Rattle: A Graphical User Interface for Data Mining using R
Welcome to the R Analytical Tool To Learn Easily!
Rattle is a popular GUI for data mining using R. It presents statistical and visual summaries of data, transforms data so that it can be readily modelled, builds both unsupervised and supervised machine learning models from the data, presents the performance of models graphically, and scores new datasets for deployment into production. A key features is that all of your interactions through the graphical user interface are captured as an R script that can be readily executed in R independently of the Rattle interface. Use it as a tool to learn and develop your skills in R and then to build your initial models in Rattle to then be tuned in R which provides considerably more powerful options.
Rattle is Free (as in Libre) Open Source Software and the source code is available from the Bitbucket repository. We give you the freedom to review the code, use it for whatever purpose you like, and to extend it however you like, without restriction, except that if you then distribute your changes you also need to distribute your source code under the same conditions.
Rattle clocks between 10,000 and 20,000 downloads per month from the RStudio CRAN node (one of over 140 nodes).
After installing R you can install Rattle using
A development version is regularly updated and a list of the latest updates is available from the NEWS file. All known issues are fixed in the latest development version. See the troubleshooting page to see if you have a new issue.
To install the latest development Version 5.1.3 dated 2017-06-20:
> install.packages("rattle", repos="https://rattle.togaware.com", type="source")
To install from the source code repository:
> install.packages("devtools") > devtools::install_bitbucket("kayontoga/rattle")
To install from the package tar file:
> install.packages("https://togaware.com/access/rattle_5.1.3.tar.gz", repos=NULL)Errata Brochure
- Other Resources
"Rattle is a tab-oriented user interface that is similar to Microsoft Office’s ribbon interface. It makes getting started with data mining in R very easy. This book covers both Rattle, the R code that Rattle creates, and writing some R code from scratch. Therefore it will appeal to both people seeking the ease-of-use that is very much missing from R, and people looking to learn R programming."
"The book is very enjoyable reading and is filled with useful information. It is aimed at both students learning data mining and data miners who are using or learning R. People are likely to read it through the first time as a text book and then later use it as a reference, especially about the details of the R language. One of the strongest aspects of this book is Dr. Williams’ ability to simplify complex topics and explain them clearly. His descriptions of bagging and boosting are the most clear that I have ever read."
Bob Muenchen, author of R for SAS and SPSS Users, 30 June 2011
For anyone looking to learn more about R, this would be a great introduction. Brian Tvenstrup (5 reviewers made a similar statement).
This book covers both Rattle, the R code that Rattle creates, and writing some R code from scratch. Robert A. Muenchen (2 reviewers made a similar statement).
In summary, I found the book very readable, the examples easy to follow, and the explanations and reasons for why different processes are done. G3N1U5 (2 reviewers made a similar statement).
Rattle is open source and freely available from Togaware. You can download Rattle and get familiar with its functionality without any obligation, except for the obligation to freely share! Organisations are also welcome to purchase Rattle, including support for installation and initial training, and ongoing data mining support. Email firstname.lastname@example.org for details.
Through a simple and logical graphical user interface based on Gnome, Rattle can be used by itself to deliver data mining projects. Rattle also provides an entry into sophisticated data mining using the open source and free statistical language R.
Rattle runs under GNU/Linux, Macintosh OS/X, and MS/Windows. The aim is to provide an intuitive interface that takes you through the basic steps of data mining, as well as illustrating the R code that is used to achieve this. Whilst the tool itself may be sufficient for all of a user's needs, it also provides a stepping stone to more sophisticated processing and modelling in R itself, for sophisticated and unconstrained data mining.
Rattle is in daily use by Australia's largest team of data scientists and by a variety of government and other enterprises, world wide. Whilst the true number of active users is hard to gauge we can observe that there are about 20,000 downloads of the package per month from a single though popular CRAN node (where CRAN has over 100 nodes).
Many independent consultants world wide also use Rattle in their day-to-day business.
Users of rattle include data scientists in ANZ Bank, Commonwealth Bank, the Australian Taxation Office (2004-), Australian Department of Immigration, Ulster Bank, Toyota Australia, Vitorian Cancer Council, US Geological Survey, Carat Media Network, Institute of Infection and Immunity of the University Hospital of Wales, US National Institutes of Health, AIMIA Loyalty Marketing, Added Value, Stanford University, V.E.S Institute of Technology Mumbai, Microsoft (2016), Chevron (2017), Seattle Children's Hospital (2017) and many more.
Rattle is also used to teach the practise of data mining. The software and the book are used as the primary tool of instruction for hands-on data mining and data science at University of Canberra, Harbin Institute of Technology, Shenzhen Graduate School (since 2006), Australian Consortium for Social and Political Research (2011), Revolution Analytics (since 2012 and now Microsoft), International Centre for Free and Open Source Software in Kerala, India (2015) and many others.
Rattle is used in teaching data science at numerous universities, including: University of South Dakota, the University of Washington Foster School (since 2017), the School of Global Policy and Strategy, UC San Diego (since 2016), the Australian National University's course on Data Mining (since 2006), University of Canberra (since 2010), University of South Australia (since 2009), Yale University, University of Liège Belgium (since 2011), University of Wollongong (since 2010), University of Southern Queensland (since 2010), University of Technology, Sydney (since 2012), Electrical Engineering courses in Reliability and Testability at Virginia University, Loyola University Chicago, Southern New Hampshire University (2017), among others.
The author of Rattle received a 2007 Australia Day Medallion, presented by the Commissioner of Taxation, for leadership and mentoring in Data Mining in the Australian Taxation Office and in Australia, and particularly cited the development and sharing of the Rattle system.
If you use Rattle please reference it according to citation("rattle"). You might also reference one of the following:
Graham Williams (2011). Data Mining with Rattle and R: The Art of Excavating Data for Knowledge Discovery, Springer, Use R!.
Graham Williams (2009). Rattle: A Data Mining GUI for R, Graham J Williams, The R Journal, 1(2):45-55.
Discussion Group and Suggestions
The Rattle Users mailing list is hosted by Google Groups. Questions and suggestions can be posted there. You can [visit the discussion archive] or subscribe by supplying your email address below and clicking the Subscribe button.
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Last Modified 2017-10-29 21:39:20 Graham Williams
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