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AMR/R/ab_selectors.R

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# ==================================================================== #
# TITLE: #
# AMR: An R Package for Working with Antimicrobial Resistance Data #
# #
# SOURCE CODE: #
# https://github.com/msberends/AMR #
# #
# PLEASE CITE THIS SOFTWARE AS: #
# Berends MS, Luz CF, Friedrich AW, et al. (2022). #
# AMR: An R Package for Working with Antimicrobial Resistance Data. #
# Journal of Statistical Software, 104(3), 1-31. #
# https://doi.org/10.18637/jss.v104.i03 #
# #
# Developed at the University of Groningen and the University Medical #
# Center Groningen in The Netherlands, in collaboration with many #
# colleagues from around the world, see our website. #
# #
# This R package is free software; you can freely use and distribute #
# it for both personal and commercial purposes under the terms of the #
# GNU General Public License version 2.0 (GNU GPL-2), as published by #
# the Free Software Foundation. #
# We created this package for both routine data analysis and academic #
# research and it was publicly released in the hope that it will be #
# useful, but it comes WITHOUT ANY WARRANTY OR LIABILITY. #
# #
# Visit our website for the full manual and a complete tutorial about #
# how to conduct AMR data analysis: https://msberends.github.io/AMR/ #
# ==================================================================== #
#' Antibiotic Selectors
#'
#' @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 [antibiotics] data set), without the need to define the columns or antibiotic abbreviations.
#'
#' In short, if you have a column name that resembles an antimicrobial drug, it will be picked up by any of these functions that matches its pharmaceutical class: "cefazolin", "kefzol", "CZO" and "J01DB04" will all be picked up by [cephalosporins()].
#' @param ab_class an antimicrobial class or a part of it, such as `"carba"` and `"carbapenems"`. The columns `group`, `atc_group1` and `atc_group2` of the [antibiotics] data set will be searched (case-insensitive) for this value.
#' @param filter an [expression] to be evaluated in the [antibiotics] data set, such as `name %like% "trim"`
#' @param only_sir_columns a [logical] to indicate whether only columns of class `sir` must be selected (default is `FALSE`), see [as.sir()]
#' @param only_treatable a [logical] to indicate whether antimicrobial drugs should be excluded that are only for laboratory tests (default is `TRUE`), such as gentamicin-high (`GEH`) and imipenem/EDTA (`IPE`)
#' @param ... ignored, only in place to allow future extensions
#' @details
#' These functions can be used in data set calls for selecting columns and filtering rows. They work with base \R, the Tidyverse, and `data.table`. They are heavily inspired by the [Tidyverse selection helpers][tidyselect::language] such as [`everything()`][tidyselect::everything()], but are not limited to `dplyr` verbs. Nonetheless, they are very convenient to use with `dplyr` functions such as [`select()`][dplyr::select()], [`filter()`][dplyr::filter()] and [`summarise()`][dplyr::summarise()], see *Examples*.
#'
#' All columns in the data in which these functions are called will be searched for known antibiotic names, abbreviations, brand names, and codes (ATC, EARS-Net, WHO, etc.) according to the [antibiotics] data set. This means that a selector such as [aminoglycosides()] will pick up column names like 'gen', 'genta', 'J01GB03', 'tobra', 'Tobracin', etc.
#'
#' The [ab_class()] function can be used to filter/select on a manually defined antibiotic class. It searches for results in the [antibiotics] data set within the columns `group`, `atc_group1` and `atc_group2`.
#' @section Full list of supported (antibiotic) 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 antibiotic_class_selectors
#' @name antibiotic_class_selectors
#' @return (internally) a [character] vector of column names, with additional class `"ab_selector"`
#' @export
#' @inheritSection AMR Reference Data Publicly Available
#' @examples
#' # `example_isolates` is a data set available in the AMR package.
#' # See ?example_isolates.
