mirror of https://github.com/msberends/AMR.git
quasiquotation, alpha for geom_rsi
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@ -1,6 +1,6 @@
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Package: AMR
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Version: 0.3.0.9001
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Date: 2018-08-21
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Version: 0.3.0.9002
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Date: 2018-08-23
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Title: Antimicrobial Resistance Analysis
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Authors@R: c(
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person(
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@ -52,8 +52,8 @@ Imports:
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xml2 (>= 1.0.0),
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knitr (>= 1.0.0),
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readr,
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rvest (>= 0.3.2),
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tibble
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rlang,
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rvest (>= 0.3.2)
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Suggests:
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testthat (>= 1.0.2),
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covr (>= 3.0.1),
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@ -127,6 +127,7 @@ importFrom(dplyr,arrange)
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importFrom(dplyr,arrange_at)
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importFrom(dplyr,as_tibble)
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importFrom(dplyr,between)
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importFrom(dplyr,bind_cols)
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importFrom(dplyr,bind_rows)
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importFrom(dplyr,case_when)
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importFrom(dplyr,desc)
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@ -171,7 +172,6 @@ importFrom(stats,mad)
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importFrom(stats,pchisq)
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importFrom(stats,predict)
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importFrom(stats,sd)
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importFrom(tibble,tibble)
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importFrom(utils,View)
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importFrom(utils,browseVignettes)
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importFrom(utils,installed.packages)
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10
NEWS.md
10
NEWS.md
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@ -1,13 +1,19 @@
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# 0.3.0.90xx (latest development version)
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#### New
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* Functions `count_R`, `count_IR`, `count_I`, `count_SI` and `count_S` to selectively count resistant or susceptibile isolates
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* Functions `count_R`, `count_IR`, `count_I`, `count_SI` and `count_S` to selectively count resistant or susceptible isolates
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* Function `is.rsi.eligible` to check for columns that have valid antimicrobial results, but do not have the `rsi` class yet. Transform the columns of your raw data with: `data %>% mutate_if(is.rsi.eligible, as.rsi)`
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#### Changed
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* Added parameters `minimum` and `as_percent` to `portion_df`
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* Support for quasiquotation in the functions series `count_*` and `portions_*`, and `n_rsi`. This allow to check for more than 2 vectors or columns.
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* `septic_patients %>% select(amox, cipr) %>% count_R()`
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* `septic_patients %>% portion_S(amcl)`
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* `septic_patients %>% portion_S(amcl, gent)`
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* `septic_patients %>% portion_S(amcl, gent, pita)`
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* Edited `ggplot_rsi` and `geom_rsi` so they can cope with `count_df`. The new `fun` parameter has value `portion_df` at default, but can be set to `count_df`.
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* Fix for `ggplot_rsi` when the `ggplot2` was not loaded
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* Fix for `ggplot_rsi` when the `ggplot2` package was not loaded
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* Added parameter `alpha` to `ggplot_rsi` and `geom_rsi`
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# 0.3.0 (latest stable version)
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**Published on CRAN: 2018-08-14**
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41
R/count.R
41
R/count.R
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@ -18,7 +18,7 @@
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#' Count isolates
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#'
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#' @description These functions can be used to count resistant/susceptible microbial isolates. All functions can be used in \code{dplyr}s \code{\link[dplyr]{summarise}} and support grouped variables, see \emph{Examples}.
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#' @description These functions can be used to count resistant/susceptible microbial isolates. 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{count_R} and \code{count_IR} can be used to count resistant isolates, \code{count_S} and \code{count_SI} can be used to count susceptible isolates.\cr
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#' @inheritParams portion
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@ -87,11 +87,9 @@
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#' group_by(hospital_id) %>%
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#' count_df(translate = FALSE)
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#'
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count_R <- function(ab1,
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ab2 = NULL) {
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rsi_calc(type = "R",
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ab1 = ab1,
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ab2 = ab2,
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count_R <- function(...) {
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rsi_calc(...,
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type = "R",
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include_I = FALSE,
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minimum = 0,
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as_percent = FALSE,
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@ -100,11 +98,9 @@ count_R <- function(ab1,
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#' @rdname count
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#' @export
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count_IR <- function(ab1,
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ab2 = NULL) {
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rsi_calc(type = "R",
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ab1 = ab1,
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ab2 = ab2,
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count_IR <- function(...) {
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rsi_calc(...,
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type = "R",
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include_I = TRUE,
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minimum = 0,
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as_percent = FALSE,
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@ -113,10 +109,9 @@ count_IR <- function(ab1,
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#' @rdname count
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#' @export
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count_I <- function(ab1) {
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rsi_calc(type = "I",
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ab1 = ab1,
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ab2 = NULL,
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count_I <- function(...) {
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rsi_calc(...,
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type = "I",
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include_I = FALSE,
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minimum = 0,
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as_percent = FALSE,
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@ -125,11 +120,9 @@ count_I <- function(ab1) {
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#' @rdname count
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#' @export
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count_SI <- function(ab1,
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ab2 = NULL) {
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rsi_calc(type = "S",
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ab1 = ab1,
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ab2 = ab2,
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count_SI <- function(...) {
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rsi_calc(...,
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type = "S",
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include_I = TRUE,
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minimum = 0,
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as_percent = FALSE,
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@ -138,11 +131,9 @@ count_SI <- function(ab1,
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#' @rdname count
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#' @export
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count_S <- function(ab1,
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ab2 = NULL) {
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rsi_calc(type = "S",
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ab1 = ab1,
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ab2 = ab2,
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count_S <- function(...) {
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rsi_calc(...,
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type = "S",
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include_I = FALSE,
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minimum = 0,
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as_percent = FALSE,
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7
R/freq.R
7
R/freq.R
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@ -55,9 +55,8 @@
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#' The function \code{top_freq} uses \code{\link[dplyr]{top_n}} internally and will include more than \code{n} rows if there are ties.
