mirror of https://github.com/msberends/AMR.git
113 lines
4.5 KiB
R
113 lines
4.5 KiB
R
# ==================================================================== #
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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) 2018-2022 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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if (AMR:::pkg_is_available("dplyr", min_version = "1.0.0") & AMR:::pkg_is_available("ggplot2")) {
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pdf(NULL) # prevent Rplots.pdf being created
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# data should be equal
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expect_equal(
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(example_isolates %>%
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select(AMC, CIP) %>%
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ggplot_rsi())$data %>%
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summarise_all(resistance) %>%
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as.double(),
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example_isolates %>%
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select(AMC, CIP) %>%
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summarise_all(resistance) %>%
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as.double()
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)
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expect_stdout(print(example_isolates %>%
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select(AMC, CIP) %>%
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ggplot_rsi(x = "interpretation", facet = "antibiotic")))
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expect_stdout(print(example_isolates %>%
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select(AMC, CIP) %>%
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ggplot_rsi(x = "antibiotic", facet = "interpretation")))
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expect_equal(
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(example_isolates %>%
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select(AMC, CIP) %>%
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ggplot_rsi(x = "interpretation", facet = "antibiotic"))$data %>%
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summarise_all(resistance) %>%
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as.double(),
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example_isolates %>%
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select(AMC, CIP) %>%
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summarise_all(resistance) %>%
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as.double()
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)
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expect_equal(
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(example_isolates %>%
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select(AMC, CIP) %>%
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ggplot_rsi(x = "antibiotic", facet = "interpretation"))$data %>%
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summarise_all(resistance) %>%
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as.double(),
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example_isolates %>%
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select(AMC, CIP) %>%
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summarise_all(resistance) %>%
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as.double()
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)
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expect_equal(
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(example_isolates %>%
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select(AMC, CIP) %>%
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ggplot_rsi(x = "antibiotic", facet = "interpretation"))$data %>%
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summarise_all(count_resistant) %>%
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as.double(),
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example_isolates %>%
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select(AMC, CIP) %>%
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summarise_all(count_resistant) %>%
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as.double()
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)
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# support for scale_type ab and mo
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expect_inherits((data.frame(mo = as.mo(c("e. coli", "s aureus")),
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n = c(40, 100)) %>%
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ggplot(aes(x = mo, y = n)) +
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geom_col())$data,
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"data.frame")
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expect_inherits((data.frame(ab = as.ab(c("amx", "amc")),
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n = c(40, 100)) %>%
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ggplot(aes(x = ab, y = n)) +
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geom_col())$data,
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"data.frame")
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expect_inherits((data.frame(ab = as.ab(c("amx", "amc")),
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n = c(40, 100)) %>%
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ggplot(aes(x = ab, y = n)) +
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geom_col())$data,
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"data.frame")
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# support for manual colours
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expect_inherits((ggplot(data.frame(x = c("Value1", "Value2", "Value3"),
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y = c(1, 2, 3),
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z = c("Value4", "Value5", "Value6"))) +
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geom_col(aes(x = x, y = y, fill = z)) +
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scale_rsi_colours(Value4 = "S", Value5 = "I", Value6 = "R"))$data,
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"data.frame")
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}
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