This is all very helpful. Thank you for your comments. I will try the
approaches suggested and let you know if I have any problems.
Thank you!!!
On Tue, Jul 23, 2013 at 5:59 PM, David Winsemius <dwinsem...@comcast.net>wrote:
>
> On Jul 23, 2013, at 2:00 PM, David Carlson wrote:
>
> > Actually the ".0" on the first variable is not needed.
> >
> > You could modify the reshape() call to search for the base
> > name of each variable so you would not need to change the code
> > if the number of replications changes:
> >
> > reshape(df5, direction="long", v.names=c("dose", "resp"),
> > varying=list(dose=grepl("dose", names(df5)),
> > resp=grepl("resp", names(df5)) )
> > )
> >
>
> That's really elegant and much more "elastic". (I hadn't realized that a
> logical vector would be accepted.) Also possible to just use 'grep' which
> would instead construct a vector of column numbers as the list elements of
> 'varying'. I've wondered for years whether the help page description of
> 'varying could be improved. It currently says:
>
> "varying :
> names of sets of variables in the wide format that correspond to single
> variables in long format (time-varying). This is canonically a list of
> vectors of variable names, but it can optionally be a matrix of names, or a
> single vector of names. In each case, the names can be replaced by indices
> which are interpreted as referring to names(data). See Details for more
> details and options."
>
> I wondered if it might say instead:
>
> "a list of sets of variables in the wide format that each correspond to
> single variables in long format (time-varying). This is canonically a
> list of vectors of column names or numbers , but it can optionally be a
> matrix of names, or a single vector of names. In each case, the names can
> be replaced by numeric or logical indices which are interpreted as
> extracting from names(data). See Details for more details and options."
>
> But it supposedly is the case that it can be a set of names, and in that
> case there is also a further promise that an effort to do the automagic
> splitting. Unfortunately the magic is often unsuccessful
>
> > reshape(df5, direction="long",
> + varying=c("dose", "resp")
> + )
> Error in guess(varying) :
> failed to guess time-varying variables from their names
>
> # Seems like it should have been possible:
> > df5
> dose.0 resp.0 dose.1 resp.1 dose.2 resp.2 dose.3 resp.3
> 1 40 40 1 4 2 4 3 4
> 2 50 50 2 5 1 5 3 5
> 3 60 60 1 4 2 4 3 4
> 4 50 50 2 5 1 5 3 5
>
> --
> David.
>
> > -------------------------------------
> > David L Carlson
> > Associate Professor of Anthropology
> > Texas A&M University
> > College Station, TX 77840-4352
> >
> > -----Original Message-----
> > From: r-help-boun...@r-project.org
> > [mailto:r-help-boun...@r-project.org] On Behalf Of David
> > Winsemius
> > Sent: Tuesday, July 23, 2013 1:12 PM
> > To: David Winsemius
> > Cc: R help; Andrea Lamont
> > Subject: Re: [R] flexible approach to subsetting data
> >
> >
> > On Jul 23, 2013, at 10:49 AM, David Winsemius wrote:
> >
> >>
> >> On Jul 23, 2013, at 10:01 AM, Adams, Jean wrote:
> >>
> >>> Check out the reshape() function of the reshape package.
> > Here's one of the
> >>> examples from ?reshape.
> >>>
> >>> Jean
> >>>
> >>>
> >>> library(reshape) # No, at least not for the
> > reshape-function
> >>
> >> The reshape function is from the 'base' package. The
> > 'reshape' and 'reshape2' packages were written (at least in
> > part) because the 'reshape'-function was so difficult to
> > understand.
> >>
> >> If you do choose to use the reshape2 package, which is
> > well-respected and often extremely helpful, the function you
> > will want to start with is 'melt'.
> >>
> >>
> >>> long <- reshape(wide, direction="long")
> >>
> >> I don't think this example will be particularly helpful
> > since the initial direction is "long" (from "wide") and more
> > input would be needed.
