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(v2.1.1.9147) scale fixes and WISCA update, fix conserved capped values

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dr. M.S. (Matthijs) Berends
2025-02-14 14:16:46 +01:00
parent bd2887bcd4
commit d94efb0f5e
19 changed files with 430 additions and 333 deletions
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R/antibiogram.R
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@ -38,7 +38,7 @@
#' @param mo_transform a character to transform microorganism input - must be `"name"`, `"shortname"` (default), `"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 or `NA` to consider all microorganisms 'unknown'.
#' @param ab_transform a character to transform antimicrobial input - must be one of the column names of the [antibiotics] data set (defaults to `"name"`): `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 isolates per antimicrobial (e.g, if for *E. coli* 200 isolates are available for ciprofloxacin and 150 for amoxicillin, the returned number will be "150-200").
#' @param add_total_n a [logical] to indicate whether `n_tested` available numbers per pathogen should be added to the table (default is `TRUE`). This will add the lowest and highest number of available isolates per antimicrobial (e.g, if for *E. coli* 200 isolates are available for ciprofloxacin and 150 for amoxicillin, the returned number will be "150-200"). This option is unavailable when `wisca = TRUE`; in that case, use [retrieve_wisca_parameters()] to get the parameters used for WISCA.
#' @param only_all_tested (for combination antibiograms): a [logical] to indicate that isolates must be tested for all antimicrobials, see *Details*
#' @param digits number of digits to use for rounding the antimicrobial coverage, defaults to 1 for WISCA and 0 otherwise
#' @param formatting_type numeric value (1–22 for WISCA, 1-12 for non-WISCA) indicating how the 'cells' of the antibiogram table should be formatted. See *Details* > *Formatting Type* for a list of options.
@ -47,7 +47,7 @@
#' @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, SDD, or I, instead of only S (default is `TRUE`)
#' @param sep a separating character for antimicrobial columns in combination antibiograms
#' @param wisca a [logical] to indicate whether a Weighted-Incidence Syndromic Combination Antibiogram (WISCA) must be generated (default is `FALSE`). This will use a Bayesian decision model to estimate regimen coverage probabilities using [Monte Carlo simulations](https://en.wikipedia.org/wiki/Monte_Carlo_method). Set `simulations` to adjust.
#' @param wisca a [logical] to indicate whether a Weighted-Incidence Syndromic Combination Antibiogram (WISCA) must be generated (default is `FALSE`). This will use a Bayesian decision model to estimate regimen coverage probabilities using [Monte Carlo simulations](https://en.wikipedia.org/wiki/Monte_Carlo_method). Set `simulations`, `conf_interval`, and `interval_side` to adjust.
#' @param simulations (for WISCA) a numerical value to set the number of Monte Carlo simulations
#' @param conf_interval (for WISCA) a numerical value to set confidence interval (default is `0.95`)
#' @param interval_side (for WISCA) the side of the confidence interval, either `"two-tailed"` (default), `"left"` or `"right"`
@ -64,7 +64,7 @@
#'
#' ### Formatting Type
#'
#' The formatting of the 'cells' of the table can be set with the argument `formatting_type`. In these examples, `5` is the antimicrobial coverage (for WISCA: `4-6` indicates the confidence level), `15` the numerator, and `300` the denominator:
#' The formatting of the 'cells' of the table can be set with the argument `formatting_type`. In these examples, `5` is the antimicrobial coverage (`4-6` indicates the confidence level), `15` the number of susceptible isolates, and `300` the number of tested (i.e., available) isolates:
#'
#' 1. 5
#' 2. 15
@ -75,13 +75,11 @@
#' 7. 5 (N=300)
#' 8. 5% (N=300)
#' 9. 5 (15/300)
#' 10. 5% (15/300) - **default for non-WISCA**
#' 10. 5% (15/300)
#' 11. 5 (N=15/300)
#' 12. 5% (N=15/300)
#'
#' Additional options for WISCA (using `antibiogram(..., wisca = TRUE)` or `wisca()`):
#' 13. 5 (4-6)
#' 14. 5% (4-6%) - **default for WISCA**
#' 14. 5% (4-6%) - **default**
#' 15. 5 (4-6,300)
#' 16. 5% (4-6%,300)
#' 17. 5 (4-6,N=300)
@ -91,7 +89,7 @@
#' 21. 5 (4-6,N=15/300)
#' 22. 5% (4-6%,N=15/300)
#'
#' The default is `14` for WISCA and `10` for non-WISCA, which can be set globally with the package option [`AMR_antibiogram_formatting_type`][AMR-options], e.g. `options(AMR_antibiogram_formatting_type = 5)`. Do note that for WISCA, the numerator and denominator are less useful to report, since these are included in the Bayesian model and apparent from the susceptibility and its confidence level.
#' The default is `14`, which can be set globally with the package option [`AMR_antibiogram_formatting_type`][AMR-options], e.g. `options(AMR_antibiogram_formatting_type = 5)`. Do note that for WISCA, the total numbers of tested and susceptible isolates are less useful to report, since these are included in the Bayesian model and apparent from the susceptibility and its confidence level.
#'
#' Set `digits` (defaults to `0`) to alter the rounding of the susceptibility percentages.
#'
@ -99,7 +97,7 @@
#'
#' There are various antibiogram types, as summarised by Klinker *et al.* (2021, \doi{10.1177/20499361211011373}), and they are all supported by [antibiogram()].
#'
#' **Use WISCA whenever possible**, since it provides more precise coverage estimates by accounting for pathogen incidence and antimicrobial susceptibility, as has been shown by Bielicki *et al.* (2020, \doi{10.1001.jamanetworkopen.2019.21124}). See the section *Explaining WISCA* on this page.
#' For clinical coverage estimations, **use WISCA whenever possible**, since it provides more precise coverage estimates by accounting for pathogen incidence and antimicrobial susceptibility, as has been shown by Bielicki *et al.* (2020, \doi{10.1001.jamanetworkopen.2019.21124}). See the section *Explaining WISCA* on this page. Do note that WISCA is pathogen-agnostic, meaning that the outcome is not stratied by pathogen, but rather by syndrome.
#'
#' 1. **Traditional Antibiogram**
#'
@ -174,14 +172,17 @@
#'
#' At admission, no pathogen information is available.
#'
#' - Action: broad-spectrum coverage is based on local resistance patterns and syndromic antibiograms.
#' - Action: broad-spectrum coverage is based on local resistance patterns and syndromic antibiograms. Using the pathogen-agnostic yet incidence-weighted WISCA is preferred.
#' - Code example:
#'
#' ```r
#' antibiogram(your_data,
#' antibiotics = selected_regimens,
#' wisca = TRUE,
#' mo_transform = NA) # all pathogens set to `NA`
#'
#' # preferred: use WISCA
#' wisca(your_data,
#' antibiotics = selected_regimens)
#' ```
#'
#' 2. **Refinement with Gram Stain Results**
@ -194,7 +195,6 @@
#' ```r
#' antibiogram(your_data,
#' antibiotics = selected_regimens,
#' wisca = TRUE,
#' mo_transform = "gramstain") # all pathogens set to Gram-pos/Gram-neg
#' ```
#'
@ -208,7 +208,6 @@
#' ```r
#' antibiogram(your_data,
#' antibiotics = selected_regimens,
#' wisca = TRUE,
#' mo_transform = "shortname") # all pathogens set to 'G. species', e.g., E. coli
#' ```
#'
@ -247,7 +246,7 @@
#'
#' @section Explaining WISCA:
#'
#' WISCA, as outlined by Bielicki *et al.* (\doi{10.1093/jac/dkv397}), stands for Weighted-Incidence Syndromic Combination Antibiogram, which estimates the probability of adequate empirical antimicrobial regimen coverage for specific infection syndromes. This method leverages a Bayesian hierarchical logistic regression framework with random effects for pathogens and regimens, enabling robust estimates in the presence of sparse data.
