494;">  NA</span>  <span style="color: #080808; background-color: #5FD7AF;">  S  </span> <span style="color: #080808; background-color: #FF5F5F;">  R  </span></span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 8</span> 2002-01-14 462729     78 M      Clinical <span style="color: #949494;">B_</span>STPHY<span style="color: #949494;">_</span>AURS <span style="color: #080808; background-color: #FF5F5F;">  R  </span> <span style="color: #949494;">  NA</span>  <span style="color: #080808; background-color: #5FD7AF;">  S  </span> <span style="color: #080808; background-color: #FF5F5F;">  R  </span></span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 9</span> 2002-01-16 067927     45 F      ICU      <span style="color: #949494;">B_</span>STPHY<span style="color: #949494;">_</span>EPDR <span style="color: #080808; background-color: #FF5F5F;">  R  </span> <span style="color: #949494;">  NA</span>  <span style="color: #080808; background-color: #FF5F5F;">  R  </span> <span style="color: #949494;">  NA</span> </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;">10</span> 2002-01-17 858515     79 F      ICU      <span style="color: #949494;">B_</span>STPHY<span style="color: #949494;">_</span>EPDR <span style="color: #080808; background-color: #FF5F5F;">  R  </span> <span style="color: #949494;">  NA</span>  <span style="color: #080808; background-color: #5FD7AF;">  S  </span> <span style="color: #949494;">  NA</span> </span></span>
+<span><span class="co">#&gt; <span style="color: #949494;"># ℹ 1,990 more rows</span></span></span>
+<span><span class="co">#&gt; <span style="color: #949494;"># ℹ 36 more variables: AMC &lt;sir&gt;, AMP &lt;sir&gt;, TZP &lt;sir&gt;, CZO &lt;sir&gt;, FEP &lt;sir&gt;,</span></span></span>
+<span><span class="co">#&gt; <span style="color: #949494;">#   CXM &lt;sir&gt;, FOX &lt;sir&gt;, CTX &lt;sir&gt;, CAZ &lt;sir&gt;, CRO &lt;sir&gt;, GEN &lt;sir&gt;,</span></span></span>
+<span><span class="co">#&gt; <span style="color: #949494;">#   TOB &lt;sir&gt;, AMK &lt;sir&gt;, KAN &lt;sir&gt;, TMP &lt;sir&gt;, SXT &lt;sir&gt;, NIT &lt;sir&gt;,</span></span></span>
+<span><span class="co">#&gt; <span style="color: #949494;">#   FOS &lt;sir&gt;, LNZ &lt;sir&gt;, CIP &lt;sir&gt;, MFX &lt;sir&gt;, VAN &lt;sir&gt;, TEC &lt;sir&gt;,</span></span></span>
+<span><span class="co">#&gt; <span style="color: #949494;">#   TCY &lt;sir&gt;, TGC &lt;sir&gt;, DOX &lt;sir&gt;, ERY &lt;sir&gt;, CLI &lt;sir&gt;, AZM &lt;sir&gt;,</span></span></span>
+<span><span class="co">#&gt; <span style="color: #949494;">#   IPM &lt;sir&gt;, MEM &lt;sir&gt;, MTR &lt;sir&gt;, CHL &lt;sir&gt;, COL &lt;sir&gt;, MUP &lt;sir&gt;, …</span></span></span>
+<span></span>
+<span><span class="co"># Add a synthetic syndrome column for demonstration</span></span>
+<span><span class="va">data</span><span class="op">$</span><span class="va">syndrome</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/ifelse.html" class="external-link">ifelse</a></span><span class="op">(</span><span class="va">data</span><span class="op">$</span><span class="va">mo</span> <span class="op"><a href="../reference/like.html">%like%</a></span> <span class="st">"coli"</span>, <span class="st">"UTI"</span>, <span class="st">"Non-UTI"</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Keep only 10 most common microorganisms</span></span>
+<span><span class="va">data</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/top_n_microorganisms.html">top_n_microorganisms</a></span><span class="op">(</span><span class="va">data</span>, n <span class="op">=</span> <span class="fl">10</span>, property <span class="op">=</span> <span class="st">"species"</span><span class="op">)</span></span>
+<span><span class="co">#&gt; <span style="color: #00BBBB;">ℹ</span> Using column <span style="color: #00BB00; font-weight: bold;">mo</span> as input for `col_mo`.</span></span></code></pre></div>
+</div>
+<div class="section level3">
+<h3 id="basic-wisca">Basic WISCA<a class="anchor" aria-label="anchor" href="#basic-wisca"></a>
+</h3>
+<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="../reference/antibiogram.html">wisca</a></span><span class="op">(</span><span class="va">data</span>,</span>
+<span>  antimicrobials <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"AMC"</span>, <span class="st">"CIP"</span>, <span class="st">"GEN"</span><span class="op">)</span></span>
+<span><span class="op">)</span></span></code></pre></div>
+<table class="table">
+<thead><tr class="header">
+<th align="left">Amoxicillin/clavulanic acid</th>
+<th align="left">Ciprofloxacin</th>
+<th align="left">Gentamicin</th>
+</tr></thead>
+<tbody><tr class="odd">
+<td align="left">76.8% (74.7-79.1%)</td>
+<td align="left">81.5% (78.9-84.1%)</td>
+<td align="left">82.9% (81-84.8%)</td>
+</tr></tbody>
+</table>
+</div>
+<div class="section level3">
+<h3 id="use-combination-regimens">Use combination regimens<a class="anchor" aria-label="anchor" href="#use-combination-regimens"></a>
+</h3>
+<p>Combination regimens are specified with a <code>+</code> separator.
+WISCA evaluates whether <em>at least one</em> agent in the combination
+covers the pathogen:</p>
+<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="../reference/antibiogram.html">wisca</a></span><span class="op">(</span><span class="va">data</span>,</span>
+<span>  antimicrobials <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"AMC"</span>, <span class="st">"AMC + CIP"</span>, <span class="st">"AMC + GEN"</span><span class="op">)</span></span>
+<span><span class="op">)</span></span></code></pre></div>
+<table class="table">
+<colgroup>
+<col width="24%">
+<col width="38%">
+<col width="36%">
+</colgroup>
+<thead><tr class="header">
+<th align="left">Amoxicillin/clavulanic acid</th>
+<th align="left">Amoxicillin/clavulanic acid + Ciprofloxacin</th>
+<th align="left">Amoxicillin/clavulanic acid + Gentamicin</th>
+</tr></thead>
+<tbody><tr class="odd">
+<td align="left">76.8% (74.6-78.9%)</td>
+<td align="left">89.6% (88-91.1%)</td>
+<td align="left">93.7% (92.5-94.9%)</td>
+</tr></tbody>
+</table>
+</div>
+<div class="section level3">
+<h3 id="stratify-by-syndrome">Stratify by syndrome<a class="anchor" aria-label="anchor" href="#stratify-by-syndrome"></a>
+</h3>
+<p>Use <code>syndromic_group</code> to produce separate WISCA estimates
+per clinical stratum. You can pass a column name or any expression:</p>
+<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">wisca_out</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/antibiogram.html">wisca</a></span><span class="op">(</span><span class="va">data</span>,</span>
+<span>  antimicrobials <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"AMC"</span>, <span class="st">"AMC + CIP"</span>, <span class="st">"AMC + GEN"</span><span class="op">)</span>,</span>
+<span>  syndromic_group <span class="op">=</span> <span class="st">"syndrome"</span></span>
+<span><span class="op">)</span></span>
+<span><span class="va">wisca_out</span></span></code></pre></div>
+<table class="table">
+<colgroup>
+<col width="12%">
+<col width="21%">
+<col width="34%">
+<col width="31%">
+</colgroup>
+<thead><tr class="header">
+<th align="left">Syndromic Group</th>
+<th align="left">Amoxicillin/clavulanic acid</th>
+<th align="left">Amoxicillin/clavulanic acid + Ciprofloxacin</th>
+<th align="left">Amoxicillin/clavulanic acid + Gentamicin</th>
+</tr></thead>
+<tbody>
+<tr class="odd">
+<td align="left">Non-UTI</td>
+<td align="left">72.5% (69.9-75.1%)</td>
+<td align="left">86.9% (84.8-89%)</td>
+<td align="left">91.4% (89.5-93%)</td>
+</tr>
+<tr class="even">
+<td align="left">UTI</td>
+<td align="left">86% (82.5-89%)</td>
+<td align="left">94.8% (92.5-96.6%)</td>
+<td align="left">97.9% (96.3-99%)</td>
+</tr>
+</tbody>
+</table>
+<p>The <code>AMR</code> package is available in 28 languages, which can
+all be used for the <code><a href="../reference/antibiogram.html">wisca()</a></code> function too:</p>
+<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="../reference/antibiogram.html">wisca</a></span><span class="op">(</span><span class="va">data</span>,</span>
+<span>  antimicrobials <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"AMC"</span>, <span class="st">"AMC + CIP"</span>, <span class="st">"AMC + GEN"</span><span class="op">)</span>,</span>
+<span>  syndromic_group <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/grep.html" class="external-link">gsub</a></span><span class="op">(</span><span class="st">"UTI"</span>, <span class="st">"UCI"</span>, <span class="va">data</span><span class="op">$</span><span class="va">syndrome</span><span class="op">)</span>,</span>
+<span>  language <span class="op">=</span> <span class="st">"Spanish"</span></span>
+<span><span class="op">)</span></span></code></pre></div>
+<table class="table">
+<colgroup>
+<col width="12%">
+<col width="21%">
+<col width="34%">
+<col width="31%">
+</colgroup>
+<thead><tr class="header">
+<th align="left">Grupo sindrómico</th>
+<th align="left">Amoxicilina/ácido clavulánico</th>
+<th align="left">Amoxicilina/ácido clavulánico + Ciprofloxacina</th>
+<th align="left">Amoxicilina/ácido clavulánico + Gentamicina</th>
+</tr></thead>
+<tbody>
+<tr class="odd">
+<td align="left">Non-UCI</td>
+<td align="left">72.6% (69.9-75.3%)</td>
+<td align="left">87% (84.9-89.1%)</td>
+<td align="left">91.4% (89.7-92.9%)</td>
+</tr>
+<tr class="even">
+<td align="left">UCI</td>
+<td align="left">86% (82.7-89%)</td>
+<td align="left">94.8% (92.7-96.4%)</td>
+<td align="left">97.9% (96.5-99%)</td>
+</tr>
+</tbody>
+</table>
+</div>
+<div class="section level3">
+<h3 id="interpreting-the-output">Interpreting the output<a class="anchor" aria-label="anchor" href="#interpreting-the-output"></a>
+</h3>
+<p>Each row shows the estimated empirical coverage for a regimen, with a
+95% credible interval. When comparing regimens:</p>
+<ul>
+<li>
+<strong>Overlapping credible intervals</strong> mean there is no
+statistically significant difference in coverage. If a narrower-spectrum
+regimen overlaps with a broader one, the narrower-spectrum option can be
+preferred on stewardship grounds.</li>
+<li>
+<strong>Non-overlapping credible intervals</strong> indicate a
+clinically meaningful difference in coverage.</li>
+</ul>
+</div>
+<div class="section level3">
+<h3 id="plotting">Plotting<a class="anchor" aria-label="anchor" href="#plotting"></a>
+</h3>
+<p>WISCA results can be visualised in several ways. All plot functions
+work on the output of <code><a href="../reference/antibiogram.html">wisca()</a></code> (or
+<code>antibiogram(..., wisca = TRUE)</code>).</p>
+<p>Below we use the <code>wisca_out</code> object that was generated
+above.</p>
+<div class="section level4">
+<h4 id="coverage-with-credible-intervals">Coverage with credible intervals<a class="anchor" aria-label="anchor" href="#coverage-with-credible-intervals"></a>
+</h4>
+<p>The extended <code><a href="https://ggplot2.tidyverse.org/reference/autoplot.html" class="external-link">autoplot()</a></code> method from the
+<code>ggplot2()</code> package produces a point-and-interval plot
+showing the coverage estimate and 95% credible interval for each
