# ==================================================================== # # TITLE # # Antimicrobial Resistance (AMR) Analysis # # # # AUTHORS # # Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl) # # # # LICENCE # # This program is free software; you can redistribute it and/or modify # # it under the terms of the GNU General Public License version 2.0, # # as published by the Free Software Foundation. # # # # This program is distributed in the hope that it will be useful, # # but WITHOUT ANY WARRANTY; without even the implied warranty of # # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # # GNU General Public License for more details. # # ==================================================================== # #' Transform to microorganism ID #' #' Use this function to determine a valid microorganism ID (\code{mo}). Determination is done using Artificial Intelligence (AI) and the complete taxonomic kingdoms \emph{Bacteria}, \emph{Fungi} and \emph{Protozoa} (see Source), so the input can be almost anything: a full name (like \code{"Staphylococcus aureus"}), an abbreviated name (like \code{"S. aureus"}), an abbreviation known in the field (like \code{"MRSA"}), or just a genus. You could also \code{\link{select}} a genus and species column, zie Examples. #' @param x a character vector or a \code{data.frame} with one or two columns #' @param Becker a logical to indicate whether \emph{Staphylococci} should be categorised into Coagulase Negative \emph{Staphylococci} ("CoNS") and Coagulase Positive \emph{Staphylococci} ("CoPS") instead of their own species, according to Karsten Becker \emph{et al.} [1]. #' #' This excludes \emph{Staphylococcus aureus} at default, use \code{Becker = "all"} to also categorise \emph{S. aureus} as "CoPS". #' @param Lancefield a logical to indicate whether beta-haemolytic \emph{Streptococci} should be categorised into Lancefield groups instead of their own species, according to Rebecca C. Lancefield [2]. These \emph{Streptococci} will be categorised in their first group, e.g. \emph{Streptococcus dysgalactiae} will be group C, although officially it was also categorised into groups G and L. #' #' This excludes \emph{Enterococci} at default (who are in group D), use \code{Lancefield = "all"} to also categorise all \emph{Enterococci} as group D. #' @param allow_uncertain a logical to indicate whether empty results should be checked for only a part of the input string. When results are found, a warning will be given about the uncertainty and the result. #' @rdname as.mo #' @aliases mo #' @keywords mo Becker becker Lancefield lancefield guess #' @details #' A microbial ID (class: \code{mo}) typically looks like these examples:\cr #' \preformatted{ #' Code Full name #' --------------- -------------------------------------- #' B_KLBSL Klebsiella #' B_KLBSL_PNE Klebsiella pneumoniae #' B_KLBSL_PNE_RHI Klebsiella pneumoniae rhinoscleromatis #' | | | | #' | | | | #' | | | ----> subspecies, a 3-4 letter acronym #' | | ----> species, a 3-4 letter acronym #' | ----> genus, a 5-7 letter acronym, mostly without vowels #' ----> taxonomic kingdom, either Bacteria (B), Fungi (F) or Protozoa (P) #' } #' #' Use the \code{\link{mo_property}} functions to get properties based on the returned code, see Examples. #' #' This function uses Artificial Intelligence (AI) to help