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Programming Language R: Empowering Open Source Data Science & Statistics

ViOffice is a proud part of the Free Software movement. We rely exclusively on Free, Open Source Software (FOSS) and regularly educate about the benefits of FOSS in our blog. Personally, we, the founders of ViOffice, have a university background in statistics and data science/econometrics and still use the (statistical) programming language R almost daily. Therefore, we thought it’s time to take R as well as statistics out of the nerdy niche and write a blog article about it.

What is the Programming Language R?

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In the world of data science and statistics, R stands as a testament to the power of Open Source collaboration. Born out of a need for flexible and robust statistical computing, R has evolved into a versatile programming language and environment that empowers researchers, analysts, and practitioners to uncover insights from complex datasets. In the following, we explain the development journey of R, its pivotal role in Open Source innovation, and its profound impact on the field of data science and statistics.

The Birth of R and Open Source

R traces its origins back to the early 1990s when two statisticians, Ross Ihaka and Robert Gentleman, began developing it at the University of Auckland, New Zealand. Their intention was to create a tool that would democratize statistical analysis, making it accessible to a wider audience. The result was R, an open source project that was released in 1995 under the GNU General Public License. This licensing choice not only ensured the software’s free availability but also encouraged a collaborative community of developers and users to contribute to its growth.

The Essence of Open Source Collaboration

Central to R’s evolution is the collaborative nature of the open source community that developed around it (often called the R Community). This community-driven approach fostered a spirit of knowledge sharing, resulting in a wide range of packages, libraries, and extensions being developed to address diverse analytical needs. These contributions not only expanded the functionalities of R but also made it adaptable to various domains beyond traditional statistics, including machine learning, data visualization, bioinformatics, and more.

R’s Flexibility and Versatility

One of R’s defining features is its flexibility. Unlike proprietary software, R’s open source nature allowed researchers to modify and extend its core functionalities according to their requirements. This adaptability paved the way for the creation of specialized packages that catered to specific niches within data science and statistics. The Comprehensive R Archive Network (CRAN) became a central repository for these packages, enabling users to easily access and incorporate the tools they needed for their projects.

The Rise of R in Data Science

As the field of data science gained prominence, R emerged as a powerful tool due to its focus on data manipulation, exploration, and visualization. Its data-centric approach made it particularly appealing for analysts and data scientists, as they could seamlessly transition from data preprocessing to model building and evaluation within a single environment. Packages like “dplyr“, “ggplot2” and “tidyr” became instrumental in reshaping how data analysis and visualization were approached.

Today, R continues to play a pivotal role in the data science ecosystem. Its integration with popular programming languages like Python and its interoperability with big data frameworks such as Apache Hadoop and Spark have further extended its reach. As organizations recognize the value of data-driven decision-making, R’s presence has grown in industries spanning finance, healthcare, e-commerce, and more.

R Shiny: Bridging Data Science & Web Development

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While R initially gained prominence in the realm of statistical computing and data analysis, its influence has extended far beyond the confines of research and analysis. One remarkable manifestation of this expansion is the development of R Shiny—a powerful web application framework that seamlessly blends the capabilities of R with the world of web development.

R Shiny, introduced by the Posit team, empowers data scientists and analysts to create interactive web applications directly from R scripts. This innovation bridges the gap between data science and web development, allowing professionals to share their insights, models, and visualizations with a broader audience in a dynamic and user-friendly manner.

At its core, R Shiny leverages the versatility of R to process data, perform calculations, and generate visualizations. What sets it apart is its ability to transform these analytical components into interactive dashboards and web applications without requiring extensive knowledge of traditional web development languages like HTML, CSS, and JavaScript.


At a time when an estimated 330 million terabytes of data are created every day, the benefits of tools like R cannot be underestimated. In almost all areas of life and work today, knowledge of R or similar programming languages is helpful and increasingly required.

So, we can definitely call ourselves R enthusiasts. We love the Open Source spirit behind R, the extremely large and helpful R Community on the internet and, last but not least, the functionality that goes beyond classical statistics, for example with R Shiny. In the future we will certainly publish more articles related to the fantastic programming language R.

A Portrait Picture of Pascal Langer.
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Pascal founded ViOffice together with Jan in the fall of 2020. He mainly takes care of marketing, finance and sales. After his degrees in political science, economics and applied statistics, he continues to work in scientific research. With ViOffice, he wants to provide access to secure software from Europe for everyone and especially support non-profit associations in their digitalization.