Question

I know it’s not the best practice in R to use the for loop because it doesn’t have an enhanced performance. For almost all cases there is a function of the family *apply that solves our problems.

However I’m facing a situation where I don’t see a workaround.

I need to calculate percent variation for consecutive values:

pv[1] <- 0
for(i in 2:length(x)) {
  pv[i] <- (x[i] - x[i-1])/x[i-1]
}

So, as you can see, I have to use both the x[i] element, but also the x[i-1] element. By using the *apply functions, I just see how to use the x[i]. Is there anyway I can avoid the forloops?

Answer 1

What you offered would be the fractional variation, but if you multiplied by 100 you get the “percent variation”:

pv<- vector("numeric",length(x))
pv[1] <- 0
pv[-1] <- 100* ( x[-1] - x[-length(x)] )/ x[-length(x)]

Vectorized solution. ( And you should note that for-loops are going to be just as slow as *apply solutions … just not as pretty. Always look for a vectorized approach.)

To explain a bit more: The x[-length(x)] is the vector, x[1:(length{x-1)], and the x[-1] is the vector, x[2:length(x)], and the vector operations in R are doing the same operations as in your for-loop body, although not using an explicit loop. R first constructs the differences in those shifted vectors, x[-length(x)] - x[-1], and then divides by x[1:(length{x-1)].