(v2.1.1.9121) support tidymodels

.Rbuildignore

@@ -25,6 +25,7 @@
 ^tests/testthat/_snaps$
 ^vignettes/AMR\.Rmd$
 ^vignettes/AMR_intro\.png$
+^vignettes/AMR_with_tidymodels\.Rmd$
 ^vignettes/benchmarks\.Rmd$
 ^vignettes/benchmarks\.Rmd\.not$
 ^vignettes/datasets\.Rmd$

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: AMR
-Version: 2.1.1.9120
+Version: 2.1.1.9121
-Date: 2024-12-15
+Date: 2024-12-19
 Title: Antimicrobial Resistance Data Analysis
 Description: Functions to simplify and standardise antimicrobial resistance (AMR)
   data analysis and to work with microbial and antimicrobial properties by
@@ -47,6 +47,7 @@ Suggests:
   rvest,
   skimr,
   tibble,
+  tidymodels,
   tidyselect,
   tinytest,
   vctrs,

NEWS.md

@@ -1,4 +1,4 @@
-# AMR 2.1.1.9120
+# AMR 2.1.1.9121
 
 *(this beta version will eventually become v3.0. We're happy to reach a new major milestone soon, which will be all about the new One Health support! Install this beta using [the instructions here](https://msberends.github.io/AMR/#latest-development-version).)*
 
@@ -30,6 +30,8 @@ This package now supports not only tools for AMR data analysis in clinical setti
   * New function `rescale_mic()`, which allows users to rescale MIC values to a manually set range. This is the powerhouse behind the `scale_*_mic()` functions, but it can be used independently to, for instance, compare equality in MIC distributions by rescaling them to the same range first.
 * **Support for Python**
   * While using R for the heavy lifting, [our 'AMR' Python Package](https://pypi.org/project/AMR/) was developed to run the AMR R package natively in Python. The Python package will always have the same version number as the R package, as it is built automatically with every code change.
+* **Support for `tidymodels`**
+  * All antimicrobial selectors (such as `aminoglycosides()` and `betalactams()`) are now supported in `tidymodels` packages such as `recipe` and `parsnip`. See for more info [our tutorial](https://msberends.github.io/AMR/articles/AMR_with_tidymodels.html) on using AMR function for predictive modelling.
 * **Other**
   * New function `mo_group_members()` to retrieve the member microorganisms of a microorganism group. For example, `mo_group_members("Strep group C")` returns a vector of all microorganisms that belong to that group.
 
@@ -73,7 +75,9 @@ This package now supports not only tools for AMR data analysis in clinical setti
 * Updated the prevalence calculation to include genera from the World Health Organization's (WHO) Priority Pathogen List
 * Improved algorithm of `first_isolate()` when using the phenotype-based method, to prioritise records with the highest availability of SIR values
 * `scale_y_percent()` can now cope with ranges outside the 0-100% range
-* Support for new Dutch national MDRO guideline (SRI-richtlijn BRMO, Nov 2024)
+* MDRO determination (using `mdro()`)
+  * Implemented the new Dutch national MDRO guideline (SRI-richtlijn BRMO, Nov 2024)
+  * Added arguments `esbl`, `carbapenemase`, `mecA`, `mecC`, `vanA`, `vanB` to denote column names or logical values indicating presence of these genes (or production of their proteins)
 
 ## Other
 * Greatly improved `vctrs` integration, a Tidyverse package working in the background for many Tidyverse functions. For users, this means that functions such as `dplyr`'s `bind_rows()`, `rowwise()` and `c_across()` are now supported for e.g. columns of class `mic`. Despite this, this `AMR` package is still zero-dependent on any other package, including `dplyr` and `vctrs`.

PythonPackage/AMR/AMR.egg-info/PKG-INFO

@@ -1,6 +1,6 @@
 Metadata-Version: 2.1
 Name: AMR
-Version: 2.1.1.9120
+Version: 2.1.1.9121
 Summary: A Python wrapper for the AMR R package
 Home-page: https://github.com/msberends/AMR
 Author: Matthijs Berends

PythonPackage/AMR/AMR/functions.py

@@ -441,9 +441,9 @@ def mdr_tb(x = None, *args, **kwargs):
 def mdr_cmi2012(x = None, *args, **kwargs):
     """See our website of the R package for the manual: https://msberends.github.io/AMR/index.html"""
     return convert_to_python(amr_r.mdr_cmi2012(x = None, *args, **kwargs))
-def eucast_exceptional_phenotypes(x = None, *args, **kwargs):
+def eucast_exceptional_phenotypes(*args, **kwargs):
     """See our website of the R package for the manual: https://msberends.github.io/AMR/index.html"""
-    return convert_to_python(amr_r.eucast_exceptional_phenotypes(x = None, *args, **kwargs))
+    return convert_to_python(amr_r.eucast_exceptional_phenotypes(*args, **kwargs))
 def mean_amr_distance(x, *args, **kwargs):
     """See our website of the R package for the manual: https://msberends.github.io/AMR/index.html"""
     return convert_to_python(amr_r.mean_amr_distance(x, *args, **kwargs))

PythonPackage/AMR/setup.py

@@ -2,7 +2,7 @@ from setuptools import setup, find_packages
 
 setup(
     name='AMR',
-    version='2.1.1.9120',
+    version='2.1.1.9121',
     packages=find_packages(),
     install_requires=[
         'rpy2',

R/aa_helper_functions.R

@@ -988,7 +988,7 @@ get_current_data <- function(arg_name, call) {
   for (env in frms[which(with_mask)]) {
     if (is.function(env$mask$current_rows) && (valid_df(env$data) || valid_df(env$`.data`))) {
       # an element `.data` or `data` (containing all data) and `mask` (containing functions) will be in the environment when using dplyr verbs
-      # we use their mask$current_rows() to get the group rows, since dplyr::cur_data_all() is deprecated and will be removed in the future
+      # we use their mask$current_rows() below to get the group rows, since dplyr::cur_data_all() is deprecated and will be removed in the future
       # e.g. for `example_isolates %>% group_by(ward) %>% mutate(first = first_isolate(.))`
       if (valid_df(env$data)) {
         # support for dplyr 1.1.x
@@ -1008,6 +1008,9 @@ get_current_data <- function(arg_name, call) {
     if (valid_df(env$`.data`)) {
       # an element `.data` will be in the environment when using dplyr::select()
       return(env$`.data`)
+    } else if (valid_df(env$data)) {
+      # an element `data` will be in the environment when using older dplyr versions, or tidymodels
+      return(env$data)
     } else if (valid_df(env$xx)) {
       # an element `xx` will be in the environment for rows + cols in base R, e.g. `example_isolates[c(1:3), carbapenems()]`
       return(env$xx)