#' WISCA, as outlined by Bielicki *et al.* (\doi{10.1093/jac/dkv397}), stands for Weighted-Incidence Syndromic Combination Antibiogram, which estimates the probability of adequate empirical antimicrobial regimen coverage for specific infection syndromes. This method leverages a Bayesian decision model with random effects for pathogen incidence and susceptibility, enabling robust estimates in the presence of sparse data.
#'
#' The Bayesian model assumes conjugate priors for parameter estimation. For example, the coverage probability \eqn{\theta} for a given antimicrobial regimen is modelled using a Beta distribution as a prior:
#'
@ -282,6 +281,7 @@
#'
#' By combining empirical data with prior knowledge, WISCA overcomes the limitations of traditional combination antibiograms, offering disease-specific, patient-stratified estimates with robust uncertainty quantification. This tool is invaluable for antimicrobial stewardship programs and empirical treatment guideline refinement.
#'
#' **Note:** WISCA never gives an output on the pathogen/species level, as all incidences and susceptibilities are already weighted for all species.
#' @source
#' * Bielicki JA *et al.* (2016). **Selecting appropriate empirical antibiotic regimens for paediatric bloodstream infections: application of a Bayesian decision model to local and pooled antimicrobial resistance surveillance data** *Journal of Antimicrobial Chemotherapy* 71(3); \doi{10.1093/jac/dkv397}
#' * Bielicki JA *et al.* (2020). **Evaluation of the coverage of 3 antibiotic regimens for neonatal sepsis in the hospital setting across Asian countries** *JAMA Netw Open.* 3(2):e1921124; \doi{10.1001.jamanetworkopen.2019.21124}
@ -307,14 +307,12 @@
#' antibiogram(example_isolates,
#' antibiotics = aminoglycosides(),
#' ab_transform = "atc",
#' mo_transform = "gramstain"
#' )
#' mo_transform = "gramstain")
#'
#' antibiogram(example_isolates,
#' antibiotics = carbapenems(),
#' ab_transform = "name",
#' mo_transform = "name"
#' )
#' mo_transform = "name")
#'
#'
#' # Combined antibiogram -------------------------------------------------
@ -322,16 +320,14 @@
#' # combined antibiotics yield higher empiric coverage
#' antibiogram(example_isolates,
#' antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"),
#' mo_transform = "gramstain"
#' )
#' mo_transform = "gramstain")
#'
#' # names of antibiotics do not need to resemble columns exactly:
#' antibiogram(example_isolates,
#' antibiotics = c("Cipro", "cipro + genta"),
#' mo_transform = "gramstain",
#' ab_transform = "name",
#' sep = " & "
#' )
#' sep = " & ")
#'
#'
#' # Syndromic antibiogram ------------------------------------------------
@ -339,8 +335,7 @@
#' # the data set could contain a filter for e.g. respiratory specimens
#' antibiogram(example_isolates,
#' antibiotics = c(aminoglycosides(), carbapenems()),
#' syndromic_group = "ward"
#' )
#' syndromic_group = "ward")
#'
#' # now define a data set with only E. coli
#' ex1 <- example_isolates[which(mo_genus() == "Escherichia"), ]
@ -353,27 +348,24 @@
#' syndromic_group = ifelse(ex1$ward == "ICU",
#' "UCI", "No UCI"
#' ),
#' language = "es"
#' )
#' language = "es")
#'
#'
#' # WISCA antibiogram ----------------------------------------------------
#'
#' # can be used for any of the above types - just add `wisca = TRUE`
#' # WISCA are not stratified by species, but rather on syndromes
#' antibiogram(example_isolates,
#' antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"),
#' mo_transform = "gramstain",
#' wisca = TRUE
#' )
#' syndromic_group = "ward",
#' wisca = TRUE)
#'
#'
#' # Print the output for R Markdown / Quarto -----------------------------
#'
#' ureido <- antibiogram(example_isolates,
#' antibiotics = ureidopenicillins(),
#' ab_transform = "name",
#' wisca = TRUE
#' )
#' syndromic_group = "name",
#' wisca = TRUE)
#'
#' # in an Rmd file, you would just need to return `ureido` in a chunk,
#' # but to be explicit here:
@ -386,14 +378,11 @@
#'
#' ab1 <- antibiogram(example_isolates,
#' antibiotics = c("AMC", "CIP", "TZP", "TZP+TOB"),
#' mo_transform = "gramstain",
#' wisca = TRUE
#' )
#' mo_transform = "gramstain")
#' ab2 <- antibiogram(example_isolates,
#' antibiotics = c("AMC", "CIP", "TZP", "TZP+TOB"),
#' mo_transform = "gramstain",
#' syndromic_group = "ward"
#' )
#' syndromic_group = "ward")
#'
#' if (requireNamespace("ggplot2")) {
#' ggplot2::autoplot(ab1)
@ -413,7 +402,7 @@ antibiogram <- function(x,
add_total_n = FALSE,
only_all_tested = FALSE,
digits = ifelse(wisca, 1, 0),
formatting_type = getOption("AMR_antibiogram_formatting_type", ifelse(wisca, 14, 10)),
formatting_type = getOption("AMR_antibiogram_formatting_type", 14),
col_mo = NULL,
language = get_AMR_locale(),
minimum = 30,
@ -437,7 +426,7 @@ antibiogram.default <- function(x,
add_total_n = FALSE,
only_all_tested = FALSE,
digits = ifelse(wisca, 1, 0),
formatting_type = getOption("AMR_antibiogram_formatting_type", ifelse(wisca, 14, 10)),
formatting_type = getOption("AMR_antibiogram_formatting_type", 14),
col_mo = NULL,
language = get_AMR_locale(),
minimum = 30,
@ -450,6 +439,13 @@ antibiogram.default <- function(x,
info = interactive()) {
meet_criteria(x, allow_class = "data.frame")
x <- ascertain_sir_classes(x, "x")
meet_criteria(wisca, allow_class = "logical", has_length = 1)
if (isTRUE(wisca)) {
if (!missing(mo_transform)) {
warning_("WISCA must be based on the species level as WISCA parameters are based on this. For that reason, `mo_transform` will be ignored.")
