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10
articles/AMR.html
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@@ -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="">3.0.1.9007</small>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9009</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 22 December 2025.</p>
generated on 23 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">2025-12-22</td>
<td align="center">2025-12-23</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">2025-12-22</td>
<td align="center">2025-12-23</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">2025-12-22</td>
<td align="center">2025-12-23</td>
<td align="center">efgh</td>
<td align="center">Escherichia coli</td>
<td align="center">R</td>

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articles/AMR.md
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@@ -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 22 December
methodology remains unchanged. This page was generated on 23 December
2025.
## Introduction
@@ -52,9 +52,9 @@ structure of your data generally look like this:
| date | patient_id | mo | AMX | CIP |
|:----------:|:----------:|:----------------:|:---:|:---:|
| 2025-12-22 | abcd | Escherichia coli | S | S |
| 2025-12-22 | abcd | Escherichia coli | S | R |
| 2025-12-22 | efgh | Escherichia coli | R | S |
| 2025-12-23 | abcd | Escherichia coli | S | S |
| 2025-12-23 | abcd | Escherichia coli | S | R |
| 2025-12-23 | efgh | Escherichia coli | R | S |
### Needed R packages

2
articles/AMR_for_Python.html
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@@ -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="">3.0.1.9007</small>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9009</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">

36
articles/AMR_with_tidymodels.html
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@@ -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="">3.0.1.9007</small>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9009</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">
@@ -419,7 +419,7 @@ ROC curve looks like this:</p>
<h3 id="conclusion">
<strong>Conclusion</strong><a class="anchor" aria-label="anchor" href="#conclusion"></a>
</h3>
<p>In this post, we demonstrated how to build a machine learning
<p>In this example, we demonstrated how to build a machine learning
pipeline with the <code>tidymodels</code> framework and the
<code>AMR</code> package. By combining selector functions like
<code><a href="../reference/antimicrobial_selectors.html">aminoglycosides()</a></code> and <code><a href="../reference/antimicrobial_selectors.html">betalactams()</a></code> with
@@ -521,10 +521,9 @@ predictors can easily and agnostically selected using the new
<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 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"># Log2 transform all MIC predictors</span></span>
<span></span>
<span><span class="va">mic_recipe</span></span>
<span><span class="fu">prep</span><span class="op">(</span><span class="va">mic_recipe</span><span class="op">)</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>
@@ -534,8 +533,11 @@ predictors can easily and agnostically selected using the new
<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; ── Training information</span></span>
<span><span class="co">#&gt; Training data contained 375 data points and no incomplete rows.</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>
<span><span class="co">#&gt; <span style="color: #00BBBB;">•</span> Log2 transformation of MIC columns: <span style="color: #0000BB;">AMC</span>, <span style="color: #0000BB;">AMP</span>, <span style="color: #0000BB;">TZP</span>, <span style="color: #0000BB;">CXM</span>, <span style="color: #0000BB;">FOX</span>, ... | <span style="font-style: italic;">Trained</span></span></span></code></pre></div>
<p><strong>Explanation:</strong></p>
<ul>
<li>
@@ -641,7 +643,7 @@ 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>
91.9% using a simplistic logistic regression model.</p>
</div>
<div class="section level3">
<h3 id="visualising-predictions">
@@ -672,17 +674,21 @@ predictions?</p>
<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/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><img src="AMR_with_tidymodels_files/figure-html/unnamed-chunk-15-1.png" class="r-plt" alt="" width="720"></p>
</div>
<div class="section level3">
<h3 id="conclusion-1">
<strong>Conclusion</strong><a class="anchor" aria-label="anchor" href="#conclusion-1"></a>
</h3>
<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>
converts MICs (with or without operators) 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>
@@ -872,7 +878,7 @@ evaluate performance.</p>
<span><span class="co"># Make predictions</span></span>
<span><span class="va">predictions_time</span> <span class="op">&lt;-</span> <span class="va">fitted_workflow_time</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://rdrr.io/r/stats/predict.html" class="external-link">predict</a></span><span class="op">(</span><span class="va">test_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"><a href="https://dplyr.tidyverse.org/reference/bind_cols.html" class="external-link">bind_cols</a></span><span class="op">(</span><span class="va">test_time</span><span class="op">)</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">test_time</span><span class="op">)</span></span>
<span></span>
<span><span class="co"># Evaluate model</span></span>
<span><span class="va">metrics_time</span> <span class="op">&lt;-</span> <span class="va">predictions_time</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
@@ -931,8 +937,8 @@ sets.</li>
<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">
<strong>Conclusion</strong><a class="anchor" aria-label="anchor" href="#conclusion-1"></a>
<h3 id="conclusion-2">
<strong>Conclusion</strong><a class="anchor" aria-label="anchor" href="#conclusion-2"></a>
</h3>
<p>In this example, we demonstrated how to analyze AMR trends over time
using <code>tidymodels</code>. By aggregating resistance rates by year

34
articles/AMR_with_tidymodels.md
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@@ -332,9 +332,9 @@ predictions %>%
### **Conclusion**
In this post, we demonstrated how to build a machine learning pipeline
with the `tidymodels` framework and the `AMR` package. By combining
selector functions like
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)
@@ -431,10 +431,9 @@ 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()
step_mic_log2(all_mic_predictors()) # Log2 transform all MIC predictors
mic_recipe
prep(mic_recipe)
#>
#> ── Recipe ──────────────────────────────────────────────────────────────────────
#>
@@ -444,8 +443,11 @@ mic_recipe
#> predictor: 17
#> undeclared role: 1
#>
#> ── Training information
#> Training data contained 375 data points and no incomplete rows.
#>
#> ── Operations
#> • Log2 transformation of MIC columns: all_mic_predictors()
#> • Log2 transformation of MIC columns: AMC, AMP, TZP, CXM, FOX, ... | Trained
```
**Explanation:**
@@ -540,8 +542,8 @@ metrics
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.
value (PPV) of 92.1% and a negative predictive value (NPV) of 91.9%
using a simplistic logistic regression model.
### **Visualising Predictions**
@@ -574,20 +576,22 @@ predictions %>%
colour = correct)) +
scale_colour_manual(values = c(Right = "green3", Wrong = "red2"),
name = "Correct?") +
geom_point() +
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**
![](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.
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.
@@ -762,7 +766,7 @@ fitted_workflow_time <- resistance_workflow_time %>%
# Make predictions
predictions_time <- fitted_workflow_time %>%
predict(test_time) %>%
bind_cols(test_time)
bind_cols(test_time)
# Evaluate model
metrics_time <- predictions_time %>%

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articles/EUCAST.html
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<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="">3.0.1.9007</small>
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@@ -30,7 +30,7 @@
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@@ -30,7 +30,7 @@
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@@ -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="">3.0.1.9007</small>
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articles/datasets.html
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@@ -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="">3.0.1.9007</small>
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@@ -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">22 December 2025</h4>
<h4 data-toc-skip class="date">23 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
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@@ -7,7 +7,7 @@
<a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9007</small>
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