R/mdro.R

@@ -32,6 +32,12 @@
 #' Determine which isolates are multidrug-resistant organisms (MDRO) according to international, national, or custom guidelines.
 #' @param x a [data.frame] with antibiotics columns, like `AMX` or `amox`. Can be left blank for automatic determination.
 #' @param guideline a specific guideline to follow, see sections *Supported international / national guidelines* and *Using Custom Guidelines* below. When left empty, the publication by Magiorakos *et al.* (see below) will be followed.
+#' @param esbl [logical] values, or a column name containing logical values, indicating the presence of an ESBL gene (or production of its proteins)
+#' @param carbapenemase [logical] values, or a column name containing logical values, indicating the presence of a carbapenemase gene (or production of its proteins)
+#' @param mecA [logical] values, or a column name containing logical values, indicating the presence of a *mecA* gene (or production of its proteins)
+#' @param mecC [logical] values, or a column name containing logical values, indicating the presence of a *mecC* gene (or production of its proteins)
+#' @param vanA [logical] values, or a column name containing logical values, indicating the presence of a *vanA* gene (or production of its proteins)
+#' @param vanB [logical] values, or a column name containing logical values, indicating the presence of a *vanB* gene (or production of its proteins)
 #' @param ... in case of [custom_mdro_guideline()]: a set of rules, see section *Using Custom Guidelines* below. Otherwise: column name of an antibiotic, see section *Antibiotics* below.
 #' @param as_factor a [logical] to indicate whether the returned value should be an ordered [factor] (`TRUE`, default), or otherwise a [character] vector
 #' @inheritParams eucast_rules
@@ -177,6 +183,12 @@
 mdro <- function(x = NULL,
                  guideline = "CMI2012",
                  col_mo = NULL,
+                 esbl = NA,
+                 carbapenemase = NA,
+                 mecA = NA,
+                 mecC = NA,
+                 vanA = NA,
+                 vanB = NA,
                  info = interactive(),
                  pct_required_classes = 0.5,
                  combine_SI = TRUE,
@@ -190,9 +202,13 @@ mdro <- function(x = NULL,
   }
   meet_criteria(x, allow_class = "data.frame") # also checks dimensions to be >0
   meet_criteria(guideline, allow_class = c("list", "character"), allow_NULL = TRUE)
-  if (!is.list(guideline)) {
+  if (!is.list(guideline)) meet_criteria(guideline, allow_class = "character", has_length = 1, allow_NULL = TRUE)
-    meet_criteria(guideline, allow_class = "character", has_length = 1, allow_NULL = TRUE)
+  meet_criteria(esbl, allow_class = c("logical", "character"), allow_NA = TRUE)
-  }
+  meet_criteria(carbapenemase, allow_class = c("logical", "character"), allow_NA = TRUE)
+  meet_criteria(mecA, allow_class = c("logical", "character"), allow_NA = TRUE)
+  meet_criteria(mecC, allow_class = c("logical", "character"), allow_NA = TRUE)
+  meet_criteria(vanA, allow_class = c("logical", "character"), allow_NA = TRUE)
+  meet_criteria(vanB, allow_class = c("logical", "character"), allow_NA = TRUE)
   meet_criteria(col_mo, allow_class = "character", has_length = 1, is_in = colnames(x), allow_NULL = TRUE)
   meet_criteria(info, allow_class = "logical", has_length = 1)
   meet_criteria(pct_required_classes, allow_class = "numeric", has_length = 1)
@@ -204,6 +220,50 @@ mdro <- function(x = NULL,
     stop_("There were no possible SIR columns found in the data set. Transform columns with `as.sir()` for valid antimicrobial interpretations.")
   }
   
+  # get gene values as TRUE/FALSE
+  if (is.character(esbl)) {
+    meet_criteria(esbl, is_in = colnames(x), allow_NA = FALSE, has_length = 1)
+    esbl <- x[[esbl]]
+    meet_criteria(esbl, allow_class = "logical", allow_NA = TRUE)
+  } else if (length(esbl) == 1) {
+    esbl <- rep(esbl, NROW(x))
+  }
+  if (is.character(carbapenemase)) {
+    meet_criteria(carbapenemase, is_in = colnames(x), allow_NA = FALSE, has_length = 1)
+    carbapenemase <- x[[carbapenemase]]
+    meet_criteria(carbapenemase, allow_class = "logical", allow_NA = TRUE)
+  } else if (length(carbapenemase) == 1) {
+    carbapenemase <- rep(carbapenemase, NROW(x))
+  }
+  if (is.character(mecA)) {
+    meet_criteria(mecA, is_in = colnames(x), allow_NA = FALSE, has_length = 1)
+    mecA <- x[[mecA]]
+    meet_criteria(mecA, allow_class = "logical", allow_NA = TRUE)
+  } else if (length(mecA) == 1) {
+    mecA <- rep(mecA, NROW(x))
+  }
+  if (is.character(mecC)) {
+    meet_criteria(mecC, is_in = colnames(x), allow_NA = FALSE, has_length = 1)
+    mecC <- x[[mecC]]
+    meet_criteria(mecC, allow_class = "logical", allow_NA = TRUE)
+  } else if (length(mecC) == 1) {
+    mecC <- rep(mecC, NROW(x))
+  }
+  if (is.character(vanA)) {
+    meet_criteria(vanA, is_in = colnames(x), allow_NA = FALSE, has_length = 1)
+    vanA <- x[[vanA]]
+    meet_criteria(vanA, allow_class = "logical", allow_NA = TRUE)
+  } else if (length(vanA) == 1) {
+    vanA <- rep(vanA, NROW(x))
+  }
+  if (is.character(vanB)) {
+    meet_criteria(vanB, is_in = colnames(x), allow_NA = FALSE, has_length = 1)
+    vanB <- x[[vanB]]
+    meet_criteria(vanB, allow_class = "logical", allow_NA = TRUE)
+  } else if (length(vanB) == 1) {
+    vanB <- rep(vanB, NROW(x))
+  }
+  
   info.bak <- info
   # don't throw info's more than once per call
   if (isTRUE(info)) {
@@ -476,7 +536,7 @@ mdro <- function(x = NULL,
   if (!"AMP" %in% names(cols_ab) && "AMX" %in% names(cols_ab)) {
     # ampicillin column is missing, but amoxicillin is available
     if (isTRUE(info)) {
-      message_("Using column '", cols_ab[names(cols_ab) == "AMX"], "' as input for ampicillin since many EUCAST rules depend on it.")
+      message_("Using column '", cols_ab[names(cols_ab) == "AMX"], "' as input for ampicillin since many MDRO rules depend on it.")
     }
     cols_ab <- c(cols_ab, c(AMP = unname(cols_ab[names(cols_ab) == "AMX"])))
   }
@@ -663,6 +723,17 @@ mdro <- function(x = NULL,
     out[is.na(out)] <- FALSE
     out
   }
+  col_values <- function(df, col, return_if_lacking = "") {
+    if (col %in% colnames(df)) {
+      df[[col]]
+    } else {
+      rep(return_if_lacking, NROW(df))
+    }
+  }
+  NA_as_FALSE <- function(x) {
+    x[is.na(x)] <- FALSE
+    x
+  }
 
