Data Visualization with ggplot2 (Part 3)
- 6 hours
- 19 Videos
- 87 Exercises
- 2,532 Participants
- 7650 XP
This course is part of these tracks:
Rick Scavetta
Data Scientist and cofounder of Science Craft
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.
See MoreCollaborator(s)
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Filip Schouwenaars
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.
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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.
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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.
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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!
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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.
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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.