+regimen, grouped by syndromic stratum. This is the most direct way to
+compare regimens: overlapping intervals suggest clinical
+non-inferiority, non-overlapping intervals indicate a meaningful
+difference.</p>
+<div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu">ggplot2</span><span class="fu">::</span><span class="fu"><a href="https://ggplot2.tidyverse.org/reference/autoplot.html" class="external-link">autoplot</a></span><span class="op">(</span><span class="va">wisca_out</span><span class="op">)</span></span></code></pre></div>
+<p><img src="WISCA_files/figure-html/unnamed-chunk-6-1.png" class="r-plt" alt="" width="720"></p>
+</div>
+<div class="section level4">
+<h4 id="susceptibility-vs--incidence-weight">Susceptibility vs. incidence weight<a class="anchor" aria-label="anchor" href="#susceptibility-vs--incidence-weight"></a>
+</h4>
+<p><code><a href="../reference/antibiogram.html">wisca_plot()</a></code> produces a scatter plot of the Monte Carlo
+simulation draws, showing each pathogen’s susceptibility (x-axis)
+against its incidence weight (y-axis) for each regimen. Each dot
+represents one of 1,000 simulated draws, so the spread reflects
+posterior uncertainty. This plot reveals <em>why</em> a regimen achieves
+its coverage: you can see which pathogens dominate the syndrome (high on
+the y-axis), how susceptible they are (position on the x-axis), and how
+uncertain both estimates are (spread of the cloud). The dashed vertical
+lines denote the point estimates, i.e., the coverage percentages. The
+ribbon behind the dashed lines denote the credible interval, which is
+95% at default.</p>
+<div class="sourceCode" id="cb7"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="../reference/antibiogram.html">wisca_plot</a></span><span class="op">(</span><span class="va">wisca_out</span><span class="op">)</span></span></code></pre></div>
+<p><img src="WISCA_files/figure-html/unnamed-chunk-7-1.png" class="r-plt" alt="" width="720"></p>
+</div>
+<div class="section level4">
+<h4 id="posterior-coverage-distributions">Posterior coverage distributions<a class="anchor" aria-label="anchor" href="#posterior-coverage-distributions"></a>
+</h4>
+<p>Setting <code>wisca_plot_type = "posterior_coverage"</code> shows the
+full posterior distribution of coverage for each regimen as a density
+curve. This is the most complete representation of what the Bayesian
+model produces: each curve shows the relative likelihood of each
+coverage value across all 1,000 simulations. Narrow, tall peaks indicate
+high certainty; wide, flat curves indicate greater uncertainty. Where
+two curves overlap, the regimens cannot be confidently
+distinguished.</p>
+<div class="sourceCode" id="cb8"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="../reference/antibiogram.html">wisca_plot</a></span><span class="op">(</span><span class="va">wisca_out</span>, wisca_plot_type <span class="op">=</span> <span class="st">"posterior_coverage"</span><span class="op">)</span></span></code></pre></div>
+<p><img src="WISCA_files/figure-html/unnamed-chunk-8-1.png" class="r-plt" alt="" width="720"></p>
+</div>
+</div>
+</div>
+<div class="section level2">
+<h2 id="sensible-defaults-which-can-be-customised">Sensible defaults, which can be customised<a class="anchor" aria-label="anchor" href="#sensible-defaults-which-can-be-customised"></a>
+</h2>
+<ul>
+<li>
+<code>simulations = 1000</code>: number of Monte Carlo draws</li>
+<li>
+<code>conf_interval = 0.95</code>: coverage interval width</li>
+<li>
+<code>combine_SI = TRUE</code>: count “I” and “SDD” as
+susceptible</li>
+</ul>
+</div>
+<div class="section level2">
+<h2 id="practical-considerations">Practical considerations<a class="anchor" aria-label="anchor" href="#practical-considerations"></a>
+</h2>
+<ul>
+<li>
+<strong>First isolates only</strong>: always deduplicate using
+<code><a href="../reference/first_isolate.html">first_isolate()</a></code> before running WISCA. Repeat isolates
+introduce bias.</li>
+<li>
+<strong>Pathogen selection</strong>: consider filtering with
+<code><a href="../reference/top_n_microorganisms.html">top_n_microorganisms()</a></code>. Including rare contaminants
+(e.g. CoNS without clinical context) can distort estimates and may
+artificially lower coverage (Cook <em>et al.</em>, 2022).</li>
+<li>
+<strong>Sample size</strong>: coverage estimates become reliable
+with approximately 100+ isolates. For smaller datasets, consider pooling
+data from multiple sites, but only after verifying that pathogen
+distributions are sufficiently similar (Bielicki <em>et al.</em>,
+2016).</li>
+<li>
+<strong>Culture request bias</strong>: WISCA is only as good as the
+data it is based on. If cultures are selectively requested (e.g. only
+after treatment failure), the dataset will be biased towards resistant
+isolates. A robust culture policy is essential for reliable
+estimates.</li>
+</ul>
+</div>
+<div class="section level2">
+<h2 id="limitations">Limitations<a class="anchor" aria-label="anchor" href="#limitations"></a>
+</h2>
+<ul>
+<li>It assumes your data are representative of the patient population
+you are treating</li>
+<li>No direct adjustment for patient-level covariates, although these
+can be passed onto the <code>syndromic_group</code> argument for
+stratification</li>
+<li>WISCA does not model resistance trends over time; for that, you
+might want to use <code>tidymodels</code>, for which we <a href="https://amr-for-r.org/articles/AMR_with_tidymodels.html">wrote a
+basic introduction</a>
+</li>
+</ul>
+</div>
+<div class="section level2">
+<h2 id="summary">Summary<a class="anchor" aria-label="anchor" href="#summary"></a>
+</h2>
+<p>WISCA enables:</p>
+<ul>
+<li>
+<strong>Empirical regimen comparison</strong>, answering the
+clinician’s actual question</li>
+<li>
+<strong>Syndrome-specific coverage estimation</strong>, stratifiable
+by any clinical variable</li>
+<li>
+<strong>Fully probabilistic interpretation</strong>, with credible
+intervals that honestly communicate uncertainty</li>
+</ul>
+<p>It is available in the <code>AMR</code> package via either:</p>
+<div class="sourceCode" id="cb9"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="../reference/antibiogram.html">wisca</a></span><span class="op">(</span><span class="va">...</span><span class="op">)</span></span>
+<span></span>
+<span><span class="fu"><a href="../reference/antibiogram.html">antibiogram</a></span><span class="op">(</span><span class="va">...</span>, wisca <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div>
+</div>
+<div class="section level2">
+<h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a>
+</h2>
+<ol style="list-style-type: decimal">
+<li>Hebert C, Ridgway J, Vekhter B, Brown EC, Weber SG, Robicsek A.
+Demonstration of the weighted-incidence syndromic combination
+antibiogram: an empiric prescribing decision aid. <em>Infect Control
+Hosp Epidemiol.</em> 2012;33(4):381-388. <a href="https://doi.org/10.1086/664768" class="external-link uri">https://doi.org/10.1086/664768</a>
+</li>
+<li>Randhawa V, Sarwar S, Walker S, Elligsen M, Palmay L, Daneman N.
+Weighted-incidence syndromic combination antibiograms to guide empiric
+treatment of critical care infections: a retrospective cohort study.
+<em>Crit Care.</em> 2014;18(3):R112. <a href="https://doi.org/10.1186/cc13901" class="external-link uri">https://doi.org/10.1186/cc13901</a>
+</li>
+<li>Bielicki JA, Sharland M, Johnson AP, Henderson KL, Cromwell DA.
+Selecting appropriate empirical antibiotic regimens for paediatric
+bloodstream infections: application of a Bayesian decision model to
+local and pooled antimicrobial resistance surveillance data. <em>J
+Antimicrob Chemother.</em> 2016;71(3):794-802. <a href="https://doi.org/10.1093/jac/dkv397" class="external-link uri">https://doi.org/10.1093/jac/dkv397</a>
+</li>
+<li>Cook A, Sharland M, Yau Y, Bielicki J. Improving empiric antibiotic
+prescribing in pediatric bloodstream infections: a potential application
+of weighted-incidence syndromic combination antibiograms (WISCA).
+<em>Expert Rev Anti Infect Ther.</em> 2022;20(3):445-456. <a href="https://doi.org/10.1080/14787210.2021.1967145" class="external-link uri">https://doi.org/10.1080/14787210.2021.1967145</a>
+</li>
+</ol>
+</div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside>
+</div>
+
+
+
+    <footer><div class="pkgdown-footer-left">
+  <p><code>AMR</code> (for R). Free and open-source, licenced under the <a target="_blank" href="https://github.com/msberends/AMR/blob/main/LICENSE" class="external-link">GNU GPL 2.0</a>. Developed at the <a target="_blank" href="https://www.rug.nl" class="external-link">University of Groningen</a> and <a target="_blank" href="https://www.umcg.nl" class="external-link">University Medical Center Groningen</a> in The Netherlands, in collaboration with <a href="https://amr-for-r.org/authors.html">many colleagues from around the world</a>.</p>
+</div>
+
+<div class="pkgdown-footer-right">
+  <p><a target="_blank" href="https://www.rug.nl" class="external-link"><img src="https://amr-for-r.org/logo_rug.svg" style="max-width: 150px;"></a><a target="_blank" href="https://www.umcg.nl" class="external-link"><img src="https://amr-for-r.org/logo_umcg.svg" style="max-width: 150px;"></a></p>
+</div>
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+</div>
+
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+  </body>
+</html>
@@ -0,0 +1,443 @@
+# Estimating Empirical Coverage with WISCA
+
+## Why WISCA?
+
+When a clinician starts empirical antimicrobial therapy, the causative
+pathogen is unknown. The question they need answered is not *“what
+proportion of* E. coli *is susceptible to ciprofloxacin?“* but rather
+*“what is the probability that this regimen will adequately cover
+whatever pathogen turns out to be causing my patient’s infection?”*
+
+The traditional cumulative antibiogram, as standardised by CLSI M39,
+cannot answer that question. It presents susceptibility percentages per
+species per antibiotic, but:
+
+- **It fragments information by organism.** The clinician must mentally
+  combine susceptibility rates across multiple species, weighting by how
+  often each species causes the syndrome, a calculation nobody does at
+  the bedside.
+- **It ignores pathogen incidence.** A species that causes 2% of
+  infections is given the same visual weight as one that causes 60%.
+- **It does not evaluate combination regimens.** Much empirical therapy
+  consists of two or more agents, but the traditional antibiogram only
+  shows monotherapy per organism.
+- **It provides no measure of uncertainty.** A reported “90%
+  susceptible” based on 50 isolates has a 95% confidence interval of
+  roughly 78-97% (Clopper-Pearson), yet the antibiogram presents it as a
+  point estimate without context.
+
+**WISCA** (Weighted-Incidence Syndromic Combination Antibiogram)
+resolves all four limitations. It estimates the probability that a
+regimen will provide adequate empirical coverage for a given infection
+syndrome, weighted by local pathogen incidence, with full uncertainty
+quantification via Bayesian inference.
