AMR/R/mo.R

# ==================================================================== #
# TITLE                                                                #
# Antimicrobial Resistance (AMR) Analysis                              #
#                                                                      #
# AUTHORS                                                              #
# Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl)           #
#                                                                      #
# LICENCE                                                              #
# This program is free software; you can redistribute it and/or modify #
# it under the terms of the GNU General Public License version 2.0,    #
# as published by the Free Software Foundation.                        #
#                                                                      #
# This program is distributed in the hope that it will be useful,      #
# but WITHOUT ANY WARRANTY; without even the implied warranty of       #
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the        #
# GNU General Public License for more details.                         #
# ==================================================================== #

#' Transform to microorganism ID
#'
#' Use this function to determine a valid microorganism ID (\code{mo}). Determination is done using Artificial Intelligence (AI) and the complete taxonomic kingdoms \emph{Bacteria}, \emph{Fungi} and \emph{Protozoa} (see Source), so the input can be almost anything: a full name (like \code{"Staphylococcus aureus"}), an abbreviated name (like \code{"S. aureus"}), an abbreviation known in the field (like \code{"MRSA"}), or just a genus. You could also \code{\link{select}} a genus and species column, zie Examples.
#' @param x a character vector or a \code{data.frame} with one or two columns
#' @param Becker a logical to indicate whether \emph{Staphylococci} should be categorised into Coagulase Negative \emph{Staphylococci} ("CoNS") and Coagulase Positive \emph{Staphylococci} ("CoPS") instead of their own species, according to Karsten Becker \emph{et al.} [1].
#'
#'   This excludes \emph{Staphylococcus aureus} at default, use \code{Becker = "all"} to also categorise \emph{S. aureus} as "CoPS".
#' @param Lancefield a logical to indicate whether beta-haemolytic \emph{Streptococci} should be categorised into Lancefield groups instead of their own species, according to Rebecca C. Lancefield [2]. These \emph{Streptococci} will be categorised in their first group, e.g. \emph{Streptococcus dysgalactiae} will be group C, although officially it was also categorised into groups G and L.
#'
#'   This excludes \emph{Enterococci} at default (who are in group D), use \code{Lancefield = "all"} to also categorise all \emph{Enterococci} as group D.
#' @param allow_uncertain a logical to indicate whether empty results should be checked for only a part of the input string. When results are found, a warning will be given about the uncertainty and the result.
#' @rdname as.mo
#' @aliases mo
#' @keywords mo Becker becker Lancefield lancefield guess
#' @details
#' A microbial ID (class: \code{mo}) typically looks like these examples:\cr
#' \preformatted{
#'   Code                Full name
#'   ---------------     --------------------------------------
#'   B_KLBSL             Klebsiella
#'   B_KLBSL_PNE         Klebsiella pneumoniae
#'   B_KLBSL_PNE_RHI     Klebsiella pneumoniae rhinoscleromatis
#'   |   |    |   |
#'   |   |    |   |
#'   |   |    |    ----> subspecies, a 3-4 letter acronym
#'   |   |     ----> species, a 3-4 letter acronym
#'   |    ----> genus, a 5-7 letter acronym, mostly without vowels
#'    ----> taxonomic kingdom, either Bacteria (B), Fungi (F) or Protozoa (P)
#' }
#'
#' Use the \code{\link{mo_property}} functions to get properties based on the returned code, see Examples.
#'
#' This function uses Artificial Intelligence (AI) to help getting more logical results, based on type of input and known prevalence of human pathogens. For example:
#' \itemize{
#'   \item{\code{"E. coli"} will return the ID of \emph{Escherichia coli} and not \emph{Entamoeba coli}, although the latter would alphabetically come first}
