split是R核心中尤其重要的函数。在数据处理方面，许多提供基于R基础解决方案的Stack Overflow答案都依赖它。任何分组操作的工作程序都是这个函数。
还有许多问题的解决方案只需要一行split。很多人不知道
split.data.frame可以按行拆分矩阵；split.default可以按列拆分数据框。也许关于split的R文档并不太好。它确实提到了第一个用途，但没有提到第二个。
R核心中split有四种方法：
methods(split)
#[1] split.data.frame split.Date split.default split.POSIXct
我会提供一份深入解释split.data.frame，split.default和C级别的.Internal(split(x, f))如何工作的答案。欢迎其他回答涉及“Date”和“POSIXct”对象。
split.data.frame 是如何工作的？function (x, f, drop = FALSE, ...)
lapply(split(x = seq_len(nrow(x)), f = f, drop = drop, ...),
function(ind) x[ind, , drop = FALSE])
它调用split.default函数来分割行索引向量seq_len(nrow(x))，然后使用lapply循环将相关行提取到列表条目中。
这并不严格是一个“data.frame”的方法。它可以通过第一维度来分割任何二维对象，包括按行分割矩阵。
split.default如何工作？function (x, f, drop = FALSE, sep = ".", lex.order = FALSE, ...)
{
if (!missing(...))
.NotYetUsed(deparse(...), error = FALSE)
if (is.list(f))
f <- interaction(f, drop = drop, sep = sep, lex.order = lex.order)
else if (!is.factor(f))
f <- as.factor(f)
else if (drop)
f <- factor(f)
storage.mode(f) <- "integer"
if (is.null(attr(x, "class")))
return(.Internal(split(x, f)))
lf <- levels(f)
y <- vector("list", length(lf))
names(y) <- lf
ind <- .Internal(split(seq_along(x), f))
for (k in lf) y[[k]] <- x[ind[[k]]]
y
}
x没有类别（主要是一个原子向量），将使用.Internal(split(x, f))；.Internal(split())来沿着x分割索引，然后使用一个for循环将相关元素提取到列表条目中。原子向量（参见?vector）是具有以下模式的向量：
带有类别的对象...额...有这么多！让我举三个例子：
在我看来，split.default写得不太好。有那么多带类别的对象，但是split.default会通过"["以相同方式处理它们。这对于“factor”和“data.frame”很好用（所以我们将沿着列拆分数据框！），但它绝对不适用于矩阵，因为我们期望它会按照另一种方式工作。
A <- matrix(1:9, 3)
# [,1] [,2] [,3]
#[1,] 1 4 7
#[2,] 2 5 8
#[3,] 3 6 9
split.default(A, c(1, 1, 2)) ## it does not split the matrix by columns!
#$`1`
#[1] 1 2 4 5 7 8
#
#$`2`
#[1] 3 6 9
实际上，已经将回收规则应用于c(1, 1, 2)，我们等效地执行如下操作：
split(c(A), rep_len(c(1,1,2), length(A)))
for (k in lf) y[[k]] <- x[, ind[[k]], drop = FALSE]
split.data.frame 函数，再转置回去。lapply(split.data.frame(t(A), c(1, 1, 2)), t)
通过 lapply(split.default(data.frame(A), c(1, 1, 2)), as.matrix) 暂时可以解决问题。但如果 A 是字符矩阵，这种方法就会出现错误。
.Internal(split(x, f)) 的工作原理是什么？这真的是核心中的核心！下面我将以一个小例子来解释:
set.seed(0)
f <- sample(factor(letters[1:3]), 10, TRUE)
# [1] c a b b c a c c b b
#Levels: a b c
x <- 0:9
基本上有三个步骤。为了提高可读性，每个步骤都提供了等效的R代码。
步骤1：制表（计算每个因子水平的出现次数）
## a factor has integer mode so `tabulate` works
tab <- tabulate(f, nbins = nlevels(f))
[1] 2 4 4
步骤2：分配结果列表的存储空间
result <- vector("list", nlevels(f))
for (i in 1:length(tab)) result[[i]] <- vector(mode(x), tab[i])
names(result) <- levels(f)
[ ]都代表该向量的一个条目。$a: [ ] [ ]
