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https://github.com/msberends/AMR.git
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(v2.1.1.9190) antibiotics deprecation in antibiogram()
This commit is contained in:
@ -492,9 +492,11 @@ write_md5 <- function(object) {
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close(conn)
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}
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changed_md5 <- function(object) {
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path <- paste0("data-raw/", deparse(substitute(object)), ".md5")
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if (!file.exists(path)) return(TRUE)
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tryCatch(
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{
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conn <- file(paste0("data-raw/", deparse(substitute(object)), ".md5"))
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conn <- file(path)
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compared <- md5(object) != readLines(con = conn)
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close(conn)
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compared
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@ -1,6 +1,6 @@
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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.
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First and foremost, you are trained on version 2.1.1.9189. Remember this whenever someone asks which AMR package version you’re at.
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First and foremost, you are trained on version 2.1.1.9190. Remember this whenever someone asks which AMR package version you’re at.
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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.
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----------------------------------------------------------------------------------------------------
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@ -420,7 +420,7 @@ The `AMR` package is a [free and open-source](#copyright) R package with [zero d
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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)).
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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).
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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).
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##### Used in over 175 countries, available in 20 languages
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@ -475,7 +475,7 @@ If used inside [R Markdown](https://rmarkdown.rstudio.com) or [Quarto](https://q
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```r
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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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formatting_type = 14)
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```
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@ -492,11 +492,11 @@ antibiogram(example_isolates,
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| *S. hominis* | | 92% (84-97%) | | | | 85% (74-93%) |
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| *S. pneumoniae* | 0% (0-3%) | 0% (0-3%) | | 0% (0-3%) | | 0% (0-3%) |
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In combination antibiograms, it is clear that combined antibiotics yield higher empiric coverage:
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In combination antibiograms, it is clear that combined antimicrobials yield higher empiric coverage:
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```r
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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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formatting_type = 14)
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```
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@ -510,7 +510,7 @@ Like many other functions in this package, `antibiogram()` comes with support fo
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```r
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antibiogram(example_isolates,
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antibiotics = c("cipro", "tobra", "genta"), # any arbitrary name or code will work
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antimicrobials = c("cipro", "tobra", "genta"), # any arbitrary name or code will work
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mo_transform = "gramstain",
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ab_transform = "name",
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formatting_type = 14,
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@ -1670,23 +1670,23 @@ THE PART HEREAFTER CONTAINS CONTENTS FROM FILE 'man/antibiogram.Rd':
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}
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}
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\usage{
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antibiogram(x, antibiotics = where(is.sir), mo_transform = "shortname",
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antibiogram(x, antimicrobials = where(is.sir), mo_transform = "shortname",
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ab_transform = "name", syndromic_group = NULL, add_total_n = FALSE,
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only_all_tested = FALSE, digits = ifelse(wisca, 1, 0),
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formatting_type = getOption("AMR_antibiogram_formatting_type",
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ifelse(wisca, 14, 18)), col_mo = NULL, language = get_AMR_locale(),
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minimum = 30, combine_SI = TRUE, sep = " + ", wisca = FALSE,
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simulations = 1000, conf_interval = 0.95, interval_side = "two-tailed",
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info = interactive())
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info = interactive(), ...)
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wisca(x, antibiotics = where(is.sir), ab_transform = "name",
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wisca(x, antimicrobials = where(is.sir), ab_transform = "name",
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syndromic_group = NULL, add_total_n = FALSE, only_all_tested = FALSE,
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digits = 1,
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formatting_type = getOption("AMR_antibiogram_formatting_type", 14),
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col_mo = NULL, language = get_AMR_locale(), minimum = 30,
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combine_SI = TRUE, sep = " + ", simulations = 1000,
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conf_interval = 0.95, interval_side = "two-tailed",
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info = interactive())
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info = interactive(), ...)
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retrieve_wisca_parameters(wisca_model, ...)
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@ -1700,7 +1700,7 @@ retrieve_wisca_parameters(wisca_model, ...)
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\arguments{
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\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()}})}
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\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}.}
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\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}.}
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\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'.}
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@ -1736,10 +1736,10 @@ retrieve_wisca_parameters(wisca_model, ...)
