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Intermediate Data Visualization with ggplot2

21 reviews

Learn to use facets, coordinate systems and statistics in ggplot2 to create meaningful explanatory plots.

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4 Hours14 Videos52 Exercises
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Course Description

This ggplot2 course builds on your knowledge from the introductory course to produce meaningful explanatory plots. Statistics will be calculated on the fly and you’ll see how Coordinates and Facets aid in communication. You’ll also explore details of data visualization best practices with ggplot2 to help make sure you have a sound understanding of what works and why. By the end of the course, you’ll have all the tools needed to make a custom plotting function to explore a large data set, combining statistics and excellent visuals.
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  1. 1



    A picture paints a thousand words, which is why R ggplot2 is such a powerful tool for graphical data analysis. In this chapter, you’ll progress from simply plotting data to applying a variety of statistical methods. These include a variety of linear models, descriptive and inferential statistics (mean, standard deviation and confidence intervals) and custom functions.

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    Stats with geoms
    50 xp
    100 xp
    Grouping variables
    100 xp
    Modifying stat_smooth
    100 xp
    Modifying stat_smooth (2)
    100 xp
    Stats: sum and quantile
    50 xp
    100 xp
    Using stat_sum
    100 xp
    Stats outside geoms
    50 xp
    100 xp
    Using position objects
    100 xp
    Plotting variations
    100 xp
  2. 2


    The Coordinates layers offer specific and very useful tools for efficiently and accurately communicating data. Here we’ll look at the various ways of effectively using these layers, so you can clearly visualize lognormal datasets, variables with units, and periodic data.

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  3. 4

    Best Practices

    Now that you have the technical skills to make great visualizations, it’s important that you make them as meaningful as possible. In this chapter, you’ll review three plot types that are commonly discouraged in the data viz community: heat maps, pie charts, and dynamite plots. You’ll learn the pitfalls with these plots and how to avoid making these mistakes yourself.

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In the following tracks

Associate Data Scientist in RData Visualization with R


Collaborator's avatar
Richie Cotton
Rick Scavetta HeadshotRick 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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from 21 reviews
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  • David C.
    6 months

    Loved it! Hope it all sticks in the future though

  • Jean-claude K.
    9 months

    Very good course.

  • Moses P.
    about 1 year

    Great walk through of important concepts and their implementation.

  • Amy C.
    over 1 year

    Great class. Learned a lot.

  • Nicolas F.
    over 1 year

    This course did a great job of building on the introduction to ggplot2, the instructor is obviously very skilled with ggplot2. I think each of these courses could be a bit longer to take a bit more time for a case study to give the learner an opportunity to do an analysis from beginning to end. also, make sure to explain all the functions in detail, and avoid quickly moving between slides without explaining things on each slide. Overall, though, I'm learning a ton from these courses and whenever I read materials online about these packages / functions (esp. dplyr / ggplot2), I can tell that I am well informed of the most current methods for using these packages and their corresponding functions.

"Loved it! Hope it all sticks in the future though"

David C.

"Very good course."

Jean-claude K.

"Great walk through of important concepts and their implementation."

Moses P.

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