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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

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