Hello all,

Before getting to my question, I would like to apologize for asking this
question here.  My question is not directly an R question, however, I still
find the topic relevant to R community of users  - especially due to only *
partial* (current) support for interactive data visualization (see here:
http://cran.r-project.org/web/views/Graphics.html  were with iplots we are
waiting for iplots extreme, and with rggobi, it currently can not run with R
2.12 and windows 7 OS).

And now for my question:

While preparing for a talk I will give soon, I recently started digging into
two major (Free) tools for interactive data visualization:
GGobi<http://www.ggobi.org/>
 and mondrian <http://rosuda.org/mondrian/> - both offer a great range of
capabilities (even if they're a bit buggy).

I wish to ask for your help in articulating (both to myself, and for my
future audience) *When is it helpful to use interactive plots? Either for
data exploration (for ourselves) and data presentation (for a "client")?*

For when explaining the data to a client, I can see the value of animation
for:

   - Using "identify/linking/brushing" for seeing which data point in the
   graph is what.
   - Presenting a sensitivity analysis of the data (e.g: "if we remove this
   point, here is what we will get)
   - Showing the effect of different groups in the data (e.g: "let's look at
   our graphs for males and now for the females")
   - Showing the effect of time (or age, or in general, offering another
   dimension to the presentation)

For when exploring the data ourselves, I can see the value of
identify/linking/brushing when exploring an outlier in a dataset we are
working on.

But other then these two examples, I am not sure what other practical use
these techniques offer. Especially for our own data exploration!

It could be argued that the interactive part is good for exploring (For
example) a different behavior of different groups/clusters in the data. But
when (in practice) I approached such situation, what I tended to do was to
run the relevant statistical procedures (and post-hoc tests) - and what I
found to be significant I would then plot with colors clearly dividing the
data to the relevant groups. From what I've seen, this is a safer approach
then "wondering around" the data (which could easily lead to data dredging
(were the scope of the multiple comparison needed for correction is not even
clear).

I'd be very happy to read your experience/thoughts on this matter.


Thanks in advance,
Tal


----------------Contact
Details:-------------------------------------------------------
Contact me: Tal.Galili@gmail.com |  972-52-7275845
Read me: www.talgalili.com (Hebrew) | www.biostatistics.co.il (Hebrew) |
www.r-statistics.com (English)
----------------------------------------------------------------------------------------------

	[[alternative HTML version deleted]]

