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first inclusion of ITIS data
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406
R/mo.R
406
R/mo.R
@ -18,38 +18,62 @@
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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). Determination is done using Artificial Intelligence (AI), 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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#' 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, i.e. \emph{Streptococcus dysgalactiae} will be group C, although officially it was also categorised into groups G and L.
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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 \code{guess_mo} is an alias of \code{as.mo}.
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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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#' Thus 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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#' 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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#' 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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#' 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} 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 (from subkingdom to the subspecies level) are included in this package.
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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 arrange
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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 dataframe that is being used to determine ID's.
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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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@ -61,22 +85,27 @@
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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: STAEPI
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#' guess_mo("S. epidermidis", Becker = TRUE) # will not remain species: STACNS
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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: STCPYO
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#' guess_mo("S. pyogenes", Lancefield = TRUE) # will not remain species: STCGRA
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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 `ESCCOL`
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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 "Negative rods"
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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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@ -96,7 +125,7 @@
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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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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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@ -118,17 +147,33 @@ as.mo <- function(x, Becker = FALSE, Lancefield = FALSE) {
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}
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}
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MOs <- AMR::microorganisms %>%
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arrange(prevalence) %>% # more expected result on multiple findings
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filter(!mo %like% '^_FAM', # don't search in those
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(nchar(mo) > 3 | mo %in% c("GNR", "GPR", "GNC", "GPC"))) # no genera
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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 <- 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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@ -138,6 +183,7 @@ as.mo <- function(x, Becker = FALSE, Lancefield = FALSE) {
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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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@ -148,111 +194,137 @@ as.mo <- function(x, Becker = FALSE, Lancefield = FALSE) {
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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], "")) {
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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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if (toupper(x_backup[i]) %in% AMR::microorganisms$mo) {
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# is already a valid MO code
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x[i] <- toupper(x_backup[i])
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next
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}
