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$Name ) )
# logically, this is the layout
tabular( Name ~ Heading()* When * Weight * Heading()*identity,
data=sampledf )
# but I want to augment the Name with the Sex but visually group the
# Sex like
# tabular( Sex*Name ~ Heading()*When * Weight * Heading()*identity,
data=sampledf )
# would except that there really is no crossing between sexes.
tabular( SexName ~ Heading()*When * Weight * Heading()*identity,
data=sampledf )
# this repeats the Sex category excessively.
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On 13-11-09 1:23 PM, Jeff Newmiller wrote:> Visually, the elimination of duplicates in hierarchical tables in the > tabular function from the tables package is very nice. I would like to do > the same thing with non-crossed factors, but am perhaps missing some > conceptual element of how this package is used. The following code > illustrates my goal (I hope): > > library(tables) > sampledf <- data.frame( Sex=rep(c("M","F"),each=6) > , Name=rep(c("John","Joe","Mark","Alice","Beth","Jane"),each=2) > , When=rep(c("Before","After"),times=6) > , Weight=c(180,190,190,180,200,200,140,145,150,140,135,135) > ) > sampledf$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 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
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