replace_nas_with_explicit: Create explicit factor level for missing values.

Description Usage Arguments Value Note Author(s) Examples

Description

Missing values are converted to a factor level. This explicit assignment can reduce the chances that missing values are inadvertantly ignored. It also allows the presence of a missing to become a predictor in models.

Usage

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replace_nas_with_explicit(scores, new_na_label = "Unknown",
  create_factor = FALSE, add_unknown_level = 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.

add_unknown_level

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.

Author(s)

Will Beasley

Examples

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


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