Functional programming in R is more than just a trendy buzzword; it’s a powerful approach that can dramatically simplify and enhance your data analysis tasks. In this section, we’ll explore real-world examples of common data analysis problems solved using functional programming in R, comparing them to traditional imperative methods. We’ll also highlight the conciseness and readability of functional code, demonstrating why it’s a game-changer for data professionals.
I think there’s a lot of value in understanding the functional programming approach for a few reasons:
- Functional programming makes more intuitive sense to data platform developers who are raised on “set-based” operations and thinking over columns rather than rows.
- Functional programming allows for ease of parallel computation. This is why Spark heavily emphasizes functional programming and why the code base itself is written in a functional programming language.
- Functional code is often terser than equivalent imperative code.
- Functional code allows you to build more “bottom-up,” starting with small functions and composing them rather than thinking in a “top-down” approach of classes and interactions between classes. For people who are not full-time application developers, the ease of writing a quick function is huge, which is part of why shell scripting languages like Powershell and Bash emphasize this approach as well.