Summarizing Data with dplyr

Tidyverse offers a coherent and expressive way to compute descriptive statistics. Instead of calling separate functions on individual vectors, tidyverse tools let you describe an entire dataset through clear workflows built with pipelines. The basic idea is always the same: You start with your data, then add layers of transformations one step at a time.

In this section, we explore how to use dplyr to compute summary statistics, work with multiple variables at once, handle missing values, and produce grouped summaries. Before continuing with this and the subsequent tutorials in this module, we recommend that you first have a look into the “Pipes” module, the ensure you understand the basics of the dplyr package.