#' example_isolates
#'
#'
#' # Examples sections below are split into 'dplyr', 'base R', and 'data.table':
#'
#' \donttest{
#' # dplyr -------------------------------------------------------------------
#'
#' if (require("dplyr")) {
#' example_isolates %>% select(carbapenems())
#' }
#'
#' if (require("dplyr")) {
#' # select columns 'mo', 'AMK', 'GEN', 'KAN' and 'TOB'
#' example_isolates %>% select(mo, aminoglycosides())
#' }
#'
#' if (require("dplyr")) {
#' # select only antibiotic columns with DDDs for oral treatment
#' example_isolates %>% select(administrable_per_os())
#' }
#'
#' if (require("dplyr")) {
#' # get AMR for all aminoglycosides e.g., per ward:
#' example_isolates %>%
#' group_by(ward) %>%
#' summarise(across(aminoglycosides(),
#' resistance))
#' }
#' if (require("dplyr")) {
#' # You can combine selectors with '&' to be more specific:
#' example_isolates %>%
#' select(penicillins() & administrable_per_os())
#' }
#' if (require("dplyr")) {
#' # get AMR for only drugs that matter - no intrinsic resistance:
#' example_isolates %>%
#' filter(mo_genus() %in% c("Escherichia", "Klebsiella")) %>%
#' group_by(ward) %>%
#' summarise_at(not_intrinsic_resistant(),
#' resistance)
#' }
#' if (require("dplyr")) {
#' # get susceptibility for antibiotics whose name contains "trim":
#' example_isolates %>%
#' filter(first_isolate()) %>%
#' group_by(ward) %>%
#' summarise(across(ab_selector(name %like% "trim"), susceptibility))
#' }
#' if (require("dplyr")) {
#' # this will select columns 'IPM' (imipenem) and 'MEM' (meropenem):
#' example_isolates %>%
#' select(carbapenems())
#' }
#' if (require("dplyr")) {
#' # this will select columns 'mo', 'AMK', 'GEN', 'KAN' and 'TOB':
#' example_isolates %>%
#' select(mo, aminoglycosides())
#' }
#' if (require("dplyr")) {
#' # any() and all() work in dplyr's filter() too:
#' example_isolates %>%
#' filter(
#' any(aminoglycosides() == "R"),
#' all(cephalosporins_2nd() == "R")
#' )
#' }
#' if (require("dplyr")) {
#' # also works with c():
#' example_isolates %>%
#' filter(any(c(carbapenems(), aminoglycosides()) == "R"))
#' }
#' if (require("dplyr")) {
#' # not setting any/all will automatically apply all():
#' example_isolates %>%
#' filter(aminoglycosides() == "R")
#' }
#' if (require("dplyr")) {
#' # this will select columns 'mo' and all antimycobacterial drugs ('RIF'):
#' example_isolates %>%
#' select(mo, ab_class("mycobact"))
#' }
#' if (require("dplyr")) {
#' # get bug/drug combinations for only glycopeptides in Gram-positives:
#' example_isolates %>%
#' filter(mo_is_gram_positive()) %>%
#' select(mo, glycopeptides()) %>%
#' bug_drug_combinations() %>%
#' format()
#' }
#' if (require("dplyr")) {
#' data.frame(
#' some_column = "some_value",
#' J01CA01 = "S"
#' ) %>% # ATC code of ampicillin
#' select(penicillins()) # only the 'J01CA01' column will be selected
#' }
#' if (require("dplyr")) {
#' # with recent versions of dplyr, this is all equal:
#' x <- example_isolates[carbapenems() == "R", ]
#' 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)
#' example_isolates[, carbapenems()]
#'
#' # select columns 'mo', 'AMK', 'GEN', 'KAN' and 'TOB'
#' example_isolates[, c("mo", aminoglycosides())]
#'
#' # select only antibiotic columns with DDDs for oral treatment
#' example_isolates[, administrable_per_os()]
#'
#' # filter using any() or all()
#' example_isolates[any(carbapenems() == "R"), ]
#' subset(example_isolates, any(carbapenems() == "R"))
#'
#' # filter on any or all results in the carbapenem columns (i.e., IPM, MEM):
#' example_isolates[any(carbapenems()), ]
#' example_isolates[all(carbapenems()), ]
#'
#' # filter with multiple antibiotic selectors using c()
#' example_isolates[all(c(carbapenems(), aminoglycosides()) == "R"), ]
#'
#' # filter + select in one go: get penicillins in carbapenem-resistant strains
#' example_isolates[any(carbapenems() == "R"), penicillins()]
#'
#' # You can combine selectors with '&' to be more specific. For example,
#' # penicillins() would select benzylpenicillin ('peni G') and
#' # administrable_per_os() would select erythromycin. Yet, when combined these
#' # drugs are both omitted since benzylpenicillin is not administrable per os
#' # and erythromycin is not a penicillin:
#' example_isolates[, penicillins() & administrable_per_os()]
#'
#' # ab_selector() applies a filter in the `antibiotics` data set and is thus
#' # very flexible. For instance, to select antibiotic columns with an oral DDD
#' # of at least 1 gram:
#' example_isolates[, ab_selector(oral_ddd > 1 & oral_units == "g")]
#'
#'
#' # data.table --------------------------------------------------------------
#'
#' # data.table is supported as well, just use it in the same way as with
#' # base R, but add `with = FALSE` if using a single AB selector.
