validate_predictors_are_numeric: Ensure predictors are all numeric
In DavisVaughan/hardhat: Construct Modeling Packages

Description Usage Arguments Details Value Validation See Also Examples

validate - asserts the following:

predictors must have numeric 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.

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validate_predictors_are_numeric(predictors)

check_predictors_are_numeric(predictors)

predictors

An object to check.

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

validate_predictors_are_numeric() returns predictors invisibly.

check_predictors_are_numeric() 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.

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

# All good
check_predictors_are_numeric(mtcars)

# Species is not numeric
check_predictors_are_numeric(iris)

# This gives an intelligent error message
try(validate_predictors_are_numeric(iris))