AMR/inst/tinytest/test-data.R

128 lines
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R

# ==================================================================== #
# TITLE #
# AMR: An R Package for Working with Antimicrobial Resistance Data #
# #
# SOURCE #
# https://github.com/msberends/AMR #
# #
# CITE AS #
# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C #
# (2022). AMR: An R Package for Working with Antimicrobial Resistance #
# Data. Journal of Statistical Software, 104(3), 1-31. #
# doi:10.18637/jss.v104.i03 #
# #
# Developed at the University of Groningen, the Netherlands, in #
# collaboration with non-profit organisations Certe Medical #
# Diagnostics & Advice, and University Medical Center Groningen. #
# #
# This R package is free software; you can freely use and distribute #
# it for both personal and commercial purposes under the terms of the #
# GNU General Public License version 2.0 (GNU GPL-2), as published by #
# the Free Software Foundation. #
# We created this package for both routine data analysis and academic #
# research and it was publicly released in the hope that it will be #
# useful, but it comes WITHOUT ANY WARRANTY OR LIABILITY. #
# #
# Visit our website for the full manual and a complete tutorial about #
# how to conduct AMR data analysis: https://msberends.github.io/AMR/ #
# ==================================================================== #
# IDs should always be unique
expect_identical(nrow(microorganisms), length(unique(microorganisms$mo)))
expect_identical(class(microorganisms$mo), c("mo", "character"))
expect_identical(nrow(antibiotics), length(unique(antibiotics$ab)))
expect_true(all(is.na(antibiotics$atc[duplicated(antibiotics$atc)])))
expect_identical(class(antibiotics$ab), c("ab", "character"))
# check cross table reference
expect_true(all(microorganisms.codes$mo %in% microorganisms$mo))
expect_true(all(example_isolates$mo %in% microorganisms$mo))
expect_true(all(rsi_translation$mo %in% microorganisms$mo))
expect_true(all(rsi_translation$ab %in% antibiotics$ab))
expect_true(all(intrinsic_resistant$mo %in% microorganisms$mo))
expect_true(all(intrinsic_resistant$ab %in% antibiotics$ab))
expect_false(any(is.na(microorganisms.codes$code)))
expect_false(any(is.na(microorganisms.codes$mo)))
expect_true(all(dosage$ab %in% antibiotics$ab))
expect_true(all(dosage$name %in% antibiotics$name))
# check valid disks/MICs
expect_false(any(is.na(as.mic(rsi_translation[which(rsi_translation$method == "MIC"), "breakpoint_S", drop = TRUE]))))
expect_false(any(is.na(as.mic(rsi_translation[which(rsi_translation$method == "MIC"), "breakpoint_R", drop = TRUE]))))
expect_false(any(is.na(as.disk(rsi_translation[which(rsi_translation$method == "DISK"), "breakpoint_S", drop = TRUE]))))
expect_false(any(is.na(as.disk(rsi_translation[which(rsi_translation$method == "DISK"), "breakpoint_R", drop = TRUE]))))
# antibiotic names must always be coercible to their original AB code
expect_identical(as.ab(antibiotics$name), antibiotics$ab)
if (AMR:::pkg_is_available("tibble", also_load = FALSE)) {
# there should be no diacritics (i.e. non ASCII) characters in the datasets (CRAN policy)
datasets <- data(package = "AMR", envir = asNamespace("AMR"))$results[, "Item", drop = TRUE]
for (i in seq_len(length(datasets))) {
dataset <- get(datasets[i], envir = asNamespace("AMR"))
expect_identical(AMR:::dataset_UTF8_to_ASCII(dataset), dataset, info = datasets[i])
}
}
df <- AMR:::AMR_env$MO_lookup
expect_true(nrow(df[which(df$prevalence == 1), , drop = FALSE]) < nrow(df[which(df$prevalence == 2), , drop = FALSE]))
expect_true(nrow(df[which(df$prevalence == 2), , drop = FALSE]) < nrow(df[which(df$prevalence == 3), , drop = FALSE]))
expect_true(all(c(
"mo", "fullname", "status", "kingdom", "phylum", "class", "order",
"family", "genus", "species", "subspecies", "rank", "ref", "source",
"lpsn", "lpsn_parent", "lpsn_renamed_to", "gbif", "gbif_parent", "gbif_renamed_to", "prevalence",
"snomed", "kingdom_index", "fullname_lower", "full_first", "species_first"
) %in% colnames(df)))
expect_inherits(AMR:::MO_CONS, "mo")
uncategorised <- subset(
microorganisms,
genus == "Staphylococcus" &
!species %in% c("", "aureus") &
!mo %in% c(AMR:::MO_CONS, AMR:::MO_COPS)
)
expect_true(NROW(uncategorised) == 0,
info = ifelse(NROW(uncategorised) == 0,
"All staphylococcal species categorised as CoNS/CoPS.",
paste0(
"Staphylococcal species not categorised as CoNS/CoPS: S. ",
uncategorised$species, " (", uncategorised$mo, ")",
collapse = "\n"
)
)
)
# THIS WILL CHECK NON-ASCII STRINGS IN ALL FILES:
# check_non_ascii <- function() {
# purrr::map_df(
# .id = "file",
# # list common text files
# .x = fs::dir_ls(
# recurse = TRUE,
# type = "file",
# # ignore images, compressed
# regexp = "\\.(png|ico|rda|ai|tar.gz|zip|xlsx|csv|pdf|psd)$",
# invert = TRUE
# ),
# .f = function(path) {
# x <- readLines(path, warn = FALSE)
# # from tools::showNonASCII()
# asc <- iconv(x, "latin1", "ASCII")
# ind <- is.na(asc) | asc != x
# # make data frame
# if (any(ind)) {
# tibble::tibble(
# row = which(ind),
# line = iconv(x[ind], "latin1", "ASCII", sub = "byte")
# )
# } else {
# tibble::tibble()
# }
# }
# )
# }
# x <- check_non_ascii() %>%
# filter(file %unlike% "^(data-raw|docs|git_)")