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(v2.1.1.9190) antibiotics deprecation in antibiogram()
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parent
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3
.github/workflows/lintr.yaml
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3
.github/workflows/lintr.yaml
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@ -65,6 +65,7 @@ jobs:
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# do not check these folders
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rm -rf data-raw
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rm -rf tests
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rm -rf vignettes
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- name: Lint
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run: |
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@ -76,7 +77,7 @@ jobs:
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linters <- linters[!grepl("^(closed_curly|open_curly|paren_brace|semicolon_terminator|consecutive_stopifnot|no_tab|single_quotes|unnecessary_nested_if|unneeded_concatenation)_linter$", linters)]
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linters <- linters[linters != "linter"]
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# and the ones we find unnnecessary
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linters <- linters[!grepl("^(extraction_operator|implicit_integer|line_length|object_length|object_name|object_usage|nonportable_path|is)_linter$", linters)]
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linters <- linters[!grepl("^(commented_code|extraction_operator|implicit_integer|indentation|line_length|namespace|nonportable_path|object_length|object_name|object_usage|is)_linter$", linters)]
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# put the functions in a list
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linters_list <- lapply(linters, function(l) eval(parse(text = paste0("lintr::", l, "()")), envir = asNamespace("lintr")))
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names(linters_list) <- linters
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@ -1,6 +1,6 @@
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Package: AMR
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Version: 2.1.1.9189
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Date: 2025-03-07
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Version: 2.1.1.9190
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Date: 2025-03-09
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Title: Antimicrobial Resistance Data Analysis
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Description: Functions to simplify and standardise antimicrobial resistance (AMR)
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data analysis and to work with microbial and antimicrobial properties by
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7
NEWS.md
7
NEWS.md
@ -1,4 +1,4 @@
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# AMR 2.1.1.9189
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# AMR 2.1.1.9190
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*(this beta version will eventually become v3.0. We're happy to reach a new major milestone soon, which will be all about the new One Health support! Install this beta using [the instructions here](https://msberends.github.io/AMR/#latest-development-version).)*
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@ -48,8 +48,9 @@ This package now supports not only tools for AMR data analysis in clinical setti
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* To get quantitative values, `as.double()` on a `sir` object will return 1 for S, 2 for SDD/I, and 3 for R (NI will become `NA`). Other functions using `sir` classes (e.g., `summary()`) are updated to reflect the change to contain NI and SDD.
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* Fix for `conserve_capped_values`, which now again works as expected: in MIC values, `<x` will always be S, `>x` will always be R
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* `antibiogram()` function
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* New argument `formatting_type` to set any of the 22 options for the formatting of all 'cells'. This defaults to `10` for non-WISCA and `14` for WISCA, changing the output of antibiograms to cells with more info.
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* For this reason, `add_total_n` is now `FALSE` at default since the denominators are added to the cells
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* Argument `antibiotics` has been renamed to `antimicrobials`. Using `antibiotics` will still work, but now returns a warning.
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* Added argument `formatting_type` to set any of the 22 options for the formatting of all 'cells'. This defaults to `18` for non-WISCA and `14` for WISCA, changing the output of antibiograms to cells with more info.
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* For this reason, `add_total_n` is now `FALSE` at default since the denominators are added to the cells for non-WISCA. For WISCA, the denominator is not useful anyway.
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* The `ab_transform` argument now defaults to `"name"`, displaying antibiotic column names instead of codes
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* Antimicrobial selectors (previously: *antibiotic selectors*)
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* 'Antibiotic selectors' are now called 'antimicrobial selectors' since their scope is broader than just antibiotics. All documentation have been updated, and `ab_class()` and `ab_selector()` have been replaced with `amr_class()` and `amr_selector()`. The old functions are now deprecated and will be removed in a future version.
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@ -1,6 +1,6 @@
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Metadata-Version: 2.2
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Name: AMR
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Version: 2.1.1.9189
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Version: 2.1.1.9190
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Summary: A Python wrapper for the AMR R package
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Home-page: https://github.com/msberends/AMR
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Author: Matthijs Berends
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@ -2,7 +2,7 @@ from setuptools import setup, find_packages
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setup(
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name='AMR',
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version='2.1.1.9189',
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version='2.1.1.9190',
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packages=find_packages(),
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install_requires=[
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'rpy2',
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180
R/antibiogram.R
180
R/antibiogram.R
@ -34,7 +34,7 @@
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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_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 antimicrobials vector of any antimicrobial name or code (will be evaluated with [as.ab()], column name of `x`, or (any combinations of) [antimicrobial selectors][antimicrobial_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 [antimicrobials] data set (defaults to `"name"`): `r vector_or(colnames(antimicrobials), 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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@ -107,7 +107,7 @@
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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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#' antimicrobials = "TZP")
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#' ```
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#'
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#' 2. **Combination Antibiogram**
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@ -118,7 +118,7 @@
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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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#' antimicrobials = 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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@ -129,7 +129,7 @@
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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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#' antimicrobials = penicillins(),
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#' syndromic_group = "ward")
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#' ```
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#'
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@ -141,12 +141,12 @@
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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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#' antimicrobials = c("TZP", "TZP+TOB", "TZP+GEN"),
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#' wisca = TRUE)
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#'
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#' # this is equal to:
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#' wisca(your_data,
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#' antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"))
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#' antimicrobials = c("TZP", "TZP+TOB", "TZP+GEN"))
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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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@ -161,7 +161,7 @@
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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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#' wisca(antimicrobials = 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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@ -177,12 +177,12 @@
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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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#' antimicrobials = selected_regimens,
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#' mo_transform = NA) # all pathogens set to `NA`
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#'
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#' # preferred: use WISCA
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#' wisca(your_data,
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#' antibiotics = selected_regimens)
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#' antimicrobials = selected_regimens)
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#' ```
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#'
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#' 2. **Refinement with Gram Stain Results**
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@ -194,7 +194,7 @@
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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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#' antimicrobials = selected_regimens,
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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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@ -207,7 +207,7 @@
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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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#' antimicrobials = selected_regimens,
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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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@ -301,17 +301,17 @@
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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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#' antimicrobials = 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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#' antimicrobials = 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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#' antimicrobials = carbapenems(),
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#' ab_transform = "name",
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#' mo_transform = "name"
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#' )
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@ -319,15 +319,15 @@
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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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#' # combined antimicrobials 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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#' antimicrobials = 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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#' # names of antimicrobials 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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#' antimicrobials = 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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@ -338,7 +338,7 @@
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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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#' antimicrobials = c(aminoglycosides(), carbapenems()),
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#' syndromic_group = "ward"
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#' )
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#'
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@ -348,7 +348,7 @@
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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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#' antimicrobials = 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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@ -361,7 +361,7 @@
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#'
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#' # WISCA are not stratified by species, but rather on syndromes
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#' antibiogram(example_isolates,
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#' antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"),
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#' antimicrobials = c("TZP", "TZP+TOB", "TZP+GEN"),
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#' syndromic_group = "ward",
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#' wisca = TRUE
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#' )
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@ -370,7 +370,7 @@
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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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#' antimicrobials = ureidopenicillins(),
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#' syndromic_group = "ward",
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#' wisca = TRUE
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#' )
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@ -385,11 +385,11 @@
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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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#' antimicrobials = 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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#' antimicrobials = 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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@ -405,7 +405,7 @@
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#' plot(ab2)
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#' }
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antibiogram <- function(x,
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antibiotics = where(is.sir),
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antimicrobials = where(is.sir),
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mo_transform = "shortname",
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ab_transform = "name",
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syndromic_group = NULL,
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@ -422,14 +422,15 @@ antibiogram <- function(x,
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simulations = 1000,
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conf_interval = 0.95,
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interval_side = "two-tailed",
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info = interactive()) {
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info = interactive(),
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...) {
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UseMethod("antibiogram")
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}
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#' @method antibiogram default
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#' @export
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antibiogram.default <- function(x,
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antibiotics = where(is.sir),
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antimicrobials = where(is.sir),
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mo_transform = "shortname",
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ab_transform = "name",
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syndromic_group = NULL,
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@ -446,7 +447,8 @@ antibiogram.default <- function(x,
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simulations = 1000,
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conf_interval = 0.95,
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interval_side = "two-tailed",
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info = interactive()) {
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info = interactive(),
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...) {
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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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meet_criteria(wisca, allow_class = "logical", has_length = 1)
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@ -456,6 +458,10 @@ antibiogram.default <- function(x,
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}
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mo_transform <- function(x) suppressMessages(suppressWarnings(paste(mo_genus(x, keep_synonyms = TRUE, language = NULL), mo_species(x, keep_synonyms = TRUE, language = NULL))))
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}
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if ("antibiotics" %in% names(list(...))) {
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deprecation_warning("antibiotics", "antimicrobials", fn = "antibiogram", is_argument = TRUE)
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antimicrobials <- list(...)$antibiotics
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}
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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, allow_NA = TRUE)
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}
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@ -514,17 +520,17 @@ antibiogram.default <- function(x,
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has_syndromic_group <- FALSE
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}
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# get antibiotics
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ab_trycatch <- tryCatch(colnames(suppressWarnings(x[, antibiotics, drop = FALSE])), error = function(e) NULL)
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# get antimicrobials
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ab_trycatch <- tryCatch(colnames(suppressWarnings(x[, antimicrobials, drop = FALSE])), error = function(e) NULL)
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if (is.null(ab_trycatch)) {
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stop_ifnot(is.character(suppressMessages(antibiotics)), "`antibiotics` must be an antimicrobial selector, or a character vector.")
