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279 lines
14 KiB
R
Executable File
279 lines
14 KiB
R
Executable File
# ==================================================================== #
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# TITLE #
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# Antimicrobial Resistance (AMR) Analysis for R #
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# #
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# SOURCE #
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# https://github.com/msberends/AMR #
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# #
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# LICENCE #
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# (c) 2018-2021 Berends MS, Luz CF et al. #
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# Developed at the University of Groningen, the Netherlands, in #
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# collaboration with non-profit organisations Certe Medical #
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# Diagnostics & Advice, and University Medical Center Groningen. #
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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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# We created this package for both routine data analysis and academic #
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# research and it was publicly released in the hope that it will be #
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# useful, but it comes WITHOUT ANY WARRANTY OR LIABILITY. #
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# #
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# Visit our website for the full manual and a complete tutorial about #
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# how to conduct AMR analysis: https://msberends.github.io/AMR/ #
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# ==================================================================== #
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#' Calculate microbial resistance
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#'
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#' @description These functions can be used to calculate the (co-)resistance or susceptibility of microbial isolates (i.e. percentage of S, SI, I, IR or R). All functions support quasiquotation with pipes, can be used in `summarise()` from the `dplyr` package and also support grouped variables, please see *Examples*.
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#'
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#' [resistance()] should be used to calculate resistance, [susceptibility()] should be used to calculate susceptibility.\cr
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#' @inheritSection lifecycle Stable lifecycle
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#' @param ... one or more vectors (or columns) with antibiotic interpretations. They will be transformed internally with [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 minimum allowed number of available (tested) isolates. Any isolate count lower than `minimum` will return `NA` with a warning. The default number of `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 `0.123456` will then be returned as `"12.3%"`.
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#' @param only_all_tested (for combination therapies, i.e. using more than one variable for `...`): a logical to indicate that isolates must be tested for all antibiotics, see section *Combination therapy* below
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#' @param data a [data.frame] containing columns with class [`rsi`] (see [as.rsi()])
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#' @param translate_ab a column name of the [antibiotics] data set to translate the antibiotic abbreviations to, using [ab_property()]
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#' @inheritParams ab_property
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#' @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 argument `combine_IR`, but this now follows the redefinition by EUCAST about the interpretation of I (increased exposure) in 2019, see section 'Interpretation of S, I and R' below. Default is `TRUE`.
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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. I+R (susceptible vs. non-susceptible). This is outdated, see argument `combine_SI`.
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#' @inheritSection as.rsi Interpretation of R and S/I
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#' @details
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#' The function [resistance()] is equal to the function [proportion_R()]. The function [susceptibility()] is equal to the function [proportion_SI()].
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#'
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#' **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 [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 proportion of resistance/susceptibility. Use the [`count()`][AMR::count()] functions to count isolates. The function [susceptibility()] is essentially equal to `count_susceptible() / count_all()`. *Low counts can influence the outcome - the `proportion` functions may camouflage this, since they only return the proportion (albeit being dependent on the `minimum` argument).*
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#'
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#' The function [proportion_df()] takes any variable from `data` that has an [`rsi`] class (created with [as.rsi()]) and calculates the proportions R, I and S. It also supports grouped variables. The function [rsi_df()] works exactly like [proportion_df()], but adds the number of isolates.
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#' @section Combination therapy:
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#' When using more than one variable for `...` (= combination therapy), use `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, Drug A and Drug B, about how [susceptibility()] works to calculate the %SI:
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#'
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#' ```
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#' --------------------------------------------------------------------
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#' only_all_tested = FALSE only_all_tested = TRUE
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#' ----------------------- -----------------------
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#' Drug A Drug B include as include as include as include as
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#' numerator denominator numerator denominator
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#' -------- -------- ---------- ----------- ---------- -----------
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#' S or I S or I X X X X
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#' R S or I X X X X
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#' <NA> S or I X X - -
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#' S or I R X X X X
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#' R R - X - X
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#' <NA> R - - - -
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#' S or I <NA> X X - -
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#' R <NA> - - - -
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#' <NA> <NA> - - - -
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#' --------------------------------------------------------------------
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#' ```
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#'
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#' Please note that, in combination therapies, for `only_all_tested = TRUE` applies that:
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#' ```
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#' count_S() + count_I() + count_R() = count_all()
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#' proportion_S() + proportion_I() + proportion_R() = 1
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#' ```
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#' and that, in combination therapies, for `only_all_tested = FALSE` applies that:
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#' ```
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#' count_S() + count_I() + count_R() >= count_all()
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#' proportion_S() + proportion_I() + proportion_R() >= 1
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#' ```
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#'
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#' Using `only_all_tested` has no impact when only using one antibiotic as input.
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#' @source **M39 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 4th Edition**, 2014, *Clinical and Laboratory Standards Institute (CLSI)*. <https://clsi.org/standards/products/microbiology/documents/m39/>.
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#' @seealso [AMR::count()] to count resistant and susceptible isolates.
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#' @return A [double] or, when `as_percent = TRUE`, a [character].
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#' @rdname proportion
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#' @aliases portion
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#' @name proportion
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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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#' # example_isolates is a data set available in the AMR package.
