AMR/R/antibiogram.R

609 lines
26 KiB
R
Raw Normal View History

# ==================================================================== #
# TITLE #
# AMR: An R Package for Working with Antimicrobial Resistance Data #
# #
# SOURCE #
# https://github.com/msberends/AMR #
# #
# CITE AS #
# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C #
# (2022). AMR: An R Package for Working with Antimicrobial Resistance #
# Data. Journal of Statistical Software, 104(3), 1-31. #
# doi:10.18637/jss.v104.i03 #
# #
# Developed at the University of Groningen and the University Medical #
# Center Groningen in The Netherlands, in collaboration with many #
# colleagues from around the world, see our website. #
# #
# This R package is free software; you can freely use and distribute #
# it for both personal and commercial purposes under the terms of the #
# GNU General Public License version 2.0 (GNU GPL-2), as published by #
# the Free Software Foundation. #
# We created this package for both routine data analysis and academic #
# research and it was publicly released in the hope that it will be #
# useful, but it comes WITHOUT ANY WARRANTY OR LIABILITY. #
# #
# Visit our website for the full manual and a complete tutorial about #
# how to conduct AMR data analysis: https://msberends.github.io/AMR/ #
# ==================================================================== #
#' Generate Antibiogram: Traditional, Combined, Syndromic, or Weighted-Incidence Syndromic Combination (WISCA)
#'
2023-02-13 10:21:43 +01:00
#' Generate an antibiogram, and communicate the results in plots or tables. These functions follow the logic of Klinker *et al.* and Barbieri *et al.* (see *Source*), and allow reporting in e.g. R Markdown and Quarto as well.
#' @param x a [data.frame] containing at least a column with microorganisms and columns with antibiotic results (class 'sir', see [as.sir()])
#' @param antibiotics vector of any antibiotic name or code (will be evaluated with [as.ab()], column name of `x`, or (any combinations of) [antibiotic selectors][antibiotic_class_selectors] such as [aminoglycosides()] or [carbapenems()]. For combination antibiograms, this can also be set to values separated with `"+"`, such as "TZP+TOB" or "cipro + genta", given that columns resembling such antibiotics exist in `x`. See *Examples*.
#' @param mo_transform a character to transform microorganism input - must be "name", "shortname", "gramstain", or one of the column names of the [microorganisms] data set: `r vector_or(colnames(microorganisms), sort = FALSE, quotes = TRUE)`. Can also be `NULL` to not transform the input.
#' @param ab_transform a character to transform antibiotic input - must be one of the column names of the [antibiotics] data set: `r vector_or(colnames(antibiotics), sort = FALSE, quotes = TRUE)`. Can also be `NULL` to not transform the input.
#' @param syndromic_group a column name of `x`, or values calculated to split rows of `x`, e.g. by using [ifelse()] or [`case_when()`][dplyr::case_when()]. See *Examples*.
#' @param add_total_n a [logical] to indicate whether total available numbers per pathogen should be added to the table (default is `TRUE`). This will add the lowest and highest number of available isolate per antibiotic (e.g, if for *E. coli* 200 isolates are available for ciprofloxacin and 150 for amoxicillin, the returned number will be "150-200").
#' @param only_all_tested (for combination antibiograms): a [logical] to indicate that isolates must be tested for all antibiotics, see *Details*
#' @param digits number of digits to use for rounding
#' @param col_mo column name of the names or codes of the microorganisms (see [as.mo()]) - the default is the first column of class [`mo`]. Values will be coerced using [as.mo()].
#' @param language language to translate text, which defaults to the system language (see [get_AMR_locale()])
#' @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*.
#' @param combine_SI a [logical] to indicate whether all susceptibility should be determined by results of either S or I, instead of only S (default is `TRUE`)
#' @param sep a separating character for antibiotic columns in combination antibiograms
#' @param info a [logical] to indicate info should be printed - the default is `TRUE` only in interactive mode
2023-02-06 12:38:52 +01:00
#' @param object an [antibiogram()] object
2023-02-23 16:27:40 +01:00
#' @param ... when used in [R Markdown or Quarto][knitr::kable()]: arguments passed on to [knitr::kable()] (otherwise, has no use)
#' @details This function returns a table with values between 0 and 100 for *susceptibility*, not resistance.
