How can I implement nested loop with inner loop using index of outer loop index as base for incrementation in R?

Question

How do I implement in R a nested loop that has the inner loop using the outer loop index. Here is the sample of the code I wrote in C#:

int[] days = [1,2,3,4,5,6,7,8,9,10];
int[] amounts = [100, 0, 300, 0 , 0 , 500 , 0 , 600, 0, 1000];
void interpolation(int[]days, int[]amounts){
    //start with 1 to avoid amounts[0] is 0 will throw loop out of bound, same for amounts.length()-1
    for (int x = 1; x < amounts.length()-1; x++){
        if (amounts[x] == 0){
            int lastAval = amounts[x-1];
            int lastDay = days[x-1];
            int nextAval, nextDay;
            for (int y = x; y < amounts.length()-1; y++) {
                if(amounts[y] != 0) {
                    nextAval = amounts[y];
                    nextDay = amounts[y];
                    break;
                }
            }
            amounts[x] = lastAval + (days[x] - lastDay) * ((nextAval-lastAval)/(nextDay-lastDay));
        }
    }
}

The purpose of this function is to find and replace any array element that equal to 0 with interpolation. I tried to apply the same function into R and I can't seem to find a way to translate this from C# into R as I unable to find a way to iterate through the R vector and using the index from outer loop and assign it to the inner loop for iterating.

Answer 1

If you want to implement your linear interpolation objective in R, you can try the code below

• Using approxfun()

# define the linear interpolation function `interpl`, using `approxfun` given non-zero value pairs
interpl <- approxfun(days[amounts!=0], amounts[amounts!=0])

then run interpl with your input days

amounts <- interpl(days)

or

amounts[amounts == 0] <- interpl(days[amounts == 0])

• Using approx()

amount[amounts==0] <- approx(x = days[amounts!=0], 
                             y = amounts[amounts!=0],
                             xout = days[amounts==0])$y

such that

> amounts
 [1]  100.0000  200.0000  300.0000  366.6667  433.3333  500.0000  550.0000  600.0000  800.0000 1000.0000