R: Rolling window function with adjustable window and step-size for irregularly spaced observations

Question

Say there is a 2-column data frame with a time or distance column which sequentially increases and an observation column which may have NAs here and there. How can I effici

Answer 1

Here are some functions that will give the same output on your first example:

partition <- function(x, window, step = 0){
    a = x[x < step]    
    b = x[x >= step]
    ia = rep(0, length(a))
    ib = cut(b, seq(step, max(b) + window, by = window))    
    c(ia, ib)
}

roll <- function(df, window, step = 0, fun, ...){
    tapply(df$measure, partition(df$time, window, step), fun, ...)
}

roll_steps <- function(df, window, steps, fun, ...){
    X = lapply(steps, roll, df = df, window = window, fun = fun, ...)
    names(X) = steps
    X
}

Output for your first example:

> roll_steps(dat, 5, c(0, 2.5), mean)
$`0`
        1         2         3         4         5 
       NA 1.0126639 0.9514456        NA        NA 

$`2.5`
        0         1         2         3         4 
1.0222694        NA 0.9965048 1.0518228        NA

You can also ignore missing values this way easily:

> roll_steps(dat, 5, c(0, 2.5), mean, na.rm = TRUE)
$`0`
        1         2         3         4         5 
0.7275438 1.0126639 0.9514456 0.9351326       NaN 

$`2.5`
        0         1         2         3         4 
1.0222694 0.8138012 0.9965048 1.0518228 0.6122983 

This can also be used for a list of data.frames:

> x = lapply(dat2, roll_steps, 5, c(0, 2.5), mean)