plyr

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:

• 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 base R functions (like split and the apply family of functions), but plyr makes it all a bit easier with:

• totally consistent names, arguments and outputs
• convenient parallelisation through the foreach package
• 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
• labels that are maintained across all transformations

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 functions.

If you are interested in the watching the development of plyr, please see the development site on github. If you’ve discovered any bugs in the plyr package, or you have thought of a killer new feature, please email me: h.wickham@gmail.com.

Installation

# Get the released version from CRAN
install.packages("plyr")

# Get the development version from github
devtools::install_github("hadley/plyr")