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test_that("data_partition works as expected", {
# not supported
expect_error(
data_partition(new.env()),
"`data` must be a data frame"
)
# to be coerced to data frames
expect_snapshot(data_partition(letters, seed = 123))
# validation checks
expect_warning(
data_partition(iris, 0.7, row_id = "Species"),
"exists"
)
expect_warning(expect_warning(
data_partition(iris, c(0.7, 0.3), row_id = "Species"),
"generated"
))
# values
out <- data_partition(mtcars, proportion = 0.8, seed = 123)
expect_identical(
out$p_0.8$.row_id,
c(
1L, 3L, 4L, 5L, 7L, 8L, 9L, 10L, 11L, 14L, 15L, 17L, 18L, 19L,
20L, 21L, 22L, 23L, 24L, 26L, 27L, 28L, 29L, 30L, 31L, 32L
)
)
expect_identical(
colnames(out$p_0.8),
c(
"mpg", "cyl", "disp", "hp", "drat", "wt", "qsec", "vs", "am",
"gear", "carb", ".row_id"
)
)
expect_identical(
lapply(out, nrow),
list(p_0.8 = 26L, test = 6L)
)
# data frames
data(iris)
expect_snapshot(str(data_partition(iris, proportion = 0.7, seed = 123)))
expect_snapshot(str(data_partition(iris, proportion = c(0.2, 0.5), seed = 123)))
expect_snapshot(str(data_partition(iris, proportion = 0.7, by = "Species", seed = 123)))
expect_snapshot(str(data_partition(iris, proportion = c(0.2, 0.5), by = "Species", seed = 123)))
})
test_that("data_partition warns if no testing set", {
expect_warning(
data_partition(iris, proportion = 1),
"sums up to 1"
)
expect_warning(
data_partition(iris, proportion = c(0.5, 0.5)),
"sums up to 1"
)
})
test_that("data_partition errors if values in proportion not between 0 and 1", {
expect_error(
data_partition(iris, proportion = 1.3),
"cannot be higher"
)
expect_error(
data_partition(iris, proportion = c(0.5, 0.6)),
"cannot be higher"
)
expect_error(
data_partition(iris, proportion = c(1.3, -1)),
"cannot be negative"
)
expect_error(
data_partition(iris, proportion = -1),
"cannot be negative"
)
})
test_that("data_partition warns if row_id already exists", {
iris2 <- iris
iris2[[".row_id"]] <- "A"
expect_warning(
data_partition(iris2, proportion = 0.5),
"already exists"
)
iris2[["foo"]] <- "A"
expect_warning(
data_partition(iris2, proportion = 0.5, row_id = "foo"),
"already exists"
)
part1 <- data_partition(iris, proportion = 0.5, seed = 123)
part2 <- suppressWarnings(data_partition(iris2, proportion = 0.5, seed = 123))
expect_identical(
part1$p_0.5[1:5],
part2$p_0.5[1:5]
)
})
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