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AMR/R/antibiogram.R

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# ==================================================================== #
# TITLE: #
# AMR: An R Package for Working with Antimicrobial Resistance Data #
# #
# SOURCE CODE: #
# https://github.com/msberends/AMR #
# #
# PLEASE CITE THIS SOFTWARE AS: #
# Berends MS, Luz CF, Friedrich AW, et al. (2022). #
# AMR: An R Package for Working with Antimicrobial Resistance Data. #
# Journal of Statistical Software, 104(3), 1-31. #
# https://doi.org/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 Traditional, Combination, Syndromic, or WISCA Antibiograms
#'
#' @description
#' Create detailed antibiograms with options for traditional, combination, syndromic, and Bayesian WISCA methods.
#'
#' Adhering to previously described approaches (see *Source*) and especially the Bayesian WISCA model (Weighted-Incidence Syndromic Combination Antibiogram) by Bielicki *et al.*, these functions provide flexible output formats including plots and tables, ideal for integration with R Markdown and Quarto reports.
#' @param x a [data.frame] containing at least a column with microorganisms and columns with antimicrobial results (class 'sir', see [as.sir()])
#' @param antibiotics vector of any antimicrobial name or code (will be evaluated with [as.ab()], column name of `x`, or (any combinations of) [antimicrobial selectors][antimicrobial_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 antimicrobials exist in `x`. See *Examples*.
#' @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 `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.
#' @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, 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`, `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"`
#' @param info a [logical] to indicate info should be printed - the default is `TRUE` only in interactive mode
#' @param object an [antibiogram()] object
#' @param ... when used in [R Markdown or Quarto][knitr::kable()]: arguments passed on to [knitr::kable()] (otherwise, has no use)
#' @details These functions return a table with values between 0 and 100 for *susceptibility*, not resistance.
#'
#' **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 with one of the four available algorithms: isolate-based, patient-based, episode-based, or phenotype-based.
#'
#' For estimating antimicrobial coverage, especially when creating a WISCA, the outcome might become more reliable by only including the top *n* species encountered in the data. You can filter on this top *n* using [top_n_microorganisms()]. For example, use `top_n_microorganisms(your_data, n = 10)` as a pre-processing step to only include the top 10 species in the data.
#'
#' The numeric values of an antibiogram are stored in a long format as the [attribute][attributes()] `long_numeric`. You can retrieve them using `attributes(x)$long_numeric`, where `x` is the outcome of [antibiogram()] or [wisca()]. This is ideal for e.g. advanced plotting.
#'
#' ### 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 (`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
#' 3. 300
#' 4. 15/300
#' 5. 5 (300)
#' 6. 5% (300)
#' 7. 5 (N=300)
#' 8. 5% (N=300)
#' 9. 5 (15/300)
#' 10. 5% (15/300)
#' 11. 5 (N=15/300)
#' 12. 5% (N=15/300)
#' 13. 5 (4-6)
#' 14. 5% (4-6%) - **default**
#' 15. 5 (4-6,300)
#' 16. 5% (4-6%,300)
#' 17. 5 (4-6,N=300)
#' 18. 5% (4-6%,N=300)
#' 19. 5 (4-6,15/300)
#' 20. 5% (4-6%,15/300)
#' 21. 5 (4-6,N=15/300)
#' 22. 5% (4-6%,N=15/300)
#'
#' 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.
#'
#' ### Antibiogram Types
#'
#' There are various antibiogram types, as summarised by Klinker *et al.* (2021, \doi{10.1177/20499361211011373}), and they are all supported by [antibiogram()].
