| Title: | Convert Values to NA |
|---|---|
| Description: | Provides a replacement for dplyr::na_if(). Allows you to specify multiple values to be replaced with NA using a single function. |
| Authors: | Alexander Rossell Hayes [aut, cre, cph]
|
| Maintainer: | Alexander Rossell Hayes <[email protected]> |
| License: | MIT + file LICENSE |
| Version: | 0.7.1 |
| Built: | 2024-12-08 03:15:19 UTC |
| Source: | https://github.com/rossellhayes/fauxnaif |
A dataset containing fake demographic data, used in the fauxnaif vignette.
faux_censusfaux_census
A tibble with 20 rows and 6 variables.
Fabricated
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.
na_if_in(x, ...) na_if_not(x, ...)na_if_in(x, ...) na_if_not(x, ...)
x |
Vector to modify |
... |
Values to replace with
|
A modified version of x with selected values replaced with
NA.
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.
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.
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)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)