check_missing() creates a specification of a recipe operation that will
check if variables contain missing values.
check_missing(
recipe,
...,
role = NA,
trained = FALSE,
columns = NULL,
skip = FALSE,
id = rand_id("missing")
)A recipe object. The check will be added to the sequence of operations for this recipe.
One or more selector functions to choose variables for this check.
See selections() for more details.
Not used by this check since no new variables are created.
A logical for whether the selectors in ... have been
resolved by prep().
A character string of the selected variable names. This field
is a placeholder and will be populated once prep() is used.
A logical. Should the check be skipped when the recipe is baked
by bake()? While all operations are baked when prep() is run, some
operations may not be able to be conducted on new data (e.g. processing the
outcome variable(s)). Care should be taken when using skip = TRUE as it
may affect the computations for subsequent operations.
A character string that is unique to this check to identify it.
An updated version of recipe with the new check added to the
sequence of any existing operations.
This check will break the bake() function if any of the checked columns
does contain NA values. If the check passes, nothing is changed to the
data.
When you tidy() this check, a tibble with column terms
(the selectors or variables selected) is returned.
Other checks:
check_class(),
check_cols(),
check_new_values(),
check_range()
data(credit_data, package = "modeldata")
is.na(credit_data) |> colSums()
#> Status Seniority Home Time Age Marital Records Job
#> 0 0 6 0 0 1 0 2
#> Expenses Income Assets Debt Amount Price
#> 0 381 47 18 0 0
# If the test passes, `new_data` is returned unaltered
recipe(credit_data) |>
check_missing(Age, Expenses) |>
prep() |>
bake(credit_data)
#> # A tibble: 4,454 × 14
#> Status Seniority Home Time Age Marital Records Job Expenses Income
#> <fct> <int> <fct> <int> <int> <fct> <fct> <fct> <int> <int>
#> 1 good 9 rent 60 30 married no freelan… 73 129
#> 2 good 17 rent 60 58 widow no fixed 48 131
#> 3 bad 10 owner 36 46 married yes freelan… 90 200
#> 4 good 0 rent 60 24 single no fixed 63 182
#> 5 good 0 rent 36 26 single no fixed 46 107
#> 6 good 1 owner 60 36 married no fixed 75 214
#> 7 good 29 owner 60 44 married no fixed 75 125
#> 8 good 9 parents 12 27 single no fixed 35 80
#> 9 good 0 owner 60 32 married no freelan… 90 107
#> 10 bad 0 parents 48 41 married no partime 90 80
#> # ℹ 4,444 more rows
#> # ℹ 4 more variables: Assets <int>, Debt <int>, Amount <int>, Price <int>
# If your training set doesn't pass, prep() will stop with an error
if (FALSE) { # \dontrun{
recipe(credit_data) |>
check_missing(Income) |>
prep()
} # }
# If `new_data` contain missing values, the check will stop `bake()`
train_data <- credit_data |> dplyr::filter(Income > 150)
test_data <- credit_data |> dplyr::filter(Income <= 150 | is.na(Income))
rp <- recipe(train_data) |>
check_missing(Income) |>
prep()
bake(rp, train_data)
#> # A tibble: 1,338 × 14
#> Status Seniority Home Time Age Marital Records Job Expenses Income
#> <fct> <int> <fct> <int> <int> <fct> <fct> <fct> <int> <int>
#> 1 bad 10 owner 36 46 married yes freelance 90 200
#> 2 good 0 rent 60 24 single no fixed 63 182
#> 3 good 1 owner 60 36 married no fixed 75 214
#> 4 good 8 owner 60 30 married no fixed 75 199
#> 5 good 19 priv 36 37 married no fixed 75 170
#> 6 good 15 priv 24 52 single no freelance 35 330
#> 7 good 33 rent 24 68 married no freelance 65 200
#> 8 good 5 owner 60 22 single no fixed 45 324
#> 9 good 19 owner 60 43 single no fixed 75 180
#> 10 good 15 owner 36 43 married no fixed 75 251
#> # ℹ 1,328 more rows
#> # ℹ 4 more variables: Assets <int>, Debt <int>, Amount <int>, Price <int>
if (FALSE) { # \dontrun{
bake(rp, test_data)
} # }