+
+The concept was introduced by Hebert *et al.* (2012), who demonstrated
+that traditional antibiogram susceptibility rates could be misleading:
+ciprofloxacin appeared 84% effective against *E. coli* in the
+traditional antibiogram, but WISCA revealed only 62% coverage for UTI
+and 37% for abdominal infections, because enterococci (intrinsically
+resistant) and other species contribute substantially to these
+syndromes. Randhawa *et al.* (2014) showed that WISCA-guided regimen
+selection could improve time-to-adequate-coverage on the ICU by over
+40%. Bielicki *et al.* (2016) introduced the Bayesian framework now used
+in this package, enabling credible intervals and multi-centre pooling.
+Cook *et al.* (2022) applied it globally across 52 hospitals in 23
+countries.
+
+## The idea
+
+WISCA asks:
+
+> “What is the **probability** that this regimen **will cover** the
+> pathogen, given the syndrome?”
+
+This means combining two quantities:
+
+- **Pathogen incidence** in the syndrome (how often each species causes
+  it),
+- **Susceptibility** of each pathogen to the regimen.
+
+We can write this as:
+
+``` math
+\text{Coverage} = \sum_i (\text{Incidence}_i \times \text{Susceptibility}_i)
+```
+
+For example, suppose in your hospital:
+
+- *E. coli* causes 60% of UTIs, and 90% of *E. coli* are susceptible to
+  a drug.
+- *Klebsiella* causes 40% of UTIs, and 70% of *Klebsiella* are
+  susceptible.
+
+Then:
+
+``` math
+\text{Coverage} = (0.6 \times 0.9) + (0.4 \times 0.7) = 0.82
+```
+
+That 82% is a far more clinically meaningful number than the
+species-level “90% of *E. coli*” and “70% of *Klebsiella*” reported
+separately in a traditional antibiogram, because it directly answers the
+question the clinician actually faces.
+
+But in real data, both incidence and susceptibility are **estimated from
+finite samples**, so they carry uncertainty. A sample of 50 isolates is
+not a census. WISCA models this uncertainty **probabilistically**, using
+conjugate Bayesian distributions.
+
+## The Bayesian engine
+
+### Pathogen incidence
+
+Let:
+
+- $`K`$ be the number of pathogens,
+- $`\boldsymbol{\alpha} = (1, 1, \ldots, 1)`$ be a $`\text{Dirichlet}`$
+  prior (uniform, non-informative),
+- $`\boldsymbol{n} = (n_1, \ldots, n_K)`$ be the observed isolate counts
+  per species.
+
+Then the posterior incidence is:
+
+``` math
+\boldsymbol{p} \sim \text{Dirichlet}(\alpha_1 + n_1, \ldots, \alpha_K + n_K)
+```
+
+To simulate from this, we use:
+
+``` math
+x_i \sim \text{Gamma}(\alpha_i + n_i,\ 1), \quad p_i = \frac{x_i}{\sum_{j=1}^{K} x_j}
+```
+
+The Dirichlet is the conjugate prior for multinomial data. With the
+non-informative prior $`\text{Dirichlet}(1, 1, \ldots, 1)`$, the
+posterior is dominated by the data once sample sizes are reasonable.
+With small samples, the posterior is appropriately more diffuse,
+reflecting genuine uncertainty, and the resulting credible intervals
+will be wider.
+
+### Susceptibility
+
+Each pathogen-regimen pair has a prior and observed data:
+
+- Default prior: $`\text{Beta}(0.5, 0.5)`$ (Jeffreys prior)
+- Intrinsically resistant pairs: $`\text{Beta}(1, 9999)`$, forcing
+  near-zero susceptibility regardless of observed data (based on EUCAST
+  Expected Resistant Phenotypes)
+- Data: $`S`$ susceptible out of $`N`$ tested
+
+The $`S`$ category could also include values SDD (susceptible,
+dose-dependent) and I (intermediate \[CLSI\], or susceptible, increased
+exposure \[EUCAST\]).
+
+Then the posterior is:
+
+``` math
+\theta \sim \text{Beta}(\alpha_0 + S,\ \beta_0 + N - S)
+```
+
+### Final coverage estimate
+
+Putting it together:
+
+1.  Simulate pathogen incidence:
+    $`\boldsymbol{p} \sim \text{Dirichlet}`$
+2.  Simulate susceptibility:
+    $`\theta_i \sim \text{Beta}(\alpha_0 + S_i,\ \beta_0 + N_i - S_i)`$
+3.  Combine:
+
+``` math
+\text{Coverage} = \sum_{i=1}^{K} p_i \cdot \theta_i
+```
+
+Repeat this simulation (e.g., 1000 times) and summarise:
+
+- **Mean** = expected coverage
+- **Quantiles** = credible interval (95% by default)
+
+Because each simulation draws from the full posterior, the resulting
+distribution of coverage estimates naturally captures the joint
+uncertainty in both pathogen incidence and susceptibility. The credible
+interval tells you how confident you can be in the coverage estimate,
+something a traditional antibiogram never provides.
+
+## When to use WISCA vs. traditional antibiograms
+
+| Goal                                  | Recommended approach      |
+|---------------------------------------|---------------------------|
+| Guide empirical therapy decisions     | **WISCA**                 |
+| Compare regimens for a syndrome       | **WISCA**                 |
+| Evaluate combination regimens         | **WISCA**                 |
+| Antimicrobial stewardship (A-team)    | **WISCA**                 |
+| Track resistance trends per species   | Traditional / Combination |
+| AMR surveillance reporting            | Traditional / Syndromic   |
+| Understand species-level epidemiology | Traditional               |
+
+In short: if the end goal involves a *patient* who does not yet have a
+culture result, WISCA is the appropriate tool. If the end goal is
+*surveillance* of resistance at the species level, the traditional
+antibiogram remains fit for purpose.
+
+## Practical use in the `AMR` package
+
+### Prepare data
+
+``` r
+
+library(AMR)
+data <- example_isolates
+
+# Structure of our data
+data
+#> # A tibble: 2,000 × 46
+#>    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX  
+#>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir>
+#>  1 2002-01-02 A77334     65 F      Clinical B_ESCHR_COLI   R     NA    NA    NA 
+#>  2 2002-01-03 A77334     65 F      Clinical B_ESCHR_COLI   R     NA    NA    NA 
+#>  3 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA 
+#>  4 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA 
+#>  5 2002-01-13 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA 
+#>  6 2002-01-13 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA 
+#>  7 2002-01-14 462729     78 M      Clinical B_STPHY_AURS   R     NA    S     R  
+#>  8 2002-01-14 462729     78 M      Clinical B_STPHY_AURS   R     NA    S     R  
+#>  9 2002-01-16 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA 
+#> 10 2002-01-17 858515     79 F      ICU      B_STPHY_EPDR   R     NA    S     NA 
+#> # ℹ 1,990 more rows
+#> # ℹ 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, CZO <sir>, FEP <sir>,
+#> #   CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, CRO <sir>, GEN <sir>,
+#> #   TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, SXT <sir>, NIT <sir>,
+#> #   FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, VAN <sir>, TEC <sir>,
+#> #   TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, CLI <sir>, AZM <sir>,
+#> #   IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, COL <sir>, MUP <sir>, …
+
+# Add a synthetic syndrome column for demonstration
+data$syndrome <- ifelse(data$mo %like% "coli", "UTI", "Non-UTI")
+
+# Keep only 10 most common microorganisms
+data <- top_n_microorganisms(data, n = 10, property = "species")
+#> ℹ Using column mo as input for `col_mo`.
+```
+
+### Basic WISCA
+
+``` r
+
+wisca(data,
+  antimicrobials = c("AMC", "CIP", "GEN")
+)
+```
+
+| Amoxicillin/clavulanic acid | Ciprofloxacin      | Gentamicin       |
+|:----------------------------|:-------------------|:-----------------|
+| 76.8% (74.7-79.1%)          | 81.5% (78.9-84.1%) | 82.9% (81-84.8%) |
+
+### Use combination regimens
+
+Combination regimens are specified with a `+` separator. WISCA evaluates
+whether *at least one* agent in the combination covers the pathogen:
+
+``` r
+
+wisca(data,
+  antimicrobials = c("AMC", "AMC + CIP", "AMC + GEN")
+)
+```
+
+| Amoxicillin/clavulanic acid | Amoxicillin/clavulanic acid + Ciprofloxacin | Amoxicillin/clavulanic acid + Gentamicin |
+|:---|:---|:---|
+| 76.8% (74.6-78.9%) | 89.6% (88-91.1%) | 93.7% (92.5-94.9%) |
+
+### Stratify by syndrome
+
+Use `syndromic_group` to produce separate WISCA estimates per clinical
+stratum. You can pass a column name or any expression:
+
+``` r
+
+wisca_out <- wisca(data,
+  antimicrobials = c("AMC", "AMC + CIP", "AMC + GEN"),
+  syndromic_group = "syndrome"
+)
+wisca_out
+```
+
+| Syndromic Group | Amoxicillin/clavulanic acid | Amoxicillin/clavulanic acid + Ciprofloxacin | Amoxicillin/clavulanic acid + Gentamicin |
+|:---|:---|:---|:---|
+| Non-UTI | 72.5% (69.9-75.1%) | 86.9% (84.8-89%) | 91.4% (89.5-93%) |
+| UTI | 86% (82.5-89%) | 94.8% (92.5-96.6%) | 97.9% (96.3-99%) |
+
+The `AMR` package is available in 28 languages, which can all be used
+for the [`wisca()`](https://amr-for-r.org/reference/antibiogram.md)
+function too:
+
+``` r
+
+wisca(data,
+  antimicrobials = c("AMC", "AMC + CIP", "AMC + GEN"),
+  syndromic_group = gsub("UTI", "UCI", data$syndrome),
+  language = "Spanish"
+)
+```
+
+| Grupo sindrómico | Amoxicilina/ácido clavulánico | Amoxicilina/ácido clavulánico + Ciprofloxacina | Amoxicilina/ácido clavulánico + Gentamicina |
+|:---|:---|:---|:---|
+| Non-UCI | 72.6% (69.9-75.3%) | 87% (84.9-89.1%) | 91.4% (89.7-92.9%) |
+| UCI | 86% (82.7-89%) | 94.8% (92.7-96.4%) | 97.9% (96.5-99%) |
+
+### Interpreting the output
+
+Each row shows the estimated empirical coverage for a regimen, with a
+95% credible interval. When comparing regimens:
+
+- **Overlapping credible intervals** mean there is no statistically
+  significant difference in coverage. If a narrower-spectrum regimen
+  overlaps with a broader one, the narrower-spectrum option can be
+  preferred on stewardship grounds.
+- **Non-overlapping credible intervals** indicate a clinically
+  meaningful difference in coverage.
+
+### Plotting
+
+WISCA results can be visualised in several ways. All plot functions work
+on the output of
+[`wisca()`](https://amr-for-r.org/reference/antibiogram.md) (or
+`antibiogram(..., wisca = TRUE)`).
+
+Below we use the `wisca_out` object that was generated above.
+
+#### Coverage with credible intervals
+
+The extended
+[`autoplot()`](https://ggplot2.tidyverse.org/reference/autoplot.html)
+method from the `ggplot2()` package produces a point-and-interval plot
+showing the coverage estimate and 95% credible interval for each
+regimen, grouped by syndromic stratum. This is the most direct way to
+compare regimens: overlapping intervals suggest clinical
+non-inferiority, non-overlapping intervals indicate a meaningful
+difference.
+
+``` r
+
+ggplot2::autoplot(wisca_out)
+```
+
+![](WISCA_files/figure-html/unnamed-chunk-6-1.png)
+
+#### Susceptibility vs. incidence weight
+
+[`wisca_plot()`](https://amr-for-r.org/reference/antibiogram.md)
+produces a scatter plot of the Monte Carlo simulation draws, showing
+each pathogen’s susceptibility (x-axis) against its incidence weight
+(y-axis) for each regimen. Each dot represents one of 1,000 simulated
+draws, so the spread reflects posterior uncertainty. This plot reveals
+*why* a regimen achieves its coverage: you can see which pathogens
+dominate the syndrome (high on the y-axis), how susceptible they are
+(position on the x-axis), and how uncertain both estimates are (spread
+of the cloud). The dashed vertical lines denote the point estimates,
+i.e., the coverage percentages. The ribbon behind the dashed lines
+denote the credible interval, which is 95% at default.
