本文介绍了如何将列表中的数据帧写入单个csv文件的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！
问题描述
我有一个包含15个数据帧的列表，它们看起来像这样
I have a list with 15 data frames and they look like this
> head(final_data[[1]])
DateTime # Unemployed Persons.csv
147 2013-03-01 2320.58
148 2013-04-01 2336.89
149 2013-05-01 2213.78
150 2013-06-01 2135.90
151 2013-07-01 2302.79
152 2013-08-01 2177.01
> head(final_data[[2]])
DateTime Business Confidence.csv
46 2013-03-01 -6.2
47 2013-04-01 -1.3
48 2013-05-01 -2.4
49 2013-06-01 -5.1
50 2013-07-01 -2.0
51 2013-08-01 -1.8
，依此类推.第一列DateTime在所有15个数据帧中都是公用的.我想创建一个最终的输出文件，将所有这些数据帧连接在一起，并包含16列这样的
and so on. The first column DateTime is common across all the 15 dataframes. I would like to create a final output file which joins all these dataframes together and contains 16 columns something like this
DateTime # Unemployed Persons.csv Business Confidence.csv
147 2013-03-01 2320.58 -6.2
148 2013-04-01 2336.89 -1.3
149 2013-05-01 2213.78 -2.4
150 2013-06-01 2135.90 -5.1
151 2013-07-01 2302.79 -2.0
152 2013-08-01 2177.01 -1.8
我可以使用合并功能手动执行此操作，但需要您的帮助才能将其循环到整个列表.
I can do this manually by using the merge function but I would need your help to loop it over the entire list.
谢谢.
如果使用reduce，则会得到以下结果；
If I use reduce, I get the following result ;
a <- Reduce(function(...) merge(..., by = "DateTime"), final_data)
view(a)
> a
DateTime Value
1 1994-01-31 455
2 1994-02-28 470
3 1994-03-31 455
4 1994-04-30 455
5 1994-05-31 356
6 1994-06-30 425
7 1994-07-31 445
8 1994-08-31 470
9 1994-09-30 470
10 1994-10-31 470
11 1994-11-30 445
12 1994-12-31 485
13 1995-01-31 497
14 1995-02-28 536
15 1995-03-31 546
16 1995-04-30 546
17 1995-05-31 556
18 1995-06-30 601
19 1995-07-31 611
20 1995-08-31 616
21 1995-09-30 641
22 1995-10-31 631
23 1995-11-30 601
24 1995-12-31 636
25 1996-01-31 620
26 1996-02-29 620
27 1996-03-31 635
28 1996-04-30 605
29 1996-05-31 605
30 1996-06-30 605
31 1996-07-31 615
32 1996-08-31 630
33 1996-09-30 630
34 1996-10-31 630
35 1996-11-30 640
36 1996-12-31 640
37 1997-01-31 640
38 1997-02-28 631
39 1997-03-31 611
40 1997-04-30 640
41 1997-05-31 640
42 1997-06-30 631
43 1997-07-31 596
44 1997-08-31 626
45 1997-09-30 650
46 1997-10-31 705
47 1997-11-30 734
48 1997-12-31 719
49 1998-01-31 690
50 1998-02-28 685
51 1998-03-31 699
52 1998-04-30 699
53 1998-05-31 709
54 1998-06-30 709
55 1998-07-31 709
56 1998-08-31 725
57 1998-09-30 748
58 1998-10-31 781
59 1998-11-30 815
60 1998-12-31 817
61 1999-01-31 842
62 1999-02-28 829
63 1999-03-31 829
64 1999-04-30 839
65 1999-05-31 814
推荐答案
我们可以将Reduce与merge
Reduce(function(...) merge(..., by = "DateTime"), final_data)
# DateTime Unemployed_Persons.csv Business_Confidence.csv
#1 2013-03-01 2320.58 -6.2
#2 2013-04-01 2336.89 -1.3
#3 2013-05-01 2213.78 -2.4
#4 2013-06-01 2135.90 -5.1
#5 2013-07-01 2302.79 -2.0
#6 2013-08-01 2177.01 -1.8
数据
final_data <- list(structure(list(DateTime = c("2013-03-01", "2013-04-01",
"2013-05-01", "2013-06-01", "2013-07-01", "2013-08-01"), Unemployed_Persons.csv = c(2320.58,
2336.89, 2213.78, 2135.9, 2302.79, 2177.01)), .Names = c("DateTime",
"Unemployed_Persons.csv"), class = "data.frame", row.names = c("147",
"148", "149", "150", "151", "152")), structure(list(DateTime = c("2013-03-01",
"2013-04-01", "2013-05-01", "2013-06-01", "2013-07-01", "2013-08-01"
), Business_Confidence.csv = c(-6.2, -1.3, -2.4, -5.1, -2, -1.8
)), .Names = c("DateTime", "Business_Confidence.csv"), class = "data.frame", row.names = c("46",
"47", "48", "49", "50", "51")))
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