(v2.1.1.9116) selectors as separate functions

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: AMR
-Version: 2.1.1.9112
+Version: 2.1.1.9116
-Date: 2024-12-06
+Date: 2024-12-13
 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 2.1.1.9112
+# AMR 2.1.1.9116
 
 *(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).)*
 
@@ -47,7 +47,8 @@ This package now supports not only tools for AMR data analysis in clinical setti
   * Added Amorolfine (`AMO`, D01AE16), which is now also part of the `antifungals()` selector
 * Antibiotic selectors
   * Added selectors `nitrofurans()` and `rifamycins()`
-  * When using antibiotic selectors such as `aminoglycosides()` that exclude non-treatable drugs like gentamicin-high, the function now always returns a warning that these can be included using `only_treatable = FALSE`
+  * When using antibiotic selectors (such as `aminoglycosides()`) that exclude non-treatable drugs (such as gentamicin-high), the function now always returns a warning that these can be included using `only_treatable = FALSE`
+  * All selectors can now be run as a separate command to retrieve a vector of all possible antimicrobials that the selector can select
 * MICs
   * Added as valid levels: 4096, 6 powers of 0.0625, and 5 powers of 192 (192, 384, 576, 768, 960)
   * Added new argument `keep_operators` to `as.mic()`. This can be `"all"` (default), `"none"`, or `"edges"`. This argument is also available in the new `rescale_mic()` and `scale_*_mic()` functions.
@@ -71,6 +72,7 @@ This package now supports not only tools for AMR data analysis in clinical setti
 * Fixed a bug for `sir_confidence_interval()` when there are no isolates available
 * 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
 
 ## 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: 2.1.1.9112
+Version: 2.1.1.9116
 Summary: A Python wrapper for the AMR R package
 Home-page: https://github.com/msberends/AMR
 Author: Dr. Matthijs Berends

PythonPackage/AMR/setup.py

@@ -2,7 +2,7 @@ from setuptools import setup, find_packages
 
 setup(
     name='AMR',
-    version='2.1.1.9112',
+    version='2.1.1.9116',
     packages=find_packages(),
     install_requires=[
         'rpy2',

R/aa_helper_functions.R

@@ -1036,6 +1036,7 @@ get_current_data <- function(arg_name, call) {
         ", e.g.:\n",
         " ", AMR_env$bullet_icon, " your_data %>% select(", fn, "())\n",
         " ", AMR_env$bullet_icon, " your_data %>% select(column_a, column_b, ", fn, "())\n",
+        " ", AMR_env$bullet_icon, " your_data %>% filter(any(", fn, "() == \"R\"))\n",
         " ", AMR_env$bullet_icon, " your_data[, ", fn, "()]\n",
         " ", AMR_env$bullet_icon, " your_data[, c(\"column_a\", \"column_b\", ", fn, "())]"
       )

R/ab_selectors.R

@@ -48,7 +48,7 @@
 #' `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 antibiotic_class_selectors
 #' @name antibiotic_class_selectors
-#' @return (internally) a [character] vector of column names, with additional class `"ab_selector"`
+#' @return When used inside selecting or filtering, this returns a [character] vector of column names, with additional class `"ab_selector"`. When used individually, this returns an ['ab' vector][as.ab()] with all possible antimicrobial that the function would be able to select or filter.
 #' @export
 #' @inheritSection AMR Reference Data Publicly Available
 #' @examples
@@ -57,9 +57,15 @@
 #' example_isolates
 #'
 #'
+#' # you can use the selectors separately to retrieve all possible antimicrobials:
+#' carbapenems()
+#'
+#'
+#' # Though they are primarily intended to use for selections and filters.
 #' # Examples sections below are split into 'dplyr', 'base R', and 'data.table':
 #' 
 #' \donttest{
+#' \dontrun{
 #' # dplyr -------------------------------------------------------------------
 #' 
 #' library(dplyr, warn.conflicts = FALSE)
@@ -145,7 +151,7 @@
 #' z <- example_isolates %>% filter(if_all(carbapenems(), ~ .x == "R"))
 #' identical(x, y) && identical(y, z)
 #'
-#'
+#' }
 #' # base R ------------------------------------------------------------------
 #'
 #' # select columns 'IPM' (imipenem) and 'MEM' (meropenem)
@@ -589,15 +595,16 @@ ab_select_exec <- function(function_name,
                            ab_class_args = NULL) {
   # get_current_data() has to run each time, for cases where e.g., filter() and select() are used in same call
   # it only takes a couple of milliseconds, so no problem
-  vars_df <- get_current_data(arg_name = NA, call = -3)
+  vars_df <- tryCatch(get_current_data(arg_name = NA, call = -3), error = function(e) NULL)
   # to improve speed, get_column_abx() will only run once when e.g. in a select or group call
+  if (!is.null(vars_df)) {
     ab_in_data <- get_column_abx(vars_df,
                                  info = FALSE, only_sir_columns = only_sir_columns,
-    sort = FALSE, fn = function_name
+                                 sort = FALSE, fn = function_name)
-  )
+  }
   
