R: looking for a more efficient way of running rbinom() taking probabilities from a large matrix

I have a 20000×90 matrix M of probabilities ranging between 0 and 1. I want to use these probabilities to return a 20000×90 matrix of 1’s and 0’s (i.e. for each probability p in the matrix I want to run rbinom(1,1,p)).

This is my current solution which works:

apply(M,1:2,function(x) rbinom(1,1,x))

However, it is quite slow and I am wondering if there is a faster way of achieving the same thing.

>Solution :

rbinom is vectorised, so you can pass the vector (or matrix) of probabilities; you just need to change the n, the number of observations, from 1 to the number of values in M.

An example:

# with loop
set.seed(74309281)
M = matrix(runif(20000*90), nr=20000, nc=90)
a1 = apply(M,1:2,function(x) rbinom(1,1,x))

# without loop
set.seed(74309281)
M = matrix(runif(20000*90), nr=20000, nc=90)
a2 = matrix(rbinom(length(M),1,M), nr=nrow(M), nc=ncol(M))

all.equal(a1, a2)
# [1] TRUE