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if (toupper(x_trimmed[i]) %in% AMR::microorganisms$mo) {
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# is already a valid MO code
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x[i] <- toupper(x_trimmed[i])
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next
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}
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if (tolower(x_backup[i]) %in% tolower(AMR::microorganisms$fullname)) {
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# is exact match in fullname
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x[i] <- AMR::microorganisms[which(AMR::microorganisms$fullname == x_backup[i]), ]$mo[1L]
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next
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}
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# CoNS/CoPS in different languages (support for German, Dutch, Spanish, Portuguese) ----
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if (tolower(x[i]) %like% '[ck]oagulas[ea] negatie?[vf]'
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| tolower(x_trimmed[i]) %like% '[ck]oagulas[ea] negatie?[vf]'
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| tolower(x[i]) %like% '[ck]o?ns[^a-z]?$') {
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# coerce S. coagulase negative
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x[i] <- 'STACNS'
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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] <- 'STACPS'
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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] <- 'STAAUR'
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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] <- 'STAEPI'
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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] <- 'ENCSPP'
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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] <- 'PSEAER'
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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] <- 'STCPNE'
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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[ABCDFHK]S$') {
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x[i] <- gsub("G([ABCDFHK])S", "STCGR\\1", x_trimmed[i])
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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)), 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[which(MOs$fullname %like% x_withspaces[i]),]$mo
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if (length(found) > 0) {
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x[i] <- found[1L]
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next
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}
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# try the same, now based on genus + species ----
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found <- MOs[which(paste(MOs$genus, MOs$species) %like% x_withspaces[i]),]$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 with genus, keeping spaces, not ending with $ ----
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found <- MOs[which(MOs$genus %like% x_withspaces_start[i] & MOs$mo %like% 'SPP$'),]$mo
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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[which(MOs$fullname %like% x_withspaces_start[i]),]$mo
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||||
found <- MOs_mostprevalent[fullname %like% x_withspaces_start[i], mo]
|
||||
if (length(found) > 0) {
|
||||
x[i] <- found[1L]
|
||||
next
|
||||
}
|
||||
|
||||
# try any match diregarding spaces ----
|
||||
found <- MOs[which(MOs$fullname %like% x[i]),]$mo
|
||||
found <- MOs_mostprevalent[fullname %like% x[i], mo]
|
||||
if (length(found) > 0) {
|
||||
x[i] <- found[1L]
|
||||
next
|
||||
@ -260,14 +332,7 @@ as.mo <- function(x, Becker = FALSE, Lancefield = FALSE) {
|
||||
|
||||
# try fullname without start and stop regex, to also find subspecies ----
|
||||
# like "K. pneu rhino" -> "Klebsiella pneumoniae (rhinoscleromatis)" = KLEPNERH
|
||||
found <- MOs[which(gsub("[\\(\\)]", "", MOs$fullname) %like% x_withspaces_all[i]),]$mo
|
||||
if (length(found) > 0) {
|
||||
x[i] <- found[1L]
|
||||
next
|
||||
}
|
||||
|
||||
# search for GLIMS code ----
|
||||
found <- AMR::microorganisms.umcg[which(toupper(AMR::microorganisms.umcg$umcg) == toupper(x_trimmed[i])),]$mo
|
||||
found <- MOs_mostprevalent[fullname %like% x_withspaces_start[i], mo]
|
||||
if (length(found) > 0) {
|
||||
x[i] <- found[1L]
|
||||
next
|
||||
@ -280,7 +345,7 @@ as.mo <- function(x, Becker = FALSE, Lancefield = FALSE) {
|
||||
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[which(MOs$fullname %like% paste0('^', x_split[i])),]$mo
|
||||