}
mo_transform <- function(x) suppressMessages(suppressWarnings(paste(mo_genus(x, keep_synonyms = TRUE, language = NULL), mo_species(x, keep_synonyms = TRUE, language = NULL))))
}
if (!is.function(mo_transform)) {
meet_criteria(mo_transform, allow_class = "character", has_length = 1, is_in = c("name", "shortname", "gramstain", colnames(AMR::microorganisms)), allow_NULL = TRUE, allow_NA = TRUE)
}
@ -460,8 +456,7 @@ antibiogram.default <- function(x,
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(wisca, allow_class = "logical", has_length = 1)
meet_criteria(formatting_type, allow_class = c("numeric", "integer"), has_length = 1, is_in = if (wisca == TRUE) c(1:22) else c(1:12))
meet_criteria(formatting_type, allow_class = c("numeric", "integer"), has_length = 1, is_in = c(1:22))
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)
@ -587,62 +582,156 @@ antibiogram.default <- function(x,
FUN = function(x) x,
include_n_rows = TRUE
)
colnames(out)[colnames(out) == "total"] <- "n_tested"
colnames(out)[colnames(out) == "total_rows"] <- "n_total"
counts <- out
wisca_params <- NULL
if (isTRUE(combine_SI)) {
out$n_susceptible <- out$S + out$I + out$SDD
} else {
out$n_susceptible <- out$S
}
if (all(out$n_tested < minimum, na.rm = TRUE) && wisca == FALSE) {
warning_("All combinations had less than `minimum = ", minimum, "` results, returning an empty antibiogram")
return(as_original_data_class(data.frame(), class(out), extra_class = "antibiogram"))
} else if (any(out$n_tested < minimum, na.rm = TRUE)) {
out <- out %pm>%
# also for WISCA, refrain from anything below 15 isolates:
subset(n_tested > 15)
mins <- sum(out$n_tested < minimum, na.rm = TRUE)
if (wisca == FALSE) {
out <- out %pm>%
subset(n_tested >= minimum)
if (isTRUE(info) && mins > 0) {
message_("NOTE: ", mins, " combinations had less than `minimum = ", minimum, "` results and were ignored", add_fn = font_red)
}
} else if (isTRUE(info)) {
warning_("Number of tested isolates per regimen should exceed ", minimum, " for each species. Coverage estimates might be inaccurate.", call = FALSE)
}
}
if (NROW(out) == 0) {
return(as_original_data_class(data.frame(), class(out), extra_class = "antibiogram"))
}
out$p_susceptible <- out$n_susceptible / out$n_tested
# add confidence levels
out$lower_ci <- NA_real_
out$upper_ci <- NA_real_
for (r in seq_len(NROW(out))) {
if (!is.na(out$n_susceptible[r]) && !is.na(out$n_tested[r]) && out$n_tested[r] > 0) {
ci <- stats::binom.test(out$n_susceptible[r], out$n_tested[r], conf.level = conf_interval)$conf.int
out$lower_ci[r] <- ci[1]
out$upper_ci[r] <- ci[2]
}
}
# regroup for summarising
if (isTRUE(has_syndromic_group)) {
colnames(out)[1] <- "syndromic_group"
out <- out %pm>%
pm_group_by(syndromic_group, mo, ab)
} else {
out <- out %pm>%
pm_group_by(mo, ab)
}
long_numeric <- out %pm>%
pm_summarise(coverage = p_susceptible,
lower_ci = lower_ci,
upper_ci = upper_ci,
n_total = n_total,
n_tested = n_tested,
n_susceptible = n_susceptible)
wisca_parameters <- data.frame()
if (wisca == TRUE) {
# WISCA ----
# set up progress bar
progress <- progress_ticker(n = NROW(out[which(out$total > 0), , drop = FALSE]),
n_min = 10,
print = info,
title = "Calculating beta/gamma parameters for WISCA")
on.exit(close(progress))
if (isTRUE(has_syndromic_group)) {
colnames(out)[1] <- "syndromic_group"
out_wisca <- out %pm>%
pm_group_by(syndromic_group, ab)
} else {
out_wisca <- out %pm>%
pm_group_by(ab)
}
out_wisca <- out_wisca %pm>%
pm_summarise(coverage = NA_real_,
lower_ci = NA_real_,
upper_ci = NA_real_,
n_total = sum(n_total, na.rm = TRUE),
n_tested = sum(n_tested, na.rm = TRUE),
n_susceptible = sum(n_susceptible, na.rm = TRUE))
out_wisca$p_susceptible <- out_wisca$n_susceptible / out_wisca$n_tested
out$coverage <- NA_real_
out$lower_ci <- NA_real_
out$upper_ci <- NA_real_
if (isTRUE(has_syndromic_group)) {
out$group <- paste(out$syndromic_group, out$ab)
out_wisca$group <- paste(out_wisca$syndromic_group, out_wisca$ab)
} else {
out$group <- out$ab
out_wisca$group <- out_wisca$ab
}
# create the WISCA parameters, including our priors/posteriors
out$gamma_posterior <- NA_real_
out$beta_posterior_1 <- NA_real_
out$beta_posterior_2 <- NA_real_
out$beta_posterior1 <- NA_real_
out$beta_posterior2 <- NA_real_
for (i in seq_len(NROW(out))) {
if (out$total[i] == 0) {
if (out$n_tested[i] == 0) {
next
}
progress$tick()
out_current <- out[i, , drop = FALSE]
priors <- calculate_priors(out_current, combine_SI = combine_SI)
out$gamma_posterior[i] = priors$gamma_posterior
out$beta_posterior_1[i] = priors$beta_posterior_1
out$beta_posterior_2[i] = priors$beta_posterior_2
out$beta_posterior1[i] = priors$beta_posterior_1
out$beta_posterior2[i] = priors$beta_posterior_2
}
wisca_parameters <- out
progress <- progress_ticker(n = length(unique(wisca_parameters$group)) * simulations,
n_min = 25,
print = info,
title = paste("Calculating WISCA for", length(unique(wisca_parameters$group)), "regimens"))
on.exit(close(progress))
# run WISCA
for (group in unique(wisca_parameters$group)) {
params_current <- wisca_parameters[which(wisca_parameters$group == group), , drop = FALSE]
if (sum(params_current$n_tested, na.rm = TRUE) == 0) {
next
}
# Monte Carlo simulation
coverage_simulations <- replicate(simulations, {
progress$tick()
# simulate pathogen incidence
# = Dirichlet (Gamma) parameters
random_incidence <- stats::runif(1, min = 0, max = 1)
simulated_incidence <- stats::qgamma(
p = random_incidence,
shape = priors$gamma_posterior,
shape = params_current$gamma_posterior,
scale = 1
)
# normalise
simulated_incidence <- simulated_incidence / sum(simulated_incidence)
simulated_incidence <- simulated_incidence / sum(simulated_incidence, na.rm = TRUE)
# simulate susceptibility
# = Beta parameters
random_susceptibity <- stats::runif(1, min = 0, max = 1)
simulated_susceptibility <- stats::qbeta(
p = random_susceptibity,
shape1 = priors$beta_posterior_1,
shape2 = priors$beta_posterior_2
shape1 = params_current$beta_posterior1,
shape2 = params_current$beta_posterior2
)
sum(simulated_incidence * simulated_susceptibility)
sum(simulated_incidence * simulated_susceptibility, na.rm = TRUE)
})
# calculate coverage statistics
@ -656,72 +745,22 @@ antibiogram.default <- function(x,
}
coverage_ci <- unname(stats::quantile(coverage_simulations, probs = probs))
