void: Void Value Utilities

In evanverse: Utility Functions for Data Analysis and Visualization

Void Value Utilities

Description

A comprehensive suite of functions for detecting, removing, and managing "void" values (NA, NULL, and empty strings) in R objects.

Usage

is_void(x, include_na = TRUE, include_null = TRUE, include_empty_str = TRUE)

any_void(x, include_na = TRUE, include_null = TRUE, include_empty_str = TRUE)

drop_void(x, include_na = TRUE, include_null = TRUE, include_empty_str = TRUE)

replace_void(
  x,
  value = NA,
  include_na = TRUE,
  include_null = TRUE,
  include_empty_str = TRUE
)

cols_with_void(
  data,
  include_na = TRUE,
  include_null = TRUE,
  include_empty_str = TRUE,
  return_names = TRUE
)

rows_with_void(
  data,
  include_na = TRUE,
  include_null = TRUE,
  include_empty_str = TRUE
)

Arguments

x	A vector or list.
include_na	Logical. Detect NA if TRUE. Default: TRUE.
include_null	Logical. Detect NULL if TRUE. Default: TRUE.
include_empty_str	Logical. Detect empty strings "" if TRUE. Default: TRUE.
value	The replacement value to use for voids. Default: NA.
data	A data.frame or tibble.
return_names	Logical. If TRUE (default), return column names; else logical vector.

Details

The void utilities family consists of:

• is_void: Core detection function returning logical vector

• any_void: Check if any void value exists

• drop_void: Remove void values from vectors/lists

• replace_void: Replace void values with custom values

• cols_with_void: Detect columns containing void values

• rows_with_void: Detect rows containing void values

All functions support customizable void detection through three parameters:

• include_na: Consider NA as void (default: TRUE)

• include_null: Consider NULL as void (default: TRUE)

• include_empty_str: Consider "" as void (default: TRUE)

Value

A logical vector indicating which elements are void.

• If x is NULL, returns a single TRUE (if include_null=TRUE) or FALSE.

• If x is an empty vector, returns logical(0).

• If x is a list, evaluates each element recursively and returns a flattened logical vector.

• For atomic vectors, returns a logical vector of the same length.

A single logical value:

• TRUE if any void values are present.

• FALSE otherwise.

• For NULL input, returns TRUE if include_null = TRUE, else FALSE.

A cleaned vector or list of the same type as input, with void values removed.

A cleaned vector or list with void values replaced.

A character vector (column names) or logical vector indicating void presence per column.

A logical vector of length nrow(data) indicating whether each row contains at least one void value.

is_void()

Check for Null / NA / Blank ("") Values

Determine whether input values are considered "void": NULL, NA, or "". Each condition is controlled by a dedicated argument.

any_void()

Check if Any Value is Void (NA / NULL / "")

Test whether any element in a vector or list is considered "void". Void values include NA, NULL, and empty strings (""), and you can customize which ones to consider.

drop_void

Remove Void Values from a Vector or List

Removes elements from a vector or list that are considered "void": NA, NULL, and empty strings (""). Each can be toggled via parameters.

replace_void

Replace void values (NA / NULL / "")

Replace elements in a vector or list considered "void" with a specified value. Void values include NA, NULL, and empty strings "" (toggle via flags).

cols_with_void()

Detect Columns Containing Void Values

Scan a data.frame or tibble and identify columns that contain any "void" values. Void values include NA, NULL, and "", which can be toggled via parameters.

rows_with_void

Detect rows containing void values (NA / NULL / "")

Scan a data.frame or tibble and identify rows that contain any "void" values. Void values include NA, NULL, and empty strings "" (toggle via flags).

Examples

is_void(c(NA, "", "text"))                  # TRUE TRUE FALSE
is_void(list(NA, "", NULL, "a"))            # TRUE TRUE TRUE FALSE
is_void("NA", include_na = FALSE)           # FALSE
is_void(NULL)                               # TRUE
any_void(c("a", "", NA))                # TRUE
any_void(list("x", NULL, "y"))          # TRUE
any_void(c("a", "b", "c"))              # FALSE
any_void(NULL)                          # TRUE
any_void("", include_empty_str = FALSE) # FALSE
drop_void(c("apple", "", NA, "banana"))
drop_void(list("A", NA, "", NULL, "B"))
drop_void(c("", NA), include_na = FALSE)
replace_void(c(NA, "", "a"), value = "N/A")
replace_void(list("A", "", NULL, NA), value = "missing")
replace_void(c("", "b"), value = 0, include_empty_str = TRUE)
df <- data.frame(name = c("A", "", "C"), score = c(1, NA, 3), id = 1:3)
cols_with_void(df)
cols_with_void(df, return_names = FALSE)
cols_with_void(df, include_na = FALSE)
df <- data.frame(id = 1:3, name = c("A", "", "C"), score = c(10, NA, 20))
rows_with_void(df)
df[rows_with_void(df), ]

evanverse documentation built on March 10, 2026, 5:07 p.m.