You have not specified the objective function you are trying to optimize with
your term "efficient", or what you do with all of these subsets once you have
them.
For notational simplification and completeness of coverage (not necessarily
computational speedup) you might want to look at "tapply" or ddply/dlply from
the plyr package. If you build lists of subsets you can index into them
according to grouping value. You can use expand.grid to build all permutations
of grouping values to use as indexes into those lists of subsets.
To reiterate, you have not indicated what you want to do with these subsets, so
there could be special-purpose functions that do what you want. As always,
reproducible code leads to reproducible answers. :)
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Sent from my phone. Please excuse my brevity.
Charles Determan Jr <deter...@umn.edu> wrote:
>Hello R users,
>
>This is more of a convenience question that I hope others might find
>useful
>if there is a better answer. I work with large datasets that requires
>multiple parsing stages for different analysis. For example, compare
>group
>3 vs. group 4. A more complicated comparison would be time B in group
>3 of
>group L with B in group 4 of group L. I normally subset each group
>with
>the following type of code.
>
>data=read(...)
>
>#L v D
>L=data[LvD %in% c("L"),]
>D=data[LvD %in% c("D"),]
>
>#Groups 3 and 4 within L and D
>group3L=L[group %in% c("3"),]
>group4L=L[group %in% c("3"),]
>
>group3D=D[group %in% c("3"),]
>group4D=D[group %in% c("3"),]
>
>#Times B, S45, FR2, FR8
>you get the idea
>
>
>Is there a more efficient way to subset groups? Thanks for any
>insight.
>
>Regards,
>Charles
>
> [[alternative HTML version deleted]]
>
>______________________________________________
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>PLEASE do read the posting guide
>http://www.R-project.org/posting-guide.html
>and provide commented, minimal, self-contained, reproducible code.
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