Question

I have a data set that looks like this:

ByYear <- data.frame( V1 = c(2005,2006,2007,2008,2005,2006,2008,2006,2007,2005,2006,2007,2008),
                      V2 = c(0.5,0.2,1,1.6,2,5,8,4,3,6,8,6,5), 
                      V3 = c('A','A','A','A','B','B','B','C','C','D','D','D','D'))

Which gives me

> ByYear
   V1   V2  V3
1  2005 0.5 A
2  2006 0.2 A
3  2007 1.0 A
4  2008 1.6 A
5  2005 2.0 B
6  2006 5.0 B
7  2008 8.0 B
8  2006 4.0 C
9  2007 3.0 C
10 2005 6.0 D
11 2006 8.0 D
12 2007 6.0 D
13 2008 5.0 D

Some of the years are missing from V1. This is due to an error in how the data were entered. I know this is a touchy subject, but I know for a fact that in this case a missing year in V1 means the value in V2 should be 0.

Is there a way I can create a new data set that adds a row with a zero value to any missing year like so:

> ByYear
  V1   V2  V3
  2005 0.5 A
  2006 0.2 A
  2007 1.0 A
  2008 1.6 A
  2005 2.0 B
  2006 5.0 B
  2007 0.0 B
  2008 8.0 B
  2005 0.0 C
  2006 4.0 C
  2007 3.0 C
  2008 0.0 C
  2005 6.0 D
  2006 0.0 D
  2007 6.0 D
  2008 5.0 D

Thanks for everyone for all your help!

Answer 1

Use table to find the missing year/group combinations.

Frequencies <- with(ByYear, as.data.frame(table(V1, V3)))
MissingValues <- subset(Frequencies, Freq == 0, c(V1, V3))

Set the value of V2 to be 0 (or NA or whatver you want), then append this to your original dataset.

MissingValues$V2 <- 0
rbind(ByYear, MissingValues)