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#' @importFrom stats fivenum sd mad
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#' @importFrom grDevices boxplot.stats
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#' @importFrom dplyr %>% select pull n_distinct group_by arrange desc mutate summarise n_distinct
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#' @importFrom dplyr %>% select pull n_distinct group_by arrange desc mutate summarise n_distinct tibble
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#' @importFrom utils browseVignettes installed.packages
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#' @importFrom tibble tibble
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#' @keywords summary summarise frequency freq
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#' @rdname freq
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#' @name freq
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@ -378,12 +377,12 @@ frequency_tbl <- function(x,
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column_names_df <- c('item', 'count', 'percent', 'cum_count', 'cum_percent', 'factor_level')
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if (any(class(x) == 'factor')) {
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df <- tibble::tibble(item = x,
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df <- tibble(item = x,
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fctlvl = x %>% as.integer()) %>%
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group_by(item, fctlvl)
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column_align <- c('l', 'r', 'r', 'r', 'r', 'r')
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} else {
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df <- tibble::tibble(item = x) %>%
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df <- tibble(item = x) %>%
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group_by(item)
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# strip factor lvl from col names
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column_names <- column_names[1:length(column_names) - 1]
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@ -25,6 +25,7 @@
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#' @param fill variable to categorise using the plots legend, either \code{"Antibiotic"} (default) or \code{"Interpretation"} or a grouping variable
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#' @param facet variable to split plots by, either \code{"Interpretation"} (default) or \code{"Antibiotic"} or a grouping variable
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#' @param translate_ab a column name of the \code{\link{antibiotics}} data set to translate the antibiotic abbreviations into, using \code{\link{abname}}. Default behaviour is to translate to official names according to the WHO. Use \code{translate_ab = FALSE} to disable translation.
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#' @param alpha opacity of the fill colours
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#' @param fun function to transform \code{data}, either \code{\link{portion_df}} (default) or \code{\link{count_df}}
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#' @param ... other parameters passed on to \code{\link[ggplot2]{facet_wrap}}
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#' @details At default, the names of antibiotics will be shown on the plots using \code{\link{abname}}. This can be set with the option \code{get_antibiotic_names} (a logical value), so change it e.g. to \code{FALSE} with \code{options(get_antibiotic_names = FALSE)}.
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@ -113,6 +114,7 @@ ggplot_rsi <- function(data,
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fill = "Interpretation",
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facet = NULL,
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translate_ab = "official",
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alpha = 1,
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fun = portion_df,
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...) {
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@ -126,7 +128,7 @@ ggplot_rsi <- function(data,
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}
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p <- ggplot2::ggplot(data = data) +
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geom_rsi(position = position, x = x, fill = fill, translate_ab = translate_ab, fun = fun) +
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geom_rsi(position = position, x = x, fill = fill, translate_ab = translate_ab, alpha = alpha, fun = fun) +
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theme_rsi()
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if (fill == "Interpretation") {
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@ -151,6 +153,7 @@ geom_rsi <- function(position = NULL,
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x = c("Antibiotic", "Interpretation"),
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fill = "Interpretation",
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translate_ab = "official",
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alpha = 1,
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fun = portion_df) {
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fun_name <- deparse(substitute(fun))
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@ -180,7 +183,7 @@ geom_rsi <- function(position = NULL,
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ggplot2::layer(geom = "bar", stat = "identity", position = position,
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mapping = ggplot2::aes_string(x = x, y = y, fill = fill),
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data = fun, params = list())
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data = fun, params = list(alpha = alpha))
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}
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28
R/n_rsi.R
28
R/n_rsi.R
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@ -18,10 +18,11 @@
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#' Count cases with antimicrobial results
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#'
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#' This counts all cases where antimicrobial interpretations are available. Its use is equal to \code{\link{n_distinct}}.
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#' @param ab1,ab2 vector of antibiotic interpretations, they will be transformed internally with \code{\link{as.rsi}} if needed
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#' This counts all cases where antimicrobial interpretations are available. The way it can be used is equal to \code{\link{n_distinct}}. Its function is equal to \code{count_S(...) + count_IR(...)}.
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#' @inheritParams portion
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#' @export
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#' @seealso The \code{\link{portion}} functions to calculate resistance and susceptibility.
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#' @seealso \code{\link[AMR]{count}_*} to count resistant and susceptibile isolates per interpretation type.\cr
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#' \code{\link{portion}_*} to calculate microbial resistance and susceptibility.
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#' @examples
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#' library(dplyr)
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#'
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#' genta_n = n_rsi(gent),
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#' combination_p = portion_S(cipr, gent, as_percent = TRUE),
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#' combination_n = n_rsi(cipr, gent))
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n_rsi <- function(ab1, ab2 = NULL) {
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if (NCOL(ab1) > 1) {
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stop('`ab1` must be a vector of antimicrobial interpretations', call. = FALSE)
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}
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if (!is.rsi(ab1)) {
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ab1 <- as.rsi(ab1)
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}
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if (!is.null(ab2)) {
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if (NCOL(ab2) > 1) {
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stop('`ab2` must be a vector of antimicrobial interpretations', call. = FALSE)
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}
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if (!is.rsi(ab2)) {
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ab2 <- as.rsi(ab2)
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}
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sum(!is.na(ab1) & !is.na(ab2))
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} else {
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sum(!is.na(ab1))
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}
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n_rsi <- function(...) {
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# only print warnings once, if needed
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count_S(...) + suppressWarnings(count_IR(...))
