plyr 这个包，提供了一组规范的数据结构转换形式。

list        |	llply()	| ldply()     |	laply()

data frame | dlply() | ddply() | daply()
array | alply() | adply() | aaply()

一个简单的例子

• 普通做法

iris.set <- iris[iris$Species == "setosa", -5]
iris.versi <- iris[iris$Species == "versicolor", -5]
iris.virg <- iris[iris$Species == "virginica", -5]
## Apply
mean.set <- colMeans(iris.set)
mean.versi <- colMeans(iris.versi)
mean.virg <- colMeans(iris.virg)
## Combine
mean.iris <- rbind(mean.set, mean.versi, mean.virg)
rownames(mean.iris) <- c("setosa", "versicolor", "virginica")
mean.iris
           Sepal.Length Sepal.Width Petal.Length Petal.Width
setosa            5.006       3.428        1.462       0.246
versicolor        5.936       2.770        4.260       1.326
virginica         6.588       2.974        5.552       2.026

• plyr

library(plyr)
ddply(iris, .variables = "Species", .fun = function(df_sub){
    colMeans(df_sub[, -5])
})
     Species Sepal.Length Sepal.Width Petal.Length Petal.Width
1     setosa        5.006       3.428        1.462       0.246
2 versicolor        5.936       2.770        4.260       1.326
3  virginica        6.588       2.974        5.552       2.026

ddply(iris, .variables = "Species", .fun = function(df_sub) {
    model <- lm(Petal.Width ~ Petal.Length, data = df_sub)
    return(c(model$coefficients, R2 = summary(model)$r.squared))
})
     Species (Intercept) Petal.Length        R2
1     setosa -0.04822033    0.2012451 0.1099785
2 versicolor -0.08428835    0.3310536 0.6188467
3  virginica  1.13603130    0.1602970 0.1037537