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# AMR with tidymodels
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> This page was entirely written by our [AMR for R
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> This page was almost entirely written by our [AMR for R
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> Assistant](https://chat.amr-for-r.org), a ChatGPT manually-trained
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> model able to answer any question about the `AMR` package.
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@@ -351,21 +351,250 @@ and reproducibly.
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In this second example, we demonstrate how to use `<mic>` columns
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directly in `tidymodels` workflows using AMR-specific recipe steps. This
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includes a transformation to `log2` scale using `step_mic_log2()`, which
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prepares MIC values for use in classification models.
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includes a transformation to `log2` scale using
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[`step_mic_log2()`](https://amr-for-r.org/reference/amr-tidymodels.md),
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which prepares MIC values for use in classification models.
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This approach and idea formed the basis for the publication [DOI:
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10.3389/fmicb.2025.1582703](https://doi.org/10.3389/fmicb.2025.1582703)
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to model the presence of extended-spectrum beta-lactamases (ESBL).
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to model the presence of extended-spectrum beta-lactamases (ESBL) based
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on MIC values.
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> NOTE: THIS EXAMPLE WILL BE AVAILABLE IN A NEXT VERSION (#TODO)
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>
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> The new AMR package version will contain new tidymodels selectors such
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> as `step_mic_log2()`.
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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
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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
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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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#> # A tibble: 500 × 19
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#> esbl genus AMC AMP TZP CXM FOX CTX CAZ GEN TOB TMP SXT
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#> <lgl> <chr> <mic> <mic> <mic> <mic> <mic> <mic> <mic> <mic> <mic> <mic> <mic>
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#> 1 FALSE Esch… 32 32 4 64 64 8.00 8.00 1 1 16.0 20
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#> 2 FALSE Esch… 32 32 4 64 64 4.00 8.00 1 1 16.0 320
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#> 3 FALSE Esch… 4 2 64 8 4 8.00 0.12 16 16 0.5 20
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#> 4 FALSE Kleb… 32 32 16 64 64 8.00 8.00 1 1 0.5 20
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#> 5 FALSE Esch… 32 32 4 4 4 0.25 2.00 1 1 16.0 320
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#> 6 FALSE Citr… 32 32 16 64 64 64.00 32.00 1 1 0.5 20
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#> 7 FALSE Morg… 32 32 4 64 64 16.00 2.00 1 1 0.5 20
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#> 8 FALSE Prot… 16 32 4 1 4 8.00 0.12 1 1 16.0 320
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#> 9 FALSE Ente… 32 32 8 64 64 32.00 4.00 1 1 0.5 20
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#> 10 FALSE Citr… 32 32 32 64 64 8.00 64.00 1 1 16.0 320
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#> # ℹ 490 more rows
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#> # ℹ 6 more variables: NIT <mic>, FOS <mic>, CIP <mic>, IPM <mic>, MEM <mic>,
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#> # COL <mic>
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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
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isolate.
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- `mutate(esbl = ...)`: Converts the target column to an ordered factor
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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
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[`step_mic_log2()`](https://amr-for-r.org/reference/amr-tidymodels.md)
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function to log2-transform MIC values, ensuring that MICs are numeric
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and properly scaled. All MIC predictors can easily and agnostically
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selected using the new
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[`all_mic_predictors()`](https://amr-for-r.org/reference/amr-tidymodels.md):
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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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#> ── Recipe ──────────────────────────────────────────────────────────────────────
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#>
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#> ── Inputs
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#> Number of variables by role
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#> outcome: 1
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#> predictor: 17
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#> undeclared role: 1
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#>
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#> ── Operations
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#> • Log2 transformation of MIC columns: all_mic_predictors()
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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()`](https://amr-for-r.org/reference/amr-tidymodels.md):
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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
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recent models such as xgboost ([link to `parsnip`
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manual](https://parsnip.tidymodels.org/reference/details_boost_tree_xgboost.html))
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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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#> Logistic Regression Model Specification (classification)
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#>
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#> Computational engine: glm
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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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#> ══ Workflow ════════════════════════════════════════════════════════════════════
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#> Preprocessor: Recipe
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#> Model: logistic_reg()
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#>
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#> ── Preprocessor ────────────────────────────────────────────────────────────────
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#> 1 Recipe Step
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#>
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#> • step_mic_log2()
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#>
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#> ── Model ───────────────────────────────────────────────────────────────────────
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#> Logistic Regression Model Specification (classification)
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#>
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#> Computational engine: glm
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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(predict(fitted, testing_data, type = "prob")) %>% # add probabilities
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bind_cols(testing_data)
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# Evaluate model performance
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our_metrics <- metric_set(accuracy, recall, precision, sensitivity, specificity, ppv, npv)
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metrics <- our_metrics(predictions, truth = esbl, estimate = .pred_class)
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metrics
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#> # A tibble: 7 × 3
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#> .metric .estimator .estimate
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#> <chr> <chr> <dbl>
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#> 1 accuracy binary 0.92
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#> 2 recall binary 0.921
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#> 3 precision binary 0.921
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#> 4 sensitivity binary 0.921
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#> 5 specificity binary 0.919
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#> 6 ppv binary 0.921
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#> 7 npv binary 0.919
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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()`](https://rdrr.io/r/stats/predict.html): Produces
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predictions for unseen test data.
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- `metric_set()`: Allows evaluating multiple classification metrics.
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This will make `our_metrics` to become a function that we can use to
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check the predictions with.
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It appears we can predict ESBL gene presence with a positive predictive
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value (PPV) of 92.1% and a negative predictive value (NPV) of 91.9 using
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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
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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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And plot the certainties too - how certain were the actual predictions?
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``` r
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predictions %>%
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mutate(certainty = ifelse(.pred_class == "FALSE",
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.pred_FALSE,
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.pred_TRUE),
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correct = ifelse(esbl == .pred_class, "Right", "Wrong")) %>%
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ggplot(aes(x = seq_len(nrow(predictions)),
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y = certainty,
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colour = correct)) +
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scale_colour_manual(values = c(Right = "green3", Wrong = "red2"),
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name = "Correct?") +
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geom_point() +
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scale_y_continuous(labels = function(x) paste0(x * 100, "%"),
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limits = c(0.5, 1)) +
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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
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simplify working with `<mic>` columns in `tidymodels`. The
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[`step_mic_log2()`](https://amr-for-r.org/reference/amr-tidymodels.md)
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transformation converts ordered MICs to log2-transformed numerics,
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improving compatibility with classification models.
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This pipeline enables realistic, reproducible, and interpretable
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modelling of antimicrobial resistance data.
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------------------------------------------------------------------------
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## Example 2: Predicting AMR Over Time
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## Example 3: Predicting AMR Over Time
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In this third example, we aim to predict antimicrobial resistance (AMR)
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trends over time using `tidymodels`. We will model resistance to three
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@@ -573,7 +802,7 @@ ggplot(predictions_time, aes(x = year)) +
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theme_minimal()
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```
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Additionally, we can visualise resistance trends in `ggplot2` and
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directly add linear models there:
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@@ -591,7 +820,7 @@ ggplot(data_time, aes(x = year, y = res_AMX, color = gramstain)) +
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theme_minimal()
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```
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### **Conclusion**
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