You can not select more than 25 topics Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.

97 lines
5.1 KiB

# ==================================================================== #
# 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/ #
# ==================================================================== #
expect_equal(count_resistant(example_isolates$AMX), count_R(example_isolates$AMX))
expect_equal(count_susceptible(example_isolates$AMX), count_SI(example_isolates$AMX))
expect_equal(count_all(example_isolates$AMX), n_rsi(example_isolates$AMX))
# AMX resistance in `example_isolates`
expect_equal(count_R(example_isolates$AMX), 804)
expect_equal(count_I(example_isolates$AMX), 3)
expect_equal(suppressWarnings(count_S(example_isolates$AMX)), 543)
expect_equal(count_R(example_isolates$AMX) + count_I(example_isolates$AMX),
suppressWarnings(count_IR(example_isolates$AMX)))
expect_equal(suppressWarnings(count_S(example_isolates$AMX)) + count_I(example_isolates$AMX),
count_SI(example_isolates$AMX))
# warning for speed loss
expect_warning(count_resistant(as.character(example_isolates$AMC)))
expect_warning(count_resistant(example_isolates$AMC,
as.character(example_isolates$GEN)))
# check for errors
expect_error(count_resistant("test", minimum = "test"))
expect_error(count_resistant("test", as_percent = "test"))
expect_error(count_susceptible("test", minimum = "test"))
expect_error(count_susceptible("test", as_percent = "test"))
expect_error(count_df(c("A", "B", "C")))
expect_error(count_df(example_isolates[, "date"]))
if (AMR:::pkg_is_available("dplyr", min_version = "1.0.0")) {
expect_equal(example_isolates %>% count_susceptible(AMC), 1433)
expect_equal(example_isolates %>% count_susceptible(AMC, GEN, only_all_tested = TRUE), 1687)
expect_equal(example_isolates %>% count_susceptible(AMC, GEN, only_all_tested = FALSE), 1764)
expect_equal(example_isolates %>% count_all(AMC, GEN, only_all_tested = TRUE), 1798)
expect_equal(example_isolates %>% count_all(AMC, GEN, only_all_tested = FALSE), 1936)
expect_identical(example_isolates %>% count_all(AMC, GEN, only_all_tested = TRUE),
example_isolates %>% count_susceptible(AMC, GEN, only_all_tested = TRUE) +
example_isolates %>% count_resistant(AMC, GEN, only_all_tested = TRUE))
# count of cases
expect_equal(example_isolates %>%
group_by(hospital_id) %>%
summarise(cipro = count_susceptible(CIP),
genta = count_susceptible(GEN),
combination = count_susceptible(CIP, GEN)) %>%
pull(combination),
c(253, 465, 192, 558))
# count_df
expect_equal(
example_isolates %>% select(AMX) %>% count_df() %>% pull(value),
c(example_isolates$AMX %>% count_susceptible(),
example_isolates$AMX %>% count_resistant())
)
expect_equal(
example_isolates %>% select(AMX) %>% count_df(combine_IR = TRUE) %>% pull(value),
c(suppressWarnings(example_isolates$AMX %>% count_S()),
suppressWarnings(example_isolates$AMX %>% count_IR()))
)
expect_equal(
example_isolates %>% select(AMX) %>% count_df(combine_SI = FALSE) %>% pull(value),
c(suppressWarnings(example_isolates$AMX %>% count_S()),
example_isolates$AMX %>% count_I(),
example_isolates$AMX %>% count_R())
)
# grouping in rsi_calc_df() (= backbone of rsi_df())
expect_true("hospital_id" %in% (example_isolates %>%
group_by(hospital_id) %>%
select(hospital_id, AMX, CIP, gender) %>%
rsi_df() %>%
colnames()))
}