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# ==================================================================== #
# TITLE #
# Antimicrobial Resistance (AMR) Analysis #
# #
# SOURCE #
# https://gitlab.com/msberends/AMR #
# #
# LICENCE #
# (c) 2018-2020 Berends MS, Luz CF et al. #
# #
# 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 more info: https://msberends.gitlab.io/AMR. #
# ==================================================================== #
#' Transform to antibiotic ID
#'
#' Use this function to determine the antibiotic code of one or more antibiotics. The data set [antibiotics] will be searched for abbreviations, official names and synonyms (brand names).
#' @inheritSection lifecycle Maturing lifecycle
#' @param x character vector to determine to antibiotic ID
#' @param ... arguments passed on to internal functions
#' @rdname as.ab
#' @inheritSection WHOCC WHOCC
#' @details All entries in the [antibiotics] data set have three different identifiers: a human readable EARS-Net code (column `ab`, used by ECDC and WHONET), an ATC code (column `atc`, used by WHO), and a CID code (column `cid`, Compound ID, used by PubChem). The data set contains more than 5,000 official brand names from many different countries, as found in PubChem.
#'
#' Use the [ab_property()] functions to get properties based on the returned antibiotic ID, see Examples.
#' @section Source:
#' World Health Organization (WHO) Collaborating Centre for Drug Statistics Methodology: \url{https://www.whocc.no/atc_ddd_index/}
#'
#' WHONET 2019 software: \url{http://www.whonet.org/software.html}
#'
#' European Commission Public Health PHARMACEUTICALS - COMMUNITY REGISTER: \url{http://ec.europa.eu/health/documents/community-register/html/atc.htm}
#' @aliases ab
#' @return Character (vector) with class [`ab`]. Unknown values will return `NA`.
#' @seealso [antibiotics] for the dataframe that is being used to determine ATCs.
#' @inheritSection AMR Read more on our website!
#' @export
#' @examples
#' # these examples all return "ERY", the ID of erythromycin:
#' as.ab("J01FA01")
#' as.ab("J 01 FA 01")
#' as.ab("Erythromycin")
#' as.ab("eryt")
#' as.ab(" eryt 123")
#' as.ab("ERYT")
#' as.ab("ERY")
#' as.ab("eritromicine") # spelled wrong, yet works
#' as.ab("Erythrocin") # trade name
#' as.ab("Romycin") # trade name
#'
#' # spelling from different languages and dyslexia are no problem
#' ab_atc("ceftriaxon")
#' ab_atc("cephtriaxone") # small spelling error
#' ab_atc("cephthriaxone") # or a bit more severe
#' ab_atc("seephthriaaksone") # and even this works
#'
#' # use ab_* functions to get a specific properties (see ?ab_property);
#' # they use as.ab() internally:
#' ab_name("J01FA01") # "Erythromycin"
#' ab_name("eryt") # "Erythromycin"
as.ab <- function(x, ...) {
check_dataset_integrity()
if (is.ab(x)) {
return(x)
}
if (all(toupper(x) %in% antibiotics$ab)) {
# valid AB code, but not yet right class
return(structure(.Data = toupper(x),
class = c("ab", "character")))
}
x_bak <- x
# remove diacritics
x <- iconv(x, from = "UTF-8", to = "ASCII//TRANSLIT")
x <- gsub('"', "", x, fixed = TRUE)
# remove suffices
x_bak_clean <- gsub("_(mic|rsi|dis[ck])$", "", x, ignore.case = TRUE)
# remove disk concentrations, like LVX_NM -> LVX
x_bak_clean <- gsub("_[A-Z]{2}[0-9_.]{0,3}$", "", x_bak_clean, ignore.case = TRUE)
# remove part between brackets if that's followed by another string
x_bak_clean <- gsub("(.*)+ [(].*[)]", "\\1", x_bak_clean)
# keep only max 1 space
x_bak_clean <- trimws(gsub(" +", " ", x_bak_clean, ignore.case = TRUE))
# non-character, space or number should be a slash
x_bak_clean <- gsub("[^A-Za-z0-9 -]", "/", x_bak_clean)
# spaces around non-characters must be removed: amox + clav -> amox/clav
x_bak_clean <- gsub("(.*[a-zA-Z0-9]) ([^a-zA-Z0-9].*)", "\\1\\2", x_bak_clean)
x_bak_clean <- gsub("(.*[^a-zA-Z0-9]) ([a-zA-Z0-9].*)", "\\1\\2", x_bak_clean)
