tests/testthat/test-cpr_classify_signif.R

In canaper: Categorical Analysis of Neo- And Paleo-Endemism

#' @srrstats {G5.2, G5.2a, G5.2b, UL7.0} tests failure if input is not valid and
#' checks warning messages
test_that("Input is valid", {
  expect_error(
    cpr_classify_signif(1, "pd"),
    "'df' must be of class 'data.frame'"
  )
  expect_error(
    cpr_classify_signif(data.frame(), "pf"),
    "Biodiversity metrics may only be selected from 'pd', 'rpd', 'pe', or 'rpe'"
  )
  expect_error(
    cpr_classify_signif(data.frame(pd_obs_p_lower = 0.5), "pd"),
    "'df' does not include percentage of times observed value was higher than random values" # nolint
  )
  expect_error(
    cpr_classify_signif(data.frame(pd_obs_p_upper = 0.5), "pd"),
    "'df' does not include percentage of times observed value was lower than random values" # nolint
  )
  expect_error(
    cpr_classify_signif(
      data.frame(pd_obs_p_upper = 2, pd_obs_p_lower = 0.5), "pd"
    ),
    "Values for percentage of times observed value was higher than random values should be between 0 and 1, inclusive" # nolint
  )
  expect_error(
    cpr_classify_signif(
      data.frame(pd_obs_p_upper = 0.5, pd_obs_p_lower = 2), "pd"
    ),
    "Values for percentage of times observed value was lower than random values should be between 0 and 1, inclusive" # nolint
  )
})

#' @srrstats {G5.4a, G5.5} Implement correctness tests
test_that("Calculations work", {
  expect_equal(
    cpr_classify_signif(
      data.frame(
        pd_obs_p_lower = c(0.98),
        pd_obs_p_upper = c(0.0001)
      ),
      metric = "pd"
    )[["pd_signif"]],
    "< 0.025"
  )
  expect_equal(
    cpr_classify_signif(
      data.frame(
        pd_obs_p_lower = c(0.5),
        pd_obs_p_upper = c(0.0001)
      ),
      metric = "pd"
    )[["pd_signif"]],
    "not significant"
  )
  expect_equal(
    cpr_classify_signif(
      data.frame(
        pd_obs_p_lower = c(0.0001),
        pd_obs_p_upper = c(0.99)
      ),
      metric = "pd"
    )[["pd_signif"]],
    "> 0.975"
  )
  expect_equal(
    cpr_classify_signif(
      data.frame(
        pd_obs_p_lower = c(0.0001),
        pd_obs_p_upper = c(0.96)
      ),
      metric = "pd",
      upper = TRUE,
      one_sided = TRUE
    )[["pd_signif"]],
    "> 0.95"
  )
  expect_equal(
    cpr_classify_signif(
      data.frame(
        pd_obs_p_lower = c(0.0001),
        pd_obs_p_upper = c(0.96)
      ),
      metric = "pd",
      upper = FALSE
    )[["pd_signif"]],
    "not significant"
  )
})

test_that("Output is formatted as expected", {
  set.seed(123)
  expect_s3_class(
    cpr_classify_signif(
      data.frame(
        pd_obs_p_lower = c(0.0001),
        pd_obs_p_upper = c(0.99)
      ),
      metric = "pd"
    ),
    "data.frame"
  )
  set.seed(123)
  expect_type(
    cpr_classify_signif(
      data.frame(
        pd_obs_p_lower = c(0.0001),
        pd_obs_p_upper = c(0.99)
      ),
      metric = "pd"
    )[["pd_signif"]],
    "character"
  )
  # Run cpr_classify_signif() a bunch of times to test the output values
  set.seed(123)
  random_results_two_sided <- cpr_classify_signif(
    data.frame(
      pd_obs_p_lower = runif(1000),
      pd_obs_p_upper = runif(1000)
    ),
    metric = "pd"
  )
  set.seed(123)
  random_results_one_sided <- cpr_classify_signif(
    data.frame(
      pd_obs_p_lower = runif(1000),
      pd_obs_p_upper = runif(1000)
    ),
    metric = "pd",
    one_sided = TRUE
  )
  expect_true(
    isTRUE(
      all(random_results_two_sided[["pd_signif"]] %in%
        c("< 0.01", "< 0.025", "> 0.975", "> 0.99", "not significant"))
    )
  )
  expect_true(
    isTRUE(
      all(random_results_one_sided[["pd_signif"]] %in%
        c("< 0.01", "< 0.05", "> 0.99", "> 0.95", "not significant"))
    )
  )
})