Select Page

# How to Apply a Function to Every Row of a Table in R using dplyr

by | Programming, R, Tips

Using the rowwise function from dplyr in combination with the mutation function, you can apply a function to every row in a table. For example,

data %>%
rowwise() %>%
# a, b, c are column names
mutate(sum_val = sum(a, b, c))

This tutorial will go through how to use dplyr to apply a row-wise function to a table in R with code examples.

## Example #1: Sum of Rows

Let’s look at an example to calculate the row-wise mean of data frame using dplyr. First, we need to install and load dplyr if we do not already have it.

install.packages("dplyr")
library("dplyr")

Next, we will create the data frame:

data <- data.frame(a=c(2, 4, 6, 8, 10),
b=c(3, 5, 7, 9, 11),
c=c(4, 16, 64, 256, 1024))

data
a  b    c
1  2  3    4
2  4  5   16
3  6  7   64
4  8  9  256
5 10 11 1024

Once we have the data frame we can use the following syntax to apply the sum() function to each row in a data frame.

data %>%
rowwise() %>%
mutate(sum_cols = sum(a, b, c))

Let’s run the code to get the result:

# A tibble: 5 × 4
# Rowwise:
a     b     c sum_cols
<dbl> <dbl> <dbl>    <dbl>
1     2     3     4        9
2     4     5    16       25
3     6     7    64       77
4     8     9   256      273
5    10    11  1024     1045

## Example #2: Mean of Rows

Let’s look at another example of using the rowwise function with mutate to calulate the mean of every row in a data frame.

data <- data.frame(a=c(2, 4, 6, 8, 10),
b=c(3, 5, 7, 9, 11),
c=c(4, 16, 64, 256, 1024))

data %>%
rowwise() %>%
mutate(mean_cols = mean(c(a, b, c)))

Let’s run the code to get the result:

# A tibble: 5 × 4
# Rowwise:
a     b     c mean_cols
<dbl> <dbl> <dbl>     <dbl>
1     2     3     4      3
2     4     5    16      8.33
3     6     7    64     25.7
4     8     9   256     91
5    10    11  1024    348.

## Example #3: Standard Deviation of Rows

Let’s look at another example of using the rowwise function with mutate to calulate the standard devation of every row in a data frame. We will make the example a bit trickier by including NA values in the data frame. We can use na.rm to remove the NA values when calculating the standard deviation.

data <- tibble::as_tibble(data.frame(a=c(NA, 4, 6, 8, 10),
b=c(3, 5, 7, 9, NA),
c=c(4, 16, NA, 256, 1024)))

data %>%
rowwise() %>%
mutate(std_cols = sd(c(a, b, c), na.rm=TRUE))

Let’s run the code to get the result:

# A tibble: 5 × 4
# Rowwise:
a     b     c std_cols
<dbl> <dbl> <dbl>    <dbl>
1    NA     3     4    0.707
2     4     5    16    6.66
3     6     7    NA    0.707
4     8     9   256  143.
5    10    NA  1024  717.

## Summary

Congratulations on reading to the end of this tutorial!

For further reading on R go to the articles:

Go to the online courses page on R to learn more about coding in R for data science and machine learning.

Have fun and happy researching!