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24f24ecaf8
* Generalise interpretive rules for multi-guideline support (#268) - Rename data-raw/eucast_rules.tsv → interpretive_rules.tsv; add rule.provider column (value: "EUCAST") to distinguish future CLSI rows - Rename EUCAST_RULES_DF → INTERPRETIVE_RULES_DF in _pre_commit_checks.R; filter by rule.provider == guideline when applying rules in interpretive_rules() - Rename custom_eucast_rules() → custom_interpretive_rules() with new S3 class "custom_interpretive_rules"; old function becomes a deprecated wrapper in zz_deprecated.R; backward-compat S3 dispatch shims added for old class - Remove stop_if(guideline == "CLSI", ...) so clsi_rules() no longer errors - Add .onLoad shim in zzz.R to create INTERPRETIVE_RULES_DF from EUCAST_RULES_DF for transitional compatibility until sysdata.rda is regenerated https://claude.ai/code/session_01D46BTsfJSPo3HnLWp3PRkP * Fix namespace load failure: remove assignInNamespace from .onLoad (#268) assignInNamespace cannot add NEW bindings to a locked package namespace (R locks namespace bindings before .onLoad runs). Replace the .onLoad shim with a runtime fallback inside interpretive_rules(): if INTERPRETIVE_RULES_DF is absent (pre-regeneration sysdata.rda), derive it from EUCAST_RULES_DF by adding the rule.provider column. This also fixes the screening_abx line to reuse the already-resolved interpretive_rules_df_total instead of a bare INTERPRETIVE_RULES_DF reference. https://claude.ai/code/session_01D46BTsfJSPo3HnLWp3PRkP * fixes * fixes --------- Co-authored-by: Claude <noreply@anthropic.com>
144 lines
6.8 KiB
R
144 lines
6.8 KiB
R
# ==================================================================== #
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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-data.R", {
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skip_on_cran()
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# IDs should always be unique
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expect_identical(nrow(microorganisms), length(unique(microorganisms$mo)))
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expect_identical(class(microorganisms$mo), c("mo", "character"))
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expect_identical(nrow(antimicrobials), length(unique(AMR::antimicrobials$ab)))
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expect_identical(class(AMR::antimicrobials$ab), c("ab", "character"))
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expect_identical(
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nrow(antimicrobials[!is.na(antimicrobials$cid), ]),
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length(unique(AMR::antimicrobials$cid[!is.na(antimicrobials$cid)]))
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)
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# check cross table reference
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expect_true(all(microorganisms.codes$mo %in% microorganisms$mo))
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expect_true(all(example_isolates$mo %in% microorganisms$mo))
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expect_true(all(microorganisms.groups$mo %in% microorganisms$mo))
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expect_true(all(microorganisms.groups$mo_group %in% microorganisms$mo))
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expect_true(all(clinical_breakpoints$mo %in% microorganisms$mo))
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expect_true(all(clinical_breakpoints$ab %in% AMR::antimicrobials$ab))
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expect_true(all(intrinsic_resistant$mo %in% microorganisms$mo))
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expect_true(all(intrinsic_resistant$ab %in% AMR::antimicrobials$ab))
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expect_false(anyNA(microorganisms.codes$code))
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expect_false(anyNA(microorganisms.codes$mo))
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expect_true(all(dosage$ab %in% AMR::antimicrobials$ab))
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expect_true(all(dosage$name %in% AMR::antimicrobials$name))
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interpretive_abx <- AMR:::INTERPRETIVE_RULES_DF$and_these_antibiotics
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interpretive_abx <- unique(unlist(strsplit(interpretive_abx[!is.na(interpretive_abx)], ", +")))
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expect_true(all(interpretive_abx %in% AMR::antimicrobials$ab),
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info = paste0(
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"Missing in `antimicrobials` data set: ",
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toString(interpretive_abx[which(!interpretive_abx %in% AMR::antimicrobials$ab)])
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)
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)
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# check valid disks/MICs
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expect_false(anyNA(as.mic(clinical_breakpoints[which(clinical_breakpoints$method == "MIC" & clinical_breakpoints$ref_tbl != "ECOFF"), "breakpoint_S", drop = TRUE])))
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expect_true(anyNA(as.mic(clinical_breakpoints[which(clinical_breakpoints$method == "MIC" & clinical_breakpoints$ref_tbl != "ECOFF"), "breakpoint_R", drop = TRUE])))
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expect_false(anyNA(as.disk(clinical_breakpoints[which(clinical_breakpoints$method == "DISK" & clinical_breakpoints$ref_tbl != "ECOFF"), "breakpoint_S", drop = TRUE])))
