mirror of https://github.com/msberends/AMR.git
248 lines
7.5 KiB
R
Executable File
248 lines
7.5 KiB
R
Executable File
# ==================================================================== #
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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://msberends.github.io/AMR/ #
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# ==================================================================== #
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# all four methods
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expect_equal(
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sum(first_isolate(x = example_isolates, method = "isolate-based", info = TRUE), na.rm = TRUE),
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1984
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)
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expect_equal(
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sum(first_isolate(x = example_isolates, method = "patient-based", info = TRUE), na.rm = TRUE),
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1265
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)
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expect_equal(
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sum(first_isolate(x = example_isolates, method = "episode-based", info = TRUE), na.rm = TRUE),
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1300
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)
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expect_equal(
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sum(first_isolate(x = example_isolates, method = "phenotype-based", info = TRUE), na.rm = TRUE),
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1373
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)
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# for phenotype determination
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expect_equal(AMR:::duplicated_antibiogram("SSSS", points_threshold = 2, ignore_I = TRUE, type = "points"),
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FALSE)
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expect_equal(AMR:::duplicated_antibiogram(c("RRR", "SSS"),
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points_threshold = 2, ignore_I = TRUE, type = "points"),
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c(FALSE, FALSE))
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expect_equal(AMR:::duplicated_antibiogram(c("RRR", "RRR", "SSS"),
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points_threshold = 2, ignore_I = TRUE, type = "points"),
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c(FALSE, TRUE, FALSE))
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expect_equal(AMR:::duplicated_antibiogram(c("RRR", "RSS", "SSS", "RSS", "RRR", "RRR", "SSS", "RSS", "RSR", "RRR"),
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points_threshold = 2, ignore_I = TRUE, type = "points"),
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c(FALSE, FALSE, FALSE, TRUE, TRUE, TRUE, TRUE, TRUE, FALSE, TRUE))
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# Phenotype-based, using key antimicrobials
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expect_equal(
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sum(first_isolate(
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x = example_isolates,
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method = "phenotype-based",
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type = "keyantimicrobials",
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antifungal = NULL, info = TRUE
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), na.rm = TRUE),
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1376
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)
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expect_equal(
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sum(first_isolate(
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x = example_isolates,
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method = "phenotype-based",
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type = "keyantimicrobials",
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antifungal = NULL, info = TRUE, ignore_I = FALSE
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), na.rm = TRUE),
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1396
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)
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# first non-ICU isolates
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expect_true(
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sum(
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first_isolate(example_isolates,
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col_mo = "mo",
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col_date = "date",
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col_patient_id = "patient",
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col_icu = example_isolates$ward == "ICU",
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info = TRUE,
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icu_exclude = TRUE
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), na.rm = TRUE) < 950
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)
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# set 1500 random observations to be of specimen type 'Urine'
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random_rows <- sample(x = 1:2000, size = 1500, replace = FALSE)
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x <- example_isolates
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x$specimen <- "Other"
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x[random_rows, "specimen"] <- "Urine"
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expect_true(
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sum(first_isolate(
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x = x,
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col_date = "date",
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col_patient_id = "patient",
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col_mo = "mo",
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col_specimen = "specimen",
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filter_specimen = "Urine",
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info = TRUE
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), na.rm = TRUE) < 1400
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)
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# same, but now exclude ICU
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expect_true(
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sum(first_isolate(
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x = x,
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col_date = "date",
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col_patient_id = "patient",
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col_mo = "mo",
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col_specimen = "specimen",
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filter_specimen = "Urine",
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col_icu = x$ward == "ICU",
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icu_exclude = TRUE,
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info = TRUE
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), na.rm = TRUE) < 1000
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)
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# "No isolates found"
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test_iso <- example_isolates
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test_iso$specimen <- "test"
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expect_message(first_isolate(test_iso,
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"date",
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"patient",
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col_mo = "mo",
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col_specimen = "specimen",
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filter_specimen = "something_unexisting",
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info = TRUE
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))
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# printing of exclusion message
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expect_message(first_isolate(example_isolates,
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col_date = "date",
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col_mo = "mo",
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col_patient_id = "patient",
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col_testcode = "gender",
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testcodes_exclude = "M",
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info = TRUE
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))
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# errors
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expect_error(first_isolate("date", "patient", col_mo = "mo"))
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expect_error(first_isolate(example_isolates,
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col_date = "non-existing col",
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col_mo = "mo"
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))
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if (AMR:::pkg_is_available("dplyr", min_version = "1.0.0", also_load = TRUE)) {
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# if mo is not an mo class, result should be the same
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expect_identical(
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example_isolates %>%
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mutate(mo = as.character(mo)) %>%
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first_isolate(
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col_date = "date",
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col_mo = "mo",
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col_patient_id = "patient",
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info = FALSE
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),
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example_isolates %>%
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first_isolate(
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col_date = "date",
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col_mo = "mo",
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col_patient_id = "patient",
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info = FALSE
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)
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)
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# support for WHONET
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expect_message(example_isolates %>%
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select(-patient) %>%
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mutate(
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`First name` = "test",
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`Last name` = "test",
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Sex = "Female"
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) %>%
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first_isolate(info = TRUE))
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# groups
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x <- example_isolates %>%
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group_by(ward) %>%
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mutate(first = first_isolate())
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y <- example_isolates %>%
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group_by(ward) %>%
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mutate(first = first_isolate(.))
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expect_identical(x, y)
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}
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# missing dates should be no problem
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df <- example_isolates
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df[1:100, "date"] <- NA
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expect_equal(
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sum(
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first_isolate(
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x = df,
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col_date = "date",
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col_patient_id = "patient",
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col_mo = "mo",
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info = TRUE
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),
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na.rm = TRUE
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),
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1376
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)
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# unknown MOs
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test_unknown <- example_isolates
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test_unknown$mo <- ifelse(test_unknown$mo == "B_ESCHR_COLI", "UNKNOWN", test_unknown$mo)
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expect_equal(
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sum(first_isolate(test_unknown, include_unknown = FALSE)),
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1106
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)
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expect_equal(
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sum(first_isolate(test_unknown, include_unknown = TRUE)),
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1589
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)
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test_unknown$mo <- ifelse(test_unknown$mo == "UNKNOWN", NA, test_unknown$mo)
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expect_equal(
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sum(first_isolate(test_unknown)),
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1106
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)
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# empty sir results
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expect_equal(
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sum(first_isolate(example_isolates, include_untested_sir = FALSE)),
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1360
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)
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# shortcuts
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expect_identical(
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filter_first_isolate(example_isolates),
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subset(example_isolates, first_isolate(example_isolates))
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)
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# notice that all mo's are distinct, so all are TRUE
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expect_true(all(first_isolate(AMR:::pm_distinct(example_isolates, mo, .keep_all = TRUE), info = TRUE) == TRUE))
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# only one isolate, so return fast
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expect_true(first_isolate(data.frame(mo = "Escherichia coli", date = Sys.Date(), patient = "patient"), info = TRUE))
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