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}
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148
R/portion.R
148
R/portion.R
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@ -18,11 +18,10 @@
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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 can be used in \code{dplyr}s \code{\link[dplyr]{summarise}} and support grouped variables, see \emph{Examples}.
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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 ab1 vector of antibiotic interpretations, they will be transformed internally with \code{\link{as.rsi}} if needed
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#' @param ab2 like \code{ab}, a vector of antibiotic interpretations. Use this 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 ... 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 minimal amount of available isolates. Any number lower than \code{minimum} will return \code{NA}. The default number of \code{30} isolates is advised by the CLSI as best practice, see Source.
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#' @param as_percent 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 data a \code{data.frame} containing columns with class \code{rsi} (see \code{\link{as.rsi}})
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@ -43,8 +42,10 @@
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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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#' Theoretically for three antibiotics:
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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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#' portion_S(septic_patients$amox)
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#' portion_SI(septic_patients$amox)
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#'
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#' # Since n_rsi counts available isolates (and is used as denominator),
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#' # you can calculate back to count e.g. non-susceptible isolates:
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#' portion_IR(septic_patients$amox) * n_rsi(septic_patients$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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@ -88,16 +92,15 @@
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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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#' portion_S(septic_patients$amcl) # S = 67.3%
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#' n_rsi(septic_patients$amcl) # n = 1570
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#' septic_patients %>% portion_S(amcl) # S = 67.3%
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#' septic_patients %>% n_rsi(amcl) # n = 1570
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#'
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#' portion_S(septic_patients$gent) # S = 74.0%
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#' n_rsi(septic_patients$gent) # n = 1842
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#' septic_patients %>% portion_S(gent) # S = 74.0%
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#' septic_patients %>% n_rsi(gent) # n = 1842
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#'
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#' septic_patients %>% portion_S(amcl, gent) # S = 92.1%
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#' septic_patients %>% n_rsi(amcl, gent) # n = 1504
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#'
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#' with(septic_patients,
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#' portion_S(amcl, gent)) # S = 92.1%
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#' with(septic_patients, # n = 1504
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#' n_rsi(amcl, gent))
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#'
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#' septic_patients %>%
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#' group_by(hospital_id) %>%
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@ -129,13 +132,11 @@
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#' summarise(p = portion_S(amox, metr), # amoxicillin with metronidazole
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#' n = n_rsi(amox, metr))
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#' }
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portion_R <- function(ab1,
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ab2 = NULL,
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portion_R <- function(...,
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minimum = 30,
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as_percent = FALSE) {
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rsi_calc(type = "R",
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ab1 = ab1,
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ab2 = ab2,
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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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@ -144,13 +145,11 @@ portion_R <- function(ab1,
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#' @rdname portion
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#' @export
