update_role_requirements() allows you to fine tune requirements of the various roles you might come across in recipes (see update_role() for general information about roles). Role requirements can only be altered for roles that exist in the original data supplied to recipe(), they are not applied to columns computed by steps.

Like update_role(), update_role_requirements() is applied to the recipe immediately, unlike the step_*() functions which do most of their work at prep() time.

## Usage

update_role_requirements(recipe, role, ..., bake = NULL)

## Arguments

recipe

A recipe.

role

A string representing the role that you'd like to modify the requirements of. This must be a role that already exists in the recipe.

...

These dots are for future extensions and must be empty.

bake

At bake() time, should a check be done to ensure that all columns of this role that were supplied to recipe() also be present in the new_data supplied to bake()?

Must be a single TRUE or FALSE. The default, NULL, won't modify this requirement.

The following represents the default bake time requirements of specific types of roles:

• "outcome": Not required at bake time. Can't be changed.

• "predictor": Required at bake time. Can't be changed.

• "case_weights": Not required at bake time by default.

• NA: Required at bake time by default.

• Custom roles: Required at bake time by default.

## Examples

df <- tibble(y = c(1, 2, 3), x = c(4, 5, 6), var = c("a", "b", "c"))

# Let's assume that you have a var column that isn't used in the recipe.
# We typically recommend that you remove this column before passing the
# data to recipe(), but for now let's pass it through and assign it an
# "id" role.
rec <- recipe(y ~ ., df) %>%
update_role(var, new_role = "id") %>%
step_center(x)

prepped <- prep(rec, df)

# Now assume you have some "new data" and you are ready to bake() it
# to prepare it for prediction purposes. Here, you might not have var
# available as a column because it isn't important to your model.
new_data <- df[c("y", "x")]

# By default var is required at bake() time because we don't know if
# you actually use it in the recipe or not
try(bake(prepped, new_data))
#> Error in bake(prepped, new_data) :
#>   The following required columns are missing from new_data: "var".
#> ℹ These columns have one of the following roles, which are required at bake() time: "id".
#> ℹ If these roles are not required at bake() time, use update_role_requirements(role = "your_role", bake = FALSE).

# You can turn off this check by using update_role_requirements() and
# setting bake = FALSE for the "id" role. We recommend doing this on
# the original unprepped recipe, but it will also work on a prepped recipe.
rec <- update_role_requirements(rec, "id", bake = FALSE)
prepped <- prep(rec, df)

# Now you can bake() on new_data even though var is missing
bake(prepped, new_data)
#> # A tibble: 3 × 2
#>       x     y
#>   <dbl> <dbl>
#> 1    -1     1
#> 2     0     2
#> 3     1     3