[R] do I need plyr, apply or something else?

[R. Michael Weylandt]

On Wed, Jul 11, 2012 at 10:05 AM, Russell Bowdrey
<Russell.Bowdrey at justretirement.com> wrote:
>
> 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)
>

Look at the TTR package and the various run** functions. Much faster.

> 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
>

This ones harder. For simple linear regressions, you can solve the
regression analytically (e.g., slope = runCov / runVar and mean
similarly) but doing it for more complicated regressions will pretty
much require a for loop of one sort or another. Can you say what sort
of model you are looking to use?

Best,
Michael

> Would appreciate any thoughts, many thanks in advance
>
>
> Russ
>
>
>
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