#'
#' 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**
#'
#' Case example: Susceptibility of *Pseudomonas aeruginosa* to piperacillin/tazobactam (TZP)
#'
#' Code example:
#'
#' ```r
#' antibiogram(your_data,
#' antibiotics = "TZP")
#' ```
#'
#' 2. **Combination Antibiogram**
#'
#' Case example: Additional susceptibility of *Pseudomonas aeruginosa* to TZP + tobramycin versus TZP alone
#'
#' Code example:
#'
#' ```r
#' antibiogram(your_data,
#' antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"))
#' ```
#'
#' 3. **Syndromic Antibiogram**
#'
#' Case example: Susceptibility of *Pseudomonas aeruginosa* to TZP among respiratory specimens (obtained among ICU patients only)
#'
#' Code example:
#'
#' ```r
#' antibiogram(your_data,
#' antibiotics = penicillins(),
#' syndromic_group = "ward")
#' ```
#'
#' 4. **Weighted-Incidence Syndromic Combination Antibiogram (WISCA)**
#'
#' WISCA can be applied to any antibiogram, see the section *Explaining WISCA* on this page for more information.
#'
#' Code example:
#'
#' ```r
#' antibiogram(your_data,
#' antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"),
#' wisca = TRUE)
#'
#' # this is equal to:
#' wisca(your_data,
#' antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"))
#' ```
#'
#' WISCA uses a sophisticated Bayesian decision model to combine both local and pooled antimicrobial resistance data. This approach not only evaluates local patterns but can also draw on multi-centre datasets to improve regimen accuracy, even in low-incidence infections like paediatric bloodstream infections (BSIs).
#'
#' ### Grouped tibbles
#'
#' For any type of antibiogram, grouped [tibbles][tibble::tibble] can also be used to calculate susceptibilities over various groups.
#'
#' Code example:
#'
#' ```r
#' library(dplyr)
#' your_data %>%
#' group_by(has_sepsis, is_neonate, sex) %>%
#' wisca(antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"))
#' ```
#'
#' ### Stepped Approach for Clinical Insight
#'
#' In clinical practice, antimicrobial coverage decisions evolve as more microbiological data becomes available. This theoretical stepped approach ensures empirical coverage can continuously assessed to improve patient outcomes:
#'
#' 1. **Initial Empirical Therapy (Admission / Pre-Culture Data)**
#'
#' At admission, no pathogen information is available.
#'
#' - 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,
#' mo_transform = NA) # all pathogens set to `NA`
#'
#' # preferred: use WISCA
#' wisca(your_data,
#' antibiotics = selected_regimens)
#' ```
#'
#' 2. **Refinement with Gram Stain Results**
#'
#' When a blood culture becomes positive, the Gram stain provides an initial and crucial first stratification (Gram-positive vs. Gram-negative).
#'
#' - Action: narrow coverage based on Gram stain-specific resistance patterns.
#' - Code example:
#'
#' ```r
#' antibiogram(your_data,
#' antibiotics = selected_regimens,
#' mo_transform = "gramstain") # all pathogens set to Gram-pos/Gram-neg
#' ```
#'
#' 3. **Definitive Therapy Based on Species Identification**
#'
#' After cultivation of the pathogen, full pathogen identification allows precise targeting of therapy.
#'
#' - Action: adjust treatment to pathogen-specific antibiograms, minimizing resistance risks.
#' - Code example:
#'
#' ```r
#' antibiogram(your_data,
#' antibiotics = selected_regimens,
#' mo_transform = "shortname") # all pathogens set to 'G. species', e.g., E. coli
#' ```
#'
#' By structuring antibiograms around this stepped approach, clinicians can make data-driven adjustments at each stage, ensuring optimal empirical and targeted therapy while reducing unnecessary broad-spectrum antimicrobial use.
#'
#' ### Inclusion in Combination Antibiograms
#'
#' 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 antimicrobials, Drug A and Drug B, about how [antibiogram()] works to calculate the %SI:
#'
#' ```
#' --------------------------------------------------------------------
#' only_all_tested = FALSE only_all_tested = TRUE
#' ----------------------- -----------------------
#' Drug A Drug B considered considered considered considered
#' susceptible tested susceptible tested
#' -------- -------- ----------- ---------- ----------- ----------
#' 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> - - - -
#' --------------------------------------------------------------------
#' ```
#'
#' ### Plotting
#'
#' All types of antibiograms as listed above can be plotted (using [ggplot2::autoplot()] or base \R's [plot()] and [barplot()]). As mentioned above, the numeric values of an antibiogram are stored in a long format as the [attribute][attributes()] `long_numeric`. You can retrieve them using `attributes(x)$long_numeric`, where `x` is the outcome of [antibiogram()] or [wisca()].
#'
#' The outcome of [antibiogram()] 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()`.
#'
#' @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 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:
#'
#' \deqn{\theta \sim \text{Beta}(\alpha_0, \beta_0)}
#'
#' where \eqn{\alpha_0} and \eqn{\beta_0} represent prior successes and failures, respectively, informed by expert knowledge or weakly informative priors (e.g., \eqn{\alpha_0 = 1, \beta_0 = 1}). The likelihood function is constructed based on observed data, where the number of covered cases for a regimen follows a binomial distribution:
#'
#' \deqn{y \sim \text{Binomial}(n, \theta)}
#'
#' Posterior parameter estimates are obtained by combining the prior and likelihood using Bayes' theorem. The posterior distribution of \eqn{\theta} is also a Beta distribution:
#'
#' \deqn{\theta | y \sim \text{Beta}(\alpha_0 + y, \beta_0 + n - y)}
#'
#' Pathogen incidence, representing the proportion of infections caused by different pathogens, is modelled using a Dirichlet distribution, which is the natural conjugate prior for multinomial outcomes. The Dirichlet distribution is parameterised by a vector of concentration parameters \eqn{\alpha}, where each \eqn{\alpha_i} corresponds to a specific pathogen. The prior is typically chosen to be uniform (\eqn{\alpha_i = 1}), reflecting an assumption of equal prior probability across pathogens.
#'
#' The posterior distribution of pathogen incidence is then given by:
#'
#' \deqn{\text{Dirichlet}(\alpha_1 + n_1, \alpha_2 + n_2, \dots, \alpha_K + n_K)}
#'
#' where \eqn{n_i} is the number of infections caused by pathogen \eqn{i} observed in the data. For practical implementation, pathogen incidences are sampled from their posterior using normalised Gamma-distributed random variables:
#'
#' \deqn{x_i \sim \text{Gamma}(\alpha_i + n_i, 1)}
#' \deqn{p_i = \frac{x_i}{\sum_{j=1}^K x_j}}
#'
#' where \eqn{x_i} represents unnormalised pathogen counts, and \eqn{p_i} is the normalised proportion for pathogen \eqn{i}.
#'
#' For hierarchical modelling, pathogen-level effects (e.g., differences in resistance patterns) and regimen-level effects are modelled using Gaussian priors on log-odds. This hierarchical structure ensures partial pooling of estimates across groups, improving stability in strata with small sample sizes. The model is implemented using Hamiltonian Monte Carlo (HMC) sampling.
#'
#' Stratified results can be provided based on covariates such as age, sex, and clinical complexity (e.g., prior antimicrobial treatments or renal/urological comorbidities) using `dplyr`'s [`group_by()`][dplyr::group_by()] as a pre-processing step before running [wisca()]. Posterior odds ratios (ORs) are derived to quantify the effect of these covariates on coverage probabilities:
#'
#' \deqn{\text{OR}_{\text{covariate}} = \frac{\exp(\beta_{\text{covariate}})}{\exp(\beta_0)}}
#'
#' 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}
#' * 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/>.
#' @author Implementation: Dr. Larisse Bolton and Dr. Matthijs Berends
#' @rdname antibiogram
#' @name antibiogram
#' @export
#' @examples
#' # example_isolates is a data set available in the AMR package.
#' # run ?example_isolates for more info.
