P281424/AMR
1
0
Fork 0
mirror of https://github.com/msberends/AMR.git synced 2025-12-25 09:10:27 +01:00
Code Releases Activity

Built site for AMR@3.0.1.9007: a5c6aa9

Browse Source
This commit is contained in:
github-actions
2025-12-22 08:48:41 +00:00
parent 7c8d0f82f9
commit 1227e2c60c
128 changed files with 1616 additions and 440 deletions
Download Patch File Download Diff File Expand all files Collapse all files

18
articles/AMR.html
View File

@@ -30,7 +30,7 @@
<a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9004</small>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9007</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">
@@ -91,7 +91,7 @@
website update since they are based on randomly created values and the
page was written in <a href="https://rmarkdown.rstudio.com/" class="external-link">R
Markdown</a>. However, the methodology remains unchanged. This page was
generated on 15 December 2025.</p>
generated on 22 December 2025.</p>
<div class="section level2">
<h2 id="introduction">Introduction<a class="anchor" aria-label="anchor" href="#introduction"></a>
</h2>
@@ -147,21 +147,21 @@ make the structure of your data generally look like this:</p>
</tr></thead>
<tbody>
<tr class="odd">
<td align="center">2025-12-15</td>
<td align="center">2025-12-22</td>
<td align="center">abcd</td>
<td align="center">Escherichia coli</td>
<td align="center">S</td>
<td align="center">S</td>
</tr>
<tr class="even">
<td align="center">2025-12-15</td>
<td align="center">2025-12-22</td>
<td align="center">abcd</td>
<td align="center">Escherichia coli</td>
<td align="center">S</td>
<td align="center">R</td>
</tr>
<tr class="odd">
<td align="center">2025-12-15</td>
<td align="center">2025-12-22</td>
<td align="center">efgh</td>
<td align="center">Escherichia coli</td>
<td align="center">R</td>
@@ -1254,7 +1254,7 @@ function on a grouped <code>tibble</code>, i.e., using
provides an extension to that function:</p>
<div class="sourceCode" id="cb22"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="fu"><a href="https://ggplot2.tidyverse.org/reference/autoplot.html" class="external-link">autoplot</a></span><span class="op">(</span><span class="va">combined_ab</span><span class="op">)</span></span></code></pre></div>
<p><img src="AMR_files/figure-html/unnamed-chunk-10-1.png" class="r-plt" width="720"></p>
<p><img src="AMR_files/figure-html/unnamed-chunk-10-1.png" class="r-plt" alt="" width="720"></p>
<p>To calculate antimicrobial resistance in a more sensible way, also by
correcting for too few results, we use the <code><a href="../reference/proportion.html">resistance()</a></code> and
<code><a href="../reference/proportion.html">susceptibility()</a></code> functions.</p>
@@ -1348,7 +1348,7 @@ categories.</p>
<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">"MIC Distribution and SIR Interpretation"</span>,</span>
<span> x <span class="op">=</span> <span class="st">"Sample Groups"</span>,</span>
<span> y <span class="op">=</span> <span class="st">"MIC (mg/L)"</span><span class="op">)</span></span></code></pre></div>
<p><img src="AMR_files/figure-html/mic_plot-1.png" class="r-plt" width="720"></p>
<p><img src="AMR_files/figure-html/mic_plot-1.png" class="r-plt" alt="" width="720"></p>
<p>This plot provides an intuitive way to assess susceptibility patterns
across different groups while incorporating clinical breakpoints.</p>
<p>For a more straightforward and less manual approach,
@@ -1357,12 +1357,12 @@ extended by this package to directly plot MIC and disk diffusion
values:</p>
<div class="sourceCode" id="cb27"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="fu"><a href="https://ggplot2.tidyverse.org/reference/autoplot.html" class="external-link">autoplot</a></span><span class="op">(</span><span class="va">mic_values</span><span class="op">)</span></span></code></pre></div>
<p><img src="AMR_files/figure-html/autoplot-1.png" class="r-plt" width="720"></p>
<p><img src="AMR_files/figure-html/autoplot-1.png" class="r-plt" alt="" width="720"></p>
<div class="sourceCode" id="cb28"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span></span>
<span><span class="co"># by providing `mo` and `ab`, colours will indicate the SIR interpretation:</span></span>
<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">mic_values</span>, mo <span class="op">=</span> <span class="st">"K. pneumoniae"</span>, ab <span class="op">=</span> <span class="st">"cipro"</span>, guideline <span class="op">=</span> <span class="st">"EUCAST 2024"</span><span class="op">)</span></span></code></pre></div>
<p><img src="AMR_files/figure-html/autoplot-2.png" class="r-plt" width="720"></p>
<p><img src="AMR_files/figure-html/autoplot-2.png" class="r-plt" alt="" width="720"></p>
<hr>
<p><em>Author: Dr. Matthijs Berends, 23rd Feb 2025</em></p>
</div>

8
articles/AMR.md
View File

@@ -3,7 +3,7 @@
**Note:** values on this page will change with every website update
since they are based on randomly created values and the page was written
in [R Markdown](https://rmarkdown.rstudio.com/). However, the
methodology remains unchanged. This page was generated on 15 December
methodology remains unchanged. This page was generated on 22 December
2025.