2023-02-12 17:10:48 +01:00
#'
#' **Remember that you should filter your data 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 with one of the four available algorithms.
2023-03-11 14:24:34 +01:00
#'
2023-02-24 10:31:36 +01:00
#' All types of antibiograms as listed below can be plotted (using [ggplot2::autoplot()] or base \R [plot()]/[barplot()]). The `antibiogram` object can also be used directly in R Markdown / Quarto (i.e., `knitr`) for reports. In this case, [knitr::kable()] will be applied automatically and microorganism names will even be printed in italics at default (see argument `italicise`). You can also use functions from specific 'table reporting' packages to transform the output of [antibiogram()] to your needs, e.g. with `flextable::as_flextable()` or `gt::gt()`.
2023-02-12 17:10:48 +01:00
#'
2023-02-24 09:43:10 +01:00
#' ### Antibiogram Types
2023-03-11 14:24:34 +01:00
#'
#' There are four antibiogram types, as proposed by Klinker *et al.* (2021, \doi{10.1177/20499361211011373}), and they are all supported by [antibiogram()]:
2023-02-12 17:10:48 +01:00
#'
#' 1. **Traditional Antibiogram**
2023-02-12 17:10:48 +01:00
#'
#' Case example: Susceptibility of *Pseudomonas aeruginosa* to piperacillin/tazobactam (TZP)
2023-02-12 17:10:48 +01:00
#'
#' Code example:
2023-02-12 17:10:48 +01:00
#'
#' ```r
#' antibiogram(your_data,
#' antibiotics = "TZP")
#' ```
2023-02-12 17:10:48 +01:00
#'
#' 2. **Combination Antibiogram**
2023-02-12 17:10:48 +01:00
#'
#' Case example: Additional susceptibility of *Pseudomonas aeruginosa* to TZP + tobramycin versus TZP alone
2023-02-12 17:10:48 +01:00
#'
#' Code example:
2023-02-12 17:10:48 +01:00
#'
#' ```r
#' antibiogram(your_data,
#' antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"))
#' ```
2023-02-12 17:10:48 +01:00
#'
#' 3. **Syndromic Antibiogram**
2023-02-12 17:10:48 +01:00
#'
#' Case example: Susceptibility of *Pseudomonas aeruginosa* to TZP among respiratory specimens (obtained among ICU patients only)
2023-02-12 17:10:48 +01:00
#'
#' Code example:
2023-02-12 17:10:48 +01:00
#'
#' ```r
#' antibiogram(your_data,
#' antibiotics = penicillins(),
#' syndromic_group = "ward")
#' ```
2023-02-12 17:10:48 +01:00
#'
#' 4. **Weighted-Incidence Syndromic Combination Antibiogram (WISCA)**
2023-02-12 17:10:48 +01:00
#'
#' Case example: Susceptibility of *Pseudomonas aeruginosa* to TZP among respiratory specimens (obtained among ICU patients only) for male patients age >=65 years with heart failure
2023-02-12 17:10:48 +01:00
#'
#' Code example:
2023-02-12 17:10:48 +01:00
#'
#' ```r
2023-02-13 10:21:43 +01:00
#' library(dplyr)
2023-03-11 14:24:34 +01:00
#' your_data %>%
#' filter(ward == "ICU" & specimen_type == "Respiratory") %>%
2023-02-13 10:21:43 +01:00
#' antibiogram(antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"),
#' syndromic_group = ifelse(.$age >= 65 &
#' .$gender == "Male" &
#' .$condition == "Heart Disease",
#' "Study Group", "Control Group"))
#' ```
2023-02-12 17:10:48 +01:00
#'
