What is R Programming?
R is a powerful programming language and software environment for statistical computing, data analysis, graphics, and reporting. R was created by Ross Ihaka and Robert Gentleman at the University of Auckland in 1993 and is developed by the R Development Core Team. Because it is free software released by the GNU project and inspired by the S language, it is often called GNU S. R is an interpreted, high-level, dynamic language that executes commands without compilation, making it flexible and easy for interactive data analysis.
It runs on Windows, OS X, and Linux, making it portable. R’s “workhorse” data structure, the vector, supports matrices, arrays, lists, and data frames, making it ideal for data-centric applications. Language uses functional and object-oriented programming ideas. Vectorization, which applies operations to vectors element-wise, makes code clearer, faster, and more compact. The language is well known for its advanced graphics tools that can build publication-quality visualizations.
Thousands of user-contributed packages on the Comprehensive R Archive Network (CRAN) make R expandable for many applications, including machine learning. Statisticians, data scientists, and academics use R for data retrieval, cleaning, complicated modeling, and presentation due to its features. The command line can be used, but most users prefer an integrated programming environment (IDE) like RStudio, which has a console, syntax-highlighting editor, charting, history, and debugging tools.
History of R
The S statistical language was created at AT&T Bell Laboratories and served as an inspiration for R. The two languages are essentially comparable. At AT&T, the renowned C language was also developed, and the moniker “S” was a reference to it. In addition to being a play on the name of its predecessor, S, R’s name pays homage to its founders, Ross Ihaka and Robert Gentleman. GNU S is another name for R because of its status and affiliation with the GNU Project. Available under the GNU General Public License, R has gained popularity over S or its for-profit variant, S-Plus, due to its free nature and the support of a sizable contributor community.
Core Nature of R
In contrast to compiled languages, R is interpreted. It’s a dynamic and intuitive environment for interactive data analysis because commands are executed immediately without a software. Its syntax is simpler than many others, although Python and Java programmers may find it strange.
R combines elements from several different programming paradigms:
Functional Programming: Functional programming, which avoids explicit iteration like for loops, underpins R programming. Vectorization, a type of “lightning-fast code” that applies operations and functions point-wise to each vector member, makes this possible. R links up vectors and conducts a set of actions on each pair of items using element-wise execution.
Object-Oriented Programming (OOP): As R is object-oriented, all data and functions are objects. S3, S4, and Reference Class class systems are supported, and generic functions enable polymorphism and encapsulation. Because of this, a single function call, like plot(), may behave differently depending on the object’s class, providing a consistent and adaptive user experience.
Key Features of R
R has gained popularity because of its strong feature set, which makes it perfect for tasks involving data:
Comprehensive Statistical Platform: An extensive, well-organized, and integrated set of tools for almost any kind of data analysis is offered by R’s comprehensive statistical platform. Many universities teach it, and it is a de facto standard among professional statisticians.
Extensibility through Packages: R’s extensive collection of user-written packages, which are accessible through the Comprehensive R Archive Network (CRAN), is one of its best features. R’s capabilities are expanded by these packages, which are frequently created by top statisticians and span everything from text analysis and image manipulation to specialized statistical techniques. R offers more than 9,100 packages that allow users to customize the language to suit their own requirements.
State-of-the-Art Graphics: R boasts well-known graphical tools that enable the production of intricate data representations of publication caliber. R offers creative ways to present complicated data sets, from simple plots to intricate, layered graphics with programs like ggplot2.
Effective Data Handling: With a collection of operators for mathematical operations on basic data structures such as vectors, lists, matrices, and data frames, the language provides efficient data management and storage capabilities. The workhorse of R is thought to be the vector.
Cross-Platform and Integration Capabilities: Portable R works with Linux, Mac OS X, and Windows. It boosts computing-intensive application performance with Python, C, C++, and FORTRAN.
Open-Source and Free: Good thing it’s free and lets anyone download. This produced a big, active, and supportive user community that grows it via forums and mailing lists.
The R Environment and RStudio
R is a programming language and software environment for statistical computation and graphics, but RStudio is a free,IDE that gives R a richer, easier-to-use interface. Since RStudio helps you write in R but does not install R, you must first install R on your PC. Windows, Mac OS, and Linux use the same four-pane RStudio interface. Run R commands by typing them into the console pane and pressing Enter.
In the editor pane, which includes syntax highlighting and permits the execution of single lines or complete scripts with “Run” buttons, most users write and change their code, creating a repeatable record of their work. One pane displays your workspace or environment, listing all R objects you’ve created and a history of your commands; the other has tabs for a file browser, a plot window, a package manager, and a comprehensive help system. With its terminal, editor, debugging, and workspace management capabilities, RStudio is important for most R users.