What are the differences between R's native pipe `|>` and the magrittr pipe `%>%`?

Piping is a powerful feature in R that allows for clear, readable, and concise code by chaining multiple operations together. Before R 4.1, the magrittr package’s pipe (%>%) was the go-to tool for this task. However, starting with R 4.1, a native pipe operator (|>) was introduced. Although they serve similar purposes, there are notable differences between the two. This article outlines these differences to help you choose the most suitable one in R Programming Language.

Introduction to Piping in R

Piping allows data to be passed from one function to another in a sequential manner. Instead of nesting multiple functions or manually creating intermediate variables, piping simplifies this process and enhances code readability.

Example of nested functions:

result <- sum(sqrt(abs(-25:25)))

Example using a pipe:

result <- -25:25 %>% abs() %>% sqrt() %>% sum()

Both the magrittr pipe (%>%) and R’s native pipe (|>) achieve this, but there are some functional and syntactical differences.

The magrittr Pipe (%>%)

The magrittr package introduced the pipe operator %>%, and it quickly became popular for its intuitive functionality in the tidyverse ecosystem. Here’s an example of how it’s used:

Output:

[1] 171.2676

This example takes a sequence of numbers, computes their absolute values, takes the square roots of those values, and sums the result. Each step is linked by %>%.

• Flexibility in Function Arguments: With %>%, the left-hand side is automatically passed as the first argument to the function on the right. You can also specify the position of the argument using a placeholder (.), making %>% highly flexible.
• Compatibility:%>% works well with legacy code and within the tidyverse ecosystem.
• Data Frame-Friendly:%>% integrates seamlessly with data manipulation functions in dplyr and other tidyverse packages, which expect data frames.

The Native Pipe (|>)

With R 4.1, a native pipe operator (|>) was introduced as part of the base R functionality. Its usage is similar to %>%, but there are some important differences:

Output:

[1] 171.2676

• Strict Argument Placement: Unlike %>%, the native pipe does not automatically place the left-hand side as the first argument. It explicitly requires the left-hand side to be passed into the right-hand function, so the function’s structure must be compatible with this style.
• Performance Improvements: The native pipe is optimized for R and tends to have better performance since it avoids dependencies on external packages (like magrittr). It leverages R’s internals, making it a faster option in some cases.
• Simplicity in Syntax:|> focuses on simplicity and avoids some of the complex features of %>%, like specifying argument positions. This makes it easier for new users, but less flexible.
• Requires R 4.1 or Later: Since |> is a native feature, it is only available in R 4.1 and beyond. Older versions of R do not support this operator.

When to Use %>% vs. |>

Here are the main concepts that show When to Use %>% vs. |>:

Use %>% when:

• You need the flexibility to pass the left-hand side into different positions in the function.
• You are working with data frames in the tidyverse and using packages like dplyr.
• You are working with R versions older than 4.1.

Use |> when:

• You are using R 4.1 or later and want better performance.
• You don’t need the extra flexibility of the magrittr pipe.
• You want to avoid dependencies on external packages.

Conclusion

both the magrittr pipe (%>%) and the native pipe (|>) allow for cleaner and more readable code in R. While the functionality is largely similar, there are differences in flexibility, performance, and compatibility:

• %>% is more flexible with argument placement and integrates well with tidyverse packages like dplyr.
• |> is faster, simpler, and built into base R, but it requires functions to handle arguments in a specific order.

Depending on your use case, workflow, and R version, either operator can be a powerful tool in your R programming toolkit. For new R users, the native pipe offers a straightforward and efficient option, while advanced users working with data frames or complex function chains may still prefer the flexibility of %>%.