Rolling average for non-uniform time-sampled data.

roll_nunif.R

##################################################################
# A rolling average function for non-uniform time-sampled data.  #
##################################################################

# Create some awkward time sampled data.
time <- cumsum(round(runif(1000, 0, 2), 2))
x    <- runif(1000, 300, 400)
df   <- data.frame(t = time, x)

# Rcpp, otherwise R would take all day...
library(Rcpp)
cppFunction('std::vector<double> roll_nunif(NumericVector x, NumericVector t, double window) {

              double n = x.size(), sum, len, tmp;

              std::vector<double> out(n);  // Return vector.

              // Find first index.
              int start = 0; while (t[start] < window) ++start;

              for (int i = start; i < n; ++i)
              {
                sum = 0; len = 0; // Reset.
                tmp = i;

                while(t[tmp] > (t[i] - window)) // Work backwards.
                {
                  sum += x[tmp]; ++len;
                  // Stop if we hit the bottom of x.
                  if ((tmp - 1) < 0)
                    break;
                  else
                    --tmp;
                }
                out[i] = sum / len;
              }
              return out;
            }
')

# 30 second rolling average:
df$roll <- roll_nunif(df$x, df$t, window = 30)

# Test ----------------------------------------------
# Pick a random timestamp...
r   <- sample(seq_along(df$t), 1)
# Chop off the top...
tmp <- df[1:r, ]
# Chop off the bottom...
tmp <- tmp[tmp$t > (df$t[r] - 30), ]

x1 <- mean(tmp$x)
x2 <- tmp$roll[nrow(tmp)]
if (x1 == x2) print("Booyah.")