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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