test_that("check_new_data works", {
  set.seed(313)
  examples <- matrix(exp(rnorm(40)), ncol = 4)
  examples <- as.data.frame(examples)

  log_trans <- recipe(~ V1 + V2 + V3 + V4, data = examples) %>%
    step_log(V1, V2, V3) %>%
    update_role(V1, V2, V3, new_role = "potato") %>%
    update_role_requirements(role = "potato", bake = FALSE)

  log_obj <- prep(log_trans, training = examples)

  expect_snapshot(bake(log_obj, examples[,2:4, drop = FALSE]), error = TRUE)
  expect_snapshot(bake(log_obj, examples[,3:4, drop = FALSE]), error = TRUE)
  expect_snapshot(bake(log_obj, examples[,  4, drop = FALSE]), error = TRUE)
})

test_that("conditionMessage method for recipes errors works", {
  res <-
    try({
      recipe(~ ., data = mtcars) %>%
        step_dummy(all_numeric_predictors()) %>%
        prep()},
      silent = TRUE
    )

  expect_s3_class(attr(res, "condition"), "recipes_error")

  expect_snapshot(conditionMessage(attr(res, "condition")))
})

test_that("validate_training_data errors are thrown", {
  expect_snapshot(
    error = TRUE,
    recipe(~., data = mtcars) %>% prep(fresh = TRUE)
  )

  expect_snapshot(
    error = TRUE,
    recipe(~., data = mtcars) %>% prep(mtcars[, 1:2], fresh = TRUE)
  )

  expect_snapshot(
    error = TRUE,
    recipe(~., data = mtcars) %>%
      step_center(disp) %>%
      prep(retain = FALSE) %>%
      prep(mtcars, fresh = FALSE)
  )

  expect_snapshot(
    tmp <- recipe(~., data = mtcars) %>%
      step_center(disp) %>%
      prep() %>%
      prep(mtcars)
  )
})

test_that("vars without role in predictor/outcome avoid string processing", {

  x <- tibble(
    real_pred = 1:5,
    chr_pred_and_lime = letters[1:5],
    chr_outcome = letters[1:5],
    chr_only_lemon = letters[1:5],
    chr_only_lime = letters[1:5]
  )
  var_info <- tibble(variable = names(x), source = "original")
  var_info <- full_join(get_types(x), var_info, by = "variable")
  var_info$role <- c("predictor", "predictor", "outcome", "lemon", "lime")
  additional_row <- var_info[2, ]
  additional_row$role <- "lime"
  var_info <- var_info %>% add_row(additional_row)

  orig_lvls <- lapply(x, get_levels)
  training <- strings2factors(x, orig_lvls)
  original_expectation <- c(FALSE, rep(TRUE, 4))
  names(original_expectation) <- names(x)
  expect_identical(has_lvls(orig_lvls), original_expectation)
  expect_identical(orig_lvls$real_pred, list(values = NA, ordered = NA))
  expect_identical(
    orig_lvls$chr_pred_and_lime,
    list(values = letters[1:5], ordered = FALSE, factor = FALSE)
  )
  expect_identical(
    orig_lvls$chr_outcome,
    list(values = letters[1:5], ordered = FALSE, factor = FALSE)
  )
  expect_identical(
    orig_lvls$chr_only_lemon, # gets converted to fctr
    list(values = letters[1:5], ordered = FALSE, factor = FALSE)
  )
  expect_identical(
    orig_lvls$chr_only_lime, # gets converted to fctr
    list(values = letters[1:5], ordered = FALSE, factor = FALSE)
  )


  new_lvls <- kill_levels(orig_lvls, var_info)
  new_expect <- original_expectation
  new_expect[4:5] <- FALSE
  expect_identical(has_lvls(new_lvls), new_expect)
  expect_identical(new_lvls$real_pred, orig_lvls$real_pred)
  # chr predictor gets converted, despite also having another role
  expect_identical(new_lvls$chr_pred_and_lime, orig_lvls$chr_pred_and_lime)
  expect_identical(new_lvls$chr_outcome, orig_lvls$chr_outcome)
  # non-predictor / non-outcome var remains chr, we don't log the levels
  expect_identical(new_lvls$chr_only_lemon, list(values = NA, ordered = NA))
  expect_identical(new_lvls$chr_only_lime, list(values = NA, ordered = NA))
})

test_that("spline error messages", {
  expect_snapshot(
    recipes:::spline_msg("Error in if (df < 0) { : missing blah blah\n"),
    error = TRUE
  )
  expect_snapshot(
    recipes:::spline_msg("craaazzyy {{}}{}{}"),
    error = TRUE
  )
})

test_that("names0() error on non-positive number", {
  expect_snapshot(
    error = TRUE,
    names0(0)
  )
})

test_that("ellipse_check() errors on empty selection", {
  expect_snapshot(
    error = TRUE,
    ellipse_check()
  )
})

test_that("ellipse_check() errors on empty selection", {
  x <- 2
  class(x) <- "dimRedResult"
  expect_snapshot(
    error = TRUE,
    uses_dim_red(x)
  )
})