从数据框中的列表列表中提取值

debugcn 发表于 Dev

用户3459293

List of 29
$ : num [1:2, 1:7] -0.424 1.84 4.125 1.84 2.935 ...
..- attr(*, "dimnames")=List of 2
.. ..$ : chr [1:2] "Training set" "Test set"

result[[1]]
                 ME     RMSE      MAE       MPE     MAPE      MASE
Training set -0.4238181 4.124630 2.934922 -5.749091 15.25211 0.8298791
Test set      1.8400343 1.840034 1.840034 10.300856 10.30086 0.5202885
               ACF1
Training set -0.0218282
Test set             NA

> dput(result)
list(structure(c(0.513396419391301, -37.8812034057995, 18.8299932348763, 
37.8812034057995, 15.9937453259579, 37.8812034057995, -11.1574789625766, 
-184.084337396461, 29.1724105151215, 184.084337396461, 0.817172411862546, 
1.93547375680242, 0.0818136754061593, NA), .Dim = c(2L, 7L), .Dimnames = list(
c("Training set", "Test set"), c("ME", "RMSE", "MAE", "MPE", 
"MAPE", "MASE", "ACF1"))), structure(c(0.513396419391301, 
-22.319971645238, 18.8299932348763, 22.319971645238, 15.9937453259579, 
22.319971645238, -11.1574789625766, -61.7607524011407, 29.1724105151215, 
61.7607524011407, 0.817172411862546, 1.14039986821851, 0.0818136754061593, 
NA), .Dim = c(2L, 7L), .Dimnames = list(c("Training set", "Test set"
), c("ME", "RMSE", "MAE", "MPE", "MAPE", "MASE", "ACF1"))), structure(c(0.513396419391301, 
27.1904085572912, 18.8299932348763, 27.1904085572912, 15.9937453259579, 
27.1904085572912, -11.1574789625766, 31.7460304729518, 29.1724105151215, 
31.7460304729518, 0.817172411862546, 1.38924631394672, 0.0818136754061593, 
NA), .Dim = c(2L, 7L), .Dimnames = list(c("Training set", "Test set"
), c("ME", "RMSE", "MAE", "MPE", "MAPE", "MASE", "ACF1"))), 
structure(c(0.513396419391301, 
    53.1089634908355, 18.8299932348763, 53.1089634908355, 15.9937453259579, 
53.1089634908355, -11.1574789625766, 47.6021790380844, 29.1724105151215, 
47.6021790380844, 0.817172411862546, 2.71350949404508, 0.0818136754061593, 
NA), .Dim = c(2L, 7L), .Dimnames = list(c("Training set", "Test set"
), c("ME", "RMSE", "MAE", "MPE", "MAPE", "MASE", "ACF1"))), structure(c(0.513396419391301, 
14.6193112181759, 18.8299932348763, 14.6193112181759, 15.9937453259579, 
14.6193112181759, -11.1574789625766, 20.0048887135367, 29.1724105151215, 
20.0048887135367, 0.817172411862546, 0.746948107804167, 0.0818136754061593, 
NA), .Dim = c(2L, 7L), .Dimnames = list(c("Training set", "Test set"
), c("ME", "RMSE", "MAE", "MPE", "MAPE", "MASE", "ACF1"))), structure(c(0.513396419391301, 
-1.06040238179536, 18.8299932348763, 1.06040238179536, 15.9937453259579, 
1.06040238179536, -11.1574789625766, -1.84742375550171, 29.1724105151215, 
1.84742375550171, 0.817172411862546, 0.0541794028988392, 0.0818136754061593, 
NA), .Dim = c(2L, 7L), .Dimnames = list(c("Training set", "Test set"
), c("ME", "RMSE", "MAE", "MPE", "MAPE", "MASE", "ACF1"))), structure(c(0.513396419391301, 
50.7310943575706, 18.8299932348763, 50.7310943575706, 15.9937453259579, 
50.7310943575706, -11.1574789625766, 46.4610981905357, 29.1724105151215, 
46.4610981905357, 0.817172411862546, 2.59201643440696, 0.0818136754061593, 
NA), .Dim = c(2L, 7L), .Dimnames = list(c("Training set", "Test set"
), c("ME", "RMSE", "MAE", "MPE", "MAPE", "MASE", "ACF1"))), structure(c(0.513396419391301, 
55.0138373829973, 18.8299932348763, 55.0138373829973, 15.9937453259579, 
55.0138373829973, -11.1574789625766, 48.4817807788847, 29.1724105151215, 
48.4817807788847, 0.817172411862546, 2.81083569006907, 0.0818136754061593, 
NA), .Dim = c(2L, 7L), .Dimnames = list(c("Training set", "Test set"
), c("ME", "RMSE", "MAE", "MPE", "MAPE", "MASE", "ACF1"))), structure(c(0.513396419391301, 
27.3624500366051, 18.8299932348763, 27.3624500366051, 15.9937453259579, 
27.3624500366051, -11.1574789625766, 31.8828547941145, 29.1724105151215, 
31.8828547941145, 0.817172411862546, 1.39803647208205, 0.0818136754061593, 
NA), .Dim = c(2L, 7L), .Dimnames = list(c("Training set", "Test set"
