AMR/inst/tinytest/test-count.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.                 #
# doi:10.18637/jss.v104.i03                                            #
#                                                                      #
# 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", drop = TRUE]))

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(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_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("ward" %in% (example_isolates %>%
    group_by(ward) %>%
    select(ward, AMX, CIP, gender) %>%
    rsi_df() %>%
    colnames()))
}