Data Visualization with ggplot2 (Part 1)
Learn to produce meaningful and beautiful data visualizations with ggplot2 by understanding the grammar of graphics.
Start Course for Free5 Hours14 Videos62 Exercises149,898 Learners
5250 XPCreate Your Free Account
or
By continuing you accept the Terms of Use and Privacy Policy. You also accept that you are aware that your data will be stored outside of the EU and that you are above the age of 16.Loved by learners at thousands of companies
Course Description
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.
- 1
Introduction
FreeAs 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. - 2
Data
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. - 3
Aesthetics
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. - 4
Geometries
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. - 5
qplot and wrap-up
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.
Vincent Vankrunkelsven
Filip SchouwenaarsPrerequisites
Intermediate R
Rick Scavetta
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.
What do other learners have to say?
“I've used other sites—Coursera, Udacity, things like that—but DataCamp's been the one that I've stuck with.”
Devon Edwards Joseph
Lloyds Banking Group
“DataCamp is the top resource I recommend for learning data science.”
Louis Maiden
Harvard Business School
“DataCamp is by far my favorite website to learn from.”
Ronald Bowers
Decision Science Analytics, USAA
Join over 6 million learners and start Data Visualization with ggplot2 (Part 1) today!
Create Your Free Account
or
By continuing you accept the Terms of Use and Privacy Policy. You also accept that you are aware that your data will be stored outside of the EU and that you are above the age of 16.