RGG: A general GUI Framework for R scripts
© Visne et al; licensee BioMed Central Ltd. 2009
Received: 01 July 2008
Accepted: 02 March 2009
Published: 02 March 2009
R is the leading open source statistics software with a vast number of biostatistical and bioinformatical analysis packages. To exploit the advantages of R, extensive scripting/programming skills are required.
We have developed a software tool called R GUI Generator (RGG) which enables the easy generation of Graphical User Interfaces (GUIs) for the programming language R by adding a few Extensible Markup Language (XML) – tags. RGG consists of an XML-based GUI definition language and a Java-based GUI engine. GUIs are generated in runtime from defined GUI tags that are embedded into the R script. User-GUI input is returned to the R code and replaces the XML-tags. RGG files can be developed using any text editor. The current version of RGG is available as a stand-alone software (RGGRunner) and as a plug-in for JGR.
RGG is a general GUI framework for R that has the potential to introduce R statistics (R packages, built-in functions and scripts) to users with limited programming skills and helps to bridge the gap between R developers and GUI-dependent users. RGG aims to abstract the GUI development from individual GUI toolkits by using an XML-based GUI definition language. Thus RGG can be easily integrated in any software. The RGG project further includes the development of a web-based repository for RGG-GUIs. RGG is an open source project licensed under the Lesser General Public License (LGPL) and can be downloaded freely at http://rgg.r-forge.r-project.org
R is an environment in which statistical techniques can be implemented . R is an open source project, licensed under the General Public License, and is used by a growing number of researchers in the field of bioinformatics/biostatistics. Many new statistical procedures are published with corresponding R scripts or packages. Over 1,600 packages can be obtained from the Comprehensive R Archive Network  and further 300 bioinformatics packages can be obtained from the website of the Bioconductor project . Notably, microarray analysis has been greatly improved by several packages like limma , affy , siggenes/samr , pamr , limmaGUI  and by software applications like BRB-ArrayTools  or by web applications like CARMAweb , MIDAW , and RACE  (all of these are using R and Bioconductor). R is based on an effective programming language that includes conditionals, loops, user-defined recursive functions as well as input and output facilities. The user frequently needs to change R code like the one for setting the working directory, reading an input file and changing parameters to new values, which usually requires profound knowledge of the R scripting language. For many applications, large parts of the code remain unchanged.
There are different approaches for the GUI-based usage of R: an option is the use of GUI toolkits such as Tcl/Tk , Gtk2 , Java Swing  and wxWidgets  employing the R packages tcltk , RGtk2 , rJava  and RwxWidgets . Every toolkit has special characteristics that require the programming of its own custom GUI. gWidgets  serve as an abstraction of the named R packages and offers a standard gateway. A GUI that is written using gWidgets is easily compatible with these toolkits and can thus be executed. All these packages including gWidgets use R to describe GUIs and require R to generate the GUI. Such GUIs depend on R and are less flexibly integrated in other softwares.
In this context, this project's aim is to develop a general GUI Framework for R. This should be independent from the GUI toolkits and integrating software and make R based statistical computing available to a wider audience less familiar with R programming.
The R GUI Generator (RGG) framework allows the R user to generate Graphical User Interfaces (GUIs) for R scripts. RGG uses a component-based approach and consists of two parts: an XML  (Extensible Markup Language)-based GUI definition language to use the existing components (GUI elements) and a GUI engine that is implemented in Java.
RGG – GUI Engine
Within the RGG framework, the GUI engine is the interpreter of an RGG file which generates the GUI in runtime. GUI engines are language-specific and contain the implementation of GUI elements. The current engine was implemented in Java using the Java Swing Library.
However, new engines using other programming languages including R based on the same RGG GUI definition language specification may further be implemented. Thus the RGG GUI definition language is independent from the GUI toolkit.
