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David Moore
Asked: 2024-08-21 05:07:32 +0800 CST

使用递归函数创建嵌套 n 次的列表

  • 772

我有以下数据框。

Data_Frame <- data.frame(Factor_1 = rep(LETTERS[1:4], each = 12, length.out = 48), Factor_2 = rep(letters[1:3], each = 4, length.out = 48), Factor_3 = rep(1:2, each = 2, length.out = 48), Response = rnorm(48, 25, 1))

我想创建一个嵌套列表，其中我已按研究中的每个因素连续拆分数据框。我将从一个包含列名的向量开始，这些列名包含我想按其拆分数据框的因素（此向量将按我希望结果列表嵌套的顺序包含这些因素）。

Factors_to_Split_by <- c("Factor_1", "Factor_2", "Factor_3")

结果列表应类似于以下Output对象。

Output <- lapply(lapply(split(Data_Frame, Data_Frame[, which(colnames(Data_Frame) == Factors_to_Split_by[1])]), function (x) {
  split(x, x[, which(colnames(x) == Factors_to_Split_by[2])])
}), function (x) {
  lapply(x, function (y) {
    split(y, y[, which(colnames(y) == Factors_to_Split_by[3])])
  })
})

我如何编写一个递归函数，将Factors_to_Split_by其用作输入并返回所需Output列表作为输出？我可能有超过 3 个因素来划分数据，并且我想要一些模块化、高效和程序化的东西。

谢谢！

2 个回答

  1. Best Answer

    以下是使用Reduce自定义函数的一种可能方法：

    split_df <- function(x, split) {
  if (is.data.frame(x)) {
    split(x, x[split])
  } else {
    lapply(x, split_df, split = split)  
  }
}

Output2 <- Reduce(split_df, Factors_to_Split_by, init = Data_Frame)

identical(Output, Output2)
#> [1] TRUE
    • 5

  2. 您可以像这样定义递归函数

    f <- function(data, fct) {
  if (length(fct) == 1) split(data, data[fct])
  lapply(split(data, data[fct[1]]), f, fct = fct[-1])
}

    使得

    > f(Data_Frame, Factors_to_Split_by)
$A
$A$a
$A$a$`1`
  Factor_1 Factor_2 Factor_3 Response
1        A        a        1 25.91996
2        A        a        1 25.12079

$A$a$`2`
  Factor_1 Factor_2 Factor_3 Response
3        A        a        2 24.88218
4        A        a        2 24.77660


$A$b
$A$b$`1`
  Factor_1 Factor_2 Factor_3 Response
5        A        b        1 25.63426
6        A        b        1 24.64074

$A$b$`2`
  Factor_1 Factor_2 Factor_3 Response
7        A        b        2 26.60224
8        A        b        2 25.17982


$A$c
$A$c$`1`
   Factor_1 Factor_2 Factor_3 Response
9         A        c        1 24.90249
10        A        c        1 26.12602

$A$c$`2`
   Factor_1 Factor_2 Factor_3 Response
11        A        c        2 25.87801
12        A        c        2 24.82886



$B
$B$a
$B$a$`1`
   Factor_1 Factor_2 Factor_3 Response
13        B        a        1 25.29955
14        B        a        1 24.74579

$B$a$`2`
   Factor_1 Factor_2 Factor_3 Response
15        B        a        2 25.06018
16        B        a        2 27.33450


$B$b
$B$b$`1`
   Factor_1 Factor_2 Factor_3 Response
17        B        b        1 25.78050
18        B        b        1 24.96464

$B$b$`2`
   Factor_1 Factor_2 Factor_3 Response
19        B        b        2 24.04945
20        B        b        2 23.52038


$B$c
$B$c$`1`
   Factor_1 Factor_2 Factor_3 Response
21        B        c        1 25.68414
22        B        c        1 25.25209

$B$c$`2`
   Factor_1 Factor_2 Factor_3 Response
23        B        c        2 24.32218
24        B        c        2 25.81953



$C
$C$a
$C$a$`1`
   Factor_1 Factor_2 Factor_3 Response
25        C        a        1 23.61297
26        C        a        1 25.52444

$C$a$`2`
   Factor_1 Factor_2 Factor_3 Response
27        C        a        2 27.80018
28        C        a        2 24.85324


$C$b
$C$b$`1`
   Factor_1 Factor_2 Factor_3 Response
29        C        b        1 24.63975
30        C        b        1 23.95888

$C$b$`2`
   Factor_1 Factor_2 Factor_3 Response
31        C        b        2 24.93261
32        C        b        2 23.85798


$C$c
$C$c$`1`
   Factor_1 Factor_2 Factor_3 Response
33        C        c        1 25.29823
34        C        c        1 25.16727

$C$c$`2`
   Factor_1 Factor_2 Factor_3 Response
35        C        c        2 25.36553
36        C        c        2 24.99169



$D
$D$a
$D$a$`1`
   Factor_1 Factor_2 Factor_3 Response
37        D        a        1 24.53971
38        D        a        1 24.72733

$D$a$`2`
   Factor_1 Factor_2 Factor_3 Response
39        D        a        2 25.74960
40        D        a        2 24.56601


$D$b
$D$b$`1`
   Factor_1 Factor_2 Factor_3 Response
41        D        b        1 22.72847
42        D        b        1 24.29836

$D$b$`2`
   Factor_1 Factor_2 Factor_3 Response
43        D        b        2 24.69552
44        D        b        2 23.77094


$D$c
$D$c$`1`
   Factor_1 Factor_2 Factor_3 Response
45        D        c        1 24.07517
46        D        c        1 26.21868

$D$c$`2`
   Factor_1 Factor_2 Factor_3 Response
47        D        c        2 26.46018
48        D        c        2 24.44250
    • 3

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