#' example_isolates
#'
#' \donttest{
#' # Traditional antibiogram ----------------------------------------------
#'
#' antibiogram(example_isolates,
#' antibiotics = c(aminoglycosides(), carbapenems())
#' )
#'
#' antibiogram(example_isolates,
#' antibiotics = aminoglycosides(),
#' ab_transform = "atc",
#' mo_transform = "gramstain")
#'
#' antibiogram(example_isolates,
#' antibiotics = carbapenems(),
#' ab_transform = "name",
#' mo_transform = "name")
#'
#'
#' # Combined antibiogram -------------------------------------------------
#'
#' # combined antibiotics yield higher empiric coverage
#' antibiogram(example_isolates,
#' 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"),
#' mo_transform = "gramstain",
#' ab_transform = "name",
#' sep = " & ")
#'
#'
#' # Syndromic antibiogram ------------------------------------------------
#'
#' # the data set could contain a filter for e.g. respiratory specimens
#' antibiogram(example_isolates,
#' antibiotics = c(aminoglycosides(), carbapenems()),
#' syndromic_group = "ward")
#'
#' # now define a data set with only E. coli
#' ex1 <- example_isolates[which(mo_genus() == "Escherichia"), ]
#'
#' # with a custom language, though this will be determined automatically
#' # (i.e., this table will be in Spanish on Spanish systems)
#' antibiogram(ex1,
#' antibiotics = aminoglycosides(),
#' ab_transform = "name",
#' syndromic_group = ifelse(ex1$ward == "ICU",
#' "UCI", "No UCI"
#' ),
#' language = "es")
#'
#'
#' # WISCA antibiogram ----------------------------------------------------
#'
#' # WISCA are not stratified by species, but rather on syndromes
#' antibiogram(example_isolates,
#' antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"),
#' syndromic_group = "ward",
#' wisca = TRUE)
#'
#'
#' # Print the output for R Markdown / Quarto -----------------------------
#'
#' ureido <- antibiogram(example_isolates,
#' antibiotics = ureidopenicillins(),
#' syndromic_group = "name",
#' wisca = TRUE)
#'
#' # in an Rmd file, you would just need to return `ureido` in a chunk,
#' # but to be explicit here:
#' if (requireNamespace("knitr")) {
#' cat(knitr::knit_print(ureido))
#' }
#'
#'
#' # Generate plots with ggplot2 or base R --------------------------------
#'
#' ab1 <- antibiogram(example_isolates,
#' antibiotics = c("AMC", "CIP", "TZP", "TZP+TOB"),
#' mo_transform = "gramstain")
#' ab2 <- antibiogram(example_isolates,
#' 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)
#' }
#'
#' plot(ab1)
#' plot(ab2)
#' }
antibiogram <- function(x,
antibiotics = where(is.sir),
mo_transform = "shortname",
ab_transform = "name",
syndromic_group = NULL,
add_total_n = FALSE,
only_all_tested = FALSE,
digits = ifelse(wisca, 1, 0),
formatting_type = getOption("AMR_antibiogram_formatting_type", 14),
col_mo = NULL,
language = get_AMR_locale(),
minimum = 30,
combine_SI = TRUE,
sep = " + ",
wisca = FALSE,
simulations = 1000,
conf_interval = 0.95,
interval_side = "two-tailed",
info = interactive()) {
UseMethod("antibiogram")
}
#' @method antibiogram default
#' @export
antibiogram.default <- function(x,
antibiotics = where(is.sir),
mo_transform = "shortname",
ab_transform = "name",
syndromic_group = NULL,
add_total_n = FALSE,
only_all_tested = FALSE,
digits = ifelse(wisca, 1, 0),
formatting_type = getOption("AMR_antibiogram_formatting_type", 14),
col_mo = NULL,
language = get_AMR_locale(),
minimum = 30,
combine_SI = TRUE,
sep = " + ",
wisca = FALSE,
simulations = 1000,
conf_interval = 0.95,
interval_side = "two-tailed",
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)
}
if (!is.function(ab_transform)) {
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(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)
meet_criteria(combine_SI, allow_class = "logical", has_length = 1)
meet_criteria(sep, allow_class = "character", has_length = 1)
meet_criteria(simulations, allow_class = c("numeric", "integer"), has_length = 1, is_finite = TRUE, is_positive = TRUE)
meet_criteria(conf_interval, allow_class = c("numeric", "integer"), has_length = 1, is_finite = TRUE, is_positive = TRUE)
meet_criteria(interval_side, allow_class = "character", has_length = 1, is_in = c("two-tailed", "left", "right"))
meet_criteria(info, allow_class = "logical", has_length = 1)
# 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, no transformation
} else if (is.function(mo_transform)) {
x$`.mo` <- mo_transform(x$`.mo`)
} else if (is.na(mo_transform)) {
x$`.mo` <- NA_character_
} else if (mo_transform == "gramstain") {
x$`.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$`.mo`, property = mo_transform, language = language)
}
x$`.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
}
# get antibiotics
ab_trycatch <- tryCatch(colnames(suppressWarnings(x[, antibiotics, drop = FALSE])), error = function(e) NULL)
if (is.null(ab_trycatch)) {
stop_ifnot(is.character(suppressMessages(antibiotics)), "`antibiotics` must be an antimicrobial selector, or a character vector.")