#' Note that for combination antibiograms, it is important to realise that susceptibility can be calculated in two ways, which can be set with the `only_all_tested` argument (default is `FALSE`). See this example for two antibiotics, Drug A and Drug B, about how [antibiogram()] works to calculate the %SI:
2023-02-12 17:10:48 +01:00
#'
#' ```
#' --------------------------------------------------------------------
#' only_all_tested = FALSE only_all_tested = TRUE
#' ----------------------- -----------------------
#' Drug A Drug B include as include as include as include as
#' numerator denominator numerator denominator
#' -------- -------- ---------- ----------- ---------- -----------
#' S or I S or I X X X X
#' R S or I X X X X
#' <NA> S or I X X - -
#' S or I R X X X X
#' R R - X - X
#' <NA> R - - - -
#' S or I <NA> X X - -
#' R <NA> - - - -
#' <NA> <NA> - - - -
#' --------------------------------------------------------------------
#' ```
2023-03-11 14:24:34 +01:00
#'
2023-02-12 17:10:48 +01:00
#' @source
#' * Klinker KP *et al.* (2021). **Antimicrobial stewardship and antibiograms: importance of moving beyond traditional antibiograms**. *Therapeutic Advances in Infectious Disease*, May 5;8:20499361211011373; \doi{10.1177/20499361211011373}
#' * Barbieri E *et al.* (2021). **Development of a Weighted-Incidence Syndromic Combination Antibiogram (WISCA) to guide the choice of the empiric antibiotic treatment for urinary tract infection in paediatric patients: a Bayesian approach** *Antimicrobial Resistance & Infection Control* May 1;10(1):74; \doi{10.1186/s13756-021-00939-2}
#' * **M39 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 5th Edition**, 2022, *Clinical and Laboratory Standards Institute (CLSI)*. <https://clsi.org/standards/products/microbiology/documents/m39/>.
#' @rdname antibiogram
#' @name antibiogram
#' @export
2023-02-12 17:10:48 +01:00
#' @examples
#' # example_isolates is a data set available in the AMR package.
#' # run ?example_isolates for more info.
#' example_isolates
2023-02-12 17:10:48 +01:00
#'
2023-02-13 16:56:25 +01:00
#' \donttest{
#' # Traditional antibiogram ----------------------------------------------
2023-02-12 17:10:48 +01:00
#'
#' antibiogram(example_isolates,
2023-02-12 17:10:48 +01:00
#' antibiotics = c(aminoglycosides(), carbapenems())
#' )
#'
#' antibiogram(example_isolates,
2023-02-12 17:10:48 +01:00
#' antibiotics = aminoglycosides(),
#' ab_transform = "atc",
#' mo_transform = "gramstain"
#' )
#'
#' antibiogram(example_isolates,
2023-02-12 17:10:48 +01:00
#' antibiotics = carbapenems(),
#' ab_transform = "name",
#' mo_transform = "name"
#' )
#'
#'
#' # Combined antibiogram -------------------------------------------------
2023-02-12 17:10:48 +01:00
#'
#' # combined antibiotics yield higher empiric coverage
#' antibiogram(example_isolates,
2023-02-12 17:10:48 +01:00
#' antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"),
#' mo_transform = "gramstain"
#' )
#'
#' # names of antibiotics do not need to resemble columns exactly:
#' antibiogram(example_isolates,
#' antibiotics = c("Cipro", "cipro + genta"),
2023-02-12 17:10:48 +01:00
#' mo_transform = "gramstain",
#' ab_transform = "name",
#' sep = " & "
#' )
#'
#'
