Pipe operator (%>% and |>) in R Programming - Cheat Sheet & Quick Revision

Recall & Review

beginner

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

The pipe operator %>% takes the output of the expression on its left and passes it as the first argument to the function on its right. It helps write code that reads like a sequence of steps.

intermediate

How is the native pipe operator |> in R different from %>%?

The native pipe |> was introduced in R 4.1.0. It passes the left side value as the first argument to the right side function, similar to %>%, but it is built into base R and does not require extra packages.

beginner

Write a simple example using %>% to take a number 5, add 3, then multiply by 2.

Example:

library(dplyr)
5 %>%
  add(3) %>%
  multiply_by(2)

This means: start with 5, add 3, then multiply the result by 2.

beginner

Why do programmers like to use pipe operators in R?

Pipes make code easier to read and write by showing a clear flow of data through steps. It feels like telling a story: take this, then do that, then do next.

intermediate

Can you use the pipe operator %>% with functions that do not take the first argument as the data?

Yes, but you need to use a placeholder like . to tell the pipe where to put the left side value. For example:

3 %>% rep(x = letters, times = .)

Here, %>% passes 3 as the times argument to rep using the . placeholder.

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

A Passes the left value as the first argument to the right function

B Creates a new variable

C Repeats a loop

D Defines a function

The pipe operator %>% passes the output on the left as the first argument to the function on the right.

Which R version introduced the native pipe operator |>?

A R 3.6.0

B R 4.0.0

C R 4.1.0

D R 4.2.0

The native pipe operator |> was introduced in R version 4.1.0.

How do you pass the left value to a function argument other than the first when using %>%?

A Use a semicolon

B You cannot do that

C Use parentheses

D Use a placeholder <code>.</code>

Using a placeholder . tells the pipe where to insert the left value if it is not the first argument.

Which package commonly provides the %>% pipe operator?

A ggplot2

B dplyr

C shiny

D tidyr

The %>% pipe operator is provided by the dplyr package, part of the tidyverse.

What is a main benefit of using pipe operators in R?

A Improves code readability and flow

B Makes code run faster

C Reduces memory usage

D Automatically fixes errors

Pipes improve code readability by showing a clear sequence of data transformations.

Explain how the pipe operator %>% works in R and give a simple example.

Think of it as passing the result from one step to the next.

Describe the difference between %>% and the native pipe |> in R.

One is from a package, the other is built into R.

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