+
+``` r
+
+wisca_plot(wisca_out)
+```
+
+![](WISCA_files/figure-html/unnamed-chunk-7-1.png)
+
+#### Posterior coverage distributions
+
+Setting `wisca_plot_type = "posterior_coverage"` shows the full
+posterior distribution of coverage for each regimen as a density curve.
+This is the most complete representation of what the Bayesian model
+produces: each curve shows the relative likelihood of each coverage
+value across all 1,000 simulations. Narrow, tall peaks indicate high
+certainty; wide, flat curves indicate greater uncertainty. Where two
+curves overlap, the regimens cannot be confidently distinguished.
+
+``` r
+
+wisca_plot(wisca_out, wisca_plot_type = "posterior_coverage")
+```
+
+![](WISCA_files/figure-html/unnamed-chunk-8-1.png)
+
+## Sensible defaults, which can be customised
+
+- `simulations = 1000`: number of Monte Carlo draws
+- `conf_interval = 0.95`: coverage interval width
+- `combine_SI = TRUE`: count “I” and “SDD” as susceptible
+
+## Practical considerations
+
+- **First isolates only**: always deduplicate using
+  [`first_isolate()`](https://amr-for-r.org/reference/first_isolate.md)
+  before running WISCA. Repeat isolates introduce bias.
+- **Pathogen selection**: consider filtering with
+  [`top_n_microorganisms()`](https://amr-for-r.org/reference/top_n_microorganisms.md).
+  Including rare contaminants (e.g. CoNS without clinical context) can
+  distort estimates and may artificially lower coverage (Cook *et al.*,
+  2022).
+- **Sample size**: coverage estimates become reliable with approximately
+  100+ isolates. For smaller datasets, consider pooling data from
+  multiple sites, but only after verifying that pathogen distributions
+  are sufficiently similar (Bielicki *et al.*, 2016).
+- **Culture request bias**: WISCA is only as good as the data it is
+  based on. If cultures are selectively requested (e.g. only after
+  treatment failure), the dataset will be biased towards resistant
+  isolates. A robust culture policy is essential for reliable estimates.
+
+## Limitations
+
+- It assumes your data are representative of the patient population you
+  are treating
+- No direct adjustment for patient-level covariates, although these can
+  be passed onto the `syndromic_group` argument for stratification
+- WISCA does not model resistance trends over time; for that, you might
+  want to use `tidymodels`, for which we [wrote a basic
+  introduction](https://amr-for-r.org/articles/AMR_with_tidymodels.html)
+
+## Summary
+
+WISCA enables:
+
+- **Empirical regimen comparison**, answering the clinician’s actual
+  question
+- **Syndrome-specific coverage estimation**, stratifiable by any
+  clinical variable
+- **Fully probabilistic interpretation**, with credible intervals that
+  honestly communicate uncertainty
+
+It is available in the `AMR` package via either:
+
+``` r
+
+wisca(...)
+
+antibiogram(..., wisca = TRUE)
+```
+
+## References
+
+1.  Hebert C, Ridgway J, Vekhter B, Brown EC, Weber SG, Robicsek A.
+    Demonstration of the weighted-incidence syndromic combination
+    antibiogram: an empiric prescribing decision aid. *Infect Control
+    Hosp Epidemiol.* 2012;33(4):381-388.
+    <https://doi.org/10.1086/664768>
+2.  Randhawa V, Sarwar S, Walker S, Elligsen M, Palmay L, Daneman N.
+    Weighted-incidence syndromic combination antibiograms to guide
+    empiric treatment of critical care infections: a retrospective
+    cohort study. *Crit Care.* 2014;18(3):R112.
+    <https://doi.org/10.1186/cc13901>
+3.  Bielicki JA, Sharland M, Johnson AP, Henderson KL, Cromwell DA.
+    Selecting appropriate empirical antibiotic regimens for paediatric
+    bloodstream infections: application of a Bayesian decision model to
+    local and pooled antimicrobial resistance surveillance data. *J
+    Antimicrob Chemother.* 2016;71(3):794-802.
+    <https://doi.org/10.1093/jac/dkv397>
+4.  Cook A, Sharland M, Yau Y, Bielicki J. Improving empiric antibiotic
+    prescribing in pediatric bloodstream infections: a potential
+    application of weighted-incidence syndromic combination antibiograms
+    (WISCA). *Expert Rev Anti Infect Ther.* 2022;20(3):445-456.
+    <https://doi.org/10.1080/14787210.2021.1967145>
@@ -0,0 +1,565 @@
+# Download data sets for download / own use
+
+All reference data (about microorganisms, antimicrobials, SIR
+interpretation, EUCAST rules, etc.) in this `AMR` package are reliable,
+up-to-date and freely available. We continually export our data sets to
+formats for use in R, MS Excel, Apache Feather, Apache Parquet, SPSS,
+and Stata. We also provide tab-separated text files that are
+machine-readable and suitable for input in any software program, such as
+laboratory information systems.
+
+> If you are working in Python, be sure to use our [AMR for
+> Python](https://amr-for-r.org/articles/AMR_for_Python.html) package.
+> It allows all relevant AMR data sets to be natively available in
+> Python.
+
+## `microorganisms`: Full Microbial Taxonomy
+
+A data set with 96 982 rows and 28 columns, containing the following
+column names:  
+*mo*, *fullname*, *status*, *domain*, *kingdom*, *phylum*, *class*,
+*order*, *family*, *genus*, *species*, *subspecies*, *rank*, *ref*,
+*oxygen_tolerance*, *morphology*, *source*, *lpsn*, *lpsn_parent*,
+*lpsn_renamed_to*, *mycobank*, *mycobank_parent*, *mycobank_renamed_to*,
+*gbif*, *gbif_parent*, *gbif_renamed_to*, *prevalence*, and *snomed*.
+
+This data set is in R available as `microorganisms`, after you load the
+`AMR` package.
+
+It was last updated on 22 June 2026 23:38:13 UTC. Find more info about
+the contents, (scientific) source, and structure of this [data set
+here](https://amr-for-r.org/reference/microorganisms.html).
+
+**Direct download links:**
+
+- Download as [original R Data Structure (RDS)
+  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.rds)
+  (2.2 MB)  
+- Download as [tab-separated text
+  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.txt)
+  (23.1 MB)  
+- Download as [Microsoft Excel
+  workbook](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.xlsx)
+  (11.4 MB)  
+- Download as [Apache Feather
+  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.feather)
+  (11 MB)  
+- Download as [Apache Parquet
+  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.parquet)
+  (4.6 MB)  
+- Download as [IBM SPSS Statistics data
+  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.sav)
+  (35.2 MB)  
+- Download as [Stata DTA
+  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.dta)
+  (96.6 MB)
+
+**NOTE: The exported files for SPSS and Stata contain only the first 50
+SNOMED codes per record, as their file size would otherwise exceed 100
+MB; the file size limit of GitHub.** Their file structures and
+compression techniques are very inefficient. Advice? Use R instead. It’s
+free and much better in many ways.
+
+The tab-separated text file and Microsoft Excel workbook both contain
+all SNOMED codes as comma separated values.
+
+**Example content**
+
+Included (sub)species per taxonomic kingdom:
+
+|      Kingdom      | Number of (sub)species |
+|:-----------------:|:----------------------:|
+|                   |           20           |
+| (unknown kingdom) |           8            |
+|     Animalia      |         2 015          |
+|      Archaea      |          150           |
+|     Bacillati     |         24 200         |
+|     Bacteria      |           2            |
+
+First 6 rows when filtering on genus *Escherichia*:
+
+| mo | fullname | status | domain | kingdom | phylum | class | order | family | genus | species | subspecies | rank | ref | oxygen_tolerance | morphology | source | lpsn | lpsn_parent | lpsn_renamed_to | mycobank | mycobank_parent | mycobank_renamed_to | gbif | gbif_parent | gbif_renamed_to | prevalence | snomed |
+|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|
+| B_ESCHR | Escherichia | accepted | Bacteria | Pseudomonadati | Pseudomonadota | Gammaproteobacteria | Enterobacterales | Enterobacteriaceae | Escherichia |  |  | genus | Castellani et al., 1919 | facultative anaerobe | rods | LPSN | 515602 | 482 |  |  |  |  | CS33H | CRYWR |  | 1 | 407310004, 407251000, 407281008, … |
+| B_ESCHR_ADCR | Escherichia adecarboxylata | synonym | Bacteria | Pseudomonadati | Pseudomonadota | Gammaproteobacteria | Enterobacterales | Enterobacteriaceae | Escherichia | adecarboxylata |  | species | Leclerc, 1962 | likely facultative anaerobe | rods | LPSN | 776052 | 515602 | 777447 |  |  |  | CS33J | CS33H | 3SVX6 | 1 |  |
+| B_ESCHR_ALBR | Escherichia albertii | accepted | Bacteria | Pseudomonadati | Pseudomonadota | Gammaproteobacteria | Enterobacterales | Enterobacteriaceae | Escherichia | albertii |  | species | Huys et al., 2003 | facultative anaerobe | rods | LPSN | 776053 | 515602 |  |  |  |  | 3BGTB | CS33H |  | 1 | 419388003 |
+| B_ESCHR_BLTT | Escherichia blattae | synonym | Bacteria | Pseudomonadati | Pseudomonadota | Gammaproteobacteria | Enterobacterales | Enterobacteriaceae | Escherichia | blattae |  | species | Burgess et al., 1973 | likely facultative anaerobe | rods | LPSN | 776056 | 515602 | 788468 |  |  |  | CS33K | CS33H | 4X4P7 | 1 |  |
+| B_ESCHR_COLI | Escherichia coli | accepted | Bacteria | Pseudomonadati | Pseudomonadota | Gammaproteobacteria | Enterobacterales | Enterobacteriaceae | Escherichia | coli |  | species | Castellani et al., 1919 | facultative anaerobe | rods | LPSN | 776057 | 515602 |  |  |  |  | NT3L7 | CS33H |  | 1 | 1095001000112106, 715307006, 737528008, … |
+| B_ESCHR_COLI_COLI | Escherichia coli coli | accepted | Bacteria | Pseudomonadati | Pseudomonadota | Gammaproteobacteria | Enterobacterales | Enterobacteriaceae | Escherichia | coli | coli | subspecies |  |  |  | GBIF |  | 776057 |  |  |  |  | 12233256 | NT3L7 |  | 1 |  |
+
+------------------------------------------------------------------------
+
+## `antimicrobials`: Antibiotic and Antifungal Drugs
+
+A data set with 505 rows and 14 columns, containing the following column
+names:  
+*ab*, *cid*, *name*, *group*, *atc*, *atc_group1*, *atc_group2*,
+*abbreviations*, *synonyms*, *oral_ddd*, *oral_units*, *iv_ddd*,
+*iv_units*, and *loinc*.
+
+This data set is in R available as `antimicrobials`, after you load the
+`AMR` package.
+
+It was last updated on 23 June 2026 12:38:59 UTC. Find more info about
+the contents, (scientific) source, and structure of this [data set
+here](https://amr-for-r.org/reference/antimicrobials.html).
