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334 lines
15 KiB
R
Executable File
334 lines
15 KiB
R
Executable File
# ==================================================================== #
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# TITLE #
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# Antimicrobial Resistance (AMR) Analysis #
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# #
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# SOURCE #
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# https://gitlab.com/msberends/AMR #
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# #
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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 #
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# it for both personal and commercial purposes under the terms of the #
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# GNU General Public License version 2.0 (GNU GPL-2), as published by #
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# the Free Software Foundation. #
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# #
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# This R package was created for academic research and was publicly #
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# released in the hope that it will be useful, but it comes WITHOUT #
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# ANY WARRANTY OR LIABILITY. #
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# Visit our website for more info: https://msberends.gitab.io/AMR. #
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# ==================================================================== #
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#' Calculate resistance of isolates
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#'
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#' @description These functions can be used to calculate the (co-)resistance of microbial isolates (i.e. percentage 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}.
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#'
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#' \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
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#' @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.
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#' @param minimum the minimal amount of available isolates. Any number 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.
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#' @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\%"}.
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#' @param also_single_tested a logical to indicate whether (in combination therapies) also observations should be included where not all antibiotics were tested, but at least one of the tested antibiotics contains a target interpretation (e.g. S in case of \code{portion_S} and R in case of \code{portion_R}). \strong{This would lead to selection bias in almost all cases.}
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#' @param data a \code{data.frame} containing columns with class \code{rsi} (see \code{\link{as.rsi}})
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#' @param translate_ab a column name of the \code{\link{antibiotics}} data set to translate the antibiotic abbreviations to, using \code{\link{abname}}. This can be set with \code{\link{getOption}("get_antibiotic_names")}.
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#' @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. IR (susceptible vs. non-susceptible)
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#' @details \strong{Remember that you should filter your table to let it contain only first isolates!} Use \code{\link{first_isolate}} to determine them in your data set.
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#'
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#' 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. \emph{Low counts can infuence the outcome - these \code{portion} functions may camouflage this, since they only return the portion albeit being dependent on the \code{minimum} parameter.}
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#'
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#' \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 variable with class \code{"rsi"}.
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#'
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#' The old \code{\link{rsi}} function is still available for backwards compatibility but is deprecated.
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#' \if{html}{
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#' \cr\cr
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#' To calculate the probability (\emph{p}) of susceptibility of one antibiotic, we use this formula:
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#' \out{<div style="text-align: center">}\figure{mono_therapy.png}\out{</div>}
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#' To calculate the probability (\emph{p}) of susceptibility of more antibiotics (i.e. combination therapy), we need to check whether one of them has a susceptible result (as numerator) and count all cases where all antibiotics were tested (as denominator). \cr
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#' \cr
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#' For two antibiotics:
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#' \out{<div style="text-align: center">}\figure{combi_therapy_2.png}\out{</div>}
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#' \cr
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#' For three antibiotics:
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#' \out{<div style="text-align: center">}\figure{combi_therapy_3.png}\out{</div>}
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#' \cr
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#' And so on.
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#' }
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#' @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/}.
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#'
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#' Wickham H. \strong{Tidy Data.} The Journal of Statistical Software, vol. 59, 2014. \url{http://vita.had.co.nz/papers/tidy-data.html}
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#' @seealso \code{\link[AMR]{count}_*} to count resistant and susceptible isolates.
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#' @keywords resistance susceptibility rsi_df rsi antibiotics isolate isolates
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#' @return Double or, when \code{as_percent = TRUE}, a character.
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#' @rdname portion
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#' @name portion
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#' @export
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#' @inheritSection AMR Read more on our website!
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#' @examples
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#' # septic_patients is a data set available in the AMR package. It is true, genuine data.
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#' ?septic_patients
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#'
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#' # Calculate resistance
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#' portion_R(septic_patients$amox)
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#' portion_IR(septic_patients$amox)
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#'
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#' # Or susceptibility
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#' portion_S(septic_patients$amox)
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#' portion_SI(septic_patients$amox)
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#'
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#' # Do the above with pipes:
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#' library(dplyr)
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#' septic_patients %>% portion_R(amox)
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#' septic_patients %>% portion_IR(amox)
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#' septic_patients %>% portion_S(amox)
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#' septic_patients %>% portion_SI(amox)
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#'
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#' septic_patients %>%
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#' group_by(hospital_id) %>%
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#' summarise(p = portion_S(cipr),
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#' n = n_rsi(cipr)) # n_rsi works like n_distinct in dplyr
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#'
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#' septic_patients %>%
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#' group_by(hospital_id) %>%
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#' summarise(R = portion_R(cipr, as_percent = TRUE),
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#' I = portion_I(cipr, as_percent = TRUE),
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#' S = portion_S(cipr, as_percent = TRUE),
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#' n = n_rsi(cipr), # works like n_distinct in dplyr
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#' total = n()) # NOT the amount of tested isolates!