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]
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", "SDD", "I")
} else {
S_values <- "S"
}
other_values <- setdiff(c("S", "SDD", "I", "R", "NI"), 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 {
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 <- ab_trycatch
}
if (isTRUE(has_syndromic_group)) {
out <- x %pm>%
pm_select(.syndromic_group, .mo, antibiotics) %pm>%
pm_group_by(.syndromic_group)
} else {
out <- x %pm>%
pm_select(.mo, antibiotics)
}
# get numbers of S, I, R (per group)
out <- out %pm>%
bug_drug_combinations(
col_mo = ".mo",
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
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 ----
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$n_tested
if (isTRUE(has_syndromic_group)) {
out$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$beta_posterior1 <- NA_real_
out$beta_posterior2 <- NA_real_
for (i in seq_len(NROW(out))) {
if (out$n_tested[i] == 0) {
next
}
out_current <- out[i, , drop = FALSE]
priors <- calculate_priors(out_current, combine_SI = combine_SI)
out$gamma_posterior[i] = priors$gamma_posterior
out$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$tick()
# simulate pathogen incidence
# = Dirichlet (Gamma) parameters
random_incidence <- stats::runif(1, min = 0, max = 1)
simulated_incidence <- stats::qgamma(
p = random_incidence,
shape = params_current$gamma_posterior,
scale = 1
)
# normalise
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 = params_current$beta_posterior1,
shape2 = params_current$beta_posterior2
)
sum(simulated_incidence * simulated_susceptibility, na.rm = TRUE)
})
# calculate coverage statistics
coverage_mean <- mean(coverage_simulations)
if (interval_side == "two-tailed") {
probs <- c((1 - conf_interval) / 2, 1 - (1 - conf_interval) / 2)
} else if (interval_side == "left") {
probs <- c(0, conf_interval)
} else if (interval_side == "right") {
probs <- c(1 - conf_interval, 1)
}
coverage_ci <- unname(stats::quantile(coverage_simulations, probs = probs))
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)
# 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
# 2. 15
# 3. 300
# 4. 15/300
# 5. 5 (300)
# 6. 5% (300)
# 7. 5 (N=300)
# 8. 5% (N=300)
# 9. 5 (15/300)
# 10. 5% (15/300)
# 11. 5 (N=15/300)
# 12. 5% (N=15/300)
# 13. 5 (4-6)
# 14. 5% (4-6%)
# 15. 5 (4-6,300)
# 16. 5% (4-6%,300)
# 17. 5 (4-6,N=300)
# 18. 5% (4-6%,N=300)
# 19. 5 (4-6,15/300)
# 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(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), ",", 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) {
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 (is.function(t)) {
a <- t(a)
} 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$ab, t = ab_transform, l = language, s = sep)
long_numeric$ab <- ab_naming_function(long_numeric$ab, t = ab_transform, l = language, s = sep)
# transform long to wide
long_to_wide <- function(object) {
if (wisca == TRUE) {
# column `mo` has already been removed, but we create here a surrogate to make the stats::reshape() work since it needs an identifier
object$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)
}
# 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")]
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])
} else {
new_df <- rbind_AMR(
new_df,
long_to_wide(out[which(out$syndromic_group == grp), , drop = FALSE])
