diff --git a/DESCRIPTION b/DESCRIPTION
index 75f341427..035f7d6ff 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,5 +1,5 @@
 Package: AMR
-Version: 2.1.1.9150
+Version: 2.1.1.9151
 Date: 2025-02-15
 Title: Antimicrobial Resistance Data Analysis
 Description: Functions to simplify and standardise antimicrobial resistance (AMR)
diff --git a/NEWS.md b/NEWS.md
index c959c0568..86fdc3319 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,4 +1,4 @@
-# AMR 2.1.1.9150
+# AMR 2.1.1.9151
 
 *(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).)*
 
diff --git a/PythonPackage/AMR/AMR.egg-info/PKG-INFO b/PythonPackage/AMR/AMR.egg-info/PKG-INFO
index 1711457fe..a63234a6d 100644
--- a/PythonPackage/AMR/AMR.egg-info/PKG-INFO
+++ b/PythonPackage/AMR/AMR.egg-info/PKG-INFO
@@ -1,6 +1,6 @@
 Metadata-Version: 2.2
 Name: AMR
-Version: 2.1.1.9150
+Version: 2.1.1.9151
 Summary: A Python wrapper for the AMR R package
 Home-page: https://github.com/msberends/AMR
 Author: Matthijs Berends
diff --git a/PythonPackage/AMR/dist/AMR-2.1.1.9150-py3-none-any.whl b/PythonPackage/AMR/dist/AMR-2.1.1.9151-py3-none-any.whl
similarity index 84%
rename from PythonPackage/AMR/dist/AMR-2.1.1.9150-py3-none-any.whl
rename to PythonPackage/AMR/dist/AMR-2.1.1.9151-py3-none-any.whl
index c0a981fd3..cc358d463 100644
Binary files a/PythonPackage/AMR/dist/AMR-2.1.1.9150-py3-none-any.whl and b/PythonPackage/AMR/dist/AMR-2.1.1.9151-py3-none-any.whl differ
diff --git a/PythonPackage/AMR/dist/amr-2.1.1.9150.tar.gz b/PythonPackage/AMR/dist/amr-2.1.1.9150.tar.gz
deleted file mode 100644
index 049e290e7..000000000
Binary files a/PythonPackage/AMR/dist/amr-2.1.1.9150.tar.gz and /dev/null differ
diff --git a/PythonPackage/AMR/dist/amr-2.1.1.9151.tar.gz b/PythonPackage/AMR/dist/amr-2.1.1.9151.tar.gz
new file mode 100644
index 000000000..6914882a1
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diff --git a/PythonPackage/AMR/setup.py b/PythonPackage/AMR/setup.py
index e4c8198c7..c3bbccec7 100644
--- a/PythonPackage/AMR/setup.py
+++ b/PythonPackage/AMR/setup.py
@@ -2,7 +2,7 @@ from setuptools import setup, find_packages
 
 setup(
     name='AMR',
-    version='2.1.1.9150',
+    version='2.1.1.9151',
     packages=find_packages(),
     install_requires=[
         'rpy2',
diff --git a/R/amr_selectors.R b/R/amr_selectors.R
index 67630c6ff..ffc72342e 100755
--- a/R/amr_selectors.R
+++ b/R/amr_selectors.R
@@ -55,8 +55,8 @@
 #' @section Full list of supported (antimicrobial) classes:
 #'
 #' `r paste0(" * ", na.omit(sapply(DEFINED_AB_GROUPS, function(ab) ifelse(tolower(gsub("^AB_", "", ab)) %in% ls(envir = asNamespace("AMR")), paste0("[", tolower(gsub("^AB_", "", ab)), "()] can select: \\cr ", vector_and(paste0(ab_name(eval(parse(text = ab), envir = asNamespace("AMR")), language = NULL, tolower = TRUE), " (", eval(parse(text = ab), envir = asNamespace("AMR")), ")"), quotes = FALSE, sort = TRUE)), character(0)), USE.NAMES = FALSE)), "\n", collapse = "")`
-#' @rdname antimicrobial_class_selectors
-#' @name antimicrobial_class_selectors
+#' @rdname antimicrobial_selectors
+#' @name antimicrobial_selectors
 #' @return When used inside selecting or filtering, this returns a [character] vector of column names, with additional class `"amr_selector"`. When used individually, this returns an ['ab' vector][as.ab()] with all possible antimicrobials that the function would be able to select or filter.
 #' @export
 #' @inheritSection AMR Reference Data Publicly Available
@@ -243,7 +243,7 @@ amr_class <- function(amr_class,
   amr_select_exec(NULL, only_sir_columns = only_sir_columns, amr_class_args = amr_class, only_treatable = only_treatable, return_all = return_all)
 }
 
-#' @rdname antimicrobial_class_selectors
+#' @rdname antimicrobial_selectors
 #' @details The [amr_selector()] function can be used to internally filter the [antibiotics] data set on any results, see *Examples*. It allows for filtering on a (part of) a certain name, and/or a group name or even a minimum of DDDs for oral treatment. This function yields the highest flexibility, but is also the least user-friendly, since it requires a hard-coded filter to set.
 #' @export
 amr_selector <- function(filter,
@@ -280,7 +280,7 @@ amr_selector <- function(filter,
   )
 }
 
-#' @rdname antimicrobial_class_selectors
+#' @rdname antimicrobial_selectors
 #' @export
 aminoglycosides <- function(only_sir_columns = FALSE, only_treatable = TRUE, return_all = TRUE, ...) {
   meet_criteria(only_sir_columns, allow_class = "logical", has_length = 1)
@@ -289,7 +289,7 @@ aminoglycosides <- function(only_sir_columns = FALSE, only_treatable = TRUE, ret
   amr_select_exec("aminoglycosides", only_sir_columns = only_sir_columns, only_treatable = only_treatable, return_all = return_all)
 }
 
-#' @rdname antimicrobial_class_selectors
+#' @rdname antimicrobial_selectors
 #' @export
 aminopenicillins <- function(only_sir_columns = FALSE, return_all = TRUE, ...) {
   meet_criteria(only_sir_columns, allow_class = "logical", has_length = 1)
@@ -297,7 +297,7 @@ aminopenicillins <- function(only_sir_columns = FALSE, return_all = TRUE, ...) {
   amr_select_exec("aminopenicillins", only_sir_columns = only_sir_columns, return_all = return_all)
 }
 
-#' @rdname antimicrobial_class_selectors
+#' @rdname antimicrobial_selectors
 #' @export
 antifungals <- function(only_sir_columns = FALSE, return_all = TRUE, ...) {
   meet_criteria(only_sir_columns, allow_class = "logical", has_length = 1)
@@ -305,7 +305,7 @@ antifungals <- function(only_sir_columns = FALSE, return_all = TRUE, ...) {
   amr_select_exec("antifungals", only_sir_columns = only_sir_columns, return_all = return_all)
 }
 
-#' @rdname antimicrobial_class_selectors
+#' @rdname antimicrobial_selectors
 #' @export
 antimycobacterials <- function(only_sir_columns = FALSE, return_all = TRUE, ...) {
   meet_criteria(only_sir_columns, allow_class = "logical", has_length = 1)
@@ -313,7 +313,7 @@ antimycobacterials <- function(only_sir_columns = FALSE, return_all = TRUE, ...)
   amr_select_exec("antimycobacterials", only_sir_columns = only_sir_columns, return_all = return_all)
 }
 
-#' @rdname antimicrobial_class_selectors
+#' @rdname antimicrobial_selectors
 #' @export
 betalactams <- function(only_sir_columns = FALSE, only_treatable = TRUE, return_all = TRUE, ...) {
   meet_criteria(only_sir_columns, allow_class = "logical", has_length = 1)
@@ -322,7 +322,7 @@ betalactams <- function(only_sir_columns = FALSE, only_treatable = TRUE, return_
   amr_select_exec("betalactams", only_sir_columns = only_sir_columns, only_treatable = only_treatable, return_all = return_all)
 }
 
-#' @rdname antimicrobial_class_selectors
+#' @rdname antimicrobial_selectors
 #' @export
 betalactams_with_inhibitor <- function(only_sir_columns = FALSE, return_all = TRUE, ...) {
   meet_criteria(only_sir_columns, allow_class = "logical", has_length = 1)
@@ -330,7 +330,7 @@ betalactams_with_inhibitor <- function(only_sir_columns = FALSE, return_all = TR
   amr_select_exec("betalactams_with_inhibitor", only_sir_columns = only_sir_columns, return_all = return_all)
 }
 
-#' @rdname antimicrobial_class_selectors
+#' @rdname antimicrobial_selectors
 #' @export
 carbapenems <- function(only_sir_columns = FALSE, only_treatable = TRUE, return_all = TRUE, ...) {
   meet_criteria(only_sir_columns, allow_class = "logical", has_length = 1)
@@ -339,7 +339,7 @@ carbapenems <- function(only_sir_columns = FALSE, only_treatable = TRUE, return_
   amr_select_exec("carbapenems", only_sir_columns = only_sir_columns, only_treatable = only_treatable, return_all = return_all)
 }
 
-#' @rdname antimicrobial_class_selectors
+#' @rdname antimicrobial_selectors
 #' @export
 cephalosporins <- function(only_sir_columns = FALSE, return_all = TRUE, ...) {
   meet_criteria(only_sir_columns, allow_class = "logical", has_length = 1)
@@ -347,7 +347,7 @@ cephalosporins <- function(only_sir_columns = FALSE, return_all = TRUE, ...) {
   amr_select_exec("cephalosporins", only_sir_columns = only_sir_columns, return_all = return_all)
 }
 
-#' @rdname antimicrobial_class_selectors
+#' @rdname antimicrobial_selectors
 #' @export
 cephalosporins_1st <- function(only_sir_columns = FALSE, return_all = TRUE, ...) {
   meet_criteria(only_sir_columns, allow_class = "logical", has_length = 1)
@@ -355,7 +355,7 @@ cephalosporins_1st <- function(only_sir_columns = FALSE, return_all = TRUE, ...)
   amr_select_exec("cephalosporins_1st", only_sir_columns = only_sir_columns, return_all = return_all)
 }
 
-#' @rdname antimicrobial_class_selectors
+#' @rdname antimicrobial_selectors
 #' @export
 cephalosporins_2nd <- function(only_sir_columns = FALSE, return_all = TRUE, ...) {
   meet_criteria(only_sir_columns, allow_class = "logical", has_length = 1)
@@ -363,7 +363,7 @@ cephalosporins_2nd <- function(only_sir_columns = FALSE, return_all = TRUE, ...)
   amr_select_exec("cephalosporins_2nd", only_sir_columns = only_sir_columns, return_all = return_all)
 }
 
-#' @rdname antimicrobial_class_selectors
+#' @rdname antimicrobial_selectors
 #' @export
 cephalosporins_3rd <- function(only_sir_columns = FALSE, return_all = TRUE, ...) {
   meet_criteria(only_sir_columns, allow_class = "logical", has_length = 1)
@@ -371,7 +371,7 @@ cephalosporins_3rd <- function(only_sir_columns = FALSE, return_all = TRUE, ...)
   amr_select_exec("cephalosporins_3rd", only_sir_columns = only_sir_columns, return_all = return_all)
 }
 
-#' @rdname antimicrobial_class_selectors
+#' @rdname antimicrobial_selectors
 #' @export
 cephalosporins_4th <- function(only_sir_columns = FALSE, return_all = TRUE, ...) {
   meet_criteria(only_sir_columns, allow_class = "logical", has_length = 1)
@@ -379,7 +379,7 @@ cephalosporins_4th <- function(only_sir_columns = FALSE, return_all = TRUE, ...)
   amr_select_exec("cephalosporins_4th", only_sir_columns = only_sir_columns, return_all = return_all)
 }
 
-#' @rdname antimicrobial_class_selectors
+#' @rdname antimicrobial_selectors
 #' @export
 cephalosporins_5th <- function(only_sir_columns = FALSE, return_all = TRUE, ...) {
   meet_criteria(only_sir_columns, allow_class = "logical", has_length = 1)
@@ -387,7 +387,7 @@ cephalosporins_5th <- function(only_sir_columns = FALSE, return_all = TRUE, ...)
   amr_select_exec("cephalosporins_5th", only_sir_columns = only_sir_columns, return_all = return_all)
 }
 
-#' @rdname antimicrobial_class_selectors
+#' @rdname antimicrobial_selectors
 #' @export
 fluoroquinolones <- function(only_sir_columns = FALSE, return_all = TRUE, ...) {
   meet_criteria(only_sir_columns, allow_class = "logical", has_length = 1)
@@ -395,7 +395,7 @@ fluoroquinolones <- function(only_sir_columns = FALSE, return_all = TRUE, ...) {
   amr_select_exec("fluoroquinolones", only_sir_columns = only_sir_columns, return_all = return_all)
 }
 
-#' @rdname antimicrobial_class_selectors
+#' @rdname antimicrobial_selectors
 #' @export
 glycopeptides <- function(only_sir_columns = FALSE, return_all = TRUE, ...) {
   meet_criteria(only_sir_columns, allow_class = "logical", has_length = 1)
@@ -403,7 +403,7 @@ glycopeptides <- function(only_sir_columns = FALSE, return_all = TRUE, ...) {
   amr_select_exec("glycopeptides", only_sir_columns = only_sir_columns, return_all = return_all)
 }
 
-#' @rdname antimicrobial_class_selectors
+#' @rdname antimicrobial_selectors
 #' @export
 isoxazolylpenicillins <- function(only_sir_columns = FALSE, return_all = TRUE, ...) {
   meet_criteria(only_sir_columns, allow_class = "logical", has_length = 1)
@@ -411,7 +411,7 @@ isoxazolylpenicillins <- function(only_sir_columns = FALSE, return_all = TRUE, .
   amr_select_exec("isoxazolylpenicillins", only_sir_columns = only_sir_columns, return_all = return_all)
 }
 