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#'
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#' # Calculate co-resistance between amoxicillin/clav acid and gentamicin,
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#' # so we can see that combination therapy does a lot more than mono therapy:
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#' septic_patients %>% portion_S(amcl) # S = 67.1%
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#' septic_patients %>% count_all(amcl) # n = 1576
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#'
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#' septic_patients %>% portion_S(gent) # S = 74.0%
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#' septic_patients %>% count_all(gent) # n = 1855
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#'
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#' septic_patients %>% portion_S(amcl, gent) # S = 92.0%
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#' septic_patients %>% count_all(amcl, gent) # n = 1517
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#'
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#'
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#' septic_patients %>%
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#' group_by(hospital_id) %>%
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#' summarise(cipro_p = portion_S(cipr, as_percent = TRUE),
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#' cipro_n = count_all(cipr),
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#' genta_p = portion_S(gent, as_percent = TRUE),
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#' genta_n = count_all(gent),
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#' combination_p = portion_S(cipr, gent, as_percent = TRUE),
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#' combination_n = count_all(cipr, gent))
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#'
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#' # Get portions S/I/R immediately of all rsi columns
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#' septic_patients %>%
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#' select(amox, cipr) %>%
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#' portion_df(translate = FALSE)
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#'
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#' # It also supports grouping variables
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#' septic_patients %>%
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#' select(hospital_id, amox, cipr) %>%
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#' group_by(hospital_id) %>%
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#' portion_df(translate = FALSE)
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#'
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#'
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#' \dontrun{
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#'
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#' # calculate current empiric combination therapy of Helicobacter gastritis:
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#' my_table %>%
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#' filter(first_isolate == TRUE,
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#' genus == "Helicobacter") %>%
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#' summarise(p = portion_S(amox, metr), # amoxicillin with metronidazole
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#' n = count_all(amox, metr))
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#' }
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portion_R <- function(...,
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minimum = 30,
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as_percent = FALSE,
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also_single_tested = FALSE) {
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rsi_calc(...,
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type = "R",
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include_I = FALSE,
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minimum = minimum,
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as_percent = as_percent,
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also_single_tested = also_single_tested,
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only_count = FALSE)
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}
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#' @rdname portion
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#' @export
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portion_IR <- function(...,
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minimum = 30,
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as_percent = FALSE,
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also_single_tested = FALSE) {
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rsi_calc(...,
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type = "R",
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include_I = TRUE,
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minimum = minimum,
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as_percent = as_percent,
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also_single_tested = also_single_tested,
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only_count = FALSE)
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}
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#' @rdname portion
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#' @export
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portion_I <- function(...,
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minimum = 30,
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as_percent = FALSE,
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also_single_tested = FALSE) {
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rsi_calc(...,
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type = "I",
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include_I = FALSE,
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minimum = minimum,
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as_percent = as_percent,
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also_single_tested = also_single_tested,
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only_count = FALSE)
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}
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#' @rdname portion
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#' @export
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portion_SI <- function(...,
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minimum = 30,
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as_percent = FALSE,
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also_single_tested = FALSE) {
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rsi_calc(...,
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type = "S",
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include_I = TRUE,
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minimum = minimum,
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as_percent = as_percent,
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also_single_tested = also_single_tested,
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only_count = FALSE)
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}
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#' @rdname portion
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#' @export
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portion_S <- function(...,
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minimum = 30,
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as_percent = FALSE,
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also_single_tested = FALSE) {
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rsi_calc(...,
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type = "S",
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include_I = FALSE,
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minimum = minimum,
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as_percent = as_percent,
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also_single_tested = also_single_tested,
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only_count = FALSE)
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}
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#' @rdname portion
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#' @importFrom dplyr %>% select_if bind_rows summarise_if mutate group_vars select everything
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#' @export
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portion_df <- function(data,
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translate_ab = getOption("get_antibiotic_names", "official"),
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minimum = 30,
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as_percent = FALSE,
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combine_IR = FALSE) {
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if (!"data.frame" %in% class(data)) {
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stop("`portion_df` must be called on a data.frame")
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}
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if (data %>% select_if(is.rsi) %>% ncol() == 0) {
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stop("No columns with class 'rsi' found. See ?as.rsi.")