   # antibiotic classes
   # nolint start
@@ -677,6 +748,10 @@ mdro <- function(x = NULL,
 
   # helper function for editing the table
   trans_tbl <- function(to, rows, cols, any_all, reason = NULL) {
+    cols.bak <- cols
+    if (identical(cols, "any")) {
+      cols <- unique(cols_ab)
+    }
     cols <- cols[!ab_missing(cols)]
     cols <- cols[!is.na(cols)]
     if (length(rows) > 0 && length(cols) > 0) {
@@ -690,7 +765,7 @@ mdro <- function(x = NULL,
       x[rows, "columns_nonsusceptible"] <<- vapply(
         FUN.VALUE = character(1),
         rows,
-        function(row, group_vct = cols) {
+        function(row, group_vct = cols_ab) {
           cols_nonsus <- vapply(
             FUN.VALUE = logical(1),
             x[row, group_vct, drop = FALSE],
@@ -717,7 +792,7 @@ mdro <- function(x = NULL,
       rows_affected <- vapply(
         FUN.VALUE = logical(1),
         x_transposed,
-        function(y) search_function(y %in% search_result, na.rm = TRUE)
+        function(y) search_function(y %in% search_result, na.rm = TRUE) | identical(cols.bak, "any")
       )
       rows_affected <- x[which(rows_affected), "row_number", drop = TRUE]
       rows_to_change <- rows[rows %in% rows_affected]
@@ -1449,62 +1524,83 @@ mdro <- function(x = NULL,
     if (length(ESBLs) > 0) {
       trans_tbl(
         2, # positive, unconfirmed
-        which(x$order == "Enterobacterales" & x[[ESBLs[1]]] == "R" & x[[ESBLs[2]]] == "R"),
+        rows = which(x$order == "Enterobacterales" & x[[ESBLs[1]]] == "R" & x[[ESBLs[2]]] == "R" & is.na(esbl)),
-        c(AMX %or% AMP, cephalosporins_3rd),
+        cols = c(AMX %or% AMP, cephalosporins_3rd),
-        "all",
+        any_all = "all",
-        reason = "Enterobacterales: ESBL"
+        reason = "Enterobacterales: potential ESBL"
       )
     }
     trans_tbl(
       3, # positive
-      which(x$order == "Enterobacterales" & (x$genus %in% c("Proteus", "Providencia") | paste(x$genus, x$species) %in% c("Serratia marcescens", "Morganella morganii"))),
+      rows = which(x$order == "Enterobacterales" & esbl == TRUE),
-      carbapenems_without_imipenem,
+      cols = "any",
-      "any",
+      any_all = "any",
-      reason = "Enterobacterales: carbapenem or carbapenemase"
+      reason = "Enterobacterales: ESBL"
     )
     trans_tbl(
       3,
-      which(x$order == "Enterobacterales" & !(x$genus %in% c("Proteus", "Providencia") | paste(x$genus, x$species) %in% c("Serratia marcescens", "Morganella morganii"))),
+      rows = which(x$order == "Enterobacterales" & (x$genus %in% c("Proteus", "Providencia") | paste(x$genus, x$species) %in% c("Serratia marcescens", "Morganella morganii"))),
-      carbapenems,
+      cols = carbapenems_without_imipenem,
-      "any",
+      any_all = "any",
-      reason = "Enterobacterales: carbapenem or carbapenemase"
+      reason = "Enterobacterales: carbapenem resistance"
     )
     trans_tbl(
       3,
-      which(x[[SXT]] == "R" &
+      rows = which(x$order == "Enterobacterales" & !(x$genus %in% c("Proteus", "Providencia") | paste(x$genus, x$species) %in% c("Serratia marcescens", "Morganella morganii"))),
+      cols = carbapenems,
+      any_all = "any",
+      reason = "Enterobacterales: carbapenem resistance"
+    )
+    trans_tbl(
+      3,
+      rows = which(x$order == "Enterobacterales" & carbapenemase == TRUE),
+      cols = "any",
+      any_all = "any",
+      reason = "Enterobacterales: carbapenemase"
+    )
+    trans_tbl(
+      3,
+      rows = which(x[[SXT]] == "R" &
               (x[[GEN]] == "R" | x[[TOB]] == "R" | x[[AMK]] == "R") &
               (x[[CIP]] == "R" | x[[NOR]] == "R" | x[[LVX]] == "R") &
               (x$genus %in% c("Enterobacter", "Providencia") | paste(x$genus, x$species) %in% c("Citrobacter freundii", "Klebsiella aerogenes", "Hafnia alvei", "Morganella morganii"))),
-      c(SXT, aminoglycosides, fluoroquinolones),
+      cols = c(SXT, aminoglycosides, fluoroquinolones),
-      "any",
+      any_all = "any",
       reason = "Enterobacterales group II: aminoglycoside + fluoroquinolone + cotrimoxazol"
     )
     trans_tbl(
       3,
-      which(x[[SXT]] == "R" &
+      rows = which(x[[SXT]] == "R" &
               x[[GEN]] == "R" &
               (x[[CIP]] == "R" | x[[NOR]] == "R" | x[[LVX]] == "R") &
               paste(x$genus, x$species) == "Serratia marcescens"),
-      c(SXT, aminoglycosides_serratia_marcescens, fluoroquinolones),
+      cols = c(SXT, aminoglycosides_serratia_marcescens, fluoroquinolones),
-      "any",
+      any_all = "any",
       reason = "Enterobacterales group II: aminoglycoside + fluoroquinolone + cotrimoxazol"
     )
     
