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# ==================================================================== #
# TITLE #
# Antimicrobial Resistance (AMR) Data Analysis for R #
# #
# SOURCE #
# https://github.com/msberends/AMR #
# #
# LICENCE #
# (c) 2018-2021 Berends MS, Luz CF et al. #
# Developed at the University of Groningen, the Netherlands, in #
# collaboration with non-profit organisations Certe Medical #
# Diagnostics & Advice, and University Medical Center Groningen. #
# #
# This R package is free software; you can freely use and distribute #
# it for both personal and commercial purposes under the terms of the #
# GNU General Public License version 2.0 (GNU GPL-2), as published by #
# the Free Software Foundation. #
# We created this package for both routine data analysis and academic #
# research and it was publicly released in the hope that it will be #
# useful, but it comes WITHOUT ANY WARRANTY OR LIABILITY. #
# #
# Visit our website for the full manual and a complete tutorial about #
# how to conduct AMR data analysis: https://msberends.github.io/AMR/ #
# ==================================================================== #
if (AMR:::pkg_is_available("dplyr", min_version = "1.0.0") & AMR:::pkg_is_available("ggplot2")) {
pdf(NULL) # prevent Rplots.pdf being created
# data should be equal
expect_equal(
(example_isolates %>%
select(AMC, CIP) %>%
ggplot_rsi())$data %>%
summarise_all(resistance) %>%
as.double(),
example_isolates %>%
select(AMC, CIP) %>%
summarise_all(resistance) %>%
as.double()
)
expect_stdout(print(example_isolates %>%
select(AMC, CIP) %>%
ggplot_rsi(x = "interpretation", facet = "antibiotic")))
expect_stdout(print(example_isolates %>%
select(AMC, CIP) %>%
ggplot_rsi(x = "antibiotic", facet = "interpretation")))
expect_equal(
(example_isolates %>%
select(AMC, CIP) %>%
ggplot_rsi(x = "interpretation", facet = "antibiotic"))$data %>%
summarise_all(resistance) %>%
as.double(),
example_isolates %>%
select(AMC, CIP) %>%
summarise_all(resistance) %>%
as.double()
)
expect_equal(
(example_isolates %>%
select(AMC, CIP) %>%
ggplot_rsi(x = "antibiotic", facet = "interpretation"))$data %>%
summarise_all(resistance) %>%
as.double(),
example_isolates %>%
select(AMC, CIP) %>%
summarise_all(resistance) %>%
as.double()
)
expect_equal(
(example_isolates %>%
select(AMC, CIP) %>%
ggplot_rsi(x = "antibiotic", facet = "interpretation"))$data %>%
summarise_all(count_resistant) %>%
as.double(),
example_isolates %>%
select(AMC, CIP) %>%
summarise_all(count_resistant) %>%
as.double()
)
# support for scale_type ab and mo
expect_inherits((data.frame(mo = as.mo(c("e. coli", "s aureus")),
n = c(40, 100)) %>%
ggplot(aes(x = mo, y = n)) +
geom_col())$data,
"data.frame")
expect_inherits((data.frame(ab = as.ab(c("amx", "amc")),
n = c(40, 100)) %>%
ggplot(aes(x = ab, y = n)) +
geom_col())$data,
"data.frame")
expect_inherits((data.frame(ab = as.ab(c("amx", "amc")),
n = c(40, 100)) %>%
ggplot(aes(x = ab, y = n)) +
geom_col())$data,
"data.frame")
# support for manual colours
expect_inherits((ggplot(data.frame(x = c("Value1", "Value2", "Value3"),
y = c(1, 2, 3),
z = c("Value4", "Value5", "Value6"))) +
geom_col(aes(x = x, y = y, fill = z)) +
scale_rsi_colours(Value4 = "S", Value5 = "I", Value6 = "R"))$data,
"data.frame")
}