The R Pipe Operator: Making Your Code Flow

Introduction

When you first start learning R, your code might look something like this:

result <- function3(function2(function1(data, arg1), arg2), arg3)

This nested approach works, but it’s hard to read and understand. What if there was a way to make your code read more like a sentence, flowing from left to right? Enter the pipe operator.

The pipe operator allows you to chain functions together in a way that’s intuitive and readable. Instead of nesting functions inside each other, you can “pipe” the output of one function directly into the next function as input.

What is the Pipe Operator?

The pipe operator takes the result from the expression on its left side and passes it as the first argument to the function on its right side. Think of it like a literal pipe in plumbing—data flows through it from one function to the next.

In R, there are two main pipe operators you’ll encounter:

  1. Native pipe |> (introduced in R 4.1.0, 2021)
  2. Magrittr pipe %>% (from the magrittr package, popularized by tidyverse)

The Native Pipe |>

The native pipe |> is built directly into R (no packages required). Here’s how it works:

Basic Syntax

data |> function1() |> function2() |> function3()

This is equivalent to:

function3(function2(function1(data)))

Simple Example

Let’s say you want to: 1. Take a vector of numbers 2. Calculate the square root of each 3. Round to 2 decimal places 4. Calculate the mean

Without pipes:

numbers <- c(4, 9, 16, 25, 36)
result <- mean(round(sqrt(numbers), 2))
result
[1] 4

With the native pipe:

numbers <- c(4, 9, 16, 25, 36)
result <- numbers |> 
  sqrt() |> 
  round(2) |> 
  mean()
result
[1] 4

Much more readable! You can follow the data flow from left to right, top to bottom.

The Magrittr Pipe %>%

The %>% pipe comes from the magrittr package and is widely used in the tidyverse ecosystem (dplyr, ggplot2, etc.). It works very similarly to the native pipe but has some additional features.

Loading the Package

library(magrittr)  # For standalone use
# OR
library(dplyr)     # Automatically loads %>%
# OR  
library(tidyverse) # Loads entire tidyverse, including %>%

Basic Usage

The same example with %>%:

library(magrittr)
numbers <- c(4, 9, 16, 25, 36)
result <- numbers %>% 
  sqrt() %>% 
  round(2) %>% 
  mean()
result

Practical Data Analysis Examples

Let’s look at more realistic examples using a dataset. We’ll use the built-in mtcars dataset.

Example 1: Data Summarization

Task: Find the average miles per gallon (mpg) for cars with more than 4 cylinders, rounded to 1 decimal place.

Without pipes:

result <- round(mean(mtcars[mtcars$cyl > 4, "mpg"]), 1)
result
[1] 16.6

With native pipe:

result <- mtcars |> 
  subset(cyl > 4) |> 
  subset(select = mpg) |> 
  unlist() |> 
  mean() |> 
  round(1)
result
[1] 16.6

Example 2: Using with dplyr

If you’re using dplyr (part of tidyverse), pipes become even more powerful:

library(dplyr)

# Find the 3 most fuel-efficient cars by transmission type
mtcars |> 
  group_by(am) |> 
  arrange(desc(mpg)) |> 
  slice_head(n = 3) |> 
  select(mpg, am, cyl)

Key Differences Between |> and %>%

While both pipes work similarly for basic operations, there are some important differences:

1. Availability

  • |> is built into R 4.1.0+ (no packages needed)
  • %>% requires the magrittr package or tidyverse

2. Placeholder Usage

Magrittr pipe %>% with placeholder:

# When you need the piped value in a position other than first argument
data %>% 
  lm(y ~ x, data = .)  # The dot (.) represents the piped data

Native pipe |> with placeholder:

# R 4.2.0+ syntax
data |> 
  lm(y ~ x, data = _)  # Underscore (_) as placeholder

# Alternative for all R 4.1.0+ versions
data |> 
  (\(x) lm(y ~ z, data = x))()  # Anonymous function

3. Performance

The native pipe |> is slightly faster since it’s built into R’s core.

When to Use Which Pipe?

Use the native pipe |> when:

  • You’re using R 4.1.0 or later
  • You want to minimize package dependencies
  • You’re doing straightforward piping (most common case)
  • Performance is critical

Use the magrittr pipe %>% when:

  • You’re working with tidyverse packages
  • You need advanced features like the dot placeholder
  • You’re working with legacy code that uses %>%
  • You’re collaborating with others who use tidyverse

Best Practices

1. Format for Readability

# Good: One function per line, properly indented
data |> 
  filter(condition) |> 
  group_by(variable) |> 
  summarise(mean_value = mean(value))

# Avoid: Everything on one line (hard to read)
data |> filter(condition) |> group_by(variable) |> summarise(mean_value = mean(value))

2. Don’t Overuse Pipes

# Sometimes simple assignment is clearer
x <- mean(data$variable)

# Instead of
x <- data |> pull(variable) |> mean()

3. Break Long Chains

If your pipe chain gets very long (>10 steps), consider breaking it into smaller chunks with intermediate variables.

Common Errors and Solutions

Error: Object not found

# This won't work - data isn't defined
data |> mean()

# Solution: Make sure your starting object exists
my_data <- c(1, 2, 3, 4, 5)
my_data |> mean()

Conclusion

The pipe operator is a powerful tool that makes R code more readable and intuitive. Whether you use the native pipe |> or the magrittr pipe %>%, the key is consistency within your projects.

Start incorporating pipes into your workflow gradually—begin with simple chains and work your way up to more complex data manipulation tasks. Your future self (and your collaborators) will thank you for the cleaner, more readable code!

Additional Resources