Rattle The R Analytical Tool To Learn Easily is a popular GUI for data mining using R. It is used world wide by independent consultants, data scientists at the major AI companies (including Microsoft, Google, Facebook), and for teaching data science at many universities (including Australian National University, Stanford, MIT, Harvard).

Install To install or to update Rattle you can follow the specific instructions available for each of the major computer operating systems.

Rattle is updated regularly, often weekly. Please check the CHANGELOG for details of updates.

You can visit the survival guide for general instructions for installing rattle. The source code is available on github.

Rattle presents statistical and visual summaries of data, transforms data that can be readily modelled, builds both unsupervised and supervised models from the data, presents the performance of models graphically, and scores new datasets. All of the underlying R code is presented as a script for learning R and for running independent of Rattle.

The Rattle book Data Mining with Rattle and R is available from Amazon. The R package has been available on CRAN for nearly 20 years. There are over 1,400 YouTube videos introducing Rattle.

book

When you are ready to move on from Rattle into programming with data, my follow up book, The Essentials of Data Science, available from Amazon, provides a guide to the template (or programming by example) approach to data science that Rattle's backend is built on. The templates build on your skill set learnt using Rattle, but using the full power of R with a focus on being accessible to all.

The source code for the Flutter front-end is available on github. The source code for the R support package is hosted on Bitbucket. Anyone is welcome and encouraged to clone, extend, and to submit pull requests through github to contribute.

The older Rattle Site (Rattle V5) is available at https://rattle.togaware.com/index.html. Instructions for installing Rattle V5 are available from the Data Science Desktop Survival Guide.

Resources


Other Installation Guides

Datasets

weatherAUS

This is the full Australian weather dataset from 2007 to today. A subset of this dataset is available directly within Rattle.

Users

Over the years Rattle has been and continues to be used for teaching Data Mining and Data Science. It is also used by many companies, from the very largest (including Microsoft) to individual consultants, in support of their data science teams and activities.

Rattle has been in daily use by Australia's largest team of pioneering 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). Now with the new easy to install Flutter version these numbers are increasing again.

Many independent consultants world wide also use Rattle in their day-to-day business.

Some known users of Rattle include Fisheries and Oceans Canada, Laboratory of Biochemical and Instrumental Analysis at the CINVESTAV Unidad Irapuato, College Raptor, RACQ, McMillan Shakespeare, University of Texas at Dallas, Public Transport Authority of Western Australia, New South Wales Department of Primary Industries, the University of California San Diego, the largest banks in India, Derby Dubai, Australia's ANZ and Commonwealth Banks, the Australian Taxation Office, Australian Department of Immigration, Ulster Bank, Toyota Australia, Victorian 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, Chevron, Siemens, 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 the Australian National University (2010-2023), 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), Swinburne University of technology (2020-2023) and many others.

Rattle is used in teaching data science at numerous universities, including: Flinders University course on Data Engineering (2025-), School of Business Administration SUNY Brockport (2022), Corporación Universitaria Lasallista Medellin Columbia (2020-), Department of Operations & Information Systems Manning School of Business University of Massachusetts Lowell (2018-), NYU School of Professional Studies (2020-), Big Data Analytics @ UC San Diego (2017-), University of South Dakota, the University of Washington Foster School (2017-), the School of Global Policy and Strategy, UC San Diego (2016-), the Australian National University's courses on Data Mining and Data Wrangling (2006-), University of Canberra (2010-), University of South Australia (2009-), Yale University, University of Liège Belgium (2011-), University of Wollongong (2010-), University of Southern Queensland (since 2010), University of Technology, Sydney (2012-), Electrical Engineering courses in Reliability and Testability at Virginia University, Loyola University Chicago, Southern New Hampshire University (2017-), Penn State University (2017-), University of Washington (2016-), Swinburne University, among others.

Award

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. Other awards include to 2020 Special Achievement Award from the Pacific Asia Conference on Knowledge Discovery and Data Mining (PAKDD).

Citation

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!.

or

Graham Williams (2009). Rattle: A Data Mining GUI for R, Graham J Williams, The R Journal, 1(2):45-55.

Contributing

You are invited to support Rattle development either through contributions to the freely available and open source software or else financially to contribute to its ongoing development and distribution, through a donation via PayPal: