(v3.0.1.9047) fix #272

DESCRIPTION

@@ -1,5 +1,5 @@
 Package: AMR
-Version: 3.0.1.9045
+Version: 3.0.1.9047
 Date: 2026-04-21
 Title: Antimicrobial Resistance Data Analysis
 Description: Functions to simplify and standardise antimicrobial resistance (AMR)

NEWS.md

@@ -1,4 +1,4 @@
-# AMR 3.0.1.9045
+# AMR 3.0.1.9047
 
 ### New
 * Support for clinical breakpoints of 2026 of both CLSI and EUCAST, by adding all of their over 5,700 new clinical breakpoints to the `clinical_breakpoints` data set for usage in `as.sir()`. EUCAST 2026 is now the new default guideline for all MIC and disk diffusion interpretations.
@@ -32,6 +32,7 @@
 * Fixed a bug to disregard `NI` for susceptibility proportion functions
 * Fixed Italian translation of CoNS to Stafilococco coagulasi-negativo and CoPS to Stafilococco coagulasi-positivo (#256)
 * Fixed SIR and MIC coercion of combined values, e.g. `as.sir("<= 0.002; S") ` or `as.mic("S; 0.002")` (#252)
+* Fixed translation of foreign languages in `sir_df()` (#272)
 
 ### Updates
 * Extensive `cli` integration for better message handling and clickable links in messages and warnings (#191, #265)

R/ab_property.R

@@ -361,7 +361,7 @@ ab_property <- function(x, property = "name", language = get_AMR_locale(), ...)
   meet_criteria(x, allow_NA = TRUE)
   meet_criteria(property, is_in = colnames(AMR::antimicrobials), has_length = 1)
   language <- validate_language(language)
-  translate_into_language(ab_validate(x = x, property = property, ...), language = language)
+  translate_into_language(ab_validate(x = x, property = property, ...), language = language, only_affect_ab_names = TRUE)
 }
 
 #' @rdname ab_property

man/amr-tidymodels.Rd

@@ -73,7 +73,6 @@ These steps integrate with \code{recipes::recipe()} and work like standard prepr
 }
 \examples{
 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.
 
@@ -92,13 +91,10 @@ if (require("tidymodels")) {
 
   # 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()) \%>\%
-
     # And apply the preparation steps
     prep()
 
@@ -119,13 +115,15 @@ if (require("tidymodels")) {
     bind_cols(out_testing)
 
   # Evaluate predictions using standard classification metrics
-  our_metrics <- metric_set(accuracy,
-                            recall,
-                            precision,
-                            sensitivity,
-                            specificity,
-                            ppv,
-                            npv)
+  our_metrics <- metric_set(
+    accuracy,
+    recall,
+    precision,
+    sensitivity,
+    specificity,
+    ppv,
+    npv
+  )
   metrics <- our_metrics(predictions, truth = esbl, estimate = .pred_class)
 
   # Show performance

man/pca.Rd

@@ -79,12 +79,12 @@ if (require("dplyr")) {
 
 # new ggplot2 plotting method using this package:
 if (require("dplyr") && require("ggplot2")) {
-    ggplot_pca(pca_result)
+  ggplot_pca(pca_result)
 }
 if (require("dplyr") && require("ggplot2")) {
-    ggplot_pca(pca_result) +
-      scale_colour_viridis_d() +
-      labs(title = "Title here")
+  ggplot_pca(pca_result) +
+    scale_colour_viridis_d() +
+    labs(title = "Title here")
 }
 }
 }

man/plot.Rd

@@ -321,7 +321,7 @@ if (require("ggplot2")) {
     theme_minimal() +
     geom_boxplot(fill = NA, colour = "grey30") +
     geom_jitter(width = 0.25)
-    labs(title = "scale_y_mic()/scale_colour_sir() automatically applied")
+  labs(title = "scale_y_mic()/scale_colour_sir() automatically applied")
 
   mic_sir_plot
 }