)
}
}
if (wisca == TRUE) {
# sort rows
new_df <- new_df %pm>% pm_arrange(syndromic_group)
# sort columns
new_df <- new_df[, c("syndromic_group", sort(colnames(new_df)[colnames(new_df) != "syndromic_group"])), drop = FALSE]
colnames(new_df)[1] <- translate_AMR("Syndromic Group", language = language)
} else {
# sort rows
new_df <- new_df %pm>% pm_arrange(mo, syndromic_group)
# sort columns
new_df <- new_df[, c("syndromic_group", "mo", sort(colnames(new_df)[!colnames(new_df) %in% c("syndromic_group", "mo")])), drop = FALSE]
colnames(new_df)[1:2] <- translate_AMR(c("Syndromic Group", "Pathogen"), language = language)
}
} else {
new_df <- long_to_wide(out)
if (wisca == TRUE) {
# sort columns
new_df <- new_df[, c(sort(colnames(new_df))), drop = FALSE]
} else {
# sort rows
new_df <- new_df %pm>% pm_arrange(mo)
# sort columns
new_df <- new_df[, c("mo", sort(colnames(new_df)[colnames(new_df) != "mo"])), drop = FALSE]
colnames(new_df)[1] <- translate_AMR("Pathogen", language = language)
}
}
# add n_tested N if indicated
if (isTRUE(add_total_n) && isFALSE(wisca)) {
if (isTRUE(has_syndromic_group)) {
n_per_mo <- counts %pm>%
pm_group_by(mo, .syndromic_group) %pm>%
pm_summarise(paste0(min(n_tested, na.rm = TRUE), "-", max(n_tested, na.rm = TRUE)))
colnames(n_per_mo) <- c("mo", "syn", "count")
count_group <- n_per_mo$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(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
}
if (NCOL(new_df) == edit_col + 1) {
# only 1 antibiotic
new_df[[edit_col]] <- paste0(new_df[[edit_col]], " (", unlist(lapply(strsplit(x = count_group, split = "-", fixed = TRUE), function(x) x[1])), ")")
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)")
}
}
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,
has_syndromic_group = has_syndromic_group,
combine_SI = combine_SI,
wisca = 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))
)
}
#' @method antibiogram grouped_df
#' @export
antibiogram.grouped_df <- function(x,
antibiotics = where(is.sir),
mo_transform = function (...) "no_mo",
ab_transform = "name",
syndromic_group = NULL,
add_total_n = FALSE,
only_all_tested = FALSE,
digits = ifelse(wisca, 1, 0),
formatting_type = getOption("AMR_antibiogram_formatting_type", 14),
col_mo = NULL,
language = get_AMR_locale(),
minimum = 30,
combine_SI = TRUE,
sep = " + ",
wisca = FALSE,
simulations = 1000,
conf_interval = 0.95,
interval_side = "two-tailed",
info = interactive()) {
stop_ifnot(is.null(syndromic_group), "`syndromic_group` must not be set if creating an antibiogram using a grouped tibble. The groups will become the variables over which the antimicrobials are calculated, making `syndromic_groups` redundant.", call = FALSE)
groups <- attributes(x)$groups
n_groups <- NROW(groups)
progress <- progress_ticker(n = n_groups,
n_min = 5,
print = info,
title = paste("Calculating AMR for", n_groups, "groups"))
on.exit(close(progress))
for (i in seq_len(n_groups)) {
if (i > 1) progress$tick()
rows <- unlist(groups[i, ]$.rows)
if (length(rows) == 0) {
next
}
new_out <- antibiogram(as.data.frame(x)[rows, , drop = FALSE],
antibiotics = antibiotics,
mo_transform = function(x) "no_mo",
ab_transform = ab_transform,
syndromic_group = NULL,
add_total_n = add_total_n,
only_all_tested = only_all_tested,
digits = digits,
formatting_type = formatting_type,
col_mo = col_mo,
language = language,
minimum = minimum,
