is a free, open-source implementation of the S statistical computing language and programming environment. Nowadays, R is widely used for statistical software development, data analysis, and machine learning. Since R is free and open source, now there are mor than 2000 user-contributed packages available. This means that anyone can fix bugs and/or add features. R can be integrated with other languages (C/C++, Java, andvPython). R can also interact with many data sources: ODBC-compliant databases (Excel and Access) and other statistical packages (SAS, Stata, SPSS, and Minitab). For the High Performance Computing Task, several R packages provide the advantage of multiple cores, either on a single machine or across a network.
Despite the aforementioned capabilities, R is a command line interface (CLI) where users type commands to perform a statistical analysis. The CLI is the preferred user interface for power users because it allows the direct control on calculations and it is flexible. However, this command-driven system requires good knowledge of the language and makes it difficult for beginners or less frequent users. To incorporate this limitation, several R projects were develop to produce user interfaces.
My talk presented in R Workshop Hasselt University Belgium and a seminar in Medical Epidemiology and Biostatistics department of Karolinska Institutet will provide a review of some R GUI projects and illustrate how to develop a simple R GUI using tcltk and some future development using R service bus and shiny. More detail of R GUI and some example of RGUIs can be found in my PhD thesis.
Some example of RGUIs: