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.gal...@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]] ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
