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
* eliminate for-loops in your code
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:
* absolutely 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 most cases it is 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 0.1.9 (2009-06-23) ---------------------------------------------------
* fix bug in rbind.fill when NULLs present in list
* improve each to recognise when all elements are numeric
* fix labelling bug in d*ply when .drop = FALSE
* additional methods for quoted objects
* add summarise helper - this function is like transform, but creates
a new data frame rather than reusing the old (thanks to Brendan
O'Connor for the neat idea)
--
http://had.co.nz/
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