#'
#' if (require("data.table")) {
#' dt <- as.data.table(example_isolates)
#'
#' # this does not work, it returns column *names*
#' dt[, carbapenems()]
#' }
#' if (require("data.table")) {
#' # so `with = FALSE` is required
#' dt[, carbapenems(), with = FALSE]
#' }
#'
#' # for multiple selections or AB selectors, `with = FALSE` is not needed:
#' if (require("data.table")) {
#' dt[, c("mo", aminoglycosides())]
#' }
#' if (require("data.table")) {
#' dt[, c(carbapenems(), aminoglycosides())]
#' }
#'
#' # row filters are also supported:
#' if (require("data.table")) {
#' dt[any(carbapenems() == "S"), ]
#' }
#' if (require("data.table")) {
#' dt[any(carbapenems() == "S"), penicillins(), with = FALSE]
#' }
#' }
ab_class <- function(ab_class,
only_sir_columns = FALSE,
only_treatable = TRUE,
...) {
meet_criteria(ab_class, allow_class = "character", has_length = 1, allow_NULL = TRUE)
meet_criteria(only_sir_columns, allow_class = "logical", has_length = 1)
meet_criteria(only_treatable, allow_class = "logical", has_length = 1)
ab_select_exec(NULL, only_sir_columns = only_sir_columns, ab_class_args = ab_class, only_treatable = only_treatable)
}
#' @rdname antibiotic_class_selectors
#' @details The [ab_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
ab_selector <- function(filter,
only_sir_columns = FALSE,
only_treatable = TRUE,
...) {
meet_criteria(only_sir_columns, allow_class = "logical", has_length = 1)
meet_criteria(only_treatable, allow_class = "logical", has_length = 1)
# get_current_data() has to run each time, for cases where e.g., filter() and select() are used in same call
# but it only takes a couple of milliseconds
vars_df <- get_current_data(arg_name = NA, call = -2)
# to improve speed, get_column_abx() will only run once when e.g. in a select or group call
ab_in_data <- get_column_abx(vars_df,
info = FALSE, only_sir_columns = only_sir_columns,
sort = FALSE, fn = "ab_selector"
)
call <- substitute(filter)
agents <- tryCatch(AMR_env$AB_lookup[which(eval(call, envir = AMR_env$AB_lookup)), "ab", drop = TRUE],
error = function(e) stop_(e$message, call = -5)
)
agents <- ab_in_data[ab_in_data %in% agents]
message_agent_names(
function_name = "ab_selector",
agents = agents,
ab_group = NULL,
examples = "",
call = call
)
structure(unname(agents),
class = c("ab_selector", "character")
)
}
#' @rdname antibiotic_class_selectors
#' @export
aminoglycosides <- function(only_sir_columns = FALSE, only_treatable = TRUE, ...) {
meet_criteria(only_sir_columns, allow_class = "logical", has_length = 1)
meet_criteria(only_treatable, allow_class = "logical", has_length = 1)
ab_select_exec("aminoglycosides", only_sir_columns = only_sir_columns, only_treatable = only_treatable)
}
#' @rdname antibiotic_class_selectors
#' @export
aminopenicillins <- function(only_sir_columns = FALSE, ...) {
meet_criteria(only_sir_columns, allow_class = "logical", has_length = 1)
ab_select_exec("aminopenicillins", only_sir_columns = only_sir_columns)
}
#' @rdname antibiotic_class_selectors
#' @export
antifungals <- function(only_sir_columns = FALSE, ...) {
meet_criteria(only_sir_columns, allow_class = "logical", has_length = 1)
ab_select_exec("antifungals", only_sir_columns = only_sir_columns)
}
#' @rdname antibiotic_class_selectors
#' @export
antimycobacterials <- function(only_sir_columns = FALSE, ...) {
meet_criteria(only_sir_columns, allow_class = "logical", has_length = 1)
ab_select_exec("antimycobacterials", only_sir_columns = only_sir_columns)
}
#' @rdname antibiotic_class_selectors
#' @export
betalactams <- function(only_sir_columns = FALSE, only_treatable = TRUE, ...) {
meet_criteria(only_sir_columns, allow_class = "logical", has_length = 1)
meet_criteria(only_treatable, allow_class = "logical", has_length = 1)
ab_select_exec("betalactams", only_sir_columns = only_sir_columns, only_treatable = only_treatable)
}
#' @rdname antibiotic_class_selectors
#' @export
carbapenems <- function(only_sir_columns = FALSE, only_treatable = TRUE, ...) {
meet_criteria(only_sir_columns, allow_class = "logical", has_length = 1)
meet_criteria(only_treatable, allow_class = "logical", has_length = 1)
ab_select_exec("carbapenems", only_sir_columns = only_sir_columns, only_treatable = only_treatable)
}
#' @rdname antibiotic_class_selectors
#' @export
cephalosporins <- function(only_sir_columns = FALSE, ...) {