getting more logical results, based on type of input and known prevalence of human pathogens. For example: #' \itemize{ #' \item{\code{"E. coli"} will return the ID of \emph{Escherichia coli} and not \emph{Entamoeba coli}, although the latter would alphabetically come first} #' \item{\code{"H. influenzae"} will return the ID of \emph{Haemophilus influenzae} and not \emph{Haematobacter influenzae} for the same reason} #' \item{Something like \code{"p aer"} will return the ID of \emph{Pseudomonas aeruginosa} and not \emph{Pasteurella aerogenes}} #' \item{Something like \code{"stau"} or \code{"S aur"} will return the ID of \emph{Staphylococcus aureus} and not \emph{Staphylococcus auricularis}} #' } #' This means that looking up human non-pathogenic microorganisms takes a longer time compares to human pathogenic microorganisms. #' #' \code{guess_mo} is an alias of \code{as.mo}. #' @section ITIS: #' \if{html}{\figure{itis_logo.jpg}{options: height=60px style=margin-bottom:5px} \cr} #' This \code{AMR} package contains the \strong{complete microbial taxonomic data} (with seven taxonomic ranks - from subkingdom to subspecies) from the publicly available Integrated Taxonomic Information System (ITIS, \url{https://www.itis.gov}). ITIS is a partnership of U.S., Canadian, and Mexican agencies and taxonomic specialists [3]. The complete taxonomic kingdoms Bacteria, Fungi and Protozoa are included in this package, as well as all previously accepted names known to ITIS. # (source as section, so it can be inherited by mo_property:) #' @section Source: #' [1] Becker K \emph{et al.} \strong{Coagulase-Negative Staphylococci}. 2014. Clin Microbiol Rev. 27(4): 870–926. \url{https://dx.doi.org/10.1128/CMR.00109-13} #' #' [2] Lancefield RC \strong{A serological differentiation of human and other groups of hemolytic streptococci}. 1933. J Exp Med. 57(4): 571–95. \url{https://dx.doi.org/10.1084/jem.57.4.571} #' #' [3] Integrated Taxonomic Information System (ITIS). Retrieved September 2018. \url{http://www.itis.gov} #' @export #' @return Character (vector) with class \code{"mo"}. Unknown values will return \code{NA}. #' @seealso \code{\link{microorganisms}} for the \code{data.frame} with ITIS content that is being used to determine ID's. \cr #' The \code{\link{mo_property}} functions (like \code{\link{mo_genus}}, \code{\link{mo_gramstain}}) to get properties based on the returned code. #' @examples #' # These examples all return "STAAUR", the ID of S. aureus: #' as.mo("stau") #' as.mo("STAU") #' as.mo("staaur") #' as.mo("S. aureus") #' as.mo("S aureus") #' as.mo("Staphylococcus aureus") #' as.mo("MRSA") # Methicillin Resistant S. aureus #' as.mo("VISA") # Vancomycin Intermediate S. aureus #' as.mo("VRSA") # Vancomycin Resistant S. aureus #' as.mo(369) # Search on TSN (Taxonomic Serial Number), a unique identifier #' # for the Integrated Taxonomic Information System (ITIS) #' #' as.mo("Streptococcus group A") #' as.mo("GAS") # Group A Streptococci #' as.mo("GBS") # Group B Streptococci #' #' # guess_mo is an alias of as.mo and works the same #' guess_mo("S. epidermidis") # will remain species: B_STPHY_EPI #' guess_mo("S. epidermidis", Becker = TRUE) # will not remain species: B_STPHY_CNS #' #' guess_mo("S. pyogenes") # will remain species: B_STRPTC_PYO #' guess_mo("S. pyogenes", Lancefield = TRUE) # will