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(v2.1.1.9140) WISCA fix
This commit is contained in:
202
R/antibiogram.R
202
R/antibiogram.R
@ -34,21 +34,21 @@
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#'
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#' 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.
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#' @param x a [data.frame] containing at least a column with microorganisms and columns with antimicrobial results (class 'sir', see [as.sir()])
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#' @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*.
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#' @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.
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#' @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*.
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#' @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'.
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#' @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.
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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 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").
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#' @param only_all_tested (for combination antibiograms): a [logical] to indicate that isolates must be tested for all antimicrobials, see *Details*
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#' @param digits number of digits to use for rounding the susceptibility percentage
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#' @param digits number of digits to use for rounding the antimicrobial coverage, defaults to 1 for WISCA and 0 otherwise
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#' @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.
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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, SDD, or I, instead of only S (default is `TRUE`)
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#' @param sep a separating character for antimicrobial columns in combination antibiograms
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#' @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 hierarchical model to estimate regimen coverage probabilities using Montecarlo simulations. Set `simulations` to adjust.
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#' @param simulations (for WISCA) a numerical value to set the number of Montecarlo simulations
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#' @param wisca a [logical] to indicate whether a Weighted-Incidence Syndromic Combination Antibiogram (WISCA) must be generated (default is `FALSE`). This will use a Bayesian decision model to estimate regimen coverage probabilities using [Monte Carlo simulations](https://en.wikipedia.org/wiki/Monte_Carlo_method). Set `simulations` to adjust.
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#' @param simulations (for WISCA) a numerical value to set the number of Monte Carlo simulations
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#' @param conf_interval (for WISCA) a numerical value to set confidence interval (default is `0.95`)
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#' @param interval_side (for WISCA) the side of the confidence interval, either `"two-tailed"` (default), `"left"` or `"right"`
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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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@ -64,7 +64,7 @@
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#'
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#' ### Formatting Type
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#'
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#' The formatting of the 'cells' of the table can be set with the argument `formatting_type`. In these examples, `5` is the susceptibility percentage (for WISCA: `4-6` indicates the confidence level), `15` the numerator, and `300` the denominator:
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#' The formatting of the 'cells' of the table can be set with the argument `formatting_type`. In these examples, `5` is the antimicrobial coverage (for WISCA: `4-6` indicates the confidence level), `15` the numerator, and `300` the denominator:
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#'
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#' 1. 5
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#' 2. 15
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@ -81,17 +81,17 @@
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#'
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#' Additional options for WISCA (using `antibiogram(..., wisca = TRUE)` or `wisca()`):
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#' 13. 5 (4-6)
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#' 14. 5% (4-6%)
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#' 14. 5% (4-6%) - **default for WISCA**
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#' 15. 5 (4-6,300)
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#' 16. 5% (4-6%,300)
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#' 17. 5 (4-6,N=300)
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#' 18. 5% (4-6%,N=300) - **default for WISCA**
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#' 18. 5% (4-6%,N=300)
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#' 19. 5 (4-6,15/300)
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#' 20. 5% (4-6%,15/300)
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#' 21. 5 (4-6,N=15/300)
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#' 22. 5% (4-6%,N=15/300)
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#'
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#' The default is `18` for WISCA and `10` for non-WISCA, which can be set globally with the package option [`AMR_antibiogram_formatting_type`][AMR-options], e.g. `options(AMR_antibiogram_formatting_type = 5)`.
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#' The default is `14` for WISCA and `10` for non-WISCA, which can be set globally with the package option [`AMR_antibiogram_formatting_type`][AMR-options], e.g. `options(AMR_antibiogram_formatting_type = 5)`. Do note that for WISCA, the numerator and denominator are less useful to report, since these are included in the Bayesian model and apparent from the susceptibility and its confidence level.
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#'
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#' Set `digits` (defaults to `0`) to alter the rounding of the susceptibility percentages.
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#'
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@ -99,7 +99,7 @@
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#'
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#' There are various antibiogram types, as summarised by Klinker *et al.* (2021, \doi{10.1177/20499361211011373}), and they are all supported by [antibiogram()].