#' # Syndromic antibiogram ------------------------------------------------
2023-02-12 17:10:48 +01:00
#'
#' # the data set could contain a filter for e.g. respiratory specimens
#' antibiogram(example_isolates,
2023-02-12 17:10:48 +01:00
#' antibiotics = c(aminoglycosides(), carbapenems()),
#' syndromic_group = "ward"
#' )
#'
#' # now define a data set with only E. coli
#' ex1 <- example_isolates[which(mo_genus() == "Escherichia"), ]
2023-02-12 17:10:48 +01:00
#'
#' # with a custom language, though this will be determined automatically
#' # (i.e., this table will be in Spanish on Spanish systems)
#' antibiogram(ex1,
2023-03-11 14:24:34 +01:00
#' antibiotics = aminoglycosides(),
#' ab_transform = "name",
#' syndromic_group = ifelse(ex1$ward == "ICU",
#' "UCI", "No UCI"
#' ),
#' language = "es"
2023-02-12 17:10:48 +01:00
#' )
#'
#'
#' # Weighted-incidence syndromic combination antibiogram (WISCA) ---------
2023-02-12 17:10:48 +01:00
#'
2023-02-13 10:21:43 +01:00
#' # the data set could contain a filter for e.g. respiratory specimens/ICU
#' antibiogram(example_isolates,
2023-03-11 14:24:34 +01:00
#' antibiotics = c("AMC", "AMC+CIP", "TZP", "TZP+TOB"),
#' mo_transform = "gramstain",
#' minimum = 10, # this should be >=30, but now just as example
#' syndromic_group = ifelse(example_isolates$age >= 65 &
#' example_isolates$gender == "M",
#' "WISCA Group 1", "WISCA Group 2"
#' )
2023-02-12 17:10:48 +01:00
#' )
2023-03-11 14:24:34 +01:00
#'
2023-02-12 17:10:48 +01:00
#'
2023-02-17 09:42:51 +01:00
#' # Print the output for R Markdown / Quarto -----------------------------
2023-03-11 14:24:34 +01:00
#'
2023-02-17 09:42:51 +01:00
#' ureido <- antibiogram(example_isolates,
2023-03-11 14:24:34 +01:00
#' antibiotics = ureidopenicillins(),
#' ab_transform = "name"
#' )
#'
2023-02-23 16:27:40 +01:00
#' # in an Rmd file, you would just need to return `ureido` in a chunk,
#' # but to be explicit here:
#' if (requireNamespace("knitr")) {
#' knitr::knit_print(ureido)
#' }
2023-03-11 14:24:34 +01:00
#'
#'
#' # Generate plots with ggplot2 or base R --------------------------------
2023-02-12 17:10:48 +01:00
#'
#' ab1 <- antibiogram(example_isolates,
2023-02-12 17:10:48 +01:00
#' antibiotics = c("AMC", "CIP", "TZP", "TZP+TOB"),
#' mo_transform = "gramstain"
#' )
#' ab2 <- antibiogram(example_isolates,
2023-02-12 17:10:48 +01:00
#' antibiotics = c("AMC", "CIP", "TZP", "TZP+TOB"),
#' mo_transform = "gramstain",
#' syndromic_group = "ward"
#' )
#'
#' if (requireNamespace("ggplot2")) {
#' ggplot2::autoplot(ab1)
#' }
#' if (requireNamespace("ggplot2")) {
#' ggplot2::autoplot(ab2)
#' }
2023-03-11 14:24:34 +01:00
#'
2023-02-17 09:42:51 +01:00
#' plot(ab1)
#' plot(ab2)
2023-02-13 16:56:25 +01:00
#' }
antibiogram <- function(x,
antibiotics = where(is.sir),
mo_transform = "shortname",
ab_transform = NULL,
syndromic_group = NULL,
add_total_n = TRUE,
only_all_tested = FALSE,
digits = 0,
col_mo = NULL,
language = get_AMR_locale(),
minimum = 30,
combine_SI = TRUE,
2023-02-17 11:39:00 +01:00
sep = " + ",
info = interactive()) {
meet_criteria(x, allow_class = "data.frame", contains_column_class = c("sir", "rsi"))