-#' @rdname antimicrobial_class_selectors
+#' @rdname antimicrobial_selectors
 #' @export
 lincosamides <- function(only_sir_columns = FALSE, only_treatable = TRUE, return_all = TRUE, ...) {
   meet_criteria(only_sir_columns, allow_class = "logical", has_length = 1)
@@ -420,7 +420,7 @@ lincosamides <- function(only_sir_columns = FALSE, only_treatable = TRUE, return
   amr_select_exec("lincosamides", only_sir_columns = only_sir_columns, only_treatable = only_treatable, return_all = return_all)
 }
 
-#' @rdname antimicrobial_class_selectors
+#' @rdname antimicrobial_selectors
 #' @export
 lipoglycopeptides <- function(only_sir_columns = FALSE, return_all = TRUE, ...) {
   meet_criteria(only_sir_columns, allow_class = "logical", has_length = 1)
@@ -428,7 +428,7 @@ lipoglycopeptides <- function(only_sir_columns = FALSE, return_all = TRUE, ...)
   amr_select_exec("lipoglycopeptides", only_sir_columns = only_sir_columns, return_all = return_all)
 }
 
-#' @rdname antimicrobial_class_selectors
+#' @rdname antimicrobial_selectors
 #' @export
 macrolides <- function(only_sir_columns = FALSE, return_all = TRUE, ...) {
   meet_criteria(only_sir_columns, allow_class = "logical", has_length = 1)
@@ -436,7 +436,7 @@ macrolides <- function(only_sir_columns = FALSE, return_all = TRUE, ...) {
   amr_select_exec("macrolides", only_sir_columns = only_sir_columns, return_all = return_all)
 }
 
-#' @rdname antimicrobial_class_selectors
+#' @rdname antimicrobial_selectors
 #' @export
 monobactams <- function(only_sir_columns = FALSE, return_all = TRUE, ...) {
   meet_criteria(only_sir_columns, allow_class = "logical", has_length = 1)
@@ -444,7 +444,7 @@ monobactams <- function(only_sir_columns = FALSE, return_all = TRUE, ...) {
   amr_select_exec("monobactams", only_sir_columns = only_sir_columns, return_all = return_all)
 }
 
-#' @rdname antimicrobial_class_selectors
+#' @rdname antimicrobial_selectors
 #' @export
 nitrofurans <- function(only_sir_columns = FALSE, return_all = TRUE, ...) {
   meet_criteria(only_sir_columns, allow_class = "logical", has_length = 1)
@@ -452,7 +452,7 @@ nitrofurans <- function(only_sir_columns = FALSE, return_all = TRUE, ...) {
   amr_select_exec("nitrofurans", only_sir_columns = only_sir_columns, return_all = return_all)
 }
 
-#' @rdname antimicrobial_class_selectors
+#' @rdname antimicrobial_selectors
 #' @export
 oxazolidinones <- function(only_sir_columns = FALSE, return_all = TRUE, ...) {
   meet_criteria(only_sir_columns, allow_class = "logical", has_length = 1)
@@ -460,7 +460,7 @@ oxazolidinones <- function(only_sir_columns = FALSE, return_all = TRUE, ...) {
   amr_select_exec("oxazolidinones", only_sir_columns = only_sir_columns, return_all = return_all)
 }
 
-#' @rdname antimicrobial_class_selectors
+#' @rdname antimicrobial_selectors
 #' @export
 penicillins <- function(only_sir_columns = FALSE, return_all = TRUE, ...) {
   meet_criteria(only_sir_columns, allow_class = "logical", has_length = 1)
@@ -468,7 +468,7 @@ penicillins <- function(only_sir_columns = FALSE, return_all = TRUE, ...) {
   amr_select_exec("penicillins", only_sir_columns = only_sir_columns, return_all = return_all)
 }
 
-#' @rdname antimicrobial_class_selectors
+#' @rdname antimicrobial_selectors
 #' @export
 phenicols <- function(only_sir_columns = FALSE, return_all = TRUE, ...) {
   meet_criteria(only_sir_columns, allow_class = "logical", has_length = 1)
@@ -476,7 +476,7 @@ phenicols <- function(only_sir_columns = FALSE, return_all = TRUE, ...) {
   amr_select_exec("phenicols", only_sir_columns = only_sir_columns, return_all = return_all)
 }
 
-#' @rdname antimicrobial_class_selectors
+#' @rdname antimicrobial_selectors
 #' @export
 polymyxins <- function(only_sir_columns = FALSE, only_treatable = TRUE, return_all = TRUE, ...) {
   meet_criteria(only_sir_columns, allow_class = "logical", has_length = 1)
@@ -485,7 +485,7 @@ polymyxins <- function(only_sir_columns = FALSE, only_treatable = TRUE, return_a
   amr_select_exec("polymyxins", only_sir_columns = only_sir_columns, only_treatable = only_treatable, return_all = return_all)
 }
 
-#' @rdname antimicrobial_class_selectors
+#' @rdname antimicrobial_selectors
 #' @export
 quinolones <- function(only_sir_columns = FALSE, return_all = TRUE, ...) {
   meet_criteria(only_sir_columns, allow_class = "logical", has_length = 1)
@@ -493,7 +493,7 @@ quinolones <- function(only_sir_columns = FALSE, return_all = TRUE, ...) {
   amr_select_exec("quinolones", only_sir_columns = only_sir_columns, return_all = return_all)
 }
 
-#' @rdname antimicrobial_class_selectors
+#' @rdname antimicrobial_selectors
 #' @export
 rifamycins <- function(only_sir_columns = FALSE, return_all = TRUE, ...) {
   meet_criteria(only_sir_columns, allow_class = "logical", has_length = 1)
@@ -501,7 +501,7 @@ rifamycins <- function(only_sir_columns = FALSE, return_all = TRUE, ...) {
   amr_select_exec("rifamycins", only_sir_columns = only_sir_columns, return_all = return_all)
 }
 
-#' @rdname antimicrobial_class_selectors
+#' @rdname antimicrobial_selectors
 #' @export
 streptogramins <- function(only_sir_columns = FALSE, return_all = TRUE, ...) {
   meet_criteria(only_sir_columns, allow_class = "logical", has_length = 1)
@@ -509,7 +509,7 @@ streptogramins <- function(only_sir_columns = FALSE, return_all = TRUE, ...) {
   amr_select_exec("streptogramins", only_sir_columns = only_sir_columns, return_all = return_all)
 }
 
-#' @rdname antimicrobial_class_selectors
+#' @rdname antimicrobial_selectors
 #' @export
 tetracyclines <- function(only_sir_columns = FALSE, return_all = TRUE, ...) {
   meet_criteria(only_sir_columns, allow_class = "logical", has_length = 1)
@@ -517,7 +517,7 @@ tetracyclines <- function(only_sir_columns = FALSE, return_all = TRUE, ...) {
   amr_select_exec("tetracyclines", only_sir_columns = only_sir_columns, return_all = return_all)
 }
 
-#' @rdname antimicrobial_class_selectors
+#' @rdname antimicrobial_selectors
 #' @export
 trimethoprims <- function(only_sir_columns = FALSE, return_all = TRUE, ...) {
   meet_criteria(only_sir_columns, allow_class = "logical", has_length = 1)
@@ -525,7 +525,7 @@ trimethoprims <- function(only_sir_columns = FALSE, return_all = TRUE, ...) {
   amr_select_exec("trimethoprims", only_sir_columns = only_sir_columns, return_all = return_all)
 }
 
-#' @rdname antimicrobial_class_selectors
+#' @rdname antimicrobial_selectors
 #' @export
 ureidopenicillins <- function(only_sir_columns = FALSE, return_all = TRUE, ...) {
   meet_criteria(only_sir_columns, allow_class = "logical", has_length = 1)
@@ -533,7 +533,7 @@ ureidopenicillins <- function(only_sir_columns = FALSE, return_all = TRUE, ...)
   amr_select_exec("ureidopenicillins", only_sir_columns = only_sir_columns, return_all = return_all)
 }
 
-#' @rdname antimicrobial_class_selectors
+#' @rdname antimicrobial_selectors
 #' @details The [administrable_per_os()] and [administrable_iv()] functions also rely on the [antibiotics] data set - antimicrobials will be matched where a DDD (defined daily dose) for resp. oral and IV treatment is available in the [antibiotics] data set.
 #' @export
 administrable_per_os <- function(only_sir_columns = FALSE, return_all = TRUE, ...) {
@@ -575,7 +575,7 @@ administrable_per_os <- function(only_sir_columns = FALSE, return_all = TRUE, ..
   )
 }
 
-#' @rdname antimicrobial_class_selectors
+#' @rdname antimicrobial_selectors
 #' @export
 administrable_iv <- function(only_sir_columns = FALSE, return_all = TRUE, ...) {
   meet_criteria(only_sir_columns, allow_class = "logical", has_length = 1)
@@ -602,7 +602,7 @@ administrable_iv <- function(only_sir_columns = FALSE, return_all = TRUE, ...) {
   )
 }
 
-#' @rdname antimicrobial_class_selectors
+#' @rdname antimicrobial_selectors
 #' @inheritParams eucast_rules
 #' @details The [not_intrinsic_resistant()] function can be used to only select antimicrobials that pose no intrinsic resistance for the microorganisms in the data set. For example, if a data set contains only microorganism codes or names of *E. coli* and *K. pneumoniae* and contains a column "vancomycin", this column will be removed (or rather, unselected) using this function. It currently applies `r format_eucast_version_nr(names(EUCAST_VERSION_EXPERT_RULES[1]))` to determine intrinsic resistance, using the [eucast_rules()] function internally. Because of this determination, this function is quite slow in terms of performance.
 #' @export
diff --git a/R/antibiogram.R b/R/antibiogram.R
index 76a346c8f..0de1d7a3d 100755
--- a/R/antibiogram.R
+++ b/R/antibiogram.R
@@ -34,7 +34,7 @@
 #' 
 #' Adhering to previously described approaches (see *Source*) and especially the Bayesian WISCA model (Weighted-Incidence Syndromic Combination Antibiogram) by Bielicki *et al.*, these functions provide flexible output formats including plots and tables, ideal for integration with R Markdown and Quarto reports.
 #' @param x a [data.frame] containing at least a column with microorganisms and columns with antimicrobial results (class 'sir', see [as.sir()])
-#' @param antibiotics vector of any antimicrobial name or code (will be evaluated with [as.ab()], column name of `x`, or (any combinations of) [antimicrobial selectors][antimicrobial_class_selectors] such as [aminoglycosides()] or [carbapenems()]. For combination antibiograms, this can also be set to values separated with `"+"`, such as `"TZP+TOB"` or `"cipro + genta"`, given that columns resembling such antimicrobials exist in `x`. See *Examples*.
+#' @param antibiotics vector of any antimicrobial name or code (will be evaluated with [as.ab()], column name of `x`, or (any combinations of) [antimicrobial selectors][antimicrobial_selectors] such as [aminoglycosides()] or [carbapenems()]. For combination antibiograms, this can also be set to values separated with `"+"`, such as `"TZP+TOB"` or `"cipro + genta"`, given that columns resembling such antimicrobials exist in `x`. See *Examples*.
 #' @param mo_transform a character to transform microorganism input - must be `"name"`, `"shortname"` (default), `"gramstain"`, or one of the column names of the [microorganisms] data set: `r vector_or(colnames(microorganisms), sort = FALSE, quotes = TRUE)`. Can also be `NULL` to not transform the input or `NA` to consider all microorganisms 'unknown'.
 #' @param ab_transform a character to transform antimicrobial input - must be one of the column names of the [antibiotics] data set (defaults to `"name"`): `r vector_or(colnames(antibiotics), sort = FALSE, quotes = TRUE)`. Can also be `NULL` to not transform the input.
 #' @param syndromic_group a column name of `x`, or values calculated to split rows of `x`, e.g. by using [ifelse()] or [`case_when()`][dplyr::case_when()]. See *Examples*.
diff --git a/_pkgdown.yml b/_pkgdown.yml
index 8b2ba6d85..192c503c3 100644
--- a/_pkgdown.yml
+++ b/_pkgdown.yml
@@ -191,7 +191,7 @@ reference:
     - "`key_antimicrobials`"
     - "`mdro`"
     - "`bug_drug_combinations`"
-    - "`antimicrobial_class_selectors`"
+    - "`antimicrobial_selectors`"
     - "`top_n_microorganisms`"
     - "`mean_amr_distance`"
     - "`resistance_predict`"
diff --git a/data-raw/gpt_training_text_v2.1.1.9150.txt b/data-raw/gpt_training_text_v2.1.1.9151.txt
similarity index 99%
rename from data-raw/gpt_training_text_v2.1.1.9150.txt
rename to data-raw/gpt_training_text_v2.1.1.9151.txt
index 8a4832554..949fcfb4f 100644
--- a/data-raw/gpt_training_text_v2.1.1.9150.txt
+++ b/data-raw/gpt_training_text_v2.1.1.9151.txt
@@ -1,6 +1,6 @@
 This knowledge base contains all context you must know about the AMR package for R. You are a GPT trained to be an assistant for the AMR package in R. You are an incredible R specialist, especially trained in this package and in the tidyverse.
 
-First and foremost, you are trained on version 2.1.1.9150. Remember this whenever someone asks which AMR package version you’re at.
+First and foremost, you are trained on version 2.1.1.9151. Remember this whenever someone asks which AMR package version you’re at.
 
