mirror of https://github.com/msberends/AMR.git
437 lines
16 KiB
R
437 lines
16 KiB
R
# ==================================================================== #
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# TITLE #
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# Antimicrobial Resistance (AMR) Analysis #
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# #
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# AUTHORS #
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# Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl) #
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# #
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# LICENCE #
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# This program is free software; you can redistribute it and/or modify #
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# it under the terms of the GNU General Public License version 2.0, #
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# as published by the Free Software Foundation. #
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# #
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# This program is distributed in the hope that it will be useful, #
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# but WITHOUT ANY WARRANTY; without even the implied warranty of #
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# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the #
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# GNU General Public License for more details. #
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# ==================================================================== #
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#' Transform to microorganism ID
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#'
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#' Use this function to determine a valid ID based on a genus (and species). This input can be a full name (like \code{"Staphylococcus aureus"}), an abbreviated name (like \code{"S. aureus"}), or just a genus. You could also \code{\link{select}} a genus and species column, zie Examples.
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#' @param x a character vector or a dataframe with one or two columns
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#' @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".
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#' @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, i.e. \emph{Streptococcus dysgalactiae} will be group C, although officially it was also categorised into groups G and L. Groups D and E will be ignored, since they are \emph{Enterococci}.
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#' @rdname as.mo
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#' @aliases mo
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#' @keywords mo Becker becker Lancefield lancefield guess
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#' @details \code{guess_mo} is an alias of \code{as.mo}.
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#'
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#' Use the \code{\link{mo_property}} functions to get properties based on the returned mo, see Examples.
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#'
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#' Some exceptions have been built in to get more logical results, based on prevalence of human pathogens. These are:
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#' \itemize{
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#' \item{\code{"E. coli"} will return the ID of \emph{Escherichia coli} and not \emph{Entamoeba coli}, although the latter would alphabetically come first}
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#' \item{\code{"H. influenzae"} will return the ID of \emph{Haemophilus influenzae} and not \emph{Haematobacter influenzae}}
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#' \item{Something like \code{"p aer"} will return the ID of \emph{Pseudomonas aeruginosa} and not \emph{Pasteurella aerogenes}}
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#' \item{Something like \code{"stau"} or \code{"staaur"} will return the ID of \emph{Staphylococcus aureus} and not \emph{Staphylococcus auricularis}}
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#' }
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#' Moreover, this function also supports ID's based on only Gram stain, when the species is not known. \cr
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#' For example, \code{"Gram negative rods"} and \code{"GNR"} will both return the ID of a Gram negative rod: \code{GNR}.
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#' @source
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#' [1] Becker K \emph{et al.} \strong{Coagulase-Negative Staphylococci}. 2014. Clin Microbiol Rev. 27(4): 870–926. \cr
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#' \url{https://dx.doi.org/10.1128/CMR.00109-13} \cr
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#' [2] Lancefield RC \strong{A serological differentiation of human and other groups of hemolytic streptococci}. 1933. J Exp Med. 57(4): 571–95. \cr
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#' \url{https://dx.doi.org/10.1084/jem.57.4.571}
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#' @export
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#' @importFrom dplyr %>% pull left_join
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#' @return Character (vector) with class \code{"mo"}. Unknown values will return \code{NA}.
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#' @seealso \code{\link{microorganisms}} for the dataframe that is being used to determine ID's.
