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# ==================================================================== #
# TITLE #
# Antimicrobial Resistance (AMR) Analysis #
# #
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# SOURCE #
# https://gitlab.com/msberends/AMR #
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# #
# LICENCE #
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# (c) 2019 Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl) #
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# #
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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 #
# GNU General Public License version 2.0 (GNU GPL-2), as published by #
# the Free Software Foundation. #
# #
# 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. #
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# Visit our website for more info: https://msberends.gitlab.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.
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#' @param x table with antibiotics coloms, like \code{AMX} or \code{amox}
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#' @param y,z characters to compare
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#' @inheritParams first_isolate
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#' @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}}.
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#' @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}}.
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#' @param warnings give warning about missing antibiotic columns, they will anyway be ignored
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#' @param ... other parameters passed on to function
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#' @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.
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#'
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#' At default, the antibiotics that are used for \strong{Gram-positive bacteria} are: \cr
#' amoxicillin, amoxicillin/clavulanic acid, cefuroxime, piperacillin/tazobactam, ciprofloxacin, trimethoprim/sulfamethoxazole (until here is universal), vancomycin, teicoplanin, tetracycline, erythromycin, oxacillin, rifampin.
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#'
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#' At default, the antibiotics that are used for \strong{Gram-negative bacteria} are: \cr
#' amoxicillin, amoxicillin/clavulanic acid, cefuroxime, piperacillin/tazobactam, ciprofloxacin, trimethoprim/sulfamethoxazole (until here is universal), gentamicin, tobramycin, colistin, cefotaxime, ceftazidime, meropenem.
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#'
#'
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#' The function \code{key_antibiotics_equal} checks the characters returned by \code{key_antibiotics} for equality, and returns a logical vector.
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#' @inheritSection first_isolate Key antibiotics
#' @rdname key_antibiotics
#' @export
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#' @importFrom dplyr %>% mutate if_else pull
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#' @importFrom crayon blue bold
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#' @seealso \code{\link{first_isolate}}
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#' @inheritSection AMR Read more on our website!
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#' @examples
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#' # `example_isolates` is a dataset available in the AMR package.
#' # See ?example_isolates.
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#'
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#' library(dplyr)
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#' # set key antibiotics to a new variable
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#' my_patients <- example_isolates %>%
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#' mutate(keyab = key_antibiotics(.)) %>%
#' mutate(
#' # now calculate first isolates
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#' first_regular = first_isolate(., col_keyantibiotics = FALSE),
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#' # and first WEIGHTED isolates
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#' first_weighted = first_isolate(., col_keyantibiotics = "keyab")
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#' )
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#'
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#' # 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)
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#'
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#'
#' # output of the `key_antibiotics` function could be like this:
#' strainA <- "SSSRR.S.R..S"
#' strainB <- "SSSIRSSSRSSS"
#'
#' key_antibiotics_equal(strainA, strainB)
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#' # TRUE, because I is ignored (as well as missing values)
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#'
#' key_antibiotics_equal(strainA, strainB, ignore_I = FALSE)
#' # FALSE, because I is not ignored and so the 4th value differs
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key_antibiotics <- function ( x ,
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col_mo = NULL ,
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universal_1 = guess_ab_col ( x , " amoxicillin" ) ,
universal_2 = guess_ab_col ( x , " amoxicillin/clavulanic acid" ) ,
universal_3 = guess_ab_col ( x , " cefuroxime" ) ,
universal_4 = guess_ab_col ( x , " piperacillin/tazobactam" ) ,
universal_5 = guess_ab_col ( x , " ciprofloxacin" ) ,
universal_6 = guess_ab_col ( x , " trimethoprim/sulfamethoxazole" ) ,
GramPos_1 = guess_ab_col ( x , " vancomycin" ) ,
GramPos_2 = guess_ab_col ( x , " teicoplanin" ) ,
GramPos_3 = guess_ab_col ( x , " tetracycline" ) ,
GramPos_4 = guess_ab_col ( x , " erythromycin" ) ,
GramPos_5 = guess_ab_col ( x , " oxacillin" ) ,
GramPos_6 = guess_ab_col ( x , " rifampin" ) ,
GramNeg_1 = guess_ab_col ( x , " gentamicin" ) ,
GramNeg_2 = guess_ab_col ( x , " tobramycin" ) ,
GramNeg_3 = guess_ab_col ( x , " colistin" ) ,
GramNeg_4 = guess_ab_col ( x , " cefotaxime" ) ,
GramNeg_5 = guess_ab_col ( x , " ceftazidime" ) ,
GramNeg_6 = guess_ab_col ( x , " meropenem" ) ,
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warnings = TRUE ,
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... ) {
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# try to find columns based on type
# -- mo
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if ( is.null ( col_mo ) ) {
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col_mo <- search_type_in_df ( x = x , type = " mo" )
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}
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if ( is.null ( col_mo ) ) {
stop ( " `col_mo` must be set." , call. = FALSE )
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}
# check columns
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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 )
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check_available_columns <- function ( x , col.list , info = TRUE ) {
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# check columns
col.list <- col.list [ ! is.na ( col.list ) & ! is.null ( col.list ) ]
