基于一列将多个两列文本文件合并为一个数据框

Question

This question is a follow-up to this one , which got negative responses and no answers. So, I'm trying to do this using R.

I have many (more than 30) files like this:

• File1

   5 A1 23 A3 1 B2 289 BX5 90 D3

• File2

   2 A1 10 A2 3 B1 1 BX4 90 D3 24 E0

• File3

   4 A0 11 A2 1 B1 2 D3

And I would like to combine all of them to produce a data frame like this:

A0 0 0 4
A1 5 2 0
A2 0 10 11
A3 23 0 0
B1 0 3 1
B2 1 0 0
BX4 0 1 0
BX5 289 0 0
D3 90 90 2
E0 0 24 0

Based on this , I tried to read two files using read.table while specifying the second column as the row names and then I merged the data frames by row names, like this:

> df1 <- read.table("File1", row.names = 2)
> df1
     V1
A1    5
A3   23
B2    1
BX5 289
D3   90
> df2 <- read.table("File2", row.names = 2)
> df2
     V1
A1    2
A2   10
B1    3
BX4   1
D3   90
E0   24
> m1 <- merge(df1, df2, by=0, all=TRUE)
> m1[is.na(m1)] <- 0
> m1
    Row.names V1.x V1.y
1          A1    5    2
2          A2    0   10
3          A3   23    0
4          B1    0    3
5          B2    1    0
6         BX4    0    1
7         BX5  289    0
8          D3   90   90
9          E0    0   24

So far so good, but when I tried to merge the resulting data frame to the third one, it doesn't work as I hoped for. And because of that, I'm not sure how I will continue to merge all the 30-something files into one data frame. Previously I thought I would modify the multmerge function described here , but now I'm stuck.

So, would anybody please help me with this? Thanks in advance.

EDIT: I would also really appreciate if anyone could suggest me a better title for this question.

Answer 1

I have tried to adapt the Reduce-part from the multmerge function for your issue.

#read in the data (can be replaced with filenames
#like f1 <- read.table(file, header=F)
#or even lapply(list.files(mypath), read.table, header=F) 
#to get all dataframes in a list
f1 <- read.table(text="5 A1
23 A3
1 B2
289 BX5
90 D3", header=F)

f2 <- read.table(text="2 A1
10 A2
3 B1
1 BX4
90 D3
24 E0", header=F)

f3 <- read.table(text="4 A0
11 A2
1 B1
2 D3", header=F)

#put files in list
myfiles <- list(f1,f2,f3)

#changing colnames because I like keeping my data in order/knowing where it came from.
myfiles <- lapply(1:length(myfiles),function(x){
  r <- myfiles[[x]]
  colnames(r) <- c(paste0("f",x),"ID")
  r
})

#using the Reduce-function
res <- Reduce(function(x,y) {merge(x,y,all=T, by="ID")}, myfiles)
res[is.na(res)]<-0
res

> res
    ID  f1 f2 f3
1   A1   5  2  0
2   A3  23  0  0
3   B2   1  0  0
4  BX5 289  0  0
5   D3  90 90  2
6   A2   0 10 11
7   B1   0  3  1
8  BX4   0  1  0
9   E0   0 24  0
10  A0   0  0  4

Answer 2

Here's how to do it with dplyr . First you need to load your data without assigning row names. Below, I reuse your file1,file2,file3 structures, but you might as well read them in the proper format like you did with df1,df2, df3. You need a Names column to join on. Then you do two consecutive full_join . I then sort the data and change NAs to 0.

file1 <-data.frame(Names=rownames(file1),V1=file1,row.names = NULL)
file2 <-data.frame(Names=rownames(file2),V1=file2,row.names = NULL)
file3 <-data.frame(Names=rownames(file3),V1=file3,row.names = NULL)

library(dplyr)
out <-file1  %>%
full_join(file2,by = "Names") %>%
full_join(file3,by = "Names") %>%
arrange(Names)
out[is.na(out)]<-0
#> out
#   Names V1.x V1.y V1
#1     A0    0    0  4
#2     A1    5    2  0
#3     A2    0   10 11
#4     A3   23    0  0
#5     B1    0    3  1
#6     B2    1    0  0
#7    BX4    0    1  0
#8    BX5  289    0  0
#9     D3   90   90  2
#10    E0    0   24  0

Update

To deal with an arbitrary number of files, we have to introduce a loop.

myfiles <- list(file1,file2,file3)
out <-file1                       #first file
for (i in myfiles[-1]){           #all but first file
out <-full_join(out,i,by = "Names")
}
out <-arrange(out,Names)
out[is.na(out)]<-0
out
> out
   Names V1.x V1.y V1
1     A0    0    0  4
2     A1    5    2  0
3     A2    0   10 11
4     A3   23    0  0
5     B1    0    3  1
6     B2    1    0  0
7    BX4    0    1  0
8    BX5  289    0  0
9     D3   90   90  2
10    E0    0   24  0