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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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stop_ifnot(is.character(suppressMessages(antimicrobials)), "`antimicrobials` must be an antimicrobial selector, or a character vector.")
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antimicrobials.bak <- antimicrobials
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# split antimicrobials on separator and make it a list
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antimicrobials <- strsplit(gsub(" ", "", antimicrobials), "+", fixed = TRUE)
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# get available antimicrobials 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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# get antimicrobials from user
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user_ab <- suppressMessages(suppressWarnings(lapply(antimicrobials, 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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@ -535,14 +541,14 @@ antibiogram.default <- function(x,
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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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warning_("The following antimicrobials 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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antimicrobials <- 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 (i in seq_len(length(antimicrobials))) {
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abx <- antimicrobials[[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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@ -568,20 +574,20 @@ antibiogram.default <- function(x,
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))
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}
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}
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antibiotics[[i]] <- new_colname
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antimicrobials[[i]] <- new_colname
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}
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antibiotics <- unlist(antibiotics)
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antimicrobials <- unlist(antimicrobials)
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} else {
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antibiotics <- ab_trycatch
|
||||
antimicrobials <- ab_trycatch
|
||||
}
|
||||
|
||||
if (isTRUE(has_syndromic_group)) {
|
||||
out <- x %pm>%
|
||||
pm_select(.syndromic_group, .mo, antibiotics) %pm>%
|
||||
pm_select(.syndromic_group, .mo, antimicrobials) %pm>%
|
||||
pm_group_by(.syndromic_group)
|
||||
} else {
|
||||
out <- x %pm>%
|
||||
pm_select(.mo, antibiotics)
|
||||
pm_select(.mo, antimicrobials)
|
||||
}
|
||||
|
||||
|
||||
@ -613,8 +619,6 @@ antibiogram.default <- function(x,
|
||||
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) {
|
||||
@ -678,6 +682,10 @@ antibiogram.default <- function(x,
|
||||
n_susceptible = sum(n_susceptible, na.rm = TRUE)
|
||||
)
|
||||
|
||||
if (any(out_wisca$n_tested < minimum, na.rm = TRUE) && message_not_thrown_before("antibiogram", wisca)) {
|
||||
warning_("Number of tested isolates should exceed ", minimum, " for each regimen (and group). WISCA coverage estimates might be inaccurate.", call = FALSE)
|
||||
}
|
||||
|
||||
out_wisca$p_susceptible <- out_wisca$n_susceptible / out_wisca$n_tested
|
||||
|
||||
if (isTRUE(has_syndromic_group)) {
|
||||
@ -690,8 +698,8 @@ antibiogram.default <- function(x,
|
||||
|
||||
# create the WISCA parameters, including our priors/posteriors
|
||||
out$gamma_posterior <- NA_real_
|
||||
out$beta_posterior1 <- NA_real_
|
||||
out$beta_posterior2 <- NA_real_
|
||||
out$beta_posterior_1 <- NA_real_
|
||||
out$beta_posterior_2 <- NA_real_
|
||||
|
||||
for (i in seq_len(NROW(out))) {
|
||||
if (out$n_tested[i] == 0) {
|
||||
@ -699,10 +707,22 @@ antibiogram.default <- function(x,
|
||||
}
|
||||
|
||||
out_current <- out[i, , drop = FALSE]
|
||||
priors <- calculate_priors(out_current, combine_SI = combine_SI)
|
||||
out$gamma_posterior[i] <- priors$gamma_posterior
|
||||
out$beta_posterior1[i] <- priors$beta_posterior_1
|
||||
out$beta_posterior2[i] <- priors$beta_posterior_2
|
||||
|
||||
## calculate priors ----
|
||||
# pathogen incidence (Dirichlet distribution)
|
||||
gamma_prior <- rep(1, length(unique(out_current$mo))) # Dirichlet prior
|
||||
gamma_posterior <- gamma_prior + out_current$n_total # Posterior parameters
|
||||
|
||||
# regimen susceptibility (Beta distribution)
|
||||
beta_prior <- rep(1, length(unique(out_current$mo))) # Beta prior
|
||||
r <- out_current$n_susceptible
|
||||
n <- out_current$n_tested
|
||||
beta_posterior_1 <- beta_prior + r # Posterior alpha
|
||||
beta_posterior_2 <- beta_prior + (n - r) # Posterior beta
|
||||
|
||||
out$gamma_posterior[i] <- gamma_posterior
|
||||
out$beta_posterior_1[i] <- beta_posterior_1
|
||||
out$beta_posterior_2[i] <- beta_posterior_2
|
||||
}
|
||||
|
||||
wisca_parameters <- out
|
||||
@ -742,8 +762,8 @@ antibiogram.default <- function(x,
|
||||
random_susceptibity <- stats::runif(1, min = 0, max = 1)
|
||||
simulated_susceptibility <- stats::qbeta(
|
||||
p = random_susceptibity,
|
||||
shape1 = params_current$beta_posterior1,
|
||||
shape2 = params_current$beta_posterior2
|
||||
shape1 = params_current$beta_posterior_1,
|
||||
shape2 = params_current$beta_posterior_2
|
||||
)
|
||||
sum(simulated_incidence * simulated_susceptibility, na.rm = TRUE)
|
||||
})
|
||||
@ -825,6 +845,9 @@ antibiogram.default <- function(x,
|
||||
# 20. 5% (4-6%,15/300)
|
||||
# 21. 5 (4-6,N=15/300)
|
||||
# 22. 5% (4-6%,N=15/300)
|
||||
if (wisca == TRUE && !formatting_type %in% c(1, 2, 13, 14) && info == TRUE && message_not_thrown_before("antibiogram", wisca, formatting_type)) {
|
||||
message_("Using WISCA with a `formatting_type` that includes the denominator is not useful")
|
||||
}
|
||||
if (formatting_type == 1) out <- out %pm>% pm_summarise(out_value = round(coverage * 100, digits = digits))
|
||||
if (formatting_type == 2) out <- out %pm>% pm_summarise(out_value = n_susceptible)
|
||||
if (formatting_type == 3) out <- out %pm>% pm_summarise(out_value = n_tested)
|
||||
@ -847,8 +870,9 @@ antibiogram.default <- function(x,
|
||||
if (formatting_type == 20) out <- out %pm>% pm_summarise(out_value = paste0(round(coverage * 100, digits = digits), "% (", round(lower_ci * 100, digits = digits), "-", round(upper_ci * 100, digits = digits), "%,", n_susceptible, "/", n_tested, ")"))
|
||||
if (formatting_type == 21) out <- out %pm>% pm_summarise(out_value = paste0(round(coverage * 100, digits = digits), " (", round(lower_ci * 100, digits = digits), "-", round(upper_ci * 100, digits = digits), ",N=", n_susceptible, "/", n_tested, ")"))
|
||||
if (formatting_type == 22) out <- out %pm>% pm_summarise(out_value = paste0(round(coverage * 100, digits = digits), "% (", round(lower_ci * 100, digits = digits), "-", round(upper_ci * 100, digits = digits), "%,N=", n_susceptible, "/", n_tested, ")"))
|
||||
out$out_value[out$out_value %like% "^NA"] <- NA_character_
|
||||
|
||||
# transform names of antibiotics
|
||||
# transform names of antimicrobials
|
||||
ab_naming_function <- function(x, t, l, s) {
|
||||
x <- strsplit(x, s, fixed = TRUE)
|
||||
out <- character(length = length(x))
|
||||
@ -979,7 +1003,7 @@ antibiogram.default <- function(x,
|
||||
#' @method antibiogram grouped_df
|
||||
#' @export
|
||||
antibiogram.grouped_df <- function(x,
|
||||
antibiotics = where(is.sir),
|
||||
antimicrobials = where(is.sir),
|
||||
mo_transform = NULL,
|
||||
ab_transform = "name",
|
||||
syndromic_group = NULL,
|
||||
@ -996,7 +1020,8 @@ antibiogram.grouped_df <- function(x,
|
||||
simulations = 1000,
|
||||
conf_interval = 0.95,
|
||||
interval_side = "two-tailed",
|
||||
info = interactive()) {
|
||||
info = interactive(),
|
||||
...) {
|
||||
stop_ifnot(is.null(mo_transform), "`mo_transform` must not be set if creating an antibiogram using a grouped tibble. The groups will become the variables over which the antimicrobials are calculated, which could include the pathogen information (though not necessary). Nonetheless, this makes `mo_transform` redundant.", call = FALSE)
|
||||
stop_ifnot(is.null(syndromic_group), "`syndromic_group` must not be set if creating an antibiogram using a grouped tibble. The groups will become the variables over which the antimicrobials are calculated, making `syndromic_groups` redundant.", call = FALSE)
|
||||
groups <- attributes(x)$groups
|
||||
@ -1020,7 +1045,7 @@ antibiogram.grouped_df <- function(x,
|
||||
next
|
||||
}
|
||||
new_out <- antibiogram(as.data.frame(x)[rows, , drop = FALSE],
|
||||
antibiotics = antibiotics,
|
||||
antimicrobials = antimicrobials,
|
||||
mo_transform = NULL,
|
||||
ab_transform = ab_transform,
|
||||
syndromic_group = NULL,
|
||||
@ -1037,7 +1062,8 @@ antibiogram.grouped_df <- function(x,
|
||||
simulations = simulations,
|
||||
conf_interval = conf_interval,
|
||||
interval_side = interval_side,
|
||||
info = FALSE
|
||||
info = FALSE,
|
||||
...
