# ==================================================================== #
# 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(sir_confidence_interval(example_isolates$AMX)[1], 0.5688204, tolerance = 0.0001)
expect_equal(sir_confidence_interval(example_isolates$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", also_load = TRUE)) {
  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_sir(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_sir(CIP),
        genta_p = proportion_SI(GEN, as_percent = TRUE),
        genta_n = n_sir(GEN),
        combination_p = proportion_SI(CIP, GEN, as_percent = TRUE),
        combination_n = n_sir(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, sir_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_sir(as.character(example_isolates$AMC, example_isolates$GEN)))
expect_equal(
  suppressWarnings(n_sir(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]))