我正在尝试合并不同列的列表。很少有其他专栏。
我在这里展示一个简单的例子。
a <- structure(list(CrashID = c("3409", "3410", "6790", "1100", "1200", "5609", "6730", "1220", "1234", "1239", "4409", "5610", "6794", "1123", "1245", "5634", "6732"),
From = c("0", "0", "0", "0", "1", "1", "3", "3", "3", "3", "8", "8", "8", "8","1", "1", "1"), To = c("1", "1", "1", "1", "3", "3", "4", "4", "4", "4", "9", "9", "9", "9", "2.2", "2.2", "2.2"), Dr = c("1", "6", "6", "6", "6", "4", "5", "4", "4", "4", "7", "7", "7", "4", "7", "7", "7"), Mn = c("4", "6", "5", "6", "3", "6", "4", "4", "4", "4", "9", "9", "9", "9", "3", "4", "7"), CSECT = c("001-01", "001-01", "001-01", "001-01", "001-01", "001-01", "001-01", "001-01", "001-01", "001-01", "001-02", "001-02","001-02","001-02","001-02","001-02","001-02")), .Names = c("CrashID", "From", "To", "Dr", "Mn", "CSECT" ), row.names = c(NA, -17L), class = "data.frame")
a
CrashID From To Dr Mn CSECT
1 3409 0 1 1 4 001-01
2 3410 0 1 6 6 001-01
3 6790 0 1 6 5 001-01
4 1100 0 1 6 6 001-01
5 1200 1 3 6 3 001-01
6 5609 1 3 4 6 001-01
7 6730 3 4 5 4 001-01
8 1220 3 4 4 4 001-01
9 1234 3 4 4 4 001-01
10 1239 3 4 4 4 001-01
11 4409 8 9 7 9 001-02
12 5610 8 9 7 9 001-02
13 6794 8 9 7 9 001-02
14 1123 8 9 4 9 001-02
15 1245 1 2.2 7 3 001-02
16 5634 1 2.2 7 4 001-02
17 6732 1 2.2 7 7 001-02
b <- structure(list(CrashID = c("12409", "12410", "62290", "13330"),
From = c("1", "2", "3", "4"), To = c("4", "4", "4", "4"), Dr = c("1", "3", "3", "3"),
CSECT = c("0301-01", "0031-01", "0301-01", "0031-01")), .Names = c("CrashID", "From", "To", "Dr", "CSECT" ), row.names = c(NA, -4L), class = "data.frame")
b
CrashID From To Dr CSECT
1 12409 1 4 1 0301-01
2 12410 2 4 3 0031-01
3 62290 3 4 3 0301-01
4 13330 4 4 3 0031-01
>
c <- structure(list(CrashID = c("3569", "3430", "6660", "1400", "1700", "5979", "6530", "1320", "1334", "1669", "4559", "5510", "6733", "3333", "1257", "544", "6632"),
To = c("1", "2", "2", "2", "7", "7", "7", "7", "7", "7", "7", "8", "8", "9", "5.2", "4.2", "8.2"), CSECT = c("090-01", "090-01", "090-01", "090-01", "090-01", "001-0w", "090-06", "001-r5", "001-44", "031-01", "031-02", "031-32","031-02","331-02","001-we","301-02","031-02")), .Names = c("CrashID", "To", "CSECT" ), row.names = c(NA, -17L), class = "data.frame")
c
CrashID To CSECT
1 3569 1 090-01
2 3430 2 090-01
3 6660 2 090-01
4 1400 2 090-01
5 1700 7 090-01
6 5979 7 001-0w
7 6530 7 090-06
8 1320 7 001-r5
9 1334 7 001-44
10 1669 7 031-01
11 4559 7 031-02
12 5510 8 031-32
13 6733 8 031-02
14 3333 9 331-02
15 1257 5.2 001-we
16 544 4.2 301-02
17 6632 8.2 031-02
我正在尝试使用＆＃34; rbind＆＃34;函数生成不会去任何地方的表。如果我手动使所有列都相同，我可以很容易地做到这一点。对于使用大量表格，这项工作是巨大的。
我喜欢得到如下的输出。
CrashID From To Dr Mn CSECT Table
1 3409 0.0 1.0 1 4 001-01 a
2 3410 0.0 1.0 6 6 001-01 a
3 6790 0.0 1.0 6 5 001-01 a
4 1100 0.0 1.0 6 6 001-01 a
5 1200 1.0 3.0 6 3 001-01 a
6 5609 1.0 3.0 4 6 001-01 a
7 6730 3.0 4.0 5 4 001-01 a
8 1220 3.0 4.0 4 4 001-01 a
9 1234 3.0 4.0 4 4 001-01 a
10 1239 3.0 4.0 4 4 001-01 a
11 4409 8.0 9.0 7 9 001-02 a
12 5610 8.0 9.0 7 9 001-02 a
13 6794 8.0 9.0 7 9 001-02 a
14 1123 8.0 9.0 4 9 001-02 a
15 1245 1.0 2.2 7 3 001-02 a
16 5634 1.0 2.2 7 4 001-02 a
17 6732 1.0 2.2 7 7 001-02 a
