(v2.1.1.9122) fix documentation

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: AMR
-Version: 2.1.1.9121
+Version: 2.1.1.9122
-Date: 2024-12-19
+Date: 2024-12-20
 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

NEWS.md

@@ -1,4 +1,4 @@
-# AMR 2.1.1.9121
+# AMR 2.1.1.9122
 
 *(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).)*
 

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

@@ -1,6 +1,6 @@
 Metadata-Version: 2.1
 Name: AMR
-Version: 2.1.1.9121
+Version: 2.1.1.9122
 Summary: A Python wrapper for the AMR R package
 Home-page: https://github.com/msberends/AMR
 Author: Matthijs Berends

PythonPackage/AMR/setup.py

@@ -2,7 +2,7 @@ from setuptools import setup, find_packages
 
 setup(
     name='AMR',
-    version='2.1.1.9121',
+    version='2.1.1.9122',
     packages=find_packages(),
     install_requires=[
         'rpy2',

R/ggplot_sir.R

@@ -40,7 +40,6 @@
 #' @inheritParams proportion
 #' @param nrow (when using `facet`) number of rows
 #' @param colours a named vactor with colour to be used for filling. The default colours are colour-blind friendly.
-#' @param aesthetics aesthetics to apply the colours to - the default is "fill" but can also be (a combination of) "alpha", "colour", "fill", "linetype", "shape" or "size"
 #' @param datalabels show datalabels using [labels_sir_count()]
 #' @param datalabels.size size of the datalabels
 #' @param datalabels.colour colour of the datalabels

R/plotting.R

@@ -42,7 +42,10 @@
 #' @param colours_SIR colours to use for filling in the bars, must be a vector of three values (in the order S, I and R). The default colours are colour-blind friendly.
 #' @param language language to be used to translate 'Susceptible', 'Increased exposure'/'Intermediate' and 'Resistant' - the default is system language (see [get_AMR_locale()]) and can be overwritten by setting the package option [`AMR_locale`][AMR-options], e.g. `options(AMR_locale = "de")`, see [translate]. Use `language = NULL` or `language = ""` to prevent translation.
 #' @param expand a [logical] to indicate whether the range on the x axis should be expanded between the lowest and highest value. For MIC values, intermediate values will be factors of 2 starting from the highest MIC value. For disk diameters, the whole diameter range will be filled.
+#' @param aesthetics aesthetics to apply the colours to - the default is "fill" but can also be (a combination of) "alpha", "colour", "fill", "linetype", "shape" or "size"
 #' @inheritParams as.sir
+#' @inheritParams ggplot_sir
+#' @inheritParams proportion
 #' @details
 #' The interpretation of "I" will be named "Increased exposure" for all EUCAST guidelines since 2019, and will be named "Intermediate" in all other cases.
 #'
@@ -80,7 +83,7 @@
 #' plot(some_disk_values, mo = "Escherichia coli", ab = "cipro", language = "nl")
 #' 
 #' 
-#' # Plotting using scale_x_mic()
+#' # Plotting using scale_x_mic() ---------------------------------------------
 #' \donttest{
 #' if (require("ggplot2")) {
 #'   mic_plot <- ggplot(data.frame(mics = as.mic(c(0.25, "<=4", 4, 8, 32, ">=32")),
@@ -120,6 +123,25 @@
 #' if (require("ggplot2")) {
 #'   autoplot(some_sir_values)
 #' }
+#' 
+#' # Plotting using scale_y_percent() -----------------------------------------
+#' if (require("ggplot2")) {
+#'   p <- ggplot(data.frame(mics = as.mic(c(0.25, "<=4", 4, 8, 32, ">=32")),
+#'                                counts = c(1, 1, 2, 2, 3, 3)),
+#'                          aes(mics, counts / sum(counts))) +
+#'     geom_col()
+#'   print(p)
+#'   
+#'   p2 <- p +
+#'     scale_y_percent() +
+#'     theme_sir()
+#'   print(p2)
+#'   
+#'   p +
+#'     scale_y_percent(breaks = seq(from = 0, to = 1, by = 0.1),
+#'                     limits = c(0, 1)) +
+#'     theme_sir()
+#' }
 #' }
 NULL
 
