Combining split() and cumsum()

Question

I am trying to produce stats for cumulative goals by season by a particular soccer player. I have used the cut function to obtain the season from the game dates. I have data which corresponds to this dataframe

df.raw <-
    data.frame(Game = 1:20,
            Goals=c(1,0,0,2,1,0,3,2,0,0,0,1,0,4,1,2,0,0,0,3),     
               season = gl(4,5,labels = c("2001", "2002","2003", "2004")))

In real life, the number of games per season may not be constant

I want to end up with data that looks like this

df.seasoned <-
    data.frame(Game = 1:20,seasonGame= rep(1:5),
            Goals=c(1,0,0,2,1,0,3,2,0,0,0,1,0,4,1,2,0,0,0,3),
            cumGoals = c(1,1,1,3,4,0,3,5,5,5,0,1,1,5,6,2,2,2,2,5),     
               season = gl(4,5,labels = c("2001", "2002","2003", "2004")))

With the goals cumulatively summed within year and a game number for the season

Answer 1

 df.raw$cumGoals <- with(df.raw,  ave(Goals, season, FUN=cumsum) )
 df.raw$seasonGame <- with(df.raw,  ave(Game, season, FUN=seq))
 df.raw

Or with transform ... the original transform, that is:

df.seas <- transform(df.raw, seasonGame = ave(Game, season, FUN=seq),
                          cumGoals = ave(Goals, season, FUN=cumsum) )
df.seas
   Game Goals season seasonGame cumGoals
1     1     1   2001          1        1
2     2     0   2001          2        1
3     3     0   2001          3        1
4     4     2   2001          4        3
5     5     1   2001          5        4
6     6     0   2002          1        0
7     7     3   2002          2        3
8     8     2   2002          3        5
9     9     0   2002          4        5
10   10     0   2002          5        5
snipped

Answer 2

Another job for ddply and transform (from the plyr package):

ddply(df.raw,.(season),transform,seasonGame = 1:NROW(piece),
                                 cumGoals = cumsum(Goals))
   Game Goals season seasonGame cumGoals
1     1     1   2001          1        1
2     2     0   2001          2        1
3     3     0   2001          3        1
4     4     2   2001          4        3
5     5     1   2001          5        4
6     6     0   2002          1        0
7     7     3   2002          2        3
8     8     2   2002          3        5
9     9     0   2002          4        5
10   10     0   2002          5        5
11   11     0   2003          1        0
12   12     1   2003          2        1
13   13     0   2003          3        1
14   14     4   2003          4        5
15   15     1   2003          5        6
16   16     2   2004          1        2
17   17     0   2004          2        2
18   18     0   2004          3        2
19   19     0   2004          4        2
20   20     3   2004          5        5