Java based RGG GUI engine
RGG – GUI definition Language
The RGG GUI definition language is based on XML and contains all basic GUI elements. RGG files are thus XML files. Rules applying to an XML file also apply to RGG files. To distinguish RGG files from other XML files, the root tag must be set to <rgg>. In between the two tags <rgg> and </rgg> the user can combine GUI elements and R code. There are common GUI elements such as <filechooser>, <slider>, <checkbox> or more complex and specific elements like a dataframe/matrix editor <matrix> or an importer of microarray data, the <maimporter>. Each GUI element consists of a given number of GUI widgets. Frequently, the basic elements like <textfield> (a label and a text field), <filechooser> (a label, a text field, and a button), consist of two or three GUI widgets. However, there are no limitations to the complexity of GUI elements. A file containing both R code and GUI elements is saved with the suffix .rgg. Such RGG files serve as templates for the GUI engine. The GUI engine loads the RGG file and dynamically creates and arranges GUI elements from the XML tags. Each GUI element converts the user settings (e.g. text in a <textfield>) into the corresponding R code; i.e., the XML tags in the RGG file are replaced by the corresponding R codes. In this way a new R script is generated which is then executed in R. Several XML tags leave the R script unchanged, but improve the GUI layout. These tags include the container elements <vbox> and <hbox>. The GUI components and elements can be arranged both horizontally and vertically, i.e. spread across several columns and rows of the GUI. By default, GUI components and elements are arranged vertically. To ensure horizontal arrangement of child elements, the container element <hbox> can be used, whereas the vertical arrangement can be restored using <vbox>. The <separator> tag introduces a horizontal line to separate groups of independent GUI elements, and the <gaprow> tag modifies the spatial separation of single GUI elements. Each GUI element has its attributes, which aids its fine-tuning. Some attributes are common in many GUI elements such as "var" and "label". The var attribute's value corresponds to the R variable name that the returned user input is assigned to. The label attribute defines the label of the element in the GUI. Each of the more than fifteen GUI elements and their definition by XML tags are described in detail on the project webpage http://rgg.r-forge.r-project.org and an HTML page (provided as Additional file 1) gives an overview of the GUI elements and their attributes. New GUI elements are continuously uploaded and documented on the website. The development of RGG was inspired by XUL, several components such as the <hbox> and <vbox> concept were directly adapted from the XUL. The reason why we could not use XUL directly is related to the choice of the programming language JAVA which should make it easier to integrate RGG in other JAVA based softwares (e.g. Taverna) .
JGR Plug-in & RGGRunner
The RGG project is going to provide a well-organized and documented web-repository for RGG files. In addition, the RGG framework will be integrated into the Taverna environment which enables the composition of bioinformatic workflows . RGG is an open source project under the Lesser General Public License. The source code of these elements and of the GUI engine is freely available via a public subversion repository (see project web-site). Downloads and installation instructions are available at the project homepage http://rgg.r-forge.r-project.org.
Results and discussion
We have developed a general GUI framework for R scripts for a broad range of R users: Developers with skills in XML and R can provide GUIs, whereas users of bioinformatics software (without programming skills) can run those GUIs with the software RGGRunner.
RGG consists of an XML based GUI definition language and a GUI engine that interprets the GUI language and generates a new R code. Describing GUIs with XML decouples RGG from any concrete GUI toolkit. Unlike R based toolkits such as gWidgets , XML simplifies the integration of RGG in software not developed in R. The current version of RGG is available as stand-alone software RGGRunner and as a plug-in for JGR.
The GUI element 'microarray importer' generalizes the import of microarray files by using a wizard system. The importer reads the raw data files and/or the experiment descriptor file and then automatically detects the microarray platform: If the raw data files are Affymetrix ('CEL') files, then the 'readAffy' function from the 'affy' package  is used and an 'AffyBatch'-object is generated. In the case of Agilent, GenePix, or other custom array data files, an 'RGlist' object is generated by the 'read.maimages' function of the 'limma' package . The user-interaction for the downstream GUI elements (an optional 'vsn' normalization procedure from the 'arrayQualityMetrics' package, an optional mapping of reporters, and the setting of parameters of the main function) is further interpreted. The resulting R commands can be checked in the log on the right window of the RGGRunner. All output from the quality control package 'arrayQualityMetrics' is saved to the selected project folder. This GUI can be extended by customized downstream analysis steps for the entire analysis of micorarray data, including subsequent pathway  and gene-ontology  analysis.
