为什么aggregate()在这里不起作用?
> r = aggregate(data.frame(var1 = 1:10, var2 = 101:110), by=list(range=cut(1:10, breaks=c(2,4,8,10))), FUN = function(x) { c(obs=length(x), avg=mean(x), sd=sd(x)) })
> class(r)
[1] "data.frame"
> dim(r)
[1] 3 3
> r[,1]
[1] (2,4] (4,8] (8,10]
Levels: (2,4] (4,8] (8,10]
> r[,2]
obs avg sd
[1,] 2 3.5 0.707107
[2,] 4 6.5 1.290994
[3,] 2 9.5 0.707107
> r[,3]
obs avg sd
[1,] 2 103.5 0.707107
[2,] 4 106.5 1.290994
[3,] 2 109.5 0.707107
> class(r[,2])
[1] "matrix"
> class(r[,3])
[1] "matrix"
更新
运行正确版本后返回的aggregate()值:
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答案 0 :(得分:3)
那是因为aggregate
没有将data.frames
传递给其FUN=
参数。它传递了观察向量。此外,[, "name"]
索引不适用于矩阵。确保传入data.frame而不是示例中的矩阵。也许你想要by
函数
by(data.frame(var1 = 1:10, var2 = 101:110),
list(range=cut(1:10, breaks=c(2,4,8,10))),
FUN = function(x) { c(obs=length(x[, "var2"]), avg=mean(x[, "var2"]), sd=sd(x[, "var2"])) })
答案 1 :(得分:3)
提供数据框并理解聚合只传递列向量,因此使用x[ , "colname"]
注定要失败,因为" x"不是数据帧:
aggregate(data.frame(var1 = 1:10, var2 = 101:110),
by=list(range=cut(1:10, breaks=c(2,4,8,10))),
FUN = function(x)
{
c(obs=length(x), avg=mean(x), sd=sd(x))
})
#------------
range var1.obs var1.avg var1.sd var2.obs var2.avg var2.sd
1 (2,4] 2.0000000 3.5000000 0.7071068 2.0000000 103.5000000 0.7071068
2 (4,8] 4.0000000 6.5000000 1.2909944 4.0000000 106.5000000 1.2909944
3 (8,10] 2.0000000 9.5000000 0.7071068 2.0000000 109.5000000 0.7071068