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Introduction to Data Visualization with ggplot2

Learn to produce meaningful and beautiful data visualizations with ggplot2 by understanding the grammar of graphics.

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4 Hours14 Videos52 Exercises79,949 Learners4300 XPData Analyst TrackData Scientist TrackData Visualization Track

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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.

  1. 1



    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.

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    50 xp
    Explore and explain
    50 xp
    Drawing your first plot
    100 xp
    Data columns types affect plot types
    100 xp
    The grammar of graphics
    50 xp
    Mapping data columns to aesthetics
    100 xp
    Understanding variables
    50 xp
    ggplot2 layers
    50 xp
    Adding geometries
    100 xp
    Changing one geom or every geom
    100 xp
    Saving plots as variables
    100 xp
  2. 2


    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.

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  3. 3


    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.

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  4. 4


    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.

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In the following tracks

Data Analyst Data ScientistData Visualization


richieRichie CottonshoninouyeShon InouyejonathanngJonathan Ng
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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.
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