#'   \item{\code{"H. influenzae"} will return the ID of \emph{Haemophilus influenzae} and not \emph{Haematobacter influenzae} for the same reason}
#'   \item{Something like \code{"p aer"} will return the ID of \emph{Pseudomonas aeruginosa} and not \emph{Pasteurella aerogenes}}
#'   \item{Something like \code{"stau"} or \code{"S aur"} will return the ID of \emph{Staphylococcus aureus} and not \emph{Staphylococcus auricularis}}
#' }
#' This means that looking up human non-pathogenic microorganisms takes a longer time compares to human pathogenic microorganisms.
#'
#' \code{guess_mo} is an alias of \code{as.mo}.
#' @section ITIS:
#' \if{html}{\figure{itis_logo.jpg}{options: height=60px style=margin-bottom:5px} \cr}
#' This \code{AMR} package contains the \strong{complete microbial taxonomic data} (with seven taxonomic ranks - from subkingdom to subspecies) from the publicly available Integrated Taxonomic Information System (ITIS, https://www.itis.gov). ITIS is a partnership of U.S., Canadian, and Mexican agencies and taxonomic specialists [3]. The complete taxonomic kingdoms Bacteria, Fungi and Protozoa are included in this package, as well as all previously accepted names known to ITIS.
#  (source as section, so it can be inherited by mo_property:)
#' @section Source:
#' [1] Becker K \emph{et al.} \strong{Coagulase-Negative Staphylococci}. 2014. Clin Microbiol Rev. 27(4): 870–926. \url{https://dx.doi.org/10.1128/CMR.00109-13}
#'
#' [2] Lancefield RC \strong{A serological differentiation of human and other groups of hemolytic streptococci}. 1933. J Exp Med. 57(4): 571–95. \url{https://dx.doi.org/10.1084/jem.57.4.571}
#'
#' [3] Integrated Taxonomic Information System (ITIS). Retrieved September 2018. \url{http://www.itis.gov}
#' @export
#' @importFrom dplyr %>% pull left_join
#' @importFrom data.table as.data.table setkey
#' @return Character (vector) with class \code{"mo"}. Unknown values will return \code{NA}.
#' @seealso \code{\link{microorganisms}} for the \code{data.frame} with ITIS content that is being used to determine ID's. \cr
#' The \code{\link{mo_property}} functions (like \code{\link{mo_genus}}, \code{\link{mo_gramstain}}) to get properties based on the returned code.
#' @examples
#' # These examples all return "STAAUR", the ID of S. aureus:
#' as.mo("stau")
#' as.mo("STAU")
#' as.mo("staaur")
#' as.mo("S. aureus")
#' as.mo("S aureus")
#' as.mo("Staphylococcus aureus")
#' as.mo("MRSA") # Methicillin Resistant S. aureus
#' as.mo("VISA") # Vancomycin Intermediate S. aureus
#' as.mo("VRSA") # Vancomycin Resistant S. aureus
#' as.mo(369)    # Search on TSN (Taxonomic Serial Number), a unique identifier
#'               # for the Integrated Taxonomic Information System (ITIS)
#'
#' as.mo("Streptococcus group A")
#' as.mo("GAS") # Group A Streptococci
#' as.mo("GBS") # Group B Streptococci
#'
#' # guess_mo is an alias of as.mo and works the same
#' guess_mo("S. epidermidis")                 # will remain species: B_STPHY_EPI
#' guess_mo("S. epidermidis", Becker = TRUE)  # will not remain species: B_STPHY_CNS
#'
#' guess_mo("S. pyogenes")                    # will remain species: B_STRPTC_PYO
#' guess_mo("S. pyogenes", Lancefield = TRUE) # will not remain species: B_STRPTC_GRA
#'
#' # Use mo_* functions to get a specific property based on `mo`
#' Ecoli <- as.mo("E. coli")     # returns `B_ESCHR_COL`
#' mo_genus(Ecoli)               # returns "Escherichia"
#' mo_gramstain(Ecoli)           # returns "Gram negative"
#' # but it uses as.mo internally too, so you could also just use:
#' mo_genus("E. coli")           # returns "Escherichia"
#'
#'
#' \dontrun{
#' df$mo <- as.mo(df$microorganism_name)
#'
#' # the select function of tidyverse is also supported:
#' library(dplyr)
#' df$mo <- df %>%
#'   select(microorganism_name) %>%
#'   guess_mo()
#'
#' # and can even contain 2 columns, which is convenient for genus/species combinations:
#' df$mo <- df %>%
#'   select(genus, species) %>%
#'   guess_mo()
#'
#' # same result:
#' df <- df %>%
#'   mutate(mo = guess_mo(paste(genus, species)))
#' }
as.mo <- function(x, Becker = FALSE, Lancefield = FALSE, allow_uncertain = FALSE) {

