# ==================================================================== #
# TITLE:                                                               #
# AMR: An R Package for Working with Antimicrobial Resistance Data     #
#                                                                      #
# SOURCE CODE:                                                         #
# https://github.com/msberends/AMR                                     #
#                                                                      #
# PLEASE CITE THIS SOFTWARE AS:                                        #
# Berends MS, Luz CF, Friedrich AW, et al. (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://amr-for-r.org              #
# ==================================================================== #

test_that("test-count.R", {
  skip_on_cran()

  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()))
  }
})