R is a segmented programming language for statistics—and was built to handle enormous amounts of data. R can do all of these tasks really well—cleaning large datasets, estimating / modeling (statistics) and plotting (graphics / visualization) to produce high-quality outputs.

R is a language specifically developed for statistical computing. It is well worth learning as R was developed, enhanced, and grew in tandem with statisticians. Although R has impact in other fields such as artificial intelligence and machine learning and in areas like financial modeling, when you open R today you are most likely using it because it is a statistical programming language.

With a ceiling of R’s applications, consistently rated as one of the most popular programing language r around the world. One other thing is that R is associated with high-paying jobs in data analytics and for jobs in the research area. R was introduced for open-source use around 1995 and it has evolved and continues to evolve to fit our modern data-filled environment.

Python and R programming are still one of the most widely used programming languages in the world, especially in R programming for data science, academic settings and statistical analysis.

R was rated 17th in the TIOBE index, a well known ranking for programming languages, in October 2023. In August 2020, R peaked at the 8th position in the TIOBE index as it has had a strong presence in programming communities. The TIOBE index only considers 50 of the over 8000+ known programming languages, so R’s ranking is impressive for consideration and incorporation into ones work.

R briefly fell out of the top 20 in popularity in May 2020, but rebounded quickly. By July of the same year R was back in the top 10 due in part, to its use in COVID-19 research and reporting.

R is still the go to programming language among statisticians and researchers around the world. R is a language of choice among universities, research centres and many data driven organizations, as it provides robust analytical functions.

R has been recognized for its analytical flexibility to process many types of data and produce actionable decisions in the past 30 years for academic studies and real world-issues respectively. R has continued to evolve and the user community continues to grow across the globe.

R was developed in the early 1990s by statisticians Ross Ihaka and Robert Gentleman at the University of Auckland in New Zealand. Both professors were becoming aware of the need for a more effective software environment for the purposes of teaching and performing statistical analysis.

Motivated by this gap, they set about developing a new programming language (the S language developed at Bell Labs served as the initial inspiration), intended as an open-source development platform for statistical computing with flexibility and fluidity as guiding principles.

While development begun in the early 1990s, the first official release (version 1.0.0) was released in February 2000, providing a transition from a research project into a globally embraced open-source software.

The name R has two origins. First, it represents the first letters in the names of its (R’s) creators — Ross Ihaka and Robert Gentleman. The new name also evokes S, the programming language from which R was developed. The S programming language was developed by John Chambers,  at Bell Labs, and was for statistical computing.

R, S, and Scheme

To understand R’s design philosophy, it is helpful to put it in relation to S. R is an open-source implementation of S, with the same syntactic style and a focus on statistical computing. R also developed aspects of functional programming based on Scheme, a minimalistic dialect of Lisp. Hence, R’s semantics and style of programming incorporates S and Scheme and also its own distinct design choices.

The S programming language was created in the mid-1970s by John Chambers and colleagues at Bell Labs. The S programming language sought to create a more interactive and user-friendly environment for statistical computing that made it easier and more efficient to communicate the results of data analysis.

In a 2013 interview, Chambers mentioned that the “mission” of S was to give people “access to the best computational methods that existed at the time… no matter where they came from.” The idea was for the user to interact easily and simply with an interactive command line, transitioning over time to more advanced programming despite their level of proficiency.

The philosophy behind S parallels the philosophy behind R, where one wanted to create access to a computing environment focused on statistical analysis within an accessible, intuitive environment where anyone—even if they did not have a conventional programming background—could find useful capabilities.

While R and S each have means for scripting and programming to accomplish more complex statistical tasks, the two differ at a foundational level—both were created as licensed products, while R is a fully open-source application.

Again, an important distinction is that R does not exist in a vacuum, but is a dialect of S. R borrows a good deal of structure and ideas from S, yet added access and extensibility through the open-source process.

With meaning and structure in diverse domains such as linguistics and computer science, the terms syntax and semantics appear. Syntax describes the rules (i.e., “grammar” and “spelling”) that determine how code should be written. Semantics refers to the “meaning” of that code, or what it does in terms of instruction interpretation and execution.

R early on established a syntax very much like S and it made sense for R to follow in S-PLUS’s footsteps in this way, as familiarity with S made it easier for S-PLUS users to adopt R. Familiarity with the code structure made a huge contribution to R’s successful adoption in academic and research circles.

R (and the R community) have adopted syntax features and semantics which are more like Scheme–a “minimal” functional programming language–but R’s initial syntax features like S helped shape R to take shape in a useful and powerful way as a clear and accessible statistical tool.

