Although I am not totally enamored of statistical graphics or their ability to convey nuances (sometimes words rule, OK?) I am indebted to Shane’s Blog for a pointer to

the R Graph Gallery

which has a whole bunch of nice graphics produced in R, together with full source code.

There is a thumbnail gallery from which you can go to more details and the source code for each of the graphics.

Here are some of the ones I like:

Violin plot- similar to a boxplot except that they show the density of the data, estimated by a kernel method (uses package SimpleR)

Boxplot and friends (uses packages Hmisc, hdrcde, vioplot)

Conditional Regression Tree (uses package Party)

Spinogram - an extension of a histogram

Mosaic plot (uses package vcd)

Conditional density plot (uses vcd)

ScatterPlot3D

Graphical representation of a sound wave (uses seewave package)

Geographic Cluster representation/thematic maps (uses packages cluster, maps, RcolorBrewer)

Other R graphics (including 3D)

• The rgl package is a visualization device system for R, using OpenGL as the rendering backend
• R Movies gallery
• CRAN taskview (an overview of the relevant packages in R) for graphics

GraphViz

is a favorite of mine .. an open source graph (network) visualization project from AT&T Research. The key apps here are dot and neato.

There is a very nice gallery here

And you can interface to it from R with RGraphviz

Update - more ideas

The Gallery of Data Visualization presents some very creative approaches, including

1. the Reorderable Matrix.
2. the Hanging Rootogram
3. the Bagplot: a Bivariate Boxplot
4. the Anamorphic Map
5. the Chi-square Map
6. the Multivariate Star Plot
7. the Enhanced Scatterplot Matrix - enhanced with a data ellipse showing the strength of the relationship
8. the CorrGram
9. the Spie Chart — a comparison of two pie charts

Some good ideas there, as long as it does not turn out to be more work to interpret the graphic than it is to look at the data itself.