Interactive Course

Data Visualization in R with lattice

Learn to visualize multivariate datasets using lattice graphics.

4 hours
17 Videos
60 Exercises
2,794 Participants
4,950 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.

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

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

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.

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

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

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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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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Controlling scales and graphical parameters

Learn how to control and customize axis limits and visual appearance.
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Customizing plots using panel functions

Learn to use panel and prepanel functions to enhance existing displays or create new ones.
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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.
View Chapter Details Play Chapter Now

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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.

Tracks

Data Visualization with R

Collaborators

Tom Jeon
Richie Cotton
Sumedh Panchadhar

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Data Visualization in R with lattice

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Course Description

Basic plotting with lattice

Introduction

Formula interface in histogram() and xyplot()

Create a histogram

Create a scatterplot

Differences between base R graphics and lattice histogram

Optional arguments

Y-axis value in histogram()

Labels

Density plots

Box and whisker plots and reordering levels

Box and whisker plot

Whisker extents and outliers

Controlling scales and graphical parameters

Combining scales

Explicit limits

Per-panel explicit limits

Customizing axis annotation

Logarithmic scales

Annotating logarithmic scales

Finding patterns by sex

Log scales in economic data

Graphical parameters

ggplot2 styling with par.settings

Changing graphical parameters in histogram

Changing plotting characters

Using simpleTheme()

Changing plotting characters revisited

Changing parameters in grouped density plot

Extensions and the lattice ecosystem

New methods

The xyplot() method for time series objects

The xyplot() method with panel.horizonplot()

New high-level functions

Map projections

Confidence bands using the segplot() function

Hexagonal binning of bivariate data

Manipulation of trellis objects

The directlabels package

Adding ggplot2-style layers

Conditioning and the formula interface

Conditioning

A conditioned plot of the airquality dataset

Conditioning in density plots

Data summary and transformation, grouping

Superposition in density plots

Enhancing the legend

Incorporating external data sources

Conditioned scatter plot

The as.table argument

Custom strips labels for box and whisker plots

The trellis object

Modifying strip labels

Matrix-like subsetting

Customizing plots using panel functions

Panel Functions

Scatter plot with rugs

Alternative panel function - panel.violin()

Alternative panel functions - panel.smoothScatter()

Prepanel Functions to control limits

Prepanel functions with scales

Optional arguments of default panel functions

Interaction plot of residuals