mirror of
https://github.com/msberends/AMR.git
synced 2024-12-25 18:46:11 +01:00
164 lines
6.2 KiB
R
Executable File
164 lines
6.2 KiB
R
Executable File
# ==================================================================== #
|
|
# 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. #
|
|
# doi: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(proportion_R(example_isolates$AMX), resistance(example_isolates$AMX))
|
|
expect_equal(proportion_SI(example_isolates$AMX), susceptibility(example_isolates$AMX))
|
|
# AMX resistance in `example_isolates`
|
|
expect_equal(proportion_R(example_isolates$AMX), 0.5955556, tolerance = 0.0001)
|
|
expect_equal(proportion_I(example_isolates$AMX), 0.002222222, tolerance = 0.0001)
|
|
expect_equal(rsi_confidence_interval(example_isolates$AMX)[1], 0.5688204, tolerance = 0.0001)
|
|
expect_equal(rsi_confidence_interval(example_isolates$AMX)[2], 0.6218738, tolerance = 0.0001)
|
|
expect_equal(
|
|
1 - proportion_R(example_isolates$AMX) - proportion_I(example_isolates$AMX),
|
|
proportion_S(example_isolates$AMX)
|
|
)
|
|
expect_equal(
|
|
proportion_R(example_isolates$AMX) + proportion_I(example_isolates$AMX),
|
|
proportion_IR(example_isolates$AMX)
|
|
)
|
|
expect_equal(
|
|
proportion_S(example_isolates$AMX) + proportion_I(example_isolates$AMX),
|
|
proportion_SI(example_isolates$AMX)
|
|
)
|
|
|
|
if (AMR:::pkg_is_available("dplyr", min_version = "1.0.0")) {
|
|
expect_equal(example_isolates %>% proportion_SI(AMC),
|
|
0.7626397,
|
|
tolerance = 0.0001
|
|
)
|
|
expect_equal(example_isolates %>% proportion_SI(AMC, GEN),
|
|
0.9408,
|
|
tolerance = 0.0001
|
|
)
|
|
expect_equal(example_isolates %>% proportion_SI(AMC, GEN, only_all_tested = TRUE),
|
|
0.9382647,
|
|
tolerance = 0.0001
|
|
)
|
|
|
|
# percentages
|
|
expect_equal(
|
|
example_isolates %>%
|
|
group_by(ward) %>%
|
|
summarise(
|
|
R = proportion_R(CIP, as_percent = TRUE),
|
|
I = proportion_I(CIP, as_percent = TRUE),
|
|
S = proportion_S(CIP, as_percent = TRUE),
|
|
n = n_rsi(CIP),
|
|
total = n()
|
|
) %>%
|
|
pull(n) %>%
|
|
sum(),
|
|
1409
|
|
)
|
|
|
|
# count of cases
|
|
expect_equal(
|
|
example_isolates %>%
|
|
group_by(ward) %>%
|
|
summarise(
|
|
cipro_p = proportion_SI(CIP, as_percent = TRUE),
|
|
cipro_n = n_rsi(CIP),
|
|
genta_p = proportion_SI(GEN, as_percent = TRUE),
|
|
genta_n = n_rsi(GEN),
|
|
combination_p = proportion_SI(CIP, GEN, as_percent = TRUE),
|
|
combination_n = n_rsi(CIP, GEN)
|
|
) %>%
|
|
pull(combination_n),
|
|
c(1181, 577, 116)
|
|
)
|
|
|
|
# proportion_df
|
|
expect_equal(
|
|
example_isolates %>% select(AMX) %>% proportion_df() %>% pull(value),
|
|
c(
|
|
example_isolates$AMX %>% proportion_SI(),
|
|
example_isolates$AMX %>% proportion_R()
|
|
)
|
|
)
|
|
expect_equal(
|
|
example_isolates %>% select(AMX) %>% proportion_df(combine_SI = FALSE) %>% pull(value),
|
|
c(
|
|
example_isolates$AMX %>% proportion_S(),
|
|
example_isolates$AMX %>% proportion_I(),
|
|
example_isolates$AMX %>% proportion_R()
|
|
)
|
|
)
|
|
|
|
expect_warning(example_isolates %>% group_by(ward) %>% summarise(across(KAN, rsi_confidence_interval)))
|
|
}
|
|
|
|
expect_warning(proportion_R(as.character(example_isolates$AMC)))
|
|
expect_warning(proportion_S(as.character(example_isolates$AMC)))
|
|
expect_warning(proportion_S(as.character(
|
|
example_isolates$AMC,
|
|
example_isolates$GEN
|
|
)))
|
|
|
|
expect_warning(n_rsi(as.character(
|
|
example_isolates$AMC,
|
|
example_isolates$GEN
|
|
)))
|
|
expect_equal(
|
|
suppressWarnings(n_rsi(as.character(
|
|
example_isolates$AMC,
|
|
example_isolates$GEN
|
|
))),
|
|
1879
|
|
)
|
|
|
|
# check for errors
|
|
expect_error(proportion_IR("test", minimum = "test"))
|
|
expect_error(proportion_IR("test", as_percent = "test"))
|
|
expect_error(proportion_I("test", minimum = "test"))
|
|
expect_error(proportion_I("test", as_percent = "test"))
|
|
expect_error(proportion_S("test", minimum = "test"))
|
|
expect_error(proportion_S("test", as_percent = "test"))
|
|
expect_error(proportion_S("test", also_single_tested = TRUE))
|
|
|
|
# check too low amount of isolates
|
|
expect_identical(
|
|
suppressWarnings(proportion_R(example_isolates$AMX, minimum = nrow(example_isolates) + 1)),
|
|
NA_real_
|
|
)
|
|
expect_identical(
|
|
suppressWarnings(proportion_I(example_isolates$AMX, minimum = nrow(example_isolates) + 1)),
|
|
NA_real_
|
|
)
|
|
expect_identical(
|
|
suppressWarnings(proportion_S(example_isolates$AMX, minimum = nrow(example_isolates) + 1)),
|
|
NA_real_
|
|
)
|
|
|
|
# warning for speed loss
|
|
expect_warning(proportion_R(as.character(example_isolates$GEN)))
|
|
expect_warning(proportion_I(as.character(example_isolates$GEN)))
|
|
expect_warning(proportion_S(example_isolates$AMC, as.character(example_isolates$GEN)))
|
|
expect_error(proportion_df(c("A", "B", "C")))
|
|
expect_error(proportion_df(example_isolates[, "date", drop = TRUE]))
|