Tables Package Grouping Factors
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.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