[R] When is *interactive* data visualization useful to use?

[Tom Hopper]

Tal,

One interactive capability that I have repeatedly wished for (but
never taken the time to develop with the existing R tools) is the
ability to interactively zoom in on and out of a data set, and to
interactively create "call-outs of sections of the data. Much of the
data that I deal with takes the form of time series where both the
full data and small section carry meaningful information.

Some of the capabilities of Deducer approach interactive graphing,
such as adjusting alpha values or smoothers, though the updates don't
happen in quite real-time.

- Tom

On Friday, February 11, 2011, Tal Galili <tal.galili at gmail.com> wrote:
> 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 at gmail.com |  972-52-7275845
> Read me: www.talgalili.com (Hebrew) | www.biostatistics.co.il (Hebrew) |
> www.r-statistics.com (English)
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