 Below are the contents of the  file, the  file, and all the  files (documentation) in the package. Every file content is split using 100 hypens.
 ----------------------------------------------------------------------------------------------------
@@ -430,7 +430,7 @@ With the help of contributors from all corners of the world, the `AMR` package i
 
 #### Filtering and selecting data
 
-One of the most powerful functions of this package, aside from calculating and plotting AMR, is selecting and filtering based on antimicrobial columns. This can be done using the so-called [antimicrobial class selectors](https://msberends.github.io/AMR/reference/antimicrobial_class_selectors.html), which work in base R, `dplyr` and `data.table`:
+One of the most powerful functions of this package, aside from calculating and plotting AMR, is selecting and filtering based on antimicrobial columns. This can be done using the so-called [antimicrobial selectors](https://msberends.github.io/AMR/reference/antimicrobial_selectors.html), which work in base R, `dplyr` and `data.table`.
 
 ```r
 # AMR works great with dplyr, but it's not required or neccesary
@@ -463,45 +463,29 @@ With only having defined a row filter on Gram-negative bacteria with intrinsic r
 |*Pseudomonas aeruginosa*       |  S  |  S  |  S  |  R  |     |  S  |
 |*Pseudomonas aeruginosa*       |  S  |  S  |  S  |  R  |  S  |  S  |
 
-A base R equivalent would be:
-
-```r
-library(AMR)
-example_isolates$bacteria <- mo_fullname(example_isolates$mo)
-example_isolates[which(mo_is_gram_negative() &
-                         mo_is_intrinsic_resistant(ab = "cefotax")),
-                 c("bacteria", aminoglycosides(), carbapenems())]
-```
-
-This base R code will work in any version of R since April 2013 (R-3.0). Moreover, this code works identically with the `data.table` package, only by starting with:
-
-```r
-example_isolates <- data.table::as.data.table(example_isolates)
-```
-
 #### Generating antibiograms
 
 The `AMR` package supports generating traditional, combined, syndromic, and even weighted-incidence syndromic combination antibiograms (WISCA).
 
-If used inside R Markdown or Quarto, the table will be printed in the right output format automatically (such as markdown, LaTeX, HTML, etc.).
+If used inside [R Markdown](https://rmarkdown.rstudio.com) or [Quarto](https://quarto.org), the table will be printed in the right output format automatically (such as markdown, LaTeX, HTML, etc.).
 
 ```r
 antibiogram(example_isolates,
             antibiotics = c(aminoglycosides(), carbapenems()))
 ```
 
-| Pathogen                | Amikacin         | Gentamicin      | Imipenem         | Kanamycin        | Meropenem        | Tobramycin       |
-|:------------------------|:----------------:|:---------------:|:----------------:|:----------------:|:----------------:|:----------------:|
-| CoNS                    | 0% (0/43)        | 86% (267/309)   | 52% (25/48)      | 0% (0/43)        | 52% (25/48)      | 22% (12/55)      |
-| *E. coli*               | 100% (171/171)   | 98% (451/460)   | 100% (422/422)   |                  | 100% (418/418)   | 97% (450/462)    |
-| *E. faecalis*           | 0% (0/39)        | 0% (0/39)       | 100% (38/38)     | 0% (0/39)        |                  | 0% (0/39)        |
-| *K. pneumoniae*         |                  | 90% (52/58)     | 100% (51/51)     |                  | 100% (53/53)     | 90% (52/58)      |
-| *P. aeruginosa*         |                  | 100% (30/30)    |                  | 0% (0/30)        |                  | 100% (30/30)     |
-| *P. mirabilis*          |                  | 94% (32/34)     | 94% (30/32)      |                  |                  | 94% (32/34)      |
-| *S. aureus*             |                  | 99% (231/233)   |                  |                  |                  | 98% (84/86)      |
-| *S. epidermidis*        | 0% (0/44)        | 79% (128/163)   |                  | 0% (0/44)        |                  | 51% (45/89)      |
-| *S. hominis*            |                  | 92% (74/80)     |                  |                  |                  | 85% (53/62)      |
-| *S. pneumoniae*         | 0% (0/117)       | 0% (0/117)      |                  | 0% (0/117)       |                  | 0% (0/117)       |
+|     Pathogen     |    Amikacin    |   Gentamicin   |    Imipenem    | Kanamycin  |   Meropenem    |   Tobramycin   |
+|:-----------------|:--------------:|:--------------:|:--------------:|:----------:|:--------------:|:--------------:|
+| CoNS             |   0% (0-8%)    |  86% (82-90%)  |  52% (37-67%)  | 0% (0-8%)  |  52% (37-67%)  |  22% (12-35%)  |
+| *E. coli*        | 100% (98-100%) |  98% (96-99%)  | 100% (99-100%) |            | 100% (99-100%) |  97% (96-99%)  |
+| *E. faecalis*    |   0% (0-9%)    |   0% (0-9%)    | 100% (91-100%) | 0% (0-9%)  |                |   0% (0-9%)    |
+| *K. pneumoniae*  |                |  90% (79-96%)  | 100% (93-100%) |            | 100% (93-100%) |  90% (79-96%)  |
+| *P. aeruginosa*  |                | 100% (88-100%) |                | 0% (0-12%) |                | 100% (88-100%) |
+| *P. mirabilis*   |                |  94% (80-99%)  |  94% (79-99%)  |            |                |  94% (80-99%)  |
+| *S. aureus*      |                | 99% (97-100%)  |                |            |                | 98% (92-100%)  |
+| *S. epidermidis* |   0% (0-8%)    |  79% (71-85%)  |                | 0% (0-8%)  |                |  51% (40-61%)  |
+| *S. hominis*     |                |  92% (84-97%)  |                |            |                |  85% (74-93%)  |
+| *S. pneumoniae*  |   0% (0-3%)    |   0% (0-3%)    |                | 0% (0-3%)  |                |   0% (0-3%)    |
 
 In combination antibiograms, it is clear that combined antibiotics yield higher empiric coverage:
 
@@ -511,10 +495,10 @@ antibiogram(example_isolates,
             mo_transform = "gramstain")
 ```
 
-| Pathogen      | Piperacillin/tazobactam | Piperacillin/tazobactam + Gentamicin | Piperacillin/tazobactam + Tobramycin |
-|:--------------|:-----------------------:|:-------------------------------------:|:------------------------------------:|
-| Gram-negative | 88% (565/641)            | 99% (681/691)                        | 98% (679/693)                       |
-| Gram-positive | 86% (296/345)            | 98% (1018/1044)                      | 95% (524/550)                       |
+|Pathogen      | Piperacillin/tazobactam | Piperacillin/tazobactam + Gentamicin | Piperacillin/tazobactam + Tobramycin |
+|:-------------|:-----------------------:|:------------------------------------:|:------------------------------------:|
+|Gram-negative |      88% (85-91%)       |             99% (97-99%)             |             98% (97-99%)             |
+|Gram-positive |      86% (82-89%)       |             98% (96-98%)             |             95% (93-97%)             |
 
 Like many other functions in this package, `antibiogram()` comes with support for 20 languages that are often detected automatically based on system language:
 
