Sliding window in R

Question

I have a dataframe DF, with two columns A and B shown below:

A                    B                  
1                    0             
3                           


        

Answer 1

The R library TTR has a number of functions for calculating averages over sliding windows

SMA = simple moving average

data$sma <- SMA(data$B, 3)

More documentation is here http://cran.r-project.org/web/packages/TTR/TTR.pdf

Answer 2

Try this:

# form input data
library(zoo)
B <- c(0, 0, 0, 1, 0, 1, 1, 1, 0)

# calculate
k <- 3
rollapply(B, 2*k-1, function(x) max(rollmean(x, k)), partial = TRUE)

The last line returns:

[1] 0.0000000 0.3333333 0.3333333 0.6666667 0.6666667 1.0000000 1.0000000
[8] 1.0000000 0.6666667

If there are NA values you might want to try this:

k <- 3
B <- c(1, 0, 1, 0, NA, 1)
rollapply(B, 2*k-1, function(x) max(rollapply(x, k, mean, na.rm = TRUE)), partial = TRUE)

where the last line gives this:

[1] 0.6666667 0.6666667 0.6666667 0.5000000 0.5000000 0.5000000

Expanding it out these are formed as:

c(mean(B[1:3], na.rm = TRUE), ##
max(mean(B[1:3], na.rm = TRUE), mean(B[2:4], na.rm = TRUE)), ##
max(mean(B[1:3], na.rm = TRUE), mean(B[2:4], na.rm = TRUE), mean(B[3:5], na.rm = TRUE)),
max(mean(B[2:4], na.rm = TRUE), mean(B[3:5], na.rm = TRUE), mean(B[4:6], na.rm = TRUE)),
max(mean(B[3:5], na.rm = TRUE), mean(B[4:6], na.rm = TRUE)), ##
mean(B[4:6], na.rm = TRUE)) ##

If you don't want the k-1 components at each end (marked with ## above) drop partial = TRUE.