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

Rainer M Krug
Mon, 14 Feb 2011 10:24:35 -0000

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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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