validate_outcomes_are_factors: Ensure that the outcome has only factor columns

View source: R/validation.R

validate_outcomes_are_factorsR Documentation

Ensure that the outcome has only factor columns


validate - asserts the following:

  • outcomes must have factor columns.

check - returns the following:

  • ok A logical. Does the check pass?

  • bad_classes A named list. The names are the names of problematic columns, and the values are the classes of the matching column.






An object to check.


The expected way to use this validation function is to supply it the $outcomes element of the result of a call to mold().


validate_outcomes_are_factors() returns outcomes invisibly.

check_outcomes_are_factors() returns a named list of two components, ok and bad_classes.


hardhat provides validation functions at two levels.

  • check_*(): check a condition, and return a list. The list always contains at least one element, ok, a logical that specifies if the check passed. Each check also has check specific elements in the returned list that can be used to construct meaningful error messages.

  • validate_*(): check a condition, and error if it does not pass. These functions call their corresponding check function, and then provide a default error message. If you, as a developer, want a different error message, then call the check_*() function yourself, and provide your own validation function.

See Also

Other validation functions: validate_column_names(), validate_no_formula_duplication(), validate_outcomes_are_binary(), validate_outcomes_are_numeric(), validate_outcomes_are_univariate(), validate_prediction_size(), validate_predictors_are_numeric()


# Not a factor column.
check_outcomes_are_factors(data.frame(x = 1))

# All good
check_outcomes_are_factors(data.frame(x = factor(c("A", "B"))))

hardhat documentation built on June 30, 2022, 5:05 p.m.