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287 lines
14 KiB
R
Executable File
287 lines
14 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.gitlab.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 \code{dplyr}s \code{\link[dplyr]{summarise}} and support grouped variables, see \emph{Examples}.
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#'
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#' \code{resistance()} should be used to calculate resistance, \code{susceptibility()} should 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 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.
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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 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
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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{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 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}.
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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 parameter \code{combine_SI}.
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#' @inheritSection as.rsi Interpretation of S, I and R
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#' @details
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#' The function \code{resistance()} is equal to the function \code{proportion_R()}. The function \code{susceptibility()} is equal to the function \code{proportion_SI()}.
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#'
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#' \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.
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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 \code{\link[AMR]{count}} functions to count isolates. The function \code{susceptibility()} is essentially equal to \code{count_susceptible() / count_all()}. \emph{Low counts can infuence the outcome - the \code{proportion} functions may camouflage this, since they only return the proportion (albeit being dependent on the \code{minimum} parameter).}
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#'
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#' The function \code{proportion_df()} takes any variable from \code{data} that has an \code{"rsi"} class (created with \code{\link{as.rsi}()}) and calculates the proportions R, I and S. The function \code{rsi_df()} works exactly like \code{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 \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{susceptibility} works to calculate the \%SI:
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#'
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#' \preformatted{
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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 \code{only_all_tested = TRUE} applies that:
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#' \preformatted{
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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 \code{only_all_tested = FALSE} applies that:
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#' \preformatted{
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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 \code{only_all_tested} has no impact when only using one antibiotic as input.
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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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#' @seealso \code{\link[AMR]{count}_*} to count resistant and susceptible isolates.
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#' @return Double or, when \code{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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#' library(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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#' 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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#'
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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 = susceptibility(AMX, MTR), # amoxicillin with metronidazole
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#' n = count_all(AMX, MTR))
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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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#' @importFrom dplyr %>% select_if bind_rows summarise_if mutate group_vars select everything
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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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