18 12409 1.0 4.0 1 NA 0301-01 b
19 12410 2.0 4.0 3 NA 0031-01 b
20 62290 3.0 4.0 3 NA 0301-01 b
21 13330 4.0 4.0 3 NA 0031-01 b
22 3569 1.0 NA NA NA 090-01 c
23 3430 2.0 NA NA NA 090-01 c
24 6660 2.0 NA NA NA 090-01 c
25 1400 2.0 NA NA NA 090-01 c
26 1700 7.0 NA NA NA 090-01 c
27 5979 7.0 NA NA NA 001-0w c
28 6530 7.0 NA NA NA 090-06 c
29 1320 7.0 NA NA NA 001-r5 c
30 1334 7.0 NA NA NA 001-44 c
31 1669 7.0 NA NA NA 031-01 c
32 4559 7.0 NA NA NA 031-02 c
33 5510 8.0 NA NA NA 031-32 c
34 6733 8.0 NA NA NA 031-02 c
35 3333 9.0 NA NA NA 331-02 c
36 1257 5.2 NA NA NA 001-we c
37 544 4.2 NA NA NA 301-02 c
38 6632 8.2 NA NA NA 031-02 c
答案 0 :(得分：4)
试试这个解决方案。在这里，我们创建了一个数据框列表，然后使用tidyr的{{1}}函数转换为一个名为unnest()的数据框。
Table请注意，在您的示例中，数据框library('devtools')
install_github('hadley/tidyr')
library('tidyr')
dfs <- list(a = a, b = b, c = c)
combined <- unnest(dfs, Table)
没有c列，但在您的最终数据集中，它具有From中To的值。
结果：
From用NAs替换空字符串：
Table CrashID From To Dr Mn CSECT
1 a 3409 0 1 1 4 001-01
2 a 3410 0 1 6 6 001-01
3 a 6790 0 1 6 5 001-01
4 a 1100 0 1 6 6 001-01
5 a 1200 1 3 6 3 001-01
6 a 5609 1 3 4 6 001-01
7 a 6730 3 4 5 4 001-01
8 a 1220 3 4 4 4 001-01
9 a 1234 3 4 4 4 001-01
10 a 1239 3 4 4 4 001-01
11 a 4409 8 9 7 9 001-02
12 a 5610 8 9 7 9 001-02
13 a 6794 8 9 7 9 001-02
14 a 1123 8 9 4 9 001-02
15 a 1245 1 2.2 7 3 001-02
16 a 5634 1 2.2 7 4 001-02
17 a 6732 1 2.2 7 7 001-02
18 b 12409 1 4 1 0301-01
19 b 12410 2 4 3 0031-01
20 b 62290 3 4 3 0301-01
21 b 13330 4 4 3 0031-01
22 c 3569 1 090-01
23 c 3430 2 090-01
24 c 6660 2 090-01
25 c 1400 2 090-01
26 c 1700 7 090-01
27 c 5979 7 001-0w
28 c 6530 7 090-06
29 c 1320 7 001-r5
30 c 1334 7 001-44
31 c 1669 7 031-01
32 c 4559 7 031-02
33 c 5510 8 031-32
34 c 6733 8 031-02
35 c 3333 9 331-02
36 c 1257 5.2 001-we
37 c 544 4.2 301-02
38 c 6632 8.2 031-02
答案 1 :(得分：4)
或者您可以使用join_all中的plyr。 （初始数据集都有character列）。使用@Kara Woo中的dfs
library(plyr)
join_all(Map(cbind, dfs, Table=names(dfs)), type="full")
# CrashID From To Dr Mn CSECT Table
#1 3409 0 1 1 4 001-01 a
#2 3410 0 1 6 6 001-01 a
#3 6790 0 1 6 5 001-01 a
#4 1100 0 1 6 6 001-01 a
#5 1200 1 3 6 3 001-01 a
#6 5609 1 3 4 6 001-01 a
#7 6730 3 4 5 4 001-01 a
#8 1220 3 4 4 4 001-01 a
#9 1234 3 4 4 4 001-01 a
#10 1239 3 4 4 4 001-01 a
#11 4409 8 9 7 9 001-02 a
#12 5610 8 9 7 9 001-02 a
#13 6794 8 9 7 9 001-02 a
#14 1123 8 9 4 9 001-02 a
#15 1245 1 2.2 7 3 001-02 a
#16 5634 1 2.2 7 4 001-02 a
#17 6732 1 2.2 7 7 001-02 a
#18 12409 1 4 1 <NA> 0301-01 b
#19 12410 2 4 3 <NA> 0031-01 b
#20 62290 3 4 3 <NA> 0301-01 b
#21 13330 4 4 3 <NA> 0031-01 b
#22 3569 <NA> 1 <NA> <NA> 090-01 c
#23 3430 <NA> 2 <NA> <NA> 090-01 c
#24 6660 <NA> 2 <NA> <NA> 090-01 c
#25 1400 <NA> 2 <NA> <NA> 090-01 c
#26 1700 <NA> 7 <NA> <NA> 090-01 c
#27 5979 <NA> 7 <NA> <NA> 001-0w c
#28 6530 <NA> 7 <NA> <NA> 090-06 c
#29 1320 <NA> 7 <NA> <NA> 001-r5 c
#30 1334 <NA> 7 <NA> <NA> 001-44 c
#31 1669 <NA> 7 <NA> <NA> 031-01 c
#32 4559 <NA> 7 <NA> <NA> 031-02 c
#33 5510 <NA> 8 <NA> <NA> 031-32 c
#34 6733 <NA> 8 <NA> <NA> 031-02 c
#35 3333 <NA> 9 <NA> <NA> 331-02 c
#36 1257 <NA> 5.2 <NA> <NA> 001-we c
#37 544 <NA> 4.2 <NA> <NA> 301-02 c
#38 6632 <NA> 8.2 <NA> <NA> 031-02 c