), c("ME", "RMSE", "MAE", "MPE", "MAPE", "MASE", "ACF1"))), structure(c(0.513396419391301, 
4.75759494704218, 18.8299932348763, 4.75759494704218, 15.9937453259579, 
4.75759494704218, -11.1574789625766, 7.52581852119078, 29.1724105151215, 
7.52581852119078, 0.817172411862546, 0.243080983115921, 0.0818136754061593, 
NA), .Dim = c(2L, 7L), .Dimnames = list(c("Training set", "Test set"
), c("ME", "RMSE", "MAE", "MPE", "MAPE", "MASE", "ACF1"))), structure(c(0.513396419391301, 
-21.5672042430535, 18.8299932348763, 21.5672042430535, 15.9937453259579, 
21.5672042430535, -11.1574789625766, -58.460100899509, 29.1724105151215, 
58.460100899509, 0.817172411862546, 1.10193853592405, 0.0818136754061593, 
NA), .Dim = c(2L, 7L), .Dimnames = list(c("Training set", "Test set"
), c("ME", "RMSE", "MAE", "MPE", "MAPE", "MASE", "ACF1"))), structure(c(0.513396419391301, 
-22.7300084731155, 18.8299932348763, 22.7300084731155, 15.9937453259579, 
22.7300084731155, -11.1574789625766, -63.6171484011973, 29.1724105151215, 
63.6171484011973, 0.817172411862546, 1.16134998195111, 0.0818136754061593, 
NA), .Dim = c(2L, 7L), .Dimnames = list(c("Training set", "Test set"
), c("ME", "RMSE", "MAE", "MPE", "MAPE", "MASE", "ACF1"))), structure(c(0.513396419391301, 
-6.93434513416145, 18.8299932348763, 6.93434513416145, 15.9937453259579, 
6.93434513416145, -11.1574789625766, -13.4582051337587, 29.1724105151215, 
13.4582051337587, 0.817172411862546, 0.354298222366539, 0.0818136754061593, 
NA), .Dim = c(2L, 7L), .Dimnames = list(c("Training set", "Test set"
), c("ME", "RMSE", "MAE", "MPE", "MAPE", "MASE", "ACF1"))), structure(c(0.513396419391301, 
26.5787861060258, 18.8299932348763, 26.5787861060258, 15.9937453259579, 
26.5787861060258, -11.1574789625766, 31.2551254927233, 29.1724105151215, 
31.2551254927233, 0.817172411862546, 1.35799653577008, 0.0818136754061593, 
NA), .Dim = c(2L, 7L), .Dimnames = list(c("Training set", "Test set"
), c("ME", "RMSE", "MAE", "MPE", "MAPE", "MASE", "ACF1"))), structure(c(0.513396419391301, 
36.5896815034013, 18.8299932348763, 36.5896815034013, 15.9937453259579, 
36.5896815034013, -11.1574789625766, 38.4955729378816, 29.1724105151215, 
38.4955729378816, 0.817172411862546, 1.86948570669616, 0.0818136754061593, 
NA), .Dim = c(2L, 7L), .Dimnames = list(c("Training set", "Test set"
), c("ME", "RMSE", "MAE", "MPE", "MAPE", "MASE", "ACF1"))), structure(c(0.513396419391301, 
18.3502949767713, 18.8299932348763, 18.3502949767713, 15.9937453259579, 
18.3502949767713, -11.1574789625766, 23.890603032012, 29.1724105151215, 
23.890603032012, 0.817172411862546, 0.937576189875915, 0.0818136754061593, 
NA), .Dim = c(2L, 7L), .Dimnames = list(c("Training set", "Test set"
), c("ME", "RMSE", "MAE", "MPE", "MAPE", "MASE", "ACF1"))), structure(c(0.513396419391301, 
-6.51749862530565, 18.8299932348763, 6.51749862530565, 15.9937453259579, 
6.51749862530565, -11.1574789625766, -12.547674868192, 29.1724105151215, 
12.547674868192, 0.817172411862546, 0.333000179908321, 0.0818136754061593, 
NA), .Dim = c(2L, 7L), .Dimnames = list(c("Training set", "Test set"
), c("ME", "RMSE", "MAE", "MPE", "MAPE", "MASE", "ACF1"))), structure(c(0.513396419391301, 
20.0687421145515, 18.8299932348763, 20.0687421145515, 15.9937453259579, 
20.0687421145515, -11.1574789625766, 25.5561206682837, 29.1724105151215, 
25.5561206682837, 0.817172411862546, 1.02537723732407, 0.0818136754061593, 
NA), .Dim = c(2L, 7L), .Dimnames = list(c("Training set", "Test set"
), c("ME", "RMSE", "MAE", "MPE", "MAPE", "MASE", "ACF1"))), structure(c(0.513396419391301, 
6.01122360728164, 18.8299932348763, 6.01122360728164, 15.9937453259579, 
6.01122360728164, -11.1574789625766, 9.32397573473786, 29.1724105151215, 
9.32397573473786, 0.817172411862546, 0.307132944366376, 0.0818136754061593, 
NA), .Dim = c(2L, 7L), .Dimnames = list(c("Training set", "Test set"
