As an active user of R for various psychometric projects, I am often amazed at the versatility and power of this open-source programming language. But have you ever wondered about the history and stories behind R, the main people involved in its creation, and its current standing and future potential? In this blog post, we’ll explore the rich and fascinating story of R.
The origins of R can be traced back to the early 1990s when two statisticians, Ross Ihaka and Robert Gentleman, both based at the University of Auckland, New Zealand, began working on a project to create a new programming language that could handle statistical computing and graphics. They were dissatisfied with the available options at the time, such as S and Matlab, which were expensive, closed-source, and had limitations in terms of customizability and extensibility.
With a vision of creating a free, open-source language that could empower statisticians and data scientists worldwide, Ihaka and Gentleman developed R, named after their first initials. They released the first version of R in 1995, and it quickly gained popularity among the academic and research communities, especially in the field of statistics.
Over the years, R has evolved and grown in popularity, thanks to its active community of developers, contributors, and users. Today, R is used not only in academia but also in various industries, such as finance, healthcare, marketing, and government. R is known for its versatility, flexibility, and robustness in handling large and complex datasets, as well as its vast array of packages and libraries for data manipulation, visualization, and machine learning.
One of the strengths of R is its community-driven development model, where users can contribute code, packages, and documentation to the R ecosystem. This has led to a diverse and vibrant community of developers and users worldwide, who collaborate and share knowledge and insights on R. The R community is known for its openness, inclusivity, and innovation, where ideas and feedback are welcomed and valued.
Looking to the future, R has a promising outlook, with continued growth and innovation. As more and more data is generated and analyzed, the demand for tools and languages for statistical computing and data analysis will only increase. R is well-positioned to meet this demand, with its adaptability and scalability, and its active community of developers and users.
As an active user of R myself, I am proud to be part of this community and to contribute to the growth of this powerful language. I invite you to join me and discover the stories and potential behind R, and to be part of this exciting journey of exploration and discovery.
Some relevant references:
- Ihaka, R., & Gentleman, R. (1996). R: A language for data analysis and graphics. Journal of Computational and Graphical Statistics, 5(3), 299-314.
- R Core Team. (2021). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.
- Wickham, H., & Grolemund, G. (2017). R for data science: Import, tidy, transform, visualize, and model data. O’Reilly Media, Inc.
- Chambers, J. M. (1998). Programming with data: A guide to the S language. Springer Science & Business Media.
- Peng, R. D. (2016). The R programming language in data science: emerging ecosystem and new challenges. In Big Data Analytics (pp. 1-17). Springer, Cham.
- Wickham, H. (2019). Welcome to the tidyverse. Journal of Open Source Software, 4(43), 1686.
- Horton, N. J., & Baumer, B. S. (2018). Learning R: A step-by-step function guide to data analysis. Sage Publications.
- Xie, Y. (2015). Dynamic documents with R and knitr. CRC Press.
- RStudio Team. (2021). RStudio: Integrated Development Environment for R. RStudio, PBC, Boston, MA URL http://www.rstudio.com/.
- Cook, D., & Swayne, D. F. (Eds.). (2007). Interactive and dynamic graphics for data analysis: With R and GGobi (Vol. 8). Springer Science & Business Media.
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