[R] Efficient way to fill a matrix

[stephen sefick]

#reshape package should do it
library(reshape)
foo <- data.frame(row=1:5, col=1:3, val=rnorm(15))
cast(foo, row~col)

On Wed, Nov 5, 2008 at 5:47 PM, Philipp Pagel <p.pagel at wzw.tum.de> wrote:
>
>        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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>



-- 
Stephen Sefick
Research Scientist
Southeastern Natural Sciences Academy

Let's not spend our time and resources thinking about things that are
so little or so large that all they really do for us is puff us up and
make us feel like gods.  We are mammals, and have not exhausted the
annoying little problems of being mammals.

								-K. Mullis