# ==================================================================== #
# 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/   #
# ==================================================================== #

# all four methods
expect_equal(
  sum(first_isolate(x = example_isolates, method = "isolate-based", info = TRUE), na.rm = TRUE),
  1984
)
expect_equal(
  sum(first_isolate(x = example_isolates, method = "patient-based", info = TRUE), na.rm = TRUE),
  1265
)
expect_equal(
  sum(first_isolate(x = example_isolates, method = "episode-based", info = TRUE), na.rm = TRUE),
  1300
)
expect_equal(
  sum(first_isolate(x = example_isolates, method = "phenotype-based", info = TRUE), na.rm = TRUE),
  1379
)

# Phenotype-based, using key antimicrobials
expect_equal(
  sum(first_isolate(
    x = example_isolates,
    method = "phenotype-based",
    type = "keyantimicrobials",
    antifungal = NULL, info = TRUE
  ), na.rm = TRUE),
  1395
)
expect_equal(
  sum(first_isolate(
    x = example_isolates,
    method = "phenotype-based",
    type = "keyantimicrobials",
    antifungal = NULL, info = TRUE, ignore_I = FALSE
  ), na.rm = TRUE),
  1418
)


# first non-ICU isolates
expect_true(
  sum(
    first_isolate(example_isolates,
      col_mo = "mo",
      col_date = "date",
      col_patient_id = "patient",
      col_icu = example_isolates$ward == "ICU",
      info = TRUE,
      icu_exclude = TRUE
  ), na.rm = TRUE) < 950
)

# set 1500 random observations to be of specimen type 'Urine'
random_rows <- sample(x = 1:2000, size = 1500, replace = FALSE)
x <- example_isolates
x$specimen <- "Other"
x[random_rows, "specimen"] <- "Urine"
expect_true(
  sum(first_isolate(
    x = x,
    col_date = "date",
    col_patient_id = "patient",
    col_mo = "mo",
    col_specimen = "specimen",
    filter_specimen = "Urine",
    info = TRUE
  ), na.rm = TRUE) < 1501
)
# same, but now exclude ICU
expect_true(
  sum(first_isolate(
    x = x,
    col_date = "date",
    col_patient_id = "patient",
    col_mo = "mo",
    col_specimen = "specimen",
    filter_specimen = "Urine",
    col_icu = x$ward == "ICU",
    icu_exclude = TRUE,
    info = TRUE
  ), na.rm = TRUE) < 1501
)

# "No isolates found"
test_iso <- example_isolates
test_iso$specimen <- "test"
expect_message(first_isolate(test_iso,
  "date",
  "patient",
  col_mo = "mo",
  col_specimen = "specimen",
  filter_specimen = "something_unexisting",
  info = TRUE
))

# printing of exclusion message
expect_message(first_isolate(example_isolates,
  col_date = "date",
  col_mo = "mo",
  col_patient_id = "patient",
  col_testcode = "gender",
  testcodes_exclude = "M",
  info = TRUE
))

# errors
expect_error(first_isolate("date", "patient", col_mo = "mo"))
expect_error(first_isolate(example_isolates,
  col_date = "non-existing col",
  col_mo = "mo"
))

if (AMR:::pkg_is_available("dplyr", min_version = "1.0.0")) {
  # if mo is not an mo class, result should be the same
  expect_identical(
    example_isolates %>%
      mutate(mo = as.character(mo)) %>%
      first_isolate(
        col_date = "date",
        col_mo = "mo",
        col_patient_id = "patient",
        info = FALSE
      ),
    example_isolates %>%
      first_isolate(
        col_date = "date",
        col_mo = "mo",
        col_patient_id = "patient",
        info = FALSE
      )
  )

  # support for WHONET
  expect_message(example_isolates %>%
    select(-patient) %>%
    mutate(
      `First name` = "test",
      `Last name` = "test",
      Sex = "Female"
    ) %>%
    first_isolate(info = TRUE))

  # groups
  x <- example_isolates %>%
    group_by(ward) %>%
    mutate(first = first_isolate())
  y <- example_isolates %>%
    group_by(ward) %>%
    mutate(first = first_isolate(.))
  expect_identical(x, y)
}

# missing dates should be no problem
df <- example_isolates
df[1:100, "date"] <- NA
expect_equal(
  sum(
    first_isolate(
      x = df,
      col_date = "date",
      col_patient_id = "patient",
      col_mo = "mo",
      info = TRUE
    ),
    na.rm = TRUE
  ),
  1382
)

# unknown MOs
test_unknown <- example_isolates
test_unknown$mo <- ifelse(test_unknown$mo == "B_ESCHR_COLI", "UNKNOWN", test_unknown$mo)
expect_equal(
  sum(first_isolate(test_unknown, include_unknown = FALSE)),
  1108
)
expect_equal(
  sum(first_isolate(test_unknown, include_unknown = TRUE)),
  1591
)

test_unknown$mo <- ifelse(test_unknown$mo == "UNKNOWN", NA, test_unknown$mo)
expect_equal(
  sum(first_isolate(test_unknown)),
  1108
)

# empty sir results
expect_equal(
  sum(first_isolate(example_isolates, include_untested_sir = FALSE)),
  1366
)

# shortcuts
expect_identical(
  filter_first_isolate(example_isolates),
  subset(example_isolates, first_isolate(example_isolates))
)


# notice that all mo's are distinct, so all are TRUE
expect_true(all(first_isolate(AMR:::pm_distinct(example_isolates, mo, .keep_all = TRUE), info = TRUE) == TRUE))

# only one isolate, so return fast
expect_true(first_isolate(data.frame(mo = "Escherichia coli", date = Sys.Date(), patient = "patient"), info = TRUE))