mirror of https://github.com/msberends/AMR.git
243 lines
9.6 KiB
R
243 lines
9.6 KiB
R
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# ==================================================================== #
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# TITLE #
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# Antimicrobial Resistance (AMR) Analysis #
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# #
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# SOURCE #
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# https://gitlab.com/msberends/AMR #
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# #
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# LICENCE #
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# (c) 2019 Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl) #
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# #
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# This R package is free software; you can freely use and distribute #
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# it for both personal and commercial purposes under the terms of the #
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# GNU General Public License version 2.0 (GNU GPL-2), as published by #
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# the Free Software Foundation. #
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# #
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# This R package was created for academic research and was publicly #
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# released in the hope that it will be useful, but it comes WITHOUT #
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# ANY WARRANTY OR LIABILITY. #
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# Visit our website for more info: https://msberends.gitlab.io/AMR. #
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# ==================================================================== #
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#' Transform to antibiotic ID
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#'
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#' Use this function to determine the antibiotic code of one or more antibiotics. The data set \code{\link{antibiotics}} will be searched for abbreviations, official names and synonyms (brand names).
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#' @param x character vector to determine to antibiotic ID
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#' @rdname as.ab
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#' @keywords atc
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#' @inheritSection WHOCC WHOCC
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#' @export
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#' @importFrom dplyr %>% filter slice pull
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#' @details Use the \code{\link{ab_property}} functions to get properties based on the returned ATC code, see Examples.
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#'
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#' In the ATC classification system, the active substances are classified in a hierarchy with five different levels. The system has fourteen main anatomical/pharmacological groups or 1st levels. Each ATC main group is divided into 2nd levels which could be either pharmacological or therapeutic groups. The 3rd and 4th levels are chemical, pharmacological or therapeutic subgroups and the 5th level is the chemical substance. The 2nd, 3rd and 4th levels are often used to identify pharmacological subgroups when that is considered more appropriate than therapeutic or chemical subgroups.
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#' Source: \url{https://www.whocc.no/atc/structure_and_principles/}
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#' @section Source:
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#' World Health Organization (WHO) Collaborating Centre for Drug Statistics Methodology: \url{https://www.whocc.no/atc_ddd_index/}
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#'
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#' WHONET 2019 software: \url{http://www.whonet.org/software.html}
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#'
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#' European Commission Public Health PHARMACEUTICALS - COMMUNITY REGISTER: \url{http://ec.europa.eu/health/documents/community-register/html/atc.htm}
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#' @return Character (vector) with class \code{"act"}. Unknown values will return \code{NA}.
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#' @seealso \code{\link{antibiotics}} for the dataframe that is being used to determine ATCs.
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#' @inheritSection AMR Read more on our website!
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#' @examples
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#' # These examples all return "ERY", the ID of Erythromycin:
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#' as.ab("J01FA01")
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#' as.ab("J 01 FA 01")
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#' as.ab("Erythromycin")
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#' as.ab("eryt")
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#' as.ab(" eryt 123")
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#' as.ab("ERYT")
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#' as.ab("ERY")
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#' as.ab("erytromicine") # spelled wrong
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#' as.ab("Erythrocin") # trade name
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#' as.ab("Romycin") # trade name
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#'
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#' # Use ab_* functions to get a specific properties (see ?ab_property);
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#' # they use as.ab() internally:
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#' ab_name("J01FA01") # "Erythromycin"
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#' ab_name("eryt") # "Erythromycin"
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as.ab <- function(x) {
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if (is.ab(x)) {
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return(x)
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}
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x_bak <- x
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# remove suffices
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x_bak_clean <- gsub("_(mic|rsi)$", "", x)
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# remove disk concentrations, like LVX_NM -> LVX
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x_bak_clean <- gsub("_[A-Z]{2}[0-9_]{0,3}$", "", x_bak_clean)
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# clean rest of it
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x_bak_clean <- gsub("[^a-zA-Z0-9/-]", "", x_bak_clean)
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# keep only a-z when it's not an ATC code or only numbers
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x_bak_clean[!x_bak_clean %like% "^([A-Z][0-9]{2}[A-Z]{2}[0-9]{2}|[0-9]+)$"] <- gsub("[^a-zA-Z]+",
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"",
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x_bak_clean[!x_bak_clean %like% "^([A-Z][0-9]{2}[A-Z]{2}[0-9]{2}|[0-9]+)$"])
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x <- unique(x_bak_clean)
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x_new <- rep(NA_character_, length(x))
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x_unknown <- character(0)
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for (i in 1:length(x)) {
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if (is.na(x[i]) | is.null(x[i])) {
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next
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}
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if (identical(x[i], "")) {
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x_unknown <- c(x_unknown, x_bak[x[i] == x_bak_clean][1])
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next
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}
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# exact AB code
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found <- AMR::antibiotics[which(AMR::antibiotics$ab == toupper(x[i])),]$ab
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if (length(found) > 0) {
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x_new[i] <- found[1L]
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next
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}
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# exact ATC code
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found <- AMR::antibiotics[which(AMR::antibiotics$atc == toupper(x[i])),]$ab
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if (length(found) > 0) {
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x_new[i] <- found[1L]
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next
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}
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# exact CID code
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found <- AMR::antibiotics[which(AMR::antibiotics$cid == x[i]),]$ab
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if (length(found) > 0) {
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x_new[i] <- found[1L]
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next
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}
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# exact name