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portion_IR <- function(ab1,
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ab2 = NULL,
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portion_IR <- function(...,
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minimum = 30,
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as_percent = FALSE) {
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rsi_calc(type = "R",
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ab1 = ab1,
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ab2 = ab2,
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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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@ -159,12 +158,11 @@ portion_IR <- function(ab1,
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#' @rdname portion
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#' @export
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portion_I <- function(ab1,
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portion_I <- function(...,
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minimum = 30,
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as_percent = FALSE) {
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rsi_calc(type = "I",
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ab1 = ab1,
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ab2 = NULL,
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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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@ -173,13 +171,11 @@ portion_I <- function(ab1,
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#' @rdname portion
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#' @export
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portion_SI <- function(ab1,
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ab2 = NULL,
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portion_SI <- function(...,
|
||||
minimum = 30,
|
||||
as_percent = FALSE) {
|
||||
rsi_calc(type = "S",
|
||||
ab1 = ab1,
|
||||
ab2 = ab2,
|
||||
rsi_calc(...,
|
||||
type = "S",
|
||||
include_I = TRUE,
|
||||
minimum = minimum,
|
||||
as_percent = as_percent,
|
||||
|
|
@ -188,13 +184,11 @@ portion_SI <- function(ab1,
|
|||
|
||||
#' @rdname portion
|
||||
#' @export
|
||||
portion_S <- function(ab1,
|
||||
ab2 = NULL,
|
||||
portion_S <- function(...,
|
||||
minimum = 30,
|
||||
as_percent = FALSE) {
|
||||
rsi_calc(type = "S",
|
||||
ab1 = ab1,
|
||||
ab2 = ab2,
|
||||
rsi_calc(...,
|
||||
type = "S",
|
||||
include_I = FALSE,
|
||||
minimum = minimum,
|
||||
as_percent = as_percent,
|
||||
|
|
@ -257,77 +251,3 @@ portion_df <- function(data,
|
|||
|
||||
res
|
||||
}
|
||||
|
||||
rsi_calc <- function(type,
|
||||
ab1,
|
||||
ab2,
|
||||
include_I,
|
||||
minimum,
|
||||
as_percent,
|
||||
only_count) {
|
||||
|
||||
if (NCOL(ab1) > 1) {
|
||||
stop('`ab1` must be a vector of antimicrobial interpretations', call. = FALSE)
|
||||
}
|
||||
if (!is.logical(include_I)) {
|
||||
stop('`include_I` must be logical', call. = FALSE)
|
||||
}
|
||||
if (!is.numeric(minimum)) {
|
||||
stop('`minimum` must be numeric', call. = FALSE)
|
||||
}
|
||||
if (!is.logical(as_percent)) {
|
||||
stop('`as_percent` must be logical', call. = FALSE)
|
||||
}
|
||||
|
||||
print_warning <- FALSE
|
||||
if (!is.rsi(ab1)) {
|
||||
ab1 <- as.rsi(ab1)
|
||||
print_warning <- TRUE
|
||||
}
|
||||
if (!is.null(ab2)) {
|
||||
# ab_name <- paste(deparse(substitute(ab1)), "and", deparse(substitute(ab2)))
|
||||
if (NCOL(ab2) > 1) {
|
||||
stop('`ab2` must be a vector of antimicrobial interpretations', call. = FALSE)
|
||||
}
|
||||
if (!is.rsi(ab2)) {
|
||||
ab2 <- as.rsi(ab2)
|
||||
print_warning <- TRUE
|
||||
}
|
||||
x <- apply(X = data.frame(ab1 = as.integer(ab1),
|
||||
ab2 = as.integer(ab2)),
|
||||
MARGIN = 1,
|
||||
FUN = min)
|
||||
} else {
|
||||
x <- ab1
|
||||
# ab_name <- deparse(substitute(ab1))
|
||||
}
|
||||
|
||||
if (print_warning == TRUE) {
|
||||
warning("Increase speed by transforming to class `rsi` on beforehand: df %>% mutate_at(vars(col10:col20), as.rsi)")
|
||||
}
|
||||
|
||||
if (type == "S") {
|
||||
found <- sum(as.integer(x) <= 1 + include_I, na.rm = TRUE)
|
||||
} else if (type == "I") {
|
||||
found <- sum(as.integer(x) == 2, na.rm = TRUE)
|
||||
} else if (type == "R") {
|
||||
found <- sum(as.integer(x) >= 3 - include_I, na.rm = TRUE)
|
||||
} else {
|
||||
stop("invalid type")
|
||||
}
|
||||
|
||||
if (only_count == TRUE) {
|
||||
return(found)
|
||||
}
|
||||
|
||||
total <- length(x) - sum(is.na(x))
|
||||
if (total < minimum) {
|
||||
return(NA)
|
||||
}
|
||||
|
||||
if (as_percent == TRUE) {
|
||||
percent(found / total, force_zero = TRUE)
|
||||
} else {
|
||||
found / total
|
||||
}
|
||||
}
|
||||
|
|
|
|||
20
R/rsi.R
20
R/rsi.R
|
|
@ -20,9 +20,10 @@
|
|||
#'
|
||||
#' This function is deprecated. Use the \code{\link{portion}} functions instead.
|
||||
#' @inheritParams portion
|
||||
#' @param ab1,ab2 vector (or column) with antibiotic interpretations. It will be transformed internally with \code{\link{as.rsi}} if needed.
|
||||
#' @param interpretation antimicrobial interpretation to check for
|
||||
#' @param ... deprecated parameters to support usage on older versions
|
||||
#' @importFrom dplyr case_when
|
||||
#' @importFrom dplyr tibble case_when
|
||||
#' @export
|
||||
rsi <- function(ab1,
|
||||
ab2 = NULL,
|
||||
|
|
@ -31,12 +32,19 @@ rsi <- function(ab1,
|
|||