), c("ME", "RMSE", "MAE", "MPE", "MAPE", "MASE", "ACF1"))), structure(c(0.513396419391301, 
12.7182212474731, 18.8299932348763, 12.7182212474731, 15.9937453259579, 
12.7182212474731, -11.1574789625766, 17.8682910031556, 29.1724105151215, 
17.8682910031556, 0.817172411862546, 0.64981524462136, 0.0818136754061593, 
NA), .Dim = c(2L, 7L), .Dimnames = list(c("Training set", "Test set"
), c("ME", "RMSE", "MAE", "MPE", "MAPE", "MASE", "ACF1"))), structure(c(0.513396419391301, 
6.99715747218507, 18.8299932348763, 6.99715747218507, 15.9937453259579, 
6.99715747218507, -11.1574789625766, 10.6897760658741, 29.1724105151215, 
10.6897760658741, 0.817172411862546, 0.357507508791413, 0.0818136754061593, 
NA), .Dim = c(2L, 7L), .Dimnames = list(c("Training set", "Test set"
), c("ME", "RMSE", "MAE", "MPE", "MAPE", "MASE", "ACF1"))), structure(c(0.513396419391301, 
14.8697440743207, 18.8299932348763, 14.8697440743207, 15.9937453259579, 
14.8697440743207, -11.1574789625766, 20.2780871725288, 29.1724105151215, 
20.2780871725288, 0.817172411862546, 0.759743536072824, 0.0818136754061593, 
NA), .Dim = c(2L, 7L), .Dimnames = list(c("Training set", "Test set"
), c("ME", "RMSE", "MAE", "MPE", "MAPE", "MASE", "ACF1"))), structure(c(0.513396419391301, 
11.879111326578, 18.8299932348763, 11.879111326578, 15.9937453259579, 
11.879111326578, -11.1574789625766, 16.8884927626238, 29.1724105151215, 
16.8884927626238, 0.817172411862546, 0.606942392521939, 0.0818136754061593, 
NA), .Dim = c(2L, 7L), .Dimnames = list(c("Training set", "Test set"
), c("ME", "RMSE", "MAE", "MPE", "MAPE", "MASE", "ACF1"))), structure(c(0.513396419391301, 
-8.12047355873076, 18.8299932348763, 8.12047355873076, 15.9937453259579, 
8.12047355873076, -11.1574789625766, -16.1316044330997, 29.1724105151215, 
16.1316044330997, 0.817172411862546, 0.414901377270531, 0.0818136754061593, 
NA), .Dim = c(2L, 7L), .Dimnames = list(c("Training set", "Test set"
), c("ME", "RMSE", "MAE", "MPE", "MAPE", "MASE", "ACF1"))), structure(c(0.513396419391301, 
-12.5107370397529, 18.8299932348763, 12.5107370397529, 15.9937453259579, 
12.5107370397529, -11.1574789625766, -27.227651862362, 29.1724105151215, 
27.227651862362, 0.817172411862546, 0.63921420233948, 0.0818136754061593, 
NA), .Dim = c(2L, 7L), .Dimnames = list(c("Training set", "Test set"
), c("ME", "RMSE", "MAE", "MPE", "MAPE", "MASE", "ACF1"))), structure(c(0.513396419391301, 
8.10859725045342, 18.8299932348763, 8.10859725045342, 15.9937453259579, 
8.10859725045342, -11.1574789625766, 12.1809274700972, 29.1724105151215, 
12.1809274700972, 0.817172411862546, 0.414294578094901, 0.0818136754061593, 
NA), .Dim = c(2L, 7L), .Dimnames = list(c("Training set", "Test set"
), c("ME", "RMSE", "MAE", "MPE", "MAPE", "MASE", "ACF1"))), structure(c(0.513396419391301, 
8.74673451746508, 18.8299932348763, 8.74673451746508, 15.9937453259579, 
8.74673451746508, -11.1574789625766, 13.0147894156911, 29.1724105151215, 
13.0147894156911, 0.817172411862546, 0.446899084353792, 0.0818136754061593, 
NA), .Dim = c(2L, 7L), .Dimnames = list(c("Training set", "Test set"
), c("ME", "RMSE", "MAE", "MPE", "MAPE", "MASE", "ACF1"))), structure(c(0.513396419391301, 
28.2300570740269, 18.8299932348763, 28.2300570740269, 15.9937453259579, 
28.2300570740269, -11.1574789625766, 32.5645862171677, 29.1724105151215, 
32.5645862171677, 0.817172411862546, 1.44236533445103, 0.0818136754061593, 
NA), .Dim = c(2L, 7L), .Dimnames = list(c("Training set", "Test set"
), c("ME", "RMSE", "MAE", "MPE", "MAPE", "MASE", "ACF1"))), structure(c(0.513396419391301, 
14.0381287176184, 18.8299932348763, 14.0381287176184, 15.9937453259579, 
14.0381287176184, -11.1574789625766, 19.3636010547358, 29.1724105151215, 
19.3636010547358, 0.817172411862546, 0.717253605607607, 0.0818136754061593, 
NA), .Dim = c(2L, 7L), .Dimnames = list(c("Training set", "Test set"
), c("ME", "RMSE", "MAE", "MPE", "MAPE", "MASE", "ACF1"))))