|
||||
)
|
||||
new_wisca_parameters <- attributes(new_out)$wisca_parameters
|
||||
new_long_numeric <- attributes(new_out)$long_numeric
|
||||
@ -1098,7 +1124,7 @@ antibiogram.grouped_df <- function(x,
|
||||
#' @export
|
||||
#' @rdname antibiogram
|
||||
wisca <- function(x,
|
||||
antibiotics = where(is.sir),
|
||||
antimicrobials = where(is.sir),
|
||||
ab_transform = "name",
|
||||
syndromic_group = NULL,
|
||||
add_total_n = FALSE,
|
||||
@ -1113,10 +1139,11 @@ wisca <- function(x,
|
||||
simulations = 1000,
|
||||
conf_interval = 0.95,
|
||||
interval_side = "two-tailed",
|
||||
info = interactive()) {
|
||||
info = interactive(),
|
||||
...) {
|
||||
antibiogram(
|
||||
x = x,
|
||||
antibiotics = antibiotics,
|
||||
antimicrobials = antimicrobials,
|
||||
ab_transform = ab_transform,
|
||||
mo_transform = NULL,
|
||||
syndromic_group = syndromic_group,
|
||||
@ -1133,7 +1160,8 @@ wisca <- function(x,
|
||||
simulations = simulations,
|
||||
conf_interval = conf_interval,
|
||||
interval_side = interval_side,
|
||||
info = info
|
||||
info = info,
|
||||
...
|
||||
)
|
||||
}
|
||||
|
||||
@ -1145,26 +1173,6 @@ retrieve_wisca_parameters <- function(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 = ","))
|
||||
@ -1185,7 +1193,7 @@ tbl_format_footer.antibiogram <- function(x, ...) {
|
||||
return(footer)
|
||||
}
|
||||
c(footer, font_subtle(paste0(
|
||||
"# Use `plot()` or `ggplot2::autoplot()` to create a plot of this antibiogram,\n",
|
||||
"# Use `ggplot2::autoplot()` or base R `plot()` 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")
|
||||
)))
|
||||
|
||||
@ -58,7 +58,7 @@
|
||||
#'
|
||||
#' The function [proportion_df()] takes any variable from `data` that has an [`sir`] class (created with [as.sir()]) and calculates the proportions S, I, and R. It also supports grouped variables. The function [sir_df()] works exactly like [proportion_df()], but adds the number of isolates.
|
||||
#' @section Combination Therapy:
|
||||
#' When using more than one variable for `...` (= combination therapy), use `only_all_tested` to only count isolates that are tested for all antibiotics/variables that you test them for. See this example for two antimicrobials, Drug A and Drug B, about how [susceptibility()] works to calculate the %SI:
|
||||
#' When using more than one variable for `...` (= combination therapy), use `only_all_tested` to only count isolates that are tested for all antimicrobials/variables that you test them for. See this example for two antimicrobials, Drug A and Drug B, about how [susceptibility()] works to calculate the %SI:
|
||||
#'
|
||||
#'
|
||||
#' ```
|
||||
|
||||
@ -235,7 +235,7 @@ sir_calc_df <- function(type, # "proportion", "count" or "both"
|
||||
translate_ab <- get_translate_ab(translate_ab)
|
||||
|
||||
data.bak <- data
|
||||
# select only groups and antibiotics
|
||||
# select only groups and antimicrobials
|
||||
if (is_null_or_grouped_tbl(data)) {
|
||||
data_has_groups <- TRUE
|
||||
groups <- get_group_names(data)
|
||||
|
||||
@ -40,22 +40,25 @@ NULL
|
||||
#' @export
|
||||
"antibiotics"
|
||||
|
||||
# REMEMBER to also remove the deprecated `antibiotics` argument in `antibiogram()`
|
||||
|
||||
#' @rdname AMR-deprecated
|
||||
#' @export
|
||||
ab_class <- function(...) {
|
||||
deprecation_warning("ab_class", "amr_class")
|
||||
deprecation_warning("ab_class", "amr_class", is_function = TRUE)
|
||||
amr_class(...)
|
||||
}
|
||||
|
||||
#' @rdname AMR-deprecated
|
||||
#' @export
|
||||
ab_selector <- function(...) {
|
||||
deprecation_warning("ab_selector", "amr_selector")
|
||||
deprecation_warning("ab_selector", "amr_selector", is_function = TRUE)
|
||||
amr_selector(...)
|
||||
}
|
||||
|
||||
## Helper function ----
|
||||
|
||||
deprecation_warning <- function(old = NULL, new = NULL, extra_msg = NULL, is_function = TRUE, is_dataset = FALSE) {
|
||||
deprecation_warning <- function(old = NULL, new = NULL, fn = NULL, extra_msg = NULL, is_function = FALSE, is_dataset = FALSE, is_argument = FALSE) {
|
||||
if (is.null(old)) {
|
||||
warning_(extra_msg)
|
||||
} else {
|
||||
@ -68,22 +71,30 @@ deprecation_warning <- function(old = NULL, new = NULL, extra_msg = NULL, is_fun
|
||||
type <- "function"
|
||||
} else if (isTRUE(is_dataset)) {
|
||||
type <- "dataset"
|
||||
} else {
|
||||
} else if (isTRUE(is_argument)) {
|
||||
type <- "argument"
|
||||
if (is.null(fn)) {
|
||||
stop("Set 'fn' in deprecation_warning()")
|
||||
}
|
||||
} else {
|
||||
stop("Set either 'is_function', 'is_dataset', or 'is_argument' to TRUE in deprecation_warning()")
|
||||
}
|
||||
warning_(
|
||||
ifelse(is.null(new),
|
||||
paste0("The `", old, "` ", type, " is no longer in use"),
|
||||
ifelse(type == "dataset",
|
||||
paste0("The `", old, "` ", type, " has been renamed to `", new, "`"),
|
||||
paste0("The `", old, "` ", type, " has been replaced with `", new, "` and will be removed in a future version")
|
||||
ifelse(type == "argument",
|
||||
paste0("The `", old, "` ", type, " in `", fn, "()` has been renamed to `", new, "`: `", fn, "(", new, " = ...)`"),
|
||||
paste0("The `", old, "` ", type, " has been replaced with `", new, "`")
|
||||
)
|
||||
)
|
||||
),
|
||||
ifelse(type == "dataset",
|
||||
". The old name will be removed in future version, so please update your code.",
|
||||
ifelse(type == "argument",
|
||||
". While the old argument still works, it will be removed in a future version, so please update your code.",
|
||||
", see `?AMR-deprecated`."