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#' @examples
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#' # These examples all return "STAAUR", the ID of S. aureus:
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#' as.mo("stau")
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#' as.mo("STAU")
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#' as.mo("staaur")
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#' as.mo("S. aureus")
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#' as.mo("S aureus")
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#' as.mo("Staphylococcus aureus")
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#' as.mo("MRSA") # Methicillin Resistant S. aureus
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#' as.mo("VISA") # Vancomycin Intermediate S. aureus
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#' as.mo("VRSA") # Vancomycin Resistant S. aureus
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#'
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#' # guess_mo is an alias of as.mo and works the same
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#' guess_mo("S. epidermidis") # will remain species: STAEPI
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#' guess_mo("S. epidermidis", Becker = TRUE) # will not remain species: STACNS
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#'
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#' guess_mo("S. pyogenes") # will remain species: STCAGA
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#' guess_mo("S. pyogenes", Lancefield = TRUE) # will not remain species: STCGRA
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#'
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#' # Use mo_* functions to get a specific property based on `mo`
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#' Ecoli <- as.mo("E. coli") # returns `ESCCOL`
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#' mo_genus(Ecoli) # returns "Escherichia"
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#' mo_gramstain(Ecoli) # returns "Negative rods"
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#'
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#' \dontrun{
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#' df$mo <- as.mo(df$microorganism_name)
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#'
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#' # the select function of tidyverse is also supported:
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#' library(dplyr)
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#' df$mo <- df %>%
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#' select(microorganism_name) %>%
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#' guess_mo()
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#'
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#' # and can even contain 2 columns, which is convenient for genus/species combinations:
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#' df$mo <- df %>%
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#' select(genus, species) %>%
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#' guess_mo()
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#'
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#' # same result:
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#' df <- df %>%
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#' mutate(mo = guess_mo(paste(genus, species)))
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#' }
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as.mo <- function(x, Becker = FALSE, Lancefield = FALSE) {
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if (NCOL(x) == 2) {
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# support tidyverse selection like: df %>% select(colA, colB)
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# paste these columns together
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x_vector <- vector("character", NROW(x))
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for (i in 1:NROW(x)) {
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x_vector[i] <- paste(pull(x[i,], 1), pull(x[i,], 2), sep = " ")
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}
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x <- x_vector
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} else {
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if (NCOL(x) > 2) {
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stop('`x` can be 2 columns at most', call. = FALSE)
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}
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# support tidyverse selection like: df %>% select(colA)
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if (!is.vector(x)) {
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x <- pull(x, 1)
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}
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}
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MOs <- AMR::microorganisms %>% filter(!mo %like% '^_FAM') # dont search in those
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failures <- character(0)
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x_input <- x
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# only check the uniques, which is way faster
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x <- unique(x)
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x_backup <- x
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# remove dots and other non-text in case of "E. coli" except spaces
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x <- gsub("[^a-zA-Z0-9 ]+", "", x)
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# but spaces before and after should be omitted
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x <- trimws(x, which = "both")
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x_trimmed <- x
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# replace space by regex sign
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x_withspaces <- gsub(" ", ".* ", x, fixed = TRUE)
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x <- gsub(" ", ".*", x, fixed = TRUE)
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# for species
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x_species <- paste(x, 'species')
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# add start en stop regex
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x <- paste0('^', x, '$')
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x_withspaces_all <- x_withspaces
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x_withspaces <- paste0('^', x_withspaces, '$')
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for (i in 1:length(x)) {
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if (identical(x_trimmed[i], "")) {
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# empty values
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x[i] <- NA
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failures <- c(failures, x_backup[i])
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next
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}
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if (x_backup[i] %in% AMR::microorganisms$mo) {
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# is already a valid MO code
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x[i] <- x_backup[i]
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next
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}
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if (x_trimmed[i] %in% AMR::microorganisms$mo) {
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# is already a valid MO code
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x[i] <- x_trimmed[i]
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next
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}
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if (tolower(x[i]) == '^e.*coli$') {
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# avoid detection of Entamoeba coli in case of E. coli
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x[i] <- 'ESCCOL'
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next
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}
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if (tolower(x[i]) == '^h.*influenzae$') {
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# avoid detection of Haematobacter influenzae in case of H. influenzae
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x[i] <- 'HAEINF'
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next
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}
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if (tolower(x[i]) == '^st.*au$'
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| tolower(x[i]) == '^stau$'
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| tolower(x[i]) == '^staaur$') {
