R function with loop append avoiding for (using lapply instead) - r

Question

I have heard that it is not recommended to use for loops in R mainly because it is slow. I have heard that I should use lapply instead because it's calling C for efficiency.
Question: Would it be possible to show me how to transform the following example into a lapply efficient code (or any other apply sapply from the same family)?
myFun <- function(loop){
result = data.frame() #init new df
for(iteration in 1:loop){
generateRnorm1 = matrix(data = rnorm(n = 1000000), nrow = 10000, ncol = 10000)
generateRnorm2 = matrix(data = rnorm(n = 1000000), nrow = 10000, ncol = 10000)
iterationResult = sum(generateRnorm1, generateRnorm2)
bindIterationResult = cbind(iteration, iterationResult)
result = rbind(result, bindIterationResult)
}
return(result)
}
test = myFun(loop = 10)

Answer 1

Here is an lapply method:
myFun2 <- function(loop){
generateRnorm1 = matrix(data = rnorm(n = 1000000), nrow = 10000, ncol = 10000)
generateRnorm2 = matrix(data = rnorm(n = 1000000), nrow = 10000, ncol = 10000)
sum(generateRnorm1, generateRnorm2)
}
# run function over 1:10
myList <- lapply(seq.int(10), myFun2)
# rbind the resulting list
result2 <- do.call(rbind, myList)
Note that there isn't much (if any) speed increase, because the body of your function takes a long time to execute. This swamps any potential speed up with lapply.
On my computer, both methods take about 20 seconds to run.