Data Visualization in R with ggvis

Learn to create interactive graphs to display distributions, relationships, model fits, and more using ggvis.
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4 Hours11 Videos45 Exercises41,066 Learners
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Course Description

<p>In our ggvis data visualization course, you will learn how to create static and interactive graphs to display distributions, relationships, model fits, and more.</p><p>The first part of the course will focus on how to think conceptually about data visualizations using the theory behind the grammar of graphics.</p><p>Next, you will dive into the syntax of the R graphics package ggvis. Step-by-step, you'll learn how to create clear R data visualizations with sliders, widgets and text fields, and you’ll become familiar with the ggvis layering scheme to create multi-layered graphs.</p><p>Once you complete this data visualization class, you will be able to create meaningful static and interactive graphs to display distributions, relationships, and more!</p>

  1. 1

    The Grammar of Graphics

    Introduction to the ggvis package and the grammar of graphics. Learn the philosophy that guides ggvis and discover a clear, logical way to think about data visualization.
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  2. 2

    Lines and Syntax

    Examine each part of the grammar of graphics, and learn the ggvis syntax to make it easier to think about plots.
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  3. 3


    Discover how to build statistical transformations with the ggvis compute functions, as well as how to visualize the results. Learn shortcuts for visualizing transformations, such as smoothed lines, binned counts, and model predictions.
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  4. 4

    Interactivity and Layers

    Practice creating graphs that can be controlled through sliders, text fields, and other widgets. You'll also practice building sophisticated, multi-layered graphs with the ggvis layering scheme.
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  5. 5

    Customizing Axes, Legends, and Scales

    Change the appearance of axes and legends in your plots, and use the ggvis scale system to zoom in and out, to change the color scheme, and to control how your plot maps data values to visual properties.
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Filip Schouwenaars
Introduction to R
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