@@ -954,7 +976,7 @@ facet_sir <- function(facet = c("interpretation", "antibiotic"), nrow = NULL) {
 
 #' @rdname plot
 #' @export
-scale_y_percent <- function(breaks = function(x) seq(0, max(x, na.rm = TRUE), 0.1), limits = NULL) {
+scale_y_percent <- function(breaks = function(x) seq(0, max(x, na.rm = TRUE), 0.1), limits = c(0, NA)) {
   stop_ifnot_installed("ggplot2")
   meet_criteria(breaks, allow_class = c("numeric", "integer", "function"))
   meet_criteria(limits, allow_class = c("numeric", "integer"), has_length = 2, allow_NULL = TRUE, allow_NA = TRUE)

data-raw/gpt_training_text_v2.1.1.9122.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 2.1.1.9121. Remember this whenever someone asks which AMR package version you’re at.
+First and foremost, you are trained on version 2.1.1.9122. Remember this whenever someone asks which AMR package version you’re at.
 --------------------------------
 
 THE PART HEREAFTER CONTAINS CONTENTS FROM FILE 'NAMESPACE':
@@ -5448,8 +5448,6 @@ geom_sir(
 \item{y.title}{text to show as y axis description}
 
 \item{...}{other arguments passed on to \code{\link[=geom_sir]{geom_sir()}} or, in case of \code{\link[=scale_sir_colours]{scale_sir_colours()}}, named values to set colours. The default colours are colour-blind friendly, while maintaining the convention that e.g. 'susceptible' should be green and 'resistant' should be red. See \emph{Examples}.}
-
-\item{aesthetics}{aesthetics to apply the colours to - the default is "fill" but can also be (a combination of) "alpha", "colour", "fill", "linetype", "shape" or "size"}
 }
 \description{
 Use these functions to create bar plots for AMR data analysis. All functions rely on \link[ggplot2:ggplot]{ggplot2} functions.
@@ -7545,7 +7543,7 @@ facet_sir(facet = c("interpretation", "antibiotic"), nrow = NULL)
 
 scale_y_percent(
   breaks = function(x) seq(0, max(x, na.rm = TRUE), 0.1),
-  limits = NULL
+  limits = c(0, NA)
 )
 
 scale_sir_colours(
@@ -7597,6 +7595,28 @@ labels_sir_count(
 \item{include_PKPD}{a \link{logical} to indicate that PK/PD clinical breakpoints must be applied as a last resort - the default is \code{TRUE}. Can also be set with the package option \code{\link[=AMR-options]{AMR_include_PKPD}}.}
 
 \item{breakpoint_type}{the type of breakpoints to use, either "ECOFF", "animal", or "human". ECOFF stands for Epidemiological Cut-Off values. The default is \code{"human"}, which can also be set with the package option \code{\link[=AMR-options]{AMR_breakpoint_type}}. If \code{host} is set to values of veterinary species, this will automatically be set to \code{"animal"}.}
+
+\item{facet}{variable to split plots by, either \code{"interpretation"} (default) or \code{"antibiotic"} or a grouping variable}
+
+\item{nrow}{(when using \code{facet}) number of rows}
+
+\item{breaks}{a \link{numeric} vector of positions}
+
+\item{limits}{a \link{numeric} vector of length two providing limits of the scale, use \code{NA} to refer to the existing minimum or maximum}
+
+\item{aesthetics}{aesthetics to apply the colours to - the default is "fill" but can also be (a combination of) "alpha", "colour", "fill", "linetype", "shape" or "size"}
+
+\item{position}{position adjustment of bars, either \code{"fill"}, \code{"stack"} or \code{"dodge"}}
+
+\item{translate_ab}{a column name of the \link{antibiotics} data set to translate the antibiotic abbreviations to, using \code{\link[=ab_property]{ab_property()}}}
+
+\item{minimum}{the minimum allowed number of available (tested) isolates. Any isolate count lower than \code{minimum} will return \code{NA} with a warning. The default number of \code{30} isolates is advised by the Clinical and Laboratory Standards Institute (CLSI) as best practice, see \emph{Source}.}
+
+\item{combine_SI}{a \link{logical} to indicate whether all values of S, SDD, and I must be merged into one, so the output only consists of S+SDD+I vs. R (susceptible vs. resistant) - the default is \code{TRUE}}
+
+\item{datalabels.size}{size of the datalabels}
+
+\item{datalabels.colour}{colour of the datalabels}
 }
 \value{
 The \code{autoplot()} functions return a \code{\link[ggplot2:ggplot]{ggplot}} model that is extendible with any \code{ggplot2} function.
@@ -7641,7 +7661,7 @@ plot(some_disk_values, mo = "Escherichia coli", ab = "cipro")
 plot(some_disk_values, mo = "Escherichia coli", ab = "cipro", language = "nl")
 
