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. > 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 >> >> >> -- >> Andrea Lamont, MA 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.
