flexible approach to subsetting data

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)) )
      )

-------------------------------------
David L Carlson
Associate Professor of Anthropology
Texas A&M University
College Station, TX 77840-4352

-----Original Message-----
From: r-help-bounces at r-project.org
[mailto:r-help-bounces at 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 10:01 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

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 at 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.