(v3.0.0.9029) fix for vignette and envir data

DESCRIPTION

@@ -1,5 +1,5 @@
 Package: AMR
-Version: 3.0.0.9028
+Version: 3.0.0.9029
 Date: 2025-09-10
 Title: Antimicrobial Resistance Data Analysis
 Description: Functions to simplify and standardise antimicrobial resistance (AMR)

NEWS.md

@@ -1,4 +1,4 @@
-# AMR 3.0.0.9028
+#  3.0.0.9029
 
 This is a bugfix release following the release of v3.0.0 in June 2025.
 

R/ab.R

@@ -203,7 +203,7 @@ as.ab <- function(x, flag_multiple_results = TRUE, language = get_AMR_locale(),
     progress <- progress_ticker(n = sum(!already_known), n_min = 25, print = info) # start if n >= 25
     on.exit(close(progress))
     if (any(x_new[!already_known & !is.na(x_new)] %in% unlist(AMR_env$AV_lookup$generalised_all, use.names = FALSE), na.rm = TRUE)) {
-      warning_("in `as.ab()`: some input seem to resemble antiviral drugs - use `as.av()` or e.g. `av_name()` for these, not `as.ab()` or e.g. `ab_name()`.")
+      warning_("in `as.ab()`: some input seems to resemble antiviral drugs - use `as.av()` or e.g. `av_name()` for these, not `as.ab()` or e.g. `ab_name()`.")
     }
   }
 

tests/testthat/test-_misc.R

@@ -70,14 +70,14 @@ test_that("test-misc.R", {
   }
 
   df <- example_isolates[, check_df("x")]
-  expect_true(is_right, info = "the environmental data cannot be found for base/x (1)")
+  expect_true(is_right, info = "the environmental data cannot be found for base `x`")
 
-  if (getRversion() < "4.0.0") {
+  # should work on R >=3.6.3 or so
+  df <- example_isolates[c(1:3), check_df("x")]
+  if (!is_right) {
+    # otherwise, this is needed for older versions
     df <- example_isolates[c(1:3), check_df("xx")]
-    expect_true(is_right, info = "the environmental data cannot be found for base/xx")
+    expect_true(is_right, info = "the environmental data cannot be found for base `x` or `xx`") 
-  } else {
-    df <- example_isolates[c(1:3), check_df("x")]
-    expect_true(is_right, info = "the environmental data cannot be found for base/x (2)")
   }
 
   if (AMR:::pkg_is_available("dplyr", min_version = "1.0.0", also_load = TRUE)) {

vignettes/AMR_with_tidymodels.Rmd

@@ -225,148 +225,12 @@ This approach and idea formed the basis for the publication [DOI: 10.3389/fmicb.
 > 
 > The new AMR package version will contain new tidymodels selectors such as `step_mic_log2()`.
 
-<!--
+<!-- TODO for AMR v3.1.0: add info from here: https://github.com/msberends/AMR/blob/2461631bcefa78ebdb37bdfad359be74cdd9165a/vignettes/AMR_with_tidymodels.Rmd#L212-L291 -->
-
-### **Objective**
-
-Our goal is to:
-
-1. Use raw MIC values to predict whether a bacterial isolate produces ESBL.
-2. Apply AMR-aware preprocessing in a `tidymodels` recipe.
-3. Train a classification model and evaluate its predictive performance.
-
-### **Data Preparation**
-
-We use the `esbl_isolates` dataset that comes with the AMR package.
-
-```{r}
-# Load required libraries
-library(AMR)
-library(tidymodels)
-
-# View the esbl_isolates data set
-esbl_isolates
-
-# Prepare a binary outcome and convert to ordered factor
-data <- esbl_isolates %>%
-  mutate(esbl = factor(esbl, levels = c(FALSE, TRUE), ordered = TRUE))
-```
-
-**Explanation:**
-
-- `esbl_isolates`: Contains MIC test results and ESBL status for each isolate.
-- `mutate(esbl = ...)`: Converts the target column to an ordered factor for classification.
-
-### **Defining the Workflow**
-
-#### 1. Preprocessing with a Recipe
-
-We use our `step_mic_log2()` function to log2-transform MIC values, ensuring that MICs are numeric and properly scaled. All MIC predictors can easily and agnostically selected using the new `all_mic_predictors()`:
-
-```{r}
-# Split into training and testing sets
-set.seed(123)
-split <- initial_split(data)
-training_data <- training(split)
-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()
-
-mic_recipe
-```
-
-**Explanation:**
-
-- `remove_role()`: Removes irrelevant variables like genus.
-- `step_mic_log2()`: Applies `log2(as.numeric(...))` to all MIC predictors in one go.
-- `prep()`: Finalises the recipe based on training data.
-
-#### 2. Specifying the Model
-
-We use a simple logistic regression to model ESBL presence, though recent models such as xgboost ([link to `parsnip` manual](https://parsnip.tidymodels.org/reference/details_boost_tree_xgboost.html)) could be much more precise.
-
-```{r}
-# Define the model
-model <- logistic_reg(mode = "classification") %>%
-  set_engine("glm")
-
-model
-```
-
-**Explanation:**
-
-- `logistic_reg()`: Specifies a binary classification model.
-- `set_engine("glm")`: Uses the base R GLM engine.
-
-#### 3. Building the Workflow
-
-```{r}
-# Create workflow
-workflow_model <- workflow() %>%
-  add_recipe(mic_recipe) %>%
-  add_model(model)
-
-workflow_model
-```
-
-### **Training and Evaluating the Model**
-
-```{r}
-# Fit the model
-fitted <- fit(workflow_model, training_data)
-
-# Generate predictions
-predictions <- predict(fitted, testing_data) %>%
-  bind_cols(testing_data)
-
-# Evaluate model performance
-our_metrics <- metric_set(accuracy, kap, ppv, npv)
-metrics <- our_metrics(predictions, truth = esbl, estimate = .pred_class)
-
-metrics
-```
-
-**Explanation:**
-
-- `fit()`: Trains the model on the processed training data.
-- `predict()`: Produces predictions for unseen test data.
-- `metric_set()`: Allows evaluating multiple classification metrics.
-
-It appears we can predict ESBL gene presence with a positive predictive value (PPV) of `r round(metrics$.estimate[3], 3) * 100`% and a negative predictive value (NPV) of `r round(metrics$.estimate[4], 3) * 100` using a simplistic logistic regression model.
-
-### **Visualising Predictions**
-
-We can visualise predictions by comparing predicted and actual ESBL status.
-
-```{r}
-library(ggplot2)
-
-ggplot(predictions, aes(x = esbl, fill = .pred_class)) +
-  geom_bar(position = "stack") +
-  labs(title = "Predicted vs Actual ESBL Status",
-       x = "Actual ESBL",
-       y = "Count") +
-  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.
-
-This pipeline enables realistic, reproducible, and interpretable modelling of antimicrobial resistance data.
-
--->
 
 ---
 
 
-## Example 3: Predicting AMR Over Time
+## Example 2: Predicting AMR Over Time
 
 In this third example, we aim to predict antimicrobial resistance (AMR) trends over time using `tidymodels`. We will model resistance to three antibiotics (amoxicillin `AMX`, amoxicillin-clavulanic acid `AMC`, and ciprofloxacin `CIP`), based on historical data grouped by year and hospital ward.