     # Acinetobacter baumannii-calcoaceticus complex
     trans_tbl(
       3,
-      which((x[[GEN]] == "R" | x[[TOB]] == "R" | x[[AMK]] == "R") &
+      rows = which((x[[GEN]] == "R" | x[[TOB]] == "R" | x[[AMK]] == "R") &
               (x[[CIP]] == "R" | x[[LVX]] == "R") &
               x[[col_mo]] %in% AMR::microorganisms.groups$mo[AMR::microorganisms.groups$mo_group_name == "Acinetobacter baumannii complex"]),
-      c(aminoglycosides, CIP, LVX),
+      cols = c(aminoglycosides, CIP, LVX),
-      "any",
+      any_all = "any",
       reason = "A. baumannii-calcoaceticus complex: aminoglycoside + ciprofloxacin or levofloxacin"
     )
     trans_tbl(
       2, # unconfirmed
-      which(x[[col_mo]] %in% AMR::microorganisms.groups$mo[AMR::microorganisms.groups$mo_group_name == "Acinetobacter baumannii complex"]),
+      rows = which(x[[col_mo]] %in% AMR::microorganisms.groups$mo[AMR::microorganisms.groups$mo_group_name == "Acinetobacter baumannii complex"] & is.na(carbapenemase)),
-      carbapenems,
+      cols = carbapenems,
-      "any",
+      any_all = "any",
+      reason = "A. baumannii-calcoaceticus complex: potential carbapenemase"
+    )
+    trans_tbl(
+      3,
+      rows = which(x[[col_mo]] %in% AMR::microorganisms.groups$mo[AMR::microorganisms.groups$mo_group_name == "Acinetobacter baumannii complex"] & carbapenemase == TRUE),
+      cols = carbapenems,
+      any_all = "any",
       reason = "A. baumannii-calcoaceticus complex: carbapenemase"
     )
     
@@ -1513,59 +1609,65 @@ mdro <- function(x = NULL,
       # take pip/tazo if just pip is not available - many labs only test for pip/tazo because of availability on a Vitek card
       PIP <- TZP
     }
-    if (!ab_missing(MEM) && !ab_missing(IPM) &&
-        !ab_missing(GEN) && !ab_missing(TOB) &&
-        !ab_missing(CIP) &&
-        !ab_missing(CAZ) &&
-        !ab_missing(PIP)) {
     x$psae <- 0
-      x[which(x[, MEM, drop = TRUE] == "R" | x[, IPM, drop = TRUE] == "R"), "psae"] <- 1 + x[which(x[, MEM, drop = TRUE] == "R" | x[, IPM, drop = TRUE] == "R"), "psae"]
+    x$psae <- x$psae + ifelse(NA_as_FALSE(col_values(x, TOB) == "R" | col_values(x, AMK) == "R"), 1, 0)
-      x[which(x[, GEN, drop = TRUE] == "R" & x[, TOB, drop = TRUE] == "R"), "psae"] <- 1 + x[which(x[, GEN, drop = TRUE] == "R" & x[, TOB, drop = TRUE] == "R"), "psae"]
+    x$psae <- x$psae + ifelse(NA_as_FALSE(col_values(x, IPM) == "R" | col_values(x, MEM) == "R"), 1, 0)
-      x[which(x[, CIP, drop = TRUE] == "R"), "psae"] <- 1 + x[which(x[, CIP, drop = TRUE] == "R"), "psae"]
+    x$psae <- x$psae + ifelse(NA_as_FALSE(col_values(x, PIP) == "R"), 1, 0)
-      x[which(x[, CAZ, drop = TRUE] == "R"), "psae"] <- 1 + x[which(x[, CAZ, drop = TRUE] == "R"), "psae"]
+    x$psae <- x$psae + ifelse(NA_as_FALSE(col_values(x, CAZ) == "R"), 1, 0)
-      x[which(x[, PIP, drop = TRUE] == "R"), "psae"] <- 1 + x[which(x[, PIP, drop = TRUE] == "R"), "psae"]
+    x$psae <- x$psae + ifelse(NA_as_FALSE(col_values(x, CIP) == "R" | col_values(x, NOR) == "R" | col_values(x, LVX) == "R"), 1, 0)
-    } else {
-      x$psae <- 0
-    }
     trans_tbl(
       3,
-      which(x$genus == "Pseudomonas" & x$species == "aeruginosa"),
+      rows = which(x$genus == "Pseudomonas" & x$species == "aeruginosa"),
-      c(CAZ, CIP, GEN, IPM, MEM, TOB, PIP),
+      cols = c(CAZ, CIP, GEN, IPM, MEM, TOB, PIP),
-      "all", # this will set all negatives to "guideline criteria not met" instead of "not covered by guideline"
+      any_all = "all", # this will set all negatives to "guideline criteria not met" instead of "not covered by guideline"
       reason = "P. aeruginosa: at least 3 classes contain R"
     )
     trans_tbl(
       3,
-      which(x$genus == "Pseudomonas" & x$species == "aeruginosa" & x$psae >= 3),
+      rows = which(x$genus == "Pseudomonas" & x$species == "aeruginosa" & x$psae >= 3),
-      c(CAZ, CIP, GEN, IPM, MEM, TOB, PIP),
+      cols = c(CAZ, CIP, GEN, IPM, MEM, TOB, PIP),
-      "any", # this is the actual one, changing the ones with x$psae >= 3
+      any_all = "any", # this is the actual one, changing the ones with x$psae >= 3
       reason = "P. aeruginosa: at least 3 classes contain R"
     )
 
     # Enterococcus faecium
     trans_tbl(
       3,
-      which(x$genus == "Enterococcus" & x$species == "faecium"),
+      rows = which(x$genus == "Enterococcus" & x$species == "faecium"),
-      c(PEN %or% AMX %or% AMP, VAN),
+      cols = c(PEN %or% AMX %or% AMP, VAN),
-      "all",
+      any_all = "all",
-      reason = "E. faecium: vancomycin or vanA/vanB gene + penicillin group"
+      reason = "E. faecium: vancomycin + penicillin group"
+    )
+    trans_tbl(
+      3,
+      rows = which(x$genus == "Enterococcus" & x$species == "faecium" & (vanA == TRUE | vanB == TRUE)),
+      cols = c(PEN, AMX, AMP, VAN),
+      any_all = "any",
+      reason = "E. faecium: vanA/vanB gene + penicillin group"
     )
     
     # Staphylococcus aureus
     trans_tbl(
       2,
-      which(x$genus == "Staphylococcus" & x$species == "aureus"),
+      rows = which(x$genus == "Staphylococcus" & x$species == "aureus" & (is.na(mecA) | is.na(mecC))),
-      c(PEN, AMX, AMP, FLC, OXA, FOX, FOX1),
+      cols = c(AMC, TZP, FLC, OXA, FOX, FOX1),
-      "any",
+      any_all = "any",
-      reason = "S. aureus: MRSA"
+      reason = "S. aureus: potential MRSA"
+    )
+    trans_tbl(
+      3,
+      rows = which(x$genus == "Staphylococcus" & x$species == "aureus" & (mecA == TRUE | mecC == TRUE)),
+      cols = "any",
+      any_all = "any",
+      reason = "S. aureus: mecA/mecC gene"
     )
     
