轉自：https://www.cnblogs.com/aloiswei/p/6032513.html

1.函數

ddply(.data, .variables, .fun = NULL, ..., .progress = "none",.inform = FALSE, .drop = TRUE, .parallel = FALSE, .paropts = NULL)

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2.例子

# Summarize a dataset by two variables dfx <- data.frame(group = c(rep('A', 8), rep('B', 15), rep('C', 6)),sex = sample(c("M", "F"), size = 29, replace = TRUE),age = runif(n = 29, min = 18, max = 54) ) head(dfx)group sex age 1 A M 22.44750 2 A M 52.92616 3 A F 30.00443 4 A M 39.56907 5 A M 18.89180 6 A F 50.81139 #Note the use of the '.' function to allow # group and sex to be used without quoting ddply(dfx, .(group, sex), summarize,mean = round(mean(age), 2),sd = round(sd(age), 2))group sex mean sd#運行結果 1 A F 40.41 14.71 2 A M 30.35 13.17 3 B F 34.81 12.76 4 B M 34.04 13.36 5 C F 35.09 13.39 6 C M 28.53 4.57

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需要加載包

library(plyr)

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釋義：也就是按照第二個參數進行分類應用第三個參數（函數處理），對group和sex均相同的分為一類，進行應用第三個參數funtion處理！。

轉載于:https://www.cnblogs.com/BlueBlueSea/p/10104924.html

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