meet_criteria(mo_transform, allow_class = "character", has_length = 1, is_in = c("name", "shortname", "gramstain", colnames(AMR::microorganisms)), allow_NULL = TRUE)
meet_criteria(ab_transform, allow_class = "character", has_length = 1, is_in = colnames(AMR::antibiotics), allow_NULL = TRUE)
meet_criteria(syndromic_group, allow_class = "character", allow_NULL = TRUE, allow_NA = TRUE)
meet_criteria(add_total_n, allow_class = "logical", has_length = 1)
meet_criteria(only_all_tested, allow_class = "logical", has_length = 1)
meet_criteria(digits, allow_class = c("numeric", "integer"), has_length = 1, is_finite = TRUE)
meet_criteria(col_mo, allow_class = "character", has_length = 1, allow_NULL = TRUE, is_in = colnames(x))
language <- validate_language(language)
meet_criteria(minimum, allow_class = c("numeric", "integer"), has_length = 1, is_positive_or_zero = TRUE, is_finite = TRUE)
meet_criteria(combine_SI, allow_class = "logical", has_length = 1)
meet_criteria(sep, allow_class = "character", has_length = 1)
2023-02-17 11:39:00 +01:00
meet_criteria(info, allow_class = "logical", has_length = 1)
2023-03-11 14:24:34 +01:00
# try to find columns based on type
if (is.null(col_mo)) {
col_mo <- search_type_in_df(x = x, type = "mo", info = interactive())
stop_if(is.null(col_mo), "`col_mo` must be set")
}
# transform MOs
x$`.mo` <- x[, col_mo, drop = TRUE]
if (is.null(mo_transform)) {
# leave as is
} else if (mo_transform == "gramstain") {
x$`.mo` <- mo_gramstain(x$`.mo`, language = language)
} else if (mo_transform == "shortname") {
x$`.mo` <- mo_shortname(x$`.mo`, language = language)
} else if (mo_transform == "name") {
x$`.mo` <- mo_name(x$`.mo`, language = language)
} else {
x$`.mo` <- mo_property(x$`.mo`, language = language)
}
x$`.mo`[is.na(x$`.mo`)] <- "(??)"
# get syndromic groups
if (!is.null(syndromic_group)) {
if (length(syndromic_group) == 1 && syndromic_group %in% colnames(x)) {
x$`.syndromic_group` <- x[, syndromic_group, drop = TRUE]
} else if (!is.null(syndromic_group)) {
x$`.syndromic_group` <- syndromic_group
}
x$`.syndromic_group`[is.na(x$`.syndromic_group`) | x$`.syndromic_group` == ""] <- paste0("(", translate_AMR("unknown", language = language), ")")
has_syndromic_group <- TRUE
} else {
has_syndromic_group <- FALSE
}
2023-02-12 17:10:48 +01:00
# get antibiotics
if (tryCatch(is.character(antibiotics), error = function(e) FALSE)) {
antibiotics.bak <- antibiotics
# split antibiotics on separator and make it a list
antibiotics <- strsplit(gsub(" ", "", antibiotics), "+", fixed = TRUE)
# get available antibiotics in data set
df_ab <- get_column_abx(x, verbose = FALSE, info = FALSE)
# get antibiotics from user
user_ab <- suppressMessages(suppressWarnings(lapply(antibiotics, as.ab, flag_multiple_results = FALSE, info = FALSE)))
non_existing <- character(0)
user_ab <- lapply(user_ab, function(x) {
out <- unname(df_ab[match(x, names(df_ab))])
non_existing <<- c(non_existing, x[is.na(out) & !is.na(x)])
# remove non-existing columns
out[!is.na(out)]
})
user_ab <- user_ab[unlist(lapply(user_ab, length)) > 0]
2023-03-11 14:24:34 +01:00
if (length(non_existing) > 0) {
