Table of Contents
Replacing NAs (missing values) with strings in R can be done using the is.na() and ifelse() functions. The is.na() function returns a logical vector with TRUE for missing values and FALSE for present values. The ifelse() function is used to create a vector which returns a value when the condition is TRUE and another value when the condition is FALSE. An example of this would be ifelse(is.na(x), “missing”, x) which returns “missing” when x is missing and x when x is present.
You can use the replace_na() function from the tidyr package to replace NAs with specific strings in a column of a data frame in R:
#replace NA values in column x with "missing"
df$x %>% replace_na('none')
You can also use this function to replace NAs with specific strings in multiple columns of a data frame:
#replace NA values in column x with "missing" and NA values in column y with "none" df %>% replace_na(list(x = 'missing', y = 'none'))
The following examples show how to use this function in practice.
Example 1: Replace NAs with Strings in One Column
The following code shows how to replace NAs with a specific string in one column of a data frame:
library(tidyr)
df <- data.frame(status=c('single', 'married', 'married', NA),
education=c('Assoc', 'Bach', NA, 'Master'),
income=c(34, 88, 92, 90))
#view data frame
df
status education income
1 single Assoc 34
2 married Bach 88
3 married <NA> 92
4 <NA> Master 90
#replace missing values with 'single' in status column
df$status <- df$status %>% replace_na('single')
#view updated data frame
df
status education income
1 single Assoc 34
2 married Bach 88
3 married <NA> 92
4 single Master 90
Example 2: Replace NAs with Strings in Multiple Columns
The following code shows how to replace NAs with a specific string in multiple columns of a data frame:
library(tidyr)
df <- data.frame(status=c('single', 'married', 'married', NA),
education=c('Assoc', 'Bach', NA, 'Master'),
income=c(34, 88, 92, 90))
#view data frame
df
status education income
1 single Assoc 34
2 married Bach 88
3 married <NA> 92
4 <NA> Master 90
#replace missing values with 'single' in status column
df <- df %>% replace_na(list(status = 'single', education = 'none'))
#view updated data frame
df
status education income
1 single Assoc 34
2 married Bach 88
3 married none 92
4 single Master 90