# remove hyphen after a starting "co"
x_bak_clean <- gsub("^co-", "co", x_bak_clean, ignore.case = TRUE)
# replace text 'and' with a slash
x_bak_clean <- gsub(" and ", "/", x_bak_clean, ignore.case = TRUE)
x <- unique(x_bak_clean)
x_new <- rep(NA_character_, length(x))
x_unknown <- character(0)
for (i in seq_len(length(x))) {
if (is.na(x[i]) | is.null(x[i])) {
next
}
if (identical(x[i], "")) {
x_unknown <- c(x_unknown, x_bak[x[i] == x_bak_clean][1])
next
}
# prevent "bacteria" from coercing to TMP, since Bacterial is a brand name of it
if (identical(tolower(x[i]), "bacteria")) {
x_unknown <- c(x_unknown, x_bak[x[i] == x_bak_clean][1])
next
}
# exact AB code
found <- antibiotics[which(antibiotics$ab == toupper(x[i])), ]$ab
if (length(found) > 0) {
x_new[i] <- found[1L]
next
}
# exact ATC code
found <- antibiotics[which(antibiotics$atc == toupper(x[i])), ]$ab
if (length(found) > 0) {
x_new[i] <- found[1L]
next
}
# exact CID code
found <- antibiotics[which(antibiotics$cid == x[i]), ]$ab
if (length(found) > 0) {
x_new[i] <- found[1L]
next
}
# exact name
found <- antibiotics[which(toupper(antibiotics$name) == toupper(x[i])), ]$ab
if (length(found) > 0) {
x_new[i] <- found[1L]
next
}
# exact LOINC code
loinc_found <- unlist(lapply(antibiotics$loinc,
function(s) if (x[i] %in% s) {
TRUE
} else {
FALSE
}))
found <- antibiotics$ab[loinc_found == TRUE]
if (length(found) > 0) {
x_new[i] <- found[1L]
next
}
# exact synonym
synonym_found <- unlist(lapply(antibiotics$synonyms,
function(s) if (toupper(x[i]) %in% toupper(s)) {
TRUE
} else {
FALSE
}))
found <- antibiotics$ab[synonym_found == TRUE]
if (length(found) > 0) {
x_new[i] <- found[1L]
next
}
# exact abbreviation
abbr_found <- unlist(lapply(antibiotics$abbreviations,
function(a) if (toupper(x[i]) %in% toupper(a)) {
TRUE
} else {
FALSE
}))
found <- antibiotics$ab[abbr_found == TRUE]
if (length(found) > 0) {
x_new[i] <- found[1L]
next
}
# first >=4 characters of name
if (nchar(x[i]) >= 4) {
found <- antibiotics[which(toupper(antibiotics$name) %like% paste0("^", x[i])), ]$ab
if (length(found) > 0) {
x_new[i] <- found[1L]
next
}
}
# allow characters that resemble others, but only continue when having more than 3 characters
if (nchar(x[i]) <= 3) {
x_unknown <- c(x_unknown, x_bak[x[i] == x_bak_clean][1])
next
}
x_spelling <- tolower(x[i])
x_spelling <- gsub("[iy]+", "[iy]+", x_spelling)
x_spelling <- gsub("(c|k|q|qu|s|z|x|ks)+", "(c|k|q|qu|s|z|x|ks)+", x_spelling)
x_spelling <- gsub("(ph|f|v)+", "(ph|f|v)+", x_spelling)
x_spelling <- gsub("(th|t)+", "(th|t)+", x_spelling)
x_spelling <- gsub("a+", "a+", x_spelling)
x_spelling <- gsub("e+", "e+", x_spelling)
x_spelling <- gsub("o+", "o+", x_spelling)
# allow any ending of -in/-ine and -im/-ime
x_spelling <- gsub("(\\[iy\\]\\+(n|m)|\\[iy\\]\\+(n|m)e\\+)$", "[iy]+(n|m)e*", x_spelling)
# allow any ending of -ol/-ole
x_spelling <- gsub("(o\\+l|o\\+le\\+)$", "o+le*", x_spelling)
# allow any ending of -on/-one
x_spelling <- gsub("(o\\+n|o\\+ne\\+)$", "o+ne*", x_spelling)
# replace multiple same characters to single one with '+', like "ll" -> "l+"
x_spelling <- gsub("(.)\\1+", "\\1+", x_spelling)
# replace spaces and slashes with a possibility on both
x_spelling <- gsub("[ /]", "( .*|.*/)", x_spelling)
# try if name starts with it
found <- antibiotics[which(antibiotics$name %like% paste0("^", x_spelling)), ]$ab
if (length(found) > 0) {
x_new[i] <- found[1L]
next
}
# and try if any synonym starts with it
synonym_found <- unlist(lapply(antibiotics$synonyms,
function(s) if (any(s %like% paste0("^", x_spelling))) {
TRUE
} else {
FALSE
}))
found <- antibiotics$ab[synonym_found == TRUE]
if (length(found) > 0) {
x_new[i] <- found[1L]
next
}
# try by removing all spaces
if (x[i] %like% " ") {
found <- suppressWarnings(as.ab(gsub(" +", "", x[i])))
if (length(found) > 0 & !is.na(found)) {
x_new[i] <- found[1L]
next
}
}