@@ -526,10 +510,47 @@ antibiogram(example_isolates,
             language = "uk") # Ukrainian
 ```
 
-| Збудник        | Гентаміцин           | Тобраміцин          | Ципрофлоксацин     |
-|:---------------|:--------------------:|:-------------------:|:------------------:|
-| Грамнегативні  | 96% (659/684)        | 96% (658/686)       | 91% (621/684)      |
-| Грампозитивні  | 63% (740/1170)       | 34% (228/665)       | 77% (560/724)      |
+|Збудник       |  Гентаміцин  |  Тобраміцин  | Ципрофлоксацин |
+|:-------------|:------------:|:------------:|:--------------:|
+|Грамнегативні | 96% (95-98%) | 96% (94-97%) |  91% (88-93%)  |
+|Грампозитивні | 63% (60-66%) | 34% (31-38%) |  77% (74-80%)  |
+
+#### Interpreting and plotting MIC and SIR values
+
+The `AMR` package allows interpretation of MIC and disk diffusion values based on CLSI and EUCAST. Moreover, the `ggplot2` package is extended with new scale functions, to allow plotting of log2-distributed MIC values and SIR values.
+
+```r
+library(ggplot2)
+library(AMR)
+
+# generate some random values
+some_mic_values <- random_mic(size = 100)
+some_groups <- sample(LETTERS[1:5], 20, replace = TRUE)
+interpretation <- as.sir(some_mic_values,
+                         guideline = "EUCAST 2024",
+                         mo = "E. coli", # or any code or name resembling a known species
+                         ab = "Cipro")   # or any code or name resembling an antibiotic
+
+# create the plot
+ggplot(data.frame(mic = some_mic_values,
+                  group = some_groups,
+                  sir = interpretation),
+       aes(x = group, y = mic, colour = sir)) +
+  theme_minimal() +
+  geom_boxplot(fill = NA, colour = "grey") +
+  geom_jitter(width = 0.25) +
+  
+  # NEW scale function: plot MIC values to x, y, colour or fill
+  scale_y_mic() +
+  
+  # NEW scale function: write out S/I/R in any of the 20 supported languages
+  #                     and set colourblind-friendly colours
+  scale_colour_sir()
+```
+
+<a href="./reference/plotting.html" title="Plotting Helpers for AMR Data Analysis">
+  <img src="./plot_readme.png" style="max-width: 600px;">
+</a>
 
 #### Calculating resistance per group
 
@@ -546,13 +567,13 @@ example_isolates %>%
                         conf_int = function(x) sir_confidence_interval(x, collapse = "-"))))
 ```
 
-|ward       | GEN_total_R|GEN_conf_int | TOB_total_R|TOB_conf_int |
-|:---------:|:----------:|:-----------:|:----------:|:-----------:|
-|Clinical   |   0.229    |0.205-0.254  |   0.315    |0.284-0.347  |
-|ICU        |   0.290    |0.253-0.330  |   0.400    |0.353-0.449  |
-|Outpatient |   0.200    |0.131-0.285  |   0.368    |0.254-0.493  |
+|ward       | GEN_total_R | GEN_conf_int | TOB_total_R | TOB_conf_int |
+|:----------|:-----------:|:------------:|:-----------:|:------------:|
+|Clinical   |  0.2289362  | 0.205-0.254  |  0.3147503  | 0.284-0.347  |
+|ICU        |  0.2902655  |  0.253-0.33  |  0.4004739  | 0.353-0.449  |
+|Outpatient |  0.2000000  | 0.131-0.285  |  0.3676471  | 0.254-0.493  |
 
-Or use [antimicrobial class selectors](https://msberends.github.io/AMR/reference/antimicrobial_class_selectors.html) to select a series of antibiotic columns:
+Or use [antimicrobial selectors](https://msberends.github.io/AMR/reference/antimicrobial_selectors.html) to select a series of antibiotic columns:
 
 ```r
 library(AMR)
@@ -1679,7 +1700,7 @@ retrieve_wisca_parameters(wisca_model, ...)
 \arguments{
 \item{x}{a \link{data.frame} containing at least a column with microorganisms and columns with antimicrobial results (class 'sir', see \code{\link[=as.sir]{as.sir()}})}
 
-\item{antibiotics}{vector of any antimicrobial name or code (will be evaluated with \code{\link[=as.ab]{as.ab()}}, column name of \code{x}, or (any combinations of) \link[=antimicrobial_class_selectors]{antimicrobial selectors} such as \code{\link[=aminoglycosides]{aminoglycosides()}} or \code{\link[=carbapenems]{carbapenems()}}. For combination antibiograms, this can also be set to values separated with \code{"+"}, such as \code{"TZP+TOB"} or \code{"cipro + genta"}, given that columns resembling such antimicrobials exist in \code{x}. See \emph{Examples}.}
+\item{antibiotics}{vector of any antimicrobial name or code (will be evaluated with \code{\link[=as.ab]{as.ab()}}, column name of \code{x}, or (any combinations of) \link[=antimicrobial_selectors]{antimicrobial selectors} such as \code{\link[=aminoglycosides]{aminoglycosides()}} or \code{\link[=carbapenems]{carbapenems()}}. For combination antibiograms, this can also be set to values separated with \code{"+"}, such as \code{"TZP+TOB"} or \code{"cipro + genta"}, given that columns resembling such antimicrobials exist in \code{x}. See \emph{Examples}.}
 
 \item{mo_transform}{a character to transform microorganism input - must be \code{"name"}, \code{"shortname"} (default), \code{"gramstain"}, or one of the column names of the \link{microorganisms} data set: "mo", "fullname", "status", "kingdom", "phylum", "class", "order", "family", "genus", "species", "subspecies", "rank", "ref", "oxygen_tolerance", "source", "lpsn", "lpsn_parent", "lpsn_renamed_to", "mycobank", "mycobank_parent", "mycobank_renamed_to", "gbif", "gbif_parent", "gbif_renamed_to", "prevalence", or "snomed". Can also be \code{NULL} to not transform the input or \code{NA} to consider all microorganisms 'unknown'.}
 
@@ -2164,13 +2185,13 @@ antivirals
 
 
 ----------------------------------------------------------------------------------------------------
-THE PART HEREAFTER CONTAINS CONTENTS FROM FILE 'man/antimicrobial_class_selectors.Rd':
+THE PART HEREAFTER CONTAINS CONTENTS FROM FILE 'man/antimicrobial_selectors.Rd':
 
 
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/amr_selectors.R
-\name{antimicrobial_class_selectors}
-\alias{antimicrobial_class_selectors}
+\name{antimicrobial_selectors}
+\alias{antimicrobial_selectors}
 \alias{amr_class}
 \alias{amr_selector}
 \alias{aminoglycosides}
diff --git a/index.md b/index.md
index 0188c42dd..810324f3b 100644
--- a/index.md
+++ b/index.md
@@ -40,7 +40,7 @@ With the help of contributors from all corners of the world, the `AMR` package i
 
 #### Filtering and selecting data
 
-One of the most powerful functions of this package, aside from calculating and plotting AMR, is selecting and filtering based on antimicrobial columns. This can be done using the so-called [antimicrobial class selectors](https://msberends.github.io/AMR/reference/antimicrobial_class_selectors.html), which work in base R, `dplyr` and `data.table`:
+One of the most powerful functions of this package, aside from calculating and plotting AMR, is selecting and filtering based on antimicrobial columns. This can be done using the so-called [antimicrobial selectors](https://msberends.github.io/AMR/reference/antimicrobial_selectors.html), which work in base R, `dplyr` and `data.table`.
 