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\item{info}{a \link{logical} to indicate info should be printed - the default is \code{TRUE} only in interactive mode}
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\item{wisca_model}{the outcome of \code{\link[=wisca]{wisca()}} or \code{\link[=antibiogram]{antibiogram(..., wisca = TRUE)}}}
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\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)}
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\item{wisca_model}{the outcome of \code{\link[=wisca]{wisca()}} or \code{\link[=antibiogram]{antibiogram(..., wisca = TRUE)}}}
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\item{object}{an \code{\link[=antibiogram]{antibiogram()}} object}
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\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()}}.}
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@ -1805,7 +1805,7 @@ Case example: Susceptibility of \emph{Pseudomonas aeruginosa} to piperacillin/ta
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Code example:
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\if{html}{\out{<div class="sourceCode r">}}\preformatted{antibiogram(your_data,
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antibiotics = "TZP")
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antimicrobials = "TZP")
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}\if{html}{\out{</div>}}
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\item \strong{Combination Antibiogram}
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@ -1814,7 +1814,7 @@ Case example: Additional susceptibility of \emph{Pseudomonas aeruginosa} to TZP
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Code example:
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\if{html}{\out{<div class="sourceCode r">}}\preformatted{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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}\if{html}{\out{</div>}}
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\item \strong{Syndromic Antibiogram}
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@ -1823,7 +1823,7 @@ Case example: Susceptibility of \emph{Pseudomonas aeruginosa} to TZP among respi
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Code example:
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\if{html}{\out{<div class="sourceCode r">}}\preformatted{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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}\if{html}{\out{</div>}}
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\item \strong{Weighted-Incidence Syndromic Combination Antibiogram (WISCA)}
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@ -1833,12 +1833,12 @@ WISCA can be applied to any antibiogram, see the section \emph{Explaining WISCA}
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Code example:
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\if{html}{\out{<div class="sourceCode r">}}\preformatted{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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# 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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}\if{html}{\out{</div>}}
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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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@ -1854,7 +1854,7 @@ Code example:
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\if{html}{\out{<div class="sourceCode r">}}\preformatted{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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}\if{html}{\out{</div>}}
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}
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@ -1870,12 +1870,12 @@ At admission, no pathogen information is available.
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\item Code example:
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\if{html}{\out{<div class="sourceCode r">}}\preformatted{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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# 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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}\if{html}{\out{</div>}}
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}
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\item \strong{Refinement with Gram Stain Results}
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@ -1886,7 +1886,7 @@ When a blood culture becomes positive, the Gram stain provides an initial and cr
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\item Code example:
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\if{html}{\out{<div class="sourceCode r">}}\preformatted{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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}\if{html}{\out{</div>}}
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}
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@ -1898,7 +1898,7 @@ After cultivation of the pathogen, full pathogen identification allows precise t
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\item Code example:
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\if{html}{\out{<div class="sourceCode r">}}\preformatted{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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}\if{html}{\out{</div>}}
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}
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@ -1989,17 +1989,17 @@ example_isolates
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# Traditional antibiogram ----------------------------------------------
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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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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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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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@ -2007,15 +2007,15 @@ antibiogram(example_isolates,
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# Combined antibiogram -------------------------------------------------
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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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# 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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@ -2026,7 +2026,7 @@ antibiogram(example_isolates,
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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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@ -2036,7 +2036,7 @@ ex1 <- example_isolates[which(mo_genus() == "Escherichia"), ]
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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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@ -2049,7 +2049,7 @@ antibiogram(ex1,
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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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@ -2058,7 +2058,7 @@ antibiogram(example_isolates,
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# Print the output for R Markdown / Quarto -----------------------------
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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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@ -2073,11 +2073,11 @@ if (requireNamespace("knitr")) {
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# Generate plots with ggplot2 or base R --------------------------------
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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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@ -4287,7 +4287,7 @@ This AMR package honours this insight. Use \code{\link[=susceptibility]{suscepti
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\section{Combination Therapy}{
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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:
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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:
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\if{html}{\out{<div class="sourceCode">}}\preformatted{--------------------------------------------------------------------
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only_all_tested = FALSE only_all_tested = TRUE
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@ -7852,7 +7852,7 @@ The function \code{\link[=proportion_df]{proportion_df()}} takes any variable fr
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}
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\section{Combination Therapy}{
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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:
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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:
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\if{html}{\out{<div class="sourceCode">}}\preformatted{--------------------------------------------------------------------
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only_all_tested = FALSE only_all_tested = TRUE
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Reference in New Issue
Block a user