## Introduction
@@ -52,9 +52,9 @@ structure of your data generally look like this:
| date | patient_id | mo | AMX | CIP |
|:----------:|:----------:|:----------------:|:---:|:---:|
| 2025-12-15 | abcd | Escherichia coli | S | S |
| 2025-12-15 | abcd | Escherichia coli | S | R |
| 2025-12-15 | efgh | Escherichia coli | R | S |
| 2025-12-22 | abcd | Escherichia coli | S | S |
| 2025-12-22 | abcd | Escherichia coli | S | R |
| 2025-12-22 | efgh | Escherichia coli | R | S |
### Needed R packages

2
articles/AMR_for_Python.html
View File

@@ -30,7 +30,7 @@
<a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9004</small>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9007</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">

305
articles/AMR_with_tidymodels.html
View File

@@ -30,7 +30,7 @@
<a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9004</small>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9007</small>
<button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -88,7 +88,7 @@
<blockquote>
<p>This page was entirely written by our <a href="https://chat.amr-for-r.org" class="external-link">AMR for R Assistant</a>, a ChatGPT
<p>This page was almost entirely written by our <a href="https://chat.amr-for-r.org" class="external-link">AMR for R Assistant</a>, a ChatGPT
manually-trained model able to answer any question about the
<code>AMR</code> package.</p>
</blockquote>
@@ -413,7 +413,7 @@ ROC curve looks like this:</p>
<code class="sourceCode R"><span><span class="va">predictions</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
<span> <span class="fu">roc_curve</span><span class="op">(</span><span class="va">mo</span>, <span class="va">`.pred_Gram-negative`</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://ggplot2.tidyverse.org/reference/autoplot.html" class="external-link">autoplot</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
<p><img src="AMR_with_tidymodels_files/figure-html/unnamed-chunk-8-1.png" class="r-plt" width="720"></p>
<p><img src="AMR_with_tidymodels_files/figure-html/unnamed-chunk-8-1.png" class="r-plt" alt="" width="720"></p>
</div>
<div class="section level3">
<h3 id="conclusion">
@@ -438,21 +438,258 @@ and reproducibly.</p>
<code>&lt;mic&gt;</code> columns directly in <code>tidymodels</code>
workflows using AMR-specific recipe steps. This includes a
transformation to <code>log2</code> scale using
<code>step_mic_log2()</code>, which prepares MIC values for use in
<code><a href="../reference/amr-tidymodels.html">step_mic_log2()</a></code>, which prepares MIC values for use in
classification models.</p>
<p>This approach and idea formed the basis for the publication <a href="https://doi.org/10.3389/fmicb.2025.1582703" class="external-link">DOI:
10.3389/fmicb.2025.1582703</a> to model the presence of
extended-spectrum beta-lactamases (ESBL).</p>
<blockquote>
<p>NOTE: THIS EXAMPLE WILL BE AVAILABLE IN A NEXT VERSION (#TODO)</p>
<p>The new AMR package version will contain new tidymodels selectors
such as <code>step_mic_log2()</code>.</p>
</blockquote>
<!-- TODO for AMR v3.1.0: add info from here: https://github.com/msberends/AMR/blob/2461631bcefa78ebdb37bdfad359be74cdd9165a/vignettes/AMR_with_tidymodels.Rmd#L212-L291 -->
extended-spectrum beta-lactamases (ESBL) based on MIC values.</p>
<div class="section level3">
<h3 id="objective-1">
<strong>Objective</strong><a class="anchor" aria-label="anchor" href="#objective-1"></a>
</h3>
<p>Our goal is to:</p>
<ol style="list-style-type: decimal">
<li>Use raw MIC values to predict whether a bacterial isolate produces
ESBL.</li>
<li>Apply AMR-aware preprocessing in a <code>tidymodels</code>
recipe.</li>
<li>Train a classification model and evaluate its predictive
performance.</li>
</ol>
</div>
<div class="section level3">
<h3 id="data-preparation-1">
<strong>Data Preparation</strong><a class="anchor" aria-label="anchor" href="#data-preparation-1"></a>
</h3>
<p>We use the <code>esbl_isolates</code> dataset that comes with the AMR
package.</p>
<div class="sourceCode" id="cb10"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="co"># Load required libraries</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>
<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://tidymodels.tidymodels.org" class="external-link">tidymodels</a></span><span class="op">)</span></span>
<span></span>
<span><span class="co"># View the esbl_isolates data set</span></span>
<span><span class="va">esbl_isolates</span></span>
<span><span class="co">#&gt; <span style="color: #949494;"># A tibble: 500 × 19</span></span></span>
<span><span class="co">#&gt; esbl genus AMC AMP TZP CXM FOX CTX CAZ GEN TOB TMP SXT</span></span>
<span><span class="co">#&gt; <span style="color: #949494; font-style: italic;">&lt;lgl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span> <span style="color: #949494; font-style: italic;">&lt;mic&gt;</span> <span style="color: #949494; font-style: italic;">&lt;mic&gt;</span> <span style="color: #949494; font-style: italic;">&lt;mic&gt;</span> <span style="color: #949494; font-style: italic;">&lt;mic&gt;</span> <span style="color: #949494; font-style: italic;">&lt;mic&gt;</span> <span style="color: #949494; font-style: italic;">&lt;mic&gt;</span> <span style="color: #949494; font-style: italic;">&lt;mic&gt;</span> <span style="color: #949494; font-style: italic;">&lt;mic&gt;</span> <span style="color: #949494; font-style: italic;">&lt;mic&gt;</span> <span style="color: #949494; font-style: italic;">&lt;mic&gt;</span> <span style="color: #949494; font-style: italic;">&lt;mic&gt;</span></span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 1</span> FALSE Esch… 32 32 4 64 64 8<span style="color: #555555;">.00</span> 8<span style="color: #555555;">.00</span> 1 1 16<span style="color: #555555;">.0</span> 20</span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 2</span> FALSE Esch… 32 32 4 64 64 4<span style="color: #555555;">.00</span> 8<span style="color: #555555;">.00</span> 1 1 16<span style="color: #555555;">.0</span> 320</span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 3</span> FALSE Esch… 4 2 64 8 4 8<span style="color: #555555;">.00</span> 0.12 16 16 0.5 20</span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 4</span> FALSE Kleb… 32 32 16 64 64 8<span style="color: #555555;">.00</span> 8<span style="color: #555555;">.00</span> 1 1 0.5 20</span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 5</span> FALSE Esch… 32 32 4 4 4 0.25 2<span style="color: #555555;">.00</span> 1 1 16<span style="color: #555555;">.0</span> 320</span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 6</span> FALSE Citr… 32 32 16 64 64 64<span style="color: #555555;">.00</span> 32<span style="color: #555555;">.00</span> 1 1 0.5 20</span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 7</span> FALSE Morg… 32 32 4 64 64 16<span style="color: #555555;">.00</span> 2<span style="color: #555555;">.00</span> 1 1 0.5 20</span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 8</span> FALSE Prot… 16 32 4 1 4 8<span style="color: #555555;">.00</span> 0.12 1 1 16<span style="color: #555555;">.0</span> 320</span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 9</span> FALSE Ente… 32 32 8 64 64 32<span style="color: #555555;">.00</span> 4<span style="color: #555555;">.00</span> 1 1 0.5 20</span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;">10</span> FALSE Citr… 32 32 32 64 64 8<span style="color: #555555;">.00</span> 64<span style="color: #555555;">.00</span> 1 1 16<span style="color: #555555;">.0</span> 320</span></span>
<span><span class="co">#&gt; <span style="color: #949494;"># ℹ 490 more rows</span></span></span>
<span><span class="co">#&gt; <span style="color: #949494;"># ℹ 6 more variables: NIT &lt;mic&gt;, FOS &lt;mic&gt;, CIP &lt;mic&gt;, IPM &lt;mic&gt;, MEM &lt;mic&gt;,</span></span></span>
<span><span class="co">#&gt; <span style="color: #949494;"># COL &lt;mic&gt;</span></span></span>
<span></span>
<span><span class="co"># Prepare a binary outcome and convert to ordered factor</span></span>
<span><span class="va">data</span> <span class="op">&lt;-</span> <span class="va">esbl_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/mutate.html" class="external-link">mutate</a></span><span class="op">(</span>esbl <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/factor.html" class="external-link">factor</a></span><span class="op">(</span><span class="va">esbl</span>, levels <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="cn">FALSE</span>, <span class="cn">TRUE</span><span class="op">)</span>, ordered <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
<p><strong>Explanation:</strong></p>
<ul>
<li>
<code>esbl_isolates</code>: Contains MIC test results and ESBL
status for each isolate.</li>
<li>
<code>mutate(esbl = ...)</code>: Converts the target column to an
ordered factor for classification.</li>
</ul>
</div>
<div class="section level3">
<h3 id="defining-the-workflow-1">
<strong>Defining the Workflow</strong><a class="anchor" aria-label="anchor" href="#defining-the-workflow-1"></a>
</h3>
<div class="section level4">
<h4 id="preprocessing-with-a-recipe-1">1. Preprocessing with a Recipe<a class="anchor" aria-label="anchor" href="#preprocessing-with-a-recipe-1"></a>
</h4>
<p>We use our <code><a href="../reference/amr-tidymodels.html">step_mic_log2()</a></code> 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
<code><a href="../reference/amr-tidymodels.html">all_mic_predictors()</a></code>:</p>
<div class="sourceCode" id="cb11"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="co"># Split into training and testing sets</span></span>
<span><span class="fu"><a href="https://rdrr.io/r/base/Random.html" class="external-link">set.seed</a></span><span class="op">(</span><span class="fl">123</span><span class="op">)</span></span>
<span><span class="va">split</span> <span class="op">&lt;-</span> <span class="fu">initial_split</span><span class="op">(</span><span class="va">data</span><span class="op">)</span></span>
<span><span class="va">training_data</span> <span class="op">&lt;-</span> <span class="fu">training</span><span class="op">(</span><span class="va">split</span><span class="op">)</span></span>
<span><span class="va">testing_data</span> <span class="op">&lt;-</span> <span class="fu">testing</span><span class="op">(</span><span class="va">split</span><span class="op">)</span></span>
<span></span>
<span><span class="co"># Define the recipe</span></span>
<span><span class="va">mic_recipe</span> <span class="op">&lt;-</span> <span class="fu">recipe</span><span class="op">(</span><span class="va">esbl</span> <span class="op">~</span> <span class="va">.</span>, data <span class="op">=</span> <span class="va">training_data</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">remove_role</span><span class="op">(</span><span class="va">genus</span>, old_role <span class="op">=</span> <span class="st">"predictor"</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"># Remove non-informative variable</span></span>
<span> <span class="fu"><a href="../reference/amr-tidymodels.html">step_mic_log2</a></span><span class="op">(</span><span class="fu"><a href="../reference/amr-tidymodels.html">all_mic_predictors</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="co">#%&gt;% # Log2 transform all MIC predictors</span></span>
<span> <span class="co"># prep()</span></span>
<span></span>
<span><span class="va">mic_recipe</span></span>
<span><span class="co">#&gt; </span></span>
<span><span class="co">#&gt; <span style="color: #00BBBB;">──</span> <span style="font-weight: bold;">Recipe</span> <span style="color: #00BBBB;">──────────────────────────────────────────────────────────────────────</span></span></span>
<span><span class="co">#&gt; </span></span>
<span><span class="co">#&gt; ── Inputs</span></span>
