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(v2.1.1.9256) unit tests
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Package: AMR
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Version: 2.1.1.9255
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Version: 2.1.1.9256
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Date: 2025-04-26
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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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2
NEWS.md
2
NEWS.md
@ -1,4 +1,4 @@
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# AMR 2.1.1.9255
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# AMR 2.1.1.9256
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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://amr-for-r.org/#get-this-package).)*
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1
R/ab.R
1
R/ab.R
@ -72,7 +72,6 @@
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#' as.ab("ERY")
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#' as.ab("eritromicine") # spelled wrong, yet works
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#' as.ab("Erythrocin") # trade name
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#' as.ab("Romycin") # trade name
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#'
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#' # spelling from different languages and dyslexia are no problem
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#' ab_atc("ceftriaxon")
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11
R/sir.R
11
R/sir.R
@ -680,8 +680,8 @@ as.sir.disk <- function(x,
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}
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#' @rdname as.sir
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#' @param parallel A [logical] to indicate if parallel computing must be used, defaults to `FALSE`.
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#' @param max_cores Maximum number of cores to use if `parallel = TRUE`. Use a negative value to subtract that number from the available number of cores, e.g. a value of `-2` on an 8-core machine means that 6 cores will be used. Defaults to `-1`. The available number of cores are detected using [parallelly::availableCores()] if that package is installed, and base \R's [parallel::detectCores()] otherwise.
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#' @param parallel A [logical] to indicate if parallel computing must be used, defaults to `FALSE`. This requires no additional packages, as the used `parallel` package is part of base \R.
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#' @param max_cores Maximum number of cores to use if `parallel = TRUE`. Use a negative value to subtract that number from the available number of cores, e.g. a value of `-2` on an 8-core machine means that at most 6 cores will be used. Defaults to `-1`. There will never be used more cores than variables to analyse. The available number of cores are detected using [parallelly::availableCores()] if that package is installed, and base \R's [parallel::detectCores()] otherwise.
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#' @export
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as.sir.data.frame <- function(x,
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...,
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@ -853,6 +853,7 @@ as.sir.data.frame <- function(x,
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# set up parallel computing
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n_cores <- get_n_cores(max_cores = max_cores)
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n_cores <- min(n_cores, length(ab_cols)) # never more cores than variables required
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run_as_sir_column <- function(i) {
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ab_col <- ab_cols[i]
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@ -952,7 +953,7 @@ as.sir.data.frame <- function(x,
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if (isTRUE(parallel) && n_cores > 1 && length(ab_cols) > 1) {
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if (isTRUE(info)) {
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message()
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message_("Running SIR interpretation in parallel mode on ", nr2char(length(ab_cols)), " columns, using ", n_cores, " out of ", get_n_cores(Inf), " cores...", as_note = FALSE, appendLF = FALSE, add_fn = font_red)
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message_("Running in parallel mode using ", n_cores, " out of ", get_n_cores(Inf), " cores, on columns ", vector_and(font_bold(ab_cols, collapse = NULL), quotes = "'", sort = FALSE), "...", as_note = FALSE, appendLF = FALSE, add_fn = font_red)
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}
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if (.Platform$OS.type == "windows") {
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cl <- parallel::makeCluster(n_cores, type = "PSOCK")
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@ -976,10 +977,10 @@ as.sir.data.frame <- function(x,
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}
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} else {
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# sequential mode (non-parallel)
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if (n_cores > 1 && isTRUE(info) && (NROW(x) > 2500 || length(ab_cols) >= 5)) {
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if (isTRUE(info) && n_cores > 1 && NROW(x) * NCOL(x) > 10000) {
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# give a note that parallel mode might be better
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message()
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message_("Running SIR interpretation in sequential mode. Consider setting `parallel = TRUE` to speed up processing on multiple cores.\n", add_fn = font_red)
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message_("Running in sequential mode. Consider setting `parallel = TRUE` to speed up processing on multiple cores.\n", add_fn = font_red)
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}
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# this will contain a progress bar already
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result_list <- lapply(seq_along(ab_cols), run_as_sir_column)
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@ -85,7 +85,6 @@ as.ab("ERYT")
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as.ab("ERY")
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as.ab("eritromicine") # spelled wrong, yet works
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as.ab("Erythrocin") # trade name
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as.ab("Romycin") # trade name
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# spelling from different languages and dyslexia are no problem
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ab_atc("ceftriaxon")
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@ -138,9 +138,9 @@ The default \code{"standard"} setting ensures cautious handling of uncertain val
