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\', \         


        

Answer 1

Use %in% and some unlisting

zz <- lapply(unique(names(df)), function(x,y) as.vector(unlist(df[which(y %in% x)])),y=names(df))
names(zz) <- unique(names(df))
as.data.frame(zz)
  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

I'm not at the computer now, so can't test this, but... this might work:

do.call(cbind, 
     lapply(names(df) function(x) do.call(rbind, df[, names(df) == x])) )

Answer 3

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.

Answer 4

I would sort the data.frame by column name, unlist, and use as.data.frame on a matrix:

A <- unique(names(df))[order(unique(names(df)))]
B <- matrix(unlist(df[, order(names(df))], use.names=FALSE), ncol = length(A))
B <- setNames(as.data.frame(B), A)
B
#   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 5

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