(v3.0.0.9011) allow names for age_groups()

R/tidymodels.R

@@ -19,56 +19,59 @@
 #' @keywords internal
 #' @export
 #' @examples
-#' library(tidymodels)
+#' if (require("tidymodels")) {
 #'
-#' # The below approach formed the basis for this paper: DOI 10.3389/fmicb.2025.1582703
-#' # Presence of ESBL genes was predicted based on raw MIC values.
+#'   # The below approach formed the basis for this paper: DOI 10.3389/fmicb.2025.1582703
+#'   # Presence of ESBL genes was predicted based on raw MIC values.
 #'
 #'
-#' # example data set in the AMR package
-#' esbl_isolates
+#'   # example data set in the AMR package
+#'   esbl_isolates
 #'
-#' # Prepare a binary outcome and convert to ordered factor
-#' data <- esbl_isolates %>%
-#'   mutate(esbl = factor(esbl, levels = c(FALSE, TRUE), ordered = TRUE))
+#'   # Prepare a binary outcome and convert to ordered factor
+#'   data <- esbl_isolates %>%
+#'     mutate(esbl = factor(esbl, levels = c(FALSE, TRUE), ordered = TRUE))
 #'
-#' # Split into training and testing sets
-#' split <- initial_split(data)
-#' training_data <- training(split)
-#' testing_data <- testing(split)
+#'   # Split into training and testing sets
+#'   split <- initial_split(data)
+#'   training_data <- training(split)
+#'   testing_data <- testing(split)
 #'
-#' # Create and prep a recipe with MIC log2 transformation
-#' mic_recipe <- recipe(esbl ~ ., data = training_data) %>%
-#'   # Optionally remove non-predictive variables
-#'   remove_role(genus, old_role = "predictor") %>%
-#'   # Apply the log2 transformation to all MIC predictors
-#'   step_mic_log2(all_mic_predictors()) %>%
-#'   prep()
+#'   # Create and prep a recipe with MIC log2 transformation
+#'   mic_recipe <- recipe(esbl ~ ., data = training_data) %>%
 #'
-#' # View prepped recipe
-#' mic_recipe
+#'     # Optionally remove non-predictive variables
+#'     remove_role(genus, old_role = "predictor") %>%
 #'
-#' # Apply the recipe to training and testing data
-#' out_training <- bake(mic_recipe, new_data = NULL)
-#' out_testing <- bake(mic_recipe, new_data = testing_data)
+#'     # Apply the log2 transformation to all MIC predictors
+#'     step_mic_log2(all_mic_predictors()) %>%
 #'
-#' # Fit a logistic regression model
-#' fitted <- logistic_reg(mode = "classification") %>%
-#'   set_engine("glm") %>%
-#'   fit(esbl ~ ., data = out_training)
+#'     # And apply the preparation steps
+#'     prep()
 #'
-#' # Generate predictions on the test set
-#' predictions <- predict(fitted, out_testing) %>%
-#'   bind_cols(out_testing)
+#'   # View prepped recipe
+#'   mic_recipe
 #'
-#' # Evaluate predictions using standard classification metrics
-#' our_metrics <- metric_set(accuracy, kap, ppv, npv)
-#' metrics <- our_metrics(predictions, truth = esbl, estimate = .pred_class)
+#'   # Apply the recipe to training and testing data
+#'   out_training <- bake(mic_recipe, new_data = NULL)
+#'   out_testing <- bake(mic_recipe, new_data = testing_data)
 #'
-#' # Show performance:
-#' # - negative predictive value (NPV) of ~98%
-#' # - positive predictive value (PPV) of ~94%
-#' metrics
+#'   # Fit a logistic regression model
+#'   fitted <- logistic_reg(mode = "classification") %>%
+#'     set_engine("glm") %>%
+#'     fit(esbl ~ ., data = out_training)
+#'
+#'   # Generate predictions on the test set
+#'   predictions <- predict(fitted, out_testing) %>%
+#'     bind_cols(out_testing)
+#'
+#'   # Evaluate predictions using standard classification metrics
+#'   our_metrics <- metric_set(accuracy, kap, ppv, npv)
+#'   metrics <- our_metrics(predictions, truth = esbl, estimate = .pred_class)
+#'
+#'   # Show performance
+#'   metrics
+#' }
 all_mic <- function() {
   x <- tidymodels_amr_select(levels(NA_mic_))
   names(x)