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Graphical methods are being increasingly used for exploratory data
analysis. Some of the many graphical tools that are useful in
this
setting are scatterplot matrices, nonparametric smoothers, and tree
diagrams. Statistical graphics for presenting information
have been
used much longer, but most of the commonly used graphics used in
papers, presentations, and the popular media, such as bar charts and
pie charts, are either poor or misleading in communicating information
to the reader. This tutorial begins with a series of
graphical horror stories from the scientific and lay press.
Then elements of
graphical
perception and good graph construction, many from the writings of Bill
Cleveland, are covered. Practical suggestions for choosing
the best
chart or graph type, making good and clear graphics, and formatting
are covered. Techniques for simultaneous presentation of
multiple
variables are described. Examples of model presentation
graphics will
also be given.
The second part of the tutorial consists of interactive demonstrations
of how to make effective statistical graphics using the freely
available R environment for data analysis and graphics
(www.r-project.org).
This will focus on base and lattice graphics as
well as graphics functions in the presenter's Hmisc package.
At the
close of the workshop some graphical marvels from the literature
(especially from Edward Tufte and Howard Wainer) are
presented.
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