library(testthat)
library(AutoEDA)

#==========================================================
# pca_analysis()
#==========================================================

test_that("pca_analysis returns PCAResult", {

  pca <- pca_analysis(iris)

  expect_s3_class(
    pca,
    "PCAResult"
  )

})

test_that("pca_analysis returns prcomp object", {

  pca <- pca_analysis(iris)

  expect_s3_class(
    pca,
    "prcomp"
  )

})

test_that("pca_analysis contains expected components", {

  pca <- pca_analysis(iris)

  expect_true("sdev" %in% names(pca))
  expect_true("rotation" %in% names(pca))
  expect_true("center" %in% names(pca))
  expect_true("scale" %in% names(pca))
  expect_true("x" %in% names(pca))

})

#==========================================================
# scale argument
#==========================================================

test_that("pca_analysis accepts scale = TRUE", {

  expect_no_error(

    pca_analysis(
      iris,
      scale = TRUE
    )

  )

})

test_that("pca_analysis accepts scale = FALSE", {

  expect_no_error(

    pca_analysis(
      iris,
      scale = FALSE
    )

  )

})

#==========================================================
# Different datasets
#==========================================================

test_that("pca_analysis works with numeric-only data", {

  pca <- pca_analysis(
    iris[,1:4]
  )

  expect_s3_class(
    pca,
    "PCAResult"
  )

})

test_that("pca_analysis works with tibble", {

  skip_if_not_installed("tibble")

  dat <- tibble::as_tibble(iris)

  expect_no_error(

    pca_analysis(dat)

  )

})

#==========================================================
# Invalid scale
#==========================================================

test_that("pca_analysis rejects numeric scale", {

  expect_error(

    pca_analysis(
      iris,
      scale = 1
    ),

    "'scale'"

  )

})

test_that("pca_analysis rejects character scale", {

  expect_error(

    pca_analysis(
      iris,
      scale = "TRUE"
    ),

    "'scale'"

  )

})

test_that("pca_analysis rejects vector scale", {

  expect_error(

    pca_analysis(
      iris,
      scale = c(TRUE, FALSE)
    ),

    "'scale'"

  )

})

test_that("pca_analysis rejects NULL scale", {

  expect_error(

    pca_analysis(
      iris,
      scale = NULL
    ),

    "'scale'"

  )

})

#==========================================================
# Invalid input
#==========================================================

test_that("pca_analysis rejects non-data.frame", {

  expect_error(

    pca_analysis(
      1:10
    )

  )

})

test_that("pca_analysis rejects NULL", {

  expect_error(

    pca_analysis(
      NULL
    )

  )

})

test_that("pca_analysis rejects character vector", {

  expect_error(

    pca_analysis(
      letters
    )

  )

})

test_that("pca_analysis rejects empty data.frame", {

  expect_error(

    pca_analysis(
      data.frame()
    )

  )

})

test_that("pca_analysis rejects one numeric variable", {

  expect_error(

    pca_analysis(

      data.frame(
        x = 1:10
      )

    )

  )

})

test_that("pca_analysis rejects non-numeric data", {

  expect_error(

    pca_analysis(

      data.frame(
        A = letters[1:10],
        B = LETTERS[1:10]
      )

    )

  )

})

test_that("pca_analysis rejects one numeric and one character variable", {

  expect_error(

    pca_analysis(

      data.frame(
        x = 1:10,
        y = letters[1:10]
      )

    )

  )

})

test_that("pca_analysis rejects missing values", {

  dat <- iris

  dat$Sepal.Length[1:10] <- NA

  expect_error(

    pca_analysis(dat),

    "Missing values"

  )

})