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(v2.1.1.9163) cleanup

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dr. M.S. (Matthijs) Berends
2025-02-27 14:04:29 +01:00
parent 68efddab3d
commit 07efc292bc
73 changed files with 2187 additions and 1715 deletions
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266
R/amr_selectors.R
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@@ -32,7 +32,7 @@
#' @description These functions allow for filtering rows and selecting columns based on antimicrobial test results that are of a specific antimicrobial class or group, without the need to define the columns or antimicrobial 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 using:
#'
#'
#' ```r
#' library(dplyr)
#' my_data_with_all_these_columns %>%
@@ -46,7 +46,7 @@
#' @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 selectors can also be used in `tidymodels` packages such as `recipe` and `parsnip`. See for more info [our tutorial](https://msberends.github.io/AMR/articles/AMR_with_tidymodels.html) on using antimicrobial selectors for predictive modelling.
#'
#' All columns in the data in which these functions are called will be searched for known antimicrobial 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.
@@ -72,88 +72,88 @@
#'
#' # 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)
#'
#'
#' example_isolates %>% select(carbapenems())
#'
#'
#' # select columns 'mo', 'AMK', 'GEN', 'KAN' and 'TOB'
#' example_isolates %>% select(mo, aminoglycosides())
#'
#'
#' # you can combine selectors like you are used with tidyverse
#' # e.g., for betalactams, but not the ones with an enzyme inhibitor:
#' example_isolates %>% select(betalactams(), -betalactams_with_inhibitor())
#'
#'
#' # select only antimicrobials with DDDs for oral treatment
#' example_isolates %>% select(administrable_per_os())
#'
#'
#' # get AMR for all aminoglycosides e.g., per ward:
#' example_isolates %>%
#' group_by(ward) %>%
#' summarise(across(aminoglycosides(),
#' resistance))
#'
#'
#' # You can combine selectors with '&' to be more specific:
#' example_isolates %>%
#' select(penicillins() & administrable_per_os())
#'
#'
#' # 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)
#'
#'
#' # get susceptibility for antimicrobials whose name contains "trim":
#' example_isolates %>%
#' filter(first_isolate()) %>%
#' group_by(ward) %>%
#' summarise(across(amr_selector(name %like% "trim"), susceptibility))
#'
#'
#' # this will select columns 'IPM' (imipenem) and 'MEM' (meropenem):
#' example_isolates %>%
#' select(carbapenems())
#'
#'
#' # this will select columns 'mo', 'AMK', 'GEN', 'KAN' and 'TOB':
#' example_isolates %>%
#' select(mo, aminoglycosides())
#'
#'
#' # any() and all() work in dplyr's filter() too:
#' example_isolates %>%
#' filter(
#' any(aminoglycosides() == "R"),
#' all(cephalosporins_2nd() == "R")
#' )
#'
#'
#' # also works with c():
#' example_isolates %>%
#' filter(any(c(carbapenems(), aminoglycosides()) == "R"))
#'
#'
#' # not setting any/all will automatically apply all():
#' example_isolates %>%
#' filter(aminoglycosides() == "R")
#'
#'
#' # this will select columns 'mo' and all antimycobacterial drugs ('RIF'):
#' example_isolates %>%
#' select(mo, amr_class("mycobact"))
#'
#'
#' # get bug/drug combinations for only glycopeptides in Gram-positives:
#' example_isolates %>%
#' filter(mo_is_gram_positive()) %>%
#' select(mo, glycopeptides()) %>%
#' bug_drug_combinations() %>%
#' format()
#'
#'
#' data.frame(
#' some_column = "some_value",
#' J01CA01 = "S"
#' ) %>% # ATC code of ampicillin
#' select(penicillins()) # only the 'J01CA01' column will be selected
#'
#'
#' # with recent versions of dplyr, this is all equal:
#' x <- example_isolates[carbapenems() == "R", ]
#' y <- example_isolates %>% filter(carbapenems() == "R")
@@ -231,57 +231,6 @@
#' dt[any(carbapenems() == "S"), penicillins(), with = FALSE]
#' }
#' }
amr_class <- function(amr_class,
only_sir_columns = FALSE,
only_treatable = TRUE,
return_all = TRUE,
...) {
meet_criteria(amr_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)
meet_criteria(return_all, allow_class = "logical", has_length = 1)
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_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,
only_sir_columns = FALSE,
only_treatable = TRUE,
return_all = TRUE,
...) {
meet_criteria(only_sir_columns, allow_class = "logical", has_length = 1)
meet_criteria(only_treatable, allow_class = "logical", has_length = 1)
meet_criteria(return_all, 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 = "amr_selector", return_all = return_all
)
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 = "amr_selector",
agents = agents,
ab_group = NULL,
examples = "",
call = call
)
structure(unname(agents),
class = c("amr_selector", "character")
)
}
#' @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)
meet_criteria(only_treatable, allow_class = "logical", has_length = 1)
@@ -536,6 +485,57 @@ ureidopenicillins <- function(only_sir_columns = FALSE, return_all = TRUE, ...)