not remain species: B_STRPTC_GRA #' #' # Use mo_* functions to get a specific property based on `mo` #' Ecoli <- as.mo("E. coli") # returns `B_ESCHR_COL` #' mo_genus(Ecoli) # returns "Escherichia" #' mo_gramstain(Ecoli) # returns "Gram negative" #' # but it uses as.mo internally too, so you could also just use: #' mo_genus("E. coli") # returns "Escherichia" #' #' #' \dontrun{ #' df$mo <- as.mo(df$microorganism_name) #' #' # the select function of tidyverse is also supported: #' library(dplyr) #' df$mo <- df %>% #' select(microorganism_name) %>% #' guess_mo() #' #' # and can even contain 2 columns, which is convenient for genus/species combinations: #' df$mo <- df %>% #' select(genus, species) %>% #' guess_mo() #' #' # same result: #' df <- df %>% #' mutate(mo = guess_mo(paste(genus, species))) #' } as.mo <- function(x, Becker = FALSE, Lancefield = FALSE, allow_uncertain = FALSE) { exec_as.mo(x = x, Becker = Becker, Lancefield = Lancefield, allow_uncertain = allow_uncertain, property = "mo") } #' @rdname as.mo #' @export is.mo <- function(x) { # bactid for older releases # remove when is.bactid will be removed identical(class(x), "mo") | identical(class(x), "bactid") } #' @rdname as.mo #' @export guess_mo <- as.mo #' @importFrom dplyr %>% pull left_join #' @importFrom data.table as.data.table setkey exec_as.mo <- function(x, Becker = FALSE, Lancefield = FALSE, allow_uncertain = FALSE, property = "mo") { if (NCOL(x) == 2) { # support tidyverse selection like: df %>% select(colA, colB) # paste these columns together x_vector <- vector("character", NROW(x)) for (i in 1:NROW(x)) { x_vector[i] <- paste(pull(x[i,], 1), pull(x[i,], 2), sep = " ") } x <- x_vector } else { if (NCOL(x) > 2) { stop('`x` can be 2 columns at most', call. = FALSE) } x[is.null(x)] <- NA # support tidyverse selection like: df %>% select(colA) if (!is.vector(x)) { x <- pull(x, 1) } } failures <- character(0) x_input <- x # only check the uniques, which is way faster x <- unique(x) MOs <- NULL # will be set later, if needed MOs_mostprevalent <- NULL # will be set later, if needed MOs_allothers <- NULL # will be set later, if needed MOs_old <- NULL # will be set later, if needed if (all(x %in% AMR::microorganisms[, property])) { # already existing mo } else if (all(x %in% AMR::microorganisms.certe[, "certe"])) { # old Certe codes suppressWarnings( x <- data.frame(certe = x, stringsAsFactors = FALSE) %>% left_join(AMR::microorganisms.certe, by = "certe") %>% left_join(AMR::microorganisms, by = "mo") %>% pull(property) ) } else if (all(x %in% AMR::microorganisms.umcg[, "umcg"])) { # old UMCG codes suppressWarnings( x <- data.frame(umcg = x, stringsAsFactors = FALSE) %>% left_join(AMR::microorganisms.umcg, by = "umcg") %>% left_join(AMR::microorganisms.certe, by = "certe") %>% left_join(AMR::microorganisms, by = "mo") %>% pull(property) ) } else { MOs <- as.data.table(AMR::microorganisms) setkey(MOs, prevalence, tsn) MOs_mostprevalent <- MOs[prevalence != 9999,] x_backup <- trimws(x, which = "both") x_species <- paste(x_backup, "species") # translate to English for supported languages of mo_property x <- gsub("(Gruppe|gruppe|groep|grupo|gruppo|groupe)", "group", x) # remove 'empty' genus and species values x <- gsub("(no MO)", "", x, fixed = TRUE) # remove dots and other non-text in case of "E. coli" except spaces x <- gsub("[^a-zA-Z0-9/ \\-]+", "", x) # but spaces before and after should be omitted x <- trimws(x, which = "both") x_trimmed <- x x_trimmed_species <- paste(x_trimmed, "species") # replace space by regex sign x_withspaces <- gsub(" ", ".