<span><span class="co">#&gt; Number of variables by role</span></span>
<span><span class="co">#&gt; outcome: 1</span></span>
<span><span class="co">#&gt; predictor: 17</span></span>
<span><span class="co">#&gt; undeclared role: 1</span></span>
<span><span class="co">#&gt; </span></span>
<span><span class="co">#&gt; ── Operations</span></span>
<span><span class="co">#&gt; <span style="color: #00BBBB;">•</span> Log2 transformation of MIC columns: <span style="color: #0000BB;">all_mic_predictors()</span></span></span></code></pre></div>
<p><strong>Explanation:</strong></p>
<ul>
<li>
<code>remove_role()</code>: Removes irrelevant variables like
genus.</li>
<li>
<code><a href="../reference/amr-tidymodels.html">step_mic_log2()</a></code>: Applies
<code>log2(as.numeric(...))</code> to all MIC predictors in one go.</li>
<li>
<code>prep()</code>: Finalises the recipe based on training
data.</li>
</ul>
</div>
<div class="section level4">
<h4 id="specifying-the-model-1">2. Specifying the Model<a class="anchor" aria-label="anchor" href="#specifying-the-model-1"></a>
</h4>
<p>We use a simple logistic regression to model ESBL presence, though
recent models such as xgboost (<a href="https://parsnip.tidymodels.org/reference/details_boost_tree_xgboost.html" class="external-link">link
to <code>parsnip</code> manual</a>) could be much more precise.</p>
<div class="sourceCode" id="cb12"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="co"># Define the model</span></span>
<span><span class="va">model</span> <span class="op">&lt;-</span> <span class="fu">logistic_reg</span><span class="op">(</span>mode <span class="op">=</span> <span class="st">"classification"</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">set_engine</span><span class="op">(</span><span class="st">"glm"</span><span class="op">)</span></span>
<span></span>
<span><span class="va">model</span></span>
<span><span class="co">#&gt; Logistic Regression Model Specification (classification)</span></span>
<span><span class="co">#&gt; </span></span>
<span><span class="co">#&gt; Computational engine: glm</span></span></code></pre></div>
<p><strong>Explanation:</strong></p>
<ul>
<li>
<code>logistic_reg()</code>: Specifies a binary classification
model.</li>
<li>
<code>set_engine("glm")</code>: Uses the base R GLM engine.</li>
</ul>
</div>
<div class="section level4">
<h4 id="building-the-workflow-1">3. Building the Workflow<a class="anchor" aria-label="anchor" href="#building-the-workflow-1"></a>
</h4>
<div class="sourceCode" id="cb13"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="co"># Create workflow</span></span>
<span><span class="va">workflow_model</span> <span class="op">&lt;-</span> <span class="fu">workflow</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="fu">add_recipe</span><span class="op">(</span><span class="va">mic_recipe</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">add_model</span><span class="op">(</span><span class="va">model</span><span class="op">)</span></span>
<span></span>
<span><span class="va">workflow_model</span></span>
<span><span class="co">#&gt; ══ Workflow ════════════════════════════════════════════════════════════════════</span></span>
<span><span class="co">#&gt; <span style="font-style: italic;">Preprocessor:</span> Recipe</span></span>
<span><span class="co">#&gt; <span style="font-style: italic;">Model:</span> logistic_reg()</span></span>
<span><span class="co">#&gt; </span></span>
<span><span class="co">#&gt; ── Preprocessor ────────────────────────────────────────────────────────────────</span></span>
<span><span class="co">#&gt; 1 Recipe Step</span></span>
<span><span class="co">#&gt; </span></span>
<span><span class="co">#&gt; • step_mic_log2()</span></span>
<span><span class="co">#&gt; </span></span>
<span><span class="co">#&gt; ── Model ───────────────────────────────────────────────────────────────────────</span></span>
<span><span class="co">#&gt; Logistic Regression Model Specification (classification)</span></span>
<span><span class="co">#&gt; </span></span>
<span><span class="co">#&gt; Computational engine: glm</span></span></code></pre></div>
</div>
</div>
<div class="section level3">
<h3 id="training-and-evaluating-the-model-1">
<strong>Training and Evaluating the Model</strong><a class="anchor" aria-label="anchor" href="#training-and-evaluating-the-model-1"></a>
</h3>
<div class="sourceCode" id="cb14"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="co"># Fit the model</span></span>
<span><span class="va">fitted</span> <span class="op">&lt;-</span> <span class="fu">fit</span><span class="op">(</span><span class="va">workflow_model</span>, <span class="va">training_data</span><span class="op">)</span></span>
<span></span>
<span><span class="co"># Generate predictions</span></span>
<span><span class="va">predictions</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/stats/predict.html" class="external-link">predict</a></span><span class="op">(</span><span class="va">fitted</span>, <span class="va">testing_data</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/bind_cols.html" class="external-link">bind_cols</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/stats/predict.html" class="external-link">predict</a></span><span class="op">(</span><span class="va">fitted</span>, <span class="va">testing_data</span>, type <span class="op">=</span> <span class="st">"prob"</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 class="co"># add probabilities</span></span>
<span> <span class="fu"><a href="https://dplyr.tidyverse.org/reference/bind_cols.html" class="external-link">bind_cols</a></span><span class="op">(</span><span class="va">testing_data</span><span class="op">)</span></span>
<span></span>
<span><span class="co"># Evaluate model performance</span></span>
