AMR/R/key_antibiotics.R

# ==================================================================== #
# TITLE                                                                #
# Antimicrobial Resistance (AMR) Analysis                              #
#                                                                      #
# SOURCE                                                               #
# https://gitlab.com/msberends/AMR                                     #
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# (c) 2019 Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl)  #
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# This R package is free software; you can freely use and distribute   #
# it for both personal and commercial purposes under the terms of the  #
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# the Free Software Foundation.                                        #
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# This R package was created for academic research and was publicly    #
# released in the hope that it will be useful, but it comes WITHOUT    #
# ANY WARRANTY OR LIABILITY.                                           #
# Visit our website for more info: https://msberends.gitab.io/AMR.     #
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#' Key antibiotics for first \emph{weighted} isolates
#'
#' These function can be used to determine first isolates (see \code{\link{first_isolate}}). Using key antibiotics to determine first isolates is more reliable than without key antibiotics. These selected isolates will then be called first \emph{weighted} isolates.
#' @param tbl table with antibiotics coloms, like \code{amox} and \code{amcl}.
#' @param x,y characters to compare
#' @inheritParams first_isolate
#' @param universal_1,universal_2,universal_3,universal_4,universal_5,universal_6 column names of \strong{broad-spectrum} antibiotics, case-insensitive. At default, the columns containing these antibiotics will be guessed with \code{\link{guess_ab_col}}.
#' @param GramPos_1,GramPos_2,GramPos_3,GramPos_4,GramPos_5,GramPos_6 column names of antibiotics for \strong{Gram positives}, case-insensitive. At default, the columns containing these antibiotics will be guessed with \code{\link{guess_ab_col}}.
#' @param GramNeg_1,GramNeg_2,GramNeg_3,GramNeg_4,GramNeg_5,GramNeg_6 column names of antibiotics for \strong{Gram negatives}, case-insensitive. At default, the columns containing these antibiotics will be guessed with \code{\link{guess_ab_col}}.
#' @param warnings give warning about missing antibiotic columns, they will anyway be ignored
#' @param ... other parameters passed on to function
#' @details The function \code{key_antibiotics} returns a character vector with 12 antibiotic results for every isolate. These isolates can then be compared using \code{key_antibiotics_equal}, to check if two isolates have generally the same antibiogram. Missing and invalid values are replaced with a dot (\code{"."}). The \code{\link{first_isolate}} function only uses this function on the same microbial species from the same patient. Using this, an MRSA will be included after a susceptible \emph{S. aureus} (MSSA) found within the same episode (see \code{episode} parameter of \code{\link{first_isolate}}). Without key antibiotic comparison it would not.
#'
#'   At default, the antibiotics that are used for \strong{Gram positive bacteria} are (colum names): \cr
#'   \code{"amox"}, \code{"amcl"}, \code{"cfur"}, \code{"pita"}, \code{"cipr"}, \code{"trsu"} (until here is universal), \code{"vanc"}, \code{"teic"}, \code{"tetr"}, \code{"eryt"}, \code{"oxac"}, \code{"rifa"}.
#'
#'   At default, the antibiotics that are used for \strong{Gram negative bacteria} are (colum names): \cr
#'   \code{"amox"}, \code{"amcl"}, \code{"cfur"}, \code{"pita"}, \code{"cipr"}, \code{"trsu"} (until here is universal), \code{"gent"}, \code{"tobr"}, \code{"coli"}, \code{"cfot"}, \code{"cfta"}, \code{"mero"}.
#'
#'
#'   The function \code{key_antibiotics_equal} checks the characters returned by \code{key_antibiotics} for equality, and returns a logical vector.
#' @inheritSection first_isolate Key antibiotics
#' @rdname key_antibiotics
#' @export
#' @importFrom dplyr %>% mutate if_else pull
#' @importFrom crayon blue bold
#' @seealso \code{\link{first_isolate}}
#' @inheritSection AMR Read more on our website!
#' @examples
#' # septic_patients is a dataset available in the AMR package
#' ?septic_patients

