Tables Package Grouping Factors

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 don't think it's easy to get what you want.  The basic assumption is 
that factors are crossed.

One hack that would get you what you want in this case is to make up a 
new variable representing person within sex (running from 1 to 3), then 
treating the Name as a statistic.  Of course, this won't work if you 
don't have equal numbers of each sex.

A better solution is more cumbersome, and only works in LaTeX (and maybe 
HTML).  Draw two tables, first for the female subset, then for the male 
subset.  Put out the headers only on the first one and the footer only 
on the second, and it will be typeset as one big table.
You'll have to fight with the fact that the factors Sex and Name 
remember their levels whether they are present or not, but it should 
work.  For example,

sampledf$Sex <- as.character(sampledf$Sex)
sampledf$Name <- as.character(sampledf$Name)
females <- subset(sampledf, Sex == "F")
males <- subset(sampledf, Sex == "M")

latex( tabular( Factor(Sex)*Factor(Name) ~ Heading()*When * Weight * 
Heading()*identity, data=females),
options = list(doFooter=FALSE, doEnd=FALSE) )

latex( tabular( Factor(Sex)*Factor(Name) ~ Heading()*When * Weight * 
Heading()*identity, data=males),
options = list(doBegin=FALSE, doHeader=FALSE) )

It would probably make sense to support nested factor notation using 
%in% to make this easier, but currently tables doesn't do that.

Duncan Murdoch