Question

我在使用简洁语法组合嵌套列表中包含的数据框时遇到问题。我有一个以下形式的嵌套列表：

nestedList <- lapply(1:3,function(y){
  lapply(1:8,function(z){
    data.frame(matrix(rnorm(20), nrow=10))
  })
})

所以nestedList包含3个列表，每个列表包含8个带有数据框的列表。我想将这些列表组合如下：

tmp1 <- nestedList[[1]][[1]]
tmp2 <- nestedList[[2]][[1]]
tmp3 <- nestedList[[3]][[1]]

expectedResult <- rbind(tmp1,tmp2,tmp3)

我原以为以下语法有效，但显然不是：

unexpectedResult <- rbind(nestedList[[1:3]][[1]])

Answer 1

试试这个。

foo <- lapply(nestedList, function(x) x[[1]])
this <- do.call("rbind", foo)

Answer 2

我使用purrr

提出了以下解决方案

my_result <- nestedList %>%
  # extract first dataframe from each nested list
  map(`[[`, 1) %>% 
  # bind rows together
  bind_rows()

并测试结果是否正确

identical(my_result, expectedResult)
[1] TRUE

Answer 3

do.call(rbind, lapply(nestedList[1:3], '[[', 1))

会做到这一点：

set.seed(123)
nestedList <- lapply(1:5,function(y){
  lapply(1:8,function(z){
    data.frame(matrix(rnorm(20), nrow=10))
  })
})

> do.call(rbind, lapply(nestedList[1:3], '[[', 1))
            X1          X2
1  -0.56047565  1.22408180
2  -0.23017749  0.35981383
3   1.55870831  0.40077145
4   0.07050839  0.11068272
5   0.12928774 -0.55584113
6   1.71506499  1.78691314
7   0.46091621  0.49785048
8  -1.26506123 -1.96661716
9  -0.68685285  0.70135590
10 -0.44566197 -0.47279141
11  1.05271147 -0.21538051
12 -1.04917701  0.06529303
13 -1.26015524 -0.03406725
14  3.24103993  2.12845190
15 -0.41685759 -0.74133610
16  0.29822759 -1.09599627
17  0.63656967  0.03778840
18 -0.48378063  0.31048075
19  0.51686204  0.43652348
20  0.36896453 -0.45836533
21  0.23743027  1.01755864
22  1.21810861 -1.18843404
23 -1.33877429 -0.72160444
24  0.66082030  1.51921771
25 -0.52291238  0.37738797
26  0.68374552 -2.05222282
27 -0.06082195 -1.36403745
28  0.63296071 -0.20078102
29  1.33551762  0.86577940
30  0.00729009 -0.10188326

Answer 4

我想指出rbindlist中的data.table函数。此功能通常比基础rbind

更有效

 library(data.table)
rbindlist(unlist(nestedList, recursive = F))

# Performance comparison
microbenchmark(times = 1000,
   datatable_rbind = rbindlist(unlist(nestedList, recursive = F)),
   base_rbind = do.call("rbind", lapply(nestedList, function(x) x[[1]])),
   base_rbind2 = do.call(rbind, lapply(nestedList[1:3], '[[', 1))
)

# Unit: microseconds
# expr     min      lq     mean   median       uq      max neval
# datatable_rbind  85.530 109.397 124.5534 124.3035 141.1110 216.816  1000
# base_rbind 135.037 152.035 190.5976 184.8475 201.0455 5912.946 1000
# base_rbind2 136.196 151.783 179.9393 186.4245 200.4225  347.564 1000

合并嵌套列表中的数据帧

4 个答案: