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R is a powerful software environment for statistical computing and graphics. Originating as an implementation of the S language, developed at Bell Laboratories, R has evolved into a robust, open-source statistical software favored by statisticians and data scientists worldwide. Its comprehensive suite includes data manipulation, calculation, and graphical display capabilities, making it versatile for data analysis and visualization. Its programming language is at the core of R's functionality,...
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Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
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RGG: a general GUI Framework for R scripts.

Ilhami Visne1, Erkan Dilaveroglu, Klemens Vierlinger

  • 1Austrian Research Centers GmbH, ARC, Molecular Diagnostics, Seibersdorf, Austria. ilhami.visne@arcs.ac.at

BMC Bioinformatics
|March 4, 2009
PubMed
Summary
This summary is machine-generated.

R GUI Generator (RGG) simplifies R programming by enabling easy creation of Graphical User Interfaces (GUIs) using XML tags. This tool makes R statistics accessible to users with limited programming skills.

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Area of Science:

  • Bioinformatics
  • Biostatistics
  • Scientific Computing

Background:

  • R is a leading open-source software for statistical analysis, widely used in biostatistics and bioinformatics.
  • Leveraging R's capabilities typically requires advanced scripting and programming expertise.
  • A gap exists between R's powerful analytical functions and users lacking extensive programming skills.

Purpose of the Study:

  • To develop a software tool, R GUI Generator (RGG), for simplifying the creation of Graphical User Interfaces (GUIs) for R.
  • To enable users with limited programming skills to utilize R's statistical and bioinformatics packages.
  • To bridge the gap between R developers and users who prefer GUI-based interactions.

Main Methods:

  • Developed R GUI Generator (RGG), an XML-based GUI definition language and Java-based GUI engine.
  • GUIs are generated at runtime by embedding XML tags within R scripts.
  • User input from the GUI is seamlessly integrated back into the R code, replacing the XML tags.

Main Results:

  • RGG enables the generation of GUIs for R by embedding simple Extensible Markup Language (XML) tags.
  • The tool abstracts GUI development from specific toolkits using an XML-based definition language, facilitating integration.
  • RGG is available as a standalone application (RGGRunner) and as a plug-in for JGR, with GUIs developed using any text editor.

Conclusions:

  • RGG provides a general framework for creating GUIs in R, making advanced statistical analyses accessible to a broader audience.
  • The framework abstracts GUI development, allowing easy integration into various software environments.
  • RGG is an open-source project (LGPL) with an accompanying web-based repository for shared GUIs, promoting collaborative development.