combine_SI = combine_SI,
sep = sep,
wisca = wisca,
simulations = simulations,
conf_interval = conf_interval,
interval_side = interval_side,
info = i == 1 && info == TRUE)
new_wisca_parameters <- attributes(new_out)$wisca_parameters
new_long_numeric <- attributes(new_out)$long_numeric
if (i == 1) progress$tick()
if (NROW(new_out) == 0) {
next
}
# remove first column 'Pathogen' (in whatever language)
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))) {
col_name <- colnames(groups)[col]
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
}
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)
}
}
close(progress)
out <- structure(as_original_data_class(out, class(x), extra_class = "antibiogram"),
has_syndromic_group = FALSE,
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)))
}
#' @export
#' @rdname antibiogram
wisca <- function(x,
antibiotics = where(is.sir),
ab_transform = "name",
syndromic_group = NULL,
add_total_n = FALSE,
only_all_tested = FALSE,
digits = 1,
formatting_type = getOption("AMR_antibiogram_formatting_type", 14),
col_mo = NULL,
language = get_AMR_locale(),
minimum = 30,
combine_SI = TRUE,
sep = " + ",
simulations = 1000,
conf_interval = 0.95,
interval_side = "two-tailed",
info = interactive()) {
antibiogram(x = x,
antibiotics = antibiotics,
ab_transform = ab_transform,
syndromic_group = syndromic_group,
add_total_n = add_total_n,
only_all_tested = only_all_tested,
digits = digits,
formatting_type = formatting_type,
col_mo = col_mo,
language = language,
minimum = minimum,
combine_SI = combine_SI,
sep = sep,
wisca = TRUE,
simulations = simulations,
conf_interval = conf_interval,
interval_side = interval_side,
info = info)
}
#' @export
#' @param wisca_model the outcome of [wisca()] or [`antibiogram(..., wisca = TRUE)`][antibiogram()]
#' @rdname antibiogram
retrieve_wisca_parameters <- function(wisca_model, ...) {
stop_ifnot(isTRUE(attributes(wisca_model)$wisca), "This function only applies to WISCA models. Use `wisca()` or `antibiogram(..., wisca = TRUE)` to create a WISCA model.")
attributes(wisca_model)$wisca_parameters
}
calculate_priors <- function(data, combine_SI = TRUE) {
# Pathogen incidence (Dirichlet distribution)
gamma_prior <- rep(1, length(unique(data$mo))) # Dirichlet prior
gamma_posterior <- gamma_prior + data$n_total # Posterior parameters
# Regimen susceptibility (Beta distribution)
beta_prior <- rep(1, length(unique(data$mo))) # Beta prior
r <- data$n_susceptible # Number of pathogens tested susceptible
n <- data$n_tested # n_tested tested
beta_posterior_1 <- beta_prior + r # Posterior alpha
beta_posterior_2 <- beta_prior + (n - r) # Posterior beta
# Return parameters as a list
list(
gamma_posterior = gamma_posterior,
beta_posterior_1 = beta_posterior_1,
beta_posterior_2 = beta_posterior_2
)
}
# will be exported in R/zzz.R
tbl_sum.antibiogram <- function(x, ...) {
dims <- paste(format(NROW(x), big.mark = ","), AMR_env$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) <- paste0("An Antibiogram (non-WISCA)")
}
dims
}
# will be exported in R/zzz.R
tbl_format_footer.antibiogram <- function(x, ...) {
footer <- NextMethod()
if (NROW(x) == 0) {
return(footer)
}
c(footer, font_subtle(paste0("# Use `plot()` or `ggplot2::autoplot()` to create a plot of this antibiogram,\n",
"# or use it directly in R Markdown or ",
font_url("https://quarto.org", "Quarto"), ", see ", word_wrap("?antibiogram"))))
}