   # untreatable drugs
-  if (only_treatable == TRUE) {
+  if (!is.null(vars_df) && only_treatable == TRUE) {
     untreatable <- AMR_env$AB_lookup[which(AMR_env$AB_lookup$name %like% "(-high|EDTA|polysorbate|macromethod|screening|nacubactam)"), "ab", drop = TRUE]
     if (any(untreatable %in% names(ab_in_data))) {
       if (message_not_thrown_before(function_name, "ab_class", "untreatable")) {
@@ -617,7 +624,7 @@ ab_select_exec <- function(function_name,
     }
   }
 
-  if (length(ab_in_data) == 0) {
+  if (!is.null(vars_df) && length(ab_in_data) == 0) {
     message_("No antimicrobial drugs found in the data.")
     return(NULL)
   }
@@ -667,6 +674,21 @@ ab_select_exec <- function(function_name,
     examples <- paste0(" (such as ", find_ab_names(ab_class_args, 2), ")")
   }
   
+  if (is.null(vars_df)) {
+    # no data found, no antimicrobials, so no input. Can happen if users run e.g. `aminoglycosides()` as a separate command.
+    examples <- paste0(
+      ", e.g.:\n",
+      "  ", AMR_env$bullet_icon, " your_data %>% select(", function_name, "())\n",
+      "  ", AMR_env$bullet_icon, " your_data %>% select(column_a, column_b, ", function_name, "())\n",
+      "  ", AMR_env$bullet_icon, " your_data %>% filter(any(", function_name, "() == \"R\"))\n",
+      "  ", AMR_env$bullet_icon, " your_data[, ", function_name, "()]\n",
+      "  ", AMR_env$bullet_icon, " your_data[, c(\"column_a\", \"column_b\", ", function_name, "())]"
+    )
+    message_("The function `" , function_name, "()` should be used inside a `dplyr` verb or `data.frame` call",
+             examples, "\n\nNow returning a vector of all possible antimicrobials that `" , function_name, "()` can select.")
+    return(sort(abx))
+  }
+
   # get the columns with a group names in the chosen ab class
   agents <- ab_in_data[names(ab_in_data) %in% abx]
 

R/ggplot_sir.R

@@ -373,17 +373,17 @@ facet_sir <- function(facet = c("interpretation", "antibiotic"), nrow = NULL) {
 
 #' @rdname ggplot_sir
 #' @export
-scale_y_percent <- function(breaks = seq(0, 1, 0.1), limits = NULL) {
+scale_y_percent <- function(breaks = function(x) seq(0, max(x, na.rm = TRUE), 0.1), limits = NULL) {
   stop_ifnot_installed("ggplot2")
-  meet_criteria(breaks, allow_class = c("numeric", "integer"))
+  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)
 
-  if (all(breaks[breaks != 0] > 1)) {
+  if (!is.function(breaks) && all(breaks[breaks != 0] > 1)) {
     breaks <- breaks / 100
   }
   ggplot2::scale_y_continuous(
     breaks = breaks,
-    labels = percentage(breaks),
+    labels = if (is.function(breaks)) function(x) percentage(breaks(x)) else percentage(breaks),
     limits = limits
   )
 }

data-raw/gpt_training_text_v2.1.1.9116.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 2.1.1.9112. Remember this whenever someone asks which AMR package version you’re at.
+First and foremost, you are trained on version 2.1.1.9116. Remember this whenever someone asks which AMR package version you’re at.
 --------------------------------
 
 THE PART HEREAFTER CONTAINS CONTENTS FROM FILE 'NAMESPACE':
@@ -811,29 +811,29 @@ Authors:
 