One of the biggest advantages of R is its large and active user community, providing a vast number of scripts, functions and packages. Through a common GUI definition language for R, the generation of R GUIs could evolve into a community effort. Each standard statistical function such as built-in functions (e.g. t-test, ANOVA) and packages (Bioconductor, CRAN, etc.) could be provided with standard GUIs. A unified concept of GUI generation would also facilitate the teaching of introductory as well as of advanced statistics lectures. In the course of the rapid methodological progress in life sciences, novel (bio)statistical approaches are permanently required. When provided on a GUI base, novel statistical procedures can be shared directly with other GUI-dependent users. The R package 'Sweave' documents R analysis by integrating R commands and output text, figures and tables into a pdf/LaTeX report  and thus could contribute to the transparency of research findings . If used together with RGG, pdf/LaTeX files could be produced much faster.
RGG is based on an intuitive GUI definition language which enables the description of GUIs declaratively, without extensive programming, directly by R users. RGG provides a GUI framework that is designed to be integrated with different R frontends and other softwares supporting R. Currently the RGG project is at the stage of a general framework but does not provide a collection of GUIs. At the moment, our focus is on writing RGG files for the standard functions of R and to extend the range of available GUI elements. However, RGG files for complex and specific statistical procedures can only be efficiently developed by the R community. Therefore, the community is invited to take advantage of the RGG framework and to submit biostatistical solutions not only as R packages/scripts but also with concomitant RGG files. Clearly, the addition of such RGG libraries, would significantly contribute to RGG's utility in many scientific studies.
We introduce a generic concept that allows R users to extend their statistical and bioinformatical procedures, which are relying on commands in the R prompt, with standard graphical user interfaces. The R script developer can use the RGG framework to make biostatistical and bioinformatical solutions directly available for routine applications. The current project will further include the development of a web repository for RGG files and integration to a workflow environment, enabling the combination of GUIs and connection to web services and databases. Such community driven collections of RGG-GUIs and analytical pipelines could help to bridge the gap between the R developer community and GUI-dependent users with limited R scripting skills.
Availability and requirements
Project name: RGG
Project home page: http://rgg.r-forge.r-project.org
Operating system(s): All major platforms, such as Linux, Windows and Mac OS X
Other requirements: R 2.8.x or newer, Java 5 or newer, JGR 1.6 or newer is recommended
Any restrictions to use by non-academics: LGPL
The project is funded by the Austrian Research Centers GmbH-ARC.
The authors declare that no conflicts of interest exist.
- R Development Core Team: R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing; 2005.Google Scholar
- The Comprehensive R Archive Network[http://cran.r-project.org/]
- Gentleman RC, Carey VJ, Bates DM, Bolstad B, Dettling M, Dudoit S, Ellis B, Gautier L, Ge Y, Gentry J, Hornik K, Hothorn T, Huber W, Iacus S, Irizarry R, Leisch F, Li C, Maechler M, Rossini AJ, Sawitzki G, Smith C, Smyth G, Tierney L, Yang JY, Zhang J: Bioconductor: open software development for computational biology and bioinformatics. Genome Biol 2004, 5: R80. 10.1186/gb-2004-5-10-r80PubMed CentralView ArticlePubMedGoogle Scholar
