Apply a moving-window standard deviation function to a numeric vector.

roll_sd(x, width = 1L, by = 1L, align = c("center", "left", "right"))

Arguments

x

Numeric vector.

width

Integer width of the rolling window.

by

Integer shift by which the window is moved each iteration.

align

Character position of the return value within the window. One of: "left" | "center" | "right".

Value

Numeric vector of the same length as x.

Details

For every index in the incoming vector x, a value is returned that is the standard deviation of all values in x that fall within a window of width width.

The align parameter determines the alignment of the return value within the window. Thus:

  • align = -1 [*------] will cause the returned vector to have width-1 NA values at the right end.

  • align = 0 [---*---] will cause the returned vector to have width/2 NA values at either end.

  • align = 1 [------*] will cause the returned vector to have width-1 NA values at the left end.

For large vectors, theby parameter can be used to force the window to jump ahead by indices for the next calculation. Indices that are skipped over will be assigned NA values so that the return vector still has the same length as the incoming vector. This can dramatically speed up calculations for high resolution time series data.

Note

No na.rm argument is provided as interpretation of the results is not at all clear.

Examples

library(MazamaRollUtils) # Example air quality time series t <- example_pm25$datetime x <- example_pm25$pm25 x[1:10]
#> [1] 13.032808 9.208778 9.413230 10.089091 10.075755 8.240968 9.507955 #> [8] 10.704980 11.232667 9.767381
roll_sd(x, width = 5)[1:10]
#> [1] NA NA 1.5427003 0.7601697 0.7525439 0.9307796 1.1562013 #> [8] 1.1562053 0.7118412 0.5531811