 Other contributors:
 \itemize{
-  \item Casper J. Albers (\href{https://orcid.org/0000-0002-9213-6743}{ORCID}) [thesis advisor]
+  \item Andrew P. Norgan (\href{https://orcid.org/0000-0002-2955-2066}{ORCID}) [contributor]
-  \item Larisse Bolton (\href{https://orcid.org/0000-0001-7879-2173}{ORCID}) [contributor]
+  \item Anita Williams (\href{https://orcid.org/0000-0002-5295-8451}{ORCID}) [contributor]
-  \item Peter Dutey-Magni (\href{https://orcid.org/0000-0002-8942-9836}{ORCID}) [contributor]
+  \item Annick Lenglet (\href{https://orcid.org/0000-0003-2013-8405}{ORCID}) [contributor]
-  \item Judith M. Fonville [contributor]
+  \item Anthony Underwood (\href{https://orcid.org/0000-0002-8547-4277}{ORCID}) [contributor]
-  \item Alex W. Friedrich (\href{https://orcid.org/0000-0003-4881-038X}{ORCID}) [thesis advisor]
+  \item Anton Mymrikov [contributor]
-  \item Corinna Glasner (\href{https://orcid.org/0000-0003-1241-1328}{ORCID}) [thesis advisor]
+  \item Bart C. Meijer [contributor]
+  \item Christian F. Luz (\href{https://orcid.org/0000-0001-5809-5995}{ORCID}) [contributor]
+  \item Dmytro Mykhailenko [contributor]
   \item Eric H. L. C. M. Hazenberg [contributor]
   \item Gwen Knight (\href{https://orcid.org/0000-0002-7263-9896}{ORCID}) [contributor]
-  \item Annick Lenglet (\href{https://orcid.org/0000-0003-2013-8405}{ORCID}) [contributor]
-  \item Christian F. Luz (\href{https://orcid.org/0000-0001-5809-5995}{ORCID}) [contributor]
-  \item Bart C. Meijer [contributor]
-  \item Dmytro Mykhailenko [contributor]
-  \item Anton Mymrikov [contributor]
-  \item Andrew P. Norgan (\href{https://orcid.org/0000-0002-2955-2066}{ORCID}) [contributor]
-  \item Sofia Ny (\href{https://orcid.org/0000-0002-2017-1363}{ORCID}) [contributor]
-  \item Matthew Saab [contributor]
-  \item Jonas Salm [contributor]
-  \item Javier Sanchez (\href{https://orcid.org/0000-0003-2605-8094}{ORCID}) [contributor]
-  \item Rogier P. Schade [contributor]
-  \item Bhanu N. M. Sinha (\href{https://orcid.org/0000-0003-1634-0010}{ORCID}) [thesis advisor]
   \item Jason Stull (\href{https://orcid.org/0000-0002-9028-8153}{ORCID}) [contributor]
-  \item Anthony Underwood (\href{https://orcid.org/0000-0002-8547-4277}{ORCID}) [contributor]
+  \item Javier Sanchez (\href{https://orcid.org/0000-0003-2605-8094}{ORCID}) [contributor]
-  \item Anita Williams (\href{https://orcid.org/0000-0002-5295-8451}{ORCID}) [contributor]
+  \item Jonas Salm [contributor]
+  \item Judith M. Fonville [contributor]
+  \item Larisse Bolton (\href{https://orcid.org/0000-0001-7879-2173}{ORCID}) [contributor]
+  \item Matthew Saab [contributor]
+  \item Peter Dutey-Magni (\href{https://orcid.org/0000-0002-8942-9836}{ORCID}) [contributor]
+  \item Rogier P. Schade [contributor]
+  \item Sofia Ny (\href{https://orcid.org/0000-0002-2017-1363}{ORCID}) [contributor]
+  \item Alex W. Friedrich (\href{https://orcid.org/0000-0003-4881-038X}{ORCID}) [thesis advisor]
+  \item Bhanu N. M. Sinha (\href{https://orcid.org/0000-0003-1634-0010}{ORCID}) [thesis advisor]
+  \item Casper J. Albers (\href{https://orcid.org/0000-0002-9213-6743}{ORCID}) [thesis advisor]
+  \item Corinna Glasner (\href{https://orcid.org/0000-0003-1241-1328}{ORCID}) [thesis advisor]
 }
 
 }
@@ -2051,7 +2051,7 @@ not_intrinsic_resistant(
 \item{version_expertrules}{the version number to use for the EUCAST Expert Rules and Intrinsic Resistance guideline. Can be "3.3", "3.2", or "3.1".}
 }
 \value{
-(internally) a \link{character} vector of column names, with additional class \code{"ab_selector"}
+When used inside selecting or filtering, this returns a \link{character} vector of column names, with additional class \code{"ab_selector"}. When used individually, this returns an \link[=as.ab]{'ab' vector} with all possible antimicrobial that the function would be able to select or filter.
 }
 \description{
 These functions allow for filtering rows and selecting columns based on antibiotic test results that are of a specific antibiotic class or group (according to the \link{antibiotics} data set), without the need to define the columns or antibiotic abbreviations.
@@ -2116,9 +2116,15 @@ All data sets in this \code{AMR} package (about microorganisms, antibiotics, SIR
 example_isolates
 