as_percent = FALSE,
|
||||
...) {
|
||||
|
||||
if (all(is.null(ab2))) {
|
||||
df <- tibble(ab1 = ab1)
|
||||
} else {
|
||||
df <- tibble(ab1 = ab1,
|
||||
ab2 = ab2)
|
||||
}
|
||||
|
||||
result <- case_when(
|
||||
interpretation == "S" ~ portion_S(ab1 = ab1, ab2 = ab2, minimum = minimum, as_percent = FALSE),
|
||||
interpretation %in% c("SI", "IS") ~ portion_SI(ab1 = ab1, ab2 = ab2, minimum = minimum, as_percent = FALSE),
|
||||
interpretation == "I" ~ portion_I(ab1 = ab1, minimum = minimum, as_percent = FALSE),
|
||||
interpretation %in% c("RI", "IR") ~ portion_IR(ab1 = ab1, ab2 = ab2, minimum = minimum, as_percent = FALSE),
|
||||
interpretation == "R" ~ portion_R(ab1 = ab1, ab2 = ab2, minimum = minimum, as_percent = FALSE),
|
||||
interpretation == "S" ~ portion_S(df, minimum = minimum, as_percent = FALSE),
|
||||
interpretation %in% c("SI", "IS") ~ portion_SI(df, minimum = minimum, as_percent = FALSE),
|
||||
interpretation == "I" ~ portion_I(df, minimum = minimum, as_percent = FALSE),
|
||||
interpretation %in% c("RI", "IR") ~ portion_IR(df, minimum = minimum, as_percent = FALSE),
|
||||
interpretation == "R" ~ portion_R(df, minimum = minimum, as_percent = FALSE),
|
||||
TRUE ~ -1
|
||||
)
|
||||
if (result == -1) {
|
||||
|
|
|
|||
|
|
@ -0,0 +1,115 @@
|
|||
# ==================================================================== #
|
||||
# TITLE #
|
||||
# Antimicrobial Resistance (AMR) Analysis #
|
||||
# #
|
||||
# AUTHORS #
|
||||
# Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl) #
|
||||
# #
|
||||
# LICENCE #
|
||||
# This program is free software; you can redistribute it and/or modify #
|
||||
# it under the terms of the GNU General Public License version 2.0, #
|
||||
# as published by the Free Software Foundation. #
|
||||
# #
|
||||
# This program is distributed in the hope that it will be useful, #
|
||||
# but WITHOUT ANY WARRANTY; without even the implied warranty of #
|
||||
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the #
|
||||
# GNU General Public License for more details. #
|
||||
# ==================================================================== #
|
||||
|
||||
#' @importFrom dplyr %>% bind_cols pull
|
||||
rsi_calc <- function(...,
|
||||
type,
|
||||
include_I,
|
||||
minimum,
|
||||
as_percent,
|
||||
only_count) {
|
||||
|
||||
if (!is.logical(include_I)) {
|
||||
stop('`include_I` must be logical', call. = FALSE)
|
||||
}
|
||||
if (!is.numeric(minimum)) {
|
||||
stop('`minimum` must be numeric', call. = FALSE)
|
||||
}
|
||||
if (!is.logical(as_percent)) {
|
||||
stop('`as_percent` must be logical', call. = FALSE)
|
||||
}
|
||||
|
||||
dots_length <- ...length()
|
||||
dots <- ...elt(1) # it needs this evaluation
|
||||
dots <- rlang::exprs(...) # or this will be a list without actual values
|
||||
|
||||
if ("data.frame" %in% class(dots[[1]]) & dots_length > 1) {
|
||||
# data.frame passed with other columns, like:
|
||||
# septic_patients %>% portion_S(amcl, gent)
|
||||
df <- dots[[1]]
|
||||
dots_df <- data.frame(col1 = df[,1])
|
||||
for (i in 2:dots_length) {
|
||||
dots_col <- as.character(dots[[i]])
|
||||
if (!dots_col %in% colnames(df)) {
|
||||
stop("variable not found: ", dots_col)
|
||||
}
|
||||
dots_df <- dots_df %>% bind_cols(data.frame(df %>% pull(dots_col)))
|
||||
}
|
||||
x <- dots_df[, -1]
|
||||
} else if (dots_length == 1) {
|
||||
# only 1 variable passed (count also be data.frame), like:
|
||||
# portion_S(septic_patients$amcl)
|
||||
# septic_patients$amcl %>% portion_S()
|
||||
x <- dots[[1]]
|
||||
} else {
|
||||
# multiple variables passed without pipe, like:
|
||||
# portion_S(septic_patients$amcl, septic_patients$gent)
|
||||
# with(septic_patients, portion_S(amcl, gent))
|
||||
x <- as.data.frame(rlang::list2(...))
|
||||
}
|
||||
|
||||
print_warning <- FALSE
|
||||
# check integrity of columns: force rsi class
|
||||
if (is.data.frame(x)) {
|
||||
for (i in 1:ncol(x)) {
|
||||
if (!is.rsi(x %>% pull(i))) {
|
||||
x[, i] <- as.rsi(x[, i])
|
||||
print_warning <- TRUE
|
||||
}
|
||||
x[, i] <- x %>% pull(i) %>% as.integer()
|
||||
}
|
||||
x <- apply(X = x,
|
||||
MARGIN = 1,
|
||||
FUN = min)
|
||||
} else {
|
||||
if (!is.rsi(x)) {
|
||||
x <- as.rsi(x)
|
||||
print_warning <- TRUE
|
||||
}
|
||||
}
|
||||
|
||||
if (print_warning == TRUE) {
|
||||
warning("Increase speed by transforming to class `rsi` on beforehand: df %>% mutate_if(is.rsi.eligible, as.rsi)",
|
||||
call. = FALSE)
|
||||
}
|
||||
|
||||
if (type == "S") {
|
||||
found <- sum(as.integer(x) <= 1 + include_I, na.rm = TRUE)
|
||||
} else if (type == "I") {
|
||||
found <- sum(as.integer(x) == 2, na.rm = TRUE)
|
||||
} else if (type == "R") {
|
||||
found <- sum(as.integer(x) >= 3 - include_I, na.rm = TRUE)
|
||||
} else {
|
||||
stop("invalid type")
|
||||
}
|
||||
|
||||
if (only_count == TRUE) {
|
||||
return(found)
|
||||
}
|
||||
|
||||
total <- length(x) - sum(is.na(x))
|
||||
if (total < minimum) {
|
||||
return(NA)
|
||||
}
|
||||
|
||||
if (as_percent == TRUE) {
|
||||
percent(found / total, force_zero = TRUE)
|
||||
} else {
|
||||
found / total
|
||||
}
|
||||
}
|
||||
16
man/count.Rd
16
man/count.Rd
|
|
@ -13,23 +13,21 @@
|
|||
Wickham H. \strong{Tidy Data.} The Journal of Statistical Software, vol. 59, 2014. \url{http://vita.had.co.nz/papers/tidy-data.html}
|
||||
}
|
||||
\usage{
|
||||
count_R(ab1, ab2 = NULL)
|
||||
count_R(...)
|
||||
|
||||
count_IR(ab1, ab2 = NULL)
|
||||
count_IR(...)
|
||||
|
||||
count_I(ab1)
|
||||
count_I(...)
