mirror of https://github.com/msberends/AMR.git
659 lines
26 KiB
R
659 lines
26 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 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.
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#' @param x a character vector or a \code{data.frame} 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].
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#'
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#' 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, e.g. \emph{Streptococcus dysgalactiae} will be group C, although officially it was also categorised into groups G and L.
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#'
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#' This excludes \emph{Enterococci} at default (who are in group D), use \code{Lancefield = "all"} to also categorise all \emph{Enterococci} as group D.
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#' @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.
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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
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#' A microbial ID (class: \code{mo}) typically looks like these examples:\cr
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#' \preformatted{
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#' Code Full name
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#' --------------- --------------------------------------
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#' B_KLBSL Klebsiella
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#' B_KLBSL_PNE Klebsiella pneumoniae
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#' B_KLBSL_PNE_RHI Klebsiella pneumoniae rhinoscleromatis
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#' | | | |
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#' | | | |
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#' | | | ----> subspecies, a 3-4 letter acronym
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#' | | ----> species, a 3-4 letter acronym
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#' | ----> genus, a 5-7 letter acronym, mostly without vowels
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#' ----> taxonomic kingdom, either Bacteria (B), Fungi (F) or Protozoa (P)
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#' }
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#'
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#' Use the \code{\link{mo_property}} functions to get properties based on the returned code, see Examples.
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#'
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#' 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:
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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} for the same reason}
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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{"S aur"} will return the ID of \emph{Staphylococcus aureus} and not \emph{Staphylococcus auricularis}}
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#' }
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#' This means that looking up human non-pathogenic microorganisms takes a longer time compares to human pathogenic microorganisms.
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#'
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#' \code{guess_mo} is an alias of \code{as.mo}.
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#' @section ITIS:
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#' \if{html}{\figure{itis_logo.jpg}{options: height=60px style=margin-bottom:5px} \cr}
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#' 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, 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.
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# (source as section, so it can be inherited by mo_property:)
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#' @section Source:
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#' [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}
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#'
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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. \url{https://dx.doi.org/10.1084/jem.57.4.571}
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#'
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#' [3] Integrated Taxonomic Information System (ITIS). Retrieved September 2018. \url{http://www.itis.gov}
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#' @export
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#' @importFrom dplyr %>% pull left_join
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#' @importFrom data.table as.data.table setkey
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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 \code{data.frame} with ITIS content that is being used to determine ID's. \cr
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#' The \code{\link{mo_property}} functions (like \code{\link{mo_genus}}, \code{\link{mo_gramstain}}) to get properties based on the returned code.
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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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#' as.mo(369) # Search on TSN (Taxonomic Serial Number), a unique identifier
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#' # for the Integrated Taxonomic Information System (ITIS)
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#'
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#' as.mo("Streptococcus group A")
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#' as.mo("GAS") # Group A Streptococci
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#' as.mo("GBS") # Group B Streptococci
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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: B_STPHY_EPI
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#' guess_mo("S. epidermidis", Becker = TRUE) # will not remain species: B_STPHY_CNS
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#'
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#' guess_mo("S. pyogenes") # will remain species: B_STRPTC_PYO
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#' guess_mo("S. pyogenes", Lancefield = TRUE) # will not remain species: B_STRPTC_GRA
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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 `B_ESCHR_COL`
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#' mo_genus(Ecoli) # returns "Escherichia"
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#' mo_gramstain(Ecoli) # returns "Gram negative"
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#' # but it uses as.mo internally too, so you could also just use:
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#' mo_genus("E. coli") # returns "Escherichia"
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#'
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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, allow_uncertain = 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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x[is.null(x)] <- NA
