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Motivated by my young friend, HongMing Song, I managed to find more handy ways to calculate aggregated statistics by group in R. They require loading additional packages, plyr, doBy, Hmisc, and gdata, and are extremely user-friendly. In terms of CPU time, while the method with summarize() is as efficient as the 2nd method with by() introduced yesterday, summaryBy() in doBy package seems the slowest.

“Learn as if you were to live forever” – Mahatma Gandhi

> # METHOD 5: USING DDPLY() > library(plyr) > summ5 <- ddply(df, .(SELFEMPL, OWNRENT), summarize, INCOME = mean(INCOME), BAD = mean(BAD)) > print(summ5) SELFEMPL OWNRENT INCOME BAD 1 0 0 2133.314 0.08470957 2 0 1 2881.201 0.06293210 3 1 0 2742.247 0.06896552 4 1 1 3487.910 0.05316973 > > # METHOD 6: USING DOBy() > library(doBy) > summ6 <- summaryBy(INCOME + BAD ~ SELFEMPL + OWNRENT, data = df, fun = c(mean), keep.names = TRUE) > print(summ6) SELFEMPL OWNRENT INCOME BAD 1 0 0 2133.314 0.08470957 2 0 1 2881.201 0.06293210 3 1 0 2742.247 0.06896552 4 1 1 3487.910 0.05316973 > > # METHOD 7: USING SUMMARIZE() > library(Hmisc) > summ7 <- summarize(df[c('INCOME', 'BAD', 'SELFEMPL', 'OWNRENT')], df[c('SELFEMPL', 'OWNRENT')], colMeans, stat.name = NULL) > print(summ7) SELFEMPL OWNRENT INCOME BAD 1 0 0 2133.314 0.08470957 2 0 1 2881.201 0.06293210 3 1 0 2742.247 0.06896552 4 1 1 3487.910 0.05316973 > > # METHOD 8: USING FRAMEAPPLY() > library(gdata) > summ8 <- frameApply(df, by = c('SELFEMPL', 'OWNRENT'), on = c('INCOME', 'BAD'), fun = colMeans) > rownames(summ8) <- NULL > print(summ8) SELFEMPL OWNRENT INCOME BAD 1 0 0 2133.314 0.08470957 2 0 1 2881.201 0.06293210 3 1 0 2742.247 0.06896552 4 1 1 3487.910 0.05316973

**Efficiency Comparison**

> test5 <- function(n){ + for (i in 1:n){ + summ5 <- ddply(df, .(SELFEMPL, OWNRENT), summarize, INCOME = mean(INCOME), BAD = mean(BAD)) + } + } > system.time(test5(10)) user system elapsed 0.524 0.068 0.622 > > test6 <- function(n){ + for (i in 1:n){ + summ6 <- summaryBy(INCOME + BAD ~ SELFEMPL + OWNRENT, data = df, fun = c(mean), keep.names = TRUE) + } + } > system.time(test6(10)) user system elapsed 1.800 0.060 1.903 > > test7 <- function(n){ + for (i in 1:n){ + summ7 <- summarize(df[c('INCOME', 'BAD', 'SELFEMPL', 'OWNRENT')], df[c('SELFEMPL', 'OWNRENT')], colMeans, stat.name = NULL) + } + } > system.time(test7(10)) user system elapsed 0.236 0.020 0.274 > > test8 <- function(n){ + for (i in 1:n){ + summ8 <- frameApply(df, by = c('SELFEMPL', 'OWNRENT'), on = c('INCOME', 'BAD'), fun = colMeans) + rownames(summ8) <- NULL + } + } > system.time(test8(10)) user system elapsed 0.580 0.008 0.668

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