R Software Environment
R is a free software environment for statistical computing and graphics. It compiles and runs on a wide variety of UNIX platforms, Windows and MacOS. R is an integrated suite of software facilities for data manipulation, calculation and graphical display.
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Open Source Software
R Software Environment : R is a free software environment for statistical computing and graphics. It compiles and runs on a wide variety of UNIX platforms, Windows and MacOS. R is an integrated suite of software facilities for data manipulation, calculation and graphical display. Some of the functionalities include an effective data handling and storage facility, a suite of operators for calculations on arrays, in particular matrices, a large, coherent, integrated collection of intermediate tools for data analysis, graphical facilities for data analysis and display either directly at the computer or on hardcopy, and well developed, simple and effective programming language which includes conditionals, loops, user defined recursive functions and input and output facilities.
R is very much a vehicle for newly developing methods of interactive data analysis. It has developed rapidly, and has been extended by a large collection of packages. The R language is widely used among statisticians and data miners for developing statistical software and data analysis.
R Software Environment
R provides a wide variety of statistical and graphical techniques, including linear and nonlinear modeling, classical statistical tests, time-series analysis, classification, clustering, and others. R is easily extensible through functions and extensions, and the R community is noted for its active contributions in terms of packages. There are some important differences, but much code written for S runs unaltered. Many of R’s standard functions are written in R itself, which makes it easy for users to follow the algorithmic choices made.
The capabilities of R are extended through user-created packages, which allow specialized statistical techniques, graphical devices such as ggplot2, import/export capabilities, reporting tools such as knitr, Sweave.
R Graphical user interfaces
• Deducer is a GUI for menu driven data analysis which is similar to SPSS/JMP/Minitab.
• Java GUI for R is a cross-platform stand-alone R terminal and editor based on Java .
• Rattle GUI is a cross-platform GUI based on RGtk2 and specifically designed for data mining.
• R Commander is a cross-platform menu-driven GUI based on tcltk.
• Revolution Analytics provides a Visual Studio based IDE.
• RGUI comes with the pre-compiled version of R for Microsoft Windows.
• RKWard is an extensible GUI and IDE for R.
• RStudio is a cross-platform open source IDE.
• RWeka allows for the use of the data mining capabilities in Weka and statistical analysis in R.
Editors and IDEs
Text editors and Integrated development environments (IDEs) with some support for R include: ConTEXT, Eclipse (StatET), Emacs (Emacs Speaks Statistics), LyX (modules for knitr and Sweave), Vim, jEdit, Kate,Revolution R Enterprise DevelopR (part of Revolution R Enterprise), RStudio, Sublime Text, TextMate, WinEdt (R Package RWinEdt), Tinn-R and Notepad++.
R functionality has been made accessible from several scripting languages such as Python,Perl,Ruby,and F#.PL/R can be used alongside, or instead of, the PL/pgSQL scripting language in the PostgreSQL and Greenplum database management system. Scripting in R itself is possible via littler.
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More Information on Predictive Analysis Process
For more information of predictive analytics process, please review the overview of each components in the predictive analytics process: data collection (data mining), data analysis, statistical analysis, predictive modeling and predictive model deployment.