     # Candida auris
     trans_tbl(
       3,
-      which(x$genus == "Candida" & x$species == "auris"),
+      rows = which(x$genus == "Candida" & x$species == "auris"),
-      character(0),
+      cols = "any",
-      "any",
+      any_all = "any",
       reason = "C. auris: regardless of resistance"
     )
   }
@@ -2040,50 +2142,51 @@ brmo <- function(x = NULL, only_sir_columns = FALSE, ...) {
   mdro(x = x, only_sir_columns = only_sir_columns, guideline = "BRMO", ...)
 }
 
+
 #' @rdname mdro
 #' @export
-mrgn <- function(x = NULL, only_sir_columns = FALSE, ...) {
+mrgn <- function(x = NULL, only_sir_columns = FALSE, verbose = FALSE, ...) {
   meet_criteria(x, allow_class = "data.frame", allow_NULL = TRUE)
   meet_criteria(only_sir_columns, allow_class = "logical", has_length = 1)
   stop_if(
     "guideline" %in% names(list(...)),
     "argument `guideline` must not be set since this is a guideline-specific function"
   )
-  mdro(x = x, only_sir_columns = only_sir_columns, guideline = "MRGN", ...)
+  mdro(x = x, only_sir_columns = only_sir_columns, verbose = verbose, guideline = "MRGN", ...)
 }
 
 #' @rdname mdro
 #' @export
-mdr_tb <- function(x = NULL, only_sir_columns = FALSE, ...) {
+mdr_tb <- function(x = NULL, only_sir_columns = FALSE, verbose = FALSE, ...) {
   meet_criteria(x, allow_class = "data.frame", allow_NULL = TRUE)
   meet_criteria(only_sir_columns, allow_class = "logical", has_length = 1)
   stop_if(
     "guideline" %in% names(list(...)),
     "argument `guideline` must not be set since this is a guideline-specific function"
   )
-  mdro(x = x, only_sir_columns = only_sir_columns, guideline = "TB", ...)
+  mdro(x = x, only_sir_columns = only_sir_columns, verbose = verbose, guideline = "TB", ...)
 }
 
 #' @rdname mdro
 #' @export
-mdr_cmi2012 <- function(x = NULL, only_sir_columns = FALSE, ...) {
+mdr_cmi2012 <- function(x = NULL, only_sir_columns = FALSE, verbose = FALSE, ...) {
   meet_criteria(x, allow_class = "data.frame", allow_NULL = TRUE)
   meet_criteria(only_sir_columns, allow_class = "logical", has_length = 1)
   stop_if(
     "guideline" %in% names(list(...)),
     "argument `guideline` must not be set since this is a guideline-specific function"
   )
-  mdro(x = x, only_sir_columns = only_sir_columns, guideline = "CMI2012", ...)
+  mdro(x = x, only_sir_columns = only_sir_columns, verbose = verbose, guideline = "CMI2012", ...)
 }
 
 #' @rdname mdro
 #' @export
-eucast_exceptional_phenotypes <- function(x = NULL, only_sir_columns = FALSE, ...) {
+eucast_exceptional_phenotypes <- function(x = NULL, only_sir_columns = FALSE, verbose = FALSE, ...) {
   meet_criteria(x, allow_class = "data.frame", allow_NULL = TRUE)
   meet_criteria(only_sir_columns, allow_class = "logical", has_length = 1)
   stop_if(
     "guideline" %in% names(list(...)),
     "argument `guideline` must not be set since this is a guideline-specific function"
   )
-  mdro(x = x, only_sir_columns = only_sir_columns, guideline = "EUCAST", ...)
+  mdro(x = x, only_sir_columns = only_sir_columns, verbose = verbose, guideline = "EUCAST", ...)
 }

_pkgdown.yml

@@ -80,6 +80,9 @@ navbar:
       - text: "Download Data Sets for Own Use"
         icon: "fa-database"
         href: "articles/datasets.html"
+      - text: "Use AMR for Predictive Modelling (tidymodels)"
+        icon: "fa-square-root-variable"
+        href: "articles/AMR_with_tidymodels.html"
       - text: "Set User- Or Team-specific Package Settings"
         icon: "fa-gear"
         href: "reference/AMR-options.html"

data-raw/gpt_training_text_v2.1.1.9121.txt

@@ -1,5 +1,5 @@
 This files contains all context you must know about the AMR package for R.
-First and foremost, you are trained on version 2.1.1.9120. Remember this whenever someone asks which AMR package version you’re at.
+First and foremost, you are trained on version 2.1.1.9121. Remember this whenever someone asks which AMR package version you’re at.
 --------------------------------
 
 THE PART HEREAFTER CONTAINS CONTENTS FROM FILE 'NAMESPACE':
@@ -6086,6 +6086,12 @@ mdro(
   x = NULL,
   guideline = "CMI2012",
   col_mo = NULL,
+  esbl = NA,
+  carbapenemase = NA,
+  mecA = NA,
+  mecC = NA,
+  vanA = NA,
+  vanB = NA,
   info = interactive(),
   pct_required_classes = 0.5,
   combine_SI = TRUE,
@@ -6098,13 +6104,18 @@ custom_mdro_guideline(..., as_factor = TRUE)
 
 brmo(x = NULL, only_sir_columns = FALSE, ...)
 
-mrgn(x = NULL, only_sir_columns = FALSE, ...)
+mrgn(x = NULL, only_sir_columns = FALSE, verbose = FALSE, ...)
 
-mdr_tb(x = NULL, only_sir_columns = FALSE, ...)
+mdr_tb(x = NULL, only_sir_columns = FALSE, verbose = FALSE, ...)
 
-mdr_cmi2012(x = NULL, only_sir_columns = FALSE, ...)
+mdr_cmi2012(x = NULL, only_sir_columns = FALSE, verbose = FALSE, ...)
 
-eucast_exceptional_phenotypes(x = NULL, only_sir_columns = FALSE, ...)
+eucast_exceptional_phenotypes(
+  x = NULL,
+  only_sir_columns = FALSE,
+  verbose = FALSE,
+  ...
+)
 }
 \arguments{
 \item{x}{a \link{data.frame} with antibiotics columns, like \code{AMX} or \code{amox}. Can be left blank for automatic determination.}
@@ -6113,6 +6124,18 @@ eucast_exceptional_phenotypes(x = NULL, only_sir_columns = FALSE, ...)
 
