R: Sort columns of a data frame by a vector of column names

Question

I have a data.frame that looks like this:

which has 1000+ columns with similar names.

And I have a vector of those column names that looks like this:

The vector is sorted by the cluster_id (which goes up to 11).

I want to sort the columns in the data frame such that the columns are in the order of the names in the vector.

A simple example of what I want is that:

Data:

 A    B    C
 1    2    3
 4    5    6

Vector: c("B","C","A")

Sorted:

 B    C    A
 2    3    1
 5    6    4

Is there a fast way to do this?

Answer 1

UPDATE, with reproducible data added by OP:

df <- read.table(h=T, text="A    B    C
    1    2    3
    4    5    6")
vec <- c("B", "C", "A")
df[vec]

Results in:

  B C A
1 2 3 1
2 5 6 4

As OP desires.

---

How about:

df[df.clust$mutation_id]

Where df is the data.frame you want to sort the columns of and df.clust is the data frame that contains the vector with the column order (mutation_id).

This basically treats df as a list and uses standard vector indexing techniques to re-order it.

Answer 2

Brodie's answer does exactly what you're asking for. However, you imply that your data are large, so I will provide an alternative using "data.table", which has a function called setcolorder that will change the column order by reference.

Here's a reproducible example.

Start with some simple data:

mydf <- data.frame(A = 1:2, B = 3:4, C = 5:6)
matches <- data.frame(X = 1:3, Y = c("C", "A", "B"), Z = 4:6)
mydf
#   A B C
# 1 1 3 5
# 2 2 4 6
matches
#   X Y Z
# 1 1 C 4
# 2 2 A 5
# 3 3 B 6

Provide proof that Brodie's answer works:

out <- mydf[matches$Y]
out
#   C A B
# 1 5 1 3
# 2 6 2 4

Show a more memory efficient way to do the same thing.

library(data.table)
setDT(mydf)
mydf
#    A B C
# 1: 1 3 5
# 2: 2 4 6

setcolorder(mydf, as.character(matches$Y))
mydf
#    C A B
# 1: 5 1 3
# 2: 6 2 4