# ==================================================================== # # TITLE # # Antimicrobial Resistance (AMR) Analysis # # # # SOURCE # # https://gitlab.com/msberends/AMR # # # # LICENCE # # (c) 2019 Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl) # # # # 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. # # Visit our website for more info: https://msberends.gitlab.io/AMR. # # ==================================================================== # #' Calculate resistance of isolates #' #' @description These functions can be used to calculate the (co-)resistance of microbial isolates (i.e. percentage of S, SI, I, IR or R). All functions support quasiquotation with pipes, can be used in \code{dplyr}s \code{\link[dplyr]{summarise}} and support grouped variables, see \emph{Examples}. #' #' \code{portion_R} and \code{portion_IR} can be used to calculate resistance, \code{portion_S} and \code{portion_SI} can be used to calculate susceptibility.\cr #' @param ... one or more vectors (or columns) with antibiotic interpretations. They will be transformed internally with \code{\link{as.rsi}} if needed. Use multiple columns to calculate (the lack of) co-resistance: the probability where one of two drugs have a resistant or susceptible result. See Examples. #' @param minimum the minimum allowed number of available (tested) isolates. Any isolate count lower than \code{minimum} will return \code{NA} with a warning. The default number of \code{30} isolates is advised by the Clinical and Laboratory Standards Institute (CLSI) as best practice, see Source. #' @param as_percent a logical to indicate whether the output must be returned as a hundred fold with \% sign (a character). A value of \code{0.123456} will then be returned as \code{"12.3\%"}. #' @param only_all_tested (for combination therapies, i.e. using more than one variable for \code{...}) a logical to indicate that isolates must be tested for all antibiotics, see section \emph{Combination therapy} below #' @param data a \code{data.frame} containing columns with class \code{rsi} (see \code{\link{as.rsi}}) #' @param translate_ab a column name of the \code{\link{antibiotics}} data set to translate the antibiotic abbreviations to, using \code{\link{ab_property}} #' @inheritParams ab_property #' @param combine_SI a logical to indicate whether all values of S and I must be merged into one, so the output only consists of S+I vs. R (susceptible vs. resistant). This used to be the parameter \code{combine_IR}, but this now follows the redefinition by EUCAST about the interpretion of I (increased exposure) in 2019, see section 'Interpretation of S, I and R' below. Default is \code{TRUE}. #' @param combine_IR a logical to indicate whether all values of I and R must be merged into one, so the output only consists of S vs. I+R (susceptible vs. non-susceptible). This is outdated, see parameter \code{combine_SI}. #' @inheritSection as.rsi Interpretation of S, I and R #' @details \strong{Remember that you should filter your table to let it contain only first isolates!} This is needed to exclude duplicates and to reduce selection bias. Use \code{\link{first_isolate}} to determine them in your data set. #' #' These functions are not meant to count isolates, but to calculate the portion of resistance/susceptibility. Use the \code{\link[AMR]{count}} functions to count isolates. The function \code{portion_SI()} is essentially equal to \code{count_SI() / count_all()}. \emph{Low counts can infuence the outcome - the \code{portion} functions may camouflage this, since they only return the portion (albeit being dependent on the \code{minimum} parameter).} #' #' The function \code{portion_df} takes any variable from \code{data} that has an \code{"rsi"} class (created with \code{\link{as.rsi}}) and calculates the portions R, I and S. The resulting \emph{tidy data} (see Source) \code{data.frame} will have three rows (S/I/R) and a column for each group and each variable with class \code{"rsi"}. #' #' The function \code{rsi_df} works exactly like \code{portion_df}, but adds the number of isolates. #' @section Combination therapy: #' When using more than one variable for \code{...} (= combination therapy)), use \code{only_all_tested} to only count isolates that are tested for all antibiotics/variables that you test them for. See this example for two antibiotics, Antibiotic A and Antibiotic B, about how \code{portion_SI} works to calculate the \%SI: #' #' \preformatted{ #' ------------------------------------------------------------------------- #' only_all_tested = FALSE only_all_tested = TRUE #' Antibiotic Antibiotic ----------------------- ----------------------- #' A B include as include as include as include as #' numerator denominator numerator denominator #' ---------- ---------- ---------- ----------- ---------- ----------- #' S S X X X X #' I S X X X X #' R S X X X X #' not tested S X X - - #' S I X X X X #' I I X X X X #' R I X X X X #' not tested I X X - - #' S R X X X X #' I R X X X X #' R R - X - X #' not tested R - - - - #' S not tested X X - - #' I not tested X X - - #' R not tested - - - - #' not tested not tested - - - - #' ------------------------------------------------------------------------- #' } #' #' Please note that, in combination therapies, for \code{only_all_tested = TRUE} applies that: #' \preformatted{ #' count_S() + count_I() + count_R() == count_all() #' portion_S() + portion_I() + portion_R() == 1 #' } #' and that, in combination therapies, for \code{only_all_tested = FALSE} applies that: #' \preformatted{ #' count_S() + count_I() + count_R() >= count_all() #' portion_S() + portion_I() + portion_R() >= 1 #' } #' #' Using \code{only_all_tested} has no impact when only using one antibiotic as input. #' @source \strong{M39 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 4th Edition}, 2014, \emph{Clinical and Laboratory Standards Institute (CLSI)}. \url{https://clsi.org/standards/products/microbiology/documents/m39/}. #' #' Wickham H. \strong{Tidy Data.