replace_nas_with_explicit: Create explicit factor level for missing values.

In OuhscBbmc/OuhscMunge: Data Manipulation Operations

Create explicit factor level for missing values.

Description

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.

The function is retained so existing code doesn't break. For new code, consider using dplyr::coalesce(). if you don't need to convert the missing code to a factor level.

Usage

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

library(OuhscMunge) #Load the package into the current R session.
missing_indices <- c(3, 6, 8, 25)
# With a character variable:
a <- letters
a[missing_indices] <- NA_character_
a <- OuhscMunge::replace_nas_with_explicit(a)

# With a factor variable:
b <- factor(letters, levels=letters)
b[missing_indices] <- NA_character_
b <- OuhscMunge::replace_nas_with_explicit(b, add_unknown_level=TRUE)

OuhscBbmc/OuhscMunge documentation built on Dec. 5, 2024, 4:34 a.m.