Data Visualization with ggplot2 (Part 3)
This course covers some advanced topics including strategies for handling large data sets and specialty plots.
- 6 hours
- 19 Videos
- 86 Exercises
- 13,205 Participants
- 7,550 XP
Create Your Free Account
By pressing Continue 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 top companies:
Course Description
In this third ggplot2 course, we'll dive into some advanced topics including geoms commonly used in maths and sciences, strategies for handling large data sets, a variety of specialty plots, and some useful features of ggplot2 internals.
-
1
Statistical plots
FreeActually, all the plots you've explored in the first two ggplot2 courses can be considered 'statistical plots'. Here, however, you'll consider those that are intended for a specialist audience that is familiar with the data: box plots and density plots.
-
Plots for specific data types (Part 2)
In this chapter, we'll continue our discussion of plots for specific data types by diving into the world of maps. You'll also have a look at animations to make your data come to life!
-
Data Munging and Visualization Case Study
In this chapter, you'll draw on some of the many tools for effective data visualization that we've covered over the three ggplot2 courses and combine them with some data munging techniques.
-
Plots for specific data types (Part 1)
In this chapter, you'll explore useful specialty plots for specific data types such as ternary plots, networks and maps. You'll also look at how to use ggplot2 to convert typical base package plots that are used to evaluate the results of statistical methods. Finally, you'll take a look at a couple ways in which you can make and appropriately use animations.
-
ggplot2 Internals
In this chapter, you'll delve into ggplot2 internals, exploring the grid package and ggproto. You'll learn how to use these tools to create unique plots.
-
1
Statistical plots
FreeActually, all the plots you've explored in the first two ggplot2 courses can be considered 'statistical plots'. Here, however, you'll consider those that are intended for a specialist audience that is familiar with the data: box plots and density plots.
-
Plots for specific data types (Part 1)
In this chapter, you'll explore useful specialty plots for specific data types such as ternary plots, networks and maps. You'll also look at how to use ggplot2 to convert typical base package plots that are used to evaluate the results of statistical methods. Finally, you'll take a look at a couple ways in which you can make and appropriately use animations.
-
Plots for specific data types (Part 2)
In this chapter, we'll continue our discussion of plots for specific data types by diving into the world of maps. You'll also have a look at animations to make your data come to life!
-
ggplot2 Internals
In this chapter, you'll delve into ggplot2 internals, exploring the grid package and ggproto. You'll learn how to use these tools to create unique plots.
-
Data Munging and Visualization Case Study
In this chapter, you'll draw on some of the many tools for effective data visualization that we've covered over the three ggplot2 courses and combine them with some data munging techniques.
What do other learners have to say?
“I've used other sites, but DataCamp's been the one that I've stuck with.”
Devon Edwards Joseph
Lloyd's 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
Rick Scavetta
Rick Scavetta is a co-founder of Scavetta Academy.
Rick Scavetta is a biologist, workshop trainer, freelance data scientist and cofounder 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.
See MoreCollaborators
-
Filip Schouwenaars