$b: [ ] [ ] [ ] [ ]
$c: [ ] [ ] [ ] [ ]
步骤三：元素分配
现在揭示因子的内部整数模式非常有用：
.f <- as.integer(f)
#[1] 3 1 2 2 3 1 3 3 2 2
我们需要扫描x和.f，将x[i]填充到result[[.f[i]]]的正确条目中，参考一个累加器缓冲向量。
ab <- integer(nlevels(f)) ## accumulator buffer
for (i in 1:length(.f)) {
fi <- .f[i]
counter <- ab[fi] + 1L
result[[fi]][counter] <- x[i]
ab[fi] <- counter
}
^ 是指向被访问或更新的元素的指针。## i = 1
x: [0] [1] [2] [3] [4] [5] [6] [7] [8] [9]
.f: [3] [1] [2] [2] [3] [1] [3] [3] [2] [2]
^
ab: [0] [0] [0] ## on entry
^
$a: [ ] [ ]
$b: [ ] [ ] [ ] [ ]
$c: [0] [ ] [ ] [ ]
^
ab: [0] [0] [1] ## on exit
^
## i = 2
x: [0] [1] [2] [3] [4] [5] [6] [7] [8] [9]
.f: [3] [1] [2] [2] [3] [1] [3] [3] [2] [2]
^
ab: [0] [0] [1] ## on entry
^
$a: [1] [ ]
^
$b: [ ] [ ] [ ] [ ]
$c: [0] [ ] [ ] [ ]
ab: [1] [0] [1] ## on exit
^
## i = 3
x: [0] [1] [2] [3] [4] [5] [6] [7] [8] [9]
.f: [3] [1] [2] [2] [3] [1] [3] [3] [2] [2]
^
ab: [1] [0] [1] ## on entry
^
$a: [1] [ ]
$b: [2] [ ] [ ] [ ]
^
$c: [0] [ ] [ ] [ ]
ab: [1] [1] [1] ## on exit
^
## i = 4
x: [0] [1] [2] [3] [4] [5] [6] [7] [8] [9]
.f: [3] [1] [2] [2] [3] [1] [3] [3] [2] [2]
^
ab: [1] [1] [1] ## on entry
^
$a: [1] [ ]
$b: [2] [3] [ ] [ ]
^
$c: [0] [ ] [ ] [ ]
ab: [1] [2] [1] ## on exit
^
## i = 5
x: [0] [1] [2] [3] [4] [5] [6] [7] [8] [9]
.f: [3] [1] [2] [2] [3] [1] [3] [3] [2] [2]
^
ab: [1] [2] [1] ## on entry
^
$a: [1] [ ]
$b: [2] [3] [ ] [ ]
$c: [0] [4] [ ] [ ]
^
ab: [1] [2] [2] ## on exit
^
## i = 6
x: [0] [1] [2] [3] [4] [5] [6] [7] [8] [9]
.f: [3] [1] [2] [2] [3] [1] [3] [3] [2] [2]
^
ab: [1] [2] [2] ## on entry
^
$a: [1] [5]
^
$b: [2] [3] [ ] [ ]
$c: [0] [4] [ ] [ ]
ab: [2] [2] [2] ## on exit
^
## i = 7
x: [0] [1] [2] [3] [4] [5] [6] [7] [8] [9]
.f: [3] [1] [2] [2] [3] [1] [3] [3] [2] [2]
^
ab: [2] [2] [2] ## on entry
^
$a: [1] [5]
$b: [2] [3] [ ] [ ]
$c: [0] [4] [6] [ ]
^
ab: [2] [2] [3] ## on exit
^
## i = 8
x: [0] [1] [2] [3] [4] [5] [6] [7] [8] [9]
.f: [3] [1] [2] [2] [3] [1] [3] [3] [2] [2]
^
ab: [2] [2] [3] ## on entry
^
$a: [1] [5]
$b: [2] [3] [ ] [ ]
$c: [0] [4] [6] [7]
^
ab: [2] [2] [4] ## on exit
^
## i = 9
x: [0] [1] [2] [3] [4] [5] [6] [7] [8] [9]
.f: [3] [1] [2] [2] [3] [1] [3] [3] [2] [2]
^
ab: [2] [2] [4] ## on entry
^
$a: [1] [5]
$b: [2] [3] [8] [ ]
^
$c: [0] [4] [6] [7]
ab: [2] [3] [4] ## on exit
^
## i = 10
x: [0] [1] [2] [3] [4] [5] [6] [7] [8] [9]
.f: [3] [1] [2] [2] [3] [1] [3] [3] [2] [2]
^
ab: [2] [3] [4] ## on entry
^
$a: [1] [5]
$b: [2] [3] [8] [9]
^
$c: [0] [4] [6] [7]
ab: [2] [4] [4] ## on exit
^