# try by removing all spaces and numbers
if (x[i] %like% " " | x[i] %like% "[0-9]") {
found <- suppressWarnings(as.ab(gsub("[ 0-9]", "", x[i])))
if (length(found) > 0 & !is.na(found)) {
x_new[i] <- found[1L]
next
}
}
if (!isFALSE(list(...)$initial_search)) {
# transform back from other languages and try again
x_translated <- paste(lapply(strsplit(x[i], "[^a-zA-Z0-9 ]"),
function(y) {
for (i in seq_len(length(y))) {
y[i] <- ifelse(tolower(y[i]) %in% tolower(translations_file$replacement),
translations_file[which(tolower(translations_file$replacement) == tolower(y[i]) &
!isFALSE(translations_file$fixed)), "pattern"],
y[i])
}
y
})[[1]],
collapse = "/")
x_translated_guess <- suppressWarnings(as.ab(x_translated, initial_search = FALSE))
if (!is.na(x_translated_guess)) {
x_new[i] <- x_translated_guess
next
}
if (!isFALSE(list(...)$initial_search2)) {
# now also try to coerce brandname combinations like "Amoxy/clavulanic acid"
x_translated <- paste(lapply(strsplit(x_translated, "[^a-zA-Z0-9 ]"),
function(y) {
for (i in seq_len(length(y))) {
y_name <- suppressWarnings(ab_name(y[i], language = NULL, initial_search = FALSE, initial_search2 = FALSE))
y[i] <- ifelse(!is.na(y_name),
y_name,
y[i])
}
y
})[[1]],
collapse = "/")
x_translated_guess <- suppressWarnings(as.ab(x_translated, initial_search = FALSE))
if (!is.na(x_translated_guess)) {
x_new[i] <- x_translated_guess
next
}
}
}
# try by removing all trailing capitals
if (x[i] %like_case% "[a-z]+[A-Z]+$") {
found <- suppressWarnings(as.ab(gsub("[A-Z]+$", "", x[i])))
if (length(found) > 0 & !is.na(found)) {
x_new[i] <- found[1L]
next
}
}
# not found
x_unknown <- c(x_unknown, x_bak[x[i] == x_bak_clean][1])
}
# take failed ATC codes apart from rest
x_unknown_ATCs <- x_unknown[x_unknown %like% "[A-Z][0-9][0-9][A-Z][A-Z][0-9][0-9]"]
x_unknown <- x_unknown[!x_unknown %in% x_unknown_ATCs]
if (length(x_unknown_ATCs) > 0) {
warning("These ATC codes are not (yet) in the antibiotics data set: ",
paste('"', sort(unique(x_unknown_ATCs)), '"', sep = "", collapse = ", "),
".",
call. = FALSE)
}
if (length(x_unknown) > 0) {
warning("These values could not be coerced to a valid antimicrobial ID: ",
paste('"', sort(unique(x_unknown)), '"', sep = "", collapse = ", "),
".",
call. = FALSE)
}
x_result <- data.frame(x = x_bak_clean, stringsAsFactors = FALSE) %>%
left_join(data.frame(x = x, x_new = x_new, stringsAsFactors = FALSE), by = "x") %>%
pull(x_new)
if (length(x_result) == 0) {
x_result <- NA_character_
}
structure(.Data = x_result,
class = c("ab", "character"))
}
#' @rdname as.ab
#' @export
is.ab <- function(x) {
inherits(x, "ab")
}
#' @exportMethod print.ab
#' @export
#' @noRd
print.ab <- function(x, ...) {
cat("Class <ab>\n")
print(as.character(x), quote = FALSE)
}
#' @exportMethod as.data.frame.ab
#' @export
#' @noRd
as.data.frame.ab <- function(x, ...) {
nm <- deparse1(substitute(x))
if (!"nm" %in% names(list(...))) {
as.data.frame.vector(as.ab(x), ..., nm = nm)
} else {
as.data.frame.vector(as.ab(x), ...)
}
}
#' @exportMethod [.ab
#' @export
#' @noRd
"[.ab" <- function(x, ...) {
y <- NextMethod()
attributes(y) <- attributes(x)
y
}
#' @exportMethod [[.ab
#' @export
#' @noRd
"[[.ab" <- function(x, ...) {
y <- NextMethod()
attributes(y) <- attributes(x)
y
}
#' @exportMethod [<-.ab
#' @export
#' @noRd
"[<-.ab" <- function(i, j, ..., value) {
y <- NextMethod()
attributes(y) <- attributes(i)
class_integrity_check(y, "antimicrobial code", antibiotics$ab)
}
#' @exportMethod [[<-.ab
#' @export
#' @noRd
"[[<-.ab" <- function(i, j, ..., value) {
y <- NextMethod()
attributes(y) <- attributes(i)
class_integrity_check(y, "antimicrobial code", antibiotics$ab)
}
#' @exportMethod c.ab
#' @export
#' @noRd
c.ab <- function(x, ...) {
y <- NextMethod()
attributes(y) <- attributes(x)
class_integrity_check(y, "antimicrobial code", antibiotics$ab)
}
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