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February 18, 2013


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I would add data.table to that list, perhaps in place of plyr if it has to be one or the other. data.table is very fast.

Very informative blog. I just used RODBC and SQLDF for the first time. Are there any R packages to handle big data?

I would add package ff to that list, perhaps in place of sqldf. sqldf allows to write a subset of native R queries in yet another language at a huge cost in terms of RAM (twice) and CPU (e.g. factor 40 slower). ff enhances R for big datasets and operations that are not possible in pure R given its RAM needs. For Revolution R Enterprise users, ff is less attractive on that list, because it has its own methods for big datasets on disk.

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R for the Enterprise

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