names ( col.list ) <- col.list
col.list.bak <- col.list
# are they available as upper case or lower case then?
for ( i in 1 : length ( col.list ) ) {
if ( is.null ( col.list [i ] ) | isTRUE ( is.na ( col.list [i ] ) ) ) {
col.list [i ] <- NA
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} else if ( toupper ( col.list [i ] ) %in% colnames ( x ) ) {
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col.list [i ] <- toupper ( col.list [i ] )
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} else if ( tolower ( col.list [i ] ) %in% colnames ( x ) ) {
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col.list [i ] <- tolower ( col.list [i ] )
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} else if ( ! col.list [i ] %in% colnames ( x ) ) {
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col.list [i ] <- NA
}
}
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if ( ! all ( col.list %in% colnames ( x ) ) ) {
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if ( info == TRUE ) {
warning ( ' Some columns do not exist and will be ignored: ' ,
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col.list.bak [ ! ( col.list %in% colnames ( x ) ) ] %>% toString ( ) ,
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' .\nTHIS MAY STRONGLY INFLUENCE THE OUTCOME.' ,
immediate. = TRUE ,
call. = FALSE )
}
}
col.list
}
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col.list <- check_available_columns ( x = x , col.list = col.list , info = warnings )
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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 ]
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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 )
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gram_positive <- gram_positive [ ! is.null ( gram_positive ) ]
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gram_positive <- gram_positive [ ! is.na ( gram_positive ) ]
if ( length ( gram_positive ) < 12 ) {
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warning ( " only using " , length ( gram_positive ) , " different antibiotics as key antibiotics for Gram-positives. See ?key_antibiotics." , call. = FALSE )
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}
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gram_negative = c ( universal ,
GramNeg_1 , GramNeg_2 , GramNeg_3 ,
GramNeg_4 , GramNeg_5 , GramNeg_6 )
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gram_negative <- gram_negative [ ! is.null ( gram_negative ) ]
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gram_negative <- gram_negative [ ! is.na ( gram_negative ) ]
if ( length ( gram_negative ) < 12 ) {
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warning ( " only using " , length ( gram_negative ) , " different antibiotics as key antibiotics for Gram-negatives. See ?key_antibiotics." , call. = FALSE )
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}
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# join to microorganisms data set
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x <- x %>%
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mutate_at ( vars ( col_mo ) , as.mo ) %>%
left_join_microorganisms ( by = col_mo ) %>%
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mutate ( key_ab = NA_character_ ,
gramstain = mo_gramstain ( pull ( ., col_mo ) ) )
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# Gram +
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x <- x %>% mutate ( key_ab =
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if_else ( gramstain == " Gram-positive" ,
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apply ( X = x [ , gram_positive ] ,
MARGIN = 1 ,
FUN = function ( x ) paste ( x , collapse = " " ) ) ,
key_ab ) )
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# Gram -
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x <- x %>% mutate ( key_ab =
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if_else ( gramstain == " Gram-negative" ,
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apply ( X = x [ , gram_negative ] ,
MARGIN = 1 ,
FUN = function ( x ) paste ( x , collapse = " " ) ) ,
key_ab ) )
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# format
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key_abs <- x %>%
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pull ( key_ab ) %>%
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gsub ( ' (NA|NULL)' , ' .' , .) %>%
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gsub ( ' [^SIR]' , ' .' , ., ignore.case = TRUE ) %>%
toupper ( )
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key_abs
}
#' @importFrom dplyr progress_estimated %>%
#' @rdname key_antibiotics
#' @export
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key_antibiotics_equal <- function ( y ,
z ,
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type = c ( " keyantibiotics" , " points" ) ,
ignore_I = TRUE ,
points_threshold = 2 ,
info = FALSE ) {
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# y is active row, z is lag
x <- y
y <- z
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type <- type [1 ]
if ( length ( x ) != length ( y ) ) {
stop ( ' Length of `x` and `y` must be equal.' )
}
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# only show progress bar on points or when at least 5000 isolates
info_needed <- info == TRUE & ( type == " points" | length ( x ) > 5000 )
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result <- logical ( length ( x ) )
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if ( info_needed == TRUE ) {
p <- dplyr :: progress_estimated ( length ( x ) )
}
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for ( i in 1 : length ( x ) ) {
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if ( info_needed == TRUE ) {
p $ tick ( ) $ print ( )
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}
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if ( is.na ( x [i ] ) ) {
x [i ] <- ' '
}
if ( is.na ( y [i ] ) ) {
y [i ] <- ' '
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}
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if ( x [i ] == y [i ] ) {
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result [i ] <- TRUE
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} else if ( nchar ( x [i ] ) != nchar ( y [i ] ) ) {
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result [i ] <- FALSE
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} else {
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x_split <- strsplit ( x [i ] , " " ) [ [1 ] ]
y_split <- strsplit ( y [i ] , " " ) [ [1 ] ]
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if ( type == ' keyantibiotics' ) {
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if ( ignore_I == TRUE ) {
x_split [x_split == " I" ] <- " ."
y_split [y_split == " I" ] <- " ."
}
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y_split [x_split == " ." ] <- " ."
x_split [y_split == " ." ] <- " ."
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result [i ] <- all ( x_split == y_split )
} else if ( type == ' points' ) {
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# 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)
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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
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} else {
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stop ( ' `' , type , ' ` is not a valid value for type, must be "points" or "keyantibiotics". See ?key_antibiotics' )
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}
}
}
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if ( info_needed == TRUE ) {
cat ( ' \n' )
}
result
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}