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(v3.0.0.9029) fix for vignette and envir data
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Package: AMR
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Version: 3.0.0.9028
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Version: 3.0.0.9029
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Date: 2025-09-10
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Title: Antimicrobial Resistance Data Analysis
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Description: Functions to simplify and standardise antimicrobial resistance (AMR)
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2
NEWS.md
2
NEWS.md
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# AMR 3.0.0.9028
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# 3.0.0.9029
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This is a bugfix release following the release of v3.0.0 in June 2025.
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2
R/ab.R
2
R/ab.R
@@ -203,7 +203,7 @@ as.ab <- function(x, flag_multiple_results = TRUE, language = get_AMR_locale(),
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progress <- progress_ticker(n = sum(!already_known), n_min = 25, print = info) # start if n >= 25
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on.exit(close(progress))
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if (any(x_new[!already_known & !is.na(x_new)] %in% unlist(AMR_env$AV_lookup$generalised_all, use.names = FALSE), na.rm = TRUE)) {
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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()`.")
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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()`.")
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}
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}
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@@ -70,14 +70,14 @@ test_that("test-misc.R", {
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}
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df <- example_isolates[, check_df("x")]
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expect_true(is_right, info = "the environmental data cannot be found for base/x (1)")
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expect_true(is_right, info = "the environmental data cannot be found for base `x`")
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if (getRversion() < "4.0.0") {
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# should work on R >=3.6.3 or so
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df <- example_isolates[c(1:3), check_df("x")]
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if (!is_right) {
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# otherwise, this is needed for older versions
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df <- example_isolates[c(1:3), check_df("xx")]
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expect_true(is_right, info = "the environmental data cannot be found for base/xx")
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} else {
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df <- example_isolates[c(1:3), check_df("x")]
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expect_true(is_right, info = "the environmental data cannot be found for base/x (2)")
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expect_true(is_right, info = "the environmental data cannot be found for base `x` or `xx`")
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}
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if (AMR:::pkg_is_available("dplyr", min_version = "1.0.0", also_load = TRUE)) {
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@@ -225,148 +225,12 @@ This approach and idea formed the basis for the publication [DOI: 10.3389/fmicb.
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>
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> The new AMR package version will contain new tidymodels selectors such as `step_mic_log2()`.
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<!--
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### **Objective**
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Our goal is to:
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1. Use raw MIC values to predict whether a bacterial isolate produces ESBL.
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2. Apply AMR-aware preprocessing in a `tidymodels` recipe.
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3. Train a classification model and evaluate its predictive performance.
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### **Data Preparation**
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We use the `esbl_isolates` dataset that comes with the AMR package.
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```{r}
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# Load required libraries
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library(AMR)
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library(tidymodels)
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# View the esbl_isolates data set
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esbl_isolates
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# Prepare a binary outcome and convert to ordered factor
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data <- esbl_isolates %>%
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mutate(esbl = factor(esbl, levels = c(FALSE, TRUE), ordered = TRUE))
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```
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**Explanation:**
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- `esbl_isolates`: Contains MIC test results and ESBL status for each isolate.
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- `mutate(esbl = ...)`: Converts the target column to an ordered factor for classification.
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### **Defining the Workflow**
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#### 1. Preprocessing with a Recipe
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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()`:
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```{r}
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# Split into training and testing sets
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set.seed(123)
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split <- initial_split(data)
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training_data <- training(split)
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testing_data <- testing(split)
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# Define the recipe
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mic_recipe <- recipe(esbl ~ ., data = training_data) %>%
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remove_role(genus, old_role = "predictor") %>% # Remove non-informative variable
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step_mic_log2(all_mic_predictors()) #%>% # Log2 transform all MIC predictors
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# prep()
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mic_recipe
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```
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**Explanation:**
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- `remove_role()`: Removes irrelevant variables like genus.
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- `step_mic_log2()`: Applies `log2(as.numeric(...))` to all MIC predictors in one go.
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- `prep()`: Finalises the recipe based on training data.
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#### 2. Specifying the Model
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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.
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```{r}
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# Define the model
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model <- logistic_reg(mode = "classification") %>%
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set_engine("glm")
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model
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```
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**Explanation:**
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- `logistic_reg()`: Specifies a binary classification model.
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- `set_engine("glm")`: Uses the base R GLM engine.
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#### 3. Building the Workflow
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```{r}
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# Create workflow
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workflow_model <- workflow() %>%
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add_recipe(mic_recipe) %>%
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add_model(model)
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workflow_model
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```
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### **Training and Evaluating the Model**
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```{r}
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# Fit the model
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fitted <- fit(workflow_model, training_data)
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# Generate predictions
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predictions <- predict(fitted, testing_data) %>%
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bind_cols(testing_data)
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# Evaluate model performance
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our_metrics <- metric_set(accuracy, kap, ppv, npv)
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metrics <- our_metrics(predictions, truth = esbl, estimate = .pred_class)
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metrics
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```
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**Explanation:**
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- `fit()`: Trains the model on the processed training data.
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- `predict()`: Produces predictions for unseen test data.
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- `metric_set()`: Allows evaluating multiple classification metrics.
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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.
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### **Visualising Predictions**
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We can visualise predictions by comparing predicted and actual ESBL status.
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```{r}
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library(ggplot2)
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ggplot(predictions, aes(x = esbl, fill = .pred_class)) +
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geom_bar(position = "stack") +
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labs(title = "Predicted vs Actual ESBL Status",
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x = "Actual ESBL",
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y = "Count") +
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theme_minimal()
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```
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<!--
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### **Conclusion**
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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.
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This pipeline enables realistic, reproducible, and interpretable modelling of antimicrobial resistance data.
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-->
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<!-- TODO for AMR v3.1.0: add info from here: https://github.com/msberends/AMR/blob/2461631bcefa78ebdb37bdfad359be74cdd9165a/vignettes/AMR_with_tidymodels.Rmd#L212-L291 -->
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---
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## Example 3: Predicting AMR Over Time
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## Example 2: Predicting AMR Over Time
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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.
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