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Course Description
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.The videos contain live transcripts you can reveal by clicking "Show transcript" at the bottom left of the videos.
The course glossary can be found on the right in the resources section.
To obtain CPE credits you need to complete the course and reach a score of 70% on the qualified assessment. You can navigate to the assessment by clicking on the CPE credits callout on the right.
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Start Course for FreeWhat you'll learn
- Assess theme customization choices that improve clarity, consistency, and audience suitability in explanatory data visualizations.
- Differentiate appropriate geometries, position adjustments, and scale types for effective visualization of continuous versus categorical variables
- Distinguish between aesthetic mappings and fixed attributes in ggplot2 code to ensure correct visual encoding of data
- Evaluate techniques such as jittering, alpha blending, and shape selection to mitigate overplotting in scatterplots
- Identify the four essential layers of the grammar of graphics in ggplot2—and their functions—when constructing a visualization
Prerequisites
Curriculum
Course outline
1
Introduction
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.
- Introduction50 XP
- Explore and explain50 XP
- Drawing your first plot100 XP
- Data columns types affect plot types100 XP
- The grammar of graphics50 XP
- Mapping data columns to aesthetics100 XP
- Understanding variables50 XP
- ggplot2 layers50 XP
- Adding geometries100 XP
- Changing one geom or every geom100 XP
- Saving plots as variables100 XP
2
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.
3
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. We’ll look at a variety of different ways to construct these plots.
4
Themes
In this chapter, we’ll explore how understanding the structure of your data makes data visualization much easier. Plus, it’s time to make our plots pretty. This is the last step in the data viz process. The Themes layer will enable you to make publication quality plots directly in R. In the next course we'll look at some extra layers to add more variables to your plots.
R
Introduction to Data Visualization with ggplot2
Course
Complete