#' @export
#' @rdname antibiogram
plot.antibiogram <- function(x, ...) {
df <- attributes(x)$long_numeric
if (!"mo" %in% colnames(df)) {
stop_("Plotting antibiograms using `plot()` is only possible if they were not created using dplyr groups. See `?antibiogram` for how to retrieve numeric values in a long format for advanced plotting.",
call = FALSE)
}
if ("syndromic_group" %in% colnames(df)) {
# barplot in base R does not support facets - paste columns together
df$mo <- paste(df$mo, "-", df$syndromic_group)
df$syndromic_group <- NULL
df <- df[order(df$mo), , drop = FALSE]
}
mo_levels <- unique(df$mo)
mfrow_old <- graphics::par()$mfrow
sqrt_levels <- sqrt(length(mo_levels))
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$mo == mo, , drop = FALSE]
bp <- barplot(
height = df_sub$coverage * 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
)
if (isTRUE(attributes(x)$wisca)) {
lower_ci <- df_sub$lower_ci * 100
upper_ci <- df_sub$upper_ci * 100
arrows(
x0 = bp, y0 = lower_ci, # Start of error bar (lower bound)
x1 = bp, y1 = upper_ci, # End of error bar (upper bound)
angle = 90, code = 3, length = 0.05, col = "black"
)
}
}
graphics::par(mfrow = mfrow_old)
}
#' @export
#' @noRd
barplot.antibiogram <- function(height, ...) {
plot(height, ...)
}
#' @method autoplot antibiogram
#' @rdname antibiogram
# will be exported using s3_register() in R/zzz.R
autoplot.antibiogram <- function(object, ...) {
df <- attributes(object)$long_numeric
if (!"mo" %in% colnames(df)) {
stop_("Plotting antibiograms using `autoplot()` is only possible if they were not created using dplyr groups. See `?antibiogram` for how to retrieve numeric values in a long format for advanced plotting.",
call = FALSE)
}
out <- ggplot2::ggplot(df,
mapping = ggplot2::aes(
x = ab,
y = coverage * 100,
fill = if ("syndromic_group" %in% colnames(df)) {
syndromic_group
} else {
NULL
}
)) +
ggplot2::geom_col(position = ggplot2::position_dodge2(preserve = "single")) +
ggplot2::facet_wrap("mo") +
ggplot2::labs(
y = ifelse(isTRUE(attributes(object)$combine_SI), "%SI", "%S"),
x = NULL,
fill = if ("syndromic_group" %in% colnames(df)) {
colnames(object)[1]
} else {
NULL
}
)
if (isTRUE(attributes(object)$wisca)) {
out <- out +
ggplot2::geom_errorbar(mapping = ggplot2::aes(ymin = lower_ci * 100, ymax = upper_ci * 100),
position = ggplot2::position_dodge2(preserve = "single"),
width = 0.5)
}
out
}
# will be exported in zzz.R
#' @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()].
#' @param na character to use for showing `NA` values
#' @rdname antibiogram
knit_print.antibiogram <- function(x, italicise = TRUE, na = getOption("knitr.kable.NA", default = ""), ...) {
stop_ifnot_installed("knitr")
meet_criteria(italicise, allow_class = "logical", has_length = 1)
meet_criteria(na, allow_class = "character", has_length = 1, allow_NA = TRUE)
if (isTRUE(italicise) && "mo" %in% colnames(attributes(x)$long_numeric)) {
# make all microorganism names italic, according to nomenclature
names_col <- ifelse(isTRUE(attributes(x)$has_syndromic_group), 2, 1)
x[[names_col]] <- italicise_taxonomy(x[[names_col]], type = "markdown")
}
old_option <- getOption("knitr.kable.NA")
options(knitr.kable.NA = na)
on.exit(options(knitr.kable.NA = old_option))
out <- paste(c("", "", knitr::kable(x, ..., output = FALSE)), collapse = "\n")
knitr::asis_output(out)
}
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