Wrapping Up

Now that you’ve completed the lessons on data wrangling and analysis, you have some of the fundamentals needed to work with R. If you want to review the examples used throughout this section of the course, the rendered HTML file is available.

Don’t forget to print the data transformation with dplyr cheat sheet and keep it close as a reference!

One of the best ways to further your learning is to watch other people code. Data scientist David Robinson does weekly screencasts (one of them is embedded below), which are an amazing resource (and he uses the Tidyverse so you’ll see all of the functions you’ve just learned). He goes through a dataset that is part of the Tidy Tuesday project, which is described as:

A weekly data project aimed at the R ecosystem. An emphasis will be placed on understanding how to summarize and arrange data to make meaningful charts with ggplot2, tidyr, dplyr, and other tools in the tidyverse ecosystem.

If you're looking for a text-based option to continue your learning, I cannot recommend R for Data Science highly enough. It is the Tidyverse bible. The material from this section is covered primarily in Chapter 5. It has exercises as well, which you can use to practice your skills. Others have also posted the solutions to these exercises so if you get stuck you have a reference.