+
+**Direct download links:**
+
+- Download as [original R Data Structure (RDS)
+  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/antimicrobials.rds)
+  (44 kB)  
+- Download as [tab-separated text
+  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/antimicrobials.txt)
+  (0.1 MB)  
+- Download as [Microsoft Excel
+  workbook](https://github.com/msberends/AMR/raw/main/data-raw/datasets/antimicrobials.xlsx)
+  (79 kB)  
+- Download as [Apache Feather
+  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/antimicrobials.feather)
+  (0.1 MB)  
+- Download as [Apache Parquet
+  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/antimicrobials.parquet)
+  (94 kB)  
+- Download as [IBM SPSS Statistics data
+  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/antimicrobials.sav)
+  (0.4 MB)  
+- Download as [Stata DTA
+  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/antimicrobials.dta)
+  (10 kB)
+
+The tab-separated text, Microsoft Excel, SPSS, and Stata files all
+contain the ATC codes, common abbreviations, trade names and LOINC codes
+as comma separated values.
+
+**Example content**
+
+| ab | cid | name | group | atc | atc_group1 | atc_group2 | abbreviations | synonyms | oral_ddd | oral_units | iv_ddd | iv_units | loinc |
+|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|
+| AMK | 37768 | Amikacin | Aminoglycosides | D06AX12, J01GB06, QD06AX12, … | Aminoglycoside antibacterials | Other aminoglycosides | ak, ami, amik, … | amikacillin, amikacina, amikacine, … |  |  | 1.0 | g | 101493-5, 11-7, 12-5, … |
+| AMX | 33613 | Amoxicillin | Aminopenicillins, Penicillins, Beta-lactams | J01CA04, QG51AA03, QJ01CA04 | Beta-lactam antibacterials, penicillins | Penicillins with extended spectrum | ac, amox, amoxic, … | acuotricina, alfamox, alfida, … | 1.5 | g | 3.0 | g | 101498-4, 15-8, 16-6, … |
+| AMC | 23665637 | Amoxicillin/clavulanic acid | Aminopenicillins, Penicillins, Beta-lactams, … | J01CR02, QJ01CR02 | Beta-lactam antibacterials, penicillins | Combinations of penicillins, incl. beta-lactamase inhibitors | a/c, amcl, aml, … | amocla, amoclan, amoclav, … | 1.5 | g | 3.0 | g |  |
+| AMP | 6249 | Ampicillin | Aminopenicillins, Penicillins, Beta-lactams | J01CA01, QJ01CA01, QJ51CA01, … | Beta-lactam antibacterials, penicillins | Penicillins with extended spectrum | am, amp, amp100, … | adobacillin, alpen, amblosin, … | 2.0 | g | 6.0 | g | 101477-8, 101478-6, 18864-9, … |
+| AZM | 447043 | Azithromycin | Macrolides | J01FA10, QJ01FA10, QS01AA26, … | Macrolides, lincosamides and streptogramins | Macrolides | az, azi, azit, … | aritromicina, aruzilina, azasite, … | 0.3 | g | 0.5 | g | 100043-9, 16420-2, 16421-0, … |
+| PEN | 5904 | Benzylpenicillin | Penicillins, Beta-lactams | J01CE01, QJ01CE01, QJ51CE01, … | Combinations of antibacterials | Combinations of antibacterials | bepe, pen, peni, … | bencilpenicilina, benzopenicillin, benzylpenicilline, … |  |  | 3.6 | g |  |
+
+------------------------------------------------------------------------
+
+## `clinical_breakpoints`: Interpretation from MIC values & disk diameters to SIR
+
+A data set with 45 555 rows and 14 columns, containing the following
+column names:  
+*guideline*, *type*, *host*, *method*, *site*, *mo*, *rank_index*, *ab*,
+*ref_tbl*, *disk_dose*, *breakpoint_S*, *breakpoint_R*, *uti*, and
+*is_SDD*.
+
+This data set is in R available as `clinical_breakpoints`, after you
+load the `AMR` package.
+
+It was last updated on 22 June 2026 23:38:13 UTC. Find more info about
+the contents, (scientific) source, and structure of this [data set
+here](https://amr-for-r.org/reference/clinical_breakpoints.html).
+
+**Direct download links:**
+
+- Download as [original R Data Structure (RDS)
+  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/clinical_breakpoints.rds)
+  (92 kB)  
+- Download as [tab-separated text
+  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/clinical_breakpoints.txt)
+  (4.2 MB)  
+- Download as [Microsoft Excel
+  workbook](https://github.com/msberends/AMR/raw/main/data-raw/datasets/clinical_breakpoints.xlsx)
+  (2.7 MB)  
+- Download as [Apache Feather
+  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/clinical_breakpoints.feather)
+  (2 MB)  
+- Download as [Apache Parquet
+  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/clinical_breakpoints.parquet)
+  (0.1 MB)  
+- Download as [IBM SPSS Statistics data
+  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/clinical_breakpoints.sav)
+  (7.5 MB)  
+- Download as [Stata DTA
+  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/clinical_breakpoints.dta)
+  (12.6 MB)
+
+**Example content**
+
+| guideline | type | host | method | site | mo | mo_name | rank_index | ab | ab_name | ref_tbl | disk_dose | breakpoint_S | breakpoint_R | uti | is_SDD |
+|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|
+| EUCAST 2026 | human | human | DISK |  | B_ACHRMB_XYLS | Achromobacter xylosoxidans | 2 | MEM | Meropenem | A. xylosoxidans | 10 mcg | 26.000 | 20.000 | FALSE | FALSE |
+| EUCAST 2026 | human | human | MIC |  | B_ACHRMB_XYLS | Achromobacter xylosoxidans | 2 | MEM | Meropenem | A. xylosoxidans |  | 1.000 | 4.000 | FALSE | FALSE |
+| EUCAST 2026 | human | human | DISK |  | B_ACHRMB_XYLS | Achromobacter xylosoxidans | 2 | SXT | Trimethoprim/sulfamethoxazole | A. xylosoxidans | 1.25/23.75 mcg | 26.000 | 26.000 | FALSE | FALSE |
+| EUCAST 2026 | human | human | MIC |  | B_ACHRMB_XYLS | Achromobacter xylosoxidans | 2 | SXT | Trimethoprim/sulfamethoxazole | A. xylosoxidans |  | 0.125 | 0.125 | FALSE | FALSE |
+| EUCAST 2026 | human | human | DISK |  | B_ACHRMB_XYLS | Achromobacter xylosoxidans | 2 | TZP | Piperacillin/tazobactam | A. xylosoxidans | 30/6 mcg | 26.000 | 26.000 | FALSE | FALSE |
+| EUCAST 2026 | human | human | MIC |  | B_ACHRMB_XYLS | Achromobacter xylosoxidans | 2 | TZP | Piperacillin/tazobactam | A. xylosoxidans |  | 4.000 | 4.000 | FALSE | FALSE |
+
+------------------------------------------------------------------------
+
+## `microorganisms.groups`: Species Groups and Microbiological Complexes
+
+A data set with 530 rows and 4 columns, containing the following column
+names:  
+*mo_group*, *mo*, *mo_group_name*, and *mo_name*.
+
+This data set is in R available as `microorganisms.groups`, after you
+load the `AMR` package.
+
+It was last updated on 22 June 2026 23:38:13 UTC. Find more info about
+the contents, (scientific) source, and structure of this [data set
+here](https://amr-for-r.org/reference/microorganisms.groups.html).
+
+**Direct download links:**
+
+- Download as [original R Data Structure (RDS)
+  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.groups.rds)
+  (6 kB)  
+- Download as [tab-separated text
+  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.groups.txt)
+  (50 kB)  
+- Download as [Microsoft Excel
+  workbook](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.groups.xlsx)
+  (19 kB)  
+- Download as [Apache Feather
+  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.groups.feather)
+  (19 kB)  
+- Download as [Apache Parquet
+  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.groups.parquet)
+  (13 kB)  
+- Download as [IBM SPSS Statistics data
+  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.groups.sav)
+  (64 kB)  
+- Download as [Stata DTA
+  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.groups.dta)
+  (82 kB)
+
+**Example content**
+
+| mo_group | mo | mo_group_name | mo_name |
+|:--:|:--:|:--:|:--:|
+| B_ACNTB_BMNN-C | B_ACNTB_BMNN | Acinetobacter baumannii complex | Acinetobacter baumannii |
+| B_ACNTB_BMNN-C | B_ACNTB_CLCC | Acinetobacter baumannii complex | Acinetobacter calcoaceticus |
+| B_ACNTB_BMNN-C | B_ACNTB_LCTC | Acinetobacter baumannii complex | Acinetobacter dijkshoorniae |
+| B_ACNTB_BMNN-C | B_ACNTB_NSCM | Acinetobacter baumannii complex | Acinetobacter nosocomialis |
+| B_ACNTB_BMNN-C | B_ACNTB_PITT | Acinetobacter baumannii complex | Acinetobacter pittii |
+| B_ACNTB_BMNN-C | B_ACNTB_SFRT | Acinetobacter baumannii complex | Acinetobacter seifertii |
+
+------------------------------------------------------------------------
+
+## `intrinsic_resistant`: Intrinsic Bacterial Resistance
+
+A data set with 294 079 rows and 2 columns, containing the following
+column names:  
+*mo* and *ab*.
+
+This data set is in R available as `intrinsic_resistant`, after you load
+the `AMR` package.
+
+It was last updated on 22 June 2026 23:38:13 UTC. Find more info about
+the contents, (scientific) source, and structure of this [data set
+here](https://amr-for-r.org/reference/intrinsic_resistant.html).
