(v3.0.1.9008) fix #246

Merge branch 'main' of https://github.com/msberends/AMR

# Conflicts:
#	DESCRIPTION
#	NEWS.md
#	R/sysdata.rda
#	data/antibiotics.rda

.github/ISSUE_TEMPLATE/1-bug-report.yml

@@ -22,9 +22,9 @@ body:
       label: Minimal Reproducible Example (optional)
       description: Please include a short R code snippet that reproduces the problem, if possible.
       placeholder: 
-        e.g.\n\n
-        ```r<br>
-        ab_name("amoxicillin/clavulanic acid", language = "es")\n
+        e.g.  
+        ```r  
+        ab_name("amoxicillin/clavulanic acid", language = "es")  
         ```
     validations:
       required: false

DESCRIPTION

@@ -1,5 +1,5 @@
 Package: AMR
-Version: 3.0.1.9007
+Version: 3.0.1.9008
 Date: 2026-01-06
 Title: Antimicrobial Resistance Data Analysis
 Description: Functions to simplify and standardise antimicrobial resistance (AMR)

NEWS.md

@@ -1,4 +1,4 @@
-# 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`

vignettes/AMR_with_tidymodels.Rmd

@@ -1,6 +1,6 @@
 ---
 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.
 
@@ -267,10 +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:**
@@ -315,7 +314,7 @@ fitted <- fit(workflow_model, training_data)
 
 # Generate predictions
 predictions <- predict(fitted, testing_data) %>%
-  bind_cols(predict(fitted, out_testing, type = "prob")) %>% # add probabilities
+  bind_cols(predict(fitted, testing_data, type = "prob")) %>% # add probabilities
   bind_cols(testing_data)
 
 # Evaluate model performance
@@ -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**
 
@@ -361,14 +360,15 @@ 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.
+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.
 
@@ -487,7 +487,7 @@ 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 %>%