warning_("The following antibiotics were not available and ignored: ", vector_and(ab_name(non_existing, language = NULL, tolower = TRUE), quotes = FALSE))
}
# make list unique
antibiotics <- unique(user_ab)
# go through list to set AMR in combinations
for (i in seq_len(length(antibiotics))) {
abx <- antibiotics[[i]]
for (ab in abx) {
# make sure they are SIR columns
x[, ab] <- as.sir(x[, ab, drop = TRUE])
}
new_colname <- paste0(trimws(abx), collapse = sep)
if (length(abx) == 1) {
next
} else {
# determine whether this new column should contain S, I, R, or NA
if (isTRUE(combine_SI)) {
S_values <- c("S", "I")
2023-02-12 17:10:48 +01:00
} else {
S_values <- "S"
}
other_values <- setdiff(c("S", "I", "R"), S_values)
x_transposed <- as.list(as.data.frame(t(x[, abx, drop = FALSE]), stringsAsFactors = FALSE))
if (isTRUE(only_all_tested)) {
x[new_colname] <- as.sir(vapply(FUN.VALUE = character(1), x_transposed, function(x) ifelse(anyNA(x), NA_character_, ifelse(any(x %in% S_values), "S", "R")), USE.NAMES = FALSE))
} else {
2023-02-12 17:10:48 +01:00
x[new_colname] <- as.sir(vapply(
FUN.VALUE = character(1), x_transposed, function(x) ifelse(any(x %in% S_values, na.rm = TRUE), "S", ifelse(anyNA(x), NA_character_, "R")),
USE.NAMES = FALSE
))
}
}
antibiotics[[i]] <- new_colname
}
antibiotics <- unlist(antibiotics)
} else {
antibiotics <- colnames(suppressWarnings(x[, antibiotics, drop = FALSE]))
}
2023-02-12 17:10:48 +01:00
if (isTRUE(has_syndromic_group)) {
2023-02-12 17:10:48 +01:00
out <- x %pm>%
pm_select(.syndromic_group, .mo, antibiotics) %pm>%
pm_group_by(.syndromic_group)
} else {
2023-02-12 17:10:48 +01:00
out <- x %pm>%
pm_select(.mo, antibiotics)
}
2023-02-12 17:10:48 +01:00
# get numbers of S, I, R (per group)
2023-02-12 17:10:48 +01:00
out <- out %pm>%
bug_drug_combinations(
col_mo = ".mo",
FUN = function(x) x
)
counts <- out
2023-03-11 14:24:34 +01:00
2023-02-13 10:21:43 +01:00
if (isTRUE(combine_SI)) {
out$numerator <- out$S + out$I
} else {
out$numerator <- out$S
}
if (any(out$total < minimum, na.rm = TRUE)) {
2023-02-17 11:39:00 +01:00
if (isTRUE(info)) {
message_("NOTE: ", sum(out$total < minimum, na.rm = TRUE), " combinations had less than `minimum = ", minimum, "` results and were ignored", add_fn = font_red)
}
2023-02-13 10:21:43 +01:00
out <- out %pm>%
subset(total >= minimum)
}
2023-02-17 11:39:00 +01:00
# regroup for summarising
if (isTRUE(has_syndromic_group)) {
colnames(out)[1] <- "syndromic_group"
2023-02-12 17:10:48 +01:00
out <- out %pm>%
pm_group_by(syndromic_group, mo, ab)
} else {
2023-02-12 17:10:48 +01:00
out <- out %pm>%
pm_group_by(mo, ab)
}
out <- out %pm>%
pm_summarise(SI = numerator / total)
2023-02-12 17:10:48 +01:00
# transform names of antibiotics
ab_naming_function <- function(x, t, l, s) {
x <- strsplit(x, s, fixed = TRUE)
out <- character(length = length(x))
for (i in seq_len(length(x))) {
a <- x[[i]]
if (is.null(t)) {
# leave as is
} else if (t == "atc") {
a <- ab_atc(a, only_first = TRUE, language = l)
} else {
a <- ab_property(a, property = t, language = l)
}
if (length(a) > 1) {
a <- paste0(trimws(a), collapse = sep)
}
out[i] <- a
}
out
}
