Merge multiple data tables with duplicate column names

Question

I am trying to merge (join) multiple data tables (obtained with fread from 5 csv files) to form a single data table. I get an error when I try to merge 5 data tables, but works fine when I merge only 4. MWE below:

# example data
DT1 <- data.table(x = letters[1:6], y = 10:15)
DT2 <- data.table(x = letters[1:6], y = 11:16)
DT3 <- data.table(x = letters[1:6], y = 12:17)
DT4 <- data.table(x = letters[1:6], y = 13:18)
DT5 <- data.table(x = letters[1:6], y = 14:19)

# this gives an error
Reduce(function(...) merge(..., all = TRUE, by = "x"), list(DT1, DT2, DT3, DT4, DT5))

Error in merge.data.table(..., all = TRUE, by = "x") : x has some duplicated column name(s): y.x,y.y. Please remove or rename the duplicate(s) and try again.

# whereas this works fine
Reduce(function(...) merge(..., all = TRUE, by = "x"), list(DT1, DT2, DT3, DT4))

    x y.x y.y y.x y.y 
 1: a  10  11  12  13 
 2: b  11  12  13  14 
 3: c  12  13  14  15 
 4: d  13  14  15  16 
 5: e  14  15  16  17 
 6: f  15  16  17  18

I have a workaround, where, if I change the 2nd column name for DT1:

setnames(DT1, "y", "new_y")

# this works now
Reduce(function(...) merge(..., all = TRUE, by = "x"), list(DT1, DT2, DT3, DT4, DT5))

Why does this happen, and is there any way to merge an arbitrary number of data tables with the same column names without changing any of the column names?

Answer 1

Here's a way of keeping a counter within Reduce, if you want to rename during the merge:

Reduce((function() {counter = 0
                    function(x, y) {
                      counter <<- counter + 1
                      d = merge(x, y, all = T, by = 'x')
                      setnames(d, c(head(names(d), -1), paste0('y.', counter)))
                    }})(), list(DT1, DT2, DT3, DT4, DT5))
#   x y.x y.1 y.2 y.3 y.4
#1: a  10  11  12  13  14
#2: b  11  12  13  14  15
#3: c  12  13  14  15  16
#4: d  13  14  15  16  17
#5: e  14  15  16  17  18
#6: f  15  16  17  18  19

Answer 2

If it's just those 5 datatables (where x is the same for all datatables), you could also use nested joins:

# set the key for each datatable to 'x'
setkey(DT1,x)
setkey(DT2,x)
setkey(DT3,x)
setkey(DT4,x)
setkey(DT5,x)

# the nested join
mergedDT1 <- DT1[DT2[DT3[DT4[DT5]]]]

Or as @Frank said in the comments:

DTlist <- list(DT1,DT2,DT3,DT4,DT5)
Reduce(function(X,Y) X[Y], DTlist)

which gives:

   x y1 y2 y3 y4 y5
1: a 10 11 12 13 14
2: b 11 12 13 14 15
3: c 12 13 14 15 16
4: d 13 14 15 16 17
5: e 14 15 16 17 18
6: f 15 16 17 18 19

This gives the same result as:

mergedDT2 <- Reduce(function(...) merge(..., all = TRUE, by = "x"), list(DT1, DT2, DT3, DT4, DT5))

> identical(mergedDT1,mergedDT2)
[1] TRUE

When your x columns do not have the same values, a nested join will not give the desired solution:

DT1[DT2[DT3[DT4[DT5[DT6]]]]]

this gives:

   x y1 y2 y3 y4 y5 y6
1: b 11 12 13 14 15 15
2: c 12 13 14 15 16 16
3: d 13 14 15 16 17 17
4: e 14 15 16 17 18 18
5: f 15 16 17 18 19 19
6: g NA NA NA NA NA 20

While:

Reduce(function(...) merge(..., all = TRUE, by = "x"), list(DT1, DT2, DT3, DT4, DT5, DT6))

gives:

   x y1 y2 y3 y4 y5 y6
1: a 10 11 12 13 14 NA
2: b 11 12 13 14 15 15
3: c 12 13 14 15 16 16
4: d 13 14 15 16 17 17
5: e 14 15 16 17 18 18
6: f 15 16 17 18 19 19
7: g NA NA NA NA NA 20

Used data:

In order to make the code with Reduce work, I changed the names of the y columns.

DT1 <- data.table(x = letters[1:6], y1 = 10:15)
DT2 <- data.table(x = letters[1:6], y2 = 11:16)
DT3 <- data.table(x = letters[1:6], y3 = 12:17)
DT4 <- data.table(x = letters[1:6], y4 = 13:18)
DT5 <- data.table(x = letters[1:6], y5 = 14:19)

DT6 <- data.table(x = letters[2:7], y6 = 15:20, key="x")