<span><span class="va">our_metrics</span> <span class="op">&lt;-</span> <span class="fu">metric_set</span><span class="op">(</span><span class="va">accuracy</span>, <span class="va">recall</span>, <span class="va">precision</span>, <span class="va">sensitivity</span>, <span class="va">specificity</span>, <span class="va">ppv</span>, <span class="va">npv</span><span class="op">)</span></span>
<span><span class="va">metrics</span> <span class="op">&lt;-</span> <span class="fu">our_metrics</span><span class="op">(</span><span class="va">predictions</span>, truth <span class="op">=</span> <span class="va">esbl</span>, estimate <span class="op">=</span> <span class="va">.pred_class</span><span class="op">)</span></span>
<span></span>
<span><span class="va">metrics</span></span>
<span><span class="co">#&gt; <span style="color: #949494;"># A tibble: 7 × 3</span></span></span>
<span><span class="co">#&gt; .metric .estimator .estimate</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></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;">1</span> accuracy binary 0.92 </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;">2</span> recall binary 0.921</span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;">3</span> precision binary 0.921</span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;">4</span> sensitivity binary 0.921</span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;">5</span> specificity binary 0.919</span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;">6</span> ppv binary 0.921</span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;">7</span> npv binary 0.919</span></span></code></pre></div>
<p><strong>Explanation:</strong></p>
<ul>
<li>
<code>fit()</code>: Trains the model on the processed training
data.</li>
<li>
<code><a href="https://rdrr.io/r/stats/predict.html" class="external-link">predict()</a></code>: Produces predictions for unseen test
data.</li>
<li>
<code>metric_set()</code>: Allows evaluating multiple classification
metrics. This will make <code>our_metrics</code> to become a function
that we can use to check the predictions with.</li>
</ul>
<p>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.</p>
</div>
<div class="section level3">
<h3 id="visualising-predictions">
<strong>Visualising Predictions</strong><a class="anchor" aria-label="anchor" href="#visualising-predictions"></a>
</h3>
<p>We can visualise predictions by comparing predicted and actual ESBL
status.</p>
<div class="sourceCode" id="cb15"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://ggplot2.tidyverse.org" class="external-link">ggplot2</a></span><span class="op">)</span></span>
<span></span>
<span><span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggplot.html" class="external-link">ggplot</a></span><span class="op">(</span><span class="va">predictions</span>, <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/aes.html" class="external-link">aes</a></span><span class="op">(</span>x <span class="op">=</span> <span class="va">esbl</span>, fill <span class="op">=</span> <span class="va">.pred_class</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
<span> <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/geom_bar.html" class="external-link">geom_bar</a></span><span class="op">(</span>position <span class="op">=</span> <span class="st">"stack"</span><span class="op">)</span> <span class="op">+</span></span>
<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">"Predicted vs Actual ESBL Status"</span>,</span>
<span> x <span class="op">=</span> <span class="st">"Actual ESBL"</span>,</span>
<span> y <span class="op">=</span> <span class="st">"Count"</span><span class="op">)</span> <span class="op">+</span></span>
<span> <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggtheme.html" class="external-link">theme_minimal</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
<p><img src="AMR_with_tidymodels_files/figure-html/unnamed-chunk-14-1.png" class="r-plt" alt="" width="720"></p>
<p>And plot the certainties too - how certain were the actual
predictions?</p>
<div class="sourceCode" id="cb16"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="va">predictions</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
<span> <span class="fu"><a href="https://dplyr.tidyverse.org/reference/mutate.html" class="external-link">mutate</a></span><span class="op">(</span>certainty <span class="op">=</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">.pred_class</span> <span class="op">==</span> <span class="st">"FALSE"</span>,</span>
<span> <span class="va">.pred_FALSE</span>,</span>
<span> <span class="va">.pred_TRUE</span><span class="op">)</span>,</span>
<span> correct <span class="op">=</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">esbl</span> <span class="op">==</span> <span class="va">.pred_class</span>, <span class="st">"Right"</span>, <span class="st">"Wrong"</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="fu"><a href="https://ggplot2.tidyverse.org/reference/ggplot.html" class="external-link">ggplot</a></span><span class="op">(</span><span class="fu"><a href="https://ggplot2.tidyverse.org/reference/aes.html" class="external-link">aes</a></span><span class="op">(</span>x <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/seq.html" class="external-link">seq_len</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/nrow.html" class="external-link">nrow</a></span><span class="op">(</span><span class="va">predictions</span><span class="op">)</span><span class="op">)</span>,</span>
<span> y <span class="op">=</span> <span class="va">certainty</span>,</span>
<span> colour <span class="op">=</span> <span class="va">correct</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
<span> <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/scale_manual.html" class="external-link">scale_colour_manual</a></span><span class="op">(</span>values <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>Right <span class="op">=</span> <span class="st">"green3"</span>, Wrong <span class="op">=</span> <span class="st">"red2"</span><span class="op">)</span>,</span>
