Efficiently Load A Sparse Matrix in R

问题

I'm having trouble efficiently loading data into a sparse matrix format in R.

Here is an (incomplete) example of my current strategy:

library(Matrix)
a1=Matrix(0,5000,100000,sparse=T)
for(i in 1:5000)
  a1[i,idxOfCols]=x

Where x is usually around length 20. This is not efficient and eventually slows to a crawl. I know there is a better way but wasn't sure how. Suggestions?

回答1:

You can populate the matrix all at once:

library(Matrix)
n <- 5000
m <- 1e5
k <- 20
idxOfCols <- sample(1:m, k)
x <- rnorm(k)

a2 <- sparseMatrix(
  i=rep(1:n, each=k),
  j=rep(idxOfCols, n),
  x=rep(x, k),
  dims=c(n,m)
)

# Compare
a1 <- Matrix(0,5000,100000,sparse=T)
for(i in 1:n) {
  a1[i,idxOfCols] <- x
}
sum(a1 - a2) # 0

回答2:

You don't need to use a for-loop. Yu can just use standard matrix indexing with a two column matrix:

 a1[ cbind(i,idxOfCols) ] <- x

来源：https://stackoverflow.com/questions/9650851/efficiently-load-a-sparse-matrix-in-r