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(v1.1.0.9004) lose dependencies

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dr. M.S. (Matthijs) Berends
2020-05-16 13:05:47 +02:00
parent 9fce546901
commit 7f3da74b17
111 changed files with 3211 additions and 2345 deletions
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10
R/proportion.R
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@ -21,7 +21,7 @@
#' Calculate microbial resistance
#'
#' @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()`][dplyr::summarise()] and also support grouped variables, please see *Examples*.
#' @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 supports grouped variables, please see *Examples*.
#'
#' [resistance()] should be used to calculate resistance, [susceptibility()] should be used to calculate susceptibility.\cr
#' @inheritSection lifecycle Stable lifecycle
@ -42,7 +42,7 @@
#'
#' 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` parameter).*
#'
#' 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. The function [rsi_df()] works exactly like [proportion_df()], but adds the number of isolates.
#' 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.
#' @section Combination therapy:
#' 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:
#'
@ -99,6 +99,7 @@
#' proportion_IR(example_isolates$AMX)
#' proportion_R(example_isolates$AMX)
#'
#' \dontrun{
#' library(dplyr)
#' example_isolates %>%
#' group_by(hospital_id) %>%
@ -135,7 +136,6 @@
#' summarise(numerator = count_susceptible(AMC, GEN, only_all_tested = TRUE),
#' denominator = count_all(AMC, GEN, only_all_tested = TRUE),
#' proportion = susceptibility(AMC, GEN, only_all_tested = TRUE))
#'
#'
#' example_isolates %>%
@ -158,9 +158,6 @@
#' group_by(hospital_id) %>%
#' proportion_df(translate = FALSE)
#'
#'
#' \dontrun{
#'
#' # calculate current empiric combination therapy of Helicobacter gastritis:
#' my_table %>%
#' filter(first_isolate == TRUE,
@ -265,7 +262,6 @@ proportion_S <- function(...,
}
#' @rdname proportion
#' @importFrom dplyr %>% select_if bind_rows summarise_if mutate group_vars select everything
#' @export
proportion_df <- function(data,
translate_ab = "name",