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(v3.0.1.9008) tidymodels vignette

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DESCRIPTION
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Package: AMR Package: AMR
Version: 3.0.1.9007 Version: 3.0.1.9008
Date: 2025-12-22 Date: 2025-12-22
Title: Antimicrobial Resistance Data Analysis Title: Antimicrobial Resistance Data Analysis
Description: Functions to simplify and standardise antimicrobial resistance (AMR) 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 ### New
* Integration with the **tidymodels** framework to allow seamless use of SIR, MIC and disk data in modelling pipelines via `recipes` * 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" title: "AMR with tidymodels"
output: output:
rmarkdown::html_vignette: rmarkdown::html_vignette:
toc: true toc: true
toc_depth: 3 toc_depth: 3
@@ -8,7 +8,7 @@ vignette: >
%\VignetteIndexEntry{AMR with tidymodels} %\VignetteIndexEntry{AMR with tidymodels}
%\VignetteEncoding{UTF-8} %\VignetteEncoding{UTF-8}
%\VignetteEngine{knitr::rmarkdown} %\VignetteEngine{knitr::rmarkdown}
editor_options: editor_options:
chunk_output_type: console chunk_output_type: console
--- ---
@@ -208,7 +208,7 @@ predictions %>%
### **Conclusion** ### **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. 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 # Define the recipe
mic_recipe <- recipe(esbl ~ ., data = training_data) %>% mic_recipe <- recipe(esbl ~ ., data = training_data) %>%
remove_role(genus, old_role = "predictor") %>% # Remove non-informative variable remove_role(genus, old_role = "predictor") %>% # Remove non-informative variable
step_mic_log2(all_mic_predictors()) #%>% # Log2 transform all MIC predictors step_mic_log2(all_mic_predictors()) %>% # Log2 transform all MIC predictors
# prep() prep()
mic_recipe mic_recipe
``` ```
@@ -361,11 +361,12 @@ predictions %>%
colour = correct)) + colour = correct)) +
scale_colour_manual(values = c(Right = "green3", Wrong = "red2"), scale_colour_manual(values = c(Right = "green3", Wrong = "red2"),
name = "Correct?") + name = "Correct?") +
geom_point() + geom_point() +
scale_y_continuous(labels = function(x) paste0(x * 100, "%"), scale_y_continuous(labels = function(x) paste0(x * 100, "%"),
limits = c(0.5, 1)) + limits = c(0.5, 1)) +
theme_minimal() theme_minimal()
``` ```
### **Conclusion** ### **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. 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 # Make predictions
predictions_time <- fitted_workflow_time %>% predictions_time <- fitted_workflow_time %>%
predict(test_time) %>% predict(test_time) %>%
bind_cols(test_time) bind_cols(test_time)
# Evaluate model # Evaluate model
metrics_time <- predictions_time %>% metrics_time <- predictions_time %>%