How to Fill NAs Using mutate in R's dplyr Package

Introduction to Fill NAs using mutate

The problem of handling missing values (NAs) in data is a common issue in data analysis and manipulation. In this article, we will explore how to fill NAs using the mutate verb from the dplyr package in R.

Background

The dplyr package provides a grammar for data manipulation that makes it easy to perform complex operations on data frames. One of its verbs, mutate, is used to add new columns or modify existing ones by applying a function to each row of the data frame.

In this article, we will show how to use the mutate verb to fill NAs in a data frame using a custom function.

The Problem

Suppose we have a data frame with two columns: v1 and alt. We want to fill the missing values (NAs) in v1 using the value of the corresponding row in alt.

We can use the following R code to solve this problem:

library(tidyverse)

v1 = c(1 , NA, 3, 5, NA, NA, 2, 12, NA, 5, NA, 0, 1, 2, 6, 8)
alt = rnorm(length(v1), 0, 1)
tb = tibble(v1, alt)

print(tb)

for (i in 1:length(v1)) {
  if( is.na(tb[i, 'v1']) ){
    tb[i, 'v1'] = tb[i-1, 'v1']*tb[i, 'alt']
  }
}

# Print the updated data frame
print(tb)

This code uses a for loop to iterate over each row of the data frame and checks if the value in v1 is NA. If it is, it fills the NA with the product of the previous non-NA value in v1 and the corresponding value in alt.

The Solution

However, using a for loop can be time-consuming for large data frames. This is where the mutate verb comes into play.

The mutate verb allows us to apply a function to each row of the data frame without having to use a loop. In this case, we want to fill NAs in v1 using the value of the corresponding row in alt.

One way to do this is by using the accumulate2 function from the purrr package.

tb %>% 
  mutate(v1 = unlist(accumulate2(v1, alt[-1], ~if(is.na(..2))..3*..1 else ..2)))

This code uses the accumulate2 function to apply a custom function to each pair of rows in the data frame. The function checks if the value in v1 is NA and fills it with the product of the previous non-NA value in v1 and the corresponding value in alt.

Explanation

The accumulate2 function takes three arguments:

The first argument is the column to which we want to apply the function.
The second argument is the column that we want to use as a reference for filling NAs. In this case, it’s alt[-1], which excludes the first row (the one with NA).
The third argument is the custom function that we want to apply.

The ~ operator is used to refer to each row in the data frame. Inside the function, ..3 refers to the value in column 3 of the previous row (alt[-1]), and ..1 refers to the value in column 1 of the current row (v1). If the value in v1 is NA, we fill it with the product of the value in alt and the previous non-NA value in v1.

Example Use Cases

Here are some examples of using the mutate verb to fill NAs in a data frame:

Example 1: Simple multiplication

Suppose we have a data frame with two columns: x and y. We want to multiply x by y where x is not NA.

library(dplyr)

df = tibble(x = c(1, NA, 3), y = rnorm(length(x), 0, 1))
df %>% 
  mutate(x = x * y)

Example 2: Log transformation

Suppose we have a data frame with two columns: x and y. We want to apply the log transformation to x where x is not NA.

library(dplyr)

df = tibble(x = c(1, NA, 3), y = rnorm(length(x), 0, 1))
df %>% 
  mutate(x = ifelse(is.na(x), log(x), x))

Example 3: Rolling mean

Suppose we have a data frame with two columns: x and y. We want to calculate the rolling mean of x over a window size of 3.

library(dplyr)

df = tibble(x = c(1, 2, NA, 4), y = rnorm(length(x), 0, 1))
df %>% 
  mutate(x_mean = rollmean(x, width = 3))

Conclusion

The mutate verb is a powerful tool for data manipulation that allows us to apply custom functions to each row of the data frame. In this article, we explored how to use the mutate verb to fill NAs in a data frame using a custom function.

We saw three examples of using the mutate verb: simple multiplication, log transformation, and rolling mean calculation. These examples demonstrate how the mutate verb can be used to perform complex operations on the data frame without having to use loops or other workarounds.

Whether you’re working with small datasets or large ones, the mutate verb is a valuable tool in your data analysis toolkit that can help streamline your workflow and improve productivity.

Last modified on 2025-03-09