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