out$ab <- ab_naming_function(out$ab, t = ab_transform, l = language, s = sep)
2023-02-12 17:10:48 +01:00
# transform long to wide
long_to_wide <- function(object, digs) {
object$SI <- round(object$SI * 100, digits = digs)
object <- object %pm>%
# an unclassed data.frame is required for stats::reshape()
2023-02-12 17:10:48 +01:00
as.data.frame(stringsAsFactors = FALSE) %pm>%
stats::reshape(direction = "wide", idvar = "mo", timevar = "ab", v.names = "SI")
colnames(object) <- gsub("^SI?[.]", "", colnames(object))
return(object)
}
2023-02-12 17:10:48 +01:00
# ungroup for long -> wide transformation
attr(out, "pm_groups") <- NULL
attr(out, "groups") <- NULL
class(out) <- class(out)[!class(out) %in% c("grouped_df", "grouped_data")]
long <- out
2023-02-12 17:10:48 +01:00
if (isTRUE(has_syndromic_group)) {
grps <- unique(out$syndromic_group)
for (i in seq_len(length(grps))) {
grp <- grps[i]
if (i == 1) {
new_df <- long_to_wide(out[which(out$syndromic_group == grp), , drop = FALSE], digs = digits)
} else {
2023-02-12 17:10:48 +01:00
new_df <- rbind2(
new_df,
long_to_wide(out[which(out$syndromic_group == grp), , drop = FALSE], digs = digits)
)
}
}
# sort rows
new_df <- new_df %pm>% pm_arrange(mo, syndromic_group)
# sort columns
new_df <- new_df[, c("syndromic_group", "mo", sort(colnames(new_df)[!colnames(new_df) %in% c("syndromic_group", "mo")])), drop = FALSE]
colnames(new_df)[1:2] <- translate_AMR(c("Syndromic Group", "Pathogen"), language = language)
} else {
new_df <- long_to_wide(out, digs = digits)
# sort rows
new_df <- new_df %pm>% pm_arrange(mo)
# sort columns
new_df <- new_df[, c("mo", sort(colnames(new_df)[colnames(new_df) != "mo"])), drop = FALSE]
colnames(new_df)[1] <- translate_AMR("Pathogen", language = language)
}
2023-02-12 17:10:48 +01:00
# add total N if indicated
if (isTRUE(add_total_n)) {
if (isTRUE(has_syndromic_group)) {
n_per_mo <- counts %pm>%
pm_group_by(mo, .syndromic_group) %pm>%
pm_summarise(paste0(min(total, na.rm = TRUE), "-", max(total, na.rm = TRUE)))
colnames(n_per_mo) <- c("mo", "syn", "count")
count_group <- n_per_mo$count[match(paste(new_df[[2]], new_df[[1]]), paste(n_per_mo$mo, n_per_mo$syn))]
edit_col <- 2
} else {
n_per_mo <- counts %pm>%
pm_group_by(mo) %pm>%
pm_summarise(paste0(min(total, na.rm = TRUE), "-", max(total, na.rm = TRUE)))
colnames(n_per_mo) <- c("mo", "count")
count_group <- n_per_mo$count[match(new_df[[1]], n_per_mo$mo)]
edit_col <- 1
}
2023-02-22 16:26:13 +01:00
if (NCOL(new_df) == edit_col + 1) {
# only 1 antibiotic
2023-02-23 16:27:40 +01:00
new_df[[edit_col]] <- paste0(new_df[[edit_col]], " (", unlist(lapply(strsplit(x = count_group, split = "-", fixed = TRUE), function(x) x[1])), ")")
2023-02-22 16:26:13 +01:00
colnames(new_df)[edit_col] <- paste(colnames(new_df)[edit_col], "(N)")
} else {
# more than 1 antibiotic
new_df[[edit_col]] <- paste0(new_df[[edit_col]], " (", count_group, ")")
colnames(new_df)[edit_col] <- paste(colnames(new_df)[edit_col], "(N min-max)")
}
}
2023-02-12 17:10:48 +01:00
2023-02-18 11:57:17 +01:00
out <- as_original_data_class(new_df, class(x), extra_class = "antibiogram")
rownames(out) <- NULL
structure(out,
