R Cookbook, 2nd Edition

Chapter 6. Data Transformations

While traditional programming languages use loops, R has traditionally encouraged using vectorized operations and the apply family of functions to crunch data in batches, greatly streamlining the calculations. There is nothing to prevent you from writing loops in R that break your data into whatever chunks you want and then doing an operation on each chunk. However, using vectorized functions can, in many cases, increase the speed, readability, and maintainability of your code.

In recent history, though, the tidyverse—specifically the purrr and dplyr packages—has introduced new idioms into R that make these concepts easier to learn and slightly more consistent. The name purrr comes from a play on the phrase “Pure R.” A “pure function” is a function whose result is determined only by its inputs, and which does not produce any side effects. This is not a functional programming concept you need to understand in order to get great value from purrr, however. All most users need to know is that purrr contains functions to help us operate “chunk by chunk” on our data in a way that meshes well with other tidyverse packages such as dplyr.

Base R has many apply functions—apply, lapply, sapply, tapply, and mapply—as well as their cousins, by and split. These are solid functions that have been workhorses in Base R for years. We struggled a bit with how much to focus on the Base R apply functions and how much to focus on the newer “tidy” approach. After much ...