 
+# you can use the selectors separately to retrieve all possible antimicrobials:
+carbapenems()
+
+
+# Though they are primarily intended to use for selections and filters.
 # Examples sections below are split into 'dplyr', 'base R', and 'data.table':
 
 \donttest{
+\dontrun{
 # dplyr -------------------------------------------------------------------
 
 library(dplyr, warn.conflicts = FALSE)
@@ -2204,7 +2210,7 @@ y <- example_isolates \%>\% filter(carbapenems() == "R")
 z <- example_isolates \%>\% filter(if_all(carbapenems(), ~ .x == "R"))
 identical(x, y) && identical(y, z)
 
-
+}
 # base R ------------------------------------------------------------------
 
 # select columns 'IPM' (imipenem) and 'MEM' (meropenem)
@@ -5405,7 +5411,10 @@ geom_sir(
 
 facet_sir(facet = c("interpretation", "antibiotic"), nrow = NULL)
 
-scale_y_percent(breaks = seq(0, 1, 0.1), limits = NULL)
+scale_y_percent(
+  breaks = function(x) seq(0, max(x, na.rm = TRUE), 0.1),
+  limits = NULL
+)
 
 scale_sir_colours(..., aesthetics = "fill")
 

inst/tinytest/test-ab_selectors.R

@@ -53,6 +53,9 @@ expect_equal(ncol(example_isolates[, tetracyclines(), drop = FALSE]), 3, toleran
 expect_equal(ncol(example_isolates[, trimethoprims(), drop = FALSE]), 2, tolerance = 0.5)
 expect_equal(ncol(example_isolates[, ureidopenicillins(), drop = FALSE]), 1, tolerance = 0.5)
 
+expect_message(carbapenems())
+expect_error(administrable_per_os())
+
 # Examples:
 
 # select columns 'mo', 'AMK', 'GEN', 'KAN' and 'TOB'

man/AMR.Rd

@@ -61,29 +61,29 @@ Authors:
 