out$coverage[i] <- coverage_mean
out$lower_ci[i] <- coverage_ci[1]
out$upper_ci[i] <- coverage_ci[2]
out_wisca$coverage[which(out_wisca$group == group)] <- coverage_mean
out_wisca$lower_ci[which(out_wisca$group == group)] <- coverage_ci[1]
out_wisca$upper_ci[which(out_wisca$group == group)] <- coverage_ci[2]
}
# remove progress bar from console
close(progress)
}
if (isTRUE(combine_SI)) {
out$numerator <- out$S + out$I + out$SDD
} else {
out$numerator <- out$S
}
if (all(out$total < minimum, na.rm = TRUE) && wisca == FALSE) {
warning_("All combinations had less than `minimum = ", minimum, "` results, returning an empty antibiogram")
return(as_original_data_class(data.frame(), class(out), extra_class = "antibiogram"))
} else if (any(out$total < minimum, na.rm = TRUE)) {
out <- out %pm>%
# also for WISCA, refrain from anything below 15 isolates:
subset(total > 15)
mins <- sum(out$total < minimum, na.rm = TRUE)
if (wisca == FALSE) {
out <- out %pm>%
subset(total >= minimum)
if (isTRUE(info) && mins > 0) {
message_("NOTE: ", mins, " combinations had less than `minimum = ", minimum, "` results and were ignored", add_fn = font_red)
}
} else if (isTRUE(info)) {
warning_("Number of tested isolates per regimen should exceed ", minimum, ". Coverage estimates will be inaccurate for ", mins, " regimen", ifelse(mins == 1, "", "s"), ".", call = FALSE)
}
}
if (NROW(out) == 0) {
return(as_original_data_class(data.frame(), class(out), extra_class = "antibiogram"))
}
# regroup for summarising
if (isTRUE(has_syndromic_group)) {
colnames(out)[1] <- "syndromic_group"
out <- out %pm>%
pm_group_by(syndromic_group, mo, ab)
} else {
out <- out %pm>%
pm_group_by(mo, ab)
}
if (wisca == TRUE) {
long_numeric <- out %pm>%
pm_summarise(coverage = coverage,
lower_ci = lower_ci,
upper_ci = upper_ci,
n_tested = total,
n_total = total_rows,
n_susceptible = numerator,
p_susceptible = numerator / total,
gamma_posterior = gamma_posterior,
beta_posterior1 = beta_posterior_1,
beta_posterior2 = beta_posterior_2)
} else {
long_numeric <- out %pm>%
pm_summarise(coverage = numerator / total,
numerator = numerator,
total = total)
# prepare for definitive output
out <- out_wisca
wisca_parameters <- wisca_parameters[, colnames(wisca_parameters)[!colnames(wisca_parameters) %in% c(levels(NA_sir_), "lower_ci", "upper_ci", "group")], drop = FALSE]
}
out$digits <- digits # since pm_sumarise() cannot work with an object outside the current frame
if (isFALSE(wisca)) {
out$coverage <- out$p_susceptible
}
# formatting type:
# 1. 5
@ -746,24 +785,28 @@ antibiogram.default <- function(x,
# 20. 5% (4-6%,15/300)
# 21. 5 (4-6,N=15/300)
# 22. 5% (4-6%,N=15/300)
if (formatting_type == 1) out <- out %pm>% pm_summarise(out_value = round((numerator / total) * 100, digits = digits))
if (formatting_type == 2) out <- out %pm>% pm_summarise(out_value = numerator)
if (formatting_type == 3) out <- out %pm>% pm_summarise(out_value = total)
if (formatting_type == 4) out <- out %pm>% pm_summarise(out_value = paste0(numerator, "/", total))
if (formatting_type == 5) out <- out %pm>% pm_summarise(out_value = paste0(round((numerator / total) * 100, digits = digits), " (", total, ")"))
if (formatting_type == 6) out <- out %pm>% pm_summarise(out_value = paste0(round((numerator / total) * 100, digits = digits), "% (", total, ")"))
if (formatting_type == 7) out <- out %pm>% pm_summarise(out_value = paste0(round((numerator / total) * 100, digits = digits), " (N=", total, ")"))
if (formatting_type == 8) out <- out %pm>% pm_summarise(out_value = paste0(round((numerator / total) * 100, digits = digits), "% (N=", total, ")"))
if (formatting_type == 9) out <- out %pm>% pm_summarise(out_value = paste0(round((numerator / total) * 100, digits = digits), " (", numerator, "/", total, ")"))
if (formatting_type == 10) out <- out %pm>% pm_summarise(out_value = paste0(round((numerator / total) * 100, digits = digits), "% (", numerator, "/", total, ")"))
if (formatting_type == 11) out <- out %pm>% pm_summarise(out_value = paste0(round((numerator / total) * 100, digits = digits), " (N=", numerator, "/", total, ")"))
if (formatting_type == 12) out <- out %pm>% pm_summarise(out_value = paste0(round((numerator / total) * 100, digits = digits), "% (N=", numerator, "/", total, ")"))
if (formatting_type == 1) out <- out %pm>% pm_summarise(out_value = round(coverage * 100, digits = digits))
if (formatting_type == 2) out <- out %pm>% pm_summarise(out_value = n_susceptible)
if (formatting_type == 3) out <- out %pm>% pm_summarise(out_value = n_tested)
if (formatting_type == 4) out <- out %pm>% pm_summarise(out_value = paste0(n_susceptible, "/", n_tested))
if (formatting_type == 5) out <- out %pm>% pm_summarise(out_value = paste0(round(coverage * 100, digits = digits), " (", n_tested, ")"))
if (formatting_type == 6) out <- out %pm>% pm_summarise(out_value = paste0(round(coverage * 100, digits = digits), "% (", n_tested, ")"))
if (formatting_type == 7) out <- out %pm>% pm_summarise(out_value = paste0(round(coverage * 100, digits = digits), " (N=", n_tested, ")"))
if (formatting_type == 8) out <- out %pm>% pm_summarise(out_value = paste0(round(coverage * 100, digits = digits), "% (N=", n_tested, ")"))
if (formatting_type == 9) out <- out %pm>% pm_summarise(out_value = paste0(round(coverage * 100, digits = digits), " (", n_susceptible, "/", n_tested, ")"))
if (formatting_type == 10) out <- out %pm>% pm_summarise(out_value = paste0(round(coverage * 100, digits = digits), "% (", n_susceptible, "/", n_tested, ")"))
if (formatting_type == 11) out <- out %pm>% pm_summarise(out_value = paste0(round(coverage * 100, digits = digits), " (N=", n_susceptible, "/", n_tested, ")"))
if (formatting_type == 12) out <- out %pm>% pm_summarise(out_value = paste0(round(coverage * 100, digits = digits), "% (N=", n_susceptible, "/", n_tested, ")"))
if (formatting_type == 13) out <- out %pm>% pm_summarise(out_value = paste0(round(coverage * 100, digits = digits), " (", round(lower_ci * 100, digits = digits), "-", round(upper_ci * 100, digits = digits), ")"))
if (formatting_type == 14) out <- out %pm>% pm_summarise(out_value = paste0(round(coverage * 100, digits = digits), "% (", round(lower_ci * 100, digits = digits), "-", round(upper_ci * 100, digits = digits), "%)"))