|
||||
" and will be removed in a future version, see `?AMR-deprecated`."
|
||||
)
|
||||
),
|
||||
ifelse(!is.null(extra_msg),
|
||||
|
||||
4
R/zzz.R
4
R/zzz.R
@ -204,14 +204,14 @@ AMR_env$cross_icon <- if (isTRUE(base::l10n_info()$`UTF-8`)) "\u00d7" else "x"
|
||||
# reference data - they have additional data to improve algorithm speed
|
||||
# they cannot be part of R/sysdata.rda since CRAN thinks it would make the package too large (+3 MB)
|
||||
if (NROW(AB_LOOKUP) != NROW(AMR::antimicrobials)) {
|
||||
# antibiotics data set was updated - run create_AB_AV_lookup() again
|
||||
# antimicrobials data set was updated - run create_AB_AV_lookup() again
|
||||
AB_LOOKUP <- create_AB_AV_lookup(AMR::antimicrobials)
|
||||
}
|
||||
|
||||
# deprecated antibiotics data set
|
||||
makeActiveBinding("antibiotics", function() {
|
||||
if (interactive()) {
|
||||
deprecation_warning(old = "antibiotics", new = "antimicrobials", is_function = FALSE, is_dataset = TRUE)
|
||||
deprecation_warning(old = "antibiotics", new = "antimicrobials", is_dataset = TRUE)
|
||||
}
|
||||
AMR::antimicrobials
|
||||
}, env = asNamespace(pkgname))
|
||||
|
||||
@ -492,9 +492,11 @@ write_md5 <- function(object) {
|
||||
close(conn)
|
||||
}
|
||||
changed_md5 <- function(object) {
|
||||
path <- paste0("data-raw/", deparse(substitute(object)), ".md5")
|
||||
if (!file.exists(path)) return(TRUE)
|
||||
tryCatch(
|
||||
{
|
||||
conn <- file(paste0("data-raw/", deparse(substitute(object)), ".md5"))
|
||||
conn <- file(path)
|
||||
compared <- md5(object) != readLines(con = conn)
|
||||
close(conn)
|
||||
compared
|
||||
|
||||
Binary file not shown.
Binary file not shown.
Binary file not shown.
@ -1,6 +1,6 @@
|
||||
This knowledge base contains all context you must know about the AMR package for R. You are a GPT trained to be an assistant for the AMR package in R. You are an incredible R specialist, especially trained in this package and in the tidyverse.
|
||||
|
||||
First and foremost, you are trained on version 2.1.1.9189. Remember this whenever someone asks which AMR package version you’re at.
|
||||
First and foremost, you are trained on version 2.1.1.9190. Remember this whenever someone asks which AMR package version you’re at.
|
||||
|
||||
Below are the contents of the file, the file, and all the files (documentation) in the package. Every file content is split using 100 hypens.
|
||||
----------------------------------------------------------------------------------------------------
|
||||
@ -420,7 +420,7 @@ The `AMR` package is a [free and open-source](#copyright) R package with [zero d
|
||||
|
||||
This work was published in the Journal of Statistical Software (Volume 104(3); [DOI 10.18637/jss.v104.i03](https://doi.org/10.18637/jss.v104.i03)) and formed the basis of two PhD theses ([DOI 10.33612/diss.177417131](https://doi.org/10.33612/diss.177417131) and [DOI 10.33612/diss.192486375](https://doi.org/10.33612/diss.192486375)).
|
||||
|
||||
After installing this package, R knows [**~52,000 distinct microbial species**](./reference/microorganisms.html) (updated December 2022) and all [**~600 antimicrobial and antiviral drugs**](./reference/antibiotics.html) by name and code (including ATC, EARS-Net, ASIARS-Net, PubChem, LOINC and SNOMED CT), and knows all about valid SIR and MIC values. The integral clinical breakpoint guidelines from CLSI and EUCAST are included, even with epidemiological cut-off (ECOFF) values. It supports and can read any data format, including WHONET data. This package works on Windows, macOS and Linux with all versions of R since R-3.0 (April 2013). **It was designed to work in any setting, including those with very limited resources**. It was created for both routine data analysis and academic research at the Faculty of Medical Sciences of the [University of Groningen](https://www.rug.nl), in collaboration with non-profit organisations [Certe Medical Diagnostics and Advice Foundation](https://www.certe.nl) and [University Medical Center Groningen](https://www.umcg.nl).
|
||||
After installing this package, R knows [**~52,000 distinct microbial species**](./reference/microorganisms.html) (updated December 2022) and all [**~600 antimicrobial and antiviral drugs**](./reference/antimicrobials.html) by name and code (including ATC, EARS-Net, ASIARS-Net, PubChem, LOINC and SNOMED CT), and knows all about valid SIR and MIC values. The integral clinical breakpoint guidelines from CLSI and EUCAST are included, even with epidemiological cut-off (ECOFF) values. It supports and can read any data format, including WHONET data. This package works on Windows, macOS and Linux with all versions of R since R-3.0 (April 2013). **It was designed to work in any setting, including those with very limited resources**. It was created for both routine data analysis and academic research at the Faculty of Medical Sciences of the [University of Groningen](https://www.rug.nl), in collaboration with non-profit organisations [Certe Medical Diagnostics and Advice Foundation](https://www.certe.nl) and [University Medical Center Groningen](https://www.umcg.nl).
|
||||
|
||||
##### Used in over 175 countries, available in 20 languages
|
||||
|
||||
@ -475,7 +475,7 @@ If used inside [R Markdown](https://rmarkdown.rstudio.com) or [Quarto](https://q
|
||||
|
||||
```r
|
||||
antibiogram(example_isolates,
|
||||
antibiotics = c(aminoglycosides(), carbapenems()),
|
||||
antimicrobials = c(aminoglycosides(), carbapenems()),
|
||||
formatting_type = 14)
|
||||
```
|
||||
|
||||
@ -492,11 +492,11 @@ antibiogram(example_isolates,
|
||||
| *S. hominis* | | 92% (84-97%) | | | | 85% (74-93%) |
|
||||
| *S. pneumoniae* | 0% (0-3%) | 0% (0-3%) | | 0% (0-3%) | | 0% (0-3%) |
|
||||
|
||||
In combination antibiograms, it is clear that combined antibiotics yield higher empiric coverage:
|
||||
In combination antibiograms, it is clear that combined antimicrobials yield higher empiric coverage:
|
||||
|
||||
```r
|
||||
antibiogram(example_isolates,
|
||||
antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"),
|
||||
antimicrobials = c("TZP", "TZP+TOB", "TZP+GEN"),
|
||||
mo_transform = "gramstain",
|
||||
formatting_type = 14)
|
||||
```
|
||||
@ -510,7 +510,7 @@ Like many other functions in this package, `antibiogram()` comes with support fo
|
||||
|
||||
```r
|
||||
antibiogram(example_isolates,
|
||||
antibiotics = c("cipro", "tobra", "genta"), # any arbitrary name or code will work
|
||||
antimicrobials = c("cipro", "tobra", "genta"), # any arbitrary name or code will work
|
||||
mo_transform = "gramstain",
|
||||
ab_transform = "name",
|
||||
formatting_type = 14,
|
||||
@ -1670,23 +1670,23 @@ THE PART HEREAFTER CONTAINS CONTENTS FROM FILE 'man/antibiogram.Rd':
|
||||
}
|
||||
}
|
||||
\usage{
|
||||
antibiogram(x, antibiotics = where(is.sir), mo_transform = "shortname",
|
||||
antibiogram(x, antimicrobials = 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",
|
||||
ifelse(wisca, 14, 18)), 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())
|
||||
info = interactive(), ...)
|
||||
|
||||
wisca(x, antibiotics = where(is.sir), ab_transform = "name",
|
||||
wisca(x, antimicrobials = 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())
|
||||
info = interactive(), ...)
|
||||
|
||||
retrieve_wisca_parameters(wisca_model, ...)
