[R-SIG-Finance] question on zoo data manipulation

[Manoj]

Hi Zoo-experts,
      I am working on the data-set below.

Ticker	Date	BrokerName	Acc_Yr	Measure	lag
XXX	20080320	BRK1	200806	2.2	0
XXX	20080320	BRK1	200906	2.5	0
XXX	20080320	BRK2	200806	2.3	0
XXX	20080320	BRK2	200906	2.8	0
XXX	20080320	BRK3	200806	3.3	0
XXX	20080218	BRK1	200806	2.2	1
XXX	20080218	BRK1	200906	2.5	1
XXX	20080218	BRK2	200806	2.4	1
XXX	20080218	BRK2	200906	2.8	1



Using zoo object, Is there a quicker/efficient way of manipulating the
data as per following criteria?

1) For any given date/lag - compute mean of column "measure" grouped
by different broker & different accounting year?
          so the output data-set should look like:

Ticker 	Date	Mean Measure	Acc_Yr	Lag
XXX	20080320	2.6	200806	0

2) For any lag >= 1, calculate returns on  aggregate "measure"
constrained on "intersection" of broker-name across lag 0 & lag 1 (so
BRK3 should drop out) ?

i.e:  the intermediate data-set should look like

Ticker 	Date	Mean Measure	Acc_Yr	Lag
XXX	20080320	2.25	200806	0
XXX	20080318	2.3	200806	1


Note that for 200806, the mean changes from 2.6 as measured above to
2.25 (since BRK3 is dropped in calculation.  The final data-set should
then be:

Ticker 	Date	Pct_Change	Acc_Yr	Lag
XXX	20080218	0.02	200806	1

--------------------

I can accomplish the results using a combination of tapply &
subsetting the data-set for each lag but I thought this kind of
data-structure is ideal for zoo manipulation, hence the help request.

Thanks in Advance.

Manoj