title: Calculating a Rolling Average with Missing Time Values
body: <p>I have a dataset described by the following:</p>
  
  <pre><code>&gt; dput(droplevels(head(sample,10)))
  structure(list(Team = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 
  1L, 1L, 1L), .Label = "Air-Force", class = "factor"), Year = c(2003L, 
  2004L, 2005L, 2006L, 2007L, 2008L, 2009L, 2011L, 2012L, 2013L
  ), Grouped_Position_3 = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 
  1L, 1L, 1L), .Label = "Skill", class = "factor"), Avg_Rating = c(0.7667, 
  0, 0.7444, 0.7222, 0, 0.7556, 0.76224, 0.596322222222222, 0.706584615384615, 
  0.767509090909091), n = c(1L, 1L, 3L, 6L, 1L, 1L, 5L, 9L, 13L, 
  11L)), .Names = c("Team", "Year", "Grouped_Position_3", "Avg_Rating", 
  "n"), row.names = c(NA, 10L), class = "data.frame")
  </code></pre>
  
  <p>In the full dataset there are multiple schools, grouped positions and years. What I'm trying to do is figure out how to generate a rolling average using the current year and four years in the past for each unique group of school, year and position. For example for 2013, Air Force and Skill position I would like the following calculation to take place (Note that 2010 is missing in the data):</p>
  
  <p>(.767+.70+.59+0+.762)/5</p>
  
  <p>The 0 comes from the missing year. I have looked at the zoo library in combination with dplyr but I haven't been able to control for missing values like this. Am I looking at having to write a loop or is there some package in r that has this capability?</p>