AMR/inst/tinytest/test-count.R

110 lines
4.9 KiB
R

# ==================================================================== #
# TITLE #
# AMR: An R Package for Working with Antimicrobial Resistance Data #
# #
# SOURCE #
# https://github.com/msberends/AMR #
# #
# CITE AS #
# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C #
# (2022). AMR: An R Package for Working with Antimicrobial Resistance #
# Data. Journal of Statistical Software, 104(3), 1-31. #
# https://doi.org/10.18637/jss.v104.i03 #
# #
# Developed at the University of Groningen and the University Medical #
# Center Groningen in The Netherlands, in collaboration with many #
# colleagues from around the world, see our website. #
# #
# 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$AMX), n_sir(example_isolates$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", drop = TRUE]))
if (AMR:::pkg_is_available("dplyr", min_version = "1.0.0", also_load = TRUE)) {
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(ward) %>%
summarise(
cipro = count_susceptible(CIP),
genta = count_susceptible(GEN),
combination = count_susceptible(CIP, GEN)
) %>%
pull(combination),
c(946, 428, 94)
)
# 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_SI = FALSE) %>% pull(value),
c(
suppressWarnings(example_isolates$AMX %>% count_S()),
example_isolates$AMX %>% count_I(),
example_isolates$AMX %>% count_R()
)
)
# grouping in sir_calc_df() (= backbone of sir_df())
expect_true("ward" %in% (example_isolates %>%
group_by(ward) %>%
select(ward, AMX, CIP, gender) %>%
sir_df() %>%
colnames()))
}