Combine two data.frames in R with differing rows

Question

I have two tables one with more rows than the other. I would like to filter the rows out that both tables share. I tried the solutions proposed here.

The problem, however, is that it is a large data-set and computation takes quite a while. Is there any simple solution? I know how to extract the shared rows of both tables using:

rownames(x1)->k
rownames(x)->l
which(rownames(x1)%in%l)->o

Here x1 and x are my data frames. But this only provides me with the shared rows. How can I get the unique rows of each table to then exclude them respectively? So that I can just cbind both tables together?

Answer 1

(I edit the whole answer) You can merge both df with merge() (from Andrie's comment). Also check ?merge to know all the options you can put in as by parameter, 0 = row.names.

The code below shows an example with what could be your data frames (different number of rows and columns)

x = data.frame(a1 = c(1,1,1,1,1), a2 = c(0,1,1,0,0), a3 = c(1,0,2,0,0), row.names = c('y1','y2','y3','y4','y5'))
x1 = data.frame(a4 = c(1,1,1,1), a5 = c(0,1,0,0), row.names = c('y1','y3','y4','y5'))

Provided that row names can be used as identifier then we put them as a new column to merge by columns:

x$id <- row.names(x)
x1$id <- row.names(x1)

# merge by column names
merge(x, x1, by = intersect(names(x), names(x1)))

# result
#   id a1 a2 a3 a4 a5
# 1 y1  1  0  1  1  0
# 2 y3  1  1  2  1  1
# 3 y4  1  0  0  1  0
# 4 y5  1  0  0  1  0

I hope this solves the problem.

EDIT: Ok, now I feel silly. If ALL columns have different names in both data frames then you don't need to put the row name as another column. Just use:

merge(x,x1, by=0)