meet_criteria(only_sir_columns, allow_class = "logical", has_length = 1)
ab_select_exec("cephalosporins", only_sir_columns = only_sir_columns)
}
#' @rdname antibiotic_class_selectors
#' @export
cephalosporins_1st <- function(only_sir_columns = FALSE, ...) {
meet_criteria(only_sir_columns, allow_class = "logical", has_length = 1)
ab_select_exec("cephalosporins_1st", only_sir_columns = only_sir_columns)
}
#' @rdname antibiotic_class_selectors
#' @export
cephalosporins_2nd <- function(only_sir_columns = FALSE, ...) {
meet_criteria(only_sir_columns, allow_class = "logical", has_length = 1)
ab_select_exec("cephalosporins_2nd", only_sir_columns = only_sir_columns)
}
#' @rdname antibiotic_class_selectors
#' @export
cephalosporins_3rd <- function(only_sir_columns = FALSE, ...) {
meet_criteria(only_sir_columns, allow_class = "logical", has_length = 1)
ab_select_exec("cephalosporins_3rd", only_sir_columns = only_sir_columns)
}
#' @rdname antibiotic_class_selectors
#' @export
cephalosporins_4th <- function(only_sir_columns = FALSE, ...) {
meet_criteria(only_sir_columns, allow_class = "logical", has_length = 1)
ab_select_exec("cephalosporins_4th", only_sir_columns = only_sir_columns)
}
#' @rdname antibiotic_class_selectors
#' @export
cephalosporins_5th <- function(only_sir_columns = FALSE, ...) {
meet_criteria(only_sir_columns, allow_class = "logical", has_length = 1)
ab_select_exec("cephalosporins_5th", only_sir_columns = only_sir_columns)
}
#' @rdname antibiotic_class_selectors
#' @export
fluoroquinolones <- function(only_sir_columns = FALSE, ...) {
meet_criteria(only_sir_columns, allow_class = "logical", has_length = 1)
ab_select_exec("fluoroquinolones", only_sir_columns = only_sir_columns)
}
#' @rdname antibiotic_class_selectors
#' @export
glycopeptides <- function(only_sir_columns = FALSE, ...) {
meet_criteria(only_sir_columns, allow_class = "logical", has_length = 1)
ab_select_exec("glycopeptides", only_sir_columns = only_sir_columns)
}
#' @rdname antibiotic_class_selectors
#' @export
lincosamides <- function(only_sir_columns = FALSE, only_treatable = TRUE, ...) {
meet_criteria(only_sir_columns, allow_class = "logical", has_length = 1)
meet_criteria(only_treatable, allow_class = "logical", has_length = 1)
ab_select_exec("lincosamides", only_sir_columns = only_sir_columns, only_treatable = only_treatable)
}
#' @rdname antibiotic_class_selectors
#' @export
lipoglycopeptides <- function(only_sir_columns = FALSE, ...) {
meet_criteria(only_sir_columns, allow_class = "logical", has_length = 1)
ab_select_exec("lipoglycopeptides", only_sir_columns = only_sir_columns)
}
#' @rdname antibiotic_class_selectors
#' @export
macrolides <- function(only_sir_columns = FALSE, ...) {
meet_criteria(only_sir_columns, allow_class = "logical", has_length = 1)
ab_select_exec("macrolides", only_sir_columns = only_sir_columns)
}
#' @rdname antibiotic_class_selectors
#' @export
nitrofurans <- function(only_sir_columns = FALSE, ...) {
meet_criteria(only_sir_columns, allow_class = "logical", has_length = 1)
ab_select_exec("nitrofurans", only_sir_columns = only_sir_columns)
}
#' @rdname antibiotic_class_selectors
#' @export
oxazolidinones <- function(only_sir_columns = FALSE, ...) {
meet_criteria(only_sir_columns, allow_class = "logical", has_length = 1)
ab_select_exec("oxazolidinones", only_sir_columns = only_sir_columns)
}
#' @rdname antibiotic_class_selectors
#' @export
penicillins <- function(only_sir_columns = FALSE, ...) {
meet_criteria(only_sir_columns, allow_class = "logical", has_length = 1)
ab_select_exec("penicillins", only_sir_columns = only_sir_columns)
}
#' @rdname antibiotic_class_selectors
#' @export
polymyxins <- function(only_sir_columns = FALSE, only_treatable = TRUE, ...) {
meet_criteria(only_sir_columns, allow_class = "logical", has_length = 1)
meet_criteria(only_treatable, allow_class = "logical", has_length = 1)
ab_select_exec("polymyxins", only_sir_columns = only_sir_columns, only_treatable = only_treatable)
}
#' @rdname antibiotic_class_selectors
#' @export
quinolones <- function(only_sir_columns = FALSE, ...) {
meet_criteria(only_sir_columns, allow_class = "logical", has_length = 1)
ab_select_exec("quinolones", only_sir_columns = only_sir_columns)
}
#' @rdname antibiotic_class_selectors
#' @export
rifamycins <- function(only_sir_columns = FALSE, ...) {
meet_criteria(only_sir_columns, allow_class = "logical", has_length = 1)
ab_select_exec("rifamycins", only_sir_columns = only_sir_columns)
}
#' @rdname antibiotic_class_selectors
#' @export
streptogramins <- function(only_sir_columns = FALSE, ...) {
meet_criteria(only_sir_columns, allow_class = "logical", has_length = 1)
ab_select_exec("streptogramins", only_sir_columns = only_sir_columns)
}
#' @rdname antibiotic_class_selectors
#' @export
tetracyclines <- function(only_sir_columns = FALSE, ...) {
meet_criteria(only_sir_columns, allow_class = "logical", has_length = 1)
ab_select_exec("tetracyclines", only_sir_columns = only_sir_columns)
}
#' @rdname antibiotic_class_selectors
#' @export
trimethoprims <- function(only_sir_columns = FALSE, ...) {
meet_criteria(only_sir_columns, allow_class = "logical", has_length = 1)
ab_select_exec("trimethoprims", only_sir_columns = only_sir_columns)
}
#' @rdname antibiotic_class_selectors
#' @export
ureidopenicillins <- function(only_sir_columns = FALSE, ...) {
meet_criteria(only_sir_columns, allow_class = "logical", has_length = 1)
ab_select_exec("ureidopenicillins", only_sir_columns = only_sir_columns)
}
#' @rdname antibiotic_class_selectors
#' @details The [administrable_per_os()] and [administrable_iv()] functions also rely on the [antibiotics] data set - antibiotic columns 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, ...) {
meet_criteria(only_sir_columns, allow_class = "logical", has_length = 1)
# get_current_data() has to run each time, for cases where e.g., filter() and select() are used in same call
# but it only takes a couple of milliseconds
vars_df <- get_current_data(arg_name = NA, call = -2)
# to improve speed, get_column_abx() will only run once when e.g. in a select or group call
ab_in_data <- get_column_abx(vars_df,
info = FALSE, only_sir_columns = only_sir_columns,
sort = FALSE, fn = "administrable_per_os"
)
agents_all <- AMR_env$AB_lookup[which(!is.na(AMR_env$AB_lookup$oral_ddd)), "ab", drop = TRUE]
agents <- AMR_env$AB_lookup[which(AMR_env$AB_lookup$ab %in% ab_in_data & !is.na(AMR_env$AB_lookup$oral_ddd)), "ab", drop = TRUE]
agents <- ab_in_data[ab_in_data %in% agents]
message_agent_names(
function_name = "administrable_per_os",
agents = agents,
ab_group = "administrable_per_os",
examples = paste0(
" (such as ",
vector_or(
ab_name(
sample(agents_all,
size = min(5, length(agents_all)),
replace = FALSE
),
tolower = TRUE,
language = NULL
),
quotes = FALSE
),
")"
)
)
structure(unname(agents),
class = c("ab_selector", "character")
)
}
#' @rdname antibiotic_class_selectors
#' @export
administrable_iv <- function(only_sir_columns = FALSE, ...) {
meet_criteria(only_sir_columns, allow_class = "logical", has_length = 1)
# get_current_data() has to run each time, for cases where e.g., filter() and select() are used in same call
# but it only takes a couple of milliseconds
vars_df <- get_current_data(arg_name = NA, call = -2)
# to improve speed, get_column_abx() will only run once when e.g. in a select or group call
ab_in_data <- get_column_abx(vars_df,
info = FALSE, only_sir_columns = only_sir_columns,
sort = FALSE, fn = "administrable_iv"
)
agents_all <- AMR_env$AB_lookup[which(!is.na(AMR_env$AB_lookup$iv_ddd)), "ab", drop = TRUE]
agents <- AMR_env$AB_lookup[which(AMR_env$AB_lookup$ab %in% ab_in_data & !is.na(AMR_env$AB_lookup$iv_ddd)), "ab", drop = TRUE]
agents <- ab_in_data[ab_in_data %in% agents]
message_agent_names(
function_name = "administrable_iv",
agents = agents,
ab_group = "administrable_iv",
examples = ""
)
structure(unname(agents),
class = c("ab_selector", "character")
)
}
#' @rdname antibiotic_class_selectors
#' @inheritParams eucast_rules
#' @details The [not_intrinsic_resistant()] function can be used to only select antibiotic columns that pose no intrinsic resistance for the microorganisms in the data set. For example, if a data set contains only microorganism codes or names of *E. coli* and *K. pneumoniae* and contains a column "vancomycin", this column will be removed (or rather, unselected) using this function. It currently applies `r format_eucast_version_nr(names(EUCAST_VERSION_EXPERT_RULES[1]))` to determine intrinsic resistance, using the [eucast_rules()] function internally. Because of this determination, this function is quite slow in terms of performance.
