New Course: R Data Visualization with ggplot2 – Part 1

[This article was first published on DataCamp Blog, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

Learn how to produce meaningful and beautiful data visualizations with DataCamp’s ggplot2 course series. Be introduced to the principles of good visualizations and the grammar of graphics plotting concept implemented in the ggplot2 package. Learn yourself how to make complex exploratory plots, and be able to make a custom plotting function to explore a large data set, combining statistics and excellent visuals. Begin learning Data Visualization with ggplot2 interactively, today.

What You’ll Learn

This 5 hour course includes 5 chapters and covers the given topics below:

  • Chapter 1: In this chapter we’ll put you in the right frame of mind for developing meaningful visualizations. You’ll learn how to think about your audience first and be introduced to the basics of ggplot2 – the 7 layers of the grammar of graphics.

  • Chapter 2: In this chapter we’ll explore the iris dataset from several different perspectives to see how the data structure affects plots in ggplot2. 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.

  • Chapter 3: Aesthetic mappings are the cornerstone of the grammar of graphics plotting concept. This is where the magic happens! Learn to convert continuous and categorical data into visual scales that provide access to a large amount of information in a very short time.

  • Chapter 4: 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.

  • Chapter 5: 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 is useful in specific instances.

Add data visualization to your skills today!

To leave a comment for the author, please follow the link and comment on their blog: DataCamp Blog. offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

Never miss an update!
Subscribe to R-bloggers to receive
e-mails with the latest R posts.
(You will not see this message again.)

Click here to close (This popup will not appear again)