doi:10.1016/S0167-9473(02)00286-4 
Copyright © 2003 Elsevier B.V. All rights reserved.
GGobi: evolving from XGobi into an extensible framework for interactive data visualization
Deborah F. Swayne
, 
, a, Duncan Temple Langb, Andreas Bujac and Dianne Cookd
a AT&T Labs-Research, Florham Park, NJ, USA
b Lucent Bell Laboratories, Murray Hill, NJ, USA
c The Wharton School, University of Pennsylvania, Philadelphia, PA, USA
d Iowa State University, Ames, IA, USA
Available online 19 November 2002.
References and further reading may be available for this article. To view references and further reading you must
purchase this article.
Abstract
GGobi is a direct descendent of a data visualization system called XGobi that has been around since the early 1990s. GGobi's new features include multiple plotting windows, a color lookup table manager, and an Extensible Markup Language file format for data. Perhaps the biggest advance is that GGobi can be easily extended, either by being embedded in other software or by the addition of plugins; either way, it can be controlled using an Application Programming Interface. An illustration of its extensibility is that it can be embedded in R. The result is a full marriage between GGobi's direct manipulation graphical environment and R's familiar extensible environment for statistical data analysis.
Author Keywords: Statistical graphics; Interoperability; R; XML; API; Plugins
Fig. 1. Comparison of XGobi (on the left) and GGobi. The most obvious differences are that GGobi now uses a multi-window design and a new toolkit. The basic elements are still similar.
Fig. 2. GGobi display for the XML sample data illustrating a tiny social network of four people. The GGobi console is in the background: note the tabs appearing above the variable selection area (the checkboxes), allowing separate variable selection for either dataset. The leftmost plot shows a scatterplot of the two variables in the “Employees” dataset, with edges added to illustrate which employees have contact with each other. The rightmost scatterplot shows a 1-D plot of the frequency of contact. Using brushing, we see that the lowest frequency of contact occurs between the employees whose salaries and levels differ the most.
Fig. 3. GGobi console window and a scatterplot of the 112 gene expression levels at the first and second time points, with the levels at embryonic day 13 (E13) plotted against the levels measured 2 days earlier.
Fig. 4. GGobi variable manipulation panel, showing the range, mean and median for each variable.
Fig. 5. GGobi's panel for choosing color, glyph and line type, together with the GTK+ color selection widget which allows any color in GGobi's colormap to be edited.
Fig. 6. A scatterplot with the expression levels on embryonic day 11 plotted vertically and the functional class variable plotted horizontally, and GGobi's tool for selectively hiding groups of points with a common glyph and color. Of the four functional classes, the third has been hidden by clicking on the corresponding
H button in the tool.
Fig. 7. Four parallel coordinate plots, each of which shows all the cases corresponding to one of the four functional classes. We generated these plots one at a time using the tool shown in
Fig. 6 to control which groups are shown and which are hidden.
Fig. 8. Dendrogram of the genes in the study, produced using R's
x11 driver. We will cut the tree so as to produce five clusters.
Fig. 9. Five parallel coordinate plots, each of which shows all the cases corresponding to one of the five groups found by using hierarchical clustering in R. These are exactly the views that we animate by executing a loop in R.
Abstract
Purchase PDF (834 K)
E-mail Article
Add to my Quick Links
Cited By in Scopus (15)
Purchase PDF (421 K)