#' @export
not_intrinsic_resistant <- function(only_sir_columns = FALSE, col_mo = NULL, version_expertrules = 3.3, ...) {
meet_criteria(only_sir_columns, allow_class = "logical", has_length = 1)
# get_current_data() has to run each time, for cases where e.g., filter() and select() are used in same call
# but it only takes a couple of milliseconds
vars_df <- get_current_data(arg_name = NA, call = -2)
# to improve speed, get_column_abx() will only run once when e.g. in a select or group call
ab_in_data <- get_column_abx(vars_df,
info = FALSE, only_sir_columns = only_sir_columns,
sort = FALSE, fn = "not_intrinsic_resistant"
)
# intrinsic vars
vars_df_R <- tryCatch(
sapply(
eucast_rules(vars_df,
col_mo = col_mo,
version_expertrules = version_expertrules,
rules = "expert",
info = FALSE
),
function(col) {
tryCatch(!any(is.na(col)) && all(col == "R"),
error = function(e) FALSE
)
}
),
error = function(e) stop_("in not_intrinsic_resistant(): ", e$message, call = FALSE)
)
agents <- ab_in_data[ab_in_data %in% names(vars_df_R[which(vars_df_R)])]
if (length(agents) > 0 &&
message_not_thrown_before("not_intrinsic_resistant", sort(agents))) {
agents_formatted <- paste0("'", font_bold(agents, collapse = NULL), "'")
agents_names <- ab_name(names(agents), tolower = TRUE, language = NULL)
need_name <- generalise_antibiotic_name(agents) != generalise_antibiotic_name(agents_names)
agents_formatted[need_name] <- paste0(agents_formatted[need_name], " (", agents_names[need_name], ")")
message_(
"For `not_intrinsic_resistant()` removing ",
ifelse(length(agents) == 1, "column ", "columns "),
vector_and(agents_formatted, quotes = FALSE, sort = FALSE)
)
}
vars_df_R <- names(vars_df_R)[which(!vars_df_R)]
# find columns that are abx, but also intrinsic R
out <- unname(intersect(ab_in_data, vars_df_R))
structure(out,
class = c("ab_selector", "character")
)
}
ab_select_exec <- function(function_name,
only_sir_columns = FALSE,
only_treatable = FALSE,
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
# but it only takes a couple of milliseconds
vars_df <- get_current_data(arg_name = NA, call = -3)
# to improve speed, get_column_abx() will only run once when e.g. in a select or group call
ab_in_data <- get_column_abx(vars_df,
info = FALSE, only_sir_columns = only_sir_columns,
sort = FALSE, fn = function_name
)
# untreatable drugs
if (only_treatable == TRUE) {
untreatable <- AMR_env$AB_lookup[which(AMR_env$AB_lookup$name %like% "-high|EDTA|polysorbate|macromethod|screening|nacubactam"), "ab", drop = TRUE]
if (any(untreatable %in% names(ab_in_data))) {
if (message_not_thrown_before(function_name, "ab_class", "untreatable")) {
warning_(
"in `", function_name, "()`: some drugs were ignored since they cannot be used for treating patients: ",
vector_and(
ab_name(names(ab_in_data)[names(ab_in_data) %in% untreatable],
language = NULL,
tolower = TRUE
),
quotes = FALSE,
sort = TRUE
), ". They can be included using `", function_name, "(only_treatable = FALSE)`."
)
}
ab_in_data <- ab_in_data[!names(ab_in_data) %in% untreatable]
}
}
if (length(ab_in_data) == 0) {
message_("No antimicrobial drugs found in the data.")
return(NULL)
}
if (is.null(ab_class_args) || isTRUE(function_name %in% c("antifungals", "antimycobacterials"))) {
ab_group <- NULL
if (isTRUE(function_name == "antifungals")) {
abx <- AMR_env$AB_lookup$ab[which(AMR_env$AB_lookup$group == "Antifungals")]
} else if (isTRUE(function_name == "antimycobacterials")) {
abx <- AMR_env$AB_lookup$ab[which(AMR_env$AB_lookup$group == "Antimycobacterials")]
} else {
# their upper case equivalent are vectors with class 'ab', created in data-raw/_pre_commit_checks.R
# carbapenems() gets its codes from AMR:::AB_CARBAPENEMS
abx <- get(paste0("AB_", toupper(function_name)), envir = asNamespace("AMR"))
# manually added codes from add_custom_antimicrobials() must also be supported
if (length(AMR_env$custom_ab_codes) > 0) {
custom_ab <- AMR_env$AB_lookup[which(AMR_env$AB_lookup$ab %in% AMR_env$custom_ab_codes), ]
check_string <- paste0(custom_ab$group, custom_ab$atc_group1, custom_ab$atc_group2)
if (function_name == "betalactams") {
find_group <- "beta-lactams"
} else if (function_name %like% "cephalosporins_") {
find_group <- gsub("_(.*)$", paste0(" (\\1 gen.)"), function_name)
} else {
find_group <- function_name
}
