Understanding the tidyverse

As you use R at a more than basic level (particularly if you search for how to do different things on the internet), you will encounter the tidyverse. The tidyverse is a collection of packages (and more broadly, a philsophy around how packages should work) that do a lot of very useful things; in a sense it is an attempt to provide more modern data science tools than were present in base R (i.e., the standard tools you get when you first install R). R was released in the early 1990s, and is required to still work almost exactly the same now as it did then; the tidyverse is newer (it first appeared as a combined package in 2016) and free from that constraint, which gives the developers flexibility to change and adapt their ecosystem over time. The main creator of the tidyverse is also the founder of the company that created Rstudio, and much of the development of the tidyverse comes from that company (now called posit).

You may choose to install and load the tidyverse packages as if they were a single package, e.g., calling:

library(tidyverse)

in practice what this does is load a list of packages that are designed to work together well and do many of the things you want to do with data (you can also load them separately if you like). These include:

• ggplot2, which is used to create data visualisations by mapping columns of data to attributes of a plot. See here for an introduction to using ggplot. This is the most popular R package in the world, and to some extent the strongest argument for using R for data science over, say, Python (particularly when combined with the rest of the tidyverse).
• dplyr and tidyr, which are used to wrangle and organise data.
• tibble, which is a tidyverse-specific data-frame like object.
• purrr, which provides more advances on tools like apply(), allowing you to use fewer loops in your code.
• a collection of libraries for reading data (typically more flexible and efficiently than base), including readr and readxl.
• lubridate, which makes working with dates much easier.
• stringr, which makes working with strings much easier.

This is quite a list of tools; you should not feel the need to learn all of them at once (nor, generally, to remember what package does what specifically). For a solid introduction to how these tools work together in a data science workflow, you can consider the free online book R for Data Science. However, it helps to remember what tools are available so you can look them up when you need to.