+
+**Direct download links:**
+
+- Download as [original R Data Structure (RDS)
+  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/intrinsic_resistant.rds)
+  (0.1 MB)  
+- Download as [tab-separated text
+  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/intrinsic_resistant.txt)
+  (10.9 MB)  
+- Download as [Microsoft Excel
+  workbook](https://github.com/msberends/AMR/raw/main/data-raw/datasets/intrinsic_resistant.xlsx)
+  (3.1 MB)  
+- Download as [Apache Feather
+  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/intrinsic_resistant.feather)
+  (2.5 MB)  
+- Download as [Apache Parquet
+  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/intrinsic_resistant.parquet)
+  (0.3 MB)  
+- Download as [IBM SPSS Statistics data
+  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/intrinsic_resistant.sav)
+  (16 MB)  
+- Download as [Stata DTA
+  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/intrinsic_resistant.dta)
+  (28.6 MB)
+
+**Example content**
+
+Example rows when filtering on *Enterobacter cloacae*:
+
+|    microorganism     |         antibiotic          |
+|:--------------------:|:---------------------------:|
+| Enterobacter cloacae |      Acetylmidecamycin      |
+| Enterobacter cloacae |      Acetylspiramycin       |
+| Enterobacter cloacae |         Amoxicillin         |
+| Enterobacter cloacae | Amoxicillin/clavulanic acid |
+| Enterobacter cloacae |         Ampicillin          |
+| Enterobacter cloacae |    Ampicillin/sulbactam     |
+| Enterobacter cloacae |          Avoparcin          |
+| Enterobacter cloacae |        Azithromycin         |
+| Enterobacter cloacae |      Benzylpenicillin       |
+| Enterobacter cloacae |          Bleomycin          |
+| Enterobacter cloacae |          Cadazolid          |
+| Enterobacter cloacae |         Cefadroxil          |
+| Enterobacter cloacae |          Cefalexin          |
+| Enterobacter cloacae |          Cefalotin          |
+| Enterobacter cloacae |          Cefazolin          |
+| Enterobacter cloacae |          Cefoxitin          |
+| Enterobacter cloacae |       Clarithromycin        |
+| Enterobacter cloacae |         Clindamycin         |
+| Enterobacter cloacae |         Cycloserine         |
+| Enterobacter cloacae |         Dalbavancin         |
+| Enterobacter cloacae |        Dirithromycin        |
+| Enterobacter cloacae |        Erythromycin         |
+| Enterobacter cloacae |       Flurithromycin        |
+| Enterobacter cloacae |        Fusidic acid         |
+| Enterobacter cloacae |        Gamithromycin        |
+| Enterobacter cloacae |          Josamycin          |
+| Enterobacter cloacae |         Kitasamycin         |
+| Enterobacter cloacae |         Lincomycin          |
+| Enterobacter cloacae |          Linezolid          |
+| Enterobacter cloacae |         Meleumycin          |
+| Enterobacter cloacae |         Midecamycin         |
+| Enterobacter cloacae |         Miocamycin          |
+| Enterobacter cloacae |        Nafithromycin        |
+| Enterobacter cloacae |        Norvancomycin        |
+| Enterobacter cloacae |        Oleandomycin         |
+| Enterobacter cloacae |         Oritavancin         |
+| Enterobacter cloacae |        Ostreogrycin         |
+| Enterobacter cloacae |         Pirlimycin          |
+| Enterobacter cloacae |          Primycin           |
+| Enterobacter cloacae |        Pristinamycin        |
+| Enterobacter cloacae |  Quinupristin/dalfopristin  |
+| Enterobacter cloacae |         Ramoplanin          |
+| Enterobacter cloacae |         Rifampicin          |
+| Enterobacter cloacae |         Rokitamycin         |
+| Enterobacter cloacae |        Roxithromycin        |
+| Enterobacter cloacae |        Solithromycin        |
+| Enterobacter cloacae |         Spiramycin          |
+| Enterobacter cloacae |          Tedizolid          |
+| Enterobacter cloacae |         Teicoplanin         |
+| Enterobacter cloacae |         Telavancin          |
+| Enterobacter cloacae |        Telithromycin        |
+| Enterobacter cloacae |        Thiacetazone         |
+| Enterobacter cloacae |        Tildipirosin         |
+| Enterobacter cloacae |         Tilmicosin          |
+| Enterobacter cloacae |       Troleandomycin        |
+| Enterobacter cloacae |        Tulathromycin        |
+| Enterobacter cloacae |           Tylosin           |
+| Enterobacter cloacae |         Tylvalosin          |
+| Enterobacter cloacae |         Vancomycin          |
+| Enterobacter cloacae |       Virginiamycine        |
+| Enterobacter cloacae |         Zorbamycin          |
+
+------------------------------------------------------------------------
+
+## `dosage`: Dosage Guidelines from EUCAST
+
+A data set with 759 rows and 9 columns, containing the following column
+names:  
+*ab*, *name*, *type*, *dose*, *dose_times*, *administration*, *notes*,
+*original_txt*, and *eucast_version*.
+
+This data set is in R available as `dosage`, after you load the `AMR`
+package.
+
+It was last updated on 20 April 2025 10:55:31 UTC. Find more info about
+the contents, (scientific) source, and structure of this [data set
+here](https://amr-for-r.org/reference/dosage.html).
+
+**Direct download links:**
+
+- Download as [original R Data Structure (RDS)
+  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/dosage.rds)
+  (4 kB)  
+- Download as [tab-separated text
+  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/dosage.txt)
+  (66 kB)  
+- Download as [Microsoft Excel
+  workbook](https://github.com/msberends/AMR/raw/main/data-raw/datasets/dosage.xlsx)
+  (37 kB)  
+- Download as [Apache Feather
+  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/dosage.feather)
+  (28 kB)  
+- Download as [Apache Parquet
+  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/dosage.parquet)
+  (9 kB)  
+- Download as [IBM SPSS Statistics data
+  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/dosage.sav)
+  (97 kB)  
+- Download as [Stata DTA
+  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/dosage.dta)
+  (0.2 MB)
+
+**Example content**
+
+| ab | name | type | dose | dose_times | administration | notes | original_txt | eucast_version |
+|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|
+| AMK | Amikacin | standard_dosage | 25-30 mg/kg | 1 | iv |  | 25-30 mg/kg x 1 iv | 15 |
+| AMX | Amoxicillin | high_dosage | 2 g | 6 | iv |  | 2 g x 6 iv | 15 |
+| AMX | Amoxicillin | standard_dosage | 1 g | 3 | iv |  | 1 g x 3-4 iv | 15 |
+| AMX | Amoxicillin | high_dosage | 0.75-1 g | 3 | oral |  | 0.75-1 g x 3 oral | 15 |
+| AMX | Amoxicillin | standard_dosage | 0.5 g | 3 | oral |  | 0.5 g x 3 oral | 15 |
+| AMX | Amoxicillin | uncomplicated_uti | 0.5 g | 3 | oral |  | 0.5 g x 3 oral | 15 |
+
+------------------------------------------------------------------------
+
+## `example_isolates`: Example Data for Practice
+
+A data set with 2 000 rows and 46 columns, containing the following
+column names:  
+*date*, *patient*, *age*, *gender*, *ward*, *mo*, *PEN*, *OXA*, *FLC*,
+*AMX*, *AMC*, *AMP*, *TZP*, *CZO*, *FEP*, *CXM*, *FOX*, *CTX*, *CAZ*,
+*CRO*, *GEN*, *TOB*, *AMK*, *KAN*, *TMP*, *SXT*, *NIT*, *FOS*, *LNZ*,
+*CIP*, *MFX*, *VAN*, *TEC*, *TCY*, *TGC*, *DOX*, *ERY*, *CLI*, *AZM*,
+*IPM*, *MEM*, *MTR*, *CHL*, *COL*, *MUP*, and *RIF*.
+
+This data set is in R available as `example_isolates`, after you load
+the `AMR` package.
+
+It was last updated on 24 June 2026 16:36:47 UTC. Find more info about
+the contents, (scientific) source, and structure of this [data set
+here](https://amr-for-r.org/reference/example_isolates.html).
+
+**Example content**
+
+| date | patient | age | gender | ward | mo | PEN | OXA | FLC | AMX | AMC | AMP | TZP | CZO | FEP | CXM | FOX | CTX | CAZ | CRO | GEN | TOB | AMK | KAN | TMP | SXT | NIT | FOS | LNZ | CIP | MFX | VAN | TEC | TCY | TGC | DOX | ERY | CLI | AZM | IPM | MEM | MTR | CHL | COL | MUP | RIF |
+|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|
+| 2002-01-02 | A77334 | 65 | F | Clinical | B_ESCHR_COLI | R |  |  |  | I |  |  |  |  | I |  |  |  |  |  |  |  |  | R | R |  |  | R |  |  | R | R | R |  |  | R | R | R |  |  |  |  |  |  | R |
+| 2002-01-03 | A77334 | 65 | F | Clinical | B_ESCHR_COLI | R |  |  |  | I |  |  |  |  | I |  |  |  |  |  |  |  |  | R | R |  |  | R |  |  | R | R | R |  |  | R | R | R |  |  |  |  |  |  | R |
+| 2002-01-07 | 067927 | 45 | F | ICU | B_STPHY_EPDR | R |  | R |  |  |  |  |  |  | R |  |  | R |  |  |  |  |  | S | S |  |  |  |  |  | S |  | S | S | S | R |  | R |  |  |  |  | R |  |  |
+| 2002-01-07 | 067927 | 45 | F | ICU | B_STPHY_EPDR | R |  | R |  |  |  |  |  |  | R |  |  | R |  |  |  |  |  | S | S |  |  |  |  |  | S |  | S | S | S | R |  | R |  |  |  |  | R |  |  |
+| 2002-01-13 | 067927 | 45 | F | ICU | B_STPHY_EPDR | R |  | R |  |  |  |  |  |  | R |  |  | R |  |  |  |  |  | R |  |  |  |  |  |  | S |  | S | S | S | R |  | R |  |  |  |  | R |  |  |
+| 2002-01-13 | 067927 | 45 | F | ICU | B_STPHY_EPDR | R |  | R |  |  |  |  |  |  | R |  |  | R |  |  |  |  |  | R |  |  |  |  |  |  | S |  | S | S | S | R | R | R |  |  |  |  | R |  |  |
+
+------------------------------------------------------------------------
+
+## `example_isolates_unclean`: Example Data for Practice
+
+A data set with 3 000 rows and 8 columns, containing the following
+column names:  
+*patient_id*, *hospital*, *date*, *bacteria*, *AMX*, *AMC*, *CIP*, and
+*GEN*.
+
+This data set is in R available as `example_isolates_unclean`, after you
+load the `AMR` package.
+
+It was last updated on 27 August 2022 18:49:37 UTC. Find more info about
+the contents, (scientific) source, and structure of this [data set
+here](https://amr-for-r.org/reference/example_isolates_unclean.html).
+
+**Example content**
+
+| patient_id | hospital |    date    |   bacteria    | AMX | AMC | CIP | GEN |
+|:----------:|:--------:|:----------:|:-------------:|:---:|:---:|:---:|:---:|
+|     J3     |    A     | 2012-11-21 |    E. coli    |  R  |  I  |  S  |  S  |
+|     R7     |    A     | 2018-04-03 | K. pneumoniae |  R  |  I  |  S  |  S  |
+|     P3     |    A     | 2014-09-19 |    E. coli    |  R  |  S  |  S  |  S  |
+|    P10     |    A     | 2015-12-10 |    E. coli    |  S  |  I  |  S  |  S  |
+|     B7     |    A     | 2015-03-02 |    E. coli    |  S  |  S  |  S  |  S  |
+|     W3     |    A     | 2018-03-31 |   S. aureus   |  R  |  S  |  R  |  S  |
+
+------------------------------------------------------------------------
+
+## `microorganisms.codes`: Common Laboratory Codes
+
+A data set with 6 029 rows and 2 columns, containing the following
+column names:  
+*code* and *mo*.
+
+This data set is in R available as `microorganisms.codes`, after you
+load the `AMR` package.
+
+It was last updated on 22 June 2026 23:38:13 UTC. Find more info about
+the contents, (scientific) source, and structure of this [data set
+here](https://amr-for-r.org/reference/microorganisms.codes.html).
+
+**Direct download links:**
+
+- Download as [original R Data Structure (RDS)
+  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.codes.rds)
+  (27 kB)  
+- Download as [tab-separated text
+  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.codes.txt)
+  (0.1 MB)  
+- Download as [Microsoft Excel
+  workbook](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.codes.xlsx)
+  (98 kB)  
+- Download as [Apache Feather
+  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.codes.feather)
+  (0.1 MB)  
+- Download as [Apache Parquet
+  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.codes.parquet)
+  (68 kB)  
+- Download as [IBM SPSS Statistics data
+  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.codes.sav)
+  (0.2 MB)  
+- Download as [Stata DTA
+  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.codes.dta)
+  (0.2 MB)
+
+**Example content**
+
+| code |      mo      |
+|:----:|:------------:|
+| 1011 |   B_GRAMP    |
+| 1012 |   B_GRAMP    |
+| 1013 |   B_GRAMN    |
+| 1014 |   B_GRAMN    |
+| 1015 |   F_YEAST    |
+| 103  | B_ESCHR_COLI |
+
+------------------------------------------------------------------------
+
+## `antivirals`: Antiviral Drugs
+
+A data set with 120 rows and 11 columns, containing the following column
+names:  
+*av*, *name*, *atc*, *cid*, *atc_group*, *synonyms*, *oral_ddd*,
+*oral_units*, *iv_ddd*, *iv_units*, and *loinc*.
+
+This data set is in R available as `antivirals`, after you load the
+`AMR` package.
+
+It was last updated on 20 October 2023 12:51:48 UTC. Find more info
+about the contents, (scientific) source, and structure of this [data set
+here](https://amr-for-r.org/reference/antimicrobials.html).