abx <- c(abx, custom_ab$ab[which(check_string %like% find_group)])
}
ab_group <- function_name
}
examples <- paste0(" (such as ", vector_or(
ab_name(sample(abx, size = min(2, length(abx)), replace = FALSE),
tolower = TRUE,
language = NULL
),
quotes = FALSE
), ")")
} else {
# this for the 'manual' ab_class() function
abx <- subset(
AMR_env$AB_lookup,
group %like% ab_class_args |
atc_group1 %like% ab_class_args |
atc_group2 %like% ab_class_args
)$ab
ab_group <- find_ab_group(ab_class_args)
function_name <- "ab_class"
examples <- paste0(" (such as ", find_ab_names(ab_class_args, 2), ")")
}
# get the columns with a group names in the chosen ab class
agents <- ab_in_data[names(ab_in_data) %in% abx]
message_agent_names(
function_name = function_name,
agents = agents,
ab_group = ab_group,
examples = examples,
ab_class_args = ab_class_args
)
structure(unname(agents),
class = c("ab_selector", "character")
)
}
#' @method print ab_selector
#' @export
#' @noRd
print.ab_selector <- function(x, ...) {
warning_("It should never be needed to print an antibiotic selector class. Are you using data.table? Then add the argument `with = FALSE`, see our examples at `?ab_selector`.",
immediate = TRUE)
cat("Class 'ab_selector'\n")
print(as.character(x), quote = FALSE)
}
#' @method c ab_selector
#' @export
#' @noRd
c.ab_selector <- function(...) {
structure(unlist(lapply(list(...), as.character)),
class = c("ab_selector", "character")
)
}
all_any_ab_selector <- function(type, ..., na.rm = TRUE) {
cols_ab <- c(...)
result <- cols_ab[toupper(cols_ab) %in% c("S", "SDD", "I", "R", "NI")]
if (length(result) == 0) {
message_("Filtering ", type, " of columns ", vector_and(font_bold(cols_ab, collapse = NULL), quotes = "'"), ' to contain value "S", "I" or "R"')
result <- c("S", "SDD", "I", "R", "NI")
}
cols_ab <- cols_ab[!cols_ab %in% result]
df <- get_current_data(arg_name = NA, call = -3)
if (type == "all") {
scope_fn <- all
} else {
scope_fn <- any
}
x_transposed <- as.list(as.data.frame(t(df[, cols_ab, drop = FALSE]), stringsAsFactors = FALSE))
vapply(
FUN.VALUE = logical(1),
X = x_transposed,
FUN = function(y) scope_fn(y %in% result, na.rm = na.rm),
USE.NAMES = FALSE
)
}
#' @method all ab_selector
#' @export
#' @noRd
all.ab_selector <- function(..., na.rm = FALSE) {
all_any_ab_selector("all", ..., na.rm = na.rm)
}
#' @method any ab_selector
#' @export
#' @noRd
any.ab_selector <- function(..., na.rm = FALSE) {
all_any_ab_selector("any", ..., na.rm = na.rm)
}
#' @method all ab_selector_any_all
#' @export
#' @noRd
all.ab_selector_any_all <- function(..., na.rm = FALSE) {
# this is all() on a logical vector from `==.ab_selector` or `!=.ab_selector`
# e.g., example_isolates %>% filter(all(carbapenems() == "R"))
# so just return the vector as is, only correcting for na.rm
out <- unclass(c(...))
if (isTRUE(na.rm)) {
out <- out[!is.na(out)]
}
out
}
#' @method any ab_selector_any_all
#' @export
#' @noRd
any.ab_selector_any_all <- function(..., na.rm = FALSE) {
# this is any() on a logical vector from `==.ab_selector` or `!=.ab_selector`
# e.g., example_isolates %>% filter(any(carbapenems() == "R"))
# so just return the vector as is, only correcting for na.rm
out <- unclass(c(...))
if (isTRUE(na.rm)) {
out <- out[!is.na(out)]
}
out
}
#' @method == ab_selector
#' @export
#' @noRd
`==.ab_selector` <- function(e1, e2) {
calls <- as.character(match.call())
fn_name <- calls[2]
fn_name <- gsub("^(c\\()(.*)(\\))$", "\\2", fn_name)
if (is_any(fn_name)) {
type <- "any"
} else if (is_all(fn_name)) {
type <- "all"
} else {
type <- "all"
if (length(e1) > 1) {
message_(
"Assuming a filter on ", type, " ", length(e1), " ", gsub("[\\(\\)]", "", fn_name),
". Wrap around `all()` or `any()` to prevent this note."
)
}
}
structure(all_any_ab_selector(type = type, e1, e2),
class = c("ab_selector_any_all", "logical")
)
}
#' @method != ab_selector
#' @export
#' @noRd
`!=.ab_selector` <- function(e1, e2) {
calls <- as.character(match.call())
fn_name <- calls[2]
fn_name <- gsub("^(c\\()(.*)(\\))$", "\\2", fn_name)
if (is_any(fn_name)) {
type <- "any"
} else if (is_all(fn_name)) {
type <- "all"
} else {
type <- "all"
if (length(e1) > 1) {
message_(
"Assuming a filter on ", type, " ", length(e1), " ", gsub("[\\(\\)]", "", fn_name),
". Wrap around `all()` or `any()` to prevent this note."
