Pipe operator (%>% and |>) in R Programming

Introduction

The pipe operator helps you write code that reads like a recipe. It passes the result of one step directly into the next step, making your code easier to follow.

When you want to apply multiple functions one after another on some data.

When you want to avoid creating many temporary variables.

When you want your code to look like a clear sequence of actions.

When working with data frames and you want to filter, transform, and summarize data step-by-step.

When you want to improve readability by writing code from left to right.

Syntax

R Programming

data %>% function1() %>% function2()

# or using native pipe in R 4.1+
data |> function1() |> function2()

The %>% operator comes from the dplyr package (part of tidyverse).

The |> operator is built into R version 4.1 and later.

Examples

This example filters cars with 6 cylinders and then calculates the average miles per gallon.

R Programming

library(dplyr)

mtcars %>%
  filter(cyl == 6) %>%
  summarise(avg_mpg = mean(mpg))

This uses the native pipe to select cars with 4 cylinders, add a new column converting mpg to kilometers per liter, and then shows the first few rows.

R Programming

mtcars |> 
  subset(cyl == 4) |> 
  transform(kpl = mpg * 0.425144) |> 
  head()

Sample Program

This program filters cars with 4 gears, converts weight from 1000 lbs to tons, then calculates the average weight in tons.

R Programming

library(dplyr)

result <- mtcars %>%
  filter(gear == 4) %>%
  mutate(weight_tons = wt / 2) %>%
  summarise(avg_weight = mean(weight_tons))

print(result)

Important Notes

Remember to load dplyr to use %>%.

The native pipe |> does not require extra packages but works slightly differently in some cases.

Using pipes makes your code easier to read and write, especially for data transformations.

Summary

The pipe operator passes the output of one function to the next.

%>% is from dplyr, |> is built-in from R 4.1.

Pipes help write clear, step-by-step code for data tasks.

Practice

1. What does the pipe operator %>% do in R?

easy

A. It creates a new variable in the environment.

B. It passes the result of one function as the first argument to the next function.

C. It comments out the following line of code.

D. It repeats a function multiple times automatically.

Solution

1. Step 1: Understand the pipe operator purpose

   The pipe operator %>% takes the output of the left side and sends it as the first argument to the function on the right side.

2. Step 2: Compare with other options

   Options B, C, and D describe unrelated actions like creating variables, commenting, or repeating functions, which are not what pipes do.

3. Final Answer:

   It passes the result of one function as the first argument to the next function. -> Option B

4. Quick Check:

   Pipe operator = passes output to next function [OK]

Hint: Pipes send output forward as input to next function [OK]

Common Mistakes:

• Thinking pipe creates variables
• Confusing pipe with comment symbol
• Assuming pipe repeats functions

2. Which of the following is the correct syntax to use the native R pipe operator introduced in R 4.1?

easy

A. data |> filter(value > 10)

B. data %>% filter(value > 10)

C. data ->| filter(value > 10)

D. data |-> filter(value > 10)

Solution

1. Step 1: Identify native pipe syntax

   The native pipe operator in R 4.1+ is |>, used as data |> function().

2. Step 2: Check other options

   data %>% filter(value > 10) uses %>% which is from dplyr, not native. Options A and D are invalid syntax.

3. Final Answer:

   data |> filter(value > 10) -> Option A

4. Quick Check:

   Native pipe = |> [OK]

Hint: Native pipe uses |> without percent signs [OK]

Common Mistakes:

• Using %>% as native pipe
• Typing invalid pipe symbols like ->|
• Confusing pipe with assignment operators

3. What is the output of this R code?

library(dplyr)
data <- tibble(x = 1:3)
data %>% mutate(y = x * 2) %>% pull(y)

medium

A. [1, 2, 3]

B. Error: object 'y' not found

C. [1, 3, 5]

D. [2, 4, 6]

Solution

1. Step 1: Understand the pipe steps

   The data tibble has x = 1, 2, 3. The mutate(y = x * 2) adds y as double x: 2, 4, 6.

2. Step 2: Extract the y column

   pull(y) extracts the y column as a vector: 2, 4, 6.

3. Final Answer:

   [2, 4, 6] -> Option D

4. Quick Check:

   Mutate doubles x, pull extracts y [OK]

Hint: Mutate adds column, pull extracts it [OK]

Common Mistakes:

• Forgetting mutate creates y
• Thinking pull returns whole data frame
• Confusing x and y values

4. Identify the error in this R code using the native pipe operator:

data <- data.frame(a = 1:3)
data |> filter(a > 1)

medium

A. filter() is not found because dplyr is not loaded

B. Native pipe operator |> is invalid syntax here

C. data.frame cannot be piped

D. No error, code runs fine

Solution

1. Step 1: Check package usage

   The function filter() is from dplyr. If dplyr is not loaded, filter() is not available.

2. Step 2: Validate pipe and data

   The native pipe |> works fine with data frames. The error is due to missing dplyr, not pipe or data type.

3. Final Answer:

   filter() is not found because dplyr is not loaded -> Option A

4. Quick Check:

   Missing dplyr causes filter error [OK]

Hint: Load dplyr before using filter() [OK]

Common Mistakes:

• Assuming pipe syntax is wrong
• Thinking data.frame can't be piped
• Ignoring missing library calls

5. Using the native pipe operator, how would you transform a numeric vector v <- c(1, 2, 3, 4) to get the sum of squares of even numbers only?

hard

A. v %>% subset(v %% 2 == 0) %>% sum()^2

B. v |> filter(v %% 2 == 0) |> sum()^2

C. v |> \(x) x[x %% 2 == 0] |> \(x) sum(x^2)

D. v |> map(~ .x^2) |> sum()

Solution

1. Step 1: Filter even numbers using native pipe and anonymous function

   The expression \(x) x[x %% 2 == 0] selects even numbers from vector v.

2. Step 2: Sum squares of filtered numbers

   The next pipe applies \(x) sum(x^2) to calculate sum of squares of those even numbers.

3. Final Answer:

   v |> \(x) x[x %% 2 == 0] |> \(x) sum(x^2) -> Option C

4. Quick Check:

   Filter evens then sum squares [OK]

Hint: Use anonymous functions with |> for complex steps [OK]

Common Mistakes:

• Using filter() without loading dplyr
• Squaring sum instead of summing squares
• Using %>% with native pipe syntax