* ", x, fixed = TRUE) x <- gsub(" ", ".*", x, fixed = TRUE) # add start en stop regex x <- paste0('^', x, '$') x_withspaces_all <- x_withspaces x_withspaces_start <- paste0('^', x_withspaces) x_withspaces <- paste0('^', x_withspaces, '$') # cat(paste0('x "', x, '"\n')) # cat(paste0('x_species "', x_species, '"\n')) # cat(paste0('x_withspaces_all "', x_withspaces_all, '"\n')) # cat(paste0('x_withspaces_start "', x_withspaces_start, '"\n')) # cat(paste0('x_withspaces "', x_withspaces, '"\n')) # cat(paste0('x_backup "', x_backup, '"\n')) # cat(paste0('x_trimmed "', x_trimmed, '"\n')) # cat(paste0('x_trimmed_species "', x_trimmed_species, '"\n')) for (i in 1:length(x)) { if (identical(x_trimmed[i], "") | is.na(x_trimmed[i])) { # empty values x[i] <- NA next } # translate known trivial abbreviations to genus + species ---- if (!is.na(x_trimmed[i])) { if (toupper(x_trimmed[i]) == 'MRSA' | toupper(x_trimmed[i]) == 'VISA' | toupper(x_trimmed[i]) == 'VRSA') { x[i] <- MOs[mo == 'B_STPHY_AUR', ..property][[1]][1L] next } if (toupper(x_trimmed[i]) == 'MRSE') { x[i] <- MOs[mo == 'B_STPHY_EPI', ..property][[1]][1L] next } if (toupper(x_trimmed[i]) == 'VRE') { x[i] <- MOs[mo == 'B_ENTRC', ..property][[1]][1L] next } if (toupper(x_trimmed[i]) == 'MRPA') { # multi resistant P. aeruginosa x[i] <- MOs[mo == 'B_PDMNS_AER', ..property][[1]][1L] next } if (toupper(x_trimmed[i]) %in% c('PISP', 'PRSP', 'VISP', 'VRSP')) { # peni I, peni R, vanco I, vanco R: S. pneumoniae x[i] <- MOs[mo == 'B_STRPTC_PNE', ..property][[1]][1L] next } if (toupper(x_trimmed[i]) %like% '^G[ABCDFGHK]S$') { x[i] <- MOs[mo == gsub("G([ABCDFGHK])S", "B_STRPTC_GR\\1", x_trimmed[i]), ..property][[1]][1L] next } # CoNS/CoPS in different languages (support for German, Dutch, Spanish, Portuguese) ---- if (tolower(x[i]) %like% '[ck]oagulas[ea] negatie?[vf]' | tolower(x_trimmed[i]) %like% '[ck]oagulas[ea] negatie?[vf]' | tolower(x[i]) %like% '[ck]o?ns[^a-z]?$') { # coerce S. coagulase negative x[i] <- MOs[mo == 'B_STPHY_CNS', ..property][[1]][1L] next } if (tolower(x[i]) %like% '[ck]oagulas[ea] positie?[vf]' | tolower(x_trimmed[i]) %like% '[ck]oagulas[ea] positie?[vf]' | tolower(x[i]) %like% '[ck]o?ps[^a-z]?$') { # coerce S. coagulase positive x[i] <- MOs[mo == 'B_STPHY_CPS', ..property][[1]][1L] next } } # FIRST TRY FULLNAMES AND CODES # if only genus is available, don't select species if (all(!c(x[i], x_trimmed[i]) %like% " ")) { found <- MOs[tolower(fullname) %in% tolower(c(x_species[i], x_trimmed_species[i])), ..property][[1]] if (length(found) > 0) { x[i] <- found[1L] next } if (nchar(x_trimmed[i]) > 4) { # not when abbr is esco, stau, klpn, etc. found <- MOs[tolower(fullname) %like% gsub(" ", ".