我想将列表中的值Training set和Test set从列表中提取到数据框中，同时保留如图所示的列名。

这可能是Training set和Test set可能是列名和其他变量像R中长格式，例如训练和测试的行ME，RMSE，MAE，MPE，MAPE，等。

阿利斯泰尔

有不同的方法可以做到这一点，但 purrr（tidyverse 的一部分）很方便：

library(tidyverse)

df_result <- result %>% 
    map(as.data.frame) %>%    # convert each matrix to data.frame
    # add rownames of data.frames as column; simplify all to one data.frame 
    # with an ID column with which list element it came from
    map_df(rownames_to_column, 'subset', .id = 'element') %>% 
    tbl_df()    # for pretty printing

df_result
#> # A tibble: 58 x 9
#>    element       subset          ME     RMSE      MAE        MPE      MAPE      MASE       ACF1
#>      <chr>        <chr>       <dbl>    <dbl>    <dbl>      <dbl>     <dbl>     <dbl>      <dbl>
#>  1       1 Training set   0.5133964 18.82999 15.99375  -11.15748  29.17241 0.8171724 0.08181368
#>  2       1     Test set -37.8812034 37.88120 37.88120 -184.08434 184.08434 1.9354738         NA
#>  3       2 Training set   0.5133964 18.82999 15.99375  -11.15748  29.17241 0.8171724 0.08181368
#>  4       2     Test set -22.3199716 22.31997 22.31997  -61.76075  61.76075 1.1403999         NA
#>  5       3 Training set   0.5133964 18.82999 15.99375  -11.15748  29.17241 0.8171724 0.08181368
#>  6       3     Test set  27.1904086 27.19041 27.19041   31.74603  31.74603 1.3892463         NA
#>  7       4 Training set   0.5133964 18.82999 15.99375  -11.15748  29.17241 0.8171724 0.08181368
#>  8       4     Test set  53.1089635 53.10896 53.10896   47.60218  47.60218 2.7135095         NA
#>  9       5 Training set   0.5133964 18.82999 15.99375  -11.15748  29.17241 0.8171724 0.08181368
#> 10       5     Test set  14.6193112 14.61931 14.61931   20.00489  20.00489 0.7469481         NA
#> # ... with 48 more rows

如果您想要长格式的数据，请转置：

df_t_result <- result %>% 
    map(t) %>%    # transpose each matrix
    map(as.data.frame) %>% 
    map_df(rownames_to_column, 'stat', .id = 'element') %>% 
    set_names(~gsub('\\s', '_', .x)) %>%    # make names syntactic
    tbl_df()

df_t_result
#> # A tibble: 203 x 4
#>    element  stat   Training_set    Test_set
#>      <chr> <chr>          <dbl>       <dbl>
#>  1       1    ME     0.51339642  -37.881203
#>  2       1  RMSE    18.82999323   37.881203
#>  3       1   MAE    15.99374533   37.881203
#>  4       1   MPE   -11.15747896 -184.084337
#>  5       1  MAPE    29.17241052  184.084337
#>  6       1  MASE     0.81717241    1.935474
#>  7       1  ACF1     0.08181368          NA
#>  8       2    ME     0.51339642  -22.319972
#>  9       2  RMSE    18.82999323   22.319972
#> 10       2   MAE    15.99374533   22.319972
#> # ... with 193 more rows

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