[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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> 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
>