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Intermediate Data Visualization with ggplot2

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

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4 Hours14 Videos52 Exercises28,208 Learners4350 XPData Scientist TrackData Visualization Track

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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.

  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

Data ScientistData Visualization


richieRichie Cotton
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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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