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# avoid detection of Staphylococcus auricularis in case of S. aureus
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x[i] <- 'STAAUR'
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next
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}
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if (tolower(x[i]) == '^p.*aer$') {
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# avoid detection of Pasteurella aerogenes in case of Pseudomonas aeruginosa
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x[i] <- 'PSEAER'
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next
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}
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if (tolower(x[i]) %like% 'coagulase negative'
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| tolower(x[i]) %like% 'cns'
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| tolower(x[i]) %like% 'cons') {
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# coerce S. coagulase negative, also as CNS and CoNS
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x[i] <- 'STACNS'
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next
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}
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# translate known trivial names to genus+species
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if (!is.na(x_trimmed[i])) {
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if (toupper(x_trimmed[i]) == 'MRSA'
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| toupper(x_trimmed[i]) == 'VISA'
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| toupper(x_trimmed[i]) == 'VRSA') {
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x[i] <- 'STAAUR'
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next
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}
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if (toupper(x_trimmed[i]) == 'MRSE') {
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x[i] <- 'STAEPI'
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next
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}
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if (toupper(x_trimmed[i]) == 'VRE') {
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x[i] <- 'ENC'
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next
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}
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if (toupper(x_trimmed[i]) == 'MRPA') {
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# multi resistant P. aeruginosa
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x[i] <- 'PSEAER'
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next
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}
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if (toupper(x_trimmed[i]) %in% c('PISP', 'PRSP', 'VISP', 'VRSP')) {
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# peni R, peni I, vanco I, vanco R: S. pneumoniae
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x[i] <- 'STCPNE'
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next
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}
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}
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# try any match keeping spaces
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found <- MOs[which(MOs$fullname %like% x_withspaces[i]),]$mo
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if (length(found) > 0) {
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x[i] <- found[1L]
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next
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}
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# try any match diregarding spaces
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found <- MOs[which(MOs$fullname %like% x[i]),]$mo
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if (length(found) > 0) {
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x[i] <- found[1L]
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next
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}
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# try exact match of only genus, with 'species' attached
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# (this prevents Streptococcus from becoming Peptostreptococcus, since "p" < "s")
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found <- MOs[which(MOs$fullname == x_species[i]),]$mo
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if (length(found) > 0) {
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x[i] <- found[1L]
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next
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}
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# try any match of only genus, with 'species' attached
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found <- MOs[which(MOs$fullname %like% x_species[i]),]$mo
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if (length(found) > 0) {
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x[i] <- found[1L]
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next
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}
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# try fullname without start and stop regex, to also find subspecies, like "K. pneu rhino"
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found <- MOs[which(gsub("[\\(\\)]", "", MOs$fullname) %like% x_withspaces_all[i]),]$mo
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if (length(found) > 0) {
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x[i] <- found[1L]
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next
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}
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# search for GLIMS code
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found <- AMR::microorganisms.umcg[which(toupper(AMR::microorganisms.umcg$umcg) == toupper(x_trimmed[i])),]$mo
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if (length(found) > 0) {
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x[i] <- found[1L]
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next
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}
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# try splitting of characters and then find ID
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# like esco = E. coli, klpn = K. pneumoniae, stau = S. aureus
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x_split <- x
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x_length <- nchar(x_trimmed[i])
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x_split[i] <- paste0(x_trimmed[i] %>% substr(1, x_length / 2) %>% trimws(),
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'.* ',
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x_trimmed[i] %>% substr((x_length / 2) + 1, x_length) %>% trimws())
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found <- MOs[which(MOs$fullname %like% paste0('^', x_split[i])),]$mo
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if (length(found) > 0) {
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x[i] <- found[1L]
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next
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}
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# try any match with text before and after original search string
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# so "negative rods" will be "GNR"
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if (x_trimmed[i] %like% "^Gram") {
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x_trimmed[i] <- gsub("^Gram", "", x_trimmed[i], ignore.case = TRUE)
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# remove leading and trailing spaces again
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x_trimmed[i] <- trimws(x_trimmed[i], which = "both")
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}
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if (!is.na(x_trimmed[i])) {
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found <- MOs[which(MOs$fullname %like% x_trimmed[i]),]$mo
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if (length(found) > 0) {
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x[i] <- found[1L]
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next
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}
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}
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# not found
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x[i] <- NA_character_
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failures <- c(failures, x_backup[i])
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}
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failures <- failures[!failures %in% c(NA, NULL, NaN)]
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if (length(failures) > 0) {
