Here is a great new tool for techies to start creating Android Apps right away- even if you have no knowledge of the platform. Of course there are existing great number of apps- including my favorite Android Data Mining App in R – called AnalyticDroid http://analyticdroid.togaware.com/
Basically it calls the Rattle (R Analytical Tool To Learn Easily) Data Mining GUI -enabling data mining from an Android Mobile using remote computing.
I dont know if any other statistical application is available on Android Mobiles- though SAS did have a presentation on using SAS on IPhone
SAS Mobile -Iphone App
All you need to do is go to http://appinventor.googlelabs.com/about/index.html and request access (yes there is a 2 week approval waiting line)
||Because App Inventor provides access to a GPS-location sensor, you can build apps that know where you are. You can build an app to help you remember where you parked your car, an app that shows the location of your friends or colleagues at a concert or conference, or your own custom tour app of your school, workplace, or a museum.
||You can write apps that use the phone features of an Android phone. You can write an app that periodically texts “missing you” to your loved ones, or an app “No Text While Driving” that responds to all texts automatically with “sorry, I’m driving and will contact you later”. You can even have the app read the incoming texts aloud to you (though this might lure you into responding).
||App Inventor provides a way for you to communicate with the web. If you know how to write web apps, you can use App Inventor to write Android apps that talk to your favorite web sites, such as Amazon and Twitter.
Here is a not so statistical Android App I am trying to create called Hang-Out
using the current GPS location of your phone to find nearest Pub, Movie or Diner and catch Bus- Train based on your location city, the GPS and time of request and schedule of those cities public transport- very much WIP
Here is a new video which shows exactly how you can use Rapid Miner and R together. Advantages of using both together is using Rapid Miner’s GUI (including the flowchart style for data mning) and adding R statistical functionality to it.
The web site features a video showing how easy R models and scripts can be integrated into the RapidMiner analysis processes. RapidMiner offers a new R perspective consisting of the known R console together with the great plotting facilities of R. All variables as well as R scripts can be stored in the RapidMiner Repository and used from there which helps to organize the usually large number of scripts. Furthermore, widely used modeling methods are directly integrated as RapidMiner operators as usual.
“This is a huge step for open source data analysis. RapidMiner offers a great user interface, a clear process structure and lots of ETL and analysis capabilities necessary for real-world problems. R adds a lot of flexibility and many analysis and data manipulation methods. The result is the by far most powerful data transformation and analysis solution worldwide. And this analysis power is now combined with the ease-of-use already known from RapidMiner.” states Dr. Ingo Mierswa, CEO of Rapid-I.
Visit the RCOMM 2010 and learn more about how to integrate analysis and preprocessing methods offered by R as well as how to use the new R perspective offering a full R console and access to all R plotters.
Thus Rapid Miner is one more mainstream software (after SPSS, SAS etc) to add R functionality to it.
It was really nice to see the latest version of R Excel at http://rcom.univie.ac.at/ and bundled together in an aptly named package called R and Friends.
The look and feel of the package as well as ease of installing are really professional. I also liked the commercial equivalent at http://www.statconn.com/
However much older-guardians and die- hards of command line, feel that GUI is like putting lipstick on a pig, but we respectfully demur.
What does R Excel do? Well for one it can put the R Commander Interface INSIDE your Excel Spreadsheet. That makes it easy to use and a familiar interface even if you are newbie to R- (assuming you have done some Excel)
Download the latest version here
This package will automatically install and configure
- R 2.11.1
- rscproxy 1.3-1
- rcom 2.2-1
It will also download and install a suitable version of the statconnDCOM server and of RExcel during installation. Therefore you will need a working Internet connection during the installation process.
This version of RAndFriends was created 20100516.
We also give you information how to download all sources for R and the R packages included in RAndFriends.
Also read a paper on R and SAS interoperability (using HMisc package from Dr Harrell) at Holland Numerics
An announcement by the Journal of Statistical Software- call for papers on R GUIs. Initial deadline is December 2010 with final versions published along 2011.
Special issue of the Journal of Statistical Software on
Graphical User Interfaces for R
Editors: Pedro Valero-Mora and Ruben Ledesma
Since it original paper from Gentleman and Ihaka was published, R has managed to gain an ever-increasing percentage of academic and professional statisticians but the spread of its use among novice and occasional users of statistics have not progressed at the same pace. Among the reasons for this relative lack of impact, the lack of a GUI or point and click interface is one of the causes most widely mentioned. But, however, in the last few years, this situation has been quietly changing and a number of projects have equipped R with a number of different GUIs, ranging from the very simple to the more advanced, and providing the casual user with what could be still a new source of trouble: choosing what is the GUI for him. We may have moved from the “too few” situation to the “too many” situation
This special issue of the JSS intends as one of its main goals to offer a general overview of the different GUIs currently available for R. Thus, we think that somebody trying to find its way among different alternatives may find useful it as starting point. However, we do not want to stop in a mere listing but we want to offer a bit of a more general discussion about what could be good GUIs for R (and how to build them). Therefore, we want to see papers submitted that discuss the whole concept of GUI in R, what elements it should include (or not), how this could be achieved, and, why not, if it is actually needed at all. Finally, despite the high success of R, this does not mean other systems may not treasure important features that we would like to see in R. Indeed, descriptions of these nice features that we do not have in R but are in other systems could be another way of driving the future progress of GUIs for R.
In summary, we envision papers for this special issue on GUIs for R in the following categories:
– General discussions on GUIs for statistics, and for R.
– Implementing GUI toolboxes for R so others can program GUIs with them.
– R GUIs examples (with two subcategories, in the desktop or in the cloud).
– Is there life beyond R? What features have other systems that R does not have and why R needs them.
Papers can be sent directly to Pedro Valero-Mora (email@example.com) or Ruben Ledesma (firstname.lastname@example.org) and they will follow the usual JSS reviewing procedure. Initial deadline is December 2010 with final versions published along 2011.
Jan de Leeuw; Distinguished Professor and Chair, UCLA Department of Statistics;
Director: UCLA Center for Environmental Statistics (CES);
Editor: Journal of Multivariate Analysis, Journal of 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 –
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.
- 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.
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
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”
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.