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}
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if (as.character(translate_ab) == "TRUE") {
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translate_ab <- "official"
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}
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options(get_antibiotic_names = translate_ab)
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resS <- summarise_if(.tbl = data,
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.predicate = is.rsi,
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.funs = portion_S,
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minimum = minimum,
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as_percent = as_percent) %>%
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mutate(Interpretation = "S") %>%
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select(Interpretation, everything())
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if (combine_IR == FALSE) {
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resI <- summarise_if(.tbl = data,
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.predicate = is.rsi,
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.funs = portion_I,
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minimum = minimum,
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as_percent = as_percent) %>%
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mutate(Interpretation = "I") %>%
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select(Interpretation, everything())
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resR <- summarise_if(.tbl = data,
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.predicate = is.rsi,
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.funs = portion_R,
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minimum = minimum,
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as_percent = as_percent) %>%
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mutate(Interpretation = "R") %>%
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select(Interpretation, everything())
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data.groups <- group_vars(data)
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res <- bind_rows(resS, resI, resR) %>%
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mutate(Interpretation = factor(Interpretation, levels = c("R", "I", "S"), ordered = TRUE)) %>%
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tidyr::gather(Antibiotic, Value, -Interpretation, -data.groups)
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} else {
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resIR <- summarise_if(.tbl = data,
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.predicate = is.rsi,
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.funs = portion_IR,
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minimum = minimum,
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as_percent = as_percent) %>%
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mutate(Interpretation = "IR") %>%
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select(Interpretation, everything())
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data.groups <- group_vars(data)
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res <- bind_rows(resS, resIR) %>%
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mutate(Interpretation = factor(Interpretation, levels = c("IR", "S"), ordered = TRUE)) %>%
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tidyr::gather(Antibiotic, Value, -Interpretation, -data.groups)
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}
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if (!translate_ab == FALSE) {
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if (!tolower(translate_ab) %in% tolower(colnames(AMR::antibiotics))) {
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stop("Parameter `translate_ab` does not occur in the `antibiotics` data set.", call. = FALSE)
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}
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res <- res %>% mutate(Antibiotic = abname(Antibiotic, from = "guess", to = translate_ab))
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}
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res
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}
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#' Calculate resistance of isolates
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#'
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#' This function is deprecated. Use the \code{\link{portion}} functions instead.
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#' @inheritParams portion
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#' @param ab1,ab2 vector (or column) with antibiotic interpretations. It will be transformed internally with \code{\link{as.rsi}} if needed.
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#' @param interpretation antimicrobial interpretation to check for
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#' @param ... deprecated parameters to support usage on older versions
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#' @importFrom dplyr tibble case_when
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#' @export
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rsi <- function(ab1,
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ab2 = NULL,
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interpretation = "IR",
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minimum = 30,
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as_percent = FALSE,
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...) {
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.Deprecated(new = paste0("portion_", interpretation))
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if (all(is.null(ab2))) {
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df <- tibble(ab1 = ab1)
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} else {
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df <- tibble(ab1 = ab1,
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ab2 = ab2)
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}
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if (!interpretation %in% c("S", "SI", "IS", "I", "RI", "IR", "R")) {
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stop("invalid interpretation")
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}
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result <- case_when(
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interpretation == "S" ~ portion_S(df, minimum = minimum, as_percent = FALSE),
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interpretation %in% c("SI", "IS") ~ portion_SI(df, minimum = minimum, as_percent = FALSE),
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interpretation == "I" ~ portion_I(df, minimum = minimum, as_percent = FALSE),
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interpretation %in% c("RI", "IR") ~ portion_IR(df, minimum = minimum, as_percent = FALSE),
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interpretation == "R" ~ portion_R(df, minimum = minimum, as_percent = FALSE))
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if (as_percent == TRUE) {
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percent(result, force_zero = TRUE)
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} else {
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result
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}
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}
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