> >
> > Here's a dataset to experiment with
> >
> > df5 <- data.frame(dose.0 =
> > c(40,50,60,50),resp.0=c(40,50,60,50),
> > dose.1 = c(1,2,1,2), resp.1=c(1,2,1,2)+3,
> > dose.2 = c(2,1,2,1), resp.2=c(1,2,1,2)+3,
> > dose.3 = c(3,3,3,3), resp.3=c(1,2,1,2)+3 )
> >
> > Notice that you would need add the ".0" to the column names
> >
> > reshape(df5, direction="long",
> > v.names=c("dose", "resp"),
> > varying=list(dose=c(1,3,5,7), resp=c(2,4,6,8) )
> > ) # succeeds
> >
> >
> >
> > So perhaps could use similar call (after append the ".0"'s)
> > with:
> >
> > varying=list(sim=seq(1,810,by=4),
> > X1= seq(2,810,by=4),
> > X2= seq(3,810,by=4),
> > X3= seq(4,810,by=4)
> > )
> >
> >>
> >>
> >>> wide
> >>> long
> >>>
> >>>
> >>>
> >>> On Tue, Jul 23, 2013 at 9:35 AM, Andrea Lamont
> > <alamont...@gmail.com> wrote:
> >>>
> >>>> Hello:
> >>>>
> >>>> I am running a simulation study and am stuck with a
> > subsetting problem.
> >>>>
> >>>> Here is the basic issue:
> >>>> I generated data and am running a simulation that uses
> > multiple imputation.
> >>>> For each generated dataset, I used multiple imputation.
> > The resultant
> >>>> dataset is in wide for where each imputation is recorded
> > as a separate
> >>>> column (though the different simulations are stacked).
> > Here is an example
> >>>> of what it looks like:
> >>>>
> >>>> sim X1 X2 X3 sim.1 X1.1 X1.1 X3.1
> >>
> >>>> 1 # # # # # #
> > #
> >>>> 1 # # # # # #
> > #
> >>>> 1 # # # # # #
> > #
> >>>> 2 # # # # # #
> > #
> >>>> 2 # # # # # #
> > #
> >>>> 2 # # # # # #
> > #
> >>>>
> >>>> sim refers to the simulated/generated dataset. X1-X3 are
> > the values for the
> >>>> first imputed dataset, X1.1-X3.1 are the values for the
> > second imputed
> >>>> dataset.
> >>>>
> >>>> The problem is that I want the data to be in long format,
> > like this:
> >>>>
> >>>> sim m X1 X2 X3
> >>>> 1 1 # # #
> >>>> 1 2 # # #
> >>>> 2 1 # # #
> >>>> 2 2 # # #
> >>>>
> >>>> where m is the imputation number.
> >>>> This will allow me to do cleaner calculations (e.g.
> > X3-X1).
> >>>>
> >>>> I know I can subset the data manually - e.g. [,1:10] and
> > save this to
> >>>> separate datasets then rbind; however, I'm looking for a
> > more flexible
> >>>> approach to do this. This manual approach would be quite
> > tedious as number
> >>>> of imputations (and therefore number of columns) increased
> > (with only 10
> >>>> imputations, there are roughly 810 columns). Also,I would
> > like to
> >>>> avoid having to recode each time I change the number of
> > imputations.
> >>>>
> >>>> THe same is true for the reshape function, which would
> > require naming
> >>>> a huge number of columns and edits each time 'm' changes.
> >>
> >> If the columns are named regularly, then 'reshape' will
> > attempt to split properly without an explicit naming. Details
> > and a better description of the problem might allow more
> > specific answers to emerge. The fact that the first instances
> > have no numeric indicators may be a problem for the algorithm.
> >
> >>
> >> Why not post dput(head( dfrm[ ,1:12]))
> >>
> >> --
> >> David.
> >>
> >>>>
> >>>>
> >>>> Is there a flexible way to approach this? I'm inclined to
> > use a for loop,
> >>>> but know that 1) this is generally inefficient and 2) am
> > having trouble
> >>>> with
> >>>> the coding regardless.
> >>>>
> >>>> Any suggestions are appreciated.
> >>>>
> >>>> Thanks,
> >>>> Andrea
> >>>>
> >
> >
> > David Winsemius
> > Alameda, CA, USA
> >
> > ______________________________________________
> > R-help@r-project.org mailing list
> > https://stat.ethz.ch/mailman/listinfo/r-help
> > PLEASE do read the posting guide
> > http://www.R-project.org/posting-guide.html
> > and provide commented, minimal, self-contained, reproducible
> > code.
> >
>
> David Winsemius
> Alameda, CA, USA
>
>
--
Andrea Lamont, MA
Clinical-Community Psychology
University of South Carolina
Barnwell College
Columbia, SC 29208
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