 ```r
 # AMR works great with dplyr, but it's not required or neccesary
@@ -73,45 +73,29 @@ With only having defined a row filter on Gram-negative bacteria with intrinsic r
 |*Pseudomonas aeruginosa*       |  S  |  S  |  S  |  R  |     |  S  |
 |*Pseudomonas aeruginosa*       |  S  |  S  |  S  |  R  |  S  |  S  |
 
-A base R equivalent would be:
-
-```r
-library(AMR)
-example_isolates$bacteria <- mo_fullname(example_isolates$mo)
-example_isolates[which(mo_is_gram_negative() &
-                         mo_is_intrinsic_resistant(ab = "cefotax")),
-                 c("bacteria", aminoglycosides(), carbapenems())]
-```
-
-This base R code will work in any version of R since April 2013 (R-3.0). Moreover, this code works identically with the `data.table` package, only by starting with:
-
-```r
-example_isolates <- data.table::as.data.table(example_isolates)
-```
-
 #### Generating antibiograms
 
 The `AMR` package supports generating traditional, combined, syndromic, and even weighted-incidence syndromic combination antibiograms (WISCA).
 
-If used inside R Markdown or Quarto, the table will be printed in the right output format automatically (such as markdown, LaTeX, HTML, etc.).
+If used inside [R Markdown](https://rmarkdown.rstudio.com) or [Quarto](https://quarto.org), the table will be printed in the right output format automatically (such as markdown, LaTeX, HTML, etc.).
 
 ```r
 antibiogram(example_isolates,
             antibiotics = c(aminoglycosides(), carbapenems()))
 ```
 
-| Pathogen                | Amikacin         | Gentamicin      | Imipenem         | Kanamycin        | Meropenem        | Tobramycin       |
-|:------------------------|:----------------:|:---------------:|:----------------:|:----------------:|:----------------:|:----------------:|
-| CoNS                    | 0% (0/43)        | 86% (267/309)   | 52% (25/48)      | 0% (0/43)        | 52% (25/48)      | 22% (12/55)      |
-| *E. coli*               | 100% (171/171)   | 98% (451/460)   | 100% (422/422)   |                  | 100% (418/418)   | 97% (450/462)    |
-| *E. faecalis*           | 0% (0/39)        | 0% (0/39)       | 100% (38/38)     | 0% (0/39)        |                  | 0% (0/39)        |
-| *K. pneumoniae*         |                  | 90% (52/58)     | 100% (51/51)     |                  | 100% (53/53)     | 90% (52/58)      |
-| *P. aeruginosa*         |                  | 100% (30/30)    |                  | 0% (0/30)        |                  | 100% (30/30)     |
-| *P. mirabilis*          |                  | 94% (32/34)     | 94% (30/32)      |                  |                  | 94% (32/34)      |
-| *S. aureus*             |                  | 99% (231/233)   |                  |                  |                  | 98% (84/86)      |
-| *S. epidermidis*        | 0% (0/44)        | 79% (128/163)   |                  | 0% (0/44)        |                  | 51% (45/89)      |
-| *S. hominis*            |                  | 92% (74/80)     |                  |                  |                  | 85% (53/62)      |
-| *S. pneumoniae*         | 0% (0/117)       | 0% (0/117)      |                  | 0% (0/117)       |                  | 0% (0/117)       |
+|     Pathogen     |    Amikacin    |   Gentamicin   |    Imipenem    | Kanamycin  |   Meropenem    |   Tobramycin   |
+|:-----------------|:--------------:|:--------------:|:--------------:|:----------:|:--------------:|:--------------:|
+| CoNS             |   0% (0-8%)    |  86% (82-90%)  |  52% (37-67%)  | 0% (0-8%)  |  52% (37-67%)  |  22% (12-35%)  |
+| *E. coli*        | 100% (98-100%) |  98% (96-99%)  | 100% (99-100%) |            | 100% (99-100%) |  97% (96-99%)  |
+| *E. faecalis*    |   0% (0-9%)    |   0% (0-9%)    | 100% (91-100%) | 0% (0-9%)  |                |   0% (0-9%)    |
+| *K. pneumoniae*  |                |  90% (79-96%)  | 100% (93-100%) |            | 100% (93-100%) |  90% (79-96%)  |
+| *P. aeruginosa*  |                | 100% (88-100%) |                | 0% (0-12%) |                | 100% (88-100%) |
+| *P. mirabilis*   |                |  94% (80-99%)  |  94% (79-99%)  |            |                |  94% (80-99%)  |
+| *S. aureus*      |                | 99% (97-100%)  |                |            |                | 98% (92-100%)  |
+| *S. epidermidis* |   0% (0-8%)    |  79% (71-85%)  |                | 0% (0-8%)  |                |  51% (40-61%)  |
+| *S. hominis*     |                |  92% (84-97%)  |                |            |                |  85% (74-93%)  |
+| *S. pneumoniae*  |   0% (0-3%)    |   0% (0-3%)    |                | 0% (0-3%)  |                |   0% (0-3%)    |
 
 In combination antibiograms, it is clear that combined antibiotics yield higher empiric coverage:
 
@@ -121,10 +105,10 @@ antibiogram(example_isolates,
             mo_transform = "gramstain")
 ```
 
-| Pathogen      | Piperacillin/tazobactam | Piperacillin/tazobactam + Gentamicin | Piperacillin/tazobactam + Tobramycin |
-|:--------------|:-----------------------:|:-------------------------------------:|:------------------------------------:|
-| Gram-negative | 88% (565/641)            | 99% (681/691)                        | 98% (679/693)                       |
-| Gram-positive | 86% (296/345)            | 98% (1018/1044)                      | 95% (524/550)                       |
+|Pathogen      | Piperacillin/tazobactam | Piperacillin/tazobactam + Gentamicin | Piperacillin/tazobactam + Tobramycin |
+|:-------------|:-----------------------:|:------------------------------------:|:------------------------------------:|
+|Gram-negative |      88% (85-91%)       |             99% (97-99%)             |             98% (97-99%)             |
+|Gram-positive |      86% (82-89%)       |             98% (96-98%)             |             95% (93-97%)             |
 
 Like many other functions in this package, `antibiogram()` comes with support for 20 languages that are often detected automatically based on system language:
 
@@ -136,10 +120,47 @@ antibiogram(example_isolates,
             language = "uk") # Ukrainian
 ```
 