if (formatting_type == 15) out <- out %pm>% pm_summarise(out_value = paste0(round(coverage * 100, digits = digits), " (", round(lower_ci * 100, digits = digits), "-", round(upper_ci * 100, digits = digits), ",", total, ")"))
if (formatting_type == 16) out <- out %pm>% pm_summarise(out_value = paste0(round(coverage * 100, digits = digits), "% (", round(lower_ci * 100, digits = digits), "-", round(upper_ci * 100, digits = digits), "%,", total, ")"))
if (formatting_type == 17) out <- out %pm>% pm_summarise(out_value = paste0(round(coverage * 100, digits = digits), " (", round(lower_ci * 100, digits = digits), "-", round(upper_ci * 100, digits = digits), ",N=", total, ")"))
if (formatting_type == 18) out <- out %pm>% pm_summarise(out_value = paste0(round(coverage * 100, digits = digits), "% (", round(lower_ci * 100, digits = digits), "-", round(upper_ci * 100, digits = digits), "%,N=", total, ")"))
if (formatting_type == 15) out <- out %pm>% pm_summarise(out_value = paste0(round(coverage * 100, digits = digits), " (", round(lower_ci * 100, digits = digits), "-", round(upper_ci * 100, digits = digits), ",", n_tested, ")"))
if (formatting_type == 16) out <- out %pm>% pm_summarise(out_value = paste0(round(coverage * 100, digits = digits), "% (", round(lower_ci * 100, digits = digits), "-", round(upper_ci * 100, digits = digits), "%,", n_tested, ")"))
if (formatting_type == 17) out <- out %pm>% pm_summarise(out_value = paste0(round(coverage * 100, digits = digits), " (", round(lower_ci * 100, digits = digits), "-", round(upper_ci * 100, digits = digits), ",N=", n_tested, ")"))
if (formatting_type == 18) out <- out %pm>% pm_summarise(out_value = paste0(round(coverage * 100, digits = digits), "% (", round(lower_ci * 100, digits = digits), "-", round(upper_ci * 100, digits = digits), "%,N=", n_tested, ")"))
if (formatting_type == 19) out <- out %pm>% pm_summarise(out_value = paste0(round(coverage * 100, digits = digits), " (", round(lower_ci * 100, digits = digits), "-", round(upper_ci * 100, digits = digits), ",", n_susceptible, "/", n_tested, ")"))
if (formatting_type == 20) out <- out %pm>% pm_summarise(out_value = paste0(round(coverage * 100, digits = digits), "% (", round(lower_ci * 100, digits = digits), "-", round(upper_ci * 100, digits = digits), "%,", n_susceptible, "/", n_tested, ")"))
if (formatting_type == 21) out <- out %pm>% pm_summarise(out_value = paste0(round(coverage * 100, digits = digits), " (", round(lower_ci * 100, digits = digits), "-", round(upper_ci * 100, digits = digits), ",N=", n_susceptible, "/", n_tested, ")"))
if (formatting_type == 22) out <- out %pm>% pm_summarise(out_value = paste0(round(coverage * 100, digits = digits), "% (", round(lower_ci * 100, digits = digits), "-", round(upper_ci * 100, digits = digits), "%,N=", n_susceptible, "/", n_tested, ")"))
# transform names of antibiotics
ab_naming_function <- function(x, t, l, s) {
@ -792,11 +835,18 @@ antibiogram.default <- function(x,
# transform long to wide
long_to_wide <- function(object) {
if (wisca == TRUE) {
# column `mo` has already been removed, but we create here a surrogate to make the stats::reshape() work since it needs an identifier
object$mo <- 1 #seq_len(NROW(object))
}
object <- object %pm>%
# an unclassed data.frame is required for stats::reshape()
as.data.frame(stringsAsFactors = FALSE) %pm>%
stats::reshape(direction = "wide", idvar = "mo", timevar = "ab", v.names = "out_value")
colnames(object) <- gsub("^out_value?[.]", "", colnames(object))
if (wisca == TRUE) {
object <- object[, colnames(object)[colnames(object) != "mo"], drop = FALSE]
}
return(object)
}
@ -818,33 +868,46 @@ antibiogram.default <- function(x,
)
}
}
# 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)
if (wisca == TRUE) {
# sort rows
new_df <- new_df %pm>% pm_arrange(syndromic_group)
# sort columns
new_df <- new_df[, c("syndromic_group", sort(colnames(new_df)[colnames(new_df) != "syndromic_group"])), drop = FALSE]
colnames(new_df)[1] <- translate_AMR("Syndromic Group", language = language)
} else {
# 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)
# 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)
if (wisca == TRUE) {
# sort columns
new_df <- new_df[, c(sort(colnames(new_df))), drop = FALSE]
} else {
# 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)
}
}
# add total N if indicated
if (isTRUE(add_total_n)) {
# add n_tested N if indicated
if (isTRUE(add_total_n) && isFALSE(wisca)) {
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)))
pm_summarise(paste0(min(n_tested, na.rm = TRUE), "-", max(n_tested, 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$syn))]
edit_col <- 2
} else {
n_per_mo <- counts %pm>%
pm_group_by(mo) %pm>%
pm_summarise(paste0(min(total, na.rm = TRUE), "-", max(total, na.rm = TRUE)))
pm_summarise(paste0(min(n_tested, na.rm = TRUE), "-", max(n_tested, na.rm = TRUE)))
colnames(n_per_mo) <- c("mo", "count")
count_group <- n_per_mo$count[match(new_df[[1]], n_per_mo$mo)]
edit_col <- 1
@ -862,6 +925,7 @@ antibiogram.default <- function(x,
out <- as_original_data_class(new_df, class(x), extra_class = "antibiogram")
rownames(out) <- NULL
rownames(wisca_parameters) <- NULL
rownames(long_numeric) <- NULL
structure(out,
@ -869,7 +933,9 @@ antibiogram.default <- function(x,
combine_SI = combine_SI,
wisca = wisca,
conf_interval = conf_interval,
long_numeric = as_original_data_class(long_numeric, class(out))
formatting_type = formatting_type,
wisca_parameters = as_original_data_class(wisca_parameters, class(x)),
long_numeric = as_original_data_class(long_numeric, class(x))
)
}
@ -883,7 +949,7 @@ antibiogram.grouped_df <- function(x,
add_total_n = FALSE,
only_all_tested = FALSE,
digits = ifelse(wisca, 1, 0),
formatting_type = getOption("AMR_antibiogram_formatting_type", ifelse(wisca, 14, 10)),
formatting_type = getOption("AMR_antibiogram_formatting_type", 14),
col_mo = NULL,
language = get_AMR_locale(),
minimum = 30,
@ -929,6 +995,7 @@ antibiogram.grouped_df <- function(x,
conf_interval = conf_interval,
interval_side = interval_side,
info = i == 1 && info == TRUE)
new_wisca_parameters <- attributes(new_out)$wisca_parameters
new_long_numeric <- attributes(new_out)$long_numeric
if (i == 1) progress$tick()
@ -938,8 +1005,10 @@ antibiogram.grouped_df <- function(x,
}
# remove first column 'Pathogen' (in whatever language)
new_out <- new_out[, -1, drop = FALSE]