<span> name <span class="op">=</span> <span class="st">"Correct?"</span><span class="op">)</span> <span class="op">+</span></span>
<span> <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/geom_point.html" class="external-link">geom_point</a></span><span class="op">(</span><span class="op">)</span> <span class="op">+</span> </span>
<span> <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/scale_continuous.html" class="external-link">scale_y_continuous</a></span><span class="op">(</span>labels <span class="op">=</span> <span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste0</a></span><span class="op">(</span><span class="va">x</span> <span class="op">*</span> <span class="fl">100</span>, <span class="st">"%"</span><span class="op">)</span>,</span>
<span> limits <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="fl">0.5</span>, <span class="fl">1</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
<span> <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggtheme.html" class="external-link">theme_minimal</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
<p><img src="AMR_with_tidymodels_files/figure-html/unnamed-chunk-15-1.png" class="r-plt" alt="" width="720">
### <strong>Conclusion</strong></p>
<p>In this example, we showcased how the new <code>AMR</code>-specific
recipe steps simplify working with <code>&lt;mic&gt;</code> columns in
<code>tidymodels</code>. The <code><a href="../reference/amr-tidymodels.html">step_mic_log2()</a></code> transformation
converts ordered MICs to log2-transformed numerics, improving
compatibility with classification models.</p>
<p>This pipeline enables realistic, reproducible, and interpretable
modelling of antimicrobial resistance data.</p>
<hr>
</div>
</div>
<div class="section level2">
<h2 id="example-2-predicting-amr-over-time">Example 2: Predicting AMR Over Time<a class="anchor" aria-label="anchor" href="#example-2-predicting-amr-over-time"></a>
<h2 id="example-3-predicting-amr-over-time">Example 3: Predicting AMR Over Time<a class="anchor" aria-label="anchor" href="#example-3-predicting-amr-over-time"></a>
</h2>
<p>In this third example, we aim to predict antimicrobial resistance
(AMR) trends over time using <code>tidymodels</code>. We will model
@@ -461,8 +698,8 @@ amoxicillin-clavulanic acid <code>AMC</code>, and ciprofloxacin
<code>CIP</code>), based on historical data grouped by year and hospital
ward.</p>
<div class="section level3">
<h3 id="objective-1">
<strong>Objective</strong><a class="anchor" aria-label="anchor" href="#objective-1"></a>
<h3 id="objective-2">
<strong>Objective</strong><a class="anchor" aria-label="anchor" href="#objective-2"></a>
</h3>
<p>Our goal is to:</p>
<ol style="list-style-type: decimal">
@@ -473,12 +710,12 @@ model.</li>
</ol>
</div>
<div class="section level3">
<h3 id="data-preparation-1">
<strong>Data Preparation</strong><a class="anchor" aria-label="anchor" href="#data-preparation-1"></a>
<h3 id="data-preparation-2">
<strong>Data Preparation</strong><a class="anchor" aria-label="anchor" href="#data-preparation-2"></a>
</h3>
<p>We start by transforming the <code>example_isolates</code> dataset
into a structured time-series format.</p>
<div class="sourceCode" id="cb10"><pre class="downlit sourceCode r">
<div class="sourceCode" id="cb17"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="co"># Load required libraries</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>
<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://tidymodels.tidymodels.org" class="external-link">tidymodels</a></span><span class="op">)</span></span>
@@ -525,15 +762,15 @@ rates by year and ward.</li>
</ul>
</div>
<div class="section level3">
<h3 id="defining-the-workflow-1">
<strong>Defining the Workflow</strong><a class="anchor" aria-label="anchor" href="#defining-the-workflow-1"></a>
<h3 id="defining-the-workflow-2">
<strong>Defining the Workflow</strong><a class="anchor" aria-label="anchor" href="#defining-the-workflow-2"></a>
</h3>
<p>We now define the modelling workflow, which consists of a
preprocessing step, a model specification, and the fitting process.</p>
<div class="section level4">
<h4 id="preprocessing-with-a-recipe-1">1. Preprocessing with a Recipe<a class="anchor" aria-label="anchor" href="#preprocessing-with-a-recipe-1"></a>
<h4 id="preprocessing-with-a-recipe-2">1. Preprocessing with a Recipe<a class="anchor" aria-label="anchor" href="#preprocessing-with-a-recipe-2"></a>
</h4>
<div class="sourceCode" id="cb11"><pre class="downlit sourceCode r">
<div class="sourceCode" id="cb18"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="co"># Define the recipe</span></span>
<span><span class="va">resistance_recipe_time</span> <span class="op">&lt;-</span> <span class="fu">recipe</span><span class="op">(</span><span class="va">res_AMX</span> <span class="op">~</span> <span class="va">year</span> <span class="op">+</span> <span class="va">gramstain</span>, data <span class="op">=</span> <span class="va">data_time</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">step_dummy</span><span class="op">(</span><span class="va">gramstain</span>, one_hot <span class="op">=</span> <span class="cn">TRUE</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"># Convert categorical to numerical</span></span>
@@ -566,10 +803,10 @@ variable.</li>
</ul>
</div>
<div class="section level4">
<h4 id="specifying-the-model-1">2. Specifying the Model<a class="anchor" aria-label="anchor" href="#specifying-the-model-1"></a>
<h4 id="specifying-the-model-2">2. Specifying the Model<a class="anchor" aria-label="anchor" href="#specifying-the-model-2"></a>
</h4>
<p>We use a linear regression model to predict resistance trends.</p>
<div class="sourceCode" id="cb12"><pre class="downlit sourceCode r">