*", x_trimmed_species[i], fixed = TRUE), ..property][[1]] if (length(found) > 0) { x[i] <- found[1L] next } } } # TRY FIRST THOUSAND MOST PREVALENT IN HUMAN INFECTIONS ---- found <- MOs_mostprevalent[tolower(fullname) %in% tolower(c(x_backup[i], x_trimmed[i])), ..property][[1]] # most probable: is exact match in fullname if (length(found) > 0) { x[i] <- found[1L] next } found <- MOs_mostprevalent[tsn == x_trimmed[i], ..property][[1]] # is a valid TSN if (length(found) > 0) { x[i] <- found[1L] next } found <- MOs_mostprevalent[mo == toupper(x_backup[i]), ..property][[1]] # is a valid mo if (length(found) > 0) { x[i] <- found[1L] next } # try any match keeping spaces ---- found <- MOs_mostprevalent[fullname %like% x_withspaces[i], ..property][[1]] if (length(found) > 0) { x[i] <- found[1L] next } # try any match keeping spaces, not ending with $ ---- found <- MOs_mostprevalent[fullname %like% x_withspaces_start[i], ..property][[1]] if (length(found) > 0) { x[i] <- found[1L] next } # try any match diregarding spaces ---- found <- MOs_mostprevalent[fullname %like% x[i], ..property][[1]] if (length(found) > 0) { x[i] <- found[1L] next } # try fullname without start and stop regex, to also find subspecies ---- # like "K. pneu rhino" -> "Klebsiella pneumoniae (rhinoscleromatis)" = KLEPNERH found <- MOs_mostprevalent[fullname %like% x_withspaces_start[i], ..property][[1]] if (length(found) > 0) { x[i] <- found[1L] next } # try splitting of characters and then find ID ---- # like esco = E. coli, klpn = K. pneumoniae, stau = S. aureus x_split <- x x_length <- nchar(x_trimmed[i]) x_split[i] <- paste0(x_trimmed[i] %>% substr(1, x_length / 2) %>% trimws(), '.* ', x_trimmed[i] %>% substr((x_length / 2) + 1, x_length) %>% trimws()) found <- MOs_mostprevalent[fullname %like% paste0('^', x_split[i]), ..property][[1]] if (length(found) > 0) { x[i] <- found[1L] next } # try any match with text before and after original search string ---- # so "negative rods" will be "GNR" # if (x_trimmed[i] %like% "^Gram") { # x_trimmed[i] <- gsub("^Gram", "", x_trimmed[i], ignore.case = TRUE) # # remove leading and trailing spaces again # x_trimmed[i] <- trimws(x_trimmed[i], which = "both") # } # if (!is.na(x_trimmed[i])) { # found <- MOs_mostprevalent[fullname %like% x_trimmed[i], ..property][[1]] # if (length(found) > 0) { # x[i] <- found[1L] # next # } # } # THEN TRY ALL OTHERS ---- if (is.null(MOs_allothers)) { MOs_allothers <- MOs[prevalence == 9999,] } found <- MOs_allothers[tolower(fullname) == tolower(x_backup[i]), ..property][[1]] # most probable: is exact match in fullname if (length(found) > 0) { x[i] <- found[1L] next } found <- MOs_allothers[tolower(fullname) == tolower(x_trimmed[i]), ..property][[1]] # most probable: is exact match in fullname if (length(found) > 0) { x[i] <- found[1L] next } found <- MOs_allothers[tsn == x_trimmed[i], ..property][[1]] # is a valid TSN if (length(found) > 0) { x[i] <- found[1L] next } found <- MOs_allothers[mo == toupper(x_backup[i]), ..property][[1]] # is a valid mo if (length(found) > 0) { x[i] <- found[1L] next } # try any match keeping spaces ---- found <- MOs_allothers[fullname %like% x_withspaces[i], ..property][[1]] if (length(found) > 0) { x[i] <- found[1L] next } # try any match keeping spaces, not ending with $ ---- found <- MOs_allothers[fullname %like% x_withspaces_start[i], ..property][[1]] if (length(found) > 0) { x[i] <- found[1L] next } # try any match diregarding spaces ---- found <- MOs_allothers[fullname %like% x[i], ..property][[1]] if (length(found) > 0) { x[i] <- found[1L] next } # try fullname without start and stop regex, to also find subspecies ---- # like "K. pneu rhino" -> "Klebsiella pneumoniae (rhinoscleromatis)" = KLEPNERH found <- MOs_allothers[fullname %like% x_withspaces_start[i], ..property][[1]] if (length(found) > 0) { x[i] <- found[1L] next } # try splitting of characters and then find ID ---- # like esco = E. coli, klpn = K. pneumoniae, stau = S. aureus x_split <- x x_length <- nchar(x_trimmed[i]) x_split[i] <- paste0(x_trimmed[i] %>% substr(1, x_length / 2) %>% trimws(), '.