|
||||
|
||||
@ -1700,7 +1700,7 @@ retrieve_wisca_parameters(wisca_model, ...)
|
||||
\arguments{
|
||||
\item{x}{a \link{data.frame} containing at least a column with microorganisms and columns with antimicrobial results (class 'sir', see \code{\link[=as.sir]{as.sir()}})}
|
||||
|
||||
\item{antibiotics}{vector of any antimicrobial name or code (will be evaluated with \code{\link[=as.ab]{as.ab()}}, column name of \code{x}, or (any combinations of) \link[=antimicrobial_selectors]{antimicrobial selectors} such as \code{\link[=aminoglycosides]{aminoglycosides()}} or \code{\link[=carbapenems]{carbapenems()}}. For combination antibiograms, this can also be set to values separated with \code{"+"}, such as \code{"TZP+TOB"} or \code{"cipro + genta"}, given that columns resembling such antimicrobials exist in \code{x}. See \emph{Examples}.}
|
||||
\item{antimicrobials}{vector of any antimicrobial name or code (will be evaluated with \code{\link[=as.ab]{as.ab()}}, column name of \code{x}, or (any combinations of) \link[=antimicrobial_selectors]{antimicrobial selectors} such as \code{\link[=aminoglycosides]{aminoglycosides()}} or \code{\link[=carbapenems]{carbapenems()}}. For combination antibiograms, this can also be set to values separated with \code{"+"}, such as \code{"TZP+TOB"} or \code{"cipro + genta"}, given that columns resembling such antimicrobials exist in \code{x}. See \emph{Examples}.}
|
||||
|
||||
\item{mo_transform}{a character to transform microorganism input - must be \code{"name"}, \code{"shortname"} (default), \code{"gramstain"}, or one of the column names of the \link{microorganisms} data set: "mo", "fullname", "status", "kingdom", "phylum", "class", "order", "family", "genus", "species", "subspecies", "rank", "ref", "oxygen_tolerance", "source", "lpsn", "lpsn_parent", "lpsn_renamed_to", "mycobank", "mycobank_parent", "mycobank_renamed_to", "gbif", "gbif_parent", "gbif_renamed_to", "prevalence", or "snomed". Can also be \code{NULL} to not transform the input or \code{NA} to consider all microorganisms 'unknown'.}
|
||||
|
||||
@ -1736,10 +1736,10 @@ retrieve_wisca_parameters(wisca_model, ...)
|
||||
|
||||
\item{info}{a \link{logical} to indicate info should be printed - the default is \code{TRUE} only in interactive mode}
|
||||
|
||||
\item{wisca_model}{the outcome of \code{\link[=wisca]{wisca()}} or \code{\link[=antibiogram]{antibiogram(..., wisca = TRUE)}}}
|
||||
|
||||
\item{...}{when used in \link[knitr:kable]{R Markdown or Quarto}: arguments passed on to \code{\link[knitr:kable]{knitr::kable()}} (otherwise, has no use)}
|
||||
|
||||
\item{wisca_model}{the outcome of \code{\link[=wisca]{wisca()}} or \code{\link[=antibiogram]{antibiogram(..., wisca = TRUE)}}}
|
||||
|
||||
\item{object}{an \code{\link[=antibiogram]{antibiogram()}} object}
|
||||
|
||||
\item{italicise}{a \link{logical} to indicate whether the microorganism names in the \link[knitr:kable]{knitr} table should be made italic, using \code{\link[=italicise_taxonomy]{italicise_taxonomy()}}.}
|
||||
@ -1805,7 +1805,7 @@ Case example: Susceptibility of \emph{Pseudomonas aeruginosa} to piperacillin/ta
|
||||
Code example:
|
||||
|
||||
\if{html}{\out{<div class="sourceCode r">}}\preformatted{antibiogram(your_data,
|
||||
antibiotics = "TZP")
|
||||
antimicrobials = "TZP")
|
||||
}\if{html}{\out{</div>}}
|
||||
\item \strong{Combination Antibiogram}
|
||||
|
||||
@ -1814,7 +1814,7 @@ Case example: Additional susceptibility of \emph{Pseudomonas aeruginosa} to TZP
|
||||
Code example:
|
||||
|
||||
\if{html}{\out{<div class="sourceCode r">}}\preformatted{antibiogram(your_data,
|
||||
antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"))
|
||||
antimicrobials = c("TZP", "TZP+TOB", "TZP+GEN"))
|
||||
}\if{html}{\out{</div>}}
|
||||
\item \strong{Syndromic Antibiogram}
|
||||
|
||||
@ -1823,7 +1823,7 @@ Case example: Susceptibility of \emph{Pseudomonas aeruginosa} to TZP among respi
|
||||
Code example:
|
||||
|
||||
\if{html}{\out{<div class="sourceCode r">}}\preformatted{antibiogram(your_data,
|
||||
antibiotics = penicillins(),
|
||||
antimicrobials = penicillins(),
|
||||
syndromic_group = "ward")
|
||||
}\if{html}{\out{</div>}}
|
||||
\item \strong{Weighted-Incidence Syndromic Combination Antibiogram (WISCA)}
|
||||
@ -1833,12 +1833,12 @@ WISCA can be applied to any antibiogram, see the section \emph{Explaining WISCA}
|
||||
Code example:
|
||||
|
||||
\if{html}{\out{<div class="sourceCode r">}}\preformatted{antibiogram(your_data,
|
||||
antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"),
|
||||
antimicrobials = c("TZP", "TZP+TOB", "TZP+GEN"),
|
||||
wisca = TRUE)
|
||||
|
||||
# this is equal to:
|
||||
wisca(your_data,
|
||||
antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"))
|
||||
antimicrobials = c("TZP", "TZP+TOB", "TZP+GEN"))
|
||||
}\if{html}{\out{</div>}}
|
||||
|
||||
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).
|
||||
@ -1854,7 +1854,7 @@ Code example:
|
||||
\if{html}{\out{<div class="sourceCode r">}}\preformatted{library(dplyr)
|
||||
your_data \%>\%
|
||||
group_by(has_sepsis, is_neonate, sex) \%>\%
|
||||
wisca(antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"))
|
||||
wisca(antimicrobials = c("TZP", "TZP+TOB", "TZP+GEN"))
|
||||
}\if{html}{\out{</div>}}
|
||||
}
|
||||
|
||||
@ -1870,12 +1870,12 @@ At admission, no pathogen information is available.
