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(v3.0.1.9008) tidymodels vignette

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DESCRIPTION
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Package: AMR
Version: 3.0.1.9007
Version: 3.0.1.9008
Date: 2025-12-22
Title: Antimicrobial Resistance Data Analysis
Description: Functions to simplify and standardise antimicrobial resistance (AMR)

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NEWS.md
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# AMR 3.0.1.9007
# AMR 3.0.1.9008
### New
* Integration with the **tidymodels** framework to allow seamless use of SIR, MIC and disk data in modelling pipelines via `recipes`

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vignettes/AMR_with_tidymodels.Rmd
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---
title: "AMR with tidymodels"
output:
output:
rmarkdown::html_vignette:
toc: true
toc_depth: 3
@@ -8,7 +8,7 @@ vignette: >
%\VignetteIndexEntry{AMR with tidymodels}
%\VignetteEncoding{UTF-8}
%\VignetteEngine{knitr::rmarkdown}
editor_options:
editor_options:
chunk_output_type: console
---
@@ -208,7 +208,7 @@ 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 `aminoglycosides()` and `betalactams()` with `tidymodels`, we efficiently prepared data, trained a model, and evaluated its performance.
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()` and `betalactams()` 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.
@@ -267,8 +267,8 @@ 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
prep()
mic_recipe
```
@@ -361,11 +361,12 @@ 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()
```
### **Conclusion**
In this example, we showcased how the new `AMR`-specific recipe steps simplify working with `<mic>` columns in `tidymodels`. The `step_mic_log2()` transformation converts ordered MICs to log2-transformed numerics, improving compatibility with classification models.
@@ -487,7 +488,7 @@ 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 %>%