moving average - applying rolling mean by group in R

Question

I'm an R newbie and I'm having a lot of trouble doing something that is probably very simple. I have a big dataset split up into groups by country code, and I want to take a 3-month rolling average of a price index, by country, and then put it into a new column that matches up to the appropriate month. I've been trying to use rollmean like this with no success (code and error messages below):

> leader$last3<-tapply(leader, leader$ccode, 
    function(x) rollmean(leader$GI_delta, 3, na.pad=T))
Error in tapply(leader, leader$ccode, function(x) rollmean(leader$GI_delta,  : 
  arguments must have same length

> leader$last3<-ddply(leader, .(ccode), 
    rollmean(GI_delta, 3, na.pad=T))

Error in llply(.data = .data, .fun = .fun, ..., .progress = .progress,  : 
  .fun is not a function.

Any help would be much appreciated!

Answer 1

If you want to make a new column, then try using ave. It resembles tapply but returns a vector of the same length as its first argument. My experience is that it is a lot faster than ddply:

require(zoo)
leader$last3<-ave(leader$GI_delta, leader$ccode, 
                         FUN= function(x) rollmean(x, k=3, na.pad=T) )