check_missing creates a specification of a recipe
operation that will check if variables contain missing values.
Usage
check_missing(
recipe,
...,
role = NA,
trained = FALSE,
columns = NULL,
skip = FALSE,
id = rand_id("missing")
)Arguments
- recipe
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.- role
Not used by this check since no new variables are created.
- trained
A logical for whether the selectors in
...have been resolved byprep().- columns
A character string of the selected variable names. This field is a placeholder and will be populated once
prep()is used.- skip
A logical. Should the check be skipped when the recipe is baked by
bake()? While all operations are baked whenprep()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 usingskip = TRUEas it may affect the computations for subsequent operations.- id
A character string that is unique to this check to identify it.
Value
An updated version of recipe with the new check added to the
sequence of any existing operations.
Details
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.
tidy() results
When you tidy() this check, a tibble with column
terms (the selectors or variables selected) is returned.
See also
Other checks:
check_class(),
check_cols(),
check_new_values(),
check_range()
Examples
data(credit_data, package = "modeldata")
is.na(credit_data) %>% colSums()
#> Status Seniority Home Time Age Marital Records
#> 0 0 6 0 0 1 0
#> Job Expenses Income Assets Debt Amount Price
#> 2 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
#> <fct> <int> <fct> <int> <int> <fct> <fct> <fct> <int>
#> 1 good 9 rent 60 30 married no freelance 73
#> 2 good 17 rent 60 58 widow no fixed 48
#> 3 bad 10 owner 36 46 married yes freelance 90
#> 4 good 0 rent 60 24 single no fixed 63
#> 5 good 0 rent 36 26 single no fixed 46
#> 6 good 1 owner 60 36 married no fixed 75
#> 7 good 29 owner 60 44 married no fixed 75
#> 8 good 9 parents 12 27 single no fixed 35
#> 9 good 0 owner 60 32 married no freelance 90
#> 10 bad 0 parents 48 41 married no partime 90
#> # ℹ 4,444 more rows
#> # ℹ 5 more variables: Income <int>, Assets <int>, Debt <int>,
#> # Amount <int>, Price <int>
# If your training set doesn't pass, prep() will stop with an error
if (FALSE) {
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
#> <fct> <int> <fct> <int> <int> <fct> <fct> <fct> <int>
#> 1 bad 10 owner 36 46 married yes freelance 90
#> 2 good 0 rent 60 24 single no fixed 63
#> 3 good 1 owner 60 36 married no fixed 75
#> 4 good 8 owner 60 30 married no fixed 75
#> 5 good 19 priv 36 37 married no fixed 75
#> 6 good 15 priv 24 52 single no freelance 35
#> 7 good 33 rent 24 68 married no freelance 65
#> 8 good 5 owner 60 22 single no fixed 45
#> 9 good 19 owner 60 43 single no fixed 75
#> 10 good 15 owner 36 43 married no fixed 75
#> # ℹ 1,328 more rows
#> # ℹ 5 more variables: Income <int>, Assets <int>, Debt <int>,
#> # Amount <int>, Price <int>
if (FALSE) {
bake(rp, test_data)
}