[R] aggregating columns in a data frame in different ways

[kavaumail-r at yahoo.com]

I would like to use aggregate() to combine statistics
for several days in a data frame. My data frame looks
similar to this:

   date        type  count  value
1  2006-04-01     A     10   99.6
2  2006-04-01     B      4   33.2
3  2006-04-02     A     22   43.2
4  2006-04-02     B      8   44.9
5  2006-04-03     A     12   12.4
6  2006-04-03     B     14   18.5

('date' is a factor, and my actual data frame has
about 100 different 'types', not just two)

I would like to sum up the 'counts' per 'type', and
get an average of the 'values' per 'type'. In other
words, I would like my results to look like this:

   type  count  value
1  A     44     51.73333
2  B     26     32.2

The way I'm doing this now is to tear the table apart
into its individual columns, then apply aggregate() to
each column individually (using the 'type' column for
the 'by' parameter), and finally putting everything
back together, like this:

> A.count = aggregate(A$count, list(type=A$type), sum)
> A.value = aggregate(A$value, list(type=A$type),
mean)
> B = data.frame(type=A.count$type, count=A.count$x,
value=A.value$x)

My actual table is a bit more involved than in this
simple example, however, so this becomes quite
tedious.

I am hoping that there is a simpler way for doing
this, for example by providing different FUN
parameters for each column to the aggregate()
function.

I would appreciate any suggestions.
Thanks
Klaus