tests/testthat/test-preprocess-and-validate-data.R

In CBASSED50: Process CBASS-Derived PAM Data

# Test for the `mandatory_columns()` function
test_that("mandatory_columns returns the correct vector of mandatory columns", {
  expected <- c(
      "Project",
      "Date",
      "Site",
      "Genotype",
      "Species",
      "Country",
      "Latitude",
      "Longitude",
      "Condition",
      "Temperature",
      "Timepoint",
      "Pam_value"
  )
  result <- mandatory_columns()
  expect_equal(result, expected)
})

# Tests for the `dataset_has_mandatory_columns()` function
test_that("dataset_has_mandatory_columns returns TRUE when all mandatory columns are present", {
  sample_data <- data.frame(
    Project = c("Project A", "Project A", "Project B", "Project B"),
    Date = c("2023-07-31", "2023-08-01", "2023-08-02","2023-08-02"),
    Site = c("Site X", "Site Y", "Site Z", "Site Z"),
    Country = c("Country A", "Country B", "Country C", "Country C"),
    Latitude = c(34.05, 36.16, 40.71, 40.71),
    Longitude = c(-118.24, -115.15, -74.01, -74.01),
    Species = c("Species 1", "Species 2", "Species 2", "Species 3"),
    Genotype = c("Genotype A", "Genotype B", "Genotype C", "Genotype D"),
    Condition = c("Condition 1", "Condition 2","Condition 2", "Condition 3"),
    Timepoint = c("Timepoint 1", "Timepoint 1", "Timepoint 2", "Timepoint 2"),
    Temperature = c(30.2, 31.5, 29.8, 35),
    Pam_value = c(0.5, 0.6, 0.8, 0.3)
  )

  result <- dataset_has_mandatory_columns(sample_data)
  expect_true(result)
})

test_that("dataset_has_mandatory_columns returns FALSE when any mandatory column is missing", {
  missing_columns_data <- data.frame(
    Label = c("A", "B", "C"),
    Date = c("2023-07-31", "2023-08-01", "2023-08-02")
  )

  result <- dataset_has_mandatory_columns(missing_columns_data)
  expect_false(result)
})

# Test for the `convert_columns()` function
test_that("convert_columns converts columns to the correct types", {
  data <- data.frame(
    Project = c("Project A", "Project A", "Project B", "Project B"),
    Date = c("2023-07-31", "2023-08-01", "2023-08-02","2023-08-02"),
    Site = c("Site X", "Site Y", "Site Z", "Site Z"),
    Country = c("Country A", "Country B", "Country C", "Country C"),
    Latitude = c(34.05, 36.16, 40.71, 40.71),
    Longitude = c(-118.24, -115.15, -74.01, -74.01),
    Species = c("Species 1", "Species 2", "Species 2", "Species 3"),
    Genotype = c("Genotype A", "Genotype B", "Genotype C", "Genotype D"),
    Condition = c("Condition 1", "Condition 2", "Condition 2", "Condition 3"),
    Timepoint = c("Timepoint 1", "Timepoint 1", "Timepoint 2", "Timepoint 2"),
    Temperature = c(30.2, 31.5, 29.8, 35),
    Pam_value = c(0.5, 0.6, 0.8, 0.3)
  )

  modified_data <- convert_columns(data)

  # Assert that the 'Site', 'Condition', 'Species', and 'Genotype' columns are factors
  expect_s3_class(modified_data$Site, "factor")
  expect_s3_class(modified_data$Condition, "factor")
  expect_s3_class(modified_data$Species, "factor")
  expect_s3_class(modified_data$Genotype, "factor")

  # Assert that the 'Temperature' and 'Pam_value' columns are numeric
  expect_type(modified_data$Temperature, "double")
  expect_type(modified_data$Pam_value, "double")
})

# Tests for the `check_enough_unique_temperatures_values` function
test_that("check_enough_unique_temperatures_values returns TRUE when there are enough unique values", {
  data <- data.frame(Temperature = c(25, 30, 25, 35, 28, 28))
  result <- check_enough_unique_temperatures_values(data)
  expect_true(result)
})

test_that("check_enough_unique_temperatures_values returns FALSE when there are not enough unique values", {
  data <- data.frame(Temperature = c(25, 25, 25, 25))
  result <- check_enough_unique_temperatures_values(data)
  expect_false(result)
})

# Tests for `validate_cbass_dataset` function
test_that("validate_cbass_dataset throws an error when mandatory columns are missing", {
  missing_columns_data <- data.frame(
    Genotype = c("A", "B", "C"),
    Date = c("2023-07-31", "2023-08-01", "2023-08-02")
  )

  expect_error(validate_cbass_dataset(missing_columns_data))
})

test_that("validate_cbass_dataset throws an error when there are not enough unique temperature values", {
  data <- data.frame(Temperature = c(25, 25, 25, 25))

  expect_error(validate_cbass_dataset(data))
})

test_that("validate_cbass_dataset returns TRUE for a valid dataset", {
  sample_data <- data.frame(
    Project = c("Project A", "Project A", "Project B", "Project B"),
    Date = c("2023-07-31", "2023-08-01", "2023-08-02", "2023-08-02"),
    Site = c("Site X", "Site Y", "Site Z", "Site Z"),
    Country = c("Country A", "Country B", "Country C", "Country C"),
    Latitude = c(34.05, 36.16, 40.71, 40.71),
    Longitude = c(-118.24, -115.15, -74.01, -74.01),
    Species = c("Species 1", "Species 2", "Species 2", "Species 3"),
    Genotype = c("Genotype A", "Genotype B", "Genotype C", "Genotype D"),
    Condition = c("Condition 1", "Condition 2", "Condition 2", "Condition 3"),
    Timepoint = c("Timepoint 1", "Timepoint 1", "Timepoint 2", "Timepoint 2"),
    Temperature = c(30.2, 31.5, 29.8, 35),
    Pam_value = c(0.5, 0.6, 0.8, 0.3)
  )

  result <- validate_cbass_dataset(sample_data)
  expect_true(result)
})