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#'
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#' **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 *Why Use WISCA?* on this page.
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#' **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.
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#'
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#' 1. **Traditional Antibiogram**
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#'
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@ -137,7 +137,7 @@
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#'
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#' 4. **Weighted-Incidence Syndromic Combination Antibiogram (WISCA)**
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#'
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#' WISCA can be applied to any antibiogram, see the section *Why Use WISCA?* on this page for more information.
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#' WISCA can be applied to any antibiogram, see the section *Explaining WISCA* on this page for more information.
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#'
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#' Code example:
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#'
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@ -152,37 +152,88 @@
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#' ```
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#'
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#' 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).
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#'
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#' ### Grouped tibbles
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#'
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#' Grouped [tibbles][tibble::tibble] can also be used to calculate susceptibilities over various groups.
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#' For any type of antibiogram, grouped [tibbles][tibble::tibble] can also be used to calculate susceptibilities over various groups.
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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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#' group_by(has_sepsis, is_neonate, sex) %>%
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#' wisca(antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"))
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#' ```
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#'
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#' ### Stepped Approach for Clinical Insight
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#'
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#' 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:
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#'
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#' 1. **Initial Empirical Therapy (Admission / Pre-Culture Data)**
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#'
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#' At admission, no pathogen information is available.
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#'
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#' - Action: broad-spectrum coverage is based on local resistance patterns and syndromic antibiograms.
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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 = selected_regimens,
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#' wisca = TRUE,
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#' mo_transform = NA) # all pathogens set to `NA`
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#' ```
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#'
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#' 2. **Refinement with Gram Stain Results**
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#'
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#' When a blood culture becomes positive, the Gram stain provides an initial and crucial first stratification (Gram-positive vs. Gram-negative).
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#'
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#' - Action: narrow coverage based on Gram stain-specific resistance patterns.
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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 = selected_regimens,
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#' wisca = TRUE,
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#' mo_transform = "gramstain") # all pathogens set to Gram-pos/Gram-neg
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#' ```
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#'
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#' 3. **Definitive Therapy Based on Species Identification**
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#'
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#' After cultivation of the pathogen, full pathogen identification allows precise targeting of therapy.
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#'
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#' - Action: adjust treatment to pathogen-specific antibiograms, minimizing resistance risks.
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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 = selected_regimens,
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#' wisca = TRUE,
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#' mo_transform = "shortname") # all pathogens set to 'G. species', e.g., E. coli
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#' ```
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#'
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#' 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.
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#'
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#' ### Inclusion in Combination Antibiogram and Syndromic Antibiogram
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#'
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#' Note that for types 2 and 3 (Combination Antibiogram and Syndromic Antibiogram), 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:
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#' ### Inclusion in Combination Antibiograms
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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 antimicrobials, 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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#' Drug A Drug B considered considered considered considered
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#' susceptible tested susceptible tested
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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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@ -194,7 +245,7 @@
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#'
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#' 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()`.
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#'
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#' @section Why Use WISCA?:
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#' @section Explaining WISCA:
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#'
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#' WISCA, as outlined by Bielicki *et al.* (\doi{10.1093/jac/dkv397}), stands for Weighted-Incidence Syndromic Combination Antibiogram, which estimates the probability of adequate empirical antimicrobial regimen coverage for specific infection syndromes. This method leverages a Bayesian hierarchical logistic regression framework with random effects for pathogens and regimens, enabling robust estimates in the presence of sparse data.