 
-# Plotting using scale_x_mic()
+# Plotting using scale_x_mic() ---------------------------------------------
 \donttest{
 if (require("ggplot2")) {
   mic_plot <- ggplot(data.frame(mics = as.mic(c(0.25, "<=4", 4, 8, 32, ">=32")),
@@ -7681,6 +7701,25 @@ if (require("ggplot2")) {
 if (require("ggplot2")) {
   autoplot(some_sir_values)
 }
+
+# Plotting using scale_y_percent() -----------------------------------------
+if (require("ggplot2")) {
+  p <- ggplot(data.frame(mics = as.mic(c(0.25, "<=4", 4, 8, 32, ">=32")),
+                               counts = c(1, 1, 2, 2, 3, 3)),
+                         aes(mics, counts / sum(counts))) +
+    geom_col()
+  print(p)
+  
+  p2 <- p +
+    scale_y_percent() +
+    theme_sir()
+  print(p2)
+  
+  p +
+    scale_y_percent(breaks = seq(from = 0, to = 1, by = 0.1),
+                    limits = c(0, 1)) +
+    theme_sir()
+}
 }
 }
 
@@ -8912,13 +8951,13 @@ THE PART HEREAFTER CONTAINS CONTENTS FROM FILE 'vignettes/AMR_with_tidymodels.Rm
 
 
 ---
-title: "`AMR` with `tidymodels`"
+title: "AMR with tidymodels"
 output: 
   rmarkdown::html_vignette:
     toc: true
     toc_depth: 3
 vignette: >
-  %\VignetteIndexEntry{`AMR` with `tidymodels`}
+  %\VignetteIndexEntry{AMR with tidymodels}
   %\VignetteEncoding{UTF-8}
   %\VignetteEngine{knitr::rmarkdown}
 editor_options: 
@@ -8935,22 +8974,20 @@ knitr::opts_chunk$set(
 )
 ```
 
+> This page was entirely written by our [AMR for R Assistant](https://chatgpt.com/g/g-M4UNLwFi5-amr-for-r-assistant), a ChatGPT manually-trained model able to answer any question about the AMR package.
+
 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.
+By leveraging the power of `tidymodels` and the `AMR` package, we’ll build a reproducible machine learning workflow to predict the Gramstain of the microorganism 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:
+Our goal is to build a predictive model using the `tidymodels` framework to determine the Gramstain of the microorganism 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.
@@ -8976,26 +9013,21 @@ data <- example_isolates %>%
           # get Gramstain of microorganisms
           mo = as.factor(mo_gramstain(mo))) %>%
   # drop NAs - the ones without a Gramstain (fungi, etc.)
-  drop_na() # %>%
+  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:
+We create a recipe to preprocess the data for modelling.
-- 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
@@ -9005,8 +9037,11 @@ 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.
+- `recipe(mo ~ ., data = data)` will take the `mo` column as outcome and all other columns as predictors.
+- `step_corr()` removes predictors (i.e., antibiotic columns) that have a higher correlation than 90%.
+
+Notice how the recipe contains just the antibiotic selector functions - no need to define the columns specifically.
 
 #### 2. Specifying the Model
 
@@ -9020,6 +9055,7 @@ logistic_model
 ```
 
 **Explanation:**
+
 - `logistic_reg()` sets up a logistic regression model.
 - `set_engine("glm")` specifies the use of R's built-in GLM engine.
 
@@ -9032,11 +9068,8 @@ We bundle the recipe and model together into a `workflow`, which organizes the e
 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.
@@ -9051,14 +9084,15 @@ 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.
 