  if (NCOL(x) == 2) {
    # support tidyverse selection like: df %>% select(colA, colB)
    # paste these columns together
    x_vector <- vector("character", NROW(x))
    for (i in 1:NROW(x)) {
      x_vector[i] <- paste(pull(x[i,], 1), pull(x[i,], 2), sep = " ")
    }
    x <- x_vector
  } else {
    if (NCOL(x) > 2) {
      stop('`x` can be 2 columns at most', call. = FALSE)
    }
    x[is.null(x)] <- NA

    # support tidyverse selection like: df %>% select(colA)
    if (!is.vector(x)) {
      x <- pull(x, 1)
    }
  }

  MOs <- as.data.table(AMR::microorganisms)
  setkey(MOs, prevalence, tsn)
  MOs_mostprevalent <- MOs[prevalence != 9999,]
  MOs_allothers <- NULL # will be set later, if needed
  MOs_old <- NULL # will be set later, if needed


  if (all(unique(x) %in% MOs[,mo])) {
    class(x) <- "mo"
    attr(x, 'package') <- 'AMR'
    attr(x, 'ITIS') <- TRUE
    return(x)
  }
  if (AMR::is.mo(x) & isTRUE(attributes(x)$ITIS)) {
    # check for new mo class, data coming from ITIS
    return(x)
  }


  failures <- character(0)
  x_input <- x

  # only check the uniques, which is way faster
  x <- unique(x)

  x_backup <- trimws(x, which = "both")
  x_species <- paste(x_backup, "species")
  # translate to English for supported languages of mo_property
  x <- gsub("(Gruppe|gruppe|groep|grupo|gruppo|groupe)", "group", x)
  # remove 'empty' genus and species values
  x <- gsub("(no MO)", "", x, fixed = TRUE)
  # remove dots and other non-text in case of "E. coli" except spaces
  x <- gsub("[^a-zA-Z0-9/ \\-]+", "", x)
  # but spaces before and after should be omitted
  x <- trimws(x, which = "both")
  x_trimmed <- x
  x_trimmed_species <- paste(x_trimmed, "species")
  # replace space by regex sign
  x_withspaces <- gsub(" ", ".* ", x, fixed = TRUE)
  x <- gsub(" ", ".*", x, fixed = TRUE)
  # add start en stop regex
  x <- paste0('^', x, '$')
  x_withspaces_all <- x_withspaces
  x_withspaces_start <- paste0('^', x_withspaces)
  x_withspaces <- paste0('^', x_withspaces, '$')

  # cat(paste0('x                  "', x, '"\n'))
  # cat(paste0('x_species          "', x_species, '"\n'))
  # cat(paste0('x_withspaces_all   "', x_withspaces_all, '"\n'))
  # cat(paste0('x_withspaces_start "', x_withspaces_start, '"\n'))
  # cat(paste0('x_withspaces       "', x_withspaces, '"\n'))
  # cat(paste0('x_backup           "', x_backup, '"\n'))
  # cat(paste0('x_trimmed          "', x_trimmed, '"\n'))
  # cat(paste0('x_trimmed_species  "', x_trimmed_species, '"\n'))

  for (i in 1:length(x)) {
    if (identical(x_trimmed[i], "") | is.na(x_trimmed[i])) {
      # empty values
      x[i] <- NA
      next
    }