<div class="sourceCode" id="cb19"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="co"># Define the linear regression model</span></span>
<span><span class="va">lm_model</span> <span class="op">&lt;-</span> <span class="fu">linear_reg</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="fu">set_engine</span><span class="op">(</span><span class="st">"lm"</span><span class="op">)</span> <span class="co"># Use linear regression</span></span>
@@ -588,10 +825,10 @@ engine.</li>
</ul>
</div>
<div class="section level4">
<h4 id="building-the-workflow-1">3. Building the Workflow<a class="anchor" aria-label="anchor" href="#building-the-workflow-1"></a>
<h4 id="building-the-workflow-2">3. Building the Workflow<a class="anchor" aria-label="anchor" href="#building-the-workflow-2"></a>
</h4>
<p>We combine the preprocessing recipe and model into a workflow.</p>
<div class="sourceCode" id="cb13"><pre class="downlit sourceCode r">
<div class="sourceCode" id="cb20"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="co"># Create workflow</span></span>
<span><span class="va">resistance_workflow_time</span> <span class="op">&lt;-</span> <span class="fu">workflow</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="fu">add_recipe</span><span class="op">(</span><span class="va">resistance_recipe_time</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
@@ -616,12 +853,12 @@ engine.</li>
</div>
</div>
<div class="section level3">
<h3 id="training-and-evaluating-the-model-1">
<strong>Training and Evaluating the Model</strong><a class="anchor" aria-label="anchor" href="#training-and-evaluating-the-model-1"></a>
<h3 id="training-and-evaluating-the-model-2">
<strong>Training and Evaluating the Model</strong><a class="anchor" aria-label="anchor" href="#training-and-evaluating-the-model-2"></a>
</h3>
<p>We split the data into training and testing sets, fit the model, and
evaluate performance.</p>
<div class="sourceCode" id="cb14"><pre class="downlit sourceCode r">
<div class="sourceCode" id="cb21"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="co"># Split the data</span></span>
<span><span class="fu"><a href="https://rdrr.io/r/base/Random.html" class="external-link">set.seed</a></span><span class="op">(</span><span class="fl">123</span><span class="op">)</span></span>
<span><span class="va">data_split_time</span> <span class="op">&lt;-</span> <span class="fu">initial_split</span><span class="op">(</span><span class="va">data_time</span>, prop <span class="op">=</span> <span class="fl">0.8</span><span class="op">)</span></span>
@@ -662,11 +899,11 @@ sets.</li>
</ul>
</div>
<div class="section level3">
<h3 id="visualising-predictions">
<strong>Visualising Predictions</strong><a class="anchor" aria-label="anchor" href="#visualising-predictions"></a>
<h3 id="visualising-predictions-1">
<strong>Visualising Predictions</strong><a class="anchor" aria-label="anchor" href="#visualising-predictions-1"></a>
</h3>
<p>We plot resistance trends over time for amoxicillin.</p>
<div class="sourceCode" id="cb15"><pre class="downlit sourceCode r">
<div class="sourceCode" id="cb22"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://ggplot2.tidyverse.org" class="external-link">ggplot2</a></span><span class="op">)</span></span>
<span></span>
<span><span class="co"># Plot actual vs predicted resistance over time</span></span>
@@ -677,10 +914,10 @@ sets.</li>
<span> x <span class="op">=</span> <span class="st">"Year"</span>,</span>
<span> y <span class="op">=</span> <span class="st">"Resistance Proportion"</span><span class="op">)</span> <span class="op">+</span></span>
<span> <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggtheme.html" class="external-link">theme_minimal</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
<p><img src="AMR_with_tidymodels_files/figure-html/unnamed-chunk-14-1.png" class="r-plt" width="720"></p>
<p><img src="AMR_with_tidymodels_files/figure-html/unnamed-chunk-21-1.png" class="r-plt" alt="" width="720"></p>
<p>Additionally, we can visualise resistance trends in
<code>ggplot2</code> and directly add linear models there:</p>
<div class="sourceCode" id="cb16"><pre class="downlit sourceCode r">
<div class="sourceCode" id="cb23"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggplot.html" class="external-link">ggplot</a></span><span class="op">(</span><span class="va">data_time</span>, <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/aes.html" class="external-link">aes</a></span><span class="op">(</span>x <span class="op">=</span> <span class="va">year</span>, y <span class="op">=</span> <span class="va">res_AMX</span>, color <span class="op">=</span> <span class="va">gramstain</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
<span> <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/geom_path.html" class="external-link">geom_line</a></span><span class="op">(</span><span class="op">)</span> <span class="op">+</span></span>
<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">"AMX Resistance Trends"</span>,</span>
@@ -691,7 +928,7 @@ sets.</li>
<span> formula <span class="op">=</span> <span class="va">y</span> <span class="op">~</span> <span class="va">x</span>,</span>
<span> alpha <span class="op">=</span> <span class="fl">0.25</span><span class="op">)</span> <span class="op">+</span></span>
<span> <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggtheme.html" class="external-link">theme_minimal</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
<p><img src="AMR_with_tidymodels_files/figure-html/unnamed-chunk-15-1.png" class="r-plt" width="720"></p>
<p><img src="AMR_with_tidymodels_files/figure-html/unnamed-chunk-22-1.png" class="r-plt" alt="" width="720"></p>
</div>
<div class="section level3">
<h3 id="conclusion-1">

251
articles/AMR_with_tidymodels.md
View File

@@ -1,6 +1,6 @@
# AMR with tidymodels
> This page was entirely written by our [AMR for R
> 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.