 \item{col_mo}{column name of the names or codes of the microorganisms (see \code{\link[=as.mo]{as.mo()}}) - the default is the first column of class \code{\link{mo}}. Values will be coerced using \code{\link[=as.mo]{as.mo()}}.}
 
+\item{esbl}{\link{logical} values, or a column name containing logical values, indicating the presence of an ESBL gene (or production of its proteins)}
+
+\item{carbapenemase}{\link{logical} values, or a column name containing logical values, indicating the presence of a carbapenemase gene (or production of its proteins)}
+
+\item{mecA}{\link{logical} values, or a column name containing logical values, indicating the presence of a \emph{mecA} gene (or production of its proteins)}
+
+\item{mecC}{\link{logical} values, or a column name containing logical values, indicating the presence of a \emph{mecC} gene (or production of its proteins)}
+
+\item{vanA}{\link{logical} values, or a column name containing logical values, indicating the presence of a \emph{vanA} gene (or production of its proteins)}
+
+\item{vanB}{\link{logical} values, or a column name containing logical values, indicating the presence of a \emph{vanB} gene (or production of its proteins)}
+
 \item{info}{a \link{logical} to indicate whether progress should be printed to the console - the default is only print while in interactive sessions}
 
 \item{pct_required_classes}{minimal required percentage of antimicrobial classes that must be available per isolate, rounded down. For example, with the default guideline, 17 antimicrobial classes must be available for \emph{S. aureus}. Setting this \code{pct_required_classes} argument to \code{0.5} (default) means that for every \emph{S. aureus} isolate at least 8 different classes must be available. Any lower number of available classes will return \code{NA} for that isolate.}
@@ -8885,6 +8908,203 @@ Whether you're cleaning data or analysing resistance patterns, the `AMR` Python
 
 
 
+THE PART HEREAFTER CONTAINS CONTENTS FROM FILE 'vignettes/AMR_with_tidymodels.Rmd':
+
+
+---
+title: "`AMR` with `tidymodels`"
+output: 
+  rmarkdown::html_vignette:
+    toc: true
+    toc_depth: 3
+vignette: >
+  %\VignetteIndexEntry{`AMR` with `tidymodels`}
+  %\VignetteEncoding{UTF-8}
+  %\VignetteEngine{knitr::rmarkdown}
+editor_options: 
+  chunk_output_type: console
+---
+
+```{r setup, include = FALSE, results = 'markup'}
+knitr::opts_chunk$set(
+  warning = FALSE,
+  collapse = TRUE,
+  comment = "#>",
+  fig.width = 7.5,
+  fig.height = 5
+)
+```
+
+Antimicrobial resistance (AMR) is a global health crisis, and understanding resistance patterns is crucial for managing effective treatments. The `AMR` R package provides robust tools for analysing AMR data, including convenient antibiotic selector functions like `aminoglycosides()` and `betalactams()`. In this post, we will explore how to use the `tidymodels` framework to predict resistance patterns in the `example_isolates` dataset. 
+
+By leveraging the power of `tidymodels` and the `AMR` package, we’ll build a reproducible machine learning workflow to predict resistance to two important antibiotic classes: aminoglycosides and beta-lactams.
+
+---
+
+### **Objective**
+
+Our goal is to build a predictive model using the `tidymodels` framework to determine resistance patterns based on microbial data. We will:
+
+1. Preprocess data using the selector functions `aminoglycosides()` and `betalactams()`.
+2. Define a logistic regression model for prediction.
+3. Use a structured `tidymodels` workflow to preprocess, train, and evaluate the model.
+
+---
+
+### **Data Preparation**
+
+We begin by loading the required libraries and preparing the `example_isolates` dataset from the `AMR` package.
+
+```{r}
+# Load required libraries
+library(tidymodels)   # For machine learning workflows, and data manipulation (dplyr, tidyr, ...)
+library(AMR)          # For AMR data analysis
+
+# Load the example_isolates dataset
+data("example_isolates")  # Preloaded dataset with AMR results
+
+# Select relevant columns for prediction
+data <- example_isolates %>%
+  # select AB results dynamically
+  select(mo, aminoglycosides(), betalactams()) %>%
+  # replace NAs with NI (not-interpretable)
+   mutate(across(where(is.sir),
+                 ~replace_na(.x, "NI")),
+          # make factors of SIR columns
+          across(where(is.sir),
+                 as.integer),
+          # get Gramstain of microorganisms
+          mo = as.factor(mo_gramstain(mo))) %>%
+  # drop NAs - the ones without a Gramstain (fungi, etc.)
+  drop_na() # %>%
+  # Cefepime is not reliable
+  #select(-FEP)
+```
+
+**Explanation:**
+- `aminoglycosides()` and `betalactams()` dynamically select columns for antibiotics in these classes.
+- `drop_na()` ensures the model receives complete cases for training.
+
+---
+
+### **Defining the Workflow**
+
+We now define the `tidymodels` workflow, which consists of three steps: preprocessing, model specification, and fitting.
+
+#### 1. Preprocessing with a Recipe
+
+We create a recipe to preprocess the data for modelling. This includes:
+- Encoding resistance results (`S`, `I`, `R`) as binary (resistant or not resistant).
+- Converting microbial organism names (`mo`) into numerical features using one-hot encoding.
+
+```{r}
+# Define the recipe for data preprocessing
+resistance_recipe <- recipe(mo ~ ., data = data) %>%
+  step_corr(c(aminoglycosides(), betalactams()), threshold = 0.9)
+resistance_recipe
+```
+
+**Explanation:**
+- `step_mutate()` transforms resistance results (`R`) into binary variables (TRUE/FALSE).
+- `step_dummy()` converts categorical organism (`mo`) names into one-hot encoded numerical features, making them compatible with the model.
+
+#### 2. Specifying the Model
+
+We define a logistic regression model since resistance prediction is a binary classification task.
+
+```{r}
+# Specify a logistic regression model
+logistic_model <- logistic_reg() %>%
+  set_engine("glm") # Use the Generalized Linear Model engine
+logistic_model
+```
+
+**Explanation:**
+- `logistic_reg()` sets up a logistic regression model.
+- `set_engine("glm")` specifies the use of R's built-in GLM engine.
+
+#### 3. Building the Workflow
+
+We bundle the recipe and model together into a `workflow`, which organizes the entire modeling process.
+
+```{r}
+# Combine the recipe and model into a workflow
+resistance_workflow <- workflow() %>%
+  add_recipe(resistance_recipe) %>% # Add the preprocessing recipe
+  add_model(logistic_model) # Add the logistic regression model
+resistance_workflow
+```
+
+---
+
+### **Training and Evaluating the Model**
+
+To train the model, we split the data into training and testing sets. Then, we fit the workflow on the training set and evaluate its performance.
+
+```{r}
+# Split data into training and testing sets
+set.seed(123) # For reproducibility
+data_split <- initial_split(data, prop = 0.8) # 80% training, 20% testing
+training_data <- training(data_split) # Training set
+testing_data <- testing(data_split)   # Testing set
+
+# Fit the workflow to the training data
+fitted_workflow <- resistance_workflow %>%
+  fit(training_data) # Train the model
+
+fitted_workflow
+```
+
+**Explanation:**
+- `initial_split()` splits the data into training and testing sets.
+- `fit()` trains the workflow on the training set.
+
+Next, we evaluate the model on the testing data.
+
+```{r}
+# Make predictions on the testing set
+predictions <- fitted_workflow %>%
+  predict(testing_data)                # Generate predictions
+probabilities <- fitted_workflow %>%
+  predict(testing_data, type = "prob") # Generate probabilities
+
+predictions <- predictions %>%
+  bind_cols(probabilities) %>%
+  bind_cols(testing_data) # Combine with true labels
+
+predictions
+
+# Evaluate model performance
+metrics <- predictions %>%
+  metrics(truth = mo, estimate = .pred_class) # Calculate performance metrics
+
+metrics
+```
+
+**Explanation:**
+- `predict()` generates predictions on the testing set.
+- `metrics()` computes evaluation metrics like accuracy and AUC.
+
+It appears we can predict the Gram based on AMR results with a `r round(metrics$.estimate[1], 3)` accuracy. The ROC curve looks like:
+
+```{r}
+predictions %>%
+  roc_curve(mo, `.pred_Gram-negative`) %>%
+  autoplot()
+```
+
+---
+
+### **Conclusion**
+
+In this post, we demonstrated how to build a machine learning pipeline with the `tidymodels` framework and the `AMR` package. By combining selector functions like `aminoglycosides()` and `betalactams()` with `tidymodels`, we efficiently prepared data, trained a model, and evaluated its performance.
+
+This workflow is extensible to other antibiotic classes and resistance patterns, empowering users to analyse AMR data systematically and reproducibly.
+
+---
+
+
+
 THE PART HEREAFTER CONTAINS CONTENTS FROM FILE 'vignettes/EUCAST.Rmd':
 