|
||||
|
||||
count_SI(ab1, ab2 = NULL)
|
||||
count_SI(...)
|
||||
|
||||
count_S(ab1, ab2 = NULL)
|
||||
count_S(...)
|
||||
|
||||
count_df(data, translate_ab = getOption("get_antibiotic_names",
|
||||
"official"))
|
||||
}
|
||||
\arguments{
|
||||
\item{ab1}{vector of antibiotic interpretations, they will be transformed internally with \code{\link{as.rsi}} if needed}
|
||||
|
||||
\item{ab2}{like \code{ab}, a vector of antibiotic interpretations. Use this to calculate (the lack of) co-resistance: the probability where one of two drugs have a resistant or susceptible result. See Examples.}
|
||||
\item{...}{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.}
|
||||
|
||||
\item{data}{a \code{data.frame} containing columns with class \code{rsi} (see \code{\link{as.rsi}})}
|
||||
|
||||
|
|
@ -39,7 +37,7 @@ count_df(data, translate_ab = getOption("get_antibiotic_names",
|
|||
Integer
|
||||
}
|
||||
\description{
|
||||
These functions can be used to count resistant/susceptible microbial isolates. All functions can be used in \code{dplyr}s \code{\link[dplyr]{summarise}} and support grouped variables, see \emph{Examples}.
|
||||
These functions can be used to count resistant/susceptible microbial isolates. 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{count_R} and \code{count_IR} can be used to count resistant isolates, \code{count_S} and \code{count_SI} can be used to count susceptible isolates.\cr
|
||||
}
|
||||
|
|
|
|||
|
|
@ -11,10 +11,10 @@
|
|||
\usage{
|
||||
ggplot_rsi(data, position = NULL, x = "Antibiotic",
|
||||
fill = "Interpretation", facet = NULL, translate_ab = "official",
|
||||
fun = portion_df, ...)
|
||||
alpha = 1, fun = portion_df, ...)
|
||||
|
||||
geom_rsi(position = NULL, x = c("Antibiotic", "Interpretation"),
|
||||
fill = "Interpretation", translate_ab = "official",
|
||||
fill = "Interpretation", translate_ab = "official", alpha = 1,
|
||||
fun = portion_df)
|
||||
|
||||
facet_rsi(facet = c("Interpretation", "Antibiotic"), ...)
|
||||
|
|
@ -38,6 +38,8 @@ theme_rsi()
|
|||
|
||||
\item{translate_ab}{a column name of the \code{\link{antibiotics}} data set to translate the antibiotic abbreviations into, using \code{\link{abname}}. Default behaviour is to translate to official names according to the WHO. Use \code{translate_ab = FALSE} to disable translation.}
|
||||
|
||||
\item{alpha}{opacity of the fill colours}
|
||||
|
||||
\item{fun}{function to transform \code{data}, either \code{\link{portion_df}} (default) or \code{\link{count_df}}}
|
||||
|
||||
\item{...}{other parameters passed on to \code{\link[ggplot2]{facet_wrap}}}
|
||||
|
|
|
|||
|
|
@ -4,13 +4,13 @@
|
|||
\alias{n_rsi}
|
||||
\title{Count cases with antimicrobial results}
|
||||
\usage{
|
||||
n_rsi(ab1, ab2 = NULL)
|
||||
n_rsi(...)
|
||||
}
|
||||
\arguments{
|
||||
\item{ab1, ab2}{vector of antibiotic interpretations, they will be transformed internally with \code{\link{as.rsi}} if needed}
|
||||
\item{...}{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.}
|
||||
}
|
||||
\description{
|
||||
This counts all cases where antimicrobial interpretations are available. Its use is equal to \code{\link{n_distinct}}.
|
||||
This counts all cases where antimicrobial interpretations are available. The way it can be used is equal to \code{\link{n_distinct}}. Its function is equal to \code{count_S(...) + count_IR(...)}.
|
||||
}
|
||||
\examples{
|
||||
library(dplyr)
|
||||
|
|
@ -25,5 +25,6 @@ septic_patients \%>\%
|
|||
combination_n = n_rsi(cipr, gent))
|
||||
}
|
||||
\seealso{
|
||||
The \code{\link{portion}} functions to calculate resistance and susceptibility.
|
||||
\code{\link[AMR]{count}_*} to count resistant and susceptibile isolates per interpretation type.\cr
|
||||
\code{\link{portion}_*} to calculate microbial resistance and susceptibility.
