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

[Rainer M Krug]

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On 02/14/2011 11:21 AM, Gesmann, Markus wrote:
> Hi Rainer,
> 
> You may want to look into the package googleVis, which provides an
> interface between the Google Visualisation API and R, see
> http://code.google.com/p/google-motion-charts-with-r/

True - forgotten about that one. It looks actually nice fore especially
time series.

Rainer

> 
> Regards,
> 
> Markus 
> 
> -----Original Message-----
> From: r-help-bounces at r-project.org [mailto:r-help-bounces at r-project.org]
> On Behalf Of Rainer M Krug
> Sent: 14 February 2011 09:43
> To: Claudia Beleites
> Cc: R Help
> Subject: Re: [R] When is *interactive* data visualization useful to use?
> 
> On 02/11/2011 08:21 PM, Claudia Beleites wrote:
>> Dear Tal, dear list,
> 
>> I think the importance of interactive graphics has a lot do with how 
>> visual your scientific discipline works. I'm spectroscopist, and I 
>> think we are very visually oriented: if I think of a spectrum I 
>> mentally see a graph.
> 
>> So for that kind of work, I need a lot of interaction (type: plot, 
>> change a bit, plot again), e.g.
>> One example is the removal of spikes from Raman spectra (caused e.g. 
>> by cosmic rays hitting the detector). It is fairly easy to compute a 
>> list of suspicious signals. It is already much more complicated to 
>> find the actual beginning and end of the spike. And it is really 
>> difficult not to have false positives by some automatic procedure, 
>> because the spectra can look very different for different samples. It 
>> would just take me far longer to find a computational description of 
>> what is a spike than interactively accepting/rejecting the
> automatically marked suspicions.
>> Even though it feels like slave work ;-)
> 
>> Roughly the same applies for the choice of pre-processing like 
>> baseline correction. A number of different physical causes can produce
> 
>> different kinds of baselines, and usually you don't know which process
> 
>> contributes to what extent. In practice, experience suggests a method,
> 
>> I apply it and look whether the result looks as expected. I'm not 
>> aware of any performance measure that would indicate success here.
> 
>> The next point where interaction is needed pops up as my data has e.g.
>> spatial and spectral dimensions. So do the models usually: e.g. in a 
>> PCA, the loadings would usually capture the spectroscopic direction, 
>> whereas the scores belong to the spatial domain. So I have "connected"
>> graphs: the spatial distribution (intensity map, score map, etc.), and
> 
>> the spectra (or loadings).
>> As soon as I have such connections I wish for interactive
> visualization:
>> I go back and forth between the plots: what is the spectrum that 
>> belongs to this region of the map? Where on the sample are high 
>> intensities of this band? What is the substance behind that: if it is 
>> x, the intensities at that other spectral band should correlate. And 
>> then I want to compare this to the scatterplot (pairs plot of the PCA 
>> score) or to a dendrogram of HCA...
> 
>> Also, exploration is not just prerequisite for models, but it 
>> frequently is already the very proper scientific work (particularly in
> 
>> basic science). The more so, if you include exploring the models: Now,
> 
>> which of the bands are actually used by my predictive models? Which 
>> samples do get their predictions because of which spectral feature?
>> And, the "statistical outliers" may very well be just the interesting 
>> part of the sample. And the outlier statistics cannot interprete the 
>> data in terms of interesting ./. crap.
> 
>> For presentation* of results, I personally think that most of the time
> 
>> a careful selection of static graphs is much better than live
> interaction.
>> *The thing where you talk to an audience far awayf from your work 
>> computer. As opposed to sitting down with your client/colleague and 
>> analysing the data together.
> 
>>> 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)
>> As long as the relevant measure exists, sure.
>> Yet as a non-statistician, my work is focused on the physical/chemical
> 
>> interpretation. Summary statistics are one set of tools for me, and 
>> interactive visualisation is another set of tools (overlapping
> though).
> 
>> I may want to subtract the influence of the overall unchanging sample 
>> matrix (that would be the minimal intensity for each wavelength). But 
>> the minimum spectrum is too noisy. So I use a quantile. Which one?
>> Depends on the data. I'll have a look at a series (say, the 2nd to 
>> 10th
>> percentile) and decide trading off noise and whether any new signals 
>> appear. I honestly think there's nothing gained if I sit down and try 
>> to write a function scoring the similarity to the minimum spectrum and
> 
>> the noise level: the more so as it just shifts the need for a decision
> 
>> (How much noise outweighs what intensity of real signal being
> subtracted?).
>> It is a decision I need to take. With number or with eye. And after 
>> all, my professional training was thought to enable me taking this 
>> decision, and I'm paid (also) for being able to take this decision 
>> efficiently (i.e. making a reasonably good choice within not too long
> time).
> 
>> After all, it may also have to do with a complaint a colleague from a 
>> computational data analysis group once had. He said the bad thing with
> 
>> us spectroscopists is that our problems are either so easy that 
>> there's no fun in solving them, or they are too hard to solve.
> 
>>> - 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).
>> Sure, yet:
>> - Isn't that what validation was invented for (I mean with a proper, 
>> new, [double] blind test set after you decided your parameters)?
>> - Summarizing a whole data set into a few numbers, without having 
>> looked at the data itself may not be safe, either:
>> - The few comparisons shouldn't come at the cost of risking a bad 
>> modeling modelling strategy and fitting parameters because the data 
>> was not properly examined.
> 
>> My 2 ct,
> 
>> Claudia (who in practice warns far more frequently of multiple 
>> comparisons and validation sets being compromised (not independent) 
>> than of too few data exploration ;-) )
> 
> These are very interesting and valid points. But which tools are
> recommended / usefull for interactive graphs for data evaluation? I
> somehow have difficulties getting my head around ggobi, and haven't yet
> tried out mondian (but I will). Are there any other ones (as we are ion
> the R list - which integrate with R) which can be recommended?
> 
> Rainer
> 
> 
> 
> 

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- -- 
Rainer M. Krug, PhD (Conservation Ecology, SUN), MSc (Conservation
Biology, UCT), Dipl. Phys. (Germany)

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