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expect_true(anyNA(as.disk(clinical_breakpoints[which(clinical_breakpoints$method == "DISK" & clinical_breakpoints$ref_tbl != "ECOFF"), "breakpoint_R", drop = TRUE])))
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# antibiotic names must always be coercible to their original AB code
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expect_identical(as.ab(AMR::antimicrobials$name), AMR::antimicrobials$ab)
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if (AMR:::pkg_is_available("tibble")) {
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# there should be no diacritics (i.e. non ASCII) characters in the datasets (CRAN policy)
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datasets <- data(package = "AMR", envir = asNamespace("AMR"))$results[, "Item", drop = TRUE]
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datasets <- datasets[datasets != "antibiotics"]
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for (i in seq_len(length(datasets))) {
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dataset <- get(datasets[i], envir = asNamespace("AMR"))
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expect_identical(AMR:::dataset_UTF8_to_ASCII(dataset), dataset, info = datasets[i])
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}
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}
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df <- AMR:::AMR_env$MO_lookup
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expect_true(all(c(
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"mo", "fullname", "status", "kingdom", "phylum", "class", "order",
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"family", "genus", "species", "subspecies", "rank", "ref", "source",
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"lpsn", "lpsn_parent", "lpsn_renamed_to", "gbif", "gbif_parent", "gbif_renamed_to", "prevalence",
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"snomed", "kingdom_index", "fullname_lower", "full_first", "species_first"
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) %in% colnames(df)))
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expect_inherits(AMR:::MO_CONS, "mo")
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uncategorised <- subset(
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microorganisms,
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genus == "Staphylococcus" &
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!species %in% c("", "aureus") &
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!mo %in% c(AMR:::MO_CONS, AMR:::MO_COPS)
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)
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expect_true(NROW(uncategorised) == 0,
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info = ifelse(NROW(uncategorised) == 0,
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"All staphylococcal species categorised as CoNS/CoPS.",
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paste0(
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"Staphylococcal species not categorised as CoNS/CoPS: S. ",
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uncategorised$species, " (", uncategorised$mo, ")",
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collapse = "\n"
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)
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)
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)
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# THIS WILL CHECK NON-ASCII STRINGS IN ALL FILES:
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# check_non_ascii <- function() {
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# purrr::map_df(
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# .id = "file",
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# # list common text files
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# .x = fs::dir_ls(
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# recurse = TRUE,
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# type = "file",
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# # ignore images, compressed
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# regexp = "\\.(png|ico|rda|ai|tar.gz|zip|xlsx|csv|pdf|psd)$",
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# invert = TRUE
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# ),
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# .f = function(path) {
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# x <- readLines(path, warn = FALSE)
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# # from tools::showNonASCII()
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# asc <- iconv(x, "latin1", "ASCII")
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# ind <- is.na(asc) | asc != x
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# # make data frame
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# if (any(ind)) {
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# tibble::tibble(
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# row = which(ind),
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# line = iconv(x[ind], "latin1", "ASCII", sub = "byte")
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# )
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# } else {
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# tibble::tibble()
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# }
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# }
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# )
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# }
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# x <- check_non_ascii() %>%
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# filter(file %unlike% "^(data-raw|docs|git_)")
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})
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