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.
Here is a new package called R ODM and it is an interface to do Data Mining via Oracle Tables through R. You can read more here http://www.oracle.com/technetwork/database/options/odm/odm-r-integration-089013.html and here http://cran.fhcrc.org/web/packages/RODM/RODM.pdf . Also there is a contest for creative use of R and ODM.
R Interface to Oracle Data Mining
The R Interface to Oracle Data Mining ( R-ODM) allows R users to access the power of Oracle Data Mining’s in-database functions using the familiar R syntax. R-ODM provides a powerful environment for prototyping data analysis and data mining methodologies.
R-ODM is especially useful for:
- Quick prototyping of vertical or domain-based applications where the Oracle Database supports the application
- Scripting of “production” data mining methodologies
- Customizing graphics of ODM data mining results (examples: classification, regression, anomaly detection)
The R-ODM interface allows R users to mine data using Oracle Data Mining from the R programming environment. It consists of a set of function wrappers written in source R language that pass data and parameters from the R environment to the Oracle RDBMS enterprise edition as standard user PL/SQL queries via an ODBC interface. The R-ODM interface code is a thin layer of logic and SQL that calls through an ODBC interface. R-ODM does not use or expose any Oracle product code as it is completely an external interface and not part of any Oracle product. R-ODM is similar to the example scripts (e.g., the PL/SQL demo code) that illustrates the use of Oracle Data Mining, for example, how to create Data Mining models, pass arguments, retrieve results etc.
R-ODM is packaged as a standard R source package and is distributed freely as part of the R environment’s Comprehensive R Archive Network ( CRAN). For information about the R environment, R packages and CRAN, see www.r-project.org.
Present and win an Apple iPod Touch!
The BI, Warehousing and Analytics (BIWA) SIG is giving an Apple iPOD Touch to the best new presenter. Be part of the TechCast series and get a chance to win!
Consider highlighting a creative use of R and ODM.
BIWA invites all Oracle professionals (experts, end users, managers, DBAs, developers, data analysts, ISVs, partners, etc.) to submit abstracts for 45 minute technical webcasts to our Oracle BIWA (IOUG SIG) Community in our Wednesday TechCast series. Note that the contest is limited to new presenters to encourage fresh participation by the BIWA community.
Also an interview with Oracle Data Mining head, Charlie Berger http://decisionstats.wordpress.com/2009/09/02/oracle/
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 (firstname.lastname@example.org) or Ruben Ledesma (email@example.com) 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;