Apply a movingwindow mean function to a numeric vector.
roll_mean( x, width = 1L, by = 1L, align = c("center", "left", "right"), na.rm = FALSE, weights = NULL )
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:

na.rm  Logical specifying whether 
weights  Numeric vector of size 
Numeric vector of the same length as x
.
For every index in the incoming vector x
, a value is returned that
is the mean 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 width1 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 width1 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.
The roll_mean()
function supports an additional weights
argument that can be used to calculate a "weighted moving average" 
a convolution of the incoming data with the kernel (weighting function)
provided in weights
.
library(MazamaRollUtils) # Example air quality time series t < example_pm25$datetime x < example_pm25$pm25 plot(t, x, pch = 16, cex = 0.5)