#' library(dplyr)
#' # set key antibiotics to a new variable
#' my_patients <- septic_patients %>%
#'   mutate(keyab = key_antibiotics(.)) %>%
#'   mutate(
#'     # now calculate first isolates
#'     first_regular = first_isolate(., col_keyantibiotics = FALSE),
#'     # and first WEIGHTED isolates
#'     first_weighted = first_isolate(., col_keyantibiotics = "keyab")
#'   )
#'
#' # Check the difference, in this data set it results in 7% more isolates:
#' sum(my_patients$first_regular, na.rm = TRUE)
#' sum(my_patients$first_weighted, na.rm = TRUE)
#'
#'
#' # output of the `key_antibiotics` function could be like this:
#' strainA <- "SSSRR.S.R..S"
#' strainB <- "SSSIRSSSRSSS"
#'
#' key_antibiotics_equal(strainA, strainB)
#' # TRUE, because I is ignored (as well as missing values)
#'
#' key_antibiotics_equal(strainA, strainB, ignore_I = FALSE)
#' # FALSE, because I is not ignored and so the 4th value differs
key_antibiotics <- function(tbl,
                            col_mo = NULL,
                            universal_1 = guess_ab_col(tbl, "amox"),
                            universal_2 = guess_ab_col(tbl, "amcl"),
                            universal_3 = guess_ab_col(tbl, "cfur"),
                            universal_4 = guess_ab_col(tbl, "pita"),
                            universal_5 = guess_ab_col(tbl, "cipr"),
                            universal_6 = guess_ab_col(tbl, "trsu"),
                            GramPos_1 = guess_ab_col(tbl, "vanc"),
                            GramPos_2 = guess_ab_col(tbl, "teic"),
                            GramPos_3 = guess_ab_col(tbl, "tetr"),
                            GramPos_4 = guess_ab_col(tbl, "eryt"),
                            GramPos_5 = guess_ab_col(tbl, "oxac"),
                            GramPos_6 = guess_ab_col(tbl, "rifa"),
                            GramNeg_1 = guess_ab_col(tbl, "gent"),
                            GramNeg_2 = guess_ab_col(tbl, "tobr"),
                            GramNeg_3 = guess_ab_col(tbl, "coli"),
                            GramNeg_4 = guess_ab_col(tbl, "cfot"),
                            GramNeg_5 = guess_ab_col(tbl, "cfta"),
                            GramNeg_6 = guess_ab_col(tbl, "mero"),
                            warnings = TRUE,
                            ...) {

  # try to find columns based on type
  # -- mo
  if (is.null(col_mo)) {
    col_mo <- search_type_in_df(tbl = tbl, type = "mo")
  }
  if (is.null(col_mo)) {
    stop("`col_mo` must be set.", call. = FALSE)
  }

  # check columns
  col.list <- c(universal_1, universal_2, universal_3, universal_4, universal_5, universal_6,
                GramPos_1, GramPos_2, GramPos_3, GramPos_4, GramPos_5, GramPos_6,
                GramNeg_1, GramNeg_2, GramNeg_3, GramNeg_4, GramNeg_5, GramNeg_6)
  col.list <- check_available_columns(tbl = tbl, col.list = col.list, info = warnings)
  universal_1 <- col.list[universal_1]
  universal_2 <- col.list[universal_2]
  universal_3 <- col.list[universal_3]
  universal_4 <- col.list[universal_4]
  universal_5 <- col.list[universal_5]
  universal_6 <- col.list[universal_6]
  GramPos_1 <- col.list[GramPos_1]
  GramPos_2 <- col.list[GramPos_2]
  GramPos_3 <- col.list[GramPos_3]
  GramPos_4 <- col.list[GramPos_4]
  GramPos_5 <- col.list[GramPos_5]
  GramPos_6 <- col.list[GramPos_6]
  GramNeg_1 <- col.list[GramNeg_1]
  GramNeg_2 <- col.list[GramNeg_2]
  GramNeg_3 <- col.list[GramNeg_3]
  GramNeg_4 <- col.list[GramNeg_4]
  GramNeg_5 <- col.list[GramNeg_5]
  GramNeg_6 <- col.list[GramNeg_6]