new_long_numeric <- new_long_numeric[, -1, drop = FALSE]
if (isFALSE(wisca)) {
new_out <- new_out[, -1, drop = FALSE]
new_long_numeric <- new_long_numeric[, -1, drop = FALSE]
}
# add group names to data set
for (col in rev(seq_len(NCOL(groups) - 1))) {
@ -947,6 +1016,12 @@ antibiogram.grouped_df <- function(x,
col_value <- groups[i, col, drop = TRUE]
new_out[, col_name] <- col_value
new_out <- new_out[, c(col_name, setdiff(names(new_out), col_name))] # set place to 1st col
if (isTRUE(wisca)) {
new_wisca_parameters[, col_name] <- col_value
new_wisca_parameters <- new_wisca_parameters[, c(col_name, setdiff(names(new_wisca_parameters), col_name))] # set place to 1st col
}
new_long_numeric[, col_name] <- col_value
new_long_numeric <- new_long_numeric[, c(col_name, setdiff(names(new_long_numeric), col_name))] # set place to 1st col
}
@ -954,9 +1029,11 @@ antibiogram.grouped_df <- function(x,
if (i == 1) {
# the first go
out <- new_out
wisca_parameters <- new_wisca_parameters
long_numeric <- new_long_numeric
} else {
out <- rbind_AMR(out, new_out)
wisca_parameters <- rbind_AMR(wisca_parameters, new_wisca_parameters)
long_numeric <- rbind_AMR(long_numeric, new_long_numeric)
}
}
@ -968,6 +1045,8 @@ antibiogram.grouped_df <- function(x,
combine_SI = isTRUE(combine_SI),
wisca = isTRUE(wisca),
conf_interval = conf_interval,
formatting_type = formatting_type,
wisca_parameters = as_original_data_class(wisca_parameters, class(x)),
long_numeric = as_original_data_class(long_numeric, class(x)))
}
@ -975,7 +1054,6 @@ antibiogram.grouped_df <- function(x,
#' @rdname antibiogram
wisca <- function(x,
antibiotics = where(is.sir),
mo_transform = "shortname",
ab_transform = "name",
syndromic_group = NULL,
add_total_n = FALSE,
@ -988,10 +1066,11 @@ wisca <- function(x,
combine_SI = TRUE,
sep = " + ",
simulations = 1000,
conf_interval = 0.95,
interval_side = "two-tailed",
info = interactive()) {
antibiogram(x = x,
antibiotics = antibiotics,
mo_transform = mo_transform,
ab_transform = ab_transform,
syndromic_group = syndromic_group,
add_total_n = add_total_n,
@ -1005,6 +1084,8 @@ wisca <- function(x,
sep = sep,
wisca = TRUE,
simulations = simulations,
conf_interval = conf_interval,
interval_side = interval_side,
info = info)
}
@ -1013,25 +1094,18 @@ wisca <- function(x,
#' @rdname antibiogram
retrieve_wisca_parameters <- function(wisca_model, ...) {
stop_ifnot(isTRUE(attributes(wisca_model)$wisca), "This function only applies to WISCA models. Use `wisca()` or `antibiogram(..., wisca = TRUE)` to create a WISCA model.")
attributes(wisca_model)$long_numeric
attributes(wisca_model)$wisca_parameters
}
calculate_priors <- function(data, combine_SI = TRUE) {
if (combine_SI == TRUE && "I" %in% colnames(data)) {
data$S <- data$S + data$I
}
if (combine_SI == TRUE && "SDD" %in% colnames(data)) {
data$S <- data$S + data$SDD
}
# Pathogen incidence (Dirichlet distribution)
gamma_prior <- rep(1, length(unique(data$mo))) # Dirichlet prior
gamma_posterior <- gamma_prior + data$total_rows # Posterior parameters
gamma_posterior <- gamma_prior + data$n_total # Posterior parameters
# Regimen susceptibility (Beta distribution)
beta_prior <- rep(1, length(unique(data$mo))) # Beta prior
r <- data$S # Number of pathogens tested susceptible
n <- data$total # Total tested
r <- data$n_susceptible # Number of pathogens tested susceptible
n <- data$n_tested # n_tested tested
beta_posterior_1 <- beta_prior + r # Posterior alpha
beta_posterior_2 <- beta_prior + (n - r) # Posterior beta
@ -1048,8 +1122,10 @@ tbl_sum.antibiogram <- function(x, ...) {
dims <- paste(format(NROW(x), big.mark = ","), AMR_env$cross_icon, format(NCOL(x), big.mark = ","))
if (isTRUE(attributes(x)$wisca)) {
names(dims) <- paste0("An Antibiogram (WISCA / ", attributes(x)$conf_interval * 100, "% CI)")
} else if (isTRUE(attributes(x)$formatting_type >= 13)) {
names(dims) <- paste0("An Antibiogram (non-WISCA / ", attributes(x)$conf_interval * 100, "% CI)")
} else {
names(dims) <- "An Antibiogram (non-WISCA)"
names(dims) <- paste0("An Antibiogram (non-WISCA)")
}
dims
}

2
R/ggplot_sir.R
View File

@ -244,7 +244,7 @@ ggplot_sir <- function(data,
theme_sir()
if (fill == "interpretation") {
p <- p + scale_sir_colours(aesthetics = "fill", colours = colours)
p <- suppressWarnings(p + scale_sir_colours(aesthetics = "fill", colours = colours))
}
if (identical(position, "fill")) {

74
R/plotting.R
View File

@ -50,11 +50,11 @@
#' @details
#' ### The `scale_*_mic()` Functions
#'
#' The functions [scale_x_mic()], [scale_y_mic()], [scale_colour_mic()], and [scale_fill_mic()] functions allow to plot the [mic][as.mic()] class (MIC values) on a continuous scale. They allow to rescale the MIC range, and retain the signs in MIC values if desired. Missing intermediate log2 levels will be plotted too.
#' The functions [scale_x_mic()], [scale_y_mic()], [scale_colour_mic()], and [scale_fill_mic()] functions allow to plot the [mic][as.mic()] class (MIC values) on a continuous, logarithmic scale. They also allow to rescale the MIC range with an 'inside' or 'outside' range if required, and retain the signs in MIC values if desired. Missing intermediate log2 levels will be plotted too.
#'
#' ### The `scale_*_sir()` Functions
#'
#' The functions [scale_x_sir()], [scale_colour_sir()], and [scale_fill_sir()] functions allow to plot the [sir][as.sir()] class (S/I/R values). They can translate the S/I/R values to any of the `r length(AMR:::LANGUAGES_SUPPORTED)` supported languages, and set colour-blind friendly colours to the `colour` and `fill` aesthetics.
#' The functions [scale_x_sir()], [scale_colour_sir()], and [scale_fill_sir()] functions allow to plot the [sir][as.sir()] class in the right order (`r paste(levels(NA_sir_), collapse = " < ")`). At default, they translate the S/I/R values to an interpretative text ("Susceptible", "Resistant", etc.) in any of the `r length(AMR:::LANGUAGES_SUPPORTED)` supported languages (use `language = NULL` to keep S/I/R). Also, except for [scale_x_sir()], they set colour-blind friendly colours to the `colour` and `fill` aesthetics.
#'
#' ### Additional `ggplot2` Functions
#'
@ -68,7 +68,7 @@
#'
#' The interpretation of "I" will be named "Increased exposure" for all EUCAST guidelines since 2019, and will be named "Intermediate" in all other cases.
