plyr is a set of tools for a common set of problems: you need to break
down a big data structure into manageable pieces, operate on each
piece and then put all the pieces back together. For example, you
might want to:
* fit the same model to subsets of a data frame
* quickly calculate summary statistics for each group
* perform group-wise transformations like scaling or standardising
It's already possible to do this with built-in functions (like split
and the apply functions), but plyr just makes it all a bit easier
with:
* totally consistent names, arguments and outputs
* input from and output to data.frames, matrices and lists
* progress bars to keep track of long running operations
* built-in error recovery, and informative error messages
Some considerable effort has been put into making plyr fast and memory
efficient, and in many cases plyr is as fast, or faster than, the
built-in functions.
You can find out more at http://had.co.nz/plyr/, including a 20 page
introductory guide, http://had.co.nz/plyr/plyr-intro.pdf. You can ask
questions about plyr (and data-manipulation in general) on the plyr
mailing list. Sign up at http://groups.google.com/group/manipulatr
plyr 1.1 (2010-07-19) ---------------------------------------------------
* *dply deals more gracefully with the case when all results are NULL
(fixes #10)
* *aply correctly orders output regardless of dimension names (fixes #11)
* join gains type = "full" which preserves all x and y rows
--
Assistant Professor / Dobelman Family Junior Chair
Department of Statistics / Rice University
http://had.co.nz/
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