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Data Visualization with lattice in R

Learn to visualize multivariate datasets using lattice graphics.

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4 Hours17 Videos60 Exercises4,963 Learners4950 XP

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Course Description

Visualization is an essential component of interactive data analysis in R. Traditional (base) graphics is powerful, but limited in its ability to deal with multivariate data. Trellis graphics is the natural successor to traditional graphics, extending its simple philosophy to gracefully handle common multivariable data visualization tasks. This course introduces the lattice package, which implements Trellis graphics for R, and illustrates its basic use.

  1. 1

    Basic plotting with lattice

    Free

    Introduction to some basic plotting functions in lattice. Draw histograms, scatter plots, density plots, and box and whisker plots.

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    Introduction
    50 xp
    Formula interface in histogram() and xyplot()
    50 xp
    Create a histogram
    100 xp
    Create a scatterplot
    100 xp
    Differences between base R graphics and lattice histogram
    50 xp
    Optional arguments
    50 xp
    Y-axis value in histogram()
    100 xp
    Labels
    100 xp
    Density plots
    100 xp
    Box and whisker plots and reordering levels
    50 xp
    Box and whisker plot
    100 xp
    Whisker extents and outliers
    100 xp
  2. 2

    Conditioning and the formula interface

    These exercises will teach you to create "conditioned" plots consisting of multiple panels using the formula interface.

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    Conditioning
    50 xp
    A conditioned plot of the airquality dataset
    100 xp
    Conditioning in density plots
    100 xp
    Data summary and transformation, grouping
    50 xp
    Superposition in density plots
    100 xp
    Enhancing the legend
    100 xp
    Incorporating external data sources
    50 xp
    Conditioned scatter plot
    100 xp
    The as.table argument
    100 xp
    Custom strips labels for box and whisker plots
    100 xp
    The trellis object
    50 xp
    Modifying strip labels
    100 xp
    Matrix-like subsetting
    100 xp
  3. 3

    Controlling scales and graphical parameters

    Learn how to control and customize axis limits and visual appearance.

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    Combining scales
    50 xp
    Explicit limits
    100 xp
    Per-panel explicit limits
    100 xp
    Customizing axis annotation
    100 xp
    Logarithmic scales
    50 xp
    Annotating logarithmic scales
    100 xp
    Finding patterns by sex
    50 xp
    Log scales in economic data
    100 xp
    Graphical parameters
    50 xp
    ggplot2 styling with par.settings
    100 xp
    Changing graphical parameters in histogram
    100 xp
    Changing plotting characters
    100 xp
    Using simpleTheme()
    50 xp
    Changing plotting characters revisited
    100 xp
    Changing parameters in grouped density plot
    100 xp
  4. 4

    Customizing plots using panel functions

    Learn to use panel and prepanel functions to enhance existing displays or create new ones.

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    Panel Functions
    50 xp
    Scatter plot with rugs
    100 xp
    Alternative panel function - panel.violin()
    100 xp
    Alternative panel functions - panel.smoothScatter()
    100 xp
    Prepanel Functions to control limits
    50 xp
    Prepanel functions with scales
    100 xp
    Optional arguments of default panel functions
    50 xp
    Interaction plot of residuals
    50 xp
    Optional arguments of panel.bwplot()
    100 xp
    Using emojis as plotting symbols
    100 xp
  5. 5

    Extensions and the lattice ecosystem

    The lattice package is not just meant to be used as a standalone collection of plotting functions. Rather, it is a framework that is used as a base by many other packages. Some of these are very specialized and beyond the scope of this course. Here we give a brief survey of extensions that are generally useful to enhance displays or create new ones.

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    New methods
    50 xp
    The xyplot() method for time series objects
    100 xp
    The xyplot() method with panel.horizonplot()
    100 xp
    New high-level functions
    50 xp
    Map projections
    100 xp
    Confidence bands using the segplot() function
    100 xp
    Hexagonal binning of bivariate data
    100 xp
    Manipulation of trellis objects
    50 xp
    The directlabels package
    100 xp
    Adding ggplot2-style layers
    100 xp

Collaborators

tommyjee
Tom Jeon
richie
Richie Cotton

Prerequisites

Data Visualization in R
Deepayan Sarkar Headshot

Deepayan Sarkar

Member of R-Core & the creator of lattice

Deepayan is an Associate Professor at the Indian Statistical Institute, and a member of R-Core. He is the creator of the lattice package, and the author of the corresponding book Lattice - Multivariate Data Visualization with R.
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