The ability to produce meaningful and beautiful data visualizations is an essential part of a data scientist skill set. This course, the first R data visualization course in the series, introduces you to the principles of good visualizations and the grammar of graphics plotting concepts implemented in the ggplot2 package. ggplot2 has become the go-to tool for flexible and professional plots in R. We’ll examine the first three essential layers for making a plot: data, aesthetics, and geometries. By the end of the course you will be able to make complex exploratory plots.
As a communications tool, visualizations require you to think about your audience first. You’ll be introduced to the basics of ggplot2—the seven different grammatical elements (layers) and aesthetic mappings.
The structure of your data will dictate how you construct plots in ggplot2. In this chapter, you’ll explore the iris dataset from several different perspectives. You’ll understand why conforming your data structure to match the plot will make visualization much easier by reviewing several data visualization examples.
Aesthetic mappings are the cornerstone of the grammar of graphics plotting concept. This is where the magic happens—converting continuous and categorical data into visual scales that provide access to a large amount of information in a very short time. In this chapter, you’ll understand how to choose the best aesthetic mappings for your data.
A plot’s geometry dictates what visual elements will be used. In this chapter, we’ll familiarize you with the geometries used in the three most common plot types you’ll encounter: scatter plots, bar charts, and line plots.
In this chapter, you'll learn about qplot, which is a quick and dirty version of ggplot2. It’s not as intuitive as the full-fledged ggplot() function, but may be useful in specific instances. This chapter also features data visualization exercises.
Rick Scavetta is a co-founder of Scavetta Academy.
Rick Scavetta is a biologist, workshop trainer, freelance data scientist and co-founder of Scavetta Academy, a company dedicated to helping scientists better understand and visualize their data. Rick's practical, hands-on exposure to a wide variety of datasets has informed him of the many problems scientists face when trying to visualize their data.