)
}
}
# this is `!=`, so turn around the values
sir <- c("S", "SDD", "I", "R", "NI")
e2 <- sir[sir != e2]
structure(all_any_ab_selector(type = type, e1, e2),
class = c("ab_selector_any_all", "logical")
)
}
#' @method & ab_selector
#' @export
#' @noRd
`&.ab_selector` <- function(e1, e2) {
# this is only required for base R, since tidyselect has already implemented this
# e.g., for: example_isolates[, penicillins() & administrable_per_os()]
structure(intersect(unclass(e1), unclass(e2)),
class = c("ab_selector", "character")
)
}
#' @method | ab_selector
#' @export
#' @noRd
`|.ab_selector` <- function(e1, e2) {
# this is only required for base R, since tidyselect has already implemented this
# e.g., for: example_isolates[, penicillins() | administrable_per_os()]
structure(union(unclass(e1), unclass(e2)),
class = c("ab_selector", "character")
)
}
is_any <- function(el1) {
syscalls <- paste0(trimws2(deparse(sys.calls())), collapse = " ")
el1 <- gsub("(.*),.*", "\\1", el1)
syscalls %like% paste0("[^_a-zA-Z0-9]any\\(", "(c\\()?", el1)
}
is_all <- function(el1) {
syscalls <- paste0(trimws2(deparse(sys.calls())), collapse = " ")
el1 <- gsub("(.*),.*", "\\1", el1)
syscalls %like% paste0("[^_a-zA-Z0-9]all\\(", "(c\\()?", el1)
}
find_ab_group <- function(ab_class_args) {
ab_class_args <- gsub("[^a-zA-Z0-9]", ".*", ab_class_args)
AMR_env$AB_lookup %pm>%
subset(group %like% ab_class_args |
atc_group1 %like% ab_class_args |
atc_group2 %like% ab_class_args) %pm>%
pm_pull(group) %pm>%
unique() %pm>%
tolower() %pm>%
sort() %pm>%
paste(collapse = "/")
}
find_ab_names <- function(ab_group, n = 3) {
ab_group <- gsub("[^a-zA-Z|0-9]", ".*", ab_group)
# try popular first, they have DDDs
drugs <- AMR_env$AB_lookup[which((!is.na(AMR_env$AB_lookup$iv_ddd) | !is.na(AMR_env$AB_lookup$oral_ddd)) &
AMR_env$AB_lookup$name %unlike% " " &
AMR_env$AB_lookup$group %like% ab_group &
AMR_env$AB_lookup$ab %unlike% "[0-9]$"), ]$name
if (length(drugs) < n) {
# now try it all
drugs <- AMR_env$AB_lookup[which((AMR_env$AB_lookup$group %like% ab_group |
AMR_env$AB_lookup$atc_group1 %like% ab_group |
AMR_env$AB_lookup$atc_group2 %like% ab_group) &
AMR_env$AB_lookup$ab %unlike% "[0-9]$"), ]$name
}
if (length(drugs) == 0) {
return("??")
}
vector_or(
ab_name(sample(drugs, size = min(n, length(drugs)), replace = FALSE),
tolower = TRUE,
language = NULL
),
quotes = FALSE
)
}
message_agent_names <- function(function_name, agents, ab_group = NULL, examples = "", ab_class_args = NULL, call = NULL) {
if (message_not_thrown_before(function_name, sort(agents))) {
if (length(agents) == 0) {
if (is.null(ab_group)) {
message_("For `", function_name, "()` no antimicrobial drugs found", examples, ".")
} else if (ab_group == "administrable_per_os") {
message_("No orally administrable drugs found", examples, ".")
} else if (ab_group == "administrable_iv") {
message_("No IV administrable drugs found", examples, ".")
} else {
message_("No antimicrobial drugs of class '", ab_group, "' found", examples, ".")
}
} else {
agents_formatted <- paste0("'", font_bold(agents, collapse = NULL), "'")
agents_names <- ab_name(names(agents), tolower = TRUE, language = NULL)
need_name <- generalise_antibiotic_name(agents) != generalise_antibiotic_name(agents_names)
agents_formatted[need_name] <- paste0(agents_formatted[need_name], " (", agents_names[need_name], ")")
message_(
"For `", function_name, "(",
ifelse(function_name == "ab_class",
paste0("\"", ab_class_args, "\""),
ifelse(!is.null(call),
paste0(deparse(call), collapse = " "),
""
)
),
")` using ",
ifelse(length(agents) == 1, "column ", "columns "),
vector_and(agents_formatted, quotes = FALSE, sort = FALSE)
)
}
}
}
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