One of the advantage of R over other popular statistical packages is that it now has “natural” support for interactive and dynamic data visualization. This is, for instance, something that is lacking with the Python ecosystem for scientific computing (Mayavi or Enthought Chaco are just too complex for what I have in mind).

Some time ago, I started drafting some tutors on interactive graphics with R. The idea was merely to give an overview of existing packages for interactive and dynamic plotting, and it was supposed to be a three-part document: first part presents basic capabilities like rgl, aplpack, and iplot (aka Acinonyx)–this actually ended up as a very coarse draft; second part should present ggobi and its R interface; third and last part would be about the Qt interface, with qtpaint and cranvas. I hope I will find some time to finish this project as it might provide useful complements to my introductory statistical course on data visualization and statistics with R.

I recently updated the Qt interface (during the summer I had some problems with the linking stage, probably because of external dependencies on the Qt framework, but it seems it has been solved in the meantime), and I’m really happy with what cranvas has to offer. On a Mac, the follwoing shortcuts are useful:

• Del/F5 to delete/undelete observations
• ? for identify mode
• S followed by Ctrl-click to vary brushing size; S then click to release and return to dynamic brushing