R project for statistical computing
R software for statistical computing & graphics
R software for statistical computing & graphics
R provides a wide variety of statistical and graphical techniques, and is highly extensible. R is often the tool of choice for research in statistical methodology.
R is an integrated suite of software facilities for data manipulation, calculation and graphical display. It includes
* 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 on-screen or on hardcopy, and
* a well-developed, simple and effective programming language which includes conditionals, loops, user-defined recursive functions and input and output facilities.
The term “environment” is intended to characterize it as a fully planned and coherent system, rather than an incremental accretion of very specific and inflexible tools, as is frequently the case with other data analysis software.
R, like S, is designed around a true computer language, and it allows users to add additional functionality by defining new functions. Much of the system is itself written in the R dialect of S, which makes it easy for users to follow the algorithmic choices made. For computationally-intensive tasks, C, C++ and Fortran code can be linked and called at run time. Advanced users can write C code to manipulate R objects directly.
Many users think of R as a statistics system. We prefer to think of it of an environment within which statistical techniques are implemented. R can be extended (easily) via packages. There are about eight packages supplied with the R distribution and many more are available through the CRAN family of Internet sites covering a very wide range of modern statistics.
R has its own LaTeX-like documentation format, which is used to supply comprehensive documentation, both on-line in a number of formats and in hardcopy.
2023 Program
Successful Projects
Contributor
Dillon Murphy
Mentor
Heike Hofmann, Susan Vanderplas, Emily Robinson
Organization
R project for statistical computing
Interactive graphics with ‘You Draw It’
I plan to create an R package that extends and adds additional functionality to the "You Draw It" tool. To achieve my goal I will use an iterative...
Contributor
Yufan Fei
Mentor
Toby Dylan Hocking, Faizan Uddin Fahad Khan
Organization
R project for statistical computing
Enhance Selection, Viz & Automation
My objective is to enhance the Animint2 package through the introduction of new features, refining data visualization functions, reworking the code...
Contributor
Siddharth_Pathak
Mentor
Dootika Vats, James Flegal
Organization
R project for statistical computing
SimTools: Output Analysis for Monte Carlo
I propose completing the R package "SimTools" before it's ready for CRAN submissions. To equip the user with almost all types of output analysis of...
Contributor
Abhishek Ulayil
Mentor
Di Cook, Mitchell O'Hara-Wild
Organization
R project for statistical computing
Converting past R Journal articles to HTML using texor
During GSoC 22, I worked on the texor package which converts the Legacy(LaTeX) Rjournal articles to modern(Rmarkdown) format. This year the focus...
Contributor
Pranay Agrawal
Mentor
Neeraj Dhanraj Bokde, Andrés Elías Feijóo Lorenzo
Organization
R project for statistical computing
Updates in CleanTS Package
The proposed project, to modify the CleanTS package to handle multivariate time series data, will fill this gap and provide a new package for...
Contributor
ampurr
Mentor
Toby Dylan Hocking, Faizan Uddin Fahad Khan
Organization
R project for statistical computing
animint2 Documentation and Bug Fix Project
The animint2 Documentation and Bug Fix Project will make the animint2 reference documentation more accessible, fix errors in the documentation, and...