 Other contributors:
 \itemize{
-  \item Casper J. Albers (\href{https://orcid.org/0000-0002-9213-6743}{ORCID}) [thesis advisor]
+  \item Andrew P. Norgan (\href{https://orcid.org/0000-0002-2955-2066}{ORCID}) [contributor]
-  \item Larisse Bolton (\href{https://orcid.org/0000-0001-7879-2173}{ORCID}) [contributor]
+  \item Anita Williams (\href{https://orcid.org/0000-0002-5295-8451}{ORCID}) [contributor]
-  \item Peter Dutey-Magni (\href{https://orcid.org/0000-0002-8942-9836}{ORCID}) [contributor]
+  \item Annick Lenglet (\href{https://orcid.org/0000-0003-2013-8405}{ORCID}) [contributor]
-  \item Judith M. Fonville [contributor]
+  \item Anthony Underwood (\href{https://orcid.org/0000-0002-8547-4277}{ORCID}) [contributor]
-  \item Alex W. Friedrich (\href{https://orcid.org/0000-0003-4881-038X}{ORCID}) [thesis advisor]
+  \item Anton Mymrikov [contributor]
-  \item Corinna Glasner (\href{https://orcid.org/0000-0003-1241-1328}{ORCID}) [thesis advisor]
+  \item Bart C. Meijer [contributor]
+  \item Christian F. Luz (\href{https://orcid.org/0000-0001-5809-5995}{ORCID}) [contributor]
+  \item Dmytro Mykhailenko [contributor]
   \item Eric H. L. C. M. Hazenberg [contributor]
   \item Gwen Knight (\href{https://orcid.org/0000-0002-7263-9896}{ORCID}) [contributor]
-  \item Annick Lenglet (\href{https://orcid.org/0000-0003-2013-8405}{ORCID}) [contributor]
-  \item Christian F. Luz (\href{https://orcid.org/0000-0001-5809-5995}{ORCID}) [contributor]
-  \item Bart C. Meijer [contributor]
-  \item Dmytro Mykhailenko [contributor]
-  \item Anton Mymrikov [contributor]
-  \item Andrew P. Norgan (\href{https://orcid.org/0000-0002-2955-2066}{ORCID}) [contributor]
-  \item Sofia Ny (\href{https://orcid.org/0000-0002-2017-1363}{ORCID}) [contributor]
-  \item Matthew Saab [contributor]
-  \item Jonas Salm [contributor]
-  \item Javier Sanchez (\href{https://orcid.org/0000-0003-2605-8094}{ORCID}) [contributor]
-  \item Rogier P. Schade [contributor]
-  \item Bhanu N. M. Sinha (\href{https://orcid.org/0000-0003-1634-0010}{ORCID}) [thesis advisor]
   \item Jason Stull (\href{https://orcid.org/0000-0002-9028-8153}{ORCID}) [contributor]
-  \item Anthony Underwood (\href{https://orcid.org/0000-0002-8547-4277}{ORCID}) [contributor]
+  \item Javier Sanchez (\href{https://orcid.org/0000-0003-2605-8094}{ORCID}) [contributor]
-  \item Anita Williams (\href{https://orcid.org/0000-0002-5295-8451}{ORCID}) [contributor]
+  \item Jonas Salm [contributor]
+  \item Judith M. Fonville [contributor]
+  \item Larisse Bolton (\href{https://orcid.org/0000-0001-7879-2173}{ORCID}) [contributor]
+  \item Matthew Saab [contributor]
+  \item Peter Dutey-Magni (\href{https://orcid.org/0000-0002-8942-9836}{ORCID}) [contributor]
+  \item Rogier P. Schade [contributor]
+  \item Sofia Ny (\href{https://orcid.org/0000-0002-2017-1363}{ORCID}) [contributor]
+  \item Alex W. Friedrich (\href{https://orcid.org/0000-0003-4881-038X}{ORCID}) [thesis advisor]
+  \item Bhanu N. M. Sinha (\href{https://orcid.org/0000-0003-1634-0010}{ORCID}) [thesis advisor]
+  \item Casper J. Albers (\href{https://orcid.org/0000-0002-9213-6743}{ORCID}) [thesis advisor]
+  \item Corinna Glasner (\href{https://orcid.org/0000-0003-1241-1328}{ORCID}) [thesis advisor]
 }
 
 }

man/antibiotic_class_selectors.Rd

@@ -124,7 +124,7 @@ not_intrinsic_resistant(
 \item{version_expertrules}{the version number to use for the EUCAST Expert Rules and Intrinsic Resistance guideline. Can be "3.3", "3.2", or "3.1".}
 }
 \value{
-(internally) a \link{character} vector of column names, with additional class \code{"ab_selector"}
+When used inside selecting or filtering, this returns a \link{character} vector of column names, with additional class \code{"ab_selector"}. When used individually, this returns an \link[=as.ab]{'ab' vector} with all possible antimicrobial that the function would be able to select or filter.
 }
 \description{
 These functions allow for filtering rows and selecting columns based on antibiotic test results that are of a specific antibiotic class or group (according to the \link{antibiotics} data set), without the need to define the columns or antibiotic abbreviations.
@@ -189,9 +189,15 @@ All data sets in this \code{AMR} package (about microorganisms, antibiotics, SIR
 example_isolates
 
 
+# you can use the selectors separately to retrieve all possible antimicrobials:
+carbapenems()
+
+
+# Though they are primarily intended to use for selections and filters.
 # Examples sections below are split into 'dplyr', 'base R', and 'data.table':
 
 \donttest{
+\dontrun{
 # dplyr -------------------------------------------------------------------
 
 library(dplyr, warn.conflicts = FALSE)
@@ -277,7 +283,7 @@ y <- example_isolates \%>\% filter(carbapenems() == "R")
 z <- example_isolates \%>\% filter(if_all(carbapenems(), ~ .x == "R"))
 identical(x, y) && identical(y, z)
 
-
+}
 # base R ------------------------------------------------------------------
 
 # select columns 'IPM' (imipenem) and 'MEM' (meropenem)

man/ggplot_sir.Rd

@@ -49,7 +49,10 @@ geom_sir(
 
 facet_sir(facet = c("interpretation", "antibiotic"), nrow = NULL)
 
-scale_y_percent(breaks = seq(0, 1, 0.1), limits = NULL)
+scale_y_percent(
+  breaks = function(x) seq(0, max(x, na.rm = TRUE), 0.1),
+  limits = NULL
+)
 
 scale_sir_colours(..., aesthetics = "fill")