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#' ?example_isolates
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#'
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#' resistance(example_isolates$AMX) # determines %R
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#' susceptibility(example_isolates$AMX) # determines %S+I
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#'
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#' # be more specific
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#' proportion_S(example_isolates$AMX)
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#' proportion_SI(example_isolates$AMX)
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#' proportion_I(example_isolates$AMX)
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#' proportion_IR(example_isolates$AMX)
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#' proportion_R(example_isolates$AMX)
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#'
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#' if (require("dplyr")) {
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#' example_isolates %>%
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#' group_by(hospital_id) %>%
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#' summarise(r = resistance(CIP),
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#' n = n_rsi(CIP)) # n_rsi works like n_distinct in dplyr, see ?n_rsi
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#'
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#' example_isolates %>%
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#' group_by(hospital_id) %>%
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#' summarise(R = resistance(CIP, as_percent = TRUE),
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#' SI = susceptibility(CIP, as_percent = TRUE),
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#' n1 = count_all(CIP), # the actual total; sum of all three
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#' n2 = n_rsi(CIP), # same - analogous to n_distinct
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#' total = n()) # NOT the number 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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#' example_isolates %>% susceptibility(AMC) # %SI = 76.3%
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#' example_isolates %>% count_all(AMC) # n = 1879
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#'
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#' example_isolates %>% susceptibility(GEN) # %SI = 75.4%
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#' example_isolates %>% count_all(GEN) # n = 1855
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#'
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#' example_isolates %>% susceptibility(AMC, GEN) # %SI = 94.1%
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#' example_isolates %>% count_all(AMC, GEN) # n = 1939
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#'
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#'
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#' # See Details on how `only_all_tested` works. Example:
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#' example_isolates %>%
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#' summarise(numerator = count_susceptible(AMC, GEN),
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#' denominator = count_all(AMC, GEN),
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#' proportion = susceptibility(AMC, GEN))
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#'
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#' example_isolates %>%
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#' summarise(numerator = count_susceptible(AMC, GEN, only_all_tested = TRUE),
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#' denominator = count_all(AMC, GEN, only_all_tested = TRUE),
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#' proportion = susceptibility(AMC, GEN, only_all_tested = TRUE))
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#'
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#'
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#' example_isolates %>%
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#' group_by(hospital_id) %>%
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#' summarise(cipro_p = susceptibility(CIP, as_percent = TRUE),
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#' cipro_n = count_all(CIP),
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#' genta_p = susceptibility(GEN, as_percent = TRUE),
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#' genta_n = count_all(GEN),
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#' combination_p = susceptibility(CIP, GEN, as_percent = TRUE),
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#' combination_n = count_all(CIP, GEN))
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#'
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#' # Get proportions S/I/R immediately of all rsi columns
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#' example_isolates %>%
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#' select(AMX, CIP) %>%
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#' proportion_df(translate = FALSE)
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#'
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#' # It also supports grouping variables
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#' example_isolates %>%
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#' select(hospital_id, AMX, CIP) %>%
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#' group_by(hospital_id) %>%
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#' proportion_df(translate = FALSE)
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#' }
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resistance <- function(...,
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minimum = 30,
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as_percent = FALSE,
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only_all_tested = FALSE) {
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rsi_calc(...,
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ab_result = "R",
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minimum = minimum,
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as_percent = as_percent,
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only_all_tested = only_all_tested,
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only_count = FALSE)
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}
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#' @rdname proportion
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#' @export
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susceptibility <- function(...,
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minimum = 30,
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as_percent = FALSE,
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only_all_tested = FALSE) {
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rsi_calc(...,
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ab_result = c("S", "I"),
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minimum = minimum,
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as_percent = as_percent,
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only_all_tested = only_all_tested,
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only_count = FALSE)
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}
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#' @rdname proportion
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#' @export
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proportion_R <- function(...,
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minimum = 30,
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as_percent = FALSE,
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only_all_tested = FALSE) {
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rsi_calc(...,
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ab_result = "R",
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minimum = minimum,
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as_percent = as_percent,
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only_all_tested = only_all_tested,
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only_count = FALSE)
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}
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#' @rdname proportion
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#' @export
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proportion_IR <- function(...,
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minimum = 30,
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as_percent = FALSE,
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only_all_tested = FALSE) {
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rsi_calc(...,
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ab_result = c("I", "R"),
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minimum = minimum,
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as_percent = as_percent,
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only_all_tested = only_all_tested,
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only_count = FALSE)
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}
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#' @rdname proportion
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#' @export
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proportion_I <- function(...,
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minimum = 30,
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as_percent = FALSE,
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only_all_tested = FALSE) {
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rsi_calc(...,
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ab_result = "I",
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minimum = minimum,
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as_percent = as_percent,
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only_all_tested = only_all_tested,
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only_count = FALSE)
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}
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#' @rdname proportion
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#' @export
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proportion_SI <- function(...,
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minimum = 30,
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as_percent = FALSE,
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only_all_tested = FALSE) {
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rsi_calc(...,
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ab_result = c("S", "I"),
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minimum = minimum,
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as_percent = as_percent,
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only_all_tested = only_all_tested,
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only_count = FALSE)
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}
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#' @rdname proportion
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#' @export
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proportion_S <- function(...,
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minimum = 30,
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as_percent = FALSE,
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only_all_tested = FALSE) {
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rsi_calc(...,
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ab_result = "S",
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minimum = minimum,
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as_percent = as_percent,
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only_all_tested = only_all_tested,
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only_count = FALSE)
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}
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#' @rdname proportion
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#' @export
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proportion_df <- function(data,
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translate_ab = "name",
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language = get_locale(),
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minimum = 30,
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as_percent = FALSE,
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combine_SI = TRUE,
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combine_IR = FALSE) {
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rsi_calc_df(type = "proportion",
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data = data,
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translate_ab = translate_ab,
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language = language,
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minimum = minimum,
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as_percent = as_percent,
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combine_SI = combine_SI,
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combine_IR = combine_IR,
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combine_SI_missing = missing(combine_SI))
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}
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