do I need plyr, apply or something else?

Dear all,

This is what I'd like to do (I have an implementation using for loops, which I designed before I realised just how slow R is at executing them - this process currently takes days to run).

I have a large dataframe containing corporate bond data, columns are:
BondID
Date (goes back 5years)
Var1
Var2
Term2Maturity

What I want to do is this:

1)      For each bond, at each given date, look back over 1 year and append some statistics to each row ( sd(Var1), cor(Var1,Var2) over that year etc)

a.       It seems I might be able to use ddply for this, but I can't work out how to code the stats function to only look back over one year, rather than the full data range

b.      For example: dfBondsWithCorr<-ddply(dfBonds, .(BondID), transform,corr=cor(Var1,Var2),.progress="text")
returns a dataframe where for each bond it has same corr for each date

2)      On each date, subset dfBondsWithCorr by certain qualification criteria, then to the qualifiers fit a regression through a Var1 and Term2Maturity, output the regression as a df of curves (say for each date, a curve represented by points every 0.5 years)

a.       I can do this pretty efficiently for a single date (and I suppose I could wrap that in a function) , but can't quite see how to do the filtering and spitting out of curves over multiple dates without using for loops

Would appreciate any thoughts, many thanks in advance


Russ



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