From 35debe25ee60b22802459f7c59c51ff1c4f719e6 Mon Sep 17 00:00:00 2001
From: Matthijs Berends <berendsms@gmail.com>
Date: Tue, 23 Dec 2025 11:02:26 +0100
Subject: [PATCH] (v3.0.1.9009) tidymodels vignette

---
 DESCRIPTION                       | 4 ++--
 NEWS.md                           | 2 +-
 vignettes/AMR_with_tidymodels.Rmd | 9 ++++-----
 3 files changed, 7 insertions(+), 8 deletions(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index 94e6ed0d0..5204ece38 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,6 @@
 Package: AMR
-Version: 3.0.1.9008
-Date: 2025-12-22
+Version: 3.0.1.9009
+Date: 2025-12-23
 Title: Antimicrobial Resistance Data Analysis
 Description: Functions to simplify and standardise antimicrobial resistance (AMR)
   data analysis and to work with microbial and antimicrobial properties by
diff --git a/NEWS.md b/NEWS.md
index 1bdfa4c4b..12d7c6178 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,4 +1,4 @@
-# AMR 3.0.1.9008
+# AMR 3.0.1.9009
 
 ### New
 * Integration with the **tidymodels** framework to allow seamless use of SIR, MIC and disk data in modelling pipelines via `recipes`
diff --git a/vignettes/AMR_with_tidymodels.Rmd b/vignettes/AMR_with_tidymodels.Rmd
index 80777686d..156dfc14e 100644
--- a/vignettes/AMR_with_tidymodels.Rmd
+++ b/vignettes/AMR_with_tidymodels.Rmd
@@ -267,10 +267,9 @@ 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)
 ```
 
 **Explanation:**
@@ -331,7 +330,7 @@ metrics
 - `predict()`: 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 `r round(metrics[metrics$.metric == "ppv", ]$.estimate, 3) * 100`% and a negative predictive value (NPV) of `r round(metrics[metrics$.metric == "npv", ]$.estimate, 3) * 100` using a simplistic logistic regression model.
+It appears we can predict ESBL gene presence with a positive predictive value (PPV) of `r round(metrics[metrics$.metric == "ppv", ]$.estimate, 3) * 100`% and a negative predictive value (NPV) of `r round(metrics[metrics$.metric == "npv", ]$.estimate, 3) * 100`% using a simplistic logistic regression model.
 
 ### **Visualising Predictions**
 
@@ -369,7 +368,7 @@ predictions %>%
 
 ### **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 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.