The History | R Programming Language

R is a programming language and environment for statistical computing and graphics. It was created by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand, in 1993. The name R is derived from the first letters of the names of the two creators, Ross and Robert.

Ihaka and Gentleman were inspired to create R by the statistical programming language S, which was developed by John Chambers and colleagues at Bell Labs. S was a powerful and versatile language, but it was not free and open-source. Ihaka and Gentleman wanted to create a language that was similar to S but that was free and open-source, so that it would be accessible to everyone.

R was first released in 1995. It quickly became popular among statisticians and data scientists. In 2000, the R Core Team was formed to oversee the development and maintenance of R. The R Core Team is a group of volunteers who are committed to making R a high-quality and user-friendly language.

R has become one of the most popular programming languages for statistical computing and graphics. It is used by a wide range of users, from students and researchers to data scientists and industry professionals. R is a powerful and versatile language that can be used for a variety of statistical tasks, including data analysis, data visualization, statistical modeling, and machine learning.

Here are some of the key events in the history of R:

1993: R's Origin

In 1993, Ross Ihaka and Robert Gentleman, both statisticians at the University of Auckland, New Zealand, embarked on the development of the R programming language. Their motivation was to create a tool that could address the evolving needs of statistical computing, providing an open-source alternative to commercial software like S-PLUS. Their vision was to create a platform that could cater to statistical analysis and data visualization, making these capabilities accessible to a broader community.

1995: First Release

In 1995, the first version of R was released, marking the beginning of its journey as an open-source programming language. This initial release laid the foundation for R's growth and popularity in the years to come.

2000: Formation of R Core Team

As R gained traction within the statistical and data science communities, the R Core Team was established in 2000. This group of dedicated individuals took on the responsibility of overseeing the development, maintenance, and enhancement of the R programming language. Their efforts ensured the continuous evolution of R and the introduction of new features, functions, and packages.

2008: RStudio IDE

The year 2008 witnessed a significant advancement with the release of the RStudio Integrated Development Environment (IDE). This user-friendly interface provided a more efficient and organized way for users to write, debug, and execute R code. RStudio's development marked a crucial moment in the accessibility and usability of R for data scientists, analysts, and researchers.

2010: Tidyverse Ecosystem

In 2010, the Tidyverse package ecosystem was introduced. Spearheaded by Hadley Wickham, Tidyverse aimed to simplify and streamline data manipulation and visualization in R. This ecosystem includes a collection of interconnected packages that adopt a consistent syntax and methodology, making data tasks more intuitive and efficient.

2020: R 4.0 Release

The year 2020 saw the release of R 4.0, a milestone version that introduced several key improvements and new features. This release marked enhancements in memory management, performance optimizations, and support for new data types. The increment in the version number also highlighted R's continued evolution and adaptability to the changing landscape of data science and analysis.


The journey of the R programming language has been characterized by visionary creators, community collaboration, and continuous innovation. From its inception at the University of Auckland to the establishment of the R Core Team, the introduction of user-friendly interfaces like RStudio, and the development of powerful ecosystems like Tidyverse, R has evolved to become a cornerstone tool in statistical computing and data analysis. Its journey is a testament to the power of open-source collaboration and the enduring pursuit of excellence in data science.