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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 <- as.data.table(AMR::microorganisms)
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setkey(MOs, prevalence, tsn)
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MOs_mostprevalent <- MOs[prevalence != 9999,]
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MOs_allothers <- NULL # will be set later, if needed
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MOs_old <- NULL # will be set later, if needed
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if (all(unique(x) %in% MOs[,mo])) {
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class(x) <- "mo"
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attr(x, 'package') <- 'AMR'
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attr(x, 'ITIS') <- TRUE
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return(x)
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}
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if (AMR::is.mo(x) & isTRUE(attributes(x)$ITIS)) {
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# check for new mo class, data coming from ITIS
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return(x)
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}
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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 <- trimws(x, which = "both")
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x_species <- paste(x_backup, "species")
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# translate to English for supported languages of mo_property
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x <- gsub("(Gruppe|gruppe|groep|grupo|gruppo|groupe)", "group", x)
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# remove 'empty' genus and species values
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x <- gsub("(no MO)", "", x, fixed = TRUE)
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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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x_trimmed_species <- paste(x_trimmed, "species")
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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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# 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_start <- paste0('^', x_withspaces)
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x_withspaces <- paste0('^', x_withspaces, '$')
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# cat(paste0('x "', x, '"\n'))
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# cat(paste0('x_species "', x_species, '"\n'))
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# cat(paste0('x_withspaces_all "', x_withspaces_all, '"\n'))
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# cat(paste0('x_withspaces_start "', x_withspaces_start, '"\n'))
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# cat(paste0('x_withspaces "', x_withspaces, '"\n'))
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# cat(paste0('x_backup "', x_backup, '"\n'))
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# cat(paste0('x_trimmed "', x_trimmed, '"\n'))
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# cat(paste0('x_trimmed_species "', x_trimmed_species, '"\n'))
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for (i in 1:length(x)) {
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if (identical(x_trimmed[i], "") | is.na(x_trimmed[i])) {
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# empty values
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x[i] <- NA
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next
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}
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# translate known trivial abbreviations 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] <- 'B_STPHY_AUR'
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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] <- 'B_STPHY_EPI'
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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] <- 'B_ENTRC'
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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] <- 'B_PDMNS_AER'
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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 I, peni R, vanco I, vanco R: S. pneumoniae
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x[i] <- 'B_STRPTC_PNE'
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next
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}
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if (toupper(x_trimmed[i]) %like% '^G[ABCDFGHK]S$') {
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x[i] <- gsub("G([ABCDFGHK])S", "B_STRPTC_GR\\1", x_trimmed[i])
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next
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}
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# CoNS/CoPS in different languages (support for German, Dutch, Spanish, Portuguese) ----
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if (tolower(x[i]) %like% '[ck]oagulas[ea] negatie?[vf]'
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| tolower(x_trimmed[i]) %like% '[ck]oagulas[ea] negatie?[vf]'
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| tolower(x[i]) %like% '[ck]o?ns[^a-z]?$') {
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# coerce S. coagulase negative
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x[i] <- 'B_STPHY_CNS'
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next
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}
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if (tolower(x[i]) %like% '[ck]oagulas[ea] positie?[vf]'
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| tolower(x_trimmed[i]) %like% '[ck]oagulas[ea] positie?[vf]'
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| tolower(x[i]) %like% '[ck]o?ps[^a-z]?$') {
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# coerce S. coagulase positive
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x[i] <- 'B_STPHY_CPS'
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next
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}
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}
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# FIRST TRY FULLNAMES AND CODES
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# if only genus is available, don't select species
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if (all(!c(x[i], x_trimmed[i]) %like% " ")) {
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found <- MOs[tolower(fullname) %in% tolower(c(x_species[i], x_trimmed_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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if (nchar(x_trimmed[i]) > 4) {
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# not when abbr is esco, stau, klpn, etc.
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found <- MOs[tolower(fullname) %like% gsub(" ", ".*", x_trimmed_species[i], fixed = TRUE), 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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}
