编辑：根据@Gregor的建议。我用idcol和fill = T替换了for循环。 希望这可以帮助。在下面的代码中，df1和df2是伪数据表。在您的情况下，它们将是CPI，CapitalAccount ... 首先，我们从每个表中选择列，在名为type的列表中的每个数据表中添加一个新列，并在该列中分配经济变量。接下来，既然您的数据表具有确切的列，我们将使用rbindlist()来绑定列表。

library(data.table)
df1 <- data.table(date = rep(seq(from = as.Date('2019-01-01'),to = as.Date('2019-01-05'),by = 'day'),5),US = runif(25),Argentina = runif(25),Thailand = runif(25),China = runif(25))
df2 <- data.table(date = rep(seq(from = as.Date('2019-01-01'),Japan = runif(25))

l1 <- list(df1,df2)
names(l1) <- c('GDP','CPI')

x <- rbindlist(l1,idcol = 'type',fill = TRUE) # this works even when the columns are different for each table

现在我们将所有数据表组合在一起，我们可以调整表的形状以使其看起来像您想要的结果。

x1 <- melt(x,id.vars = c('date','type'),measure.vars = c('US','Argentina'),variable.name = 'country',value.name = 'value')
dcast(x1,date + country ~ type,value.var = 'value')

考虑具有reshape，链merge和split的基数R，以获取转换后的数据帧命名列表。辅助函数包括高阶Map和Reduce。

proc_reshape <- function(df,nm) {

    within(data.frame(reshape(df,varying = names(df)[-1],times = names(df)[-1],v.names = nm,timevar = "Country",direction = "long"),row.names = NULL),{
           Date <- as.Date(as.POSIXct(Date,origin = "1970-01-01"))
           rm(id)
    })

}

# ELEMENTWISE LOOP THROUGH DFs AND THEIR NAMES
long_list <- Map(proc_reshape,my_list,names(my_list))

# CHAIN MERGE (FULL JOIN FOR MISMATCHED DATES BY COUNTRY)
merged_df <- Reduce(function(x,y) merge(x,y,by = c("Country","Date"),all = TRUE),long_list)

# CREATE NEW NAMED LIST OF DFs
new_list <- split(merged_df,merged_df$Country)

输出

new_list  

$Argentina 
   Country         Date  CPI CapitalAccount 
1  Argentina 2018-06-29   NA           10.9 
2  Argentina 2018-12-30   NA           16.2 
3  Argentina 2019-03-30   NA           32.2 
4  Argentina 2019-05-31 57.3             NA 
5  Argentina 2019-06-29   NA           13.8 
6  Argentina 2019-06-30 55.8             NA 
7  Argentina 2019-07-31 54.4             NA 

$Brazil 
    Country      Date CPI CapitalAccount 
8   Brazil 2018-06-29  NA           82.0 
9   Brazil 2018-12-30  NA           59.8 
10  Brazil 2019-03-30  NA           98.1 
11  Brazil 2019-06-29  NA           49.0 

$China 
    Country       Date CPI CapitalAccount 
12  China   2018-06-29  NA          -50.6 
13  China   2018-12-30  NA         -213.1 
14  China   2019-03-30  NA          -26.3 
15  China   2019-06-29  NA          -58.5 

$India 
    Country       Date CPI CapitalAccount 
16  India   2019-05-31 3.0             NA 
17  India   2019-06-30 3.2             NA 
18  India   2019-07-31 3.1             NA 

$Indonesia 
   Country         Date CPI CapitalAccount 
19 Indonesia 2019-05-31 3.3             NA 
20 Indonesia 2019-06-30 3.3             NA 
21 Indonesia 2019-07-31 3.3             NA 

$Philippines 
   Country           Date CPI CapitalAccount 
22 Philippines 2019-05-31 3.2             NA 
23 Philippines 2019-06-30 2.7             NA 
24 Philippines 2019-07-31 2.4             NA 

$Thai 
   Country       Date CPI CapitalAccount 
25 Thai    2018-06-29  NA            0.0 
26 Thai    2018-12-30  NA            0.0 
27 Thai    2019-03-30  NA            0.0 
28 Thai    2019-06-29  NA           27.9 

$Turkey 
   Country       Date CPI CapitalAccount 
29  Turkey 2018-06-29  NA           -2.7 
30  Turkey 2018-12-30  NA            0.5 
31  Turkey 2019-03-30  NA           21.4 
32  Turkey 2019-06-29  NA           -7.2 

$US 
    Country       Date CPI CapitalAccount 
33       US 2018-06-29  NA             -5 
34       US 2018-12-30  NA           2721 
35       US 2019-03-30  NA              0 
36       US 2019-05-31 1.8             NA 
37       US 2019-06-29  NA              0 
38       US 2019-06-30 1.6             NA 
39       US 2019-07-31 1.8             NA 

$Vietnam 
   Country       Date CPI CapitalAccount 
40 Vietnam 2019-05-31 2.9             NA 
41 Vietnam 2019-06-30 2.2             NA 
42 Vietnam 2019-07-31 2.4             NA 

Demo