Rick Scavetta is a biologist, workshop trainer, freelance data scientist and cofounder of Science Craft, 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.
The ability to produce meaningful and beautiful data visualizations is an essential part of your skill set as a data scientist. This course, the first R data visualization tutorial 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. Here, 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.
In this chapter we’ll get you into the right frame of mind for developing meaningful visualizations with R. You’ll understand that as a communications tool, visualizations require you to think about your audience first. You’ll also be introduced to the basics of ggplot2 - the 7 different grammatical elements (layers) and aesthetic mappings.
The structure of your data will dictate how you construct plots in ggplot2. In this chapter, we’ll explore the iris dataset from several different perspectives to showcase this concept. You’ll see that making your data conform to a structure that matches the plot in mind will make the task of visualization much easier through several R 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 we’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. We’ll look at a variety of different ways to construct these plots.
In this chapter you'll learn about qplot; it is a quick and dirty form of ggplot2. It’s not as intuitive as the full-fledged ggplot() function but may be useful in specific instances. This chapter also features a wrap-up video and corresponding data visualization exercises.