Rattle (the R Analytic Tool To Learn Easily) provides a
Gnome based open source interface to R functionality for binary
classification tasks and data
mining. It is also available as a product within Information Builders'
business intelligence suite known as RStat.
The aim is to provide a simple and intuitive interface
that allows a user to quickly load data from a CSV file (or via
ODBC), transform and explore the data, build and evaluate
models, and export models as PMML (predictive modelling markup
language) or as scores.
All of this with knowing little about
R. All R commands are logged and commented through the log tab.
Thus they are available to the user as a script file or as an
aide for the user to learn R or to copy-and-paste directly into
R itself. Rattle also exports a number of utility functions and
the graphical user interface, invoked as rattle(), does not
need to be run to deploy these.
Rattle is used in business, government, research and for
teaching data mining in Australia and internationally.
| Rattle 3.0.4
Free to download
GNU GPL v2
R 2.8.0 or higher
pmml 1.2.13 or higher
colorspace, ada, amap, arules
biclust, cairoDevice, cba, descr
doBy, e1071, ellipse, fEcofin
fBasics, foreign, fpc, gdata
gtools, gplots, gWidgetsRGtk2
Hmisc, kernlab, latticist, Matrix
mice, network, nnet, odfWeave
party, playwith, psych, randomForest
reshape, rggobi, RGtk2Extras, ROCR
RODBC, rpart, RSvgDevice, survival
timeDate, XML, methods, graph
RBGL, bitops, grid, pkgDeptools
- Extensive collection of R packages
- More than a graphical user interface to R
- File Inputs: CSV, TXT, Excel, ARFF, ODBC, R Dataset, RData
File, Library Packages Datasets, Corpus, and Scripts.
- Statistics: Min, Max, Quartiles, Mean, St Dev, Missing,
Medium, Sum, Variance, Skewness, Kurtosis, chi square.
- Statistical tests: Correlation, Wilcoxon-Smirnov, Wilcoxon
Rank Sum, T-Test, F-Test, and Wilcoxon Signed Rank.
- Clustering: KMeans, Clara, Hierarchical, and BiCluster.
- Modeling: Decision Trees, Random Forests, ADA Boost,
Support Vector Machine, Logistic Regression, and Neural Net.
- Evaluation: Confusion Matrix, Risk Charts, Cost Curve,
Hand, Lift, ROC, Precision, Sensitivity.
- Charts: Box Plot, Histogram, Correlations, Dendrograms,
Cumulative, Principle Components, Benford, Bar Plot, Dot Pot,and Mosaic.
- Transformations: Rescale (Recenter, Scale 0-1, Median/MAD,
Natural Log, and Matrix) - Impute ( Zero/Missing, Mean, Medium, Mode
& Constant), Recode (Binning, Kmeans, Equal Widths, Indicator,
Join Categories) - Cleanup (Delete Ignored, Delete Selected, Delete
Missing, Delete Obs with Missing)
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Last Updated Monday, May 26 2014 @ 11:28 AM EDT