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warning("These values could not be coerced to a valid mo: ",
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paste('"', unique(failures), '"', sep = "", collapse = ', '),
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".",
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call. = FALSE)
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}
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if (Becker == TRUE | Becker == "all") {
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# See Source. It's this figure:
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# https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4187637/figure/F3/
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CoNS <- MOs %>%
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filter(genus == "Staphylococcus",
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species %in% c("arlettae", "auricularis", "capitis",
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"caprae", "carnosus", "cohnii", "condimenti",
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"devriesei", "epidermidis", "equorum",
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"fleurettii", "gallinarum", "haemolyticus",
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"hominis", "jettensis", "kloosii", "lentus",
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"lugdunensis", "massiliensis", "microti",
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"muscae", "nepalensis", "pasteuri", "petrasii",
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"pettenkoferi", "piscifermentans", "rostri",
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"saccharolyticus", "saprophyticus", "sciuri",
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"stepanovicii", "simulans", "succinus",
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"vitulinus", "warneri", "xylosus")) %>%
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pull(mo)
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CoPS <- MOs %>%
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filter(genus == "Staphylococcus",
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species %in% c("simiae", "agnetis", "chromogenes",
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"delphini", "felis", "lutrae",
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"hyicus", "intermedius",
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"pseudintermedius", "pseudointermedius",
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"schleiferi")) %>%
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pull(mo)
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x[x %in% CoNS] <- "STACNS"
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x[x %in% CoPS] <- "STACPS"
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if (Becker == "all") {
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x[x == "STAAUR"] <- "STACPS"
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}
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}
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if (Lancefield == TRUE) {
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# group A
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x[x == "STCPYO"] <- "STCGRA" # S. pyogenes
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# group B
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x[x == "STCAGA"] <- "STCGRB" # S. agalactiae
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# group C
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S_groupC <- MOs %>% filter(genus == "Streptococcus",
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species %in% c("equisimilis", "equi",
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"zooepidemicus", "dysgalactiae")) %>%
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pull(mo)
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x[x %in% S_groupC] <- "STCGRC" # S. agalactiae
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# group F
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x[x == "STCANG"] <- "STCGRF" # S. anginosus
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# group H
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x[x == "STCSAN"] <- "STCGRH" # S. sanguis
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# group K
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x[x == "STCSAL"] <- "STCGRK" # S. salivarius
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}
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# left join the found results to the original input values (x_input)
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df_found <- data.frame(input = as.character(unique(x_input)),
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found = x,
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stringsAsFactors = FALSE)
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df_input <- data.frame(input = as.character(x_input),
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stringsAsFactors = FALSE)
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x <- df_input %>%
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left_join(df_found,
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by = "input") %>%
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pull(found)
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class(x) <- "mo"
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attr(x, 'package') <- 'AMR'
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x
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}
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#' @rdname as.mo
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#' @export
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is.mo <- function(x) {
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# bactid for older releases
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# remove when is.bactid will be removed
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identical(class(x), "mo") | identical(class(x), "bactid")
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}
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#' @rdname as.mo
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#' @export
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guess_mo <- as.mo
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#' @exportMethod print.mo
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#' @export
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#' @noRd
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print.mo <- function(x, ...) {
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cat("Class 'mo'\n")
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print.default(as.character(x), quote = FALSE)
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}
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#' @exportMethod as.data.frame.mo
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#' @export
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#' @noRd
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as.data.frame.mo <- 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.mo
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#' @export
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#' @importFrom dplyr pull
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#' @noRd
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pull.mo <- function(.data, ...) {
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pull(as.data.frame(.data), ...)
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}
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#' @exportMethod print.bactid
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#' @export
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#' @noRd
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print.bactid <- function(x, ...) {
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cat("Class 'bactid'\n")
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print.default(as.character(x), quote = FALSE)
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}
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#' @exportMethod as.data.frame.bactid
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#' @export
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#' @noRd
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as.data.frame.bactid <- 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.bactid
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#' @export
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#' @importFrom dplyr pull
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#' @noRd
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pull.bactid <- function(.data, ...) {
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pull(as.data.frame(.data), ...)
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}
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