使用分组变量按列拆分data.frame

Question

data.frame根据分组因子分割a 行相当容易.但是如何按列拆分并可能应用函数？

my.df <- data.frame(a = runif(10),
        b = runif(10),
        c = runif(10),
        d = runif(10))
grp <- as.factor(c(1,1, 2,2))

我想要的是群体的col柱.

到目前为止我所做的是一个穷人的申请.

lapply(as.list(as.numeric(levels(grp))), FUN = function(x, cn, data) {
            rowMeans(data[grp %in% x])
        }, cn = grp, data = my.df)

编辑 谢谢大家的参与.我运行了10次重复*,我的工作data.frame大约有22000行.这些是几秒钟内的结果.

Roman: 2.19
Joris: 4.60
Joris #2: 3.79 #changed sapply to lapply as suggested by Joris in the [R chatroom][1].
Gavin: 4.70
James & EDi: > 200 # * ran only one replicate due to the large order of magnitude difference

令我感到奇怪的是,手头的任务没有包装函数.也许总有一天我们能够做到

apply(X = my.df, MARGIN = 3, INDEX = my.groups, FUN = mean) # :)

Answer 1

您可以使用相同的逻辑,但以更方便的形式:

sapply(levels(grp),function(x)rowMeans(my.df[which(grp==x)]))

Answer 2

转换my.df为列表并将其拆分,然后在强制转换为数据框后将您的函数应用于列表的每个组件子集:

lapply(split(as.list(my.df), grp), function(x) rowMeans(as.data.frame(x)))

这给出了:

> lapply(split(as.list(my.df), grp), function(x) rowMeans(as.data.frame(x)))
$`1`
 [1] 0.8229189 0.4901288 0.2057578 0.6531641 0.3897858 0.4225179
 [7] 0.3905410 0.3928784 0.1715857 0.3973192

$`2`
 [1] 0.61348623 0.61229702 0.31938521 0.28325342 0.25857158
 [6] 0.49071991 0.01179999 0.57639186 0.38407240 0.17467337

这相当于@ Roman的"穷人的申请":

> roman <- lapply(as.list(as.numeric(levels(grp))), 
+                 FUN = function(x, cn, data) {
+                     rowMeans(data[grp %in% x])
+                 }, cn = grp, data = my.df)
> gavin <- lapply(split(as.list(my.df), grp), 
+                 function(x) rowMeans(as.data.frame(x)))
> all.equal(roman, gavin)
[1] "names for current but not for target"

除了组件上的名称.