 

inst/tinytest/test-mdro.R

@@ -45,7 +45,7 @@ expect_identical(class(outcome), c("ordered", "factor"))
 # example_isolates should have these finding using Dutch guidelines
 expect_equal(
   as.double(table(outcome)),
-  c(1994, 0, 6)
+  c(1977, 23, 0)
 )
 
 expect_equal(

man/mdro.Rd

@@ -23,6 +23,12 @@ mdro(
   x = NULL,
   guideline = "CMI2012",
   col_mo = NULL,
+  esbl = NA,
+  carbapenemase = NA,
+  mecA = NA,
+  mecC = NA,
+  vanA = NA,
+  vanB = NA,
   info = interactive(),
   pct_required_classes = 0.5,
   combine_SI = TRUE,
@@ -35,13 +41,18 @@ custom_mdro_guideline(..., as_factor = TRUE)
 
 brmo(x = NULL, only_sir_columns = FALSE, ...)
 
-mrgn(x = NULL, only_sir_columns = FALSE, ...)
+mrgn(x = NULL, only_sir_columns = FALSE, verbose = FALSE, ...)
 
-mdr_tb(x = NULL, only_sir_columns = FALSE, ...)
+mdr_tb(x = NULL, only_sir_columns = FALSE, verbose = FALSE, ...)
 
-mdr_cmi2012(x = NULL, only_sir_columns = FALSE, ...)
+mdr_cmi2012(x = NULL, only_sir_columns = FALSE, verbose = FALSE, ...)
 
-eucast_exceptional_phenotypes(x = NULL, only_sir_columns = FALSE, ...)
+eucast_exceptional_phenotypes(
+  x = NULL,
+  only_sir_columns = FALSE,
+  verbose = FALSE,
+  ...
+)
 }
 \arguments{
 \item{x}{a \link{data.frame} with antibiotics columns, like \code{AMX} or \code{amox}. Can be left blank for automatic determination.}
@@ -50,6 +61,18 @@ eucast_exceptional_phenotypes(x = NULL, only_sir_columns = FALSE, ...)
 
 \item{col_mo}{column name of the names or codes of the microorganisms (see \code{\link[=as.mo]{as.mo()}}) - the default is the first column of class \code{\link{mo}}. Values will be coerced using \code{\link[=as.mo]{as.mo()}}.}
 
+\item{esbl}{\link{logical} values, or a column name containing logical values, indicating the presence of an ESBL gene (or production of its proteins)}
+
+\item{carbapenemase}{\link{logical} values, or a column name containing logical values, indicating the presence of a carbapenemase gene (or production of its proteins)}
+
+\item{mecA}{\link{logical} values, or a column name containing logical values, indicating the presence of a \emph{mecA} gene (or production of its proteins)}
+
+\item{mecC}{\link{logical} values, or a column name containing logical values, indicating the presence of a \emph{mecC} gene (or production of its proteins)}
+
+\item{vanA}{\link{logical} values, or a column name containing logical values, indicating the presence of a \emph{vanA} gene (or production of its proteins)}
+
+\item{vanB}{\link{logical} values, or a column name containing logical values, indicating the presence of a \emph{vanB} gene (or production of its proteins)}
+
 \item{info}{a \link{logical} to indicate whether progress should be printed to the console - the default is only print while in interactive sessions}
 
