Hi:

A good place to start would be package vcd and its suite of demos and
vignettes, as well as the vcdExtra package, which adds a few more goodies
and a very nice introductory vignette by Michael Friendly. You can't fault
the package for a lack of documentation :)

You might also find the following link useful:  http://www.datavis.ca/R/
Scroll down to 'vcd and vcdExtra', and further down to 'tableplot', which
was recently released on CRAN.

HTH,
Dennis

On Thu, Nov 11, 2010 at 2:09 PM, Lara Poplarski <larapoplarski@gmail.com>wrote:

> Dear List,
>
>
> I am looking to perform exploratory analyses of two (relatively) large
> datasets of categorical data. The first one is a binary 80x100 matrix, in
> the form:
>
>
> matrix(sample(c(0,1),25,replace=TRUE), nrow = 5, ncol=5, dimnames = list(c(
> "group1", "group2","group3", "group4","group5"), c("V.1", "V.2", "V.3",
> "V.4", "V.5")))
>
>
> and the second one is a multistate 750x1500 matrix, with up to 15
> *unordered* states per variable, in the form:
>
>
> matrix(sample(c(1:15),25,replace=TRUE), nrow = 5, ncol=5, dimnames =
> list(c(
> "group1", "group2","group3", "group4","group5"), c("V.1", "V.2", "V.3",
> "V.4", "V.5")))
>
>
> Specifically, I am looking to see which pairs of variables are correlated.
> For continuos data, I would use cor() and cov() to generate the correlation
> matrix and the variance-covariance matrix, which I would then visualize
> with
> symnum() or image(). However, it is not clear to me whether this approach
> is
> suitable for categorical data of this kind.
>
>
> Since I am new to R, I would greatly appreciate any input on how to
> approach
> this task and on efficient visualization of the results.
>
>
> Many thanks in advance,
>
> Lara
>
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>
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