+Notice how in `fit()`, the antibiotic selector functions are internally called again. For training, these functions are called since they are stored in the recipe.
+
 Next, we evaluate the model on the testing data.
 
 ```{r}
@@ -9082,10 +9116,11 @@ 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$.estimate[1], 3)` accuracy. The ROC curve looks like:
+- `predict()` generates predictions on the testing set.
+- `metrics()` computes evaluation metrics like accuracy and kappa.
+
+It appears we can predict the Gram based on AMR results with a `r round(metrics$.estimate[1], 3)` accuracy based on AMR results of aminoglycosides and beta-lactam antibiotics. The ROC curve looks like this:
 
 ```{r}
 predictions %>%
@@ -9093,16 +9128,12 @@ predictions %>%
   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':

man/ggplot_sir.Rd

@@ -86,8 +86,6 @@ geom_sir(
 \item{y.title}{text to show as y axis description}
 
 \item{...}{other arguments passed on to \code{\link[=geom_sir]{geom_sir()}} or, in case of \code{\link[=scale_sir_colours]{scale_sir_colours()}}, named values to set colours. The default colours are colour-blind friendly, while maintaining the convention that e.g. 'susceptible' should be green and 'resistant' should be red. See \emph{Examples}.}
-
-\item{aesthetics}{aesthetics to apply the colours to - the default is "fill" but can also be (a combination of) "alpha", "colour", "fill", "linetype", "shape" or "size"}
 }
 \description{
 Use these functions to create bar plots for AMR data analysis. All functions rely on \link[ggplot2:ggplot]{ggplot2} functions.

man/plot.Rd

@@ -123,7 +123,7 @@ facet_sir(facet = c("interpretation", "antibiotic"), nrow = NULL)
 
 scale_y_percent(
   breaks = function(x) seq(0, max(x, na.rm = TRUE), 0.1),
-  limits = NULL
+  limits = c(0, NA)
 )
 
 scale_sir_colours(
@@ -175,6 +175,28 @@ labels_sir_count(
 \item{include_PKPD}{a \link{logical} to indicate that PK/PD clinical breakpoints must be applied as a last resort - the default is \code{TRUE}. Can also be set with the package option \code{\link[=AMR-options]{AMR_include_PKPD}}.}
 
 \item{breakpoint_type}{the type of breakpoints to use, either "ECOFF", "animal", or "human". ECOFF stands for Epidemiological Cut-Off values. The default is \code{"human"}, which can also be set with the package option \code{\link[=AMR-options]{AMR_breakpoint_type}}. If \code{host} is set to values of veterinary species, this will automatically be set to \code{"animal"}.}
+
+\item{facet}{variable to split plots by, either \code{"interpretation"} (default) or \code{"antibiotic"} or a grouping variable}
+
+\item{nrow}{(when using \code{facet}) number of rows}
+
+\item{breaks}{a \link{numeric} vector of positions}
+
+\item{limits}{a \link{numeric} vector of length two providing limits of the scale, use \code{NA} to refer to the existing minimum or maximum}
+
+\item{aesthetics}{aesthetics to apply the colours to - the default is "fill" but can also be (a combination of) "alpha", "colour", "fill", "linetype", "shape" or "size"}
+
+\item{position}{position adjustment of bars, either \code{"fill"}, \code{"stack"} or \code{"dodge"}}
+
+\item{translate_ab}{a column name of the \link{antibiotics} data set to translate the antibiotic abbreviations to, using \code{\link[=ab_property]{ab_property()}}}
+
+\item{minimum}{the minimum allowed number of available (tested) isolates. Any isolate count lower than \code{minimum} will return \code{NA} with a warning. The default number of \code{30} isolates is advised by the Clinical and Laboratory Standards Institute (CLSI) as best practice, see \emph{Source}.}
+
+\item{combine_SI}{a \link{logical} to indicate whether all values of S, SDD, and I must be merged into one, so the output only consists of S+SDD+I vs. R (susceptible vs. resistant) - the default is \code{TRUE}}
+
+\item{datalabels.size}{size of the datalabels}
+
+\item{datalabels.colour}{colour of the datalabels}
 }
 \value{
 The \code{autoplot()} functions return a \code{\link[ggplot2:ggplot]{ggplot}} model that is extendible with any \code{ggplot2} function.
@@ -219,7 +241,7 @@ plot(some_disk_values, mo = "Escherichia coli", ab = "cipro")
 plot(some_disk_values, mo = "Escherichia coli", ab = "cipro", language = "nl")
 
