Introduction to R programming is the first step for anyone who wants to enter the field of data science, statistics, and data analysis. R is an open‑source programming language and software environment specifically designed for statistical computing, data handling, and data visualization. Unlike general‑purpose languages, R was created by statisticians for statisticians, which makes it extremely powerful for analytical work.
R provides a rich ecosystem of packages that allow users to clean data, perform exploratory data analysis, build statistical models, and create high‑quality visualizations. Because it is open source, thousands of contributors worldwide continuously improve R by developing new libraries and tools. This makes R highly flexible and future‑proof.
One of the main strengths of R programming is its ability to handle data efficiently. Whether the data comes from spreadsheets, databases, APIs, or big data systems, R can import, transform, and analyze it with ease. R also supports vectorized operations, which allow calculations on entire datasets without writing complex loops.
R is widely used in academia, research institutions, finance, healthcare, and technology companies. Universities across the world teach R as a primary language for statistics and data science. Researchers prefer R because it supports reproducible research through scripts, markdown documents, and notebooks.
Another major advantage of R programming is visualization. Packages like ggplot2 allow users to create professional‑quality charts that clearly communicate insights. These visualizations are often used in research papers, reports, and presentations.
In summary, R programming is a foundational skill for anyone working with data. It combines statistical power, visualization capabilities, and a strong community, making it one of the most important tools in modern data science.
History of R
It is always beneficial to know the history of things that we are learning about. So, let us begin with the history of R programming language.
• R evolved from the S programming language which was developed in 1976 at Bell Laboratories—formerly AT&T and now called Lucent Technologies. The S programming language, as the letter S indicates, was intended for statistical computations. The goal of the S programming language was to give the user an interactive experience of doing statistical computations and focus more on the interaction with data and relatively less on the programming aspects.
• R programming language was developed in 1993 as an advanced or extended version of the S programming language. You can also think of it as a modern implementation of the S programming language. It offers or supports the same statistical computations that the language ‘S’ supported.
• R also supports advanced graphics capabilities for producing high-quality graphical output and has packages that are particularly designed for data science applications. R itself is an open-source software designed and controlled by a core group called the R Core Group and people from the R Foundation.
• The source code for R was originally written in programming languages such as C and Fortran which allows R to be run on multiple hardware platforms ensuring superior performance. Another interesting aspect is that R also allows users to develop new packages by coding or programming directly in R itself. One of the popular graphics packages in R called ggplot2 was in fact developed using R itself. We can say that R is an integrated suite of software facilities for data handling, analysis, and visualisation.