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110 lines
4.9 KiB
R
110 lines
4.9 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://msberends.github.io/AMR/ #
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# ==================================================================== #
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expect_equal(count_resistant(example_isolates$AMX), count_R(example_isolates$AMX))
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expect_equal(count_susceptible(example_isolates$AMX), count_SI(example_isolates$AMX))
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expect_equal(count_all(example_isolates$AMX), n_sir(example_isolates$AMX))
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# AMX resistance in `example_isolates`
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expect_equal(count_R(example_isolates$AMX), 804)
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expect_equal(count_I(example_isolates$AMX), 3)
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expect_equal(suppressWarnings(count_S(example_isolates$AMX)), 543)
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expect_equal(
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count_R(example_isolates$AMX) + count_I(example_isolates$AMX),
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suppressWarnings(count_IR(example_isolates$AMX))
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)
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expect_equal(
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suppressWarnings(count_S(example_isolates$AMX)) + count_I(example_isolates$AMX),
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count_SI(example_isolates$AMX)
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)
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# warning for speed loss
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# expect_warning(count_resistant(as.character(example_isolates$AMC)))
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# expect_warning(count_resistant(example_isolates$AMC, as.character(example_isolates$GEN)))
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# check for errors
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expect_error(count_resistant("test", minimum = "test"))
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expect_error(count_resistant("test", as_percent = "test"))
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expect_error(count_susceptible("test", minimum = "test"))
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expect_error(count_susceptible("test", as_percent = "test"))
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expect_error(count_df(c("A", "B", "C")))
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expect_error(count_df(example_isolates[, "date", drop = TRUE]))
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if (AMR:::pkg_is_available("dplyr", min_version = "1.0.0", also_load = TRUE)) {
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expect_equal(example_isolates %>% count_susceptible(AMC), 1433)
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expect_equal(example_isolates %>% count_susceptible(AMC, GEN, only_all_tested = TRUE), 1687)
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expect_equal(example_isolates %>% count_susceptible(AMC, GEN, only_all_tested = FALSE), 1764)
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expect_equal(example_isolates %>% count_all(AMC, GEN, only_all_tested = TRUE), 1798)
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expect_equal(example_isolates %>% count_all(AMC, GEN, only_all_tested = FALSE), 1936)
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expect_identical(
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example_isolates %>% count_all(AMC, GEN, only_all_tested = TRUE),
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example_isolates %>% count_susceptible(AMC, GEN, only_all_tested = TRUE) +
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example_isolates %>% count_resistant(AMC, GEN, only_all_tested = TRUE)
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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 = count_susceptible(CIP),
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genta = count_susceptible(GEN),
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combination = count_susceptible(CIP, GEN)
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) %>%
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pull(combination),
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c(946, 428, 94)
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)
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# count_df
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expect_equal(
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example_isolates %>% select(AMX) %>% count_df() %>% pull(value),
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c(
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example_isolates$AMX %>% count_susceptible(),
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example_isolates$AMX %>% count_resistant()
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)
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)
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expect_equal(
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example_isolates %>% select(AMX) %>% count_df(combine_SI = FALSE) %>% pull(value),
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c(
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suppressWarnings(example_isolates$AMX %>% count_S()),
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example_isolates$AMX %>% count_I(),
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example_isolates$AMX %>% count_R()
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)
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)
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# grouping in sir_calc_df() (= backbone of sir_df())
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expect_true("ward" %in% (example_isolates %>%
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group_by(ward) %>%
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select(ward, AMX, CIP, gender) %>%
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sir_df() %>%
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colnames()))
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}
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