    # translate known trivial abbreviations to genus + species ----
    if (!is.na(x_trimmed[i])) {
      if (toupper(x_trimmed[i]) == 'MRSA'
          | toupper(x_trimmed[i]) == 'VISA'
          | toupper(x_trimmed[i]) == 'VRSA') {
        x[i] <- 'B_STPHY_AUR'
        next
      }
      if (toupper(x_trimmed[i]) == 'MRSE') {
        x[i] <- 'B_STPHY_EPI'
        next
      }
      if (toupper(x_trimmed[i]) == 'VRE') {
        x[i] <- 'B_ENTRC'
        next
      }
      if (toupper(x_trimmed[i]) == 'MRPA') {
        # multi resistant P. aeruginosa
        x[i] <- 'B_PDMNS_AER'
        next
      }
      if (toupper(x_trimmed[i]) %in% c('PISP', 'PRSP', 'VISP', 'VRSP')) {
        # peni I, peni R, vanco I, vanco R: S. pneumoniae
        x[i] <- 'B_STRPTC_PNE'
        next
      }
      if (toupper(x_trimmed[i]) %like% '^G[ABCDFGHK]S$') {
        x[i] <- gsub("G([ABCDFGHK])S", "B_STRPTC_GR\\1", x_trimmed[i])
        next
      }
      # CoNS/CoPS in different languages (support for German, Dutch, Spanish, Portuguese) ----
      if (tolower(x[i]) %like% '[ck]oagulas[ea] negatie?[vf]'
          | tolower(x_trimmed[i]) %like% '[ck]oagulas[ea] negatie?[vf]'
          | tolower(x[i]) %like% '[ck]o?ns[^a-z]?$') {
        # coerce S. coagulase negative
        x[i] <- 'B_STPHY_CNS'
        next
      }
      if (tolower(x[i]) %like% '[ck]oagulas[ea] positie?[vf]'
          | tolower(x_trimmed[i]) %like% '[ck]oagulas[ea] positie?[vf]'
          | tolower(x[i]) %like% '[ck]o?ps[^a-z]?$') {
        # coerce S. coagulase positive
        x[i] <- 'B_STPHY_CPS'
        next
      }
    }

    # FIRST TRY FULLNAMES AND CODES
    # if only genus is available, don't select species
    if (all(!c(x[i], x_trimmed[i]) %like% " ")) {
      found <- MOs[tolower(fullname) %in% tolower(c(x_species[i], x_trimmed_species[i])), mo]
      if (length(found) > 0) {
        x[i] <- found[1L]
        next
      }
      if (nchar(x_trimmed[i]) > 4) {
        # not when abbr is esco, stau, klpn, etc.
        found <- MOs[tolower(fullname) %like% gsub(" ", ".*", x_trimmed_species[i], fixed = TRUE), 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
    if (length(found) > 0) {
      x[i] <- MOs[mo.old == found, mo][1L]
      next
    }

    # TRY FIRST THOUSAND MOST PREVALENT IN HUMAN INFECTIONS ----

    found <- MOs_mostprevalent[tolower(fullname) %in% tolower(c(x_backup[i], x_trimmed[i])), mo]
    # most probable: is exact match in fullname
    if (length(found) > 0) {
      x[i] <- found[1L]
      next
    }
    found <- MOs_mostprevalent[tsn == x_trimmed[i], mo]
    # is a valid TSN
    if (length(found) > 0) {
      x[i] <- found[1L]
      next
    }
    found <- MOs_mostprevalent[mo == toupper(x_backup[i]), mo]
    # is a valid mo
    if (length(found) > 0) {
      x[i] <- found[1L]
      next
    }
    found <- MOs_mostprevalent[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_mostprevalent[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_mostprevalent[fullname %like% x_withspaces_start[i], mo]
    if (length(found) > 0) {
      x[i] <- found[1L]
      next
    }

    # try any match diregarding spaces ----
    found <- MOs_mostprevalent[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_mostprevalent[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_mostprevalent[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_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,]
    }

    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])
                           | (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), ...)
}