 
-# Plotting using scale_x_mic()
+# Plotting using scale_x_mic() ---------------------------------------------
 \donttest{
 if (require("ggplot2")) {
   mic_plot <- ggplot(data.frame(mics = as.mic(c(0.25, "<=4", 4, 8, 32, ">=32")),
@@ -259,5 +281,24 @@ if (require("ggplot2")) {
 if (require("ggplot2")) {
   autoplot(some_sir_values)
 }
+
+# Plotting using scale_y_percent() -----------------------------------------
+if (require("ggplot2")) {
+  p <- ggplot(data.frame(mics = as.mic(c(0.25, "<=4", 4, 8, 32, ">=32")),
+                               counts = c(1, 1, 2, 2, 3, 3)),
+                         aes(mics, counts / sum(counts))) +
+    geom_col()
+  print(p)
+  
+  p2 <- p +
+    scale_y_percent() +
+    theme_sir()
+  print(p2)
+  
+  p +
+    scale_y_percent(breaks = seq(from = 0, to = 1, by = 0.1),
+                    limits = c(0, 1)) +
+    theme_sir()
+}
 }
 }

vignettes/AMR_with_tidymodels.Rmd

@@ -1,11 +1,11 @@
 ---
-title: "`AMR` with `tidymodels`"
+title: "AMR with tidymodels"
 output: 
   rmarkdown::html_vignette:
     toc: true
     toc_depth: 3
 vignette: >
-  %\VignetteIndexEntry{`AMR` with `tidymodels`}
+  %\VignetteIndexEntry{AMR with tidymodels}
   %\VignetteEncoding{UTF-8}
   %\VignetteEngine{knitr::rmarkdown}
 editor_options: 
@@ -22,22 +22,20 @@ knitr::opts_chunk$set(
 )
 ```
 
+> This page was entirely written by our [AMR for R Assistant](https://chatgpt.com/g/g-M4UNLwFi5-amr-for-r-assistant), a ChatGPT manually-trained model able to answer any question about the AMR package.
+
 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.
+By leveraging the power of `tidymodels` and the `AMR` package, we’ll build a reproducible machine learning workflow to predict the Gramstain of the microorganism 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:
+Our goal is to build a predictive model using the `tidymodels` framework to determine the Gramstain of the microorganism 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.
@@ -63,26 +61,21 @@ data <- example_isolates %>%
           # get Gramstain of microorganisms
           mo = as.factor(mo_gramstain(mo))) %>%
   # drop NAs - the ones without a Gramstain (fungi, etc.)
-  drop_na() # %>%
+  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:
+We create a recipe to preprocess the data for modelling.
-- 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
@@ -92,8 +85,11 @@ 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.
+- `recipe(mo ~ ., data = data)` will take the `mo` column as outcome and all other columns as predictors.
+- `step_corr()` removes predictors (i.e., antibiotic columns) that have a higher correlation than 90%.
+
+Notice how the recipe contains just the antibiotic selector functions - no need to define the columns specifically.
 
 #### 2. Specifying the Model
 
@@ -107,6 +103,7 @@ logistic_model
 ```
 
 **Explanation:**
+
 - `logistic_reg()` sets up a logistic regression model.
 - `set_engine("glm")` specifies the use of R's built-in GLM engine.
 
@@ -119,11 +116,8 @@ We bundle the recipe and model together into a `workflow`, which organizes the e
 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.
@@ -138,14 +132,15 @@ 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.
 
+Notice how in `fit()`, the antibiotic selector functions are internally called again. For training, these functions are called since they are stored in the recipe.
+
 Next, we evaluate the model on the testing data.
 
 ```{r}
@@ -169,10 +164,11 @@ 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$.estimate[1], 3)` accuracy. The ROC curve looks like:
+- `predict()` generates predictions on the testing set.
+- `metrics()` computes evaluation metrics like accuracy and kappa.
+
+It appears we can predict the Gram based on AMR results with a `r round(metrics$.estimate[1], 3)` accuracy based on AMR results of aminoglycosides and beta-lactam antibiotics. The ROC curve looks like this:
 
 ```{r}
 predictions %>%
@@ -180,12 +176,8 @@ predictions %>%
   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.
-
----