} The Journal of Statistical Software, vol. 59, 2014. \url{http://vita.had.co.nz/papers/tidy-data.html} #' @seealso \code{\link[AMR]{count}_*} to count resistant and susceptible isolates. #' @keywords resistance susceptibility rsi_df rsi antibiotics isolate isolates #' @return Double or, when \code{as_percent = TRUE}, a character. #' @rdname portion #' @name portion #' @export #' @inheritSection AMR Read more on our website! #' @examples #' # septic_patients is a data set available in the AMR package. It is true, genuine data. #' ?septic_patients #' #' # Calculate resistance #' portion_R(septic_patients$AMX) #' portion_IR(septic_patients$AMX) #' #' # Or susceptibility #' portion_S(septic_patients$AMX) #' portion_SI(septic_patients$AMX) #' #' # Do the above with pipes: #' library(dplyr) #' septic_patients %>% portion_R(AMX) #' septic_patients %>% portion_IR(AMX) #' septic_patients %>% portion_S(AMX) #' septic_patients %>% portion_SI(AMX) #' #' septic_patients %>% #' group_by(hospital_id) %>% #' summarise(p = portion_SI(CIP), #' n = n_rsi(CIP)) # n_rsi works like n_distinct in dplyr #' #' septic_patients %>% #' group_by(hospital_id) %>% #' summarise(R = portion_R(CIP, as_percent = TRUE), #' I = portion_I(CIP, as_percent = TRUE), #' S = portion_S(CIP, as_percent = TRUE), #' n1 = count_all(CIP), # the actual total; sum of all three #' n2 = n_rsi(CIP), # same - analogous to n_distinct #' total = n()) # NOT the number of tested isolates! #' #' # Calculate co-resistance between amoxicillin/clav acid and gentamicin, #' # so we can see that combination therapy does a lot more than mono therapy: #' septic_patients %>% portion_SI(AMC) # %SI = 76.3% #' septic_patients %>% count_all(AMC) # n = 1879 #' #' septic_patients %>% portion_SI(GEN) # %SI = 75.4% #' septic_patients %>% count_all(GEN) # n = 1855 #' #' septic_patients %>% portion_SI(AMC, GEN) # %SI = 94.1% #' septic_patients %>% count_all(AMC, GEN) # n = 1939 #' #' #' # See Details on how `only_all_tested` works. Example: #' septic_patients %>% #' summarise(numerator = count_SI(AMC, GEN), #' denominator = count_all(AMC, GEN), #' portion = portion_SI(AMC, GEN)) #' # numerator denominator portion #' # 1764 1936 0.9408 #' septic_patients %>% #' summarise(numerator = count_SI(AMC, GEN, only_all_tested = TRUE), #' denominator = count_all(AMC, GEN, only_all_tested = TRUE), #' portion = portion_SI(AMC, GEN, only_all_tested = TRUE)) #' # numerator denominator portion #' # 1687 1798 0.9383 #' #' #' septic_patients %>% #' group_by(hospital_id) %>% #' summarise(cipro_p = portion_SI(CIP, as_percent = TRUE), #' cipro_n = count_all(CIP), #' genta_p = portion_SI(GEN, as_percent = TRUE), #' genta_n = count_all(GEN), #' combination_p = portion_SI(CIP, GEN, as_percent = TRUE), #' combination_n = count_all(CIP, GEN)) #' #' # Get portions S/I/R immediately of all rsi columns #' septic_patients %>% #' select(AMX, CIP) %>% #' portion_df(translate = FALSE) #' #' # It also supports grouping variables #' septic_patients %>% #' select(hospital_id, AMX, CIP) %>% #' group_by(hospital_id) %>% #' portion_df(translate = FALSE) #' #' #' \dontrun{ #' #' # calculate current empiric combination therapy of Helicobacter gastritis: #' my_table %>% #' filter(first_isolate == TRUE, #' genus == "Helicobacter") %>% #' summarise(p = portion_S(AMX, MTR), # amoxicillin with metronidazole #' n = count_all(AMX, MTR)) #' } portion_R <- function(..., minimum = 30, as_percent = FALSE, only_all_tested = FALSE) { rsi_calc(..., ab_result = "R", minimum = minimum, as_percent = as_percent, only_all_tested = only_all_tested, only_count = FALSE) } #' @rdname portion #' @export portion_IR <- function(..., minimum = 30, as_percent = FALSE, only_all_tested = FALSE) { rsi_calc(..., ab_result = c("I", "R"), minimum = minimum, as_percent = as_percent, only_all_tested = only_all_tested, only_count = FALSE) } #' @rdname portion #' @export portion_I <- function(..., minimum = 30, as_percent = FALSE, only_all_tested = FALSE) { rsi_calc(..., ab_result = "I", minimum = minimum, as_percent = as_percent, only_all_tested = only_all_tested, only_count = FALSE) } #' @rdname portion #' @export portion_SI <- function(..., minimum = 30, as_percent = FALSE, only_all_tested = FALSE) { rsi_calc(..., ab_result = c("S", "I"), minimum = minimum, as_percent = as_percent, only_all_tested = only_all_tested, only_count = FALSE) } #' @rdname portion #' @export portion_S <- function(..., minimum = 30, as_percent = FALSE, only_all_tested = FALSE) { rsi_calc(..., ab_result = "S", minimum = minimum, as_percent = as_percent, only_all_tested = only_all_tested, only_count = FALSE) } #' @rdname portion #' @importFrom dplyr %>% select_if bind_rows summarise_if mutate group_vars select everything #' @export portion_df <- function(data, translate_ab = "name", language = get_locale(), minimum = 30, as_percent = FALSE, combine_SI = TRUE, combine_IR = FALSE) { rsi_calc_df(type = "portion", data = data, translate_ab = translate_ab, language = language, minimum = minimum, as_percent = as_percent, combine_SI = combine_SI, combine_IR = combine_IR, combine_SI_missing = missing(combine_SI)) }