The Top Statistical Softwares (GUI)

The list of top Statistical Softwares (GUI) is continued below. You can see the earlier post here

6. R Commander– While initially aimed at being a basic statistics GUI, the tremendous popularity of R Commander and the extensions in the form of plugins has helped make this one of the most widely used GUI. In short if you dont know ANY R, and still want to do basic descriptive stats and modeling this will come in handy- with an added script window for custom code for advanced users and extensions like that for DoE (design of experiments) and QCC (Quality Control) packages the e-plugins are a great way to extend this. I suspect the only thing holding it back is Dr Fox and the rest of R Core’s reluctance to fully embrace GUI as a software medium. You can read his earlier interview here-https://decisionstats.wordpress.com/2009/09/14/interview-professor-john-fox-creator-r-commander/

Technically it is possible to convert just about any package to a GUI menu in R Commander using the e-plugins.

7. SAS GUIs

Enterprise (Guide)

SAS Enterprise Guide was the higher end (and higher priced solution) to enhanced editor’s lack of menu driven commands. It works but many people I know prefer the text editor just as well.


The Enterprise Miner is a separate software and works more like Red R or SPSS Modeler does. Again EM is one of the major DM softwares out there, but the similarity in names is a bit confusing.

Even the Base SAS Enhanced Editor does have some menus for importing data, or querying etc, but it is rarely confused for being a GUI.

8. Oracle Data Miner and Knime

I like both the ODM and Knime but I find the lack of advertising or promotional support puzzling. Both these softwares can do well to combine technical excellence with some marketing. And since they are both free you can check them out yourself here

Oracle Data Mining

You can download it here-(note- the Oracle Web Site itself is a bit aging 🙂 )

http://www.oracle.com/technology/products/bi/odm/odminer.html

Knime is the open source GUI which can be found here-

http://www.knime.org/introduction/features

9. RAwkard

Another R GUI- it stands out on the comprehensive ways you can customize your code in menus rather than writing all or learning by rote the syntax.

From http://sourceforge.net/apps/mediawiki/rkward/index.php?title=Main_Page

you can see it below. I recommend this GUI over other GUIs especially if you are new to R and do more data visualization which needs custom graphics.

10. Red R and R JGR/ Deducer

Red R and RJGR/Deducer are both up and coming GUIs for R. While REd R is R version for Enterprise Miner, Deducer is coming up with a new GUI for ggplot the powerful graphics package in R.

Some GUIs excluded from this list are – Statistica, MatLab, EViews(?) because I dont really work with them, and thought it best to turn them over to someone who knows them better.

Hope this list of GUIs helps you- note most of the softwares can be learnt within a quick hour and two if you know basic software skills/data manipulation so going through the GUI list is a faster way of adding value to your resume/knowledge base as well.


Top 10 Graphical User Interfaces in Statistical Software

Here is a list of top 10 GUIs in Statistical Software. The overall criterion is based on-

  • User Friendly Nature for a New User to begin click and point and learn.
  • Cleanliness of Automated Code or Log generated.
  • Practical application in consulting and corporate world.
  • Cost and Ease of Ownership (including purchase,install,training,maintainability,renewal)
  • Aesthetics (or just plain pretty)

However this list is not in order of ranking- ( as beauty (of GUI) lies in eyes of the beholder). For a list of top 10 GUI in R language only please see –

https://rforanalytics.wordpress.com/graphical-user-interfaces-for-r/

This is only a GUI based list so it excludes notable command line or text editor submit commands based softwares which are also very powerful and user friendly.

  1. JMP –

While critics of SAS Institute often complain on the premium pricing of the basic model (especially AFTER the entry of another SAS language software WPS from http://www.teamwpc.co.uk/products/wps – they should try out JMP from http://jmp.com – it has a 1 month free evaluation, is much less expensive and the GUI makes it very very easy to do basic statistical analysis and testing. The learning curve is surprisingly fast to pick it up (as it should be for well designed interfaces) and it allows for very good quality output graphics as well.

2.SPSS

The original GUI in this class of softwares- it has now expanded to a big portfolio of products. However SPSS 18 is nice with the increasing focus on Python and an early adoptee of R compatible interfaces, SPSS does offer a much affordable solution as well with a free evaluation. See especially http://www.spss.com/statistics/ and http://www.spss.com/software/modeling/modeler-pro/

the screenshot here is of SPSS Modeler

3. WPS

While it offers an alternative to Base SAS and SAS /Access software , I really like the affordability (1 Month Free Evaluation and overall lower cost especially for multiple CPU servers ), speed (on the desktop but not on the IBM OS version ) and the intuitive design as well as extensibility of the Workbench. It may look like an integrated development environment and not a proper GUI, but with all the menu features it does qualify as a GUI in my opinion. Continue reading “Top 10 Graphical User Interfaces in Statistical Software”

Towards better Statistical Interfaces

I was just walking about the U Tenn campus thinking about my next month departure from the school back to India when I ran into Bob Muenchen , head of the Stats consulting centre and more famously the author of ” R for SAS and SPSS users” . Bob mentioned that the edition for R for Stata should be ready for next month. It was also his idea for the article on Red R.

