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Data Visualization in R with ggvis

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4 hours
3,700 XP
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Course Description

In our ggvis data visualization course, you will learn how to create static and interactive graphs to display distributions, relationships, model fits, and more.

The first part of the course will focus on how to think conceptually about data visualizations using the theory behind the grammar of graphics.

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.

Once you complete this data visualization class, you will be able to create meaningful static and interactive graphs to display distributions, relationships, and more!

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  1. 1

    The Grammar of Graphics

    Free

    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.

    Play Chapter Now
    Section 1 - Introduction to ggvis
    50 xp
    Load ggvis and start to explore
    100 xp
    ggvis and its capabilities
    100 xp
    Section 2 - The grammar of ggvis
    50 xp
    ggvis grammar ~ graphics grammar
    100 xp
    4 essential components of a graph
    50 xp
  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.

    Play Chapter Now
    Section 3 - Three new types of syntax
    50 xp
    Three operators: %>%, = and :=
    100 xp
    Referring to different objects
    50 xp
    Referring to different objects (2)
    50 xp
    Properties for points
    100 xp
    Section 4 - the line, a special type of mark
    50 xp
    Properties for lines
    100 xp
    Path marks and polygons
    100 xp
    Display model fits
    100 xp
    compute_smooth() to simplify model fits
    100 xp
  3. 3

    Transformations

    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.

    Play Chapter Now
    Section 5 - Compute functions
    50 xp
    Histograms (1)
    100 xp
    Histograms (2)
    100 xp
    Density plots
    100 xp
    Shortcuts
    100 xp
    Section 6 - ggvis and dplyr
    50 xp
    ggvis and group_by
    100 xp
    group_by() versus interaction()
    100 xp
    Chaining is a virtue
    50 xp
  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.

    Play Chapter Now
    Section 7 - Interactive plots
    50 xp
    The basics of interactive plots
    100 xp
    Input widgets in more detail
    100 xp
    Input widgets in more detail (2)
    100 xp
    Control parameters and values
    100 xp
    Section 8 - Multi-layered plots
    50 xp
    Multi-layered plots and their properties
    100 xp
    Multi-layered plots and their properties (2)
    100 xp
    There is no limit on the number of layers!
    100 xp
    Taking local and global to the next level
    50 xp
  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.

    Play Chapter Now
    Section 9 - Axes and Legends
    50 xp
    Axes
    100 xp
    Legends
    100 xp
    Legends (2)
    100 xp
    Section 10 - Customize property mappings
    50 xp
    Scale types
    100 xp
    Adjust any visual property
    100 xp
    Adjust any visual property (2)
    100 xp
    "=" versus ":="
    100 xp
    Interview with Winston Chang
    50 xp
For Business

GroupTraining 2 or more people?

Get your team access to the full DataCamp library, with centralized reporting, assignments, projects and more

Collaborators

Collaborator's avatar
Filip Schouwenaars

Prerequisites

Introduction to R

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