2023-02-24 09:43:10 +01:00
has_syndromic_group = has_syndromic_group,
2023-02-12 17:10:48 +01:00
long = long,
combine_SI = combine_SI
)
}
#' @export
#' @rdname antibiogram
plot.antibiogram <- function(x, ...) {
df <- attributes(x)$long
if ("syndromic_group" %in% colnames(df)) {
# barplot in base R does not support facets - paste columns together
df$mo <- paste(df$mo, "-", df$syndromic_group)
df$syndromic_group <- NULL
df <- df[order(df$mo), , drop = FALSE]
}
2023-02-12 17:10:48 +01:00
mo_levels <- unique(df$mo)
2023-02-06 12:38:52 +01:00
mfrow_old <- graphics::par()$mfrow
sqrt_levels <- sqrt(length(mo_levels))
2023-02-06 12:38:52 +01:00
graphics::par(mfrow = c(ceiling(sqrt_levels), floor(sqrt_levels)))
for (i in seq_along(mo_levels)) {
mo <- mo_levels[i]
df_sub <- df[df$mo == mo, , drop = FALSE]
2023-02-12 17:10:48 +01:00
barplot(
height = df_sub$SI * 100,
xlab = NULL,
ylab = ifelse(isTRUE(attributes(x)$combine_SI), "%SI", "%S"),
names.arg = df_sub$ab,
col = "#aaaaaa",
beside = TRUE,
main = mo,
legend = NULL
)
}
2023-02-06 12:38:52 +01:00
graphics::par(mfrow = mfrow_old)
}
#' @export
#' @noRd
2023-02-06 12:38:52 +01:00
barplot.antibiogram <- function(height, ...) {
plot(height, ...)
}
#' @method autoplot antibiogram
#' @rdname antibiogram
# will be exported using s3_register() in R/zzz.R
autoplot.antibiogram <- function(object, ...) {
df <- attributes(object)$long
ggplot2::ggplot(df) +
2023-02-12 17:10:48 +01:00
ggplot2::geom_col(
ggplot2::aes(
x = ab,
y = SI * 100,
fill = if ("syndromic_group" %in% colnames(df)) {
syndromic_group
} else {
NULL
}
),
position = ggplot2::position_dodge2(preserve = "single")
) +
ggplot2::facet_wrap("mo") +
2023-02-12 17:10:48 +01:00
ggplot2::labs(
y = ifelse(isTRUE(attributes(object)$combine_SI), "%SI", "%S"),
x = NULL,
fill = if ("syndromic_group" %in% colnames(df)) {
colnames(object)[1]
} else {
NULL
}
)
}
2023-02-23 16:27:40 +01:00
# will be exported in zzz.R
2023-02-24 09:43:10 +01:00
#' @method knit_print antibiogram
#' @param italicise a [logical] to indicate whether the microorganism names in the [knitr][knitr::kable()] table should be made italic, using [italicise_taxonomy()].
2023-02-23 16:27:40 +01:00
#' @param na character to use for showing `NA` values
#' @rdname antibiogram
2023-02-23 16:27:40 +01:00
knit_print.antibiogram <- function(x, italicise = TRUE, na = getOption("knitr.kable.NA", default = ""), ...) {
stop_ifnot_installed("knitr")
2023-02-17 09:42:51 +01:00
meet_criteria(italicise, allow_class = "logical", has_length = 1)
meet_criteria(na, allow_class = "character", has_length = 1, allow_NA = TRUE)
2023-02-24 09:43:10 +01:00
if (isTRUE(italicise)) {
# make all microorganism names italic, according to nomenclature
names_col <- ifelse(isTRUE(attributes(x)$has_syndromic_group), 2, 1)
x[[names_col]] <- italicise_taxonomy(x[[names_col]], type = "markdown")
}
2023-02-23 16:27:40 +01:00
old_option <- getOption("knitr.kable.NA")
options(knitr.kable.NA = na)
on.exit(options(knitr.kable.NA = old_option))
2023-02-24 09:43:10 +01:00
out <- paste(c("", "", knitr::kable(x, ..., output = FALSE)), collapse = "\n")
knitr::asis_output(out)
}