How to Use the Pipe Operator in R (With Examples)

Posted on July 22, 2022 by Zach

You can use the pipe operator (%>%) in R to “pipe” together a sequence of operations.

This operator is most commonly used with the dplyr package in R to perform a sequence of operations on a data frame.

The basic syntax for the pipe operator is:

Example 1: Use Pipe Operator to Summarize One Variable

The following code shows how to use the pipe (%>%) operator to group by the cyl variable and then summarize the mean value of the mpg variable:

library(dplyr)#summarize mean mpg grouped by cylmtcars %>% group_by(cyl) %>% summarise(mean_mpg = mean(mpg))# A tibble: 3 x 2 cyl mean_mpg 1 4 26.72 6 19.73 8 15.1

From the output we can see:

The mean mpg value for the cars with a cyl value of 4 is 26.7.
The mean mpg value for the cars with a cyl value of 6 is 19.7.
The mean mpg value for the cars with a cyl value of 8 is 15.1.

Notice how easy the pipe operator makes it to interpret the code as well.

Essentially, it says:

Take the mtcars data frame.
Group it by the cyl variable.
Then summarize the mean value of the mpg variable.

Example 2: Use Pipe Operator to Group & Summarize Multiple Variables

The following code shows how to use the pipe (%>%) operator to group by the cyl and am variables, and then summarize the mean of the mpg variable and the standard deviation of the hp variable:

library(dplyr)#summarize mean mpg and standard dev of hp grouped by cyl and ammtcars %>% group_by(cyl, am) %>% summarise(mean_mpg = mean(mpg), sd_hp = sd(hp))# A tibble: 6 x 4# Groups: cyl [3] cyl am mean_mpg sd_hp 1 4 0 22.9 19.7 2 4 1 28.1 22.7 3 6 0 19.1 9.184 6 1 20.6 37.5 5 8 0 15.0 33.4 6 8 1 15.4 50.2

From the output we can see:

For cars with a cyl value of 4 and am value of 0, the mean mpg value is 22.9 and the standard deviation of the hp value is 19.7.
For cars with a cyl value of 4 and am value of 1, the mean mpg value is 28.1 and the standard deviation of the hp value is 22.7.

And so on.

Once again, notice how easy the pipe operator makes it to interpret the code as well.

Essentially, it says:

Take the mtcars data frame.
Group it by the cyl and the am variables.
Then summarize the mean value of the mpg variable and the standard deviation of the hp variable.

Example 3: Use Pipe Operator to Create New Variables

The following code shows how to use the pipe (%>%) operator along with the mutate function from the dplyr package to create two new variables in the mtcars data frame:

library(dplyr)#add two new variables in mtcarsnew_mtcars <- mtcars %>% mutate(mpg2 = mpg*2, mpg_root = sqrt(mpg))#view first six rows of new data framehead(new_mtcars) mpg cyl disp hp drat wt qsec vs am gear carb mpg2 mpg_root1 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4 42.0 4.5825762 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4 42.0 4.5825763 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1 45.6 4.7749354 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1 42.8 4.6260135 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2 37.4 4.3243506 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1 36.2 4.254409

From the output we can see:

The new mpg2 column contains the values of the mpg column multiplied by 2.
The new mpg_root column contains the square root of the values in the mpg column.

Once again, notice how easy the pipe operator makes it to interpret the code as well.

Essentially, it says:

Take the mtcars data frame.
Create a new column called mpg2 and a new column called mpg_root.

Additional Resources

The following tutorials explain how to use other common functions in R:

How to Use the Tilde Operator (~) in R
How to Use Dollar Sign ($) Operator in R
How to Use “NOT IN” Operator in R
How to Use %in% Operator in R

How to Use the Pipe Operator in R (With Examples) - Statology (2024)

Example 1: Use Pipe Operator to Summarize One Variable

Example 2: Use Pipe Operator to Group & Summarize Multiple Variables

Example 3: Use Pipe Operator to Create New Variables

Additional Resources