![]() ![]() The first official 1.0 version was released on 29 February 2000. R officially became a GNU project on 5 December 1997 when version 0.60 released. Mailing lists for the R project began on 1 April 1997 preceding the release of version 0.50. In June 1995, statistician Martin Mächler convinced Ihaka and Gentleman to make R free and open-source under the GNU General Public License. Ihaka and Gentleman first shared binaries of R on the data archive StatLib and the s-news mailing list in August 1993. The name of the language comes from being an S language successor and the shared first letter of the authors, Ross and Robert. The language took heavy inspiration from the S programming language with most S programs able to run unaltered in R as well as from Scheme's lexical scoping allowing for local variables. R was started by professors Ross Ihaka and Robert Gentleman as a programming language to teach introductory statistics at the University of Auckland. Multiple third-party graphical user interfaces are also available, such as RStudio, an integrated development environment, and Jupyter, a notebook interface. Precompiled executables are provided for various operating systems. It is written primarily in C, Fortran, and R itself (partially self-hosting). The official R software environment is an open-source free software environment released as part of the GNU Project and available under the GNU General Public License. As of April 2023, R ranks 16th in the TIOBE index, a measure of programming language popularity, in which the language peaked in 8th place in August 2020. The core R language is augmented by a large number of extension packages containing reusable code and documentation.Īccording to user surveys and studies of scholarly literature databases, R is one of the most commonly used programming languages in data mining. ![]() Created by statisticians Ross Ihaka and Robert Gentleman, R is used among data miners, bioinformaticians and statisticians for data analysis and developing statistical software. ![]() WHICH DO YOU PREFER? WHY? vote and answer below.R is a programming language for statistical computing and graphics supported by the R Core Team and the R Foundation for Statistical Computing. look ahead biasĬan not develop stand alone applications at all. Hard to detect biases in trading systems (it was built for math and engineering simulations) so extensive testing may be required. Well tested and supported due to it being a commercial productĮasy to manage multithreaded support and garbage collectionĬan not execute - must be translated into another languageĮxpensive ~1000 per license and 50+ per additional individual packageĬan not integrate well with other langauages Very short scripts considering high integration of all packagesīest visualization of plots and interactive charts Silent errors that can take a very long time to track down (even with visual debuggers / IDE)įastest mathematical and computational platform especially vectorized operations/ linear matrix algebraĬommercial level packages for all fields of mathematics and trading More code required for same operations vs R or Matlab Some packages are not compatible with others or contain overlap Immature packages especially trading packages Trading Packages(zipline, pybacktest, pyalgotrade)īest for general programming and application developmentĬan be a "glue" language to connect R, C++ and others (cython, Rpy etc)įastest general speed especially in iterative loops Open source packages( Pandas, Numpy, scipy) Limited capabilities in creating stand alone applications Worse plotting than matlab and difficult to implement interactive charts Slow vs Python especially in iterative loops and non vectorized funtions Mature quantitative trading packages( quantstrat, quantmod, performanceanalyitics, xts) Rapid development speed (60% less lines vs python, ~500% less than C) I will list a few pros and cons of each and then let the voting start.Įnd To End development to execution (some brokers packages allows execution, IB) All of them are high level languages with time series, linear / matrix computation, and trading system development packages. But for the strategy development, modeling, and prototyping I think these are the 3 major languages out there. C/C++ i did not include because they are almost always used at the institutional level for infrastructure or HFT/UHFT strategies anyway. There are 3 major players in this arena, not including c/c++. ![]()
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