R 中意外的应用函数行为 [英] Unexpected apply function behaviour in R

问题描述

我通过 apply 发现了一个令人惊讶的行为，我想知道是否有人可以解释一下.让我们看一个简单的矩阵:

I've discovered a surprising behaviour by apply that I wonder if anyone can explain. Lets take a simple matrix:

> (m = matrix(1:8,ncol=4))
     [,1] [,2] [,3] [,4]
[1,]    1    3    5    7
[2,]    2    4    6    8

我们可以这样垂直翻转它:

We can flip it vertically thus:

> apply(m, MARGIN=2, rev)
     [,1] [,2] [,3] [,4]
[1,]    2    4    6    8
[2,]    1    3    5    7

这将 rev() 向量反转函数迭代地应用于每一列.但是当我们尝试按行应用 rev 时，我们得到:

This applies the rev() vector reversal function iteratively to each column. But when we try to apply rev by row we get:

> apply(m, MARGIN=1, rev)
     [,1] [,2]
[1,]    7    8
[2,]    5    6
[3,]    3    4
[4,]    1    2

...逆时针旋转 90 度！Apply 使用 FUN=function(v) {v[length(v):1]} 提供相同的结果，所以这绝对不是 rev 的错.

.. a 90 degree anti-clockwise rotation! Apply delivers the same result using FUN=function(v) {v[length(v):1]} so it is definitely not rev's fault.

对此有什么解释吗?

推荐答案

文档指出

如果每次调用 FUN 都返回一个长度为 n 的向量，则 apply 返回一个维度为 c(n, dim(X)[MARGIN]) 如果 n > 1 的数组.

If each call to FUN returns a vector of length n, then apply returns an array of dimension c(n, dim(X)[MARGIN]) if n > 1.

从这个角度来看，这种行为无论如何都不是错误，而是它的工作方式.

From that perspective, this behaviour is not a bug whatsoever, that's how it intended to work.

有人可能想知道为什么选择它作为默认设置，而不是保留原始矩阵的结构.考虑以下示例:

One may wonder why this is chosen to be a default setting, instead of preserving the structure of the original matrix. Consider the following example:

> apply(m, 1, quantile)
     [,1] [,2]
0%    1.0  2.0
25%   2.5  3.5
50%   4.0  5.0
75%   5.5  6.5
100%  7.0  8.0

> apply(m, 2, quantile)
     [,1] [,2] [,3] [,4]
0%   1.00 3.00 5.00 7.00
25%  1.25 3.25 5.25 7.25
50%  1.50 3.50 5.50 7.50
75%  1.75 3.75 5.75 7.75
100% 2.00 4.00 6.00 8.00

> all(rownames(apply(m, 2, quantile)) == rownames(apply(m, 1, quantile)))
[1] TRUE

一致?的确，我们为什么要期待别的?

Consistent? Indeed, why would we expect anything else?

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