plyr

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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:

ldply : Split list, apply function, and return results in a data...

The package I am focusing on today is the plyr package. I am just going to barely dip into this package, as I am only go to cover two functions from the package (laply and ldply). I am covering these as I will be using them in a later lesson how to perform sentiment analysis on Twitter data.

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What is the dplyr equivalent of plyr::ldply (tapply) in R?

Viewed 9k times. Part of R Language Collective. 15. Ultimately, I am trying to achieve something similar to the following, but leveraging dplyr instead of plyr: library (dplyr) probs = seq (0, 1, 0.1) plyr::ldply (tapply (mtcars$mpg, mtcars$cyl, function (x) { quantile (x, probs = probs) })) # .id 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% # 1 ...

dlply function - RDocumentation

Say I want to have the baseball data (from the plyr package) listed according to 'id' and 'year'. There is a difference between creating the list according to either: 1. mylist1 <- dlply(baseball, .(id, year), identity) and. 2. mylist2 <- dlply(baseball, .(id), dlply, .(year), identity)

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