Binding columns with similar column names in the same dataframe in R

Question

I have a data frame that looks somewhat like this:

df <- data.frame(0:2, 1:3, 2:4, 5:7, 6:8, 2:4, 0:2, 1:3, 2:4)
colnames(df) <- rep(c('a', 'b', 'c'), 3)
> df
  a b c a b c a b c
1 0 1 2 5 6 2 0 1 2
2 1 2 3 6 7 3 1 2 3
3 2 3 4 7 8 4 2 3 4

There are multiple columns that have the same name. I would like to rearrange the data frame so that the columns with the same names combine into their own supercolumn, so that there are only unique column names left, for example:

> df
  a b c
1 0 1 2
2 1 2 3
3 2 3 4
4 5 6 2
5 6 7 3
6 7 8 4
7 0 1 2
8 1 2 3
9 2 3 4

Any thoughts on how to do this? Thanks in advance!

Answer 1

This will do the trick, I suppose.

Explanation

df[,names(df) == 'a'] will select all columns with name a

unlist will convert above columns into 1 single vector

unname will remove some stray rownames given to these vectors.

unique(names(df)) will give you unique column names in df

sapply will apply the inline function to all values of unique(names(df))

> df
  a b c a b c a b c
1 0 1 2 5 6 2 0 1 2
2 1 2 3 6 7 3 1 2 3
3 2 3 4 7 8 4 2 3 4
> sapply(unique(names(df)), function(x) unname(unlist(df[,names(df)==x])))
      a b c
 [1,] 0 1 2
 [2,] 1 2 3
 [3,] 2 3 4
 [4,] 5 6 2
 [5,] 6 7 3
 [6,] 7 8 4
 [7,] 0 1 2
 [8,] 1 2 3
 [9,] 2 3 4

Answer 2

My version:

library(reshape)
as.data.frame(with(melt(df), split(value, variable)))
  a b c
1 0 1 2
2 1 2 3
3 2 3 4
4 0 1 2
5 1 2 3
6 2 3 4
7 0 1 2
8 1 2 3
9 2 3 4

In the step using melt I transform the dataset:

> melt(df)
Using  as id variables
   variable value
1         a     0
2         a     1
3         a     2
4         b     1
5         b     2
6         b     3
7         c     2
8         c     3
9         c     4
10        a     0
11        a     1
12        a     2
13        b     1
14        b     2
15        b     3
16        c     2
17        c     3
18        c     4
19        a     0
20        a     1
21        a     2
22        b     1
23        b     2
24        b     3
25        c     2
26        c     3
27        c     4

Then I split up the value column for each unique level of variable using split:

$a
[1] 0 1 2 0 1 2 0 1 2

$b
[1] 1 2 3 1 2 3 1 2 3

$c
[1] 2 3 4 2 3 4 2 3 4

then this only needs an as.data.frame to become the data structure you need.