na_if_in {fauxnaif}

Convert values to NA

Description

This is a replacement for dplyr::na_if(). It is useful if you want to convert annoying values to NA. Unlike dplyr::na_if(), this function allows you to specify multiple values to be replaced with NA at the same time.

• na_if_in() replaces values that match its arguments with NA.

• na_if_not() replaces values that do not match its arguments with NA.

Usage

na_if_in(x, ...)

na_if_not(x, ...)

Arguments

x

Vector to modify

...

Values to replace with NA, specified as either:

• An object, vector of objects, or list of objects.

• A function (including a purrr-style lambda function) that returns a logical vector of the same length as x. See section "Formulas" for more details.

Value

A modified version of x with selected values replaced with NA.

Formulas

These functions accept one-sided formulas that can evaluate to logical vectors of the same length as x. The input is represented in these conditional statements as ".". Valid formulas take the form ~ . < 0. See examples.

See Also

dplyr::na_if() to replace a single value with NA.

dplyr::coalesce() to replace missing values with a specified value.

tidyr::replace_na() to replace NA with a value.

dplyr::recode() and dplyr::case_when() to more generally replace values.

Examples

x <- sample(c(1:5, 99))
# We can replace 99...
# ... explicitly
na_if_in(x, 99)
# ... by specifying values to keep
na_if_not(x, 1:5)
# ... or by using a formula
na_if_in(x, ~ . > 5)

messy_string <- c("abc", "", "def", "NA", "ghi", 42, "jkl", "NULL", "mno")
# We can replace unwanted values...
# ... one at a time
clean_string <- na_if_in(messy_string, "")
clean_string <- na_if_in(clean_string, "NA")
clean_string <- na_if_in(clean_string, 42)
clean_string <- na_if_in(clean_string, "NULL")
clean_string
# ... or all at once
na_if_in(messy_string, "", "NA", "NULL", 1:100)
na_if_in(messy_string, c("", "NA", "NULL", 1:100))
na_if_in(messy_string, list("", "NA", "NULL", 1:100))
# ... or using a clever formula
grepl("[a-z]{3,}", messy_string)
na_if_not(messy_string, ~ grepl("[a-z]{3,}", .))

# na_if_in() is particularly useful inside dplyr::mutate
library(dplyr)
faux_census %>%
  mutate(
    state = na_if_in(state, "Canada"),
    age   = na_if_in(age, ~ . < 18, ~ . > 120)
  )

# This function handles vector values differently than dplyr,
# and returns a different result with vector replacement values:
na_if_in(1:5, 5:1)
dplyr::na_if(1:5, 5:1)