R is a software environment for statistical statistics, data manipulation, graphics description, and reporting, in addition to being a programming language.
R is an open-source programming language that is available for free under the GNU General Public License. R GNU Package is the name of the language.
R Programming is compatible with Linux, Windows, Mac, and nearly every other operating system/platform. It is also utilized in programming, online technologies, and cloud-based data analysis.
Robert Gentleman and Ross Ihaka created R Language in August 1993 at Auckland University in New Zealand. The R programming language is named after the first letters of these two people’s names. R Development Core Team is currently working on this language.
It can handle more data points and run numerous commands at once than Excel. It’s commonly utilized in data mining and statistics. R’s software environment is written in the C and FORTRAN programming languages.
What is R Programming?
In August 1993, at Auckland University in New Zealand, Robert Gentleman and Ross Ihaka created the R Language. R is a programming language and environment that is extensively used in statistical computing, data analytics, and scientific research. R is called from the initial letters of these two names, and it is presently being developed by the R Development Core Team.
The R programming language supports machine learning algorithms, linear regression, time series, and statistical inference, to mention a few. While most R libraries are written in R, C, C++, and Fortran programs are favored for heavy computational tasks. R programming is used by many significant firms, including Uber, Google, Airbnb, Facebook, and others.
R Programming has a 20-30 year history. R was created at the University of Auckland in New Zealand by Ross Lhaka and Robert Gentleman and is maintained by the R Development Core Team. The name of this programming language is derived from the names of both developers, and the initial project was proposed in 1992. The first version was launched in 1995, with a stable beta version following in 2000.
R Programming history
The lexical scoping semantics from Scheme are merged with the S programming language in R, an open-source implementation that enables the definition of objects in predefined blocks rather than across the whole program. S was developed at Bell Labs in the early ’70s by Rick Becker, John Chambers, Doug Dunn, Jean McRae, and Judy Schilling. The language, which was created for statistical analysis, is an interpreted language whose code may be run directly without a compiler. Many S-written programs function flawlessly in R. Gerald J. Sussman and Guy L. Steele Jr. developed the Lisp dialect known as Scheme at MIT circa 1975.
What is R used for?
- Statistical inference
- Data analysis
- Machine learning algorithm
R Programming Language’s Unique Features
- R is a vector-based programming language that can do many calculations.
- R is an interpreted language, which means that its code may be executed without the need for a compiler.
- It effectively connects many databases and extracts data from Microsoft Excel, Microsoft Access, MySQL, SQLite, Oracle, and other databases.
- It can accomplish a lot of things outside statistics, such as Cloud Data Analysis and Web Technology, for example.
R Programming Implementations
R, C, and Fortran are used to write the core R implementation. Several different implementations were made to improve performance or extensibility.
Radford M. Neal’s pqR (pretty quick R) implementation is similar, with enhanced memory management and support for automated multithreading. Renjin and FastR are Java implementations of R designed to run on a Java Virtual Machine.
C++ implementations of R include CXXR, rho, and Riposte. Renjin, Riposte, and pqR use many cores and postponed evaluation to boost performance. Most of these alternative implementations are experimental and unfinished, with few users compared to the official R Development Core Team implementation.
TIBCO, which formerly provided the commercial solution S-PLUS, created the TERR runtime engine, which is included in Spotfire.
Microsoft R Open (MRO) is a completely compatible R distribution with multi-threaded processing enhancements. Microsoft began to phase down MRO in favor of CRAN distribution on June 30, 2021.
R Programming Interfaces
R code may be edited or run using a variety of apps.
The command line console was chosen by early developers, who were followed by others who preferred an IDE. Rattle GUI, R Commander, RKWard, RStudio, and Tinn-R are R IDEs (in alphabetical order). R is also supported by Eclipse’s StatET plugin and Visual Studio’s R Tools for Visual Studio. RStudio is the most often utilized of them.
Emacs, Vim (Nvim-R plugin), Kate, LyX, Notepad++, Visual Studio Code, WinEdt, and Tinn-R are among the editors that support R. Jupyter Notebook may also be used to modify and execute R code.
Scripting languages such as Python, Perl, Ruby, F#, and Julia may access R capabilities. There are interfaces to other high-level programming languages, such as Java and.NET C#.
R vs. Other Technologies is a comparison
Data handling Capabilities – There is greater data handling capability and parallel processing choices as well.
Advancement in Tool– R Language contains the most up-to-date features for the most up-to-date technologies.
Graphical capabilities– The graphics capabilities of R are the most advanced.
Job Scenario– This is a more cost-effective choice for start-ups and businesses looking to integrate new technologies.
Ease of Learning– R Language is a low-level programming language with a simple learning curve.
Availability / Cost– R Language is a free and open-source programming language that may be used everywhere.
The R environment
R is an integrated set of tools for calculating, manipulating data, and displaying graphics. It contains:
- A collection of operators for doing calculations on arrays, especially matrices.
- A capability for managing and storing data effectively.
- Graphic tools for data processing and presentation, either digitally or physically.
- A sizable, well-organized, and integrated group of intermediate data analysis tools.
- A well-designed, straightforward, and efficient programming language that supports input and output as well as conditionals, loops, and user-defined recursive functions.
Instead of a gradual accumulation of extremely particular and rigid tools, as is commonly the case with other data analysis software, the word “environment” is meant to define it as a fully organized and integrated system.
Similar to S, R is built around a real computer language and enables users to extend its capabilities by creating new functions. Users can easily understand the algorithmic decisions since a large portion of the system is written in the R dialect of S. Fortran, C++, and C code can be linked together and invoked at run time for computationally heavy tasks. For direct object manipulation with R, advanced users can write C code.
In order to provide thorough documentation, both online in a variety of forms and in hardcopy, R has its own documentation format that is similar to LaTeX.
R users may connect, exchange ideas, and learn in local communities across the world.
Users may connect at an increasing number of R events, including conferences (such as useR!, WhyR?, conectaR, and SatRdays), meetings, and R-Ladies clubs that support gender diversity. The taskforce of the R Foundation prioritizes women and other underrepresented populations.