#' @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
amr_class <- function(amr_class,
only_sir_columns = FALSE,
only_treatable = TRUE,
return_all = TRUE,
...) {
meet_criteria(amr_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)
meet_criteria(return_all, allow_class = "logical", has_length = 1)
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_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,
only_sir_columns = FALSE,
only_treatable = TRUE,
return_all = TRUE,
...) {
meet_criteria(only_sir_columns, allow_class = "logical", has_length = 1)
meet_criteria(only_treatable, allow_class = "logical", has_length = 1)
meet_criteria(return_all, 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 = "amr_selector", return_all = return_all
)
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 = "amr_selector",
agents = agents,
ab_group = NULL,
examples = "",
call = call
)
structure(unname(agents),
class = c("amr_selector", "character")
)
}
#' @rdname antimicrobial_selectors
#' @export
administrable_per_os <- function(only_sir_columns = FALSE, return_all = TRUE, ...) {
meet_criteria(only_sir_columns, allow_class = "logical", has_length = 1)
meet_criteria(return_all, allow_class = "logical", has_length = 1)
@@ -544,8 +544,8 @@ administrable_per_os <- function(only_sir_columns = FALSE, return_all = TRUE, ..
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", return_all = return_all
info = FALSE, only_sir_columns = only_sir_columns,
sort = FALSE, fn = "administrable_per_os", return_all = return_all
)
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]
@@ -559,8 +559,8 @@ administrable_per_os <- function(only_sir_columns = FALSE, return_all = TRUE, ..
vector_or(
ab_name(
sample(agents_all,
size = min(5, length(agents_all)),
replace = FALSE
size = min(5, length(agents_all)),
replace = FALSE
),
tolower = TRUE,
language = NULL
@@ -571,7 +571,7 @@ administrable_per_os <- function(only_sir_columns = FALSE, return_all = TRUE, ..
)
)
structure(unname(agents),
class = c("amr_selector", "character")
class = c("amr_selector", "character")
)
}
@@ -585,8 +585,8 @@ administrable_iv <- function(only_sir_columns = FALSE, return_all = TRUE, ...) {
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", return_all = return_all
info = FALSE, only_sir_columns = only_sir_columns,
sort = FALSE, fn = "administrable_iv", return_all = return_all
)
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]
@@ -598,7 +598,7 @@ administrable_iv <- function(only_sir_columns = FALSE, return_all = TRUE, ...) {
examples = ""
)
structure(unname(agents),
class = c("amr_selector", "character")
class = c("amr_selector", "character")
)
}
@@ -613,30 +613,30 @@ not_intrinsic_resistant <- function(only_sir_columns = FALSE, col_mo = NULL, ver
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", return_all = TRUE
info = FALSE, only_sir_columns = only_sir_columns,
sort = FALSE, fn = "not_intrinsic_resistant", return_all = TRUE
)
# intrinsic vars
vars_df_R <- tryCatch(
sapply(
eucast_rules(vars_df,
col_mo = col_mo,
version_expertrules = version_expertrules,
rules = "expert",
info = FALSE
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) FALSE
)
}
),
error = function(e) stop_("in not_intrinsic_resistant(): ", e$message, call = FALSE)
)
agents <- ab_in_data[ab_in_data %in% names(vars_df_R[which(vars_df_R)])]
if (length(agents) > 0 &&
message_not_thrown_before("not_intrinsic_resistant", sort(agents))) {
message_not_thrown_before("not_intrinsic_resistant", sort(agents))) {
agents_formatted <- paste0("'", font_bold(agents, collapse = NULL), "'")
agents_names <- ab_name(names(agents), tolower = TRUE, language = NULL)
need_name <- generalise_antibiotic_name(agents) != generalise_antibiotic_name(agents_names)
@@ -647,12 +647,12 @@ not_intrinsic_resistant <- function(only_sir_columns = FALSE, col_mo = NULL, ver
vector_and(agents_formatted, quotes = FALSE, sort = FALSE)
)
}
vars_df_R <- names(vars_df_R)[which(!vars_df_R)]
# find columns that are abx, but also intrinsic R
out <- unname(intersect(ab_in_data, vars_df_R))
structure(out,
class = c("amr_selector", "character")
class = c("amr_selector", "character")
)
}
@@ -667,13 +667,14 @@ amr_select_exec <- function(function_name,
# to improve speed, get_column_abx() will only run once when e.g. in a select or group call
if (!is.null(vars_df)) {
ab_in_data <- get_column_abx(vars_df,
info = FALSE,
only_sir_columns = only_sir_columns,
sort = FALSE,
fn = function_name,
return_all = return_all)
info = FALSE,
only_sir_columns = only_sir_columns,
sort = FALSE,
fn = function_name,
return_all = return_all
)
}
# untreatable drugs
if (!is.null(vars_df) && only_treatable == TRUE) {
untreatable <- AMR_env$AB_lookup[which(AMR_env$AB_lookup$name %like% "(-high|EDTA|polysorbate|macromethod|screening|nacubactam)"), "ab", drop = TRUE]
@@ -683,8 +684,8 @@ amr_select_exec <- function(function_name,
"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
language = NULL,
tolower = TRUE
),
quotes = FALSE,
sort = TRUE
@@ -694,12 +695,12 @@ amr_select_exec <- function(function_name,
ab_in_data <- ab_in_data[!names(ab_in_data) %in% untreatable]
}
}
if (!is.null(vars_df) && length(ab_in_data) == 0) {
message_("No antimicrobial drugs found in the data.")
return(NULL)
}
if (is.null(amr_class_args) || isTRUE(function_name %in% c("antifungals", "antimycobacterials"))) {
ab_group <- NULL
if (isTRUE(function_name == "antifungals")) {
@@ -727,8 +728,8 @@ amr_select_exec <- function(function_name,
}
examples <- paste0(" (such as ", vector_or(
ab_name(sample(abx, size = min(2, length(abx)), replace = FALSE),
tolower = TRUE,
language = NULL
tolower = TRUE,
language = NULL
),
quotes = FALSE
), ")")
@@ -744,16 +745,16 @@ amr_select_exec <- function(function_name,
function_name <- "amr_class"
examples <- paste0(" (such as ", find_ab_names(amr_class_args, 2), ")")
}
if (is.null(vars_df)) {
# no data found, no antimicrobials, so no input. Happens if users run e.g. `aminoglycosides()` as a separate command.
# print.ab will cover the additional printing text
return(structure(sort(abx), amr_selector = function_name))
}
# 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,
@@ -761,9 +762,9 @@ amr_select_exec <- function(function_name,
examples = examples,
amr_class_args = amr_class_args
)
structure(unname(agents),
class = c("amr_selector", "character")
class = c("amr_selector", "character")
)
}
@@ -772,7 +773,8 @@ amr_select_exec <- function(function_name,
#' @noRd
print.amr_selector <- function(x, ...) {
warning_("It should never be needed to print an antimicrobial selector class. Are you using data.table? Then add the argument `with = FALSE`, see our examples at `?amr_selector`.",
immediate = TRUE)
immediate = TRUE
)
cat("Class 'amr_selector'\n")
print(as.character(x), quote = FALSE)
}
@@ -782,7 +784,7 @@ print.amr_selector <- function(x, ...) {
#' @noRd
c.amr_selector <- function(...) {
structure(unlist(lapply(list(...), as.character)),
class = c("amr_selector", "character")
class = c("amr_selector", "character")
)
}
@@ -795,13 +797,13 @@ all_any_amr_selector <- function(type, ..., na.rm = TRUE) {
}
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),
@@ -875,7 +877,7 @@ any.amr_selector_any_all <- function(..., na.rm = FALSE) {
}
}
structure(all_any_amr_selector(type = type, e1, e2),
class = c("amr_selector_any_all", "logical")
class = c("amr_selector_any_all", "logical")
)
}
@@ -903,7 +905,7 @@ any.amr_selector_any_all <- function(..., na.rm = FALSE) {
sir <- c("S", "SDD", "I", "R", "NI")
e2 <- sir[sir != e2]
structure(all_any_amr_selector(type = type, e1, e2),
class = c("amr_selector_any_all", "logical")
class = c("amr_selector_any_all", "logical")
)
}
@@ -914,7 +916,7 @@ any.amr_selector_any_all <- function(..., na.rm = FALSE) {
# 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("amr_selector", "character")
class = c("amr_selector", "character")
)
}
#' @method | amr_selector
@@ -924,7 +926,7 @@ any.amr_selector_any_all <- function(..., na.rm = FALSE) {
# 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("amr_selector", "character")
class = c("amr_selector", "character")
)
}
@@ -943,8 +945,8 @@ find_ab_group <- function(amr_class_args) {
amr_class_args <- gsub("[^a-zA-Z0-9]", ".*", amr_class_args)
AMR_env$AB_lookup %pm>%
subset(group %like% amr_class_args |
atc_group1 %like% amr_class_args |
atc_group2 %like% amr_class_args) %pm>%
atc_group1 %like% amr_class_args |
atc_group2 %like% amr_class_args) %pm>%
pm_pull(group) %pm>%
unique() %pm>%
tolower() %pm>%
@@ -954,26 +956,26 @@ find_ab_group <- function(amr_class_args) {
find_ab_names <- function(ab_group, n = 3) {
ab_group <- gsub("[^a-zA-Z|0-9]", ".*", ab_group)
# try popular first, they have DDDs
drugs <- AMR_env$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
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$ab %unlike% "[0-9]$"), ]$name
AMR_env$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
tolower = TRUE,
language = NULL
),
quotes = FALSE
)
@@ -999,11 +1001,11 @@ message_agent_names <- function(function_name, agents, ab_group = NULL, examples
message_(
"For `", function_name, "(",
ifelse(function_name == "amr_class",
paste0("\"", amr_class_args, "\""),
ifelse(!is.null(call),
paste0(deparse(call), collapse = " "),
""
)
paste0("\"", amr_class_args, "\""),
ifelse(!is.null(call),
paste0(deparse(call), collapse = " "),
""
)
),
")` using ",
ifelse(length(agents) == 1, "column ", "columns "),