Hands-On Data Science with Anaconda

R package plyr

The objective of the plyr R package is about the split-apply-combine paradigm for R. This is quite common in data analysis: we solve a complex problem by breaking it down into small pieces, doing something to each piece, and then combining the results back together again. The following is an example borrowed from its menu with a minor modification:

library(plyr) d1<-c(rep('A', 8), rep('B', 15), rep('C', 6)) d2<-sample(c("M", "F"), size = 29, replace = TRUE) d3<-runif(n = 29, min = 18, max = 54) # dfx <- data.frame(group =d1,sex=d2,age=d3) # ddply(dfx, .(group, sex), summarize, mean = round(mean(age), 2), sd = round(sd(age), 2)) # ddply(baseball[1:100,], ~ year, nrow) ddply(baseball, .(lg), c("nrow", "ncol")) rbi<-ddply(baseball, ...