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Norman Nie: R GUI and More


Train in R

Predictive Analytics- The Book

Here is an interview from Norman Nie, SPSS Founder and CEO, REvolution Computing (R Platform).

Some notable thoughts

For example, SPSS was really among the first to deliver rich GUIs that make it easier to use by more people. This is why one of the first things you’ll see from REvolution is a GUI for R – to make R more accessible and hereby further accelerate adoption.

This is good news if executed- I have often written (in agony actually because I use it) for the need for GUIs for R. My last post on that was here. Indeed the one reason SPSS was easily adopted by business school students (like me) in India in 2001-3 was the much better GUI over SAS ‘s GUIs.

However some self delusion/ PR / cognitive dissonance seems at play at Dr Nie’s words

If you look at the last 40 years of university curriculum, SPSS – the product I helped build – has been the dominant player, even becoming the common thread uniting a diverse range of disciplines, which have in turn been applied to business. Data is ubiquitous: tools and data warehouses allow you to query a given set of data repeatedly. R does these things better than the alternatives out there; it is indeed the wave of the future.

SPSS has been a strong number 2- but it has never overtaken SAS. Part of that is SAS handles much bigger datasets much more easily than SPSS did ( and that is where R’s RAM only size can be a concern). Given the decreasing prices of RAM memory, the BIG-LM like packages, and the shift for cloud based computing(with rampable memory on demand) this can be less of an issue- but analysts generally like to have a straight way of handling bigger datasets. Indeed SAS with vertical focus and the recent social media analytics continues to innovate both itself as well as through its alliance partnerships in the Enterprise software world- and REvolution Computing would further need to tie up or sew these analytical partners especially data warehousing or BI providers to ensure R’s analytical functions can be used where there is maximum value for their usage to the corporate customer as well as the academic customer.

Part 2 of Nie’s interview should be interesting .

2010-2011 would likely see

Round 2 : Red Corner ( Nie)                             Gray Corner (Goodnight)


Norman Nie can truly deliver a REvolution in Computing

or else

he becomes number two again the second time around to Jim Goodnight’s software giant.


  1. Thoams says:

    Hi Tal

    I wrote my comment after reading your ‘R GUI wars’ comment. I am sorry I haven’t seen Ian’s website. Maybe I should research the subject some more before I post my comments. Would it be possible for you to post the link.


  2. Tal Galili says:

    I am not sure who you wrote this to, but regarding deducer – it is open source and GPL. BTW: Ian writes in his website why he decided to write deducer, even that R commander exists – interesting read…

  3. Thoams says:

    What about the R Commander by John Fox? http://socserv.mcmaster.ca/jfox/Misc/Rcmdr/

    Why no collaborate on that or something else. Wouldn’t that the spirit of the free software movement?


  4. Tal Galili says:

    This is so interesting.
    I wonder if that means a type of “R GUI wars” might start, since I know of another project which is SPSS inspired, that works on extending the R GUI, which is called deducer.
    I recently wrote on it’s author getting accepted to google summer of code, for writing an deducer extension for R’s ggplot2:



  5. Ellie K. says:

    I concur about the superior usability of the interface being the determining factor for student stats packages. Long ago, in the Wharton School MBA program, we easily grasped basics of SPSS. Even the very limited Minitab lives on, as students don’t waste time on the interface, instead focusing on the app as a computing engine.

    However, in the workplace, be it financial or insurance, gov’t, transportation, pharma or epi health sciences, SAS has been dominant since 1985. SPSS is second, with the exception of users in the social sciences or others who are cost-constrained and don’t work with 1 mil+ obs datasets.

    Final consideration: data security. SAS is always going to win out overcloud-based computing or similar, even with rampable RAM. SAS on an IBM mainframe running MVS with RACF and TSO/ JCL is orders of magnitude away from Web 2.0, but is used, probably preferred for sensitive data e.g. CRM of protected health info, predictive analytics despite masking of identifiable data. Ironic that SAS+IBM is such a good pair… might it ever become SPSS+IBM, post-acquisition?

    Interesting interview of Dr. Nie, and excellent commentary by you, Ajay!

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