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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] <- MOs[mo.old == found, mo][1L]
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next
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}
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# TRY FIRST THOUSAND MOST PREVALENT IN HUMAN INFECTIONS ----
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found <- MOs_mostprevalent[tolower(fullname) %in% tolower(c(x_backup[i], x_trimmed[i])), mo]
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# most probable: is exact match in fullname
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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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found <- MOs_mostprevalent[tsn == x_trimmed[i], mo]
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# is a valid TSN
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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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found <- MOs_mostprevalent[mo == toupper(x_backup[i]), mo]
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# is a valid 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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found <- MOs_mostprevalent[mo.old == toupper(x_backup[i])
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| (substr(x_backup[i], 4, 6) == "SPP" & mo.old == substr(x_backup[i], 1, 3))
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| mo.old == substr(x_backup[i], 1, 3), mo]
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# is a valid old 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 keeping spaces ----
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found <- MOs_mostprevalent[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 keeping spaces, not ending with $ ----
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found <- MOs_mostprevalent[fullname %like% x_withspaces_start[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_mostprevalent[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 fullname without start and stop regex, to also find subspecies ----
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# like "K. pneu rhino" -> "Klebsiella pneumoniae (rhinoscleromatis)" = KLEPNERH
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found <- MOs_mostprevalent[fullname %like% x_withspaces_start[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_mostprevalent[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_mostprevalent[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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# THEN TRY ALL OTHERS ----
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if (is.null(MOs_allothers)) {
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MOs_allothers <- MOs[prevalence == 9999,]
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}
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found <- MOs_allothers[tolower(fullname) == tolower(x_backup[i]), mo]
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# most probable: is exact match in fullname
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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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found <- MOs_allothers[tolower(fullname) == tolower(x_trimmed[i]), mo]
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# most probable: is exact match in fullname
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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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found <- MOs_allothers[tsn == x_trimmed[i], mo]
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# is a valid TSN
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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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found <- MOs_allothers[mo == toupper(x_backup[i]), mo]
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# is a valid 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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found <- MOs_allothers[mo.old == toupper(x_backup[i])
|
||
| (substr(x_backup[i], 4, 6) == "SPP" & mo.old == substr(x_backup[i], 1, 3))
|
||
| mo.old == substr(x_backup[i], 1, 3), mo]
|
||
# is a valid old mo
|
||
if (length(found) > 0) {
|
||
x[i] <- found[1L]
|
||
next
|
||
}
|
||
|
||
# try any match keeping spaces ----
|
||
found <- MOs_allothers[fullname %like% x_withspaces[i], mo]
|
||
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], mo]
|
||
if (length(found) > 0) {
|
||
x[i] <- found[1L]
|
||
next
|
||
}
|
||
|
||
# try any match diregarding spaces ----
|
||
found <- MOs_allothers[fullname %like% x[i], mo]
|
||
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], mo]
|
||
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]), mo]
|
||
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], mo]
|
||
# 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], mo]
|
||
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], mo]
|
||
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/
|
||
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"), mo]
|
||
CoPS <- MOs_staph[species %in% c("simiae", "agnetis", "chromogenes",
|
||
"delphini", "felis", "lutrae",
|
||
"hyicus", "intermedius",
|
||
"pseudintermedius", "pseudointermedius",
|
||
"schleiferi"), mo]
|
||
x[x %in% CoNS] <- "B_STPHY_CNS"
|
||
x[x %in% CoPS] <- "B_STPHY_CPS"
|
||
if (Becker == "all") {
|
||
x[x == "B_STPHY_AUR"] <- "B_STPHY_CPS"
|
||
}
|
||
}
|
||
|
||
# Lancefield ----
|
||
if (Lancefield == TRUE | Lancefield == "all") {
|
||
# group A
|
||
x[x == "B_STRPTC_PYO"] <- "B_STRPTC_GRA" # S. pyogenes
|
||
# group B
|
||
x[x == "B_STRPTC_AGA"] <- "B_STRPTC_GRB" # S. agalactiae
|
||
# group C
|
||
S_groupC <- MOs %>% filter(genus == "Streptococcus",
|
||
species %in% c("equisimilis", "equi",
|
||
"zooepidemicus", "dysgalactiae")) %>%
|
||
pull(mo)
|
||
x[x %in% S_groupC] <- "B_STRPTC_GRC" # S. agalactiae
|
||
if (Lancefield == "all") {
|
||
x[substr(x, 1, 7) == "B_ENTRC"] <- "B_STRPTC_GRD" # all Enterococci
|
||
}
|
||
# group F
|
||
x[x == "B_STRPTC_ANG"] <- "B_STRPTC_GRF" # S. anginosus
|
||
# group H
|
||
x[x == "B_STRPTC_SAN"] <- "B_STRPTC_GRH" # S. sanguinis
|
||
# group K
|
||
x[x == "B_STRPTC_SAL"] <- "B_STRPTC_GRK" # S. salivarius
|
||
}
|
||
|
||
# left join the found results to the original input values (x_input)
|
||
DT_found <- data.table(input = as.character(unique(x_input)),
|
||
found = x,
|
||
key = "input",
|
||
stringsAsFactors = FALSE)
|
||
DT_input <- data.table(input = as.character(x_input),
|
||
key = "input",
|
||
stringsAsFactors = FALSE)
|
||
x <- DT_found[DT_input, on = "input", found]
|
||
|
||
# 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)
|
||
|
||
class(x) <- "mo"
|
||
attr(x, 'package') <- 'AMR'
|
||
attr(x, 'ITIS') <- TRUE
|
||
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)
|
||
}
|
||
|
||
|
||
#' @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
|
||
|
||
#' @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), ...)
|
||
}
|