|
||||
\item Code example:
|
||||
|
||||
\if{html}{\out{<div class="sourceCode r">}}\preformatted{antibiogram(your_data,
|
||||
antibiotics = selected_regimens,
|
||||
antimicrobials = selected_regimens,
|
||||
mo_transform = NA) # all pathogens set to `NA`
|
||||
|
||||
# preferred: use WISCA
|
||||
wisca(your_data,
|
||||
antibiotics = selected_regimens)
|
||||
antimicrobials = selected_regimens)
|
||||
}\if{html}{\out{</div>}}
|
||||
}
|
||||
\item \strong{Refinement with Gram Stain Results}
|
||||
@ -1886,7 +1886,7 @@ When a blood culture becomes positive, the Gram stain provides an initial and cr
|
||||
\item Code example:
|
||||
|
||||
\if{html}{\out{<div class="sourceCode r">}}\preformatted{antibiogram(your_data,
|
||||
antibiotics = selected_regimens,
|
||||
antimicrobials = selected_regimens,
|
||||
mo_transform = "gramstain") # all pathogens set to Gram-pos/Gram-neg
|
||||
}\if{html}{\out{</div>}}
|
||||
}
|
||||
@ -1898,7 +1898,7 @@ After cultivation of the pathogen, full pathogen identification allows precise t
|
||||
\item Code example:
|
||||
|
||||
\if{html}{\out{<div class="sourceCode r">}}\preformatted{antibiogram(your_data,
|
||||
antibiotics = selected_regimens,
|
||||
antimicrobials = selected_regimens,
|
||||
mo_transform = "shortname") # all pathogens set to 'G. species', e.g., E. coli
|
||||
}\if{html}{\out{</div>}}
|
||||
}
|
||||
@ -1989,17 +1989,17 @@ example_isolates
|
||||
# Traditional antibiogram ----------------------------------------------
|
||||
|
||||
antibiogram(example_isolates,
|
||||
antibiotics = c(aminoglycosides(), carbapenems())
|
||||
antimicrobials = c(aminoglycosides(), carbapenems())
|
||||
)
|
||||
|
||||
antibiogram(example_isolates,
|
||||
antibiotics = aminoglycosides(),
|
||||
antimicrobials = aminoglycosides(),
|
||||
ab_transform = "atc",
|
||||
mo_transform = "gramstain"
|
||||
)
|
||||
|
||||
antibiogram(example_isolates,
|
||||
antibiotics = carbapenems(),
|
||||
antimicrobials = carbapenems(),
|
||||
ab_transform = "name",
|
||||
mo_transform = "name"
|
||||
)
|
||||
@ -2007,15 +2007,15 @@ antibiogram(example_isolates,
|
||||
|
||||
# Combined antibiogram -------------------------------------------------
|
||||
|
||||
# combined antibiotics yield higher empiric coverage
|
||||
# combined antimicrobials yield higher empiric coverage
|
||||
antibiogram(example_isolates,
|
||||
antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"),
|
||||
antimicrobials = c("TZP", "TZP+TOB", "TZP+GEN"),
|
||||
mo_transform = "gramstain"
|
||||
)
|
||||
|
||||
# names of antibiotics do not need to resemble columns exactly:
|
||||
# names of antimicrobials do not need to resemble columns exactly:
|
||||
antibiogram(example_isolates,
|
||||
antibiotics = c("Cipro", "cipro + genta"),
|
||||
antimicrobials = c("Cipro", "cipro + genta"),
|
||||
mo_transform = "gramstain",
|
||||
ab_transform = "name",
|
||||
sep = " & "
|
||||
@ -2026,7 +2026,7 @@ antibiogram(example_isolates,
|
||||
|
||||
# the data set could contain a filter for e.g. respiratory specimens
|
||||
antibiogram(example_isolates,
|
||||
antibiotics = c(aminoglycosides(), carbapenems()),
|
||||
antimicrobials = c(aminoglycosides(), carbapenems()),
|
||||
syndromic_group = "ward"
|
||||
)
|
||||
|
||||
@ -2036,7 +2036,7 @@ 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(),
|
||||
antimicrobials = aminoglycosides(),
|
||||
ab_transform = "name",
|
||||
syndromic_group = ifelse(ex1$ward == "ICU",
|
||||
"UCI", "No UCI"
|
||||
@ -2049,7 +2049,7 @@ antibiogram(ex1,
|
||||
|
||||
# WISCA are not stratified by species, but rather on syndromes
|
||||
antibiogram(example_isolates,
|
||||
antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"),
|
||||
antimicrobials = c("TZP", "TZP+TOB", "TZP+GEN"),
|
||||
syndromic_group = "ward",
|
||||
wisca = TRUE
|
||||
)
|
||||
@ -2058,7 +2058,7 @@ antibiogram(example_isolates,
|
||||
# Print the output for R Markdown / Quarto -----------------------------
|
||||
|
||||
ureido <- antibiogram(example_isolates,
|
||||
antibiotics = ureidopenicillins(),
|
||||
antimicrobials = ureidopenicillins(),
|
||||
syndromic_group = "ward",
|
||||
wisca = TRUE
|
||||
)
|
||||
@ -2073,11 +2073,11 @@ if (requireNamespace("knitr")) {
|
||||
# Generate plots with ggplot2 or base R --------------------------------
|
||||
|
||||
ab1 <- antibiogram(example_isolates,
|
||||
antibiotics = c("AMC", "CIP", "TZP", "TZP+TOB"),
|
||||
antimicrobials = c("AMC", "CIP", "TZP", "TZP+TOB"),
|
||||
mo_transform = "gramstain"
|
||||
)
|
||||
ab2 <- antibiogram(example_isolates,
|
||||
antibiotics = c("AMC", "CIP", "TZP", "TZP+TOB"),
|
||||
antimicrobials = c("AMC", "CIP", "TZP", "TZP+TOB"),
|
||||
mo_transform = "gramstain",
|
||||
syndromic_group = "ward"
|
||||
)
|
||||
@ -4287,7 +4287,7 @@ This AMR package honours this insight. Use \code{\link[=susceptibility]{suscepti
|
||||
|
||||
\section{Combination Therapy}{
|
||||
|
||||
When using more than one variable for \code{...} (= combination therapy), use \code{only_all_tested} to only count isolates that are tested for all antibiotics/variables that you test them for. See this example for two antimicrobials, Drug A and Drug B, about how \code{\link[=susceptibility]{susceptibility()}} works to calculate the \%SI:
|
||||
When using more than one variable for \code{...} (= combination therapy), use \code{only_all_tested} to only count isolates that are tested for all antimicrobials/variables that you test them for. See this example for two antimicrobials, Drug A and Drug B, about how \code{\link[=susceptibility]{susceptibility()}} works to calculate the \%SI:
|
||||
|
||||
\if{html}{\out{<div class="sourceCode">}}\preformatted{--------------------------------------------------------------------
|
||||
only_all_tested = FALSE only_all_tested = TRUE
|
||||
@ -7852,7 +7852,7 @@ The function \code{\link[=proportion_df]{proportion_df()}} takes any variable fr
|
||||
}
|
||||
\section{Combination Therapy}{
|
||||
|
||||
When using more than one variable for \code{...} (= combination therapy), use \code{only_all_tested} to only count isolates that are tested for all antibiotics/variables that you test them for. See this example for two antimicrobials, Drug A and Drug B, about how \code{\link[=susceptibility]{susceptibility()}} works to calculate the \%SI:
|
||||
When using more than one variable for \code{...} (= combination therapy), use \code{only_all_tested} to only count isolates that are tested for all antimicrobials/variables that you test them for. See this example for two antimicrobials, Drug A and Drug B, about how \code{\link[=susceptibility]{susceptibility()}} works to calculate the \%SI:
|
||||
|
||||
\if{html}{\out{<div class="sourceCode">}}\preformatted{--------------------------------------------------------------------
|
||||
only_all_tested = FALSE only_all_tested = TRUE
|
||||
10
index.md
10
index.md
@ -26,7 +26,7 @@ The `AMR` package is a [free and open-source](#copyright) R package with [zero d
|
||||
|
||||
This work was published in the Journal of Statistical Software (Volume 104(3); [DOI 10.18637/jss.v104.i03](https://doi.org/10.18637/jss.v104.i03)) and formed the basis of two PhD theses ([DOI 10.33612/diss.177417131](https://doi.org/10.33612/diss.177417131) and [DOI 10.33612/diss.192486375](https://doi.org/10.33612/diss.192486375)).
|
||||
|
||||
After installing this package, R knows [**~52,000 distinct microbial species**](./reference/microorganisms.html) (updated December 2022) and all [**~600 antimicrobial and antiviral drugs**](./reference/antibiotics.html) by name and code (including ATC, EARS-Net, ASIARS-Net, PubChem, LOINC and SNOMED CT), and knows all about valid SIR and MIC values. The integral clinical breakpoint guidelines from CLSI and EUCAST are included, even with epidemiological cut-off (ECOFF) values. It supports and can read any data format, including WHONET data. This package works on Windows, macOS and Linux with all versions of R since R-3.0 (April 2013). **It was designed to work in any setting, including those with very limited resources**. It was created for both routine data analysis and academic research at the Faculty of Medical Sciences of the [University of Groningen](https://www.rug.nl), in collaboration with non-profit organisations [Certe Medical Diagnostics and Advice Foundation](https://www.certe.nl) and [University Medical Center Groningen](https://www.umcg.nl).
|
||||
After installing this package, R knows [**~52,000 distinct microbial species**](./reference/microorganisms.html) (updated December 2022) and all [**~600 antimicrobial and antiviral drugs**](./reference/antimicrobials.html) by name and code (including ATC, EARS-Net, ASIARS-Net, PubChem, LOINC and SNOMED CT), and knows all about valid SIR and MIC values. The integral clinical breakpoint guidelines from CLSI and EUCAST are included, even with epidemiological cut-off (ECOFF) values. It supports and can read any data format, including WHONET data. This package works on Windows, macOS and Linux with all versions of R since R-3.0 (April 2013). **It was designed to work in any setting, including those with very limited resources**. It was created for both routine data analysis and academic research at the Faculty of Medical Sciences of the [University of Groningen](https://www.rug.nl), in collaboration with non-profit organisations [Certe Medical Diagnostics and Advice Foundation](https://www.certe.nl) and [University Medical Center Groningen](https://www.umcg.nl).