|
||||
}
|
||||
|
|
|
|||
|
|
@ -15,23 +15,21 @@
|
|||
Wickham H. \strong{Tidy Data.} The Journal of Statistical Software, vol. 59, 2014. \url{http://vita.had.co.nz/papers/tidy-data.html}
|
||||
}
|
||||
\usage{
|
||||
portion_R(ab1, ab2 = NULL, minimum = 30, as_percent = FALSE)
|
||||
portion_R(..., minimum = 30, as_percent = FALSE)
|
||||
|
||||
portion_IR(ab1, ab2 = NULL, minimum = 30, as_percent = FALSE)
|
||||
portion_IR(..., minimum = 30, as_percent = FALSE)
|
||||
|
||||
portion_I(ab1, minimum = 30, as_percent = FALSE)
|
||||
portion_I(..., minimum = 30, as_percent = FALSE)
|
||||
|
||||
portion_SI(ab1, ab2 = NULL, minimum = 30, as_percent = FALSE)
|
||||
portion_SI(..., minimum = 30, as_percent = FALSE)
|
||||
|
||||
portion_S(ab1, ab2 = NULL, minimum = 30, as_percent = FALSE)
|
||||
portion_S(..., minimum = 30, as_percent = FALSE)
|
||||
|
||||
portion_df(data, translate_ab = getOption("get_antibiotic_names",
|
||||
"official"), minimum = 30, as_percent = FALSE)
|
||||
}
|
||||
\arguments{
|
||||
\item{ab1}{vector of antibiotic interpretations, they will be transformed internally with \code{\link{as.rsi}} if needed}
|
||||
|
||||
\item{ab2}{like \code{ab}, a vector of antibiotic interpretations. Use this to calculate (the lack of) co-resistance: the probability where one of two drugs have a resistant or susceptible result. See Examples.}
|
||||
\item{...}{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.}
|
||||
|
||||
\item{minimum}{minimal amount of available isolates. Any number lower than \code{minimum} will return \code{NA}. The default number of \code{30} isolates is advised by the CLSI as best practice, see Source.}
|
||||
|
||||
|
|
@ -45,7 +43,7 @@ portion_df(data, translate_ab = getOption("get_antibiotic_names",
|
|||
Double or, when \code{as_percent = TRUE}, a character.
|
||||
}
|
||||
\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 can be used in \code{dplyr}s \code{\link[dplyr]{summarise}} and support grouped variables, see \emph{Examples}.
|
||||
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}.
|
||||
|
||||
\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
|
||||
}
|
||||
|
|
@ -66,8 +64,10 @@ The old \code{\link{rsi}} function is still available for backwards compatibilit
|
|||
For two antibiotics:
|
||||
\out{<div style="text-align: center">}\figure{combi_therapy_2.png}\out{</div>}
|
||||
\cr
|
||||
Theoretically for three antibiotics:
|
||||
For three antibiotics:
|
||||
\out{<div style="text-align: center">}\figure{combi_therapy_3.png}\out{</div>}
|
||||
\cr
|
||||
And so on.
|
||||
}
|
||||
}
|
||||
\examples{
|
||||
|
|
@ -82,11 +82,13 @@ portion_IR(septic_patients$amox)
|
|||
portion_S(septic_patients$amox)
|
||||
portion_SI(septic_patients$amox)
|
||||
|
||||
# Since n_rsi counts available isolates (and is used as denominator),
|
||||
# you can calculate back to count e.g. non-susceptible isolates:
|
||||
portion_IR(septic_patients$amox) * n_rsi(septic_patients$amox)
|
||||
|
||||
# Do the above with pipes:
|
||||
library(dplyr)
|
||||
septic_patients \%>\% portion_R(amox)
|
||||
septic_patients \%>\% portion_IR(amox)
|
||||
septic_patients \%>\% portion_S(amox)
|
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septic_patients \%>\% portion_SI(amox)
|
||||
|
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septic_patients \%>\%
|
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group_by(hospital_id) \%>\%
|
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summarise(p = portion_S(cipr),
|
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|
|
@ -102,16 +104,15 @@ septic_patients \%>\%
|
|||
|
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# Calculate co-resistance between amoxicillin/clav acid and gentamicin,
|
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# so we can see that combination therapy does a lot more than mono therapy:
|
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portion_S(septic_patients$amcl) # S = 67.3\%
|
||||
n_rsi(septic_patients$amcl) # n = 1570
|
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septic_patients \%>\% portion_S(amcl) # S = 67.3\%
|
||||
septic_patients \%>\% n_rsi(amcl) # n = 1570
|
||||
|
||||
portion_S(septic_patients$gent) # S = 74.0\%
|
||||
n_rsi(septic_patients$gent) # n = 1842
|
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septic_patients \%>\% portion_S(gent) # S = 74.0\%
|
||||
septic_patients \%>\% n_rsi(gent) # n = 1842
|
||||
|
||||
septic_patients \%>\% portion_S(amcl, gent) # S = 92.1\%
|
||||
septic_patients \%>\% n_rsi(amcl, gent) # n = 1504
|
||||
|
||||
with(septic_patients,
|
||||
portion_S(amcl, gent)) # S = 92.1\%
|
||||
with(septic_patients, # n = 1504
|
||||
n_rsi(amcl, gent))
|
||||
|
||||
septic_patients \%>\%
|
||||
group_by(hospital_id) \%>\%
|
||||
|
|
|
|||
|
|
@ -8,9 +8,7 @@ rsi(ab1, ab2 = NULL, interpretation = "IR", minimum = 30,
|
|||
as_percent = FALSE, ...)