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#'
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@ -361,8 +412,8 @@ antibiogram <- function(x,
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syndromic_group = NULL,
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add_total_n = FALSE,
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only_all_tested = FALSE,
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digits = 0,
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formatting_type = getOption("AMR_antibiogram_formatting_type", ifelse(wisca, 18, 10)),
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digits = ifelse(wisca, 1, 0),
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formatting_type = getOption("AMR_antibiogram_formatting_type", ifelse(wisca, 14, 10)),
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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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@ -385,8 +436,8 @@ antibiogram.default <- function(x,
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syndromic_group = NULL,
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add_total_n = FALSE,
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only_all_tested = FALSE,
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digits = 0,
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formatting_type = getOption("AMR_antibiogram_formatting_type", ifelse(wisca, 18, 10)),
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digits = ifelse(wisca, 1, 0),
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formatting_type = getOption("AMR_antibiogram_formatting_type", ifelse(wisca, 14, 10)),
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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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@ -400,7 +451,7 @@ antibiogram.default <- function(x,
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meet_criteria(x, allow_class = "data.frame")
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x <- ascertain_sir_classes(x, "x")
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if (!is.function(mo_transform)) {
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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(mo_transform, allow_class = "character", has_length = 1, is_in = c("name", "shortname", "gramstain", colnames(AMR::microorganisms)), allow_NULL = TRUE, allow_NA = TRUE)
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}
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if (!is.function(ab_transform)) {
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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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@ -429,7 +480,9 @@ antibiogram.default <- function(x,
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# transform MOs
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x$`.mo` <- x[, col_mo, drop = TRUE]
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if (is.null(mo_transform)) {
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# leave as is
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# leave as is, no transformation
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} else if (is.na(mo_transform)) {
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x$`.mo` <- NA_character_
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} else if (is.function(mo_transform)) {
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x$`.mo` <- mo_transform(x$`.mo`)
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} else if (mo_transform == "gramstain") {
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@ -536,6 +589,7 @@ antibiogram.default <- function(x,
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)
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counts <- out
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wisca_params <- NULL
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if (wisca == TRUE) {
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# WISCA ----
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@ -547,9 +601,12 @@ antibiogram.default <- function(x,
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title = "Calculating beta/gamma parameters for WISCA")
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on.exit(close(progress))
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out$percentage = NA_real_
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out$lower = NA_real_
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out$upper = NA_real_
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out$coverage <- NA_real_
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out$lower <- NA_real_
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out$upper <- NA_real_
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out$gamma_posterior <- NA_real_
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out$beta_posterior_1 <- NA_real_
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out$beta_posterior_2 <- NA_real_
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for (i in seq_len(NROW(out))) {
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if (out$total[i] == 0) {
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@ -559,22 +616,29 @@ antibiogram.default <- function(x,
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out_current <- out[i, , drop = FALSE]
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priors <- calculate_priors(out_current, combine_SI = combine_SI)
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out$gamma_posterior[i] = priors$gamma_posterior
