Abstract: If you are using R and you think you're in hell, this is a map for you.

See: http://docs.julialang.org/en/latest/packages/packagelist/?hi... and: http://docs.julialang.org/en/latest/packages/packagelist/?hi...

Are the points they describe (easy faceting, bulk application of aesthetic and visual parameters across categorical variables, pleasing default color palettes for categorical data, etc.) really that much of a pain point with matplotlib?

I used it for two or three projects but not really extensively and especially not for complex statistical plotting, thats why I ask.

The core goals of the project are to offer: interactive, easy-to-specify, powerful novel graphics, with good support for statistical plots, in a web browser. That's quite a handful of things to balance, but we are picking up the best ideas from already existing projects: matplotlib, chaco, ggplot, protovis, stencil. The project is still nacent but we have promising results so far. (For instance, here is an ipython notebook export with interactive ggplot-style faceted plots of a Pandas DataFrame: http://htmlpreview.github.com/?https://github.com/ContinuumI...)

This kind of composite plot is a one-liner in the grammar of graphics. It would be much more with Matplotlib or Chaco. There are also marvelous novel graphics that are possible with Protovis-style Marks & Glyphs: http://mbostock.github.com/protovis/ex/ Although these are technically possible with existing libraries, the amount of programming skill required to create them is outside the capability of most data analysts. Our goal with Bokeh is to make these all accessible to that larger audience.

"Although it is a Python library, its primary output backend is HTML5 Canvas."

Perhaps currying/function compositions could be encouraged to avoid(hard, to(read(nested, functions))). However, being unexperienced with currying/function compositions, I have no idea if that would complicate the language...

http://www.revolutionanalytics.com/why-revolution-r/whitepap...