|
||||
|
||||
##### Used in over 175 countries, available in 20 languages
|
||||
|
||||
@ -81,7 +81,7 @@ If used inside [R Markdown](https://rmarkdown.rstudio.com) or [Quarto](https://q
|
||||
|
||||
```r
|
||||
antibiogram(example_isolates,
|
||||
antibiotics = c(aminoglycosides(), carbapenems()),
|
||||
antimicrobials = c(aminoglycosides(), carbapenems()),
|
||||
formatting_type = 14)
|
||||
```
|
||||
|
||||
@ -98,11 +98,11 @@ antibiogram(example_isolates,
|
||||
| *S. hominis* | | 92% (84-97%) | | | | 85% (74-93%) |
|
||||
| *S. pneumoniae* | 0% (0-3%) | 0% (0-3%) | | 0% (0-3%) | | 0% (0-3%) |
|
||||
|
||||
In combination antibiograms, it is clear that combined antibiotics yield higher empiric coverage:
|
||||
In combination antibiograms, it is clear that combined antimicrobials yield higher empiric coverage:
|
||||
|
||||
```r
|
||||
antibiogram(example_isolates,
|
||||
antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"),
|
||||
antimicrobials = c("TZP", "TZP+TOB", "TZP+GEN"),
|
||||
mo_transform = "gramstain",
|
||||
formatting_type = 14)
|
||||
```
|
||||
@ -116,7 +116,7 @@ Like many other functions in this package, `antibiogram()` comes with support fo
|
||||
|
||||
```r
|
||||
antibiogram(example_isolates,
|
||||
antibiotics = c("cipro", "tobra", "genta"), # any arbitrary name or code will work
|
||||
antimicrobials = c("cipro", "tobra", "genta"), # any arbitrary name or code will work
|
||||
mo_transform = "gramstain",
|
||||
ab_transform = "name",
|
||||
formatting_type = 14,
|
||||
|
||||
@ -18,23 +18,23 @@
|
||||
}
|
||||
}
|
||||
\usage{
|
||||
antibiogram(x, antibiotics = where(is.sir), mo_transform = "shortname",
|
||||
antibiogram(x, antimicrobials = 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",
|
||||
ifelse(wisca, 14, 18)), 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())
|
||||
info = interactive(), ...)
|
||||
|
||||
wisca(x, antibiotics = where(is.sir), ab_transform = "name",
|
||||
wisca(x, antimicrobials = 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())
|
||||
info = interactive(), ...)
|
||||
|
||||
retrieve_wisca_parameters(wisca_model, ...)
|
||||
|
||||
@ -48,7 +48,7 @@ retrieve_wisca_parameters(wisca_model, ...)
|
||||
\arguments{
|
||||
\item{x}{a \link{data.frame} containing at least a column with microorganisms and columns with antimicrobial results (class 'sir', see \code{\link[=as.sir]{as.sir()}})}
|
||||
|
||||
\item{antibiotics}{vector of any antimicrobial name or code (will be evaluated with \code{\link[=as.ab]{as.ab()}}, column name of \code{x}, or (any combinations of) \link[=antimicrobial_selectors]{antimicrobial selectors} such as \code{\link[=aminoglycosides]{aminoglycosides()}} or \code{\link[=carbapenems]{carbapenems()}}. For combination antibiograms, this can also be set to values separated with \code{"+"}, such as \code{"TZP+TOB"} or \code{"cipro + genta"}, given that columns resembling such antimicrobials exist in \code{x}. See \emph{Examples}.}
|
||||
\item{antimicrobials}{vector of any antimicrobial name or code (will be evaluated with \code{\link[=as.ab]{as.ab()}}, column name of \code{x}, or (any combinations of) \link[=antimicrobial_selectors]{antimicrobial selectors} such as \code{\link[=aminoglycosides]{aminoglycosides()}} or \code{\link[=carbapenems]{carbapenems()}}. For combination antibiograms, this can also be set to values separated with \code{"+"}, such as \code{"TZP+TOB"} or \code{"cipro + genta"}, given that columns resembling such antimicrobials exist in \code{x}. See \emph{Examples}.}
|
||||
|
||||
\item{mo_transform}{a character to transform microorganism input - must be \code{"name"}, \code{"shortname"} (default), \code{"gramstain"}, or one of the column names of the \link{microorganisms} data set: "mo", "fullname", "status", "kingdom", "phylum", "class", "order", "family", "genus", "species", "subspecies", "rank", "ref", "oxygen_tolerance", "source", "lpsn", "lpsn_parent", "lpsn_renamed_to", "mycobank", "mycobank_parent", "mycobank_renamed_to", "gbif", "gbif_parent", "gbif_renamed_to", "prevalence", or "snomed". Can also be \code{NULL} to not transform the input or \code{NA} to consider all microorganisms 'unknown'.}
|
||||
|
||||
@ -84,10 +84,10 @@ retrieve_wisca_parameters(wisca_model, ...)
|
||||
|
||||
\item{info}{a \link{logical} to indicate info should be printed - the default is \code{TRUE} only in interactive mode}
|
||||
|
||||
\item{wisca_model}{the outcome of \code{\link[=wisca]{wisca()}} or \code{\link[=antibiogram]{antibiogram(..., wisca = TRUE)}}}
|
||||
|
||||
\item{...}{when used in \link[knitr:kable]{R Markdown or Quarto}: arguments passed on to \code{\link[knitr:kable]{knitr::kable()}} (otherwise, has no use)}
|
||||
|
||||
\item{wisca_model}{the outcome of \code{\link[=wisca]{wisca()}} or \code{\link[=antibiogram]{antibiogram(..., wisca = TRUE)}}}
|
||||
|
||||
\item{object}{an \code{\link[=antibiogram]{antibiogram()}} object}
|
||||
|
||||
\item{italicise}{a \link{logical} to indicate whether the microorganism names in the \link[knitr:kable]{knitr} table should be made italic, using \code{\link[=italicise_taxonomy]{italicise_taxonomy()}}.}
|
||||
@ -153,7 +153,7 @@ Case example: Susceptibility of \emph{Pseudomonas aeruginosa} to piperacillin/ta
|
||||
Code example:
|
||||
|
||||
\if{html}{\out{<div class="sourceCode r">}}\preformatted{antibiogram(your_data,
|
||||
antibiotics = "TZP")
|
||||
antimicrobials = "TZP")
|
||||
}\if{html}{\out{</div>}}
|
||||
\item \strong{Combination Antibiogram}
|
||||
|
||||
@ -162,7 +162,7 @@ Case example: Additional susceptibility of \emph{Pseudomonas aeruginosa} to TZP
|
||||
Code example:
|
||||
|
||||
\if{html}{\out{<div class="sourceCode r">}}\preformatted{antibiogram(your_data,
|
||||
antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"))
|
||||
antimicrobials = c("TZP", "TZP+TOB", "TZP+GEN"))
|
||||
}\if{html}{\out{</div>}}
|
||||
\item \strong{Syndromic Antibiogram}
|
||||
|
||||
@ -171,7 +171,7 @@ Case example: Susceptibility of \emph{Pseudomonas aeruginosa} to TZP among respi
|
||||
Code example:
|
||||
|
||||
\if{html}{\out{<div class="sourceCode r">}}\preformatted{antibiogram(your_data,
|
||||
antibiotics = penicillins(),
|
||||
antimicrobials = penicillins(),
|
||||
syndromic_group = "ward")
|
||||
}\if{html}{\out{</div>}}
|
||||
\item \strong{Weighted-Incidence Syndromic Combination Antibiogram (WISCA)}
|
||||
@ -181,12 +181,12 @@ WISCA can be applied to any antibiogram, see the section \emph{Explaining WISCA}
|
||||
Code example:
|
||||
|
||||
\if{html}{\out{<div class="sourceCode r">}}\preformatted{antibiogram(your_data,
|
||||
antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"),
|
||||
antimicrobials = c("TZP", "TZP+TOB", "TZP+GEN"),
|
||||
wisca = TRUE)
|
||||
|
||||
# this is equal to:
|
||||
wisca(your_data,
|
||||
antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"))
|
||||
antimicrobials = c("TZP", "TZP+TOB", "TZP+GEN"))
|
||||
}\if{html}{\out{</div>}}
|
||||
|
||||
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).