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out$beta_posterior_1[i] = priors$beta_posterior_1
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out$beta_posterior_2[i] = priors$beta_posterior_2
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# Monte Carlo simulation
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coverage_simulations <- replicate(simulations, {
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# simulate pathogen incidence
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# = Dirichlet (Gamma) parameters
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simulated_incidence <- stats::rgamma(
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n = length(priors$gamma_posterior),
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random_incidence <- runif(1, min = 0, max = 1)
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simulated_incidence <- stats::qgamma(
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p = random_incidence,
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shape = priors$gamma_posterior,
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rate = 1 # Scale = 1 for gamma
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scale = 1
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)
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# normalise
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simulated_incidence <- simulated_incidence / sum(simulated_incidence)
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# simulate susceptibility
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# = Beta parameters
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simulated_susceptibility <- stats::rbeta(
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n = length(priors$beta_posterior_1),
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random_susceptibity <- runif(1, min = 0, max = 1)
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simulated_susceptibility <- stats::qbeta(
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p = random_susceptibity,
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shape1 = priors$beta_posterior_1,
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shape2 = priors$beta_posterior_2
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)
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@ -592,7 +656,7 @@ antibiogram.default <- function(x,
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}
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coverage_ci <- unname(stats::quantile(coverage_simulations, probs = probs))
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out$percentage[i] <- coverage_mean
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out$coverage[i] <- coverage_mean
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out$lower[i] <- coverage_ci[1]
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out$upper[i] <- coverage_ci[2]
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}
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@ -640,19 +704,25 @@ antibiogram.default <- function(x,
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if (wisca == TRUE) {
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long_numeric <- out %pm>%
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pm_summarise(percentage = percentage,
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lower = lower,
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upper = upper,
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numerator = numerator,
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total = total)
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pm_summarise(coverage = coverage,
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lower_ci = lower,
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upper_ci = upper,
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n_tested = total,
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n_total = total_rows,
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n_susceptible = numerator,
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p_susceptible = numerator / total,
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gamma_posterior = gamma_posterior,
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beta_posterior1 = beta_posterior_1,
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beta_posterior2 = beta_posterior_2)
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} else {
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long_numeric <- out %pm>%
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pm_summarise(percentage = numerator / total,
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pm_summarise(coverage = numerator / total,
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numerator = numerator,
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total = total)
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}
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out$digits <- digits # since pm_sumarise() cannot work with an object outside the current frame
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# formatting type:
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# 1. 5
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# 2. 15
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@ -688,12 +758,12 @@ antibiogram.default <- function(x,
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if (formatting_type == 10) out <- out %pm>% pm_summarise(out_value = paste0(round((numerator / total) * 100, digits = digits), "% (", numerator, "/", total, ")"))
