# ==================================================================== #
# TITLE                                                                #
# Antimicrobial Resistance (AMR) Data Analysis for R                   #
#                                                                      #
# SOURCE                                                               #
# https://github.com/msberends/AMR                                     #
#                                                                      #
# LICENCE                                                              #
# (c) 2018-2022 Berends MS, Luz CF et al.                              #
# 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/   #
# ==================================================================== #

if (AMR:::pkg_is_available("dplyr", min_version = "1.0.0") & AMR:::pkg_is_available("ggplot2")) {
  
  pdf(NULL) # prevent Rplots.pdf being created
  
  # data should be equal
  expect_equal(
    (example_isolates %>%
       select(AMC, CIP) %>%
       ggplot_rsi())$data %>%
      summarise_all(resistance) %>%
      as.double(),
    example_isolates %>%
      select(AMC, CIP) %>%
      summarise_all(resistance) %>%
      as.double()
  )
  
  expect_stdout(print(example_isolates %>%
                        select(AMC, CIP) %>%
                        ggplot_rsi(x = "interpretation", facet = "antibiotic")))
  expect_stdout(print(example_isolates %>%
                        select(AMC, CIP) %>%
                        ggplot_rsi(x = "antibiotic", facet = "interpretation")))
  
  expect_equal(
    (example_isolates %>%
       select(AMC, CIP) %>%
       ggplot_rsi(x = "interpretation", facet = "antibiotic"))$data %>%
      summarise_all(resistance) %>%
      as.double(),
    example_isolates %>%
      select(AMC, CIP) %>%
      summarise_all(resistance) %>%
      as.double()
  )
  
  expect_equal(
    (example_isolates %>%
       select(AMC, CIP) %>%
       ggplot_rsi(x = "antibiotic", facet = "interpretation"))$data %>%
      summarise_all(resistance) %>%
      as.double(),
    example_isolates %>%
      select(AMC, CIP) %>%
      summarise_all(resistance) %>%
      as.double()
  )
  
  expect_equal(
    (example_isolates %>%
       select(AMC, CIP) %>%
       ggplot_rsi(x = "antibiotic", facet = "interpretation"))$data %>%
      summarise_all(count_resistant) %>%
      as.double(),
    example_isolates %>%
      select(AMC, CIP) %>%
      summarise_all(count_resistant) %>%
      as.double()
  )
  
  # support for scale_type ab and mo
  expect_inherits((data.frame(mo = as.mo(c("e. coli", "s aureus")),
                              n = c(40, 100)) %>%
                     ggplot(aes(x = mo, y = n)) +
                     geom_col())$data,
                  "data.frame")
  expect_inherits((data.frame(ab = as.ab(c("amx", "amc")),
                              n = c(40, 100)) %>%
                     ggplot(aes(x = ab, y = n)) +
                     geom_col())$data,
                  "data.frame")
  
  expect_inherits((data.frame(ab = as.ab(c("amx", "amc")),
                              n = c(40, 100)) %>%
                     ggplot(aes(x = ab, y = n)) +
                     geom_col())$data,
                  "data.frame")
  
  # support for manual colours
  expect_inherits((ggplot(data.frame(x = c("Value1", "Value2", "Value3"),
                                     y = c(1, 2, 3),
                                     z = c("Value4", "Value5", "Value6"))) +
                     geom_col(aes(x = x, y = y, fill = z)) +
                     scale_rsi_colours(Value4 = "S", Value5 = "I", Value6 = "R"))$data,
                  "data.frame")
  
}
