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(v1.6.0.9031) tinytest unit tests
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# ==================================================================== #
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# TITLE #
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# Antimicrobial Resistance (AMR) Data Analysis for R #
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# #
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# SOURCE #
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# https://github.com/msberends/AMR #
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# #
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# LICENCE #
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# (c) 2018-2021 Berends MS, Luz CF et al. #
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# Developed at the University of Groningen, the Netherlands, in #
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# collaboration with non-profit organisations Certe Medical #
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# Diagnostics & Advice, and University Medical Center Groningen. #
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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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context("first_isolate.R")
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test_that("first isolates work", {
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skip_on_cran()
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# all four methods
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expect_equal(sum(first_isolate(x = example_isolates, method = "isolate-based", info = TRUE), na.rm = TRUE),
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1984)
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expect_equal(sum(first_isolate(x = example_isolates, method = "patient-based", info = TRUE), na.rm = TRUE),
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1265)
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expect_equal(sum(first_isolate(x = example_isolates, method = "episode-based", info = TRUE), na.rm = TRUE),
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1300)
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expect_equal(sum(first_isolate(x = example_isolates, method = "phenotype-based", info = TRUE), na.rm = TRUE),
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1379)
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# Phenotype-based, using key antimicrobials
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expect_equal(sum(first_isolate(x = example_isolates,
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method = "phenotype-based",
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type = "keyantimicrobials",
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antifungal = NULL, info = TRUE), na.rm = TRUE),
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1395)
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expect_equal(sum(first_isolate(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), na.rm = TRUE),
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1418)
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# first non-ICU isolates
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expect_equal(
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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_id",
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col_icu = "ward_icu",
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info = TRUE,
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icu_exclude = TRUE),
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na.rm = TRUE),
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941)
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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_lt(
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sum(
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first_isolate(x = x,
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col_date = "date",
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col_patient_id = "patient_id",
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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),
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1501)
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# same, but now exclude ICU
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expect_lt(
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sum(
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first_isolate(x = x,
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col_date = "date",
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col_patient_id = "patient_id",
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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 = "ward_icu",
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icu_exclude = TRUE,
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info = TRUE),
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na.rm = TRUE),
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1501)
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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_id",
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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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# 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_id",
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col_testcode = "gender",
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testcodes_exclude = "M",
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info = TRUE))
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# errors
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expect_error(first_isolate("date", "patient_id", 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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if (suppressWarnings(require("dplyr"))) {
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# if mo is not an mo class, result should be the same
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expect_identical(example_isolates %>%
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mutate(mo = as.character(mo)) %>%
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first_isolate(col_date = "date",
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col_mo = "mo",
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col_patient_id = "patient_id",
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info = FALSE),
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example_isolates %>%
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first_isolate(col_date = "date",
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col_mo = "mo",
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col_patient_id = "patient_id",
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info = FALSE))
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# support for WHONET
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expect_message(example_isolates %>%
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select(-patient_id) %>%
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mutate(`First name` = "test",
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`Last name` = "test",
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Sex = "Female") %>%
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first_isolate(info = TRUE))
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# groups
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x <- example_isolates %>% group_by(ward_icu) %>% mutate(first = first_isolate())
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y <- example_isolates %>% group_by(ward_icu) %>% 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(x = df,
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col_date = "date",
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col_patient_id = "patient_id",
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col_mo = "mo",
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info = TRUE),
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na.rm = TRUE),
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1382)
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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$mo)
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expect_equal(sum(first_isolate(test_unknown, include_unknown = FALSE)),
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1108)
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expect_equal(sum(first_isolate(test_unknown, include_unknown = TRUE)),
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1591)
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test_unknown$mo <- ifelse(test_unknown$mo == "UNKNOWN", NA, test_unknown$mo)
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expect_equal(sum(first_isolate(test_unknown)),
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1108)
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# empty rsi results
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expect_equal(sum(first_isolate(example_isolates, include_untested_rsi = FALSE)),
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1366)
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# shortcuts
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expect_identical(filter_first_isolate(example_isolates),
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subset(example_isolates, first_isolate(example_isolates)))
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# notice that all mo's are distinct, so all are TRUE
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expect_true(all(example_isolates %pm>%
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pm_distinct(mo, .keep_all = TRUE) %pm>%
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first_isolate(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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})
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