R Programming Language

R is a programming language and software environment developed for statistical computing and graphics. R, created under the GNU Project, has a structure similar to the S language developed by John Chambers and colleagues at Bell Laboratories. It supports a wide range of statistical and graphical techniques including linear and nonlinear modeling, statistical tests, time series analysis, classification, and clustering. As open-source software, R integrates data processing, computation, and graphical visualization functions and can run on different operating systems.

History

The R programming language is derived from the S language developed at Bell Laboratories. The S language was designed as a proprietary statistical software. Due to its inability to adapt to new analytical methods, R was developed as an open-source system by Ross Ihaka and Robert Gentleman. R emerged as a free alternative to the commercial S-PLUS version and was released at no cost. Named after the first letters of its developers’ names, R was completed after six years of work in New Zealand.

R’s open-source structure has enabled researchers to access algorithms and modify them for their specific purposes. Its development is currently maintained by the R Core Team. The tradition of freely sharing software, prevalent in the 1960s, ended in the late 1960s when programs began to be sold separately; R revived this free software philosophy. Today, R is used in many international institutions.

Key Features of the Language

R is an open-source programming language designed for statistical analysis and graphical operations. It integrates data processing, computation, and visualization functions. It operates on Windows, Linux, macOS, and Unix operating systems. Users can extend the system by defining new functions and can interface seamlessly with C, C++, and Fortran.

R has a programming structure that includes conditions, loops, user-defined functions, and input-output operations. Users can modify functions, create new ones, and save them in libraries. Its open-source structure provides access to and adaptability of algorithms. Although R shares similarities with Basic, Fortran, and C, it has a more flexible syntax. The program includes numerous packages tailored for various analytical domains and can be executed via the command line or from saved files.

The R Environment

The R environment provides an integrated structure for data processing, computation, and graphical display. Data storage units, operators for manipulating arrays, analytical tools, and visual outputs coexist within this environment. Conditions, loops, user-defined functions, and input-output operations are all supported. Users can add new functionalities by defining their own functions.

The structure developed as an adaptation of the S language to R allows for the examination and modification of algorithms. Code written in C, C++, and Fortran can be called at runtime within the R environment. All R packages come with help files. This structure enables users to understand the system’s operation and apply functions correctly. The open-source nature of the R environment makes it continuously adaptable, and it runs on Mac, Windows, and Linux systems.

Basic Operators and Usage

The R language includes various operators for mathematical and logical operations. Addition is performed with “+”, subtraction with “-”, multiplication with “*”, division with “/”, exponentiation with “^” or “**”, and modulo with “%%”. Variable assignment is done using “<-” or “=”. Character data is enclosed in quotation marks, while numeric data is assigned directly. Data types can be identified using the class() function.

In R, one-dimensional data sets are defined as vectors and created using the c() function. Vectors can contain numeric, character, or logical data. Indexing is performed using square brackets “[]”. Functions such as sum(), mean(), var(), and sd() are used for basic calculations.

Data Structures

The fundamental data structures in R are vectors, matrices, lists, and data frames. Vectors are one-dimensional arrays consisting of elements of the same type. Matrices are two-dimensional numerical structures. Lists can hold data of different types together. Data frames are tabular structures composed of rows and columns and are used in statistical data analysis. These structures form the foundation of data storage, access, and analysis in R.

Basic Statistical Operations

The R language includes many functions commonly used in statistical analysis. Functions such as max(x), min(x), sum(x), mean(x), median(x), range(x), sd(x), var(x), mad(x), and cor(x,y) are used to calculate basic statistical properties of data. Additionally, functions like cumsum(), cumprod(), cummax(), and cummin() are used for cumulative operations.

Graphical Capabilities

The R programming language offers advanced graphical features for data visualization. Users can create histograms, scatter plots, box plots, and time series graphs. Axis labels, colors, and shapes in graphs can be customized by the user. The program enables the production of publication-quality graphics and can be extended through various packages.

Comparison with Other Statistical Packages

Compared to statistical software such as Minitab and SPSS, R is open-source, free, and extensible. Its strength as a full programming language, which allows users to create their own statistical packages, and its open-source structure are its primary advantages. R can import data from various programs, supports functional programming, and supports a wide range of statistical analyses. Comparisons with Minitab and SPSS show that R provides comprehensive functionality in areas such as graphical techniques, parametric and nonparametric tests, regression, and analysis of variance.

Applications

The R programming language is used in various fields including statistics, data science, economics, bioinformatics, and agriculture. Data analysis, modeling, statistical tests, and graphical presentations are carried out within the R environment. Its open-source nature allows researchers to develop and share their own analyses. R is a widely used statistical analysis tool in academic research and scientific studies.