Dear R experts,
Suppose I have a data frame of three variables:
> foo <- data.frame(row=1:5, col=1:3, val=rnorm(15))
> foo
row col val
1 1 1 -1.00631642
2 2 2 0.77715344
3 3 3 0.17358793
4 4 1 -1.67226988
5 5 2 1.08218836
6 1 3 1.32961329
7 2 1 -0.51186267
8 3 2 -1.20990127
9 4 3 -0.57786899
10 5 1 0.67102887
11 1 2 0.05646411
12 2 3 0.01146612
13 3 1 -3.12094409
14 4 2 -1.01932191
15 5 3 0.76736702
I want to turn this into a matrix of val according to row and col. Let's also
assume that some combinations of row and col are missing - i.e. there will be
NAs in the resulting Matrix. My current approach is simple and works but is
slow for large datasets:
mat <- matrix(nrow=max(foo$row), ncol=max(foo$col))
for (line in 1:dim(foo)[1]) {
mat[foo[line, 'row'], foo[line, 'col']] <- foo[line, 'val']
}
> mat
[,1] [,2] [,3]
[1,] -1.0063164 0.05646411 1.32961329
[2,] -0.5118627 0.77715344 0.01146612
[3,] -3.1209441 -1.20990127 0.17358793
[4,] -1.6722699 -1.01932191 -0.57786899
[5,] 0.6710289 1.08218836 0.76736702
Can anyone think of a more efficient way?
cu
Philipp
--
Dr. Philipp Pagel
Lehrstuhl für Genomorientierte Bioinformatik
Technische Universität München
Wissenschaftszentrum Weihenstephan
85350 Freising, Germany
http://mips.gsf.de/staff/pagel
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