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if (formatting_type == 11) out <- out %pm>% pm_summarise(out_value = paste0(round((numerator / total) * 100, digits = digits), " (N=", numerator, "/", total, ")"))
|
||||
if (formatting_type == 12) out <- out %pm>% pm_summarise(out_value = paste0(round((numerator / total) * 100, digits = digits), "% (N=", numerator, "/", total, ")"))
|
||||
if (formatting_type == 13) out <- out %pm>% pm_summarise(out_value = paste0(round(percentage * 100, digits = digits), " (", round(lower * 100, digits = digits), "-", round(upper * 100, digits = digits), ")"))
|
||||
if (formatting_type == 14) out <- out %pm>% pm_summarise(out_value = paste0(round(percentage * 100, digits = digits), "% (", round(lower * 100, digits = digits), "-", round(upper * 100, digits = digits), "%)"))
|
||||
if (formatting_type == 15) out <- out %pm>% pm_summarise(out_value = paste0(round(percentage * 100, digits = digits), " (", round(lower * 100, digits = digits), "-", round(upper * 100, digits = digits), ",", total, ")"))
|
||||
if (formatting_type == 16) out <- out %pm>% pm_summarise(out_value = paste0(round(percentage * 100, digits = digits), "% (", round(lower * 100, digits = digits), "-", round(upper * 100, digits = digits), "%,", total, ")"))
|
||||
if (formatting_type == 17) out <- out %pm>% pm_summarise(out_value = paste0(round(percentage * 100, digits = digits), " (", round(lower * 100, digits = digits), "-", round(upper * 100, digits = digits), ",N=", total, ")"))
|
||||
if (formatting_type == 18) out <- out %pm>% pm_summarise(out_value = paste0(round(percentage * 100, digits = digits), "% (", round(lower * 100, digits = digits), "-", round(upper * 100, digits = digits), "%,N=", total, ")"))
|
||||
if (formatting_type == 13) out <- out %pm>% pm_summarise(out_value = paste0(round(coverage * 100, digits = digits), " (", round(lower * 100, digits = digits), "-", round(upper * 100, digits = digits), ")"))
|
||||
if (formatting_type == 14) out <- out %pm>% pm_summarise(out_value = paste0(round(coverage * 100, digits = digits), "% (", round(lower * 100, digits = digits), "-", round(upper * 100, digits = digits), "%)"))
|
||||
if (formatting_type == 15) out <- out %pm>% pm_summarise(out_value = paste0(round(coverage * 100, digits = digits), " (", round(lower * 100, digits = digits), "-", round(upper * 100, digits = digits), ",", total, ")"))
|
||||
if (formatting_type == 16) out <- out %pm>% pm_summarise(out_value = paste0(round(coverage * 100, digits = digits), "% (", round(lower * 100, digits = digits), "-", round(upper * 100, digits = digits), "%,", total, ")"))
|
||||
if (formatting_type == 17) out <- out %pm>% pm_summarise(out_value = paste0(round(coverage * 100, digits = digits), " (", round(lower * 100, digits = digits), "-", round(upper * 100, digits = digits), ",N=", total, ")"))
|
||||
if (formatting_type == 18) out <- out %pm>% pm_summarise(out_value = paste0(round(coverage * 100, digits = digits), "% (", round(lower * 100, digits = digits), "-", round(upper * 100, digits = digits), "%,N=", total, ")"))
|
||||
|
||||
# transform names of antibiotics
|
||||
ab_naming_function <- function(x, t, l, s) {
|
||||
@ -812,8 +882,8 @@ antibiogram.grouped_df <- function(x,
|
||||
syndromic_group = NULL,
|
||||
add_total_n = FALSE,
|
||||
only_all_tested = FALSE,
|
||||
digits = 0,
|
||||
formatting_type = getOption("AMR_antibiogram_formatting_type", ifelse(wisca, 18, 10)),
|
||||
digits = ifelse(wisca, 1, 0),
|
||||
formatting_type = getOption("AMR_antibiogram_formatting_type", ifelse(wisca, 14, 10)),
|
||||
col_mo = NULL,
|
||||
language = get_AMR_locale(),
|
||||
minimum = 30,
|
||||
@ -910,8 +980,8 @@ wisca <- function(x,
|
||||
syndromic_group = NULL,
|
||||
add_total_n = FALSE,
|
||||
only_all_tested = FALSE,
|
||||
digits = 0,
|
||||
formatting_type = getOption("AMR_antibiogram_formatting_type", 18),
|
||||
digits = 1,
|
||||
formatting_type = getOption("AMR_antibiogram_formatting_type", 14),
|
||||
col_mo = NULL,
|
||||
language = get_AMR_locale(),
|
||||
minimum = 30,
|
||||
@ -938,9 +1008,15 @@ wisca <- function(x,
|
||||
info = info)
|
||||
}
|
||||
|
||||
#' @export
|
||||
#' @param wisca_model the outcome of [wisca()] or [antibiogram(..., wisca = TRUE)]
|
||||
#' @rdname antibiogram
|
||||
retrieve_wisca_parameters <- function(wisca_model, ...) {
|
||||
stop_ifnot(isTRUE(attributes(wisca_model)$wisca), "This function only applies to WISCA models. Use `wisca()` or `antibiogram(..., wisca = TRUE)` to create a WISCA model.")
|
||||
attributes(wisca_model)$long_numeric
|
||||
}
|
||||
|
||||
calculate_priors <- function(data, combine_SI = TRUE) {
|
||||
# Ensure data has required columns
|
||||
stopifnot(all(c("mo", "total_rows", "total", "S") %in% colnames(data)))
|
||||
if (combine_SI == TRUE && "I" %in% colnames(data)) {
|
||||
data$S <- data$S + data$I
|
||||
}
|
||||
@ -1013,7 +1089,7 @@ plot.antibiogram <- function(x, ...) {
|
||||
df_sub <- df[df$mo == mo, , drop = FALSE]
|
||||
|
||||
bp <- barplot(
|
||||
height = df_sub$percentage * 100,
|
||||
height = df_sub$coverage * 100,
|
||||
xlab = NULL,
|
||||
ylab = ifelse(isTRUE(attributes(x)$combine_SI), "%SI", "%S"),
|
||||
names.arg = df_sub$ab,
|
||||
@ -1055,7 +1131,7 @@ autoplot.antibiogram <- function(object, ...) {
|
||||
out <- ggplot2::ggplot(df,
|
||||
mapping = ggplot2::aes(
|
||||
x = ab,
|
||||
y = percentage * 100,
|
||||
y = coverage * 100,
|
||||
fill = if ("syndromic_group" %in% colnames(df)) {
|
||||
syndromic_group
|
||||
} else {
|
||||
|
||||
Reference in New Issue
Block a user