mirror of https://github.com/msberends/AMR.git
164 lines
6.2 KiB
R
Executable File
164 lines
6.2 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 #
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# https://github.com/msberends/AMR #
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# #
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# CITE AS #
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# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C #
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# (2022). AMR: An R Package for Working with Antimicrobial Resistance #
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# Data. Journal of Statistical Software, 104(3), 1-31. #
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# doi:10.18637/jss.v104.i03 #
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# #
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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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expect_equal(proportion_R(example_isolates$AMX), resistance(example_isolates$AMX))
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expect_equal(proportion_SI(example_isolates$AMX), susceptibility(example_isolates$AMX))
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# AMX resistance in `example_isolates`
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expect_equal(proportion_R(example_isolates$AMX), 0.5955556, tolerance = 0.0001)
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expect_equal(proportion_I(example_isolates$AMX), 0.002222222, tolerance = 0.0001)
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expect_equal(rsi_confidence_interval(example_isolates$AMX)[1], 0.5688204, tolerance = 0.0001)
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expect_equal(rsi_confidence_interval(example_isolates$AMX)[2], 0.6218738, tolerance = 0.0001)
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expect_equal(
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1 - proportion_R(example_isolates$AMX) - proportion_I(example_isolates$AMX),
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proportion_S(example_isolates$AMX)
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)
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expect_equal(
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proportion_R(example_isolates$AMX) + proportion_I(example_isolates$AMX),
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proportion_IR(example_isolates$AMX)
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)
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expect_equal(
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proportion_S(example_isolates$AMX) + proportion_I(example_isolates$AMX),
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proportion_SI(example_isolates$AMX)
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)
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if (AMR:::pkg_is_available("dplyr", min_version = "1.0.0")) {
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expect_equal(example_isolates %>% proportion_SI(AMC),
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0.7626397,
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tolerance = 0.0001
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)
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expect_equal(example_isolates %>% proportion_SI(AMC, GEN),
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0.9408,
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tolerance = 0.0001
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)
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expect_equal(example_isolates %>% proportion_SI(AMC, GEN, only_all_tested = TRUE),
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0.9382647,
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tolerance = 0.0001
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)
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# percentages
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expect_equal(
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example_isolates %>%
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group_by(ward) %>%
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summarise(
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R = proportion_R(CIP, as_percent = TRUE),
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I = proportion_I(CIP, as_percent = TRUE),
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S = proportion_S(CIP, as_percent = TRUE),
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n = n_rsi(CIP),
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total = n()
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) %>%
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pull(n) %>%
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sum(),
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1409
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)
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# count of cases
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expect_equal(
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example_isolates %>%
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group_by(ward) %>%
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summarise(
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cipro_p = proportion_SI(CIP, as_percent = TRUE),
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cipro_n = n_rsi(CIP),
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genta_p = proportion_SI(GEN, as_percent = TRUE),
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genta_n = n_rsi(GEN),
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combination_p = proportion_SI(CIP, GEN, as_percent = TRUE),
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combination_n = n_rsi(CIP, GEN)
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) %>%
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pull(combination_n),
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c(1181, 577, 116)
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)
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# proportion_df
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expect_equal(
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example_isolates %>% select(AMX) %>% proportion_df() %>% pull(value),
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c(
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example_isolates$AMX %>% proportion_SI(),
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example_isolates$AMX %>% proportion_R()
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)
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)
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expect_equal(
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example_isolates %>% select(AMX) %>% proportion_df(combine_SI = FALSE) %>% pull(value),
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c(
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example_isolates$AMX %>% proportion_S(),
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example_isolates$AMX %>% proportion_I(),
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example_isolates$AMX %>% proportion_R()
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)
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)
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expect_warning(example_isolates %>% group_by(ward) %>% summarise(across(KAN, rsi_confidence_interval)))
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}
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expect_warning(proportion_R(as.character(example_isolates$AMC)))
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expect_warning(proportion_S(as.character(example_isolates$AMC)))
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expect_warning(proportion_S(as.character(
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example_isolates$AMC,
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example_isolates$GEN
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)))
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expect_warning(n_rsi(as.character(
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example_isolates$AMC,
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example_isolates$GEN
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)))
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expect_equal(
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suppressWarnings(n_rsi(as.character(
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example_isolates$AMC,
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example_isolates$GEN
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))),
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1879
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)
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# check for errors
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expect_error(proportion_IR("test", minimum = "test"))
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expect_error(proportion_IR("test", as_percent = "test"))
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expect_error(proportion_I("test", minimum = "test"))
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expect_error(proportion_I("test", as_percent = "test"))
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expect_error(proportion_S("test", minimum = "test"))
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expect_error(proportion_S("test", as_percent = "test"))
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expect_error(proportion_S("test", also_single_tested = TRUE))
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# check too low amount of isolates
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expect_identical(
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suppressWarnings(proportion_R(example_isolates$AMX, minimum = nrow(example_isolates) + 1)),
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NA_real_
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)
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expect_identical(
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suppressWarnings(proportion_I(example_isolates$AMX, minimum = nrow(example_isolates) + 1)),
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NA_real_
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)
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expect_identical(
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suppressWarnings(proportion_S(example_isolates$AMX, minimum = nrow(example_isolates) + 1)),
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NA_real_
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)
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# warning for speed loss
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expect_warning(proportion_R(as.character(example_isolates$GEN)))
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expect_warning(proportion_I(as.character(example_isolates$GEN)))
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expect_warning(proportion_S(example_isolates$AMC, as.character(example_isolates$GEN)))
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expect_error(proportion_df(c("A", "B", "C")))
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expect_error(proportion_df(example_isolates[, "date", drop = TRUE]))
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