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Moving average in a data table

2 messages · Roman Naumenko, Gabor Grothendieck

Original
Roman Naumenko
# 
Hi,

I'm trying to figure out common approach on calculating MA on a dataset 
that contains column "time".

After digging around, I believe functions rollmean and rollaply should 
be used.
However I don't quite understand the requirements for the underlying data.
Should it be zoo object type? Formatted in a special way?

As an example, I'm looking to get calculated avg=MA(variable) over 15 
sec period on "time_sec" column:

  date           variable   time_sec         avg
  2011-05-17     132.55     11:29:59.579     132.55
  2011-05-17     132.65     11:29:59.946     132.60
  2011-05-17     132.5      11:29:59.946     132.57
  2011-05-17     132.5      11:29:59.946     132.55
  2011-05-17     132.55     11:29:59.946     132.55
  2011-05-17     132.6      11:29:59.946     132.56
  2011-05-17     132.55     11:29:59.946     132.56
  2011-05-17     132.65     11:29:59.947     132.57
  2011-05-17     132.85     11:30:00.45      132.60
  2011-05-17     132.9      11:30:00.45      132.63
  2011-05-17     133.05     11:30:00.45      132.67
  2011-05-17     132.2      11:30:00.45      132.63
  2011-05-17     132.5      11:30:00.45      132.62
  2011-05-17     132.7      11:30:00.50      132.63
  2011-05-17     132.75     11:30:00.57      132.63
  2011-05-17     132.55     11:30:00.70      132.63
  2011-05-17     132.25     11:30:00.70      132.61
  2011-05-17     132.25     11:30:00.71      132.59
  2011-05-17     132.35     11:30:00.173     132.57
  2011-05-17     132.45     11:30:00.173     132.57


Any help is really appreciated.

Thanks,
--Roman N.
Gabor Grothendieck
#
On Sat, Jun 25, 2011 at 3:15 PM, Roman Naumenko <roman at naumenko.ca> wrote:
rollapply and rollmean are for fixed offsets such as 5 rows before and
after.  For this problem modify the following depending on your
precise requirements:

Lines <- "date           variable   time_sec         avg
 2011-05-17     132.55     11:29:59.579     132.55
 2011-05-17     132.65     11:29:59.946     132.60
 2011-05-17     132.5      11:29:59.946     132.57
 2011-05-17     132.5      11:29:59.946     132.55
 2011-05-17     132.55     11:29:59.946     132.55
 2011-05-17     132.6      11:29:59.946     132.56
 2011-05-17     132.55     11:29:59.946     132.56
 2011-05-17     132.65     11:29:59.947     132.57
 2011-05-17     132.85     11:30:00.45      132.60
 2011-05-17     132.9      11:30:00.45      132.63
 2011-05-17     133.05     11:30:00.45      132.67
 2011-05-17     132.2      11:30:00.45      132.63
 2011-05-17     132.5      11:30:00.45      132.62
 2011-05-17     132.7      11:30:00.50      132.63
 2011-05-17     132.75     11:30:00.57      132.63
 2011-05-17     132.55     11:30:00.70      132.63
 2011-05-17     132.25     11:30:00.70      132.61
 2011-05-17     132.25     11:30:00.71      132.59
 2011-05-17     132.35     11:30:00.173     132.57
 2011-05-17     132.45     11:30:00.173     132.57"

DF <- read.table(textConnection(Lines), header = TRUE)
DF <- transform(DF, datetime = as.POSIXct(paste(date, time_sec)))

f <- function(i) {
	is.near <- abs(as.numeric(DF$datetime[i] - DF$datetime)) < 7.5
	mean(DF$variable[is.near])
}
sapply(1:nrow(DF), f)