@@ -351,21 +351,250 @@ and reproducibly.
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()`, which
prepares MIC values for use in classification models.
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).
to model the presence of extended-spectrum beta-lactamases (ESBL) based
on MIC values.
> NOTE: THIS EXAMPLE WILL BE AVAILABLE IN A NEXT VERSION (#TODO)
>
> The new AMR package version will contain new tidymodels selectors such
> as `step_mic_log2()`.
### **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
#>
#> ── Operations
#> • Log2 transformation of MIC columns: all_mic_predictors()
```
**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 ordered MICs to log2-transformed numerics,
improving compatibility with classification models.
This pipeline enables realistic, reproducible, and interpretable
modelling of antimicrobial resistance data.
------------------------------------------------------------------------
## Example 2: Predicting AMR Over Time
## 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
@@ -573,7 +802,7 @@ ggplot(predictions_time, aes(x = year)) +
theme_minimal()
```
![](AMR_with_tidymodels_files/figure-html/unnamed-chunk-14-1.png)
![](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:
@@ -591,7 +820,7 @@ ggplot(data_time, aes(x = year, y = res_AMX, color = gramstain)) +
theme_minimal()
```
![](AMR_with_tidymodels_files/figure-html/unnamed-chunk-15-1.png)
![](AMR_with_tidymodels_files/figure-html/unnamed-chunk-22-1.png)
### **Conclusion**

BIN
articles/AMR_with_tidymodels_files/figure-html/unnamed-chunk-14-1.png
View File

Binary file not shown.
Side by Side Swipe Overlay

Before

Width:  |  Height:  |  Size: 64 KiB

After

Width:  |  Height:  |  Size: 31 KiB

BIN
articles/AMR_with_tidymodels_files/figure-html/unnamed-chunk-15-1.png
View File

Binary file not shown.
Side by Side Swipe Overlay

Before

Width:  |  Height:  |  Size: 137 KiB

After

Width:  |  Height:  |  Size: 70 KiB

BIN
articles/AMR_with_tidymodels_files/figure-html/unnamed-chunk-21-1.png Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 64 KiB

BIN
articles/AMR_with_tidymodels_files/figure-html/unnamed-chunk-22-1.png Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 137 KiB

2
articles/EUCAST.html
View File

@@ -30,7 +30,7 @@
<a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9004</small>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9007</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">

8
articles/PCA.html
View File

@@ -30,7 +30,7 @@
<a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9004</small>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9007</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">
@@ -210,18 +210,18 @@ per drug explain the difference per microorganism.</p>
</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" width="750"></p>
<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" width="750"></p>
<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" width="750"></p>
<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>

4
articles/WHONET.html
View File

@@ -30,7 +30,7 @@
<a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9004</small>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9007</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">
@@ -311,7 +311,7 @@ using the included <code><a href="../reference/ggplot_sir.html">ggplot_sir()</a>
<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" width="720"></p>
<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>

2
articles/WISCA.html
View File

@@ -30,7 +30,7 @@
<a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9004</small>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9007</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">

4
articles/datasets.html
View File

@@ -30,7 +30,7 @@
<a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9004</small>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9007</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">
@@ -80,7 +80,7 @@
<main id="main" class="col-md-9"><div class="page-header">
<img src="../logo.svg" class="logo" alt=""><h1>Download data sets for download / own use</h1>
<h4 data-toc-skip class="date">15 December 2025</h4>
<h4 data-toc-skip class="date">22 December 2025</h4>
<small class="dont-index">Source: <a href="https://github.com/msberends/AMR/blob/main/vignettes/datasets.Rmd" class="external-link"><code>vignettes/datasets.Rmd</code></a></small>
<div class="d-none name"><code>datasets.Rmd</code></div>

2
articles/index.html
View File

@@ -7,7 +7,7 @@
<a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9004</small>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9007</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">