Re: [R] Tables Package Grouping Factors

[Duncan Murdoch]

On 13-11-09 5:56 PM, Jeff Newmiller wrote:
The problem that prompted this question involved manufacturers and their model 
numbers, so I think the cross everything and throw away most of it will get out 
of hand quickly. The number of models per manufacturer definitely varies. I 
think I will work on the print segments of the table successively approach. 
Thanks for the ideas.

I've just added cbind() and rbind() methods for tabular objects, so that 
approach will be a lot easier.  Just do the table of the first subset, 
then rbind on the subsets for the rest.  Will commit to R-forge after a 
bit more testing and documentation.

Duncan Murdoch

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Duncan Murdoch <murdoch.dun...@gmail.com> wrote:
On 13-11-09 1:23 PM, Jeff Newmiller wrote:
Visually, the elimination of duplicates in hierarchical tables in the
tabular function from the tables package is very nice. I would like
to do
the same thing with non-crossed factors, but am perhaps missing some
conceptual element of how this package is used. The following code
illustrates my goal (I hope):

library(tables)
sampledf <- data.frame( Sex=rep(c("M","F"),each=6)
              ,
Name=rep(c("John","Joe","Mark","Alice","Beth","Jane"),each=2)
              , When=rep(c("Before","After"),times=6)
              ,
Weight=c(180,190,190,180,200,200,140,145,150,140,135,135)
              )
sampledf$SexName <- factor( paste( sampledf$Sex, sampledf$Name ) )

# logically, this is the layout
tabular( Name ~ Heading()* When * Weight * Heading()*identity,
data=sampledf )

# but I want to augment the Name with the Sex but visually group the
# Sex like
#   tabular( Sex*Name ~ Heading()*When * Weight * Heading()*identity,
data=sampledf )
# would except that there really is no crossing between sexes.
tabular( SexName ~ Heading()*When * Weight * Heading()*identity,
data=sampledf )
# this repeats the Sex category excessively.

I forgot, there's a simpler way to do this.  Build the full table with
the junk values, then take a subset:

full <- tabular( Sex*Name ~ Heading()*When * Weight *
Heading()*identity, data=sampledf )

full[c(1:3, 10:12), ]

Figuring out which rows you want to keep can be a little tricky, but
doing something like this might be good:

counts <- tabular( Sex*Name ~ 1, data=sampledf )
full[ as.logical(counts), ]

Duncan Murdoch


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