Mastering Nested Syntactic Expressions (NSE) with dplyr: Workarounds for Complex Operations.

NSE in dplyr: Nesting Functions Inside `mutate`

As a fan of the dplyr package in R, I’ve often found myself wrestling with non-trivial operations involving multiple functions. One common pain point is dealing with Nested Syntactic Expressions (NSE), where we want to nest functions inside each other for more complex operations. In this article, we’ll delve into NSE and explore its implications in dplyr.

What are Nested Syntactic Expressions?

Nested Syntactic Expressions refer to a situation where you have an expression that contains another expression as part of its definition. This can lead to unexpected behavior when the expressions are evaluated. In our case, we want to use mutate with a function that takes another function as an argument.

A Simple Example

To understand NSE better, let’s consider a simple example:

temp <- data.frame(A = 1:3, B = 4:6)

We can then apply the sum function to columns B and C using select and rowSums:

temp %>% {
  rowSums(select(., B:C), na.rm = TRUE)
}

This works as expected. However, we want to achieve the same result inside mutate. Unfortunately, this leads us into NSE territory.

The Problem with NSE in dplyr

The problem arises when trying to nest functions inside mutate:

temp %>% {
  mutate(Sum = rowSums(select(., B:C), na.rm = TRUE))
}

This code will throw an error because the select and rowSums functions are not being evaluated correctly.

Understanding the Error

The error message is cryptic but informative:

Error: Position must be between 0 and n
In addition: Warning messages:
1: In 4:6:8:10 : numerical expression has 3 elements: only the first used
2: In 4:6:8:10 : numerical expression has 3 elements: only the first used

Let’s break down what’s happening here. The select function is being applied to each row in our data frame, which returns a numeric vector containing the values of columns B and C for each row.

The rowSums function then takes this numeric vector as input and calculates the sum of its elements.

However, there’s a catch: when we nest rowSums inside mutate, it tries to apply the select expression to each row in our data frame. This results in an error because the position must be between 0 and n (the number of rows), but the select expression is producing more than one value per row.

A Potential Solution

To work around this issue, we can use a different approach using mutate_at. The mutate_at function allows us to apply a list of functions to specific columns in our data frame. We can define a list containing the desired function and then use mutate_at to apply it:

temp %>% {
  mutate_at(vars(B:C), rowSums, na.rm = TRUE)
}

Here, we’re telling mutate_at to apply the rowSums function to columns B and C. This approach avoids NSE and gets us around the error.

Conclusion

While dplyr is an incredibly powerful package for data manipulation in R, it can be tricky to deal with nested functions and NSE. By understanding how these concepts work and using alternative approaches like mutate_at, we can overcome common challenges and achieve more complex operations.

In this article, we’ve seen firsthand the issues that arise when trying to nest functions inside mutate. We’ve also explored a potential solution using mutate_at, which provides a flexible way to apply multiple functions to specific columns in our data frame.

Last modified on 2024-05-01

NSE in dplyr: Nesting Functions Inside mutate