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\item{col_mo}{Column name of the names or codes of the microorganisms (see \code{\link[=as.mo]{as.mo()}}) - the default is the first column of class \code{\link{mo}}. Values will be coerced using \code{\link[=as.mo]{as.mo()}}.}
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\item{parallel}{A \link{logical} to indicate if parallel computing must be used, defaults to \code{FALSE}.}
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\item{parallel}{A \link{logical} to indicate if parallel computing must be used, defaults to \code{FALSE}. This requires no additional packages, as the used \code{parallel} package is part of base \R.}
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\item{max_cores}{Maximum number of cores to use if \code{parallel = TRUE}. Use a negative value to subtract that number from the available number of cores, e.g. a value of \code{-2} on an 8-core machine means that 6 cores will be used. Defaults to \code{-1}. The available number of cores are detected using \code{\link[parallelly:availableCores]{parallelly::availableCores()}} if that package is installed, and base \R's \code{\link[parallel:detectCores]{parallel::detectCores()}} otherwise.}
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\item{max_cores}{Maximum number of cores to use if \code{parallel = TRUE}. Use a negative value to subtract that number from the available number of cores, e.g. a value of \code{-2} on an 8-core machine means that at most 6 cores will be used. Defaults to \code{-1}. There will never be used more cores than variables to analyse. The available number of cores are detected using \code{\link[parallelly:availableCores]{parallelly::availableCores()}} if that package is installed, and base \R's \code{\link[parallel:detectCores]{parallel::detectCores()}} otherwise.}
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\item{clean}{A \link{logical} to indicate whether previously stored results should be forgotten after returning the 'logbook' with results.}
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}
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@ -41,10 +41,9 @@ test_that("test-ab.R", {
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"ERYT",
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"ERY",
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"erytromicine",
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"Erythrocin",
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"Romycin"
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"Erythrocin"
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))),
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rep("ERY", 9)
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rep("ERY", 8)
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)
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expect_identical(class(as.ab("amox")), c("ab", "character"))
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@ -39,8 +39,7 @@ test_that("test-antibiogram.R", {
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ab2 <- antibiogram(example_isolates,
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antimicrobials = aminoglycosides(),
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ab_transform = "atc",
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mo_transform = "gramstain",
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add_total_n = TRUE
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mo_transform = "gramstain"
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)
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ab3 <- antibiogram(example_isolates,
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@ -1,124 +0,0 @@
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# ==================================================================== #
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# TITLE: #
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# AMR: An R Package for Working with Antimicrobial Resistance Data #
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# #
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# SOURCE CODE: #
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# https://github.com/msberends/AMR #
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# #
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# PLEASE CITE THIS SOFTWARE AS: #
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# Berends MS, Luz CF, Friedrich AW, et al. (2022). #
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# AMR: An R Package for Working with Antimicrobial Resistance Data. #
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# Journal of Statistical Software, 104(3), 1-31. #
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# https://doi.org/10.18637/jss.v104.i03 #
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# #
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# Developed at the University of Groningen and the University Medical #
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# Center Groningen in The Netherlands, in collaboration with many #
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# colleagues from around the world, see our website. #
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# #
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# This R package is free software; you can freely use and distribute #
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# it for both personal and commercial purposes under the terms of the #
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# GNU General Public License version 2.0 (GNU GPL-2), as published by #
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# the Free Software Foundation. #
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# We created this package for both routine data analysis and academic #
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# research and it was publicly released in the hope that it will be #
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# useful, but it comes WITHOUT ANY WARRANTY OR LIABILITY. #
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# #
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# Visit our website for the full manual and a complete tutorial about #
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# how to conduct AMR data analysis: https://amr-for-r.org #
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# ==================================================================== #
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test_that("test-resistance_predict.R", {
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skip_on_cran()
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if (AMR:::pkg_is_available("dplyr", min_version = "1.0.0", also_load = TRUE)) {
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expect_output(AMX_R <- example_isolates %>%
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filter(mo == "B_ESCHR_COLI") %>%
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sir_predict(
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col_ab = "AMX",
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col_date = "date",
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model = "binomial",
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minimum = 10,
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info = TRUE
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) %>%
