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mirror of https://github.com/msberends/AMR.git synced 2026-09-11 14:58:57 +02:00

(v3.0.1.9090) unit test

This commit is contained in:
2026-09-04 19:47:30 +02:00
parent 177e83aaf8
commit 3f50d6b6c3
4 changed files with 16 additions and 12 deletions

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@@ -1,6 +1,6 @@
Package: AMR
Version: 3.0.1.9089
Date: 2026-09-03
Version: 3.0.1.9090
Date: 2026-09-04
Title: Antimicrobial Resistance Data Analysis
Description: Functions to simplify and standardise antimicrobial resistance (AMR)
data analysis and to work with microbial and antimicrobial properties by

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@@ -1,4 +1,4 @@
# AMR 3.0.1.9089
# AMR 3.0.1.9090
Planned as v3.1.0, end of September 2026.

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@@ -972,10 +972,14 @@ meet_criteria <- function(object, # can be literally `list(...)` for `allow_argu
if ("logical" %in% allow_class) {
or_values <- paste0(or_values, ", or TRUE or FALSE")
}
stop_ifnot(all(object %in% is_in.bak, na.rm = TRUE), "argument {.arg ", obj_name, "} ",
stop_ifnot(all(object %in% is_in.bak, na.rm = TRUE),
"argument {.arg ", obj_name, "} ",
ifelse(!is.null(has_length) && length(has_length) == 1 && has_length == 1 && length(is_in.bak) == 1,
"must be ",
ifelse(!is.null(has_length) && length(has_length) == 1 && has_length == 1,
"must be either ",
"must only contain values "
)
),
or_values,
ifelse(allow_NA == TRUE, ", or NA", ""),

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@@ -108,17 +108,17 @@ test_that("test-interpretive_rules.R", {
)
expect_equal(suppressWarnings(interpretive_rules(a, "mo", info = FALSE)), b)
# piperacillin must be R in Enterobacteriaceae when tica is R
# piperacillin must be R in E. coli when ampi is R
if (AMR:::pkg_is_available("dplyr", min_version = "1.0.0", also_load = TRUE)) {
expect_equal(
suppressWarnings(
example_isolates %>%
filter(mo_family(mo) == "Enterobacteriaceae") %>%
filter(mo_name(mo) == "Escherichia coli") %>%
mutate(
TIC = as.sir("R"),
PIP = as.sir("S")
AMP = as.sir("R"),
PIP = as.sir(NA)
) %>%
interpretive_rules(col_mo = "mo", version_expertrules = 3.1, rules = "expert", info = FALSE, overwrite = TRUE) %>%
interpretive_rules(col_mo = "mo", version_expertrules = 3.3, rules = "expert", info = FALSE, overwrite = TRUE) %>%
pull(PIP) %>%
unique() %>%
as.character()