  universal <- c(universal_1, universal_2, universal_3,
                 universal_4, universal_5, universal_6)

  gram_positive = c(universal,
                    GramPos_1, GramPos_2, GramPos_3,
                    GramPos_4, GramPos_5, GramPos_6)
  gram_positive <- gram_positive[!is.null(gram_positive)]

  gram_negative = c(universal,
                    GramNeg_1, GramNeg_2, GramNeg_3,
                    GramNeg_4, GramNeg_5, GramNeg_6)
  gram_negative <- gram_negative[!is.null(gram_negative)]

  # join to microorganisms data set
  tbl <- tbl %>%
    mutate_at(vars(col_mo), as.mo) %>%
    left_join_microorganisms(by = col_mo) %>%
    mutate(key_ab = NA_character_,
           gramstain = mo_gramstain(pull(., col_mo)))

  # Gram +
  tbl <- tbl %>% mutate(key_ab =
                          if_else(gramstain == "Gram positive",
                                  apply(X = tbl[, gram_positive],
                                        MARGIN = 1,
                                        FUN = function(x) paste(x, collapse = "")),
                                  key_ab))

  # Gram -
  tbl <- tbl %>% mutate(key_ab =
                          if_else(gramstain == "Gram negative",
                                  apply(X = tbl[, gram_negative],
                                        MARGIN = 1,
                                        FUN = function(x) paste(x, collapse = "")),
                                  key_ab))

  # format
  key_abs <- tbl %>%
    pull(key_ab) %>%
    gsub('(NA|NULL)', '.', .) %>%
    gsub('[^SIR]', '.', ., ignore.case = TRUE)

  key_abs

}

#' @importFrom dplyr progress_estimated %>%
#' @rdname key_antibiotics
#' @export
key_antibiotics_equal <- function(x,
                                  y,
                                  type = c("keyantibiotics", "points"),
                                  ignore_I = TRUE,
                                  points_threshold = 2,
                                  info = FALSE) {
  # x is active row, y is lag

  type <- type[1]

  if (length(x) != length(y)) {
    stop('Length of `x` and `y` must be equal.')
  }

  # only show progress bar on points or when at least 5000 isolates
  info_needed <- info == TRUE & (type == "points" | length(x) > 5000)

  result <- logical(length(x))

  if (info_needed == TRUE) {
    p <- dplyr::progress_estimated(length(x))
  }

  for (i in 1:length(x)) {

    if (info_needed == TRUE) {
      p$tick()$print()
    }

    if (is.na(x[i])) {
      x[i] <- ''
    }
    if (is.na(y[i])) {
      y[i] <- ''
    }

    if (x[i] == y[i]) {

      result[i] <- TRUE

    } else if (nchar(x[i]) != nchar(y[i])) {

      result[i] <- FALSE

    } else {

      x_split <- strsplit(x[i], "")[[1]]
      y_split <- strsplit(y[i], "")[[1]]

      if (type == 'keyantibiotics') {

        if (ignore_I == TRUE) {
          x_split[x_split == "I"] <- "."
          y_split[y_split == "I"] <- "."
        }

        y_split[x_split == "."] <- "."
        x_split[y_split == "."] <- "."

        result[i] <- all(x_split == y_split)

      } else if (type == 'points') {
        # count points for every single character:
        # - no change is 0 points
        # - I <-> S|R is 0.5 point
        # - S|R <-> R|S is 1 point
        # use the levels of as.rsi (S = 1, I = 2, R = 3)

        suppressWarnings(x_split <- x_split %>% as.rsi() %>% as.double())
        suppressWarnings(y_split <- y_split %>% as.rsi() %>% as.double())

        points <- (x_split - y_split) %>% abs() %>% sum(na.rm = TRUE) / 2
        result[i] <- points >= points_threshold

      } else {
        stop('`', type, '` is not a valid value for type, must be "points" or "keyantibiotics". See ?first_isolate.')
      }
    }
  }
  if (info_needed == TRUE) {
    cat('\n')
  }
  result
}