In fact what perplexes users of statistical software like me is why complex softwares like R or SAS choose interfaces that are clearly not as well designed in simplicity as they are in statistical rigor. I think SPSS to some extent and JMP to a much greater extent represent well designed user interfaces. While Rattle , R Commander , R Analytical Flow and Red R are examples for R interfaces SAS also invested in the Enterprise class interfaces.

On all these I belive there is a much greater need for say a Pro UI designer and clean it up. I was reading Prof Maeda’s laws of simplicity ( see http://lawsofsimplicity.com ) and just comparing and contrasting that with some of the softwares I end up using.

The Principles of Reduce ( Shrink, Hide , Embody ) and Organize ( Sort , Label , Integrate and Priortize ) need to be looked into by the Chief Software Interface designers for analytics and BI. While attempts to create more and more robust and faster algorithms and prettier dashboards are important is it not important to simplify the process and procedures to do so . The software which is easier to learn and pick up will tend to have an edge over less visually designed softwares. Keeping it simple helped Apple in the retail electronics and software , it needs to be seen who or which enterprise BI or BA software will make attempts to do the same. An ideal stats or BI interface should be simple and powerful enough to be used by decision makers directly on occasion rather rely on the middleware of analysts and consultants solely.

R is an epic fail or is it just overhyped

I came across this nice post from someone who is both knowledgeable and experienced in data. I mean I totally agree that data visualization , user interfaces and unstructured data mining are the trends of the future.

What caught my attention were the words from http://www.thejuliagroup.com/blog/?p=433

However, for me personally and for most users, both individual and organizational, the much greater cost of software is the time it takes to install it, maintain it, learn it and document it. On that, R is an epic fail. It does NOT fit with the way the vast majority of people in the world use computers. The vast majority of people are NOT programmers. They are used to looking at things and clicking on things.

Let me analyze this scientifically and dispassionately

R Documentation

I believe that the SAS Online Doc and the SPSS Documentation are both good examples of structured documentation. I do belive that despite the many corporate R products floating- the quality of R documentation is both very extensive and perhaps too big to be put in a neat document something like the ” The Little R Book” or “R Online Doc” would really help.

Entering ? or ?? to search for documentation seems like too difficult work and complex for corporate users it seems. However the documentation for R is not really enterprise software quality is a valid enough point.

Maintaining R

It takes a single line of code or even a single click to update and maintain R.

Apparently the author of the fore mentioned post that existing corporate users are too STUPID OR LAZY to do this.

I like to think most corporate users of statistical software are actually way smarter ( One Hint : They earn money doing that stuff)

Installing R

Anyone who mentions installation costs of software as a reason for enhanced software costs and then mentions R is either biased against R or has not worked with R. Or Both

Learn R

I think anyone cannot learn all R packages just as you cannot learn all the modules of SAS ( like ETS, Stat, etc etc)

R does have more time to learn than Base SAS and this is a valid enough point.

However two R GUI like Rattle and R Commander can help the execution time for this learning.

And increasingly R is taught in universities which is where the battle for future developers or users for platforms like SAS , SPSS , Stata or R would ultimately be decided while the short term monetization of other softwares dazzles people R has too many passionate developers or users to allow it to fail.

However,

R is not perfect. It does need a better corporate version than is currently offered especially to people who are simple users not developers , and it could also to well to better the marketability and visibility of R.

Regarding software costs, ironically while it is easier to estimate how much SAS will cost you in terms of licenses and training time. A similar comparitive document between R and SAS in terms of costs and estimated training costs etc should settle this debate more rationally and more dispassionately than is currently the norm in comparing softwares

Top ten RRReasons R is bad for you ?

 

This is the original symbol of the Perl progra...
Image via Wikipedia

 

R stands for programming language based out of www.r-project.org

R is bad for you because –

1) It is slower with bigger datasets than SPSS language and SAS language .If you use bigger datasets, then you should either consider more hardware , or try and wait for some of the ODBC connect packages.

2) It needs more time to learn than SAS language .Much more time to learn how to do much more.

3) R programmers are lesser paid than SAS programmers.They prefer it that way.It equates the satisfaction of creating a package in development with a world wide community with the satisfaction of using a package and earning much more money per hour.

4) It forces you to learn the exact details of what you are doing due to its object oriented structure. Thus you either get no answer or get an exact answer. Your customer pays you by the hour not by the correct answers.

5) You can not push a couple of buttons or refer to a list of top ten most commonly used commands to finish the project.

6) It is free. And open for all. It is socialism expressed in code. Some of the packages are built by university professors. It is free.Free is bad. Who pays for the mortgage of the software programmers if all softwares were free ? Who pays for the Friday picnics. Who pays for the Good Night cruises?

7) It is free. Your organization will not commend you for saving them money- they will question why you did not recommend this before. And why did you approve all those packages that expire in 2011.R is fReeeeee. Customers feel good while spending money.The more software budgets you approve the more your salary is. R thReatens all that.

8) It is impossible to install a package you do not need or want. There is no one calling you on the phone to consider one more package or solution. R can make you lonely.

9) R uses mostly Command line. Command line is from the Seventies. Or the Eighties. The GUI’s RCmdr and Rattle are there but still…..

10) R forces you to learn new stuff by the month. You prefer to only earn by the month. Till the day your job got offshored…

Written by a R user in English language

( which fortunately was not copyrighted otherwise we would be paying Britain for each word)

the above post was reprinted by request.