[R] [R-pkgs] plyr version 1.6

Hadley Wickham hadley at rice.edu
Sat Jul 30 15:32:39 CEST 2011

# 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 each patient 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

  * 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 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

Version 1.6

* documentation improved using new features of `roxygen2`

* fixed namespacing issue which lead to lost labels when subsetting the
  results of `*lply`

* `colwise` automatically strips off split variables.

* `rlply` now correctly deals with `rlply(4, NULL)` (thanks to bug report from
  Eric Goldlust)

* `rbind.fill` tries harder to keep attributes, retaining the attributes from
  the first occurrence of each column it finds. It also now works with
  variables of class `POSIXlt` and preserves the ordered status of factors.

* `arrange` now works with one column data frames

Assistant Professor / Dobelman Family Junior Chair
Department of Statistics / Rice University

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