ls_explicit_na: Encode Categorical Missing Values in a 'list' of 'data.frame'

In dunlin: Preprocessing Tools for Clinical Trial Data

Encode Categorical Missing Values in a list of data.frame

Description

Encode Categorical Missing Values in a list of data.frame

Usage

ls_explicit_na(
  data,
  omit_tables = NULL,
  omit_columns = NULL,
  char_as_factor = TRUE,
  na_level = "<Missing>"
)

Arguments

data	(list of data.frame) to be transformed.
omit_tables	(character) the names of the tables to omit from processing.
omit_columns	(character) the names of the columns to omit from processing.
char_as_factor	(logical) should character columns be converted into factor.
na_level	(string) the label to encode missing levels.

Details

This is a helper function to encode missing values (i.e NA and ⁠empty string⁠) of every character and factor variable found in a list of data.frame. The label attribute of the columns is preserved.

Value

list of data.frame object with explicit missing levels.

Examples

df1 <- data.frame(
  "char" = c("a", "b", NA, "a", "k", "x"),
  "char2" = c("A", "B", NA, "A", "K", "X"),
  "fact" = factor(c("f1", "f2", NA, NA, "f1", "f1")),
  "logi" = c(NA, FALSE, TRUE, NA, FALSE, NA)
)
df2 <- data.frame(
  "char" = c("a", "b", NA, "a", "k", "x"),
  "fact" = factor(c("f1", "f2", NA, NA, "f1", "f1")),
  "num" = c(1:5, NA)
)
df3 <- data.frame(
  "char" = c(NA, NA, "A")
)

db <- list(df1 = df1, df2 = df2, df3 = df3)

ls_explicit_na(db)
ls_explicit_na(db, omit_tables = "df3", omit_columns = "char2")

dunlin documentation built on Oct. 31, 2024, 5:07 p.m.