A set of tools that solves a common set of problems: you need to break a big problem down into manageable pieces, operate on each piece and then put all the pieces back together. For example, you might want to fit a model to each spatial location or time point in your study, summarise data by panels or collapse high-dimensional arrays to simpler summary statistics. The development of 'plyr' has been generously supported by 'Becton Dickinson'.
plyr is a set of tools for a common set of problems: you need to split up a big data structure into homogeneous pieces, apply a function to each piece and then combine all the results back together. For example, you might want to:
It's already possible to do this with base R functions (like split and the apply family of functions), but plyr makes it all a bit easier with:
Considerable effort has been put into making plyr fast and memory efficient, and in many cases plyr is as fast as, or faster than, the built-in equivalents.
A detailed introduction to plyr has been published in JSS: "The Split-Apply-Combine Strategy for Data Analysis", http://www.jstatsoft.org/v40/i01/. You can find out more at http://had.co.nz/plyr/, or track development at http://github.com/hadley/plyr. 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.