[R] Tables Package Grouping Factors

[Jeff Newmiller]

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.
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Sent from my phone. Please excuse my brevity.

Duncan Murdoch <murdoch.duncan at 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