+
+**Direct download links:**
+
+- Download as [original R Data Structure (RDS)
+  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/antivirals.rds)
+  (6 kB)  
+- Download as [tab-separated text
+  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/antivirals.txt)
+  (17 kB)  
+- Download as [Microsoft Excel
+  workbook](https://github.com/msberends/AMR/raw/main/data-raw/datasets/antivirals.xlsx)
+  (16 kB)  
+- Download as [Apache Feather
+  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/antivirals.feather)
+  (16 kB)  
+- Download as [Apache Parquet
+  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/antivirals.parquet)
+  (13 kB)  
+- Download as [IBM SPSS Statistics data
+  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/antivirals.sav)
+  (32 kB)  
+- Download as [Stata DTA
+  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/antivirals.dta)
+  (78 kB)
+
+The tab-separated text, Microsoft Excel, SPSS, and Stata files all
+contain the trade names and LOINC codes as comma separated values.
+
+**Example content**
+
+| av | name | atc | cid | atc_group | synonyms | oral_ddd | oral_units | iv_ddd | iv_units | loinc |
+|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|
+| ABA | Abacavir | J05AF06 | 441300 | Nucleoside and nucleotide reverse transcriptase inhibitors | abacavir sulfate, avacavir, ziagen | 0.6 | g |  |  | 29113-8, 30273-7, 30287-7, … |
+| ACI | Aciclovir | J05AB01 | 135398513 | Nucleosides and nucleotides excl. reverse transcriptase inhibitors | acicloftal, aciclovier, aciclovirum, … | 4.0 | g | 4 | g |  |
+| ADD | Adefovir dipivoxil | J05AF08 | 60871 | Nucleoside and nucleotide reverse transcriptase inhibitors | adefovir di, adefovir di ester, adefovir dipivoxyl, … | 10.0 | mg |  |  |  |
+| AME | Amenamevir | J05AX26 | 11397521 | Other antivirals | amenalief | 0.4 | g |  |  |  |
+| AMP | Amprenavir | J05AE05 | 65016 | Protease inhibitors | agenerase, carbamate, prozei | 1.2 | g |  |  | 29114-6, 30296-8, 30297-6, … |
+| ASU | Asunaprevir | J05AP06 | 16076883 | Antivirals for treatment of HCV infections | sunvepra, sunvepratrade | 0.2 | g |  |  |  |
@@ -0,0 +1,89 @@
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+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Articles • AMR (for R)</title><!-- favicons --><link rel="icon" type="image/png" sizes="96x96" href="../favicon-96x96.png"><link rel="icon" type="”image/svg+xml”" href="../favicon.svg"><link rel="apple-touch-icon" sizes="180x180" href="../apple-touch-icon.png"><link rel="icon" sizes="any" href="../favicon.ico"><link rel="manifest" href="../site.webmanifest"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.8/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.8/bootstrap.bundle.min.js"></script><link href="../deps/Lato-0.4.10/font.css" rel="stylesheet"><link href="../deps/Fira_Code-0.4.10/font.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><script src="../extra.js"></script><meta property="og:title" content="Articles"><meta property="og:image" content="https://amr-for-r.org/logo.svg"><link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/katex@0.16.11/dist/katex.min.css" integrity="sha384-nB0miv6/jRmo5UMMR1wu3Gz6NLsoTkbqJghGIsx//Rlm+ZU03BU6SQNC66uf4l5+" crossorigin="anonymous"><script defer src="https://cdn.jsdelivr.net/npm/katex@0.16.11/dist/katex.min.js" integrity="sha384-7zkQWkzuo3B5mTepMUcHkMB5jZaolc2xDwL6VFqjFALcbeS9Ggm/Yr2r3Dy4lfFg" crossorigin="anonymous"></script><script defer src="https://cdn.jsdelivr.net/npm/katex@0.16.11/dist/contrib/auto-render.min.js" integrity="sha384-43gviWU0YVjaDtb/GhzOouOXtZMP/7XUzwPTstBeZFe/+rCMvRwr4yROQP43s0Xk" crossorigin="anonymous" onload="renderMathInElement(document.body);"></script></head><body>
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+    <li><a class="dropdown-item" href="../reference/ab_property.html"><span class="fa fa-capsules"></span> Get Properties of an Antibiotic Drug</a></li>
+    <li><a class="dropdown-item" href="../reference/av_property.html"><span class="fa fa-capsules"></span> Get Properties of an Antiviral Drug</a></li>
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+      <img src="../logo.svg" class="logo" alt=""><h1>Articles</h1>
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+    <div class="section ">
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+      <div class="section-desc"></div>
+
+      <dl><dt><a href="AMR_for_Python.html">AMR for Python</a></dt>
+        <dd>
+        </dd><dt><a href="AMR_with_tidymodels.html">AMR with tidymodels</a></dt>
+        <dd>
+        </dd><dt><a href="AMR.html">Conduct AMR data analysis</a></dt>
+        <dd>
+        </dd><dt><a href="datasets.html">Download data sets for download / own use</a></dt>
+        <dd>
+        </dd><dt><a href="EUCAST.html">Apply EUCAST rules</a></dt>
+        <dd>
+        </dd><dt><a href="PCA.html">Conduct principal component analysis (PCA) for AMR</a></dt>
+        <dd>
+        </dd><dt><a href="WHONET.html">Work with WHONET data</a></dt>
+        <dd>
+        </dd><dt><a href="WISCA.html">Estimating Empirical Coverage with WISCA</a></dt>
+        <dd>
+      </dd></dl></div>
+  </main></div>
+
+
+    <footer><div class="pkgdown-footer-left">
+  <p><code>AMR</code> (for R). Free and open-source, licenced under the <a target="_blank" href="https://github.com/msberends/AMR/blob/main/LICENSE" class="external-link">GNU GPL 2.0</a>. Developed at the <a target="_blank" href="https://www.rug.nl" class="external-link">University of Groningen</a> and <a target="_blank" href="https://www.umcg.nl" class="external-link">University Medical Center Groningen</a> in The Netherlands, in collaboration with <a href="https://amr-for-r.org/authors.html">many colleagues from around the world</a>.</p>
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+</div>
+
+    </footer></div>
+
+
+
+
+
+  </body></html>
+
@@ -0,0 +1,16 @@
+# Articles
+
+### All vignettes
+
+- [AMR for Python](https://amr-for-r.org/articles/AMR_for_Python.md):
+- [AMR with
+  tidymodels](https://amr-for-r.org/articles/AMR_with_tidymodels.md):
+- [Conduct AMR data analysis](https://amr-for-r.org/articles/AMR.md):
+- [Download data sets for download / own
+  use](https://amr-for-r.org/articles/datasets.md):
+- [Apply EUCAST rules](https://amr-for-r.org/articles/EUCAST.md):
+- [Conduct principal component analysis (PCA) for
+  AMR](https://amr-for-r.org/articles/PCA.md):
+- [Work with WHONET data](https://amr-for-r.org/articles/WHONET.md):
+- [Estimating Empirical Coverage with
+  WISCA](https://amr-for-r.org/articles/WISCA.md):
@@ -0,0 +1,215 @@
+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Authors and Citation • AMR (for R)</title><!-- favicons --><link rel="icon" type="image/png" sizes="96x96" href="favicon-96x96.png"><link rel="icon" type="”image/svg+xml”" href="favicon.svg"><link rel="apple-touch-icon" sizes="180x180" href="apple-touch-icon.png"><link rel="icon" sizes="any" href="favicon.ico"><link rel="manifest" href="site.webmanifest"><script src="deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="deps/bootstrap-5.3.8/bootstrap.min.css" rel="stylesheet"><script src="deps/bootstrap-5.3.8/bootstrap.bundle.min.js"></script><link href="deps/Lato-0.4.10/font.css" rel="stylesheet"><link href="deps/Fira_Code-0.4.10/font.css" rel="stylesheet"><link href="deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="deps/headroom-0.11.0/headroom.min.js"></script><script src="deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="deps/search-1.0.0/fuse.min.js"></script><script src="deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="pkgdown.js"></script><link href="extra.css" rel="stylesheet"><script src="extra.js"></script><meta property="og:title" content="Authors and Citation"><meta property="og:image" content="https://amr-for-r.org/logo.svg"><link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/katex@0.16.11/dist/katex.min.css" integrity="sha384-nB0miv6/jRmo5UMMR1wu3Gz6NLsoTkbqJghGIsx//Rlm+ZU03BU6SQNC66uf4l5+" crossorigin="anonymous"><script defer src="https://cdn.jsdelivr.net/npm/katex@0.16.11/dist/katex.min.js" integrity="sha384-7zkQWkzuo3B5mTepMUcHkMB5jZaolc2xDwL6VFqjFALcbeS9Ggm/Yr2r3Dy4lfFg" crossorigin="anonymous"></script><script defer src="https://cdn.jsdelivr.net/npm/katex@0.16.11/dist/contrib/auto-render.min.js" integrity="sha384-43gviWU0YVjaDtb/GhzOouOXtZMP/7XUzwPTstBeZFe/+rCMvRwr4yROQP43s0Xk" crossorigin="anonymous" onload="renderMathInElement(document.body);"></script></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-primary" data-bs-theme="dark" aria-label="Site navigation"><div class="container">
+
+    <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="">3.0.1.9075</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">
+      <span class="navbar-toggler-icon"></span>
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+      <ul class="navbar-nav me-auto"><li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-how-to" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true"><span class="fa fa-question-circle"></span> How to</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-how-to"><li><a class="dropdown-item" href="articles/AMR.html"><span class="fa fa-directions"></span> Conduct AMR Analysis</a></li>
+    <li><a class="dropdown-item" href="reference/antibiogram.html"><span class="fa fa-file-prescription"></span> Generate Antibiogram (Trad./Syndromic/WISCA)</a></li>
+    <li><a class="dropdown-item" href="articles/AMR_with_tidymodels.html"><span class="fa fa-square-root-variable"></span> Use AMR for Predictive Modelling (tidymodels)</a></li>
+    <li><a class="dropdown-item" href="articles/datasets.html"><span class="fa fa-database"></span> Download Data Sets for Own Use</a></li>
+    <li><a class="dropdown-item" href="reference/AMR-options.html"><span class="fa fa-gear"></span> Set User- Or Team-specific Package Settings</a></li>
+    <li><a class="dropdown-item" href="articles/PCA.html"><span class="fa fa-compress"></span> Conduct Principal Component Analysis for AMR</a></li>
+    <li><a class="dropdown-item" href="reference/mdro.html"><span class="fa fa-skull-crossbones"></span> Determine Multi-Drug Resistance (MDR)</a></li>
+    <li><a class="dropdown-item" href="articles/WHONET.html"><span class="fa fa-globe-americas"></span> Work with WHONET Data</a></li>
+    <li><a class="dropdown-item" href="articles/EUCAST.html"><span class="fa fa-exchange-alt"></span> Apply EUCAST Rules</a></li>
+    <li><a class="dropdown-item" href="reference/mo_property.html"><span class="fa fa-bug"></span> Get Taxonomy of a Microorganism</a></li>
+    <li><a class="dropdown-item" href="reference/ab_property.html"><span class="fa fa-capsules"></span> Get Properties of an Antibiotic Drug</a></li>
+    <li><a class="dropdown-item" href="reference/av_property.html"><span class="fa fa-capsules"></span> Get Properties of an Antiviral Drug</a></li>
+  </ul></li>
+<li class="nav-item"><a class="nav-link" href="articles/AMR_for_Python.html"><span class="fa fab fa-python"></span> AMR for Python</a></li>
+<li class="nav-item"><a class="nav-link" href="reference/index.html"><span class="fa fa-book-open"></span> Manual</a></li>
+<li class="active nav-item"><a class="nav-link" href="authors.html"><span class="fa fa-users"></span> Authors</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="search.json"></form></li>
+<li class="nav-item"><a class="nav-link" href="news/index.html"><span class="fa fa-newspaper"></span> Changelog</a></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/msberends/AMR"><span class="fa fa-github"></span> Source Code</a></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-citation-authors">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="logo.svg" class="logo" alt=""><h1>Authors and Citation</h1>
+    </div>
+
+    <div class="section level2">
+      <h2>Authors</h2>
+
+      <ul class="list-unstyled"><li>
+          <p><strong>Matthijs S. Berends</strong>. Author, maintainer. <a href="https://orcid.org/0000-0001-7620-1800" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
+          </p>
+        </li>
+        <li>
+          <p><strong>Dennis Souverein</strong>. Author, contributor. <a href="https://orcid.org/0000-0003-0455-0336" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
+          </p>
+        </li>
+        <li>
+          <p><strong>Erwin E. A. Hassing</strong>. Author, contributor.
