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") )
recipe | A recipe object. The check will be added to the sequence of operations for this recipe. |
---|---|
... | One or more selector functions to choose which
variables are checked in the check. See |
role | Not used by this check since no new variables are created. |
trained | A logical for whether the selectors in |
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 |
values | A named list with the allowed values.
This is |
skip | A logical. Should the check be skipped when the
recipe is baked by |
id | A character string that is unique to this step to identify it. |
An updated version of recipe
with the new check
added to the sequence of existing operations (if any). For the
tidy
method, a tibble with columns terms
(the
selectors or variables selected).
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.
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 x 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 #> # … 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 x 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 #> # … with 4,444 more rows, and 4 more variables: Assets <int>, Debt <int>, #> # Amount <int>, Price <int>