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pull("value"))
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# AMX resistance will increase according to data set `example_isolates`
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expect_true(AMX_R[3] < AMX_R[20])
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}
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expect_output(x <- suppressMessages(resistance_predict(example_isolates,
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col_ab = "AMX",
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year_min = 2010,
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model = "binomial",
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info = TRUE
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)))
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pdf(NULL) # prevent Rplots.pdf being created
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expect_silent(plot(x))
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if (AMR:::pkg_is_available("ggplot2")) {
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expect_silent(ggplot_sir_predict(x))
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expect_silent(ggplot2::autoplot(x))
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expect_error(ggplot_sir_predict(example_isolates))
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}
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expect_output(sir_predict(
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x = subset(example_isolates, mo == "B_ESCHR_COLI"),
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model = "binomial",
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col_ab = "AMX",
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col_date = "date",
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info = TRUE
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))
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expect_output(sir_predict(
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x = subset(example_isolates, mo == "B_ESCHR_COLI"),
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model = "loglin",
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col_ab = "AMX",
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col_date = "date",
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info = TRUE
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))
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expect_output(sir_predict(
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x = subset(example_isolates, mo == "B_ESCHR_COLI"),
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model = "lin",
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col_ab = "AMX",
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col_date = "date",
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info = TRUE
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))
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expect_error(sir_predict(
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x = subset(example_isolates, mo == "B_ESCHR_COLI"),
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model = "INVALID MODEL",
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col_ab = "AMX",
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col_date = "date",
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info = TRUE
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))
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expect_error(sir_predict(
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x = subset(example_isolates, mo == "B_ESCHR_COLI"),
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model = "binomial",
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col_ab = "NOT EXISTING COLUMN",
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col_date = "date",
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info = TRUE
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))
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expect_error(sir_predict(
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x = subset(example_isolates, mo == "B_ESCHR_COLI"),
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model = "binomial",
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col_ab = "AMX",
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col_date = "NOT EXISTING COLUMN",
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info = TRUE
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))
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expect_error(sir_predict(
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x = subset(example_isolates, mo == "B_ESCHR_COLI"),
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col_ab = "AMX",
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col_date = "NOT EXISTING COLUMN",
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info = TRUE
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))
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expect_error(sir_predict(
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x = subset(example_isolates, mo == "B_ESCHR_COLI"),
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col_ab = "AMX",
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col_date = "date",
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info = TRUE
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))
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# almost all E. coli are MEM S in the Netherlands :)
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expect_error(resistance_predict(
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x = subset(example_isolates, mo == "B_ESCHR_COLI"),
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model = "binomial",
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col_ab = "MEM",
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col_date = "date",
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info = TRUE
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))
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})
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@ -325,12 +325,14 @@ test_that("test-sir.R", {
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expect_message(as.sir(data.frame(
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mo = "E. coli",
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NIT = c("<= 2", 32),
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uti = TRUE
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uti = TRUE,
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info = TRUE
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)))
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expect_message(as.sir(data.frame(
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mo = "E. coli",
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NIT = c("<= 2", 32),
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specimen = c("urine", "blood")
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specimen = c("urine", "blood"),
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info = TRUE
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)))
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# SDD vs I in CLSI 2024
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