-| Збудник        | Гентаміцин           | Тобраміцин          | Ципрофлоксацин     |
-|:---------------|:--------------------:|:-------------------:|:------------------:|
-| Грамнегативні  | 96% (659/684)        | 96% (658/686)       | 91% (621/684)      |
-| Грампозитивні  | 63% (740/1170)       | 34% (228/665)       | 77% (560/724)      |
+|Збудник       |  Гентаміцин  |  Тобраміцин  | Ципрофлоксацин |
+|:-------------|:------------:|:------------:|:--------------:|
+|Грамнегативні | 96% (95-98%) | 96% (94-97%) |  91% (88-93%)  |
+|Грампозитивні | 63% (60-66%) | 34% (31-38%) |  77% (74-80%)  |
+
+#### Interpreting and plotting MIC and SIR values
+
+The `AMR` package allows interpretation of MIC and disk diffusion values based on CLSI and EUCAST. Moreover, the `ggplot2` package is extended with new scale functions, to allow plotting of log2-distributed MIC values and SIR values.
+
+```r
+library(ggplot2)
+library(AMR)
+
+# generate some random values
+some_mic_values <- random_mic(size = 100)
+some_groups <- sample(LETTERS[1:5], 20, replace = TRUE)
+interpretation <- as.sir(some_mic_values,
+                         guideline = "EUCAST 2024",
+                         mo = "E. coli", # or any code or name resembling a known species
+                         ab = "Cipro")   # or any code or name resembling an antibiotic
+
+# create the plot
+ggplot(data.frame(mic = some_mic_values,
+                  group = some_groups,
+                  sir = interpretation),
+       aes(x = group, y = mic, colour = sir)) +
+  theme_minimal() +
+  geom_boxplot(fill = NA, colour = "grey") +
+  geom_jitter(width = 0.25) +
+  
+  # NEW scale function: plot MIC values to x, y, colour or fill
+  scale_y_mic() +
+  
+  # NEW scale function: write out S/I/R in any of the 20 supported languages
+  #                     and set colourblind-friendly colours
+  scale_colour_sir()
+```
+
+<a href="./reference/plotting.html" title="Plotting Helpers for AMR Data Analysis">
+  <img src="./plot_readme.png" style="max-width: 600px;">
+</a>
 
 #### Calculating resistance per group
 
@@ -156,13 +177,13 @@ example_isolates %>%
                         conf_int = function(x) sir_confidence_interval(x, collapse = "-"))))
 ```
 
-|ward       | GEN_total_R|GEN_conf_int | TOB_total_R|TOB_conf_int |
-|:---------:|:----------:|:-----------:|:----------:|:-----------:|
-|Clinical   |   0.229    |0.205-0.254  |   0.315    |0.284-0.347  |
-|ICU        |   0.290    |0.253-0.330  |   0.400    |0.353-0.449  |
-|Outpatient |   0.200    |0.131-0.285  |   0.368    |0.254-0.493  |
+|ward       | GEN_total_R | GEN_conf_int | TOB_total_R | TOB_conf_int |
+|:----------|:-----------:|:------------:|:-----------:|:------------:|
+|Clinical   |  0.2289362  | 0.205-0.254  |  0.3147503  | 0.284-0.347  |
+|ICU        |  0.2902655  |  0.253-0.33  |  0.4004739  | 0.353-0.449  |
+|Outpatient |  0.2000000  | 0.131-0.285  |  0.3676471  | 0.254-0.493  |
 
-Or use [antimicrobial class selectors](https://msberends.github.io/AMR/reference/antimicrobial_class_selectors.html) to select a series of antibiotic columns:
+Or use [antimicrobial selectors](https://msberends.github.io/AMR/reference/antimicrobial_selectors.html) to select a series of antibiotic columns:
 
 ```r
 library(AMR)
diff --git a/man/antibiogram.Rd b/man/antibiogram.Rd
index 3d42a63a9..e82ca0583 100644
--- a/man/antibiogram.Rd
+++ b/man/antibiogram.Rd
@@ -48,7 +48,7 @@ retrieve_wisca_parameters(wisca_model, ...)
 \arguments{
 \item{x}{a \link{data.frame} containing at least a column with microorganisms and columns with antimicrobial results (class 'sir', see \code{\link[=as.sir]{as.sir()}})}
 
-\item{antibiotics}{vector of any antimicrobial name or code (will be evaluated with \code{\link[=as.ab]{as.ab()}}, column name of \code{x}, or (any combinations of) \link[=antimicrobial_class_selectors]{antimicrobial selectors} such as \code{\link[=aminoglycosides]{aminoglycosides()}} or \code{\link[=carbapenems]{carbapenems()}}. For combination antibiograms, this can also be set to values separated with \code{"+"}, such as \code{"TZP+TOB"} or \code{"cipro + genta"}, given that columns resembling such antimicrobials exist in \code{x}. See \emph{Examples}.}
+\item{antibiotics}{vector of any antimicrobial name or code (will be evaluated with \code{\link[=as.ab]{as.ab()}}, column name of \code{x}, or (any combinations of) \link[=antimicrobial_selectors]{antimicrobial selectors} such as \code{\link[=aminoglycosides]{aminoglycosides()}} or \code{\link[=carbapenems]{carbapenems()}}. For combination antibiograms, this can also be set to values separated with \code{"+"}, such as \code{"TZP+TOB"} or \code{"cipro + genta"}, given that columns resembling such antimicrobials exist in \code{x}. See \emph{Examples}.}
 
 \item{mo_transform}{a character to transform microorganism input - must be \code{"name"}, \code{"shortname"} (default), \code{"gramstain"}, or one of the column names of the \link{microorganisms} data set: "mo", "fullname", "status", "kingdom", "phylum", "class", "order", "family", "genus", "species", "subspecies", "rank", "ref", "oxygen_tolerance", "source", "lpsn", "lpsn_parent", "lpsn_renamed_to", "mycobank", "mycobank_parent", "mycobank_renamed_to", "gbif", "gbif_parent", "gbif_renamed_to", "prevalence", or "snomed". Can also be \code{NULL} to not transform the input or \code{NA} to consider all microorganisms 'unknown'.}
 
diff --git a/man/antimicrobial_class_selectors.Rd b/man/antimicrobial_selectors.Rd
similarity index 99%
rename from man/antimicrobial_class_selectors.Rd
rename to man/antimicrobial_selectors.Rd
index 7746615b0..f9d74ab39 100644
--- a/man/antimicrobial_class_selectors.Rd
+++ b/man/antimicrobial_selectors.Rd
@@ -1,7 +1,7 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/amr_selectors.R
-\name{antimicrobial_class_selectors}
-\alias{antimicrobial_class_selectors}
+\name{antimicrobial_selectors}
+\alias{antimicrobial_selectors}
 \alias{amr_class}
 \alias{amr_selector}
 \alias{aminoglycosides}
diff --git a/pkgdown/assets/plot_readme.png b/pkgdown/assets/plot_readme.png
new file mode 100644
index 000000000..5926ea8b6
Binary files /dev/null and b/pkgdown/assets/plot_readme.png differ