- Smyth GK: Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Stat Appl Genet Mol Biol 2004., 3:Google Scholar
- Gautier L, Cope L, Bolstad BM, Irizarry RA: affy – analysis of Affymetrix GeneChip data at the probe level. Bioinformatics 2004, 20: 307–315. 10.1093/bioinformatics/btg405View ArticlePubMedGoogle Scholar
- Tusher VG, Tibshirani R, Chu G: Significance analysis of microarrays applied to the ionizing radiation response. Proc Natl Acad Sci USA 2001, 98: 5116–5121. 10.1073/pnas.091062498PubMed CentralView ArticlePubMedGoogle Scholar
- Tibshirani R, Hastie T, Narasimhan B, Chu G: Diagnosis of multiple cancer types by shrunken centroids of gene expression. Proc Natl Acad Sci USA 2002, 99: 6567–6572. 10.1073/pnas.082099299PubMed CentralView ArticlePubMedGoogle Scholar
- Wettenhall JM, Smyth GK: limmaGUI: a graphical user interface for linear modeling of microarray data. Bioinformatics 2004, 20: 3705–3706. 10.1093/bioinformatics/bth449View ArticlePubMedGoogle Scholar
- Xu X, Zhao Y, Simon R: Gene Set Expression Comparison kit for BRB-ArrayTools. Bioinformatics 2008, 24: 137–139. 10.1093/bioinformatics/btm541View ArticlePubMedGoogle Scholar
- Rainer J, Sanchez-Cabo F, Stocker G, Sturn A, Trajanoski Z: CARMAweb: comprehensive R- and bioconductor-based web service for microarray data analysis. Nucleic Acids Res 2006, 34: W498-W503. 10.1093/nar/gkl038PubMed CentralView ArticlePubMedGoogle Scholar
- Romualdi C, Vitulo N, Del FM, Lanfranchi G: MIDAW: a web tool for statistical analysis of microarray data. Nucleic Acids Res 2005, 33: W644-W649. 10.1093/nar/gki497PubMed CentralView ArticlePubMedGoogle Scholar
- Psarros M, Heber S, Sick M, Thoppae G, Harshman K, Sick B: RACE: Remote Analysis Computation for gene Expression data. Nucleic Acids Res 2005, 33: W638-W643. 10.1093/nar/gki490PubMed CentralView ArticlePubMedGoogle Scholar
- Java Swing[http://java.sun.com/docs/books/tutorial/uiswing]
- Dalgaard P: The R-Tcl/Tk Interface. In Proceedings of the 2nd International Workshop on Distributed Statistical Computing: March 15–17 2001; Vienna Edited by: Hornik K, Leisch F.Google Scholar
- Fox J: Rcmdr: The R Commander: A Basic-Statistics Graphical User Interface to R. Journalof Statistical Software 2005, 14: 1–42.Google Scholar
- Qt Toolkit[http://trolltech.com/products]
- Extensible Markup Language[http://www.w3.org/XML/]
- XML User Interface Language[http://www.mozilla.org/projects/xul/]
- Helbig M, Theus M, Urbanek S: JGR: Java GUI for R. Statistical Computing and Graphics Newsletter 2005, 16: 9–12.Google Scholar
- Oinn T, Addis M, Ferris J, Marvin D, Senger M, Greenwood M, Carver T, Glover K, Pocock MR, Wipat A, Li P: Taverna: a tool for the composition and enactment of bioinformatics workflows. Bioinformatics 2004, 20: 3045–3054. 10.1093/bioinformatics/bth361View ArticlePubMedGoogle Scholar
- Kanehisa M, Goto S, Hattori M, Aoki-Kinoshita KF, Itoh M, Kawashima S, Katayama T, Araki M, Hirakawa M: From genomics to chemical genomics: new developments in KEGG. Nucleic Acids Res 2006, 34: D354-D357. 10.1093/nar/gkj102PubMed CentralView ArticlePubMedGoogle Scholar
- Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, Harris MA, Hill DP, Issel-Tarver L, Kasarskis A, Lewis S, Matese JC, Richardson JE, Ringwald M, Rubin GM, Sherlock G: Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet 2000, 25: 25–29. 10.1038/75556PubMed CentralView ArticlePubMedGoogle Scholar
- Leisch F: Sweave: dynamic generation of statistical reports using literate data analysis. In Proceedings in Computational Statistics. Edited by: Härdle W, Rönz B. Physica Verlag; 2002:575–580.Google Scholar
- Ioannidis JP, Polyzos NP, Trikalinos TA: Selective discussion and transparency in microarray research findings for cancer outcomes. Eur J Cancer 2007, 43: 1999–2010. 10.1016/j.ejca.2007.05.019View ArticlePubMedGoogle Scholar
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