* ', x_trimmed[i] %>% substr((x_length / 2) + 1, x_length) %>% trimws()) found <- MOs_allothers[fullname %like% paste0('^', x_split[i]), ..property][[1]] if (length(found) > 0) { x[i] <- found[1L] next } # # try any match with text before and after original search string ---- # # so "negative rods" will be "GNR" # if (x_trimmed[i] %like% "^Gram") { # x_trimmed[i] <- gsub("^Gram", "", x_trimmed[i], ignore.case = TRUE) # # remove leading and trailing spaces again # x_trimmed[i] <- trimws(x_trimmed[i], which = "both") # } # if (!is.na(x_trimmed[i])) { # found <- MOs_allothers[fullname %like% x_trimmed[i], ..property][[1]] # if (length(found) > 0) { # x[i] <- found[1L] # next # } # } # MISCELLANEOUS ---- # look for old taxonomic names ---- if (is.null(MOs_old)) { MOs_old <- as.data.table(AMR::microorganisms.old) setkey(MOs_old, name, tsn_new) } found <- MOs_old[tolower(name) == tolower(x_backup[i]) | tsn == x_trimmed[i],] if (NROW(found) > 0) { x[i] <- MOs[tsn == found[1, tsn_new], ..property][[1]] renamed_note(name_old = found[1, name], name_new = MOs[tsn == found[1, tsn_new], fullname], authors = found[1, authors], year = found[1, year]) next } # check for uncertain results ---- if (allow_uncertain == TRUE) { # (1) look again for old taxonomic names, now for G. species ---- found <- MOs_old[name %like% x_withspaces[i] | name %like% x_withspaces_start[i] | name %like% x[i],] if (NROW(found) > 0) { x[i] <- MOs[tsn == found[1, tsn_new], ..property][[1]] warning("Uncertain interpretation: '", x_backup[i], "' -> '", found[1, name], "'", call. = FALSE, immediate. = TRUE) renamed_note(name_old = found[1, name], name_new = MOs[tsn == found[1, tsn_new], fullname], authors = found[1, authors], year = found[1, year]) next } # (2) try to strip off one element and check the remains x_strip <- x_backup[i] %>% strsplit(" ") %>% unlist() x_strip <- x_strip[1:length(x_strip) - 1] x[i] <- suppressWarnings(suppressMessages(as.mo(x_strip))) if (!is.na(x[i])) { warning("Uncertain interpretation: '", x_backup[i], "' -> '", MOs[mo == x[i], fullname], "' (", x[i], ")", call. = FALSE, immediate. = TRUE) next } } # not found ---- x[i] <- NA_character_ failures <- c(failures, x_backup[i]) } } failures <- failures[!failures %in% c(NA, NULL, NaN)] if (length(failures) > 0) { warning("These ", length(failures) , " values could not be coerced (try again with allow_uncertain = TRUE):\n", paste('"', unique(failures), '"', sep = "", collapse = ', '), ".", call. = FALSE) } # Becker ---- if (Becker == TRUE | Becker == "all") { # See Source. It's this figure: # https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4187637/figure/F3/ if (is.null(MOs)) { MOs <- as.data.table(AMR::microorganisms) setkey(MOs, prevalence, tsn) } MOs_staph <- MOs[genus == "Staphylococcus"] setkey(MOs_staph, species) CoNS <- MOs_staph[species %in% c("arlettae", "auricularis", "capitis", "caprae", "carnosus", "cohnii", "condimenti", "devriesei", "epidermidis", "equorum", "fleurettii", "gallinarum", "haemolyticus", "hominis", "jettensis", "kloosii", "lentus", "lugdunensis", "massiliensis", "microti", "muscae", "nepalensis", "pasteuri", "petrasii", "pettenkoferi", "piscifermentans", "rostri", "saccharolyticus", "saprophyticus", "sciuri", "stepanovicii", "simulans", "succinus", "vitulinus", "warneri", "xylosus"), ..property][[1]] CoPS <- MOs_staph[species %in% c("simiae", "agnetis", "chromogenes", "delphini", "felis", "lutrae", "hyicus", "intermedius", "pseudintermedius", "pseudointermedius", "schleiferi"), ..property][[1]] x[x %in% CoNS] <- MOs[mo == 'B_STPHY_CNS', ..property][[1]][1L] x[x %in% CoPS] <- MOs[mo == 'B_STPHY_CPS', ..property][[1]][1L] if (Becker == "all") { x[x == MOs[mo == 'B_STPHY_AUR', ..property][[1]][1L]] <- MOs[mo == 'B_STPHY_CPS', ..property][[1]][1L] } } # Lancefield ---- if (Lancefield == TRUE | Lancefield == "all") { if (is.null(MOs)) { MOs <- as.data.table(AMR::microorganisms) setkey(MOs, prevalence, tsn) } # group A - S. pyogenes x[x == MOs[mo == 'B_STRPTC_PYO', ..property][[1]][1L]] <- MOs[mo == 'B_STRPTC_GRA', ..property][[1]][1L] # group B - S. agalactiae x[x == MOs[mo == 'B_STRPTC_AGA', ..property][[1]][1L]] <- MOs[mo == 'B_STRPTC_GRB', ..property][[1]][1L] # group C S_groupC <- MOs %>% filter(genus == "Streptococcus", species %in% c("equisimilis", "equi", "zooepidemicus", "dysgalactiae")) %>% pull(property) x[x %in% S_groupC] <- MOs[mo == 'B_STRPTC_GRC', ..property][[1]][1L] if (Lancefield == "all") { # all Enterococci x[x %like% "^(Enterococcus|B_ENTRC)"] <- MOs[mo == 'B_STRPTC_GRD', ..property][[1]][1L] } # group F - S. anginosus x[x == MOs[mo == 'B_STRPTC_ANG', ..property][[1]][1L]] <- MOs[mo == 'B_STRPTC_GRF', ..property][[1]][1L] # group H - S. sanguinis x[x == MOs[mo == 'B_STRPTC_SAN', ..property][[1]][1L]] <- MOs[mo == 'B_STRPTC_GRH', ..property][[1]][1L] # group K - S. salivarius x[x == MOs[mo == 'B_STRPTC_SAL', ..property][[1]][1L]] <- MOs[mo == 'B_STRPTC_GRK', ..property][[1]][1L] } # left join the found results to the original input values (x_input) df_found <- data.frame(input = as.character(unique(x_input)), found = x, stringsAsFactors = FALSE) df_input <- data.frame(input = as.character(x_input), stringsAsFactors = FALSE) x <- df_input %>% left_join(df_found, by = "input") %>% pull(found) if (property == "mo") { class(x) <- "mo" attr(x, 'package') <- 'AMR' attr(x, 'ITIS') <- TRUE } else if (property == "tsn") { x <- as.integer(x) } x } renamed_note <- function(name_old, name_new, authors, year) { msg <- paste0("Note: '", name_old, "' was renamed to '", name_new, "'") if (!authors %in% c("", NA)) { msg <- paste0(msg, " by ", authors) } if (!year %in% c("", NA)) { msg <- paste0(msg, " in ", year) } base::message(msg) } #' @exportMethod print.mo #' @export #' @noRd print.mo <- function(x, ...) { cat("Class 'mo'\n") print.default(as.character(x), quote = FALSE) } #' @exportMethod as.data.frame.mo #' @export #' @noRd as.data.frame.mo <- function (x, ...) { # same as as.data.frame.character but with removed stringsAsFactors nm <- paste(deparse(substitute(x), width.cutoff = 500L), collapse = " ") if (!"nm" %in% names(list(...))) { as.data.frame.vector(x, ..., nm = nm) } else { as.data.frame.vector(x, ...) } } #' @exportMethod pull.mo #' @export #' @importFrom dplyr pull #' @noRd pull.mo <- function(.data, ...) { pull(as.data.frame(.data), ...) }