+          </p>
+        </li>
+        <li>
+          <p><strong>Aislinn Cook</strong>. Contributor. <a href="https://orcid.org/0000-0002-9189-7815" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
+          </p>
+        </li>
+        <li>
+          <p><strong>Andrew P. Norgan</strong>. Contributor. <a href="https://orcid.org/0000-0002-2955-2066" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
+          </p>
+        </li>
+        <li>
+          <p><strong>Anita Williams</strong>. Contributor. <a href="https://orcid.org/0000-0002-5295-8451" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
+          </p>
+        </li>
+        <li>
+          <p><strong>Annick Lenglet</strong>. Contributor. <a href="https://orcid.org/0000-0003-2013-8405" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
+          </p>
+        </li>
+        <li>
+          <p><strong>Anthony Underwood</strong>. Contributor. <a href="https://orcid.org/0000-0002-8547-4277" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
+          </p>
+        </li>
+        <li>
+          <p><strong>Anton Mymrikov</strong>. Contributor.
+          </p>
+        </li>
+        <li>
+          <p><strong>Bart C. Meijer</strong>. Contributor.
+          </p>
+        </li>
+        <li>
+          <p><strong>Christian F. Luz</strong>. Contributor. <a href="https://orcid.org/0000-0001-5809-5995" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
+          </p>
+        </li>
+        <li>
+          <p><strong>Dmytro Mykhailenko</strong>. Contributor.
+          </p>
+        </li>
+        <li>
+          <p><strong>Eric H. L. C. M. Hazenberg</strong>. Contributor.
+          </p>
+        </li>
+        <li>
+          <p><strong>Gwen Knight</strong>. Contributor. <a href="https://orcid.org/0000-0002-7263-9896" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
+          </p>
+        </li>
+        <li>
+          <p><strong>Jane Hawkey</strong>. Contributor. <a href="https://orcid.org/0000-0001-9661-5293" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
+          </p>
+        </li>
+        <li>
+          <p><strong>Jason Stull</strong>. Contributor. <a href="https://orcid.org/0000-0002-9028-8153" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
+          </p>
+        </li>
+        <li>
+          <p><strong>Javier Sanchez</strong>. Contributor. <a href="https://orcid.org/0000-0003-2605-8094" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
+          </p>
+        </li>
+        <li>
+          <p><strong>Jonas Salm</strong>. Contributor.
+          </p>
+        </li>
+        <li>
+          <p><strong>Judith M. Fonville</strong>. Contributor.
+          </p>
+        </li>
+        <li>
+          <p><strong>Kathryn Holt</strong>. Contributor. <a href="https://orcid.org/0000-0003-3949-2471" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
+          </p>
+        </li>
+        <li>
+          <p><strong>Larisse Bolton</strong>. Contributor. <a href="https://orcid.org/0000-0001-7879-2173" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
+          </p>
+        </li>
+        <li>
+          <p><strong>Matthew Saab</strong>. Contributor. <a href="https://orcid.org/0009-0008-6626-7919" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
+          </p>
+        </li>
+        <li>
+          <p><strong>Natacha Couto</strong>. Contributor. <a href="https://orcid.org/0000-0002-9152-5464" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
+          </p>
+        </li>
+        <li>
+          <p><strong>Peter Dutey-Magni</strong>. Contributor. <a href="https://orcid.org/0000-0002-8942-9836" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
+          </p>
+        </li>
+        <li>
+          <p><strong>Rogier P. Schade</strong>. Contributor. <a href="https://orcid.org/0000-0002-9487-4467" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
+          </p>
+        </li>
+        <li>
+          <p><strong>Sofia Ny</strong>. Contributor. <a href="https://orcid.org/0000-0002-2017-1363" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
+          </p>
+        </li>
+        <li>
+          <p><strong>Alex W. Friedrich</strong>. Thesis advisor. <a href="https://orcid.org/0000-0003-4881-038X" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
+          </p>
+        </li>
+        <li>
+          <p><strong>Bhanu N. M. Sinha</strong>. Thesis advisor. <a href="https://orcid.org/0000-0003-1634-0010" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
+          </p>
+        </li>
+        <li>
+          <p><strong>Casper J. Albers</strong>. Thesis advisor. <a href="https://orcid.org/0000-0002-9213-6743" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
+          </p>
+        </li>
+        <li>
+          <p><strong>Corinna Glasner</strong>. Thesis advisor. <a href="https://orcid.org/0000-0003-1241-1328" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
+          </p>
+        </li>
+      </ul></div>
+
+    <div class="section level2">
+      <h2 id="citation">Citation</h2>
+      <p><small class="dont-index">Source: <a href="https://github.com/msberends/AMR/blob/main/inst/CITATION" class="external-link"><code>inst/CITATION</code></a></small></p>
+
+      <p>Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C (2022).
+“AMR: An R Package for Working with Antimicrobial Resistance Data.”
+<em>Journal of Statistical Software</em>, <b>104</b>(3), 1–31.
+<a href="https://doi.org/10.18637/jss.v104.i03" class="external-link">doi:10.18637/jss.v104.i03</a>.
+</p>
+      <pre>@Article{,
+  title = {{AMR}: An {R} Package for Working with Antimicrobial Resistance Data},
+  author = {Matthijs S. Berends and Christian F. Luz and Alexander W. Friedrich and Bhanu N. M. Sinha and Casper J. Albers and Corinna Glasner},
+  journal = {Journal of Statistical Software},
+  year = {2022},
+  volume = {104},
+  number = {3},
+  pages = {1--31},
+  doi = {10.18637/jss.v104.i03},
+}</pre>
+    </div>
+
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
+
+
+    <footer><div class="pkgdown-footer-left">
+  <p><code>AMR</code> (for R). Free and open-source, licenced under the <a target="_blank" href="https://github.com/msberends/AMR/blob/main/LICENSE" class="external-link">GNU GPL 2.0</a>. Developed at the <a target="_blank" href="https://www.rug.nl" class="external-link">University of Groningen</a> and <a target="_blank" href="https://www.umcg.nl" class="external-link">University Medical Center Groningen</a> in The Netherlands, in collaboration with <a href="https://amr-for-r.org/authors.html">many colleagues from around the world</a>.</p>
+</div>
+
+<div class="pkgdown-footer-right">
+  <p><a target="_blank" href="https://www.rug.nl" class="external-link"><img src="https://amr-for-r.org/logo_rug.svg" style="max-width: 150px;"></a><a target="_blank" href="https://www.umcg.nl" class="external-link"><img src="https://amr-for-r.org/logo_umcg.svg" style="max-width: 150px;"></a></p>
+</div>
+
+    </footer></div>
+
+
+
+
+
+  </body></html>
+
@@ -0,0 +1,106 @@
+# Authors and Citation
+
+## Authors
+
+- **Matthijs S. Berends**. Author, maintainer.
+  [](https://orcid.org/0000-0001-7620-1800)
+
+- **Dennis Souverein**. Author, contributor.
+  [](https://orcid.org/0000-0003-0455-0336)
+
+- **Erwin E. A. Hassing**. Author, contributor.
+
+- **Aislinn Cook**. Contributor.
+  [](https://orcid.org/0000-0002-9189-7815)
+
+- **Andrew P. Norgan**. Contributor.
+  [](https://orcid.org/0000-0002-2955-2066)
+
+- **Anita Williams**. Contributor.
+  [](https://orcid.org/0000-0002-5295-8451)
+
+- **Annick Lenglet**. Contributor.
+  [](https://orcid.org/0000-0003-2013-8405)
+
+- **Anthony Underwood**. Contributor.
+  [](https://orcid.org/0000-0002-8547-4277)
+
+- **Anton Mymrikov**. Contributor.
+
+- **Bart C. Meijer**. Contributor.
+
+- **Christian F. Luz**. Contributor.
+  [](https://orcid.org/0000-0001-5809-5995)
+
+- **Dmytro Mykhailenko**. Contributor.
+
+- **Eric H. L. C. M. Hazenberg**. Contributor.
+
+- **Gwen Knight**. Contributor.
+  [](https://orcid.org/0000-0002-7263-9896)
+
+- **Jane Hawkey**. Contributor.
+  [](https://orcid.org/0000-0001-9661-5293)
+
+- **Jason Stull**. Contributor.
+  [](https://orcid.org/0000-0002-9028-8153)
+
+- **Javier Sanchez**. Contributor.
+  [](https://orcid.org/0000-0003-2605-8094)
+
+- **Jonas Salm**. Contributor.
+
+- **Judith M. Fonville**. Contributor.
+
+- **Kathryn Holt**. Contributor.
+  [](https://orcid.org/0000-0003-3949-2471)
+
+- **Larisse Bolton**. Contributor.
+  [](https://orcid.org/0000-0001-7879-2173)
+
+- **Matthew Saab**. Contributor.
+  [](https://orcid.org/0009-0008-6626-7919)
+
+- **Natacha Couto**. Contributor.
+  [](https://orcid.org/0000-0002-9152-5464)
+
+- **Peter Dutey-Magni**. Contributor.
+  [](https://orcid.org/0000-0002-8942-9836)
+
+- **Rogier P. Schade**. Contributor.
+  [](https://orcid.org/0000-0002-9487-4467)
+
+- **Sofia Ny**. Contributor. [](https://orcid.org/0000-0002-2017-1363)
+
+- **Alex W. Friedrich**. Thesis advisor.
+  [](https://orcid.org/0000-0003-4881-038X)
+
+- **Bhanu N. M. Sinha**. Thesis advisor.
+  [](https://orcid.org/0000-0003-1634-0010)
+
+- **Casper J. Albers**. Thesis advisor.
+  [](https://orcid.org/0000-0002-9213-6743)
+
+- **Corinna Glasner**. Thesis advisor.
+  [](https://orcid.org/0000-0003-1241-1328)
+
+## Citation
+
+Source:
+[`inst/CITATION`](https://github.com/msberends/AMR/blob/main/inst/CITATION)
+
+Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C
+(2022). “AMR: An R Package for Working with Antimicrobial Resistance
+Data.” *Journal of Statistical Software*, **104**(3), 1–31.
+[doi:10.18637/jss.v104.i03](https://doi.org/10.18637/jss.v104.i03).
+
+    @Article{,
+      title = {{AMR}: An {R} Package for Working with Antimicrobial Resistance Data},
+      author = {Matthijs S. Berends and Christian F. Luz and Alexander W. Friedrich and Bhanu N. M. Sinha and Casper J. Albers and Corinna Glasner},
+      journal = {Journal of Statistical Software},
+      year = {2022},
+      volume = {104},
+      number = {3},
+      pages = {1--31},
+      doi = {10.18637/jss.v104.i03},
+    }
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+}
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+}
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+}
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+  font-family: 'Lato';
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+}
--- a/Show More
+++ b/Show More