I'm having trouble loading my data set into a sparse matrix in R. I am using the Matrix package. The data I have is in the form x y value. For example:
  V1 V2 V3
  1  2  .34
  7  4  .56
  4  5  .62
where I would want to do the equivalent of
myMatrix[1,2] = .34
myMatrix[7,4] = .56 
myMatrix[4,5] = .62 
in an automated fashion.
I want to do something like:
myMatrix = Matrix(nrow=numrows, ncol=numcols)
myMatrix[mydata[1:numrows, 1], mydata[1:numrows, 2]] <- mydata[1:numrows, 3]
but this makes my matrix a lgeMatrix when I need a numeric matrix.
I have also tried:
myMatrix = Matrix(nrow=numrows, ncol=numcols)
for(i in 1:numrows){
    myMatrix[mydata[i, 1], mydata[i, 2]] <- mydata[i, 3]
}
Which creates the kind of matrix I want, but it takes way too long (more than 5 minutes). I know it works because when I stop it I check the first few values and they're correct, but the last values are NA. I'm working with a 7095 by 5896 matrix with 247158 values to enter so a for loop is out of the question, unless I'm just being impatient.
My question is: What is the preferred way to do this in R?
update:
I figured it out using sparseMatrix instead:
myMatrix = sparseMatrix(i = mydata[1:numrows,1], j = mydata[1:numrows,2],
                     x = mydata[1:numrows,3])
didn't understand the sparseMatrix usage in the other post
 
     
    