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Data Visualization in R

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4 hours
5,250 XP
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Course Description

This course provides a comprehensive introduction on how to plot data with R’s default graphics system, base graphics.

After an introduction to base graphics, we look at a number of R plotting examples, from simple graphs such as scatterplots to plotting correlation matrices. The course finishes with exercises in plot customization. This includes using R plot colors effectively and creating and saving complex plots in R.

Base Graphics Background
R supports four different graphics systems: base graphics, grid graphics, lattice graphics, and ggplot2. Base graphics is the default graphics system in R, the easiest of the four systems to learn to use, and provides a wide variety of useful tools, especially for exploratory graphics where we wish to learn what is in an unfamiliar dataset.

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  1. 1

    A quick introduction to base R graphics

    Free

    This chapter gives a brief overview of some of the things you can do with base graphics in R. This graphics system is one of four available in R and it forms the basis for this course because it is both the easiest to learn and extremely useful both in preparing exploratory data visualizations to help you see what's in a dataset and in preparing explanatory data visualizations to help others see what we have found.

    Play Chapter Now
    The world of data visualization
    50 xp
    Creating an exploratory plot array
    100 xp
    Creating an explanatory scatterplot
    100 xp
    The plot() function is generic
    100 xp
    A preview of some more and less useful techniques
    50 xp
    Adding details to a plot using point shapes, color, and reference lines
    100 xp
    Creating multiple plot arrays
    100 xp
    Avoid pie charts
    100 xp
  2. 2

    Different plot types

    This chapter introduces several Base R supported plot types that are particularly useful for visualizing important features in a dataset. We start with simple tools like histograms and density plots for characterizing one variable at a time, move on to scatter plots and other useful tools for showing how two variables relate, and finally introduce some tools for visualizing more complex relationships in our dataset.

    Play Chapter Now
    Characterizing a single variable
    50 xp
    The hist() and truehist() functions
    100 xp
    Density plots as smoothed histograms
    100 xp
    Using the qqPlot() function to see many details in data
    100 xp
    Visualizing relations between two variables
    50 xp
    The sunflowerplot() function for repeated numerical data
    100 xp
    Useful options for the boxplot() function
    100 xp
    Using the mosaicplot() function
    100 xp
    Showing more complex relations between variables
    50 xp
    Using the bagplot() function
    100 xp
    Plotting correlation matrices with the corrplot() function
    100 xp
    Building and plotting rpart() models
    100 xp
  3. 3

    Adding details to plots

    Most base R graphics functions support many optional arguments and parameters that allow us to customize our plots to get exactly what we want. In this chapter, we will learn how to modify point shapes and sizes, line types and widths, add points and lines to plots, add explanatory text and generate multiple plot arrays.

    Play Chapter Now
    The plot() function and its options
    50 xp
    Introduction to the par() function
    100 xp
    Exploring the type option
    100 xp
    The surprising utility of the type "n" option
    100 xp
    Adding lines and points to plots
    50 xp
    The lines() function and line types
    100 xp
    The points() function and point types
    100 xp
    Adding trend lines from linear regression models
    100 xp
    Adding text to plots
    50 xp
    Using the text() function to label plot features
    100 xp
    Adjusting text position, size, and font
    100 xp
    Rotating text with the srt argument
    100 xp
    Adding or modifying other plot details
    50 xp
    Using the legend() function
    100 xp
    Adding custom axes with the axis() function
    100 xp
    Using the supsmu() function to add smooth trend curves
    100 xp
  4. 4

    How much is too much?

    As we have seen, base R graphics provides tremendous flexibility in creating plots with multiple lines, points of different shapes and sizes, and added text, along with arrays of multiple plots. If we attempt to add too many details to a plot or too many plots to an array, however, the result can become too complicated to be useful. This chapter focuses on how to manage this visual complexity so the results remain useful to ourselves and to others.

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    Managing visual complexity
    50 xp
    Too much is too much
    100 xp
    Deciding how many scatterplots is too many
    100 xp
    How many words is too many?
    100 xp
    Creating plot arrays with the mfrow parameter
    50 xp
    The Anscombe quartet
    100 xp
    The utility of common scaling and individual titles
    100 xp
    Using multiple plots to give multiple views of a dataset
    100 xp
    Creating plot arrays with the layout() function
    50 xp
    Constructing and displaying layout matrices
    100 xp
    Creating a triangular array of plots
    100 xp
    Creating arrays with different sized plots
    100 xp
  5. 5

    Advanced plot customization and beyond

    This final chapter introduces a number of important topics, including the use of numerical plot details returned invisibly by functions like barplot() to enhance our plots, and saving plots to external files so they don't vanish when we end our current R session. This chapter also offers some guidelines for using color effectively in data visualizations, and it concludes with a brief introduction to the other three graphics systems in R.

    Play Chapter Now
    Creating and saving more complex plots
    50 xp
    Some plot functions also return useful information
    100 xp
    Using the symbols() function to display relations between more than two variables
    100 xp
    Saving plot results as files
    100 xp
    Using color effectively
    50 xp
    Iliinsky and Steele's 12 recommended colors
    100 xp
    Using color to enhance a bubbleplot
    100 xp
    Using color to enhance stacked barplots
    100 xp
    Other graphics systems in R
    50 xp
    The tabplot package and grid graphics
    100 xp
    A lattice graphics example
    100 xp
    A ggplot2 graphics example
    100 xp

Collaborators

Collaborator's avatar
Nick Carchedi
Collaborator's avatar
Tom Jeon

Prerequisites

Introduction to R
Ronald Pearson HeadshotRonald Pearson

PhD in Electrical Engineering and Computer Science from M.I.T.

Ron has been actively involved in data analysis and predictive modeling in a variety of technical positions, both academic and commercial, including the DuPont Company, the Swiss Federal Institute of Technology (ETH Zurich), the Tampere University of Technology in Tampere, Finland, the Travelers Companies and DataRobot. He holds a PhD in Electrical Engineering and Computer Science from M.I.T. and has written or co-written five books, including Exploring Data in Engineering, the Sciences, and Medicine (Oxford University Press, 2011) and Nonlinear Digital Filtering with Python (CRC Press, 2016, with Moncef Gabbouj). Ron is the author and maintainer of the GoodmanKruskal R package, and one of the authors of the datarobot R package.
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