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In my R program I have a "for" loop of the following form:
for(i in 1:I)
{
res[i] <- a[i:I] %*% b[i:I]
}
where res, a and b are vectors of length I.
Is there any straightforward way to avoid this loop and calculate res directly? If so, would that be more efficient?
Thanks in advance!
334
Just like with repeat and while loops, you can break out of a for loop completely by using the break statement.
Breaking the while loop in R To do this, we can use another break statement. Again, this functions the same way in a while loop that it does in a for loop; once the condition is met and break is executed, the loop ends.
For that reason, it might make sense for you to avoid for-loops and to use functions such as lapply instead. This might speed up the R syntax and can save a lot of computational power! The next example explains how to use the lapply function in R.
The apply functions (apply, sapply, lapply etc.) are marginally faster than a regular for loop, but still do their looping in R, rather than dropping down to the lower level of C code. For a beginner, it can also be difficult to understand why you would want to use one of these functions with their arcane syntax.
This is the "reverse cumsum" of a*b
rev(cumsum(rev(a) * rev(b)))
So long as res is already of length I, the for loop isn't "incorrect" and the apply solutions will not really be any faster. However, using apply can be more succinct...(if potentially less readable)
Something like this:
res <- sapply(seq_along(a), function(i) a[i:I] %*% b[i:I])
should work as a one-liner.
Expanding on my first sentence. While using the inherent vectorization available in R is very handy and often the fastest way to go, it isn't always critical to avoid for loops. Underneath, the apply family determines the size of the output and pre-allocates it before "looping".
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