|
||||
}
|
||||
\arguments{
|
||||
\item{ab1}{vector of antibiotic interpretations, they will be transformed internally with \code{\link{as.rsi}} if needed}
|
||||
|
||||
\item{ab2}{like \code{ab}, a vector of antibiotic interpretations. Use this to calculate (the lack of) co-resistance: the probability where one of two drugs have a resistant or susceptible result. See Examples.}
|
||||
\item{ab1, ab2}{vector (or column) with antibiotic interpretations. It will be transformed internally with \code{\link{as.rsi}} if needed.}
|
||||
|
||||
\item{interpretation}{antimicrobial interpretation to check for}
|
||||
|
||||
|
|
|
|||
|
|
@ -1,8 +1,7 @@
|
|||
context("atc.R")
|
||||
|
||||
test_that("atc_property works", {
|
||||
skip_on_travis() # relies on internet connection of server, don't test
|
||||
|
||||
skip_on_cran() # relies on internet connection of server, don't test
|
||||
skip_on_appveyor() # security error on AppVeyor
|
||||
|
||||
if (!is.null(curl::nslookup("www.whocc.no", error = FALSE))) {
|
||||
|
|
|
|||
|
|
@ -0,0 +1,41 @@
|
|||
context("count.R")
|
||||
|
||||
test_that("counts work", {
|
||||
# amox resistance in `septic_patients`
|
||||
expect_equal(count_R(septic_patients$amox), 659)
|
||||
expect_equal(count_I(septic_patients$amox), 3)
|
||||
expect_equal(count_S(septic_patients$amox), 336)
|
||||
expect_equal(count_R(septic_patients$amox) + count_I(septic_patients$amox),
|
||||
count_IR(septic_patients$amox))
|
||||
expect_equal(count_S(septic_patients$amox) + count_I(septic_patients$amox),
|
||||
count_SI(septic_patients$amox))
|
||||
|
||||
expect_equal(septic_patients %>% count_S(amcl), 1056)
|
||||
expect_equal(septic_patients %>% count_S(amcl, gent), 1385)
|
||||
|
||||
# count of cases
|
||||
expect_equal(septic_patients %>%
|
||||
group_by(hospital_id) %>%
|
||||
summarise(cipro = count_S(cipr),
|
||||
genta = count_S(gent),
|
||||
combination = count_S(cipr, gent)) %>%
|
||||
pull(combination),
|
||||
c(192, 440, 184, 474))
|
||||
|
||||
# warning for speed loss
|
||||
expect_warning(count_R(as.character(septic_patients$amcl)))
|
||||
expect_warning(count_I(as.character(septic_patients$amcl)))
|
||||
expect_warning(count_S(as.character(septic_patients$amcl,
|
||||
septic_patients$gent)))
|
||||
expect_warning(count_S(septic_patients$amcl,
|
||||
as.character(septic_patients$gent)))
|
||||
|
||||
# check for errors
|
||||
expect_error(count_IR("test", minimum = "test"))
|
||||
expect_error(count_IR("test", as_percent = "test"))
|
||||
expect_error(count_I("test", minimum = "test"))
|
||||
expect_error(count_I("test", as_percent = "test"))
|
||||
expect_error(count_S("test", minimum = "test"))
|
||||
expect_error(count_S("test", as_percent = "test"))
|
||||
|
||||
})
|
||||
|
|
@ -11,12 +11,19 @@ test_that("portions works", {
|
|||
expect_equal(portion_S(septic_patients$amox) + portion_I(septic_patients$amox),
|
||||
portion_SI(septic_patients$amox))
|
||||
|
||||
# pita+genta susceptibility around 98.09%
|
||||
expect_equal(suppressWarnings(rsi(septic_patients$pita,
|
||||
expect_equal(septic_patients %>% portion_S(amcl),
|
||||
0.673,
|
||||
tolerance = 0.001)
|
||||
expect_equal(septic_patients %>% portion_S(amcl, gent),
|
||||
0.921,
|
||||
tolerance = 0.001)
|
||||
|
||||
# amcl+genta susceptibility around 92.1%
|
||||
expect_equal(suppressWarnings(rsi(septic_patients$amcl,
|
||||
septic_patients$gent,
|
||||
interpretation = "S")),
|
||||
0.9535,
|
||||
tolerance = 0.0001)
|
||||
0.9208777,
|
||||
tolerance = 0.000001)
|
||||
|
||||
# percentages
|
||||
expect_equal(septic_patients %>%
|
||||
|
|
@ -46,25 +53,19 @@ test_that("portions works", {
|
|||
expect_warning(portion_S(as.character(septic_patients$amcl)))
|
||||
expect_warning(portion_S(as.character(septic_patients$amcl,
|
||||
septic_patients$gent)))
|
||||
expect_equal(n_rsi(as.character(septic_patients$amcl,
|
||||
septic_patients$gent)),
|
||||
expect_warning(n_rsi(as.character(septic_patients$amcl,
|
||||
septic_patients$gent)))
|
||||
expect_equal(suppressWarnings(n_rsi(as.character(septic_patients$amcl,
|
||||
septic_patients$gent))),
|
||||
1570)
|
||||
|
||||
|
||||
# check for errors
|
||||
expect_error(portion_IR(septic_patients %>% select(amox, amcl)))
|
||||
expect_error(portion_IR("test", minimum = "test"))
|
||||
expect_error(portion_IR("test", as_percent = "test"))
|
||||
expect_error(portion_I(septic_patients %>% select(amox, amcl)))
|
||||
expect_error(portion_I("test", minimum = "test"))
|
||||
expect_error(portion_I("test", as_percent = "test"))
|
||||
expect_error(portion_S("test", minimum = "test"))
|
||||
expect_error(portion_S("test", as_percent = "test"))
|
||||
expect_error(portion_S(septic_patients %>% select(amox, amcl)))
|
||||
expect_error(portion_S("R", septic_patients %>% select(amox, amcl)))
|
||||
expect_error(n_rsi(septic_patients %>% select(amox, amcl)))
|
||||
expect_error(n_rsi(septic_patients$amox, septic_patients %>% select(amox, amcl)))
|
||||
|
||||
|
||||
# check too low amount of isolates
|
||||
expect_identical(portion_R(septic_patients$amox, minimum = nrow(septic_patients) + 1),
|
||||
|
|
|
|||
Loading…
Reference in New Issue