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R Programming is a programming language that is used for carrying out statistical and data computing analysis which is developed and supported by the R software foundation.
R Programming Language is widely used among data analysts, miners and statisticians for developing statistical software and data analysis tools. Data mining surveys, polls, and studies of scholarly literature databases show a large increase in popularity.
R Programming Language is one of the most popular languages if not the most popular that is used by data scientists, statisticians, to get, tidy up, study, visualize and display data.
Due to the expressive syntax and easy-to-use interface of R, its popularity has grown in recent times. A GNU package, source code for the R software development environment is written basically in C, Fortran, and in R itself and is available for free under the GNU General Public License. Binary versions that are pre-compiled are made available for various software operating systems. Although R has a robust command-line interface, there are several graphical user interfaces, such as RStudio, which is an integrated development environment.
R Programming Language is basically an implementation of the S programming language joined together with lexical scoping semantics, that is inspired by Scheme. The S programming language was created by John Chambers in 1976 while working at Bell Labs. R was developed by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand, and is presently managed and developed by the R Development Core Team (of which Chambers is also a member). R was given its name, partly after the first names of the first two R authors and partly as a run on the name of S. The project idea was formed in 1992, with the release of its first version in 1995 and a stable beta version in 2000.
R Programming Language and all of its libraries were built with wide types of both statistical and graphical techniques and methods, including linear and nonlinear modeling, classical statistical tests, classification, clustering, among others. R is structured, modular, scalable and can be easily extended with the help of extensions and functions, and the R community is recognized for its very active contributions in the section of packages.
Most of R's default functions are written in R, this makes it quite easy for developers to know about the algorithmic choices that are made. For jobs that are computationally heavy, C, C++, and Fortran code can be linked together and ran at run time. More experienced users can write C, C++,.NET, Java, or Python code to play and manipulate objects in R directly. R is highly extensible by the use of user-developed packages for carrying out particular functions or in specific areas of study. Due to its origin from S, R has a much stronger object-oriented programming features than most statistical computing languages. Extending R is also made easy by its lexical scoping rules.
R is extensible by the means of functions and extensions, and the R community is noted and known for its regular contributions in terms of packages. Advanced users can write C, C++, Java, .NET or Python code to manipulate and use R objects directly. Due to its S heritage, R has stronger object-oriented programming facilities than most of these statistical computing languages. Extending R is also easy because of its lexical scoping rules.
R Programming is also used to generate publication-quality graphs, and also mathematical symbols. Dynamic and interactive graphics are available by means of additional packages.
R also has Rd, its own LaTeX-like documentation style, which is used to produce comprehensive documentation, both online in a number of styles and in hard copy.
There are many features of R Programming that makes it the most used development platform for large scale statistical computing, some of them are:
1. R is a fully-developed, simple and effective software programming language that includes conditionals, loops, user-defined recursive functions with input and output facilities.
2. R has a very effective facility for data handling and storage.
3. R provides a suite of operators for calculations on arrays, lists, vectors, and matrices.
4. R provides a large, coherent and integrated collection of tools for data analysis.
5. R provides graphical facilities for data analysis and display either directly at the computer or printing on papers.
6. R runs on all platforms: The R software that you are writing on one particular platform can easily be converted to another platform without any issues. Cross-platform compatibility is an essential feature to have in the computing world of this generation, even Microsoft is making its software cross-platform like the .NET platform which is currently available on all platforms after realizing the benefits of technology that runs on all systems.
There are many benefits and advantages of R and some of them are:
1. R is free and Open Source meaning that you don't have to pay to use.
2. R has exemplary support for Data Wrangling.
3. R comes with an array of Packages and extensions.
4. R allows you to Plot and Graph trends.
5. R allows you to carry out machine learning operations.
6. R is very popular and its popularity is still increasing.
7. Learning and Understanding R will triple your chances of getting a job since it's one of the most sort after languages for Data Analytics and science.
8. R is being used by the biggest giants of the tech world so it's going to be around for a very long time.
1. Data Science.
2. Statistical computing.
3. Machine Learning.
6. Health Care.
11. R is mainly used for descriptive statistics like central tendency, measurement of variability.
12. R is widely applied to do exploration data analysis.
13. R is also applied for analyzing both discrete and continuous probability distribution.
14. R is also used for building and developing statistical software packages and to carry out analytical processing in other software suites.
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