I work in cognitive science where we collect one or more data files per participant in an experiment then merge those files to perform subsequent analyses. Sometimes some files are in wide format and others are in long format, necessitating reshaping. I've found R entirely satisfactory for this.*
Indeed, I would be wary of an approach that attempts data manipulation *outside* of R as I'm of the "raw data in, results out" school of thought that it's dangerous to isolate your data manipulation from your record of analysis. If you leave your raw data files untouched and perform all manipulation & analysis in one system (R) then there is a complete record of what's happened to the data from start to finish and it's easier to catch/correct errors. The reshape package is great for reshaping between long & wide data formats, and the ply package is great for computing summary statistics within cells of the design. Mike *note: I typically use Python for data collection (showing visual stimuli, recording responses, etc), but have it spit out raw text files of the trial-by-trial data, and thus use it for only a bare minimum of processing. -- Mike Lawrence Graduate Student Department of Psychology Dalhousie University Looking to arrange a meeting? Check my public calendar: http://tr.im/mikes_public_calendar ~ Certainty is folly... I think. ~ ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
