Missing values are converted to a factor level. This explicit assignment can reduce the chances that missing values are inadvertently ignored. It also allows the presence of a missing to become a predictor in models.
replace_nas_with_explicit( scores, new_na_label = "Unknown", create_factor = FALSE, add_unknown_level = FALSE )
An array of values, ideally either factor or character. Required
The factor label assigned to the missing value.
scores into a factor, if it isn't one
already. Defaults to
Should a new factor level be created?
TRUE if it already exists.) Defaults to
An array of values, where the
NA values are now a factor level,
with the label specified by the
create_factor parameter is respected only if
scores isn't already
a factor. Otherwise, levels without any values would be lost.
stop error will be thrown if the operation fails to convert all the