title: R Rolling average from irregular time series
body: <p>I've encountered this problem several times over the years so maybe I'm just misunderstanding something or somehow just being silly about this. I've met a wierd problem when doing a rolling average on irregular time series. A good overview of the available methods in packages and simple script is here: <a href="https://stackoverflow.com/questions/743812/calculating-moving-average-in-r">Calculating moving average in R</a></p>
  
  <p>I may be misreading some of them, but I see an issue in dealing with irregular time series. For example the common method of Rolling means in the <code>zoo</code> package requires unique values for each data. But in this case this is not the rolling average, but a rolling average of averages per time unit - time units will less data points will have comparably more influence on the average than ones with more.</p>
  
  <p>A true moving average seems to me to need to work not with aggregates, but with distributions for each calculated point. </p>
  
  <p>Given that I have the following data frame or irregular values, how can I best create a moving average measure for each of the values.</p>
  
  <pre><code>df &lt;- data.frame(year = c(rep(2000,3),rep(2001,1),rep(2004,4),rep(2005,3),+
        rep(2006,3),rep(2007,1),rep(2008,2),rep(2009,6),rep(2010,8)),+
        value1=rnorm(31), value2=rnorm(31), value3=rnorm(31))
  </code></pre>
  
  <p>I found an easy way to do it via subsetting that I'll post as an initial answer, but this works in limited circumstances and needs to be customized each time. I'm wondering what is a general solution. Also, if anyone is able to comment on the practices of using averages of averages vs averages of distributions in rolling means calculations, that would be extra helpful. Thanks!</p>