|
||||
@ -202,7 +202,7 @@ Code example:
|
||||
\if{html}{\out{<div class="sourceCode r">}}\preformatted{library(dplyr)
|
||||
your_data \%>\%
|
||||
group_by(has_sepsis, is_neonate, sex) \%>\%
|
||||
wisca(antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"))
|
||||
wisca(antimicrobials = c("TZP", "TZP+TOB", "TZP+GEN"))
|
||||
}\if{html}{\out{</div>}}
|
||||
}
|
||||
|
||||
@ -218,12 +218,12 @@ At admission, no pathogen information is available.
|
||||
\item Code example:
|
||||
|
||||
\if{html}{\out{<div class="sourceCode r">}}\preformatted{antibiogram(your_data,
|
||||
antibiotics = selected_regimens,
|
||||
antimicrobials = selected_regimens,
|
||||
mo_transform = NA) # all pathogens set to `NA`
|
||||
|
||||
# preferred: use WISCA
|
||||
wisca(your_data,
|
||||
antibiotics = selected_regimens)
|
||||
antimicrobials = selected_regimens)
|
||||
}\if{html}{\out{</div>}}
|
||||
}
|
||||
\item \strong{Refinement with Gram Stain Results}
|
||||
@ -234,7 +234,7 @@ When a blood culture becomes positive, the Gram stain provides an initial and cr
|
||||
\item Code example:
|
||||
|
||||
\if{html}{\out{<div class="sourceCode r">}}\preformatted{antibiogram(your_data,
|
||||
antibiotics = selected_regimens,
|
||||
antimicrobials = selected_regimens,
|
||||
mo_transform = "gramstain") # all pathogens set to Gram-pos/Gram-neg
|
||||
}\if{html}{\out{</div>}}
|
||||
}
|
||||
@ -246,7 +246,7 @@ After cultivation of the pathogen, full pathogen identification allows precise t
|
||||
\item Code example:
|
||||
|
||||
\if{html}{\out{<div class="sourceCode r">}}\preformatted{antibiogram(your_data,
|
||||
antibiotics = selected_regimens,
|
||||
antimicrobials = selected_regimens,
|
||||
mo_transform = "shortname") # all pathogens set to 'G. species', e.g., E. coli
|
||||
}\if{html}{\out{</div>}}
|
||||
}
|
||||
@ -337,17 +337,17 @@ example_isolates
|
||||
# Traditional antibiogram ----------------------------------------------
|
||||
|
||||
antibiogram(example_isolates,
|
||||
antibiotics = c(aminoglycosides(), carbapenems())
|
||||
antimicrobials = c(aminoglycosides(), carbapenems())
|
||||
)
|
||||
|
||||
antibiogram(example_isolates,
|
||||
antibiotics = aminoglycosides(),
|
||||
antimicrobials = aminoglycosides(),
|
||||
ab_transform = "atc",
|
||||
mo_transform = "gramstain"
|
||||
)
|
||||
|
||||
antibiogram(example_isolates,
|
||||
antibiotics = carbapenems(),
|
||||
antimicrobials = carbapenems(),
|
||||
ab_transform = "name",
|
||||
mo_transform = "name"
|
||||
)
|
||||
@ -355,15 +355,15 @@ antibiogram(example_isolates,
|
||||
|
||||
# Combined antibiogram -------------------------------------------------
|
||||
|
||||
# combined antibiotics yield higher empiric coverage
|
||||
# combined antimicrobials yield higher empiric coverage
|
||||
antibiogram(example_isolates,
|
||||
antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"),
|
||||
antimicrobials = c("TZP", "TZP+TOB", "TZP+GEN"),
|
||||
mo_transform = "gramstain"
|
||||
)
|
||||
|
||||
# names of antibiotics do not need to resemble columns exactly:
|
||||
# names of antimicrobials do not need to resemble columns exactly:
|
||||
antibiogram(example_isolates,
|
||||
antibiotics = c("Cipro", "cipro + genta"),
|
||||
antimicrobials = c("Cipro", "cipro + genta"),
|
||||
mo_transform = "gramstain",
|
||||
ab_transform = "name",
|
||||
sep = " & "
|
||||
@ -374,7 +374,7 @@ antibiogram(example_isolates,
|
||||
|
||||
# the data set could contain a filter for e.g. respiratory specimens
|
||||
antibiogram(example_isolates,
|
||||
antibiotics = c(aminoglycosides(), carbapenems()),
|
||||
antimicrobials = c(aminoglycosides(), carbapenems()),
|
||||
syndromic_group = "ward"
|
||||
)
|
||||
|
||||
@ -384,7 +384,7 @@ 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(),
|
||||
antimicrobials = aminoglycosides(),
|
||||
ab_transform = "name",
|
||||
syndromic_group = ifelse(ex1$ward == "ICU",
|
||||
"UCI", "No UCI"
|
||||
@ -397,7 +397,7 @@ antibiogram(ex1,
|
||||
|
||||
# WISCA are not stratified by species, but rather on syndromes
|
||||
antibiogram(example_isolates,
|
||||
antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"),
|
||||
antimicrobials = c("TZP", "TZP+TOB", "TZP+GEN"),
|
||||
syndromic_group = "ward",
|
||||
wisca = TRUE
|
||||
)
|
||||
@ -406,7 +406,7 @@ antibiogram(example_isolates,
|
||||
# Print the output for R Markdown / Quarto -----------------------------
|
||||
|
||||
ureido <- antibiogram(example_isolates,
|
||||
antibiotics = ureidopenicillins(),
|
||||
antimicrobials = ureidopenicillins(),
|
||||
syndromic_group = "ward",
|
||||
wisca = TRUE
|
||||
)
|
||||
@ -421,11 +421,11 @@ if (requireNamespace("knitr")) {
|
||||
# Generate plots with ggplot2 or base R --------------------------------
|
||||
|
||||
ab1 <- antibiogram(example_isolates,
|
||||
antibiotics = c("AMC", "CIP", "TZP", "TZP+TOB"),
|
||||
antimicrobials = c("AMC", "CIP", "TZP", "TZP+TOB"),
|
||||
mo_transform = "gramstain"
|
||||
)
|
||||
ab2 <- antibiogram(example_isolates,
|
||||
antibiotics = c("AMC", "CIP", "TZP", "TZP+TOB"),
|
||||
antimicrobials = c("AMC", "CIP", "TZP", "TZP+TOB"),
|
||||
mo_transform = "gramstain",
|
||||
syndromic_group = "ward"
|
||||
)
|
||||
|
||||
@ -85,7 +85,7 @@ This AMR package honours this insight. Use \code{\link[=susceptibility]{suscepti
|
||||
|
||||
\section{Combination Therapy}{
|
||||
|
||||
When using more than one variable for \code{...} (= combination therapy), use \code{only_all_tested} to only count isolates that are tested for all antibiotics/variables that you test them for. See this example for two antimicrobials, Drug A and Drug B, about how \code{\link[=susceptibility]{susceptibility()}} works to calculate the \%SI:
|
||||
When using more than one variable for \code{...} (= combination therapy), use \code{only_all_tested} to only count isolates that are tested for all antimicrobials/variables that you test them for. See this example for two antimicrobials, Drug A and Drug B, about how \code{\link[=susceptibility]{susceptibility()}} works to calculate the \%SI:
|
||||
|
||||
\if{html}{\out{<div class="sourceCode">}}\preformatted{--------------------------------------------------------------------
|
||||
only_all_tested = FALSE only_all_tested = TRUE
|
||||
|
||||
@ -99,7 +99,7 @@ The function \code{\link[=proportion_df]{proportion_df()}} takes any variable fr
|
||||
}
|
||||
\section{Combination Therapy}{
|
||||
|
||||
When using more than one variable for \code{...} (= combination therapy), use \code{only_all_tested} to only count isolates that are tested for all antibiotics/variables that you test them for. See this example for two antimicrobials, Drug A and Drug B, about how \code{\link[=susceptibility]{susceptibility()}} works to calculate the \%SI:
|
||||
When using more than one variable for \code{...} (= combination therapy), use \code{only_all_tested} to only count isolates that are tested for all antimicrobials/variables that you test them for. See this example for two antimicrobials, Drug A and Drug B, about how \code{\link[=susceptibility]{susceptibility()}} works to calculate the \%SI:
|
||||
|
||||
\if{html}{\out{<div class="sourceCode">}}\preformatted{--------------------------------------------------------------------
|
||||
only_all_tested = FALSE only_all_tested = TRUE
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user