 \item{pct_required_classes}{minimal required percentage of antimicrobial classes that must be available per isolate, rounded down. For example, with the default guideline, 17 antimicrobial classes must be available for \emph{S. aureus}. Setting this \code{pct_required_classes} argument to \code{0.5} (default) means that for every \emph{S. aureus} isolate at least 8 different classes must be available. Any lower number of available classes will return \code{NA} for that isolate.}

vignettes/AMR_with_tidymodels.Rmd

@@ -0,0 +1,191 @@
+---
+title: "`AMR` with `tidymodels`"
+output: 
+  rmarkdown::html_vignette:
+    toc: true
+    toc_depth: 3
+vignette: >
+  %\VignetteIndexEntry{`AMR` with `tidymodels`}
+  %\VignetteEncoding{UTF-8}
+  %\VignetteEngine{knitr::rmarkdown}
+editor_options: 
+  chunk_output_type: console
+---
+
+```{r setup, include = FALSE, results = 'markup'}
+knitr::opts_chunk$set(
+  warning = FALSE,
+  collapse = TRUE,
+  comment = "#>",
+  fig.width = 7.5,
+  fig.height = 5
+)
+```
+
+Antimicrobial resistance (AMR) is a global health crisis, and understanding resistance patterns is crucial for managing effective treatments. The `AMR` R package provides robust tools for analysing AMR data, including convenient antibiotic selector functions like `aminoglycosides()` and `betalactams()`. In this post, we will explore how to use the `tidymodels` framework to predict resistance patterns in the `example_isolates` dataset. 
+
+By leveraging the power of `tidymodels` and the `AMR` package, we’ll build a reproducible machine learning workflow to predict resistance to two important antibiotic classes: aminoglycosides and beta-lactams.
+
+---
+
+### **Objective**
+
+Our goal is to build a predictive model using the `tidymodels` framework to determine resistance patterns based on microbial data. We will:
+
+1. Preprocess data using the selector functions `aminoglycosides()` and `betalactams()`.
+2. Define a logistic regression model for prediction.
+3. Use a structured `tidymodels` workflow to preprocess, train, and evaluate the model.
+
+---
+
+### **Data Preparation**
+
+We begin by loading the required libraries and preparing the `example_isolates` dataset from the `AMR` package.
+
+```{r}
+# Load required libraries
+library(tidymodels)   # For machine learning workflows, and data manipulation (dplyr, tidyr, ...)
+library(AMR)          # For AMR data analysis
+
+# Load the example_isolates dataset
+data("example_isolates")  # Preloaded dataset with AMR results
+
+# Select relevant columns for prediction
+data <- example_isolates %>%
+  # select AB results dynamically
+  select(mo, aminoglycosides(), betalactams()) %>%
+  # replace NAs with NI (not-interpretable)
+   mutate(across(where(is.sir),
+                 ~replace_na(.x, "NI")),
+          # make factors of SIR columns
+          across(where(is.sir),
+                 as.integer),
+          # get Gramstain of microorganisms
+          mo = as.factor(mo_gramstain(mo))) %>%
+  # drop NAs - the ones without a Gramstain (fungi, etc.)
+  drop_na() # %>%
+  # Cefepime is not reliable
+  #select(-FEP)
+```
+
+**Explanation:**
+- `aminoglycosides()` and `betalactams()` dynamically select columns for antibiotics in these classes.
+- `drop_na()` ensures the model receives complete cases for training.
+
+---
+
+### **Defining the Workflow**
+
+We now define the `tidymodels` workflow, which consists of three steps: preprocessing, model specification, and fitting.
+
+#### 1. Preprocessing with a Recipe
+
+We create a recipe to preprocess the data for modelling. This includes:
+- Encoding resistance results (`S`, `I`, `R`) as binary (resistant or not resistant).
+- Converting microbial organism names (`mo`) into numerical features using one-hot encoding.
+
+```{r}
+# Define the recipe for data preprocessing
+resistance_recipe <- recipe(mo ~ ., data = data) %>%
+  step_corr(c(aminoglycosides(), betalactams()), threshold = 0.9)
+resistance_recipe
+```
+
+**Explanation:**
+- `step_mutate()` transforms resistance results (`R`) into binary variables (TRUE/FALSE).
+- `step_dummy()` converts categorical organism (`mo`) names into one-hot encoded numerical features, making them compatible with the model.
+
+#### 2. Specifying the Model
+
+We define a logistic regression model since resistance prediction is a binary classification task.
+
+```{r}
+# Specify a logistic regression model
+logistic_model <- logistic_reg() %>%
+  set_engine("glm") # Use the Generalized Linear Model engine
+logistic_model
+```
+
+**Explanation:**
+- `logistic_reg()` sets up a logistic regression model.
+- `set_engine("glm")` specifies the use of R's built-in GLM engine.
+
+#### 3. Building the Workflow
+
+We bundle the recipe and model together into a `workflow`, which organizes the entire modeling process.
+
+```{r}
+# Combine the recipe and model into a workflow
+resistance_workflow <- workflow() %>%
+  add_recipe(resistance_recipe) %>% # Add the preprocessing recipe
+  add_model(logistic_model) # Add the logistic regression model
+resistance_workflow
+```
+
+---
+
+### **Training and Evaluating the Model**
+
+To train the model, we split the data into training and testing sets. Then, we fit the workflow on the training set and evaluate its performance.
+
+```{r}
+# Split data into training and testing sets
+set.seed(123) # For reproducibility
+data_split <- initial_split(data, prop = 0.8) # 80% training, 20% testing
+training_data <- training(data_split) # Training set
+testing_data <- testing(data_split)   # Testing set
+
+# Fit the workflow to the training data
+fitted_workflow <- resistance_workflow %>%
+  fit(training_data) # Train the model
+
+fitted_workflow
+```
+
+**Explanation:**
+- `initial_split()` splits the data into training and testing sets.
+- `fit()` trains the workflow on the training set.
+
+Next, we evaluate the model on the testing data.
+
+```{r}
+# Make predictions on the testing set
+predictions <- fitted_workflow %>%
+  predict(testing_data)                # Generate predictions
+probabilities <- fitted_workflow %>%
+  predict(testing_data, type = "prob") # Generate probabilities
+
+predictions <- predictions %>%
+  bind_cols(probabilities) %>%
+  bind_cols(testing_data) # Combine with true labels
+
+predictions
+
+# Evaluate model performance
+metrics <- predictions %>%
+  metrics(truth = mo, estimate = .pred_class) # Calculate performance metrics
+
+metrics
+```
+
+**Explanation:**
+- `predict()` generates predictions on the testing set.
+- `metrics()` computes evaluation metrics like accuracy and AUC.
+
+It appears we can predict the Gram based on AMR results with a `r round(metrics$.estimate[1], 3)` accuracy. The ROC curve looks like:
+
+```{r}
+predictions %>%
+  roc_curve(mo, `.pred_Gram-negative`) %>%
+  autoplot()
+```
+
+---
+
+### **Conclusion**
+
+In this post, we demonstrated how to build a machine learning pipeline with the `tidymodels` framework and the `AMR` package. By combining selector functions like `aminoglycosides()` and `betalactams()` with `tidymodels`, we efficiently prepared data, trained a model, and evaluated its performance.
+
+This workflow is extensible to other antibiotic classes and resistance patterns, empowering users to analyse AMR data systematically and reproducibly.
+
+---