found <- MOs_mostprevalent[fullname %like% paste0('^', x_split[i]), mo]
|
||||
if (length(found) > 0) {
|
||||
x[i] <- found[1L]
|
||||
next
|
||||
@ -288,15 +353,137 @@ as.mo <- function(x, Becker = FALSE, Lancefield = FALSE) {
|
||||
|
||||
# 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 (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], mo]
|
||||
# if (length(found) > 0) {
|
||||
# x[i] <- found[1L]
|
||||
# next
|
||||
# }
|
||||
# }
|
||||
|
||||
# THEN TRY ALL OTHERS ----
|
||||
if (is.null(MOs_allothers)) {
|
||||
MOs_allothers <- MOs[prevalence == 9999,]
|
||||
}
|
||||
if (!is.na(x_trimmed[i])) {
|
||||
found <- MOs[which(MOs$fullname %like% x_trimmed[i]),]$mo
|
||||
if (length(found) > 0) {
|
||||
x[i] <- found[1L]
|
||||
|
||||
found <- MOs_allothers[tolower(fullname) == tolower(x_backup[i]), mo]
|
||||
# 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]), mo]
|
||||
# most probable: is exact match in fullname
|
||||
if (length(found) > 0) {
|
||||
x[i] <- found[1L]
|
||||
next
|
||||
}
|
||||
found <- MOs_allothers[tsn == x_trimmed[i], mo]
|
||||
# is a valid TSN
|
||||
if (length(found) > 0) {
|
||||
x[i] <- found[1L]
|
||||
next
|
||||
}
|
||||
found <- MOs_allothers[mo == toupper(x_backup[i]), mo]
|
||||
# is a valid mo
|
||||
if (length(found) > 0) {
|
||||
x[i] <- found[1L]
|
||||
next
|
||||
}
|
||||
found <- MOs_allothers[mo.old == toupper(x_backup[i]), 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(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]
|
||||
message("Note: '", found[1, name], "' was renamed to '",
|
||||
MOs[tsn == found[1, tsn_new], fullname], "' by ",
|
||||
found[1, authors], " in ", found[1, year])
|
||||
next
|
||||
}
|
||||
|
||||
# check for uncertain results ----
|
||||
# (1) try to strip off one element and check the remains
|
||||
if (allow_uncertain == TRUE) {
|
||||
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 result: '", x_backup[i], "' -> '", MOs[mo == x[i], fullname], "' (", x[i], ")")
|
||||
next
|
||||
}
|
||||
}
|
||||
@ -309,7 +496,7 @@ as.mo <- function(x, Becker = FALSE, Lancefield = FALSE) {
|
||||
|
||||
failures <- failures[!failures %in% c(NA, NULL, NaN)]
|
||||
if (length(failures) > 0) {
|
||||
warning("These ", length(failures) , " values could not be coerced to a valid mo: ",
|
||||
warning("These ", length(failures) , " values could not be coerced (try again with allow_uncertain = TRUE):\n",
|
||||
paste('"', unique(failures), '"', sep = "", collapse = ', '),
|
||||
".",
|
||||
call. = FALSE)
|
||||
@ -341,43 +528,36 @@ as.mo <- function(x, Becker = FALSE, Lancefield = FALSE) {
|
||||
"pseudintermedius", "pseudointermedius",
|
||||
"schleiferi")) %>%
|
||||
pull(mo)
|
||||
x[x %in% CoNS] <- "STACNS"
|
||||
x[x %in% CoPS] <- "STACPS"
|
||||
x[x %in% CoNS] <- "B_STPHY_CNS"
|
||||
x[x %in% CoPS] <- "B_STPHY_CPS"
|
||||
if (Becker == "all") {
|
||||
x[x == "STAAUR"] <- "STACPS"
|
||||
x[x == "B_STPHY_AUR"] <- "B_STPHY_CPS"
|
||||
}
|
||||
}
|
||||
|
||||
# Lancefield ----
|
||||
if (Lancefield == TRUE | Lancefield == "all") {
|
||||
# group A
|
||||
x[x == "STCPYO"] <- "STCGRA" # S. pyogenes
|
||||
x[x == "B_STRPTC_PYO"] <- "B_STRPTC_GRA" # S. pyogenes
|
||||
# group B
|
||||
x[x == "STCAGA"] <- "STCGRB" # S. agalactiae
|
||||
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] <- "STCGRC" # S. agalactiae
|
||||
x[x %in% S_groupC] <- "B_STRPTC_GRC" # S. agalactiae
|
||||
if (Lancefield == "all") {
|
||||
x[substr(x, 1, 3) == "ENC"] <- "STCGRD" # all Enterococci
|
||||
x[substr(x, 1, 7) == "B_ENTRC"] <- "B_STRPTC_GRD" # all Enterococci
|
||||
}
|
||||
# group F
|
||||
x[x == "STCANG"] <- "STCGRF" # S. anginosus
|
||||
x[x == "B_STRPTC_ANG"] <- "B_STRPTC_GRF" # S. anginosus
|
||||
# group H
|
||||
x[x == "STCSAN"] <- "STCGRH" # S. sanguis
|
||||
x[x == "B_STRPTC_SAN"] <- "B_STRPTC_GRH" # S. sanguinis
|
||||
# group K
|
||||
x[x == "STCSAL"] <- "STCGRK" # S. salivarius
|
||||
x[x == "B_STRPTC_SAL"] <- "B_STRPTC_GRK" # S. salivarius
|
||||
}
|
||||
|
||||
# for the returned genera without species, add species ----
|
||||
# like "ESC" -> "ESCSPP", but only where the input contained it
|
||||
indices <- nchar(unique(x)) == 3 & !x %like% "[A-Z]{3}SPP" & !x %in% c("GNR", "GPR", "GNC", "GPC",
|
||||
"GNS", "GPS", "GNK", "GPK")
|
||||
indices <- indices[!is.na(indices)]
|
||||
x[indices] <- paste0(x[indices], 'SPP')
|
||||
|
||||
# left join the found results to the original input values (x_input)
|
||||
df_found <- data.frame(input = as.character(unique(x_input)),
|
||||
found = x,
|
||||
@ -392,9 +572,11 @@ as.mo <- function(x, Becker = FALSE, Lancefield = FALSE) {
|
||||
|
||||
class(x) <- "mo"
|
||||
attr(x, 'package') <- 'AMR'
|
||||
attr(x, 'ITIS') <- TRUE
|
||||
x
|
||||
}
|
||||
|
||||
|
||||
#' @rdname as.mo
|
||||
#' @export
|
||||
is.mo <- function(x) {
|
||||
|
||||
Reference in New Issue
Block a user