check_new_values creates a specification of a recipe operation that will check if variables contain new values.

check_new_values(
  recipe,
  ...,
  role = NA,
  trained = FALSE,
  columns = NULL,
  ignore_NA = TRUE,
  values = NULL,
  skip = FALSE,
  id = rand_id("new_values")
)

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 by prep().

columns

A character string of variable names that will be populated (eventually) by the terms argument.

ignore_NA

A logical that indicates if we should consider missing values as value or not. Defaults to TRUE.

values

A named list with the allowed values. This is NULL until computed by prep.recipe().

skip

A logical. Should the check be skipped when the recipe is baked by bake.recipe()? While all operations are baked when prep.recipe() 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.

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 values it did not contain when prep was called on the recipe. If the check passes, nothing is changed to the data.

When you tidy() this check, a tibble with columns terms (the selectors or variables selected) is returned.

See also

Examples

library(modeldata)
data(credit_data)

# If the test passes, `new_data` is returned unaltered
recipe(credit_data) %>%
  check_new_values(Home) %>%
  prep() %>%
  bake(new_data = 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      freelance       73    129
#>  2 good          17 rent       60    58 widow   no      fixed           48    131
#>  3 bad           10 owner      36    46 married yes     freelance       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      freelance       90    107
#> 10 bad            0 parents    48    41 married no      partime         90     80
#> # … with 4,444 more rows, and 4 more variables: Assets <int>, Debt <int>,
#> #   Amount <int>, Price <int>

# If `new_data` contains values not in `x` at the `prep()` function,
# the `bake()` function will break.
if (FALSE) {
recipe(credit_data %>% dplyr::filter(Home != "rent")) %>%
  check_new_values(Home) %>%
  prep() %>%
  bake(new_data = credit_data)
}

# By default missing values are ignored, so this passes.
recipe(credit_data %>% dplyr::filter(!is.na(Home))) %>%
  check_new_values(Home) %>%
  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      freelance       73    129
#>  2 good          17 rent       60    58 widow   no      fixed           48    131
#>  3 bad           10 owner      36    46 married yes     freelance       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      freelance       90    107
#> 10 bad            0 parents    48    41 married no      partime         90     80
#> # … with 4,444 more rows, and 4 more variables: Assets <int>, Debt <int>,
#> #   Amount <int>, Price <int>

# Use `ignore_NA = FALSE` if you consider missing values  as a value,
# that should not occur when not observed in the train set.
if (FALSE) {
recipe(credit_data %>% dplyr::filter(!is.na(Home))) %>%
  check_new_values(Home, ignore_NA = FALSE) %>%
  prep() %>%
  bake(credit_data)
}