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,
)

Arguments

scores

An array of values, ideally either factor or character. Required

new_na_label

The factor label assigned to the missing value. Defaults to Unknown.

create_factor

Converts scores into a factor, if it isn't one already. Defaults to FALSE.

Should a new factor level be created? (Specify TRUE if it already exists.) Defaults to FALSE.

Value

An array of values, where the NA values are now a factor level, with the label specified by the new_na_label value.

Note

The create_factor parameter is respected only if scores isn't already a factor. Otherwise, levels without any values would be lost.

A stop error will be thrown if the operation fails to convert all the NA values.

Will Beasley

Examples

library(REDCapR) # Load the package into the current R session.