Why is `[` better than `subset`?

Is [ better than subset?

As a tech writer, I come across various programming concepts and functions that have their own pros and cons. Today, we'll dive into a common question in R: why is [ better than subset? 🤔

The Scenario

Let's consider a scenario where we need to filter a data.frame based on certain conditions. One way to achieve this is by using the subset function, like this:

subset(airquality, Month == 8 & Temp > 90)

The alternative approach involves using the [ function:

airquality[airquality$Month == 8 & airquality$Temp > 90, ]

Preference for subset

At first glance, using subset might seem more appealing for two main reasons:

1. Readability: The code reads from left to right, making it more intuitive. Even non-R coders can understand what the subset statement is doing without prior knowledge.

2. Simplicity: When using subset, we can refer to columns as variables in the filtering expression. This eliminates the need to repeat the data.frame name multiple times. In the previous example, we only had to type airquality once with subset, but three times with [.

A Warning Discovered

For a while, I happily used subset, appreciating its brevity and readability. Little did I know that I was playing with fire. During my explorations, I stumbled upon a section in the subset documentation that made me question my approach:

Warning

This is a convenience function intended for use interactively. For programming, it is better to use the standard subsetting functions like [, and in particular, the non-standard evaluation of the 'subset' argument can have unanticipated consequences.

Clarifying the Warning

Let's demystify this warning by unraveling its key points:

"For use interactively"

The phrase "for use interactively" refers to situations when we directly interact with R in a live session. It includes running commands line by line, exploring data, and getting immediate feedback. By contrast, running a script in BATCH mode involves executing a set of commands without user interaction.

Non-standard evaluation of argument subset

The warning specifically highlights the non-standard evaluation of the subset argument. In R, non-standard evaluation involves treating unevaluated code as data, allowing for flexible expressions. However, this flexibility comes with potential dangers, especially when used in programming contexts.

An Example to Illustrate the Danger

To better understand the consequences of non-standard evaluation, let's consider the following example:

column_name <- "Month"
filter_value <- 8

subset(airquality, column_name == filter_value)

At first glance, it looks like this should work fine. However, due to non-standard evaluation, R treats column_name as a variable name instead of using its value. As a result, the filtering condition becomes Month == filter_value, which is equivalent to airquality$Month == filter_value. This unexpected behavior can lead to incorrect results or even errors in more complex scenarios.

Embracing [ for Programming

Given the potential risks associated with subset in programming contexts, it is recommended to use the standard subsetting function [ instead. While it might require a few extra keystrokes, it ensures predictable behavior and avoids unexpected consequences.

airquality[airquality$Month == 8 & airquality$Temp > 90, ]

By using [, we have explicit control over the evaluated expressions, eliminating any confusion or surprises.

Call-to-Action: Share Your Thoughts

Have you ever encountered unexpected results while using subset? What are your thoughts on the warning and the preference for [? Share your experiences and insights in the comments below! Let's discuss and learn from each other's perspectives. 📝💬

Remember, being aware of the intricacies of R functions can save us from potential headaches and help us write more robust code in the long run. Happy coding! 💻🚀