#'
#' For interpreting MIC values as well as disk diffusion diameters, supported guidelines to be used as input for the `guideline` argument are: `r vector_and(AMR::clinical_breakpoints$guideline, quotes = TRUE, reverse = TRUE)`. Simply using `"CLSI"` or `"EUCAST"` as input will automatically select the latest version of that guideline.
#' For interpreting MIC values as well as disk diffusion diameters, the default guideline is `r AMR::clinical_breakpoints$guideline[1]`, unless the package option [`AMR_guideline`][AMR-options] is set. See [as.sir()] for more information.
#' @name plot
#' @rdname plot
#' @return The `autoplot()` functions return a [`ggplot`][ggplot2::ggplot()] model that is extendible with any `ggplot2` function.
@ -231,7 +231,8 @@ create_scale_mic <- function(aest, keep_operators, mic_range, ...) {
ggplot_fn <- getExportedValue(paste0("scale_", aest, "_continuous"),
ns = asNamespace("ggplot2"))
args <- list(...)
args[c("trans", "transform", "transform_df", "breaks", "labels", "limits")] <- NULL
# do not take these arguments into account, as they will be overwritten and seem to allow weird behaviour
args[c("aesthetics", "trans", "transform", "transform_df", "breaks", "labels", "limits")] <- NULL
scale <- do.call(ggplot_fn, args)
scale$transform <- function(x) {
@ -294,8 +295,8 @@ scale_colour_mic <- function(keep_operators = "edges", mic_range = NULL, ...) {
}
#' @export
#' @inheritParams as.mic
#' @rdname plot
#' @usage NULL
scale_color_mic <- scale_colour_mic
#' @export
@ -307,32 +308,27 @@ scale_fill_mic <- function(keep_operators = "edges", mic_range = NULL, ...) {
create_scale_mic("fill", keep_operators = keep_operators, mic_range = mic_range, ...)
}
create_scale_sir <- function(aest, colours_SIR, language, eucast_I, ...) {
ggplot_fn <- getExportedValue(paste0("scale_", aest, "_discrete"),
ns = asNamespace("ggplot2"))
create_scale_sir <- function(aesthetics, colours_SIR, language, eucast_I, ...) {
args <- list(...)
args[c("aesthetics", "value", "labels", "limits")] <- NULL
args[c("value", "labels", "limits")] <- NULL
if (aest == "x") {
if (identical(aesthetics, "x")) {
ggplot_fn <- ggplot2::scale_x_discrete
args <- c(args,
list(limits = base::force))
} else {
ggplot_fn <- ggplot2::scale_discrete_manual
args <- c(args,
list(aesthetics = aest,
list(aesthetics = aesthetics,
values = c(S = colours_SIR[1],
SDD = colours_SIR[2],
I = colours_SIR[2],
R = colours_SIR[3],
NI = "grey30"),
limits = base::force))
NI = "grey30")))
}
scale <- do.call(ggplot_fn, args)
scale$labels <- function(x) {
stop_ifnot(all(x %in% levels(NA_sir_)),
"Apply `scale_", aest, "_sir()` to a variable of class 'sir', see `?as.sir`.",
stop_ifnot(all(x %in% c(levels(NA_sir_), NA)),
"Apply `scale_", aesthetics[1], "_sir()` to a variable of class 'sir', see `?as.sir`.",
call = FALSE)
x <- as.character(as.sir(x))
if (!is.null(language)) {
@ -351,7 +347,7 @@ create_scale_sir <- function(aest, colours_SIR, language, eucast_I, ...) {
}
scale$limits <- function(x, ...) {
# force SIR in the right order
x[match(x, levels(NA_sir_))]
as.character(sort(factor(x, levels = levels(NA_sir_))))
}
scale
@ -366,7 +362,7 @@ scale_x_sir <- function(colours_SIR = c("#3CAEA3", "#F6D55C", "#ED553B"),
meet_criteria(colours_SIR, allow_class = "character", has_length = c(1, 3))
language <- validate_language(language)
meet_criteria(eucast_I, allow_class = "logical", has_length = 1)
create_scale_sir("x", colours_SIR = colours_SIR, language = language, eucast_I = eucast_I, ...)
create_scale_sir(aesthetics = "x", colours_SIR = colours_SIR, language = language, eucast_I = eucast_I)
}
#' @rdname plot
@ -378,9 +374,21 @@ scale_colour_sir <- function(colours_SIR = c("#3CAEA3", "#F6D55C", "#ED553B"),
meet_criteria(colours_SIR, allow_class = "character", has_length = c(1, 3))
language <- validate_language(language)
meet_criteria(eucast_I, allow_class = "logical", has_length = 1)
create_scale_sir("colour", colours_SIR = colours_SIR, language = language, eucast_I = eucast_I, ...)
args <- list(...)
args$colours_SIR <- colours_SIR
args$language <- language
args$eucast_I <- eucast_I
if (is.null(args$aesthetics)) {
args$aesthetics <- "colour"
}
do.call(create_scale_sir, args)
}
#' @export
#' @rdname plot
#' @usage NULL
scale_color_sir <- scale_colour_sir
#' @rdname plot
#' @export
scale_fill_sir <- function(colours_SIR = c("#3CAEA3", "#F6D55C", "#ED553B"),
@ -390,7 +398,14 @@ scale_fill_sir <- function(colours_SIR = c("#3CAEA3", "#F6D55C", "#ED553B"),
meet_criteria(colours_SIR, allow_class = "character", has_length = c(1, 3))
language <- validate_language(language)
meet_criteria(eucast_I, allow_class = "logical", has_length = 1)
create_scale_sir("fill", colours_SIR = colours_SIR, language = language, eucast_I = eucast_I, ...)
args <- list(...)
