Using plyr to get intimate with your data

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I gave a short tutorial [pdf slides] at the LSA summer institute on one of my favorite R packages: plyr (another brilliant Hadley Wickham creation). This package provides a set of very nice and semantically clean functions for exploring and manipulating data. The basic process that these functions carry out is to split data up in some way, do something to each piece, and then combine the results from each piece back together again.

One of the most common tasks that I use this for is to do some analysis to data from each subject in an experiment, and collect the results in a data frame. For instance, to calculate the mean and variance of each subject’s reaction time, you could use:

ddply(my.data, "subject.number", function(d) {
  return(data.frame(mean.RT=mean(d$RT), var.RT=mean(d$RT)))
})

Plyr also provides a whole host of convenience functions. For instance, you could accomplish the same thing using a one-liner:

ddply(my.data, "subject.number", summarise, mean.RT=mean(RT), var.RT=var(RT))

There are lots more examples (as well as more background on functional programming in general and the other use cases for plyr) in the slides [pdf] (knitr source is here, too).

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