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found <- AMR::antibiotics[which(toupper(AMR::antibiotics$name) == toupper(x[i])),]$ab
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if (length(found) > 0) {
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x_new[i] <- found[1L]
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next
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}
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# exact synonym
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synonym_found <- unlist(lapply(AMR::antibiotics$synonyms,
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function(s) if (toupper(x[i]) %in% toupper(s)) {
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TRUE
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} else {
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FALSE
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}))
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found <- AMR::antibiotics$ab[synonym_found == TRUE]
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if (length(found) > 0) {
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x_new[i] <- found[1L]
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next
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}
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# exact abbreviation
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abbr_found <- unlist(lapply(AMR::antibiotics$abbreviations,
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function(a) if (toupper(x[i]) %in% toupper(a)) {
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TRUE
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} else {
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FALSE
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}))
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found <- AMR::antibiotics$ab[abbr_found == TRUE]
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if (length(found) > 0) {
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x_new[i] <- found[1L]
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next
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}
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# first >=4 characters of name
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if (nchar(x[i]) >= 4) {
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found <- AMR::antibiotics[which(toupper(AMR::antibiotics$name) %like% paste0("^", x[i])),]$ab
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if (length(found) > 0) {
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x_new[i] <- found[1L]
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next
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}
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}
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# allow characters that resemble others, but only continue when having more than 3 characters
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if (nchar(x[i]) <= 3) {
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x_unknown <- c(x_unknown, x_bak[x[i] == x_bak_clean][1])
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next
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}
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x_spelling <- x[i]
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x_spelling <- gsub("[iy]+", "[iy]+", x_spelling, ignore.case = TRUE)
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x_spelling <- gsub("[sz]+", "[sz]+", x_spelling, ignore.case = TRUE)
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x_spelling <- gsub("(c|k|q|qu)+", "(c|k|q|qu)+", x_spelling, ignore.case = TRUE)
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x_spelling <- gsub("(ph|f|v)+", "(ph|f|v)+", x_spelling, ignore.case = TRUE)
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x_spelling <- gsub("(th|t)+", "(th|t)+", x_spelling, ignore.case = TRUE)
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x_spelling <- gsub("a+", "a+", x_spelling, ignore.case = TRUE)
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x_spelling <- gsub("e+", "e+", x_spelling, ignore.case = TRUE)
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x_spelling <- gsub("o+", "o+", x_spelling, ignore.case = TRUE)
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# allow any ending of -in/-ine and -im/-ime
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x_spelling <- gsub("(\\[iy\\]\\+(n|m)|\\[iy\\]\\+(n|m)e\\+)$", "[iy]+(n|m)e*", x_spelling, ignore.case = TRUE)
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# allow any ending of -ol/-ole
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x_spelling <- gsub("(o\\+l|o\\+le\\+)$", "o+le*", x_spelling, ignore.case = TRUE)
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# try if name starts with it
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found <- AMR::antibiotics[which(AMR::antibiotics$name %like% paste0("^", x_spelling)),]$ab
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if (length(found) > 0) {
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x_new[i] <- found[1L]
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next
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}
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# and try if any synonym starts with it
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synonym_found <- unlist(lapply(AMR::antibiotics$synonyms,
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function(s) if (any(s %like% paste0("^", x_spelling))) {
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TRUE
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} else {
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FALSE
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}))
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found <- AMR::antibiotics$ab[synonym_found == TRUE]
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if (length(found) > 0) {
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x_new[i] <- found[1L]
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next
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}
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# not found
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x_unknown <- c(x_unknown, x_bak[x[i] == x_bak_clean][1])
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}
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if (length(x_unknown) > 0) {
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warning("These values could not be coerced to a valid antibiotic ID: ",
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paste('"', sort(unique(x_unknown)), '"', sep = "", collapse = ', '),
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".",
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call. = FALSE)
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}
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x_result <- data.frame(x = x_bak_clean, stringsAsFactors = FALSE) %>%
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left_join(data.frame(x = x, x_new = x_new, stringsAsFactors = FALSE), by = "x") %>%
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pull(x_new)
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structure(.Data = x_result,
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class = "ab")
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}
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#' @rdname as.atc
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#' @export
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is.ab <- function(x) {
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identical(class(x), "ab")
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}
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#' @exportMethod print.ab
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#' @export
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#' @noRd
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print.ab <- function(x, ...) {
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cat("Class 'ab'\n")
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print.default(as.character(x), quote = FALSE)
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}
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#' @exportMethod as.data.frame.ab
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#' @export
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#' @noRd
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as.data.frame.ab <- function (x, ...) {
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# same as as.data.frame.character but with removed stringsAsFactors
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nm <- paste(deparse(substitute(x), width.cutoff = 500L),
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collapse = " ")
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if (!"nm" %in% names(list(...))) {
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as.data.frame.vector(x, ..., nm = nm)
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} else {
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as.data.frame.vector(x, ...)
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}
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}
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#' @exportMethod pull.ab
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#' @export
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#' @importFrom dplyr pull
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#' @noRd
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pull.ab <- function(.data, ...) {
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pull(as.data.frame(.data), ...)
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}
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