args$colours_SIR <- colours_SIR
args$language <- language
args$eucast_I <- eucast_I
if (is.null(args$aesthetics)) {
args$aesthetics <- "fill"
}
do.call(create_scale_sir, args)
}
#' @method plot mic
@ -400,7 +415,7 @@ scale_fill_sir <- function(colours_SIR = c("#3CAEA3", "#F6D55C", "#ED553B"),
plot.mic <- function(x,
mo = NULL,
ab = NULL,
guideline = "EUCAST",
guideline = getOption("AMR_guideline", "EUCAST"),
main = deparse(substitute(x)),
ylab = translate_AMR("Frequency", language = language),
xlab = translate_AMR("Minimum Inhibitory Concentration (mg/L)", language = language),
@ -489,7 +504,7 @@ plot.mic <- function(x,
barplot.mic <- function(height,
mo = NULL,
ab = NULL,
guideline = "EUCAST",
guideline = getOption("AMR_guideline", "EUCAST"),
main = deparse(substitute(height)),
ylab = translate_AMR("Frequency", language = language),
xlab = translate_AMR("Minimum Inhibitory Concentration (mg/L)", language = language),
@ -530,7 +545,7 @@ barplot.mic <- function(height,
autoplot.mic <- function(object,
mo = NULL,
ab = NULL,
guideline = "EUCAST",
guideline = getOption("AMR_guideline", "EUCAST"),
title = deparse(substitute(object)),
ylab = translate_AMR("Frequency", language = language),
xlab = translate_AMR("Minimum Inhibitory Concentration (mg/L)", language = language),
@ -640,7 +655,7 @@ plot.disk <- function(x,
xlab = translate_AMR("Disk diffusion diameter (mm)", language = language),
mo = NULL,
ab = NULL,
guideline = "EUCAST",
guideline = getOption("AMR_guideline", "EUCAST"),
colours_SIR = c("#3CAEA3", "#F6D55C", "#ED553B"),
language = get_AMR_locale(),
expand = TRUE,
@ -727,7 +742,7 @@ barplot.disk <- function(height,
xlab = translate_AMR("Disk diffusion diameter (mm)", language = language),
mo = NULL,
ab = NULL,
guideline = "EUCAST",
guideline = getOption("AMR_guideline", "EUCAST"),
colours_SIR = c("#3CAEA3", "#F6D55C", "#ED553B"),
language = get_AMR_locale(),
expand = TRUE,
@ -766,7 +781,7 @@ autoplot.disk <- function(object,
title = deparse(substitute(object)),
ylab = translate_AMR("Frequency", language = language),
xlab = translate_AMR("Disk diffusion diameter (mm)", language = language),
guideline = "EUCAST",
guideline = getOption("AMR_guideline", "EUCAST"),
colours_SIR = c("#3CAEA3", "#F6D55C", "#ED553B"),
language = get_AMR_locale(),
expand = TRUE,
@ -1268,6 +1283,11 @@ scale_sir_colours <- function(...,
ggplot2::scale_discrete_manual(aesthetics = aesthetics, values = cols, limits = force)
}
#' @export
#' @rdname plot
#' @usage NULL
scale_sir_colors <- scale_sir_colours
#' @rdname plot
#' @export
theme_sir <- function() {

14
R/sir.R
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@ -43,7 +43,7 @@
#' @param ab a vector (or column name) with [character]s that can be coerced to a valid antimicrobial drug code with [as.ab()]
#' @param uti (Urinary Tract Infection) a vector (or column name) with [logical]s (`TRUE` or `FALSE`) to specify whether a UTI specific interpretation from the guideline should be chosen. For using [as.sir()] on a [data.frame], this can also be a column containing [logical]s or when left blank, the data set will be searched for a column 'specimen', and rows within this column containing 'urin' (such as 'urine', 'urina') will be regarded isolates from a UTI. See *Examples*.
#' @inheritParams first_isolate
#' @param guideline defaults to EUCAST `r max(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, guideline %like% "EUCAST")$guideline)))` (the latest implemented EUCAST guideline in the [AMR::clinical_breakpoints] data set), but can be set with the package option [`AMR_guideline`][AMR-options]. Currently supports EUCAST (`r min(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, guideline %like% "EUCAST")$guideline)))`-`r max(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, guideline %like% "EUCAST")$guideline)))`) and CLSI (`r min(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, guideline %like% "CLSI")$guideline)))`-`r max(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, guideline %like% "CLSI")$guideline)))`), see *Details*.
#' @param guideline defaults to `r AMR::clinical_breakpoints$guideline[1]` (the latest implemented EUCAST guideline in the [AMR::clinical_breakpoints] data set), but can be set with the package option [`AMR_guideline`][AMR-options]. Currently supports EUCAST (`r min(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, guideline %like% "EUCAST")$guideline)))`-`r max(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, guideline %like% "EUCAST")$guideline)))`) and CLSI (`r min(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, guideline %like% "CLSI")$guideline)))`-`r max(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, guideline %like% "CLSI")$guideline)))`), see *Details*.
#' @param conserve_capped_values a [logical] to indicate that MIC values starting with `">"` (but not `">="`) must always return "R" , and that MIC values starting with `"<"` (but not `"<="`) must always return "S"
#' @param add_intrinsic_resistance *(only useful when using a EUCAST guideline)* a [logical] to indicate whether intrinsic antibiotic resistance must also be considered for applicable bug-drug combinations, meaning that e.g. ampicillin will always return "R" in *Klebsiella* species. Determination is based on the [intrinsic_resistant] data set, that itself is based on `r format_eucast_version_nr(3.3)`.
#' @param include_screening a [logical] to indicate that clinical breakpoints for screening are allowed - the default is `FALSE`. Can also be set with the package option [`AMR_include_screening`][AMR-options].
@ -1361,7 +1361,7 @@ as_sir_method <- function(method_short,
mo = vectorise_log_entry(mo_current, length(rows)),
host = vectorise_log_entry(host_current, length(rows)),
method = vectorise_log_entry(method_coerced, length(rows)),
input = vectorise_log_entry(as.double(values), length(rows)),
input = vectorise_log_entry(as.character(values), length(rows)),
outcome = vectorise_log_entry(NA_sir_, length(rows)),
notes = vectorise_log_entry("NO BREAKPOINT AVAILABLE", length(rows)),
guideline = vectorise_log_entry(guideline_coerced, length(rows)),
@ -1430,10 +1430,18 @@ as_sir_method <- function(method_short,
if (any(breakpoints_current$site %like% "screen", na.rm = TRUE) | any(breakpoints_current$ref_tbl %like% "screen", na.rm = TRUE)) {
notes_current <- c(notes_current, "Some screening breakpoints were applied - use `include_screening = FALSE` to prevent this")
}
if (method == "mic" && conserve_capped_values == TRUE && any(as.character(values) %like% "^[<][0-9]")) {
notes_current <- c(notes_current, "MIC values 'lower than' are all considered 'S' since conserve_capped_values = TRUE")
}
if (method == "mic" && conserve_capped_values == TRUE && any(as.character(values) %like% "^[>][0-9]")) {
notes_current <- c(notes_current, "MIC values 'greater than' are all considered 'R' since conserve_capped_values = TRUE")
}
if (method == "mic") {
new_sir <- case_when_AMR(
is.na(values) ~ NA_sir_,
conserve_capped_values == TRUE & as.character(values) %like% "^[<][0-9]" ~ as.sir("S"),
conserve_capped_values == TRUE & as.character(values) %like% "^[>][0-9]" ~ as.sir("R"),
values <= breakpoints_current$breakpoint_S ~ as.sir("S"),
guideline_coerced %like% "EUCAST" & values > breakpoints_current$breakpoint_R ~ as.sir("R"),
guideline_coerced %like% "CLSI" & values >= breakpoints_current$breakpoint_R ~ as.sir("R"),
@ -1471,7 +1479,7 @@ as_sir_method <- function(method_short,
mo = vectorise_log_entry(breakpoints_current[, "mo", drop = TRUE], length(rows)),
host = vectorise_log_entry(breakpoints_current[, "host", drop = TRUE], length(rows)),
method = vectorise_log_entry(method_coerced, length(rows)),
input = vectorise_log_entry(as.double(values), length(rows)),
input = vectorise_log_entry(as.character(values), length(rows)),
outcome = vectorise_log_entry(as.sir(new_sir), length(rows)),
notes = vectorise_log_entry(paste0(font_stripstyle(notes_current), collapse = "\n"), length(rows)),
guideline = vectorise_log_entry(guideline_coerced, length(rows)),

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R/zzz.R
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@ -65,7 +65,7 @@ AMR_env$sir_interpretation_history <- data.frame(
mo = set_clean_class(character(0), c("mo", "character")),
host = character(0),
method = character(0),
input = double(0),
input = character(0),
outcome = NA_sir_[0],
notes = character(0),
guideline = character(0),