Data Visualization with lattice in R
Learn to visualize multivariate datasets using lattice graphics.
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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
Basic plotting with lattice
FreeIntroduction to some basic plotting functions in lattice. Draw histograms, scatter plots, density plots, and box and whisker plots.
Introduction50 xpFormula interface in histogram() and xyplot()50 xpCreate a histogram100 xpCreate a scatterplot100 xpDifferences between base R graphics and lattice histogram50 xpOptional arguments50 xpY-axis value in histogram()100 xpLabels100 xpDensity plots100 xpBox and whisker plots and reordering levels50 xpBox and whisker plot100 xpWhisker extents and outliers100 xp - 2
Conditioning and the formula interface
These exercises will teach you to create "conditioned" plots consisting of multiple panels using the formula interface.
Conditioning50 xpA conditioned plot of the airquality dataset100 xpConditioning in density plots100 xpData summary and transformation, grouping50 xpSuperposition in density plots100 xpEnhancing the legend100 xpIncorporating external data sources50 xpConditioned scatter plot100 xpThe as.table argument100 xpCustom strips labels for box and whisker plots100 xpThe trellis object50 xpModifying strip labels100 xpMatrix-like subsetting100 xp - 3
Controlling scales and graphical parameters
Learn how to control and customize axis limits and visual appearance.
Combining scales50 xpExplicit limits100 xpPer-panel explicit limits100 xpCustomizing axis annotation100 xpLogarithmic scales50 xpAnnotating logarithmic scales100 xpFinding patterns by sex50 xpLog scales in economic data100 xpGraphical parameters50 xpggplot2 styling with par.settings100 xpChanging graphical parameters in histogram100 xpChanging plotting characters100 xpUsing simpleTheme()50 xpChanging plotting characters revisited100 xpChanging parameters in grouped density plot100 xp - 4
Customizing plots using panel functions
Learn to use panel and prepanel functions to enhance existing displays or create new ones.
Panel Functions50 xpScatter plot with rugs100 xpAlternative panel function - panel.violin()100 xpAlternative panel functions - panel.smoothScatter()100 xpPrepanel Functions to control limits50 xpPrepanel functions with scales100 xpOptional arguments of default panel functions50 xpInteraction plot of residuals50 xpOptional arguments of panel.bwplot()100 xpUsing emojis as plotting symbols100 xp - 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.
New methods50 xpThe xyplot() method for time series objects100 xpThe xyplot() method with panel.horizonplot()100 xpNew high-level functions50 xpMap projections100 xpConfidence bands using the segplot() function100 xpHexagonal binning of bivariate data100 xpManipulation of trellis objects50 xpThe directlabels package100 xpAdding ggplot2-style layers100 xp
Tom Jeon
Richie CottonPrerequisites
Data Visualization in R
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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