R is classified as a high-level programming language because high-level languages intend to be as readable and convenient as possible, while attempting to abstract away from machine-level operations.  Low-level languages often require an intimate understanding of a machines architecture, memory access and management, where high-level languages such as R are made to be approachable by those without any intention of understanding how a computer ‘works’—in this case, the language is intended for statisticians, analysts and researchers.

While R is powerful, highly flexible and extraordinarily extensible, it is definitely more complex than languages made for beginner programmers–such as Python. However, R is not as difficult to learn to use as many may think. I find learning R easy, although it

is important to look at the big picture, the core concepts, and practice consistently. Familiarity with programming will also no-doubt, help you learn and use R.

Regardless, all programming languages can keep you in a steep learning curve when you first start learning them. The key is to learn the basics and build your skills over time.  If you have the right resources and plan, R could be a great addition to the tools you use for data.

R is an open-source product from the beginning, making it continually enterprising and the community continually growth-oriented.  This has generated an enormous increase in package offerings, with tens of thousands of packages are available and greatly increased the scope of R’s use in diverse industries and disciplines.

The language itself has evolved – with regular releases that enhance performance and capabilities – and the breadth of R’s applications have expanded significantly. R has moved from being a tool for academic statisticians, to being a core tool in data science, finance and healthcare, and beyond.

Before we can discuss sociocultural consequences of such a huge impact on the landscape of researchers and institutions, we should first review a few of the notable milestones in the development of R:

1991 – Ross Ihaka and Robert Gentleman start developing R as a dialect of the S language while at the University of Auckland, Department of Statistics.

1993 – R is made public, via the StatLib archive and the S-news mailing list.

1995 – Statistician Martin Mächler persuades the founders to licens R under the GNU General Public License, enabling R to be free and open-source, this was to be a historic decision, launching the global explosion of R.

The R community is a global, dynamic network of users, developers, and contributors who create, support, and grow the R (and IRL) ecosystem. This community is important for R to sustain itself, grow, and thrive, through individuals developing new packages, maintaining existing packages on CRAN (The Comprehensive R Archive Network), and/or contributing to any other aspect of programming (like programming in R) and also knowledge sharing.

R users are active. They maintain forum posts, develop blogs, respond to questions on Stack Overflow, and rant about other items on R mailing lists. Aside from these online forums for R users, they are also tangible at major conferences and meetups (like useR! and RStudio Conference) that bring together thousands of R professionals and users each year.

Because of this community dressed in collaboration, R continues to expand functionality. Today there are almost 20,000 packages on CRAN available for R users to use; that’s everything from visualizing data to calculating statistics, conducting analyses related to finance, genomics, and machine learning, and so on. The Tidyverse packages are good examples of packages that have become staples in the data science world.

If you are trying to solve a problem with R, there is definitely a package (or few) that can help you, and there is a community of users around the world to help you with your problem.

If you’ve explored R at all, you would have undoubtedly heard of the Tidyverse; it is one of the most significant ecosystems of the R programming world.

The Tidyverse is not a single package, but a collection of interrelated R packages designed specifically for data science and analytics. The packages within the Tidyverse focus on the specific parts of the data workflow: importing, cleaning and transforming, visualizing, and modeling the data, while all using similar syntax and behaving as if they were designed to work together.

The Tidyverse, created by Hadley Wickham, Chief Scientist at RStudio, and co-author of R for Data Science, has been described as an “opinionated collection of R packages” which systematically enforces the clean and standard way of working with data. Therefore, it has become a very popular collection of packages among data professionals everywhere.

In today’s data-driven world, it’s basically essential for anyone working with R to understand the Tidyverse, as it is considered the toolkit of data analysis for modern data analysis practice. If you are new to it, I suggest a simple introductory course (DataCamp’s “Introduction to the Tidyverse”) or perhaps take a full skill track on the fundamentals of the Tidyverse.

No discussion of how the R language has grown would be complete without acknowledging the corresponding rise of data science as a distinctive field of study in the digital world.

With digital systems replacing analog systems in the late 20th century, data quickly became one of the most valuable commodities. Data is now the driving force behind decision making in nearly every field, from business and healthcare to government and education.

For organizations to reap the competitive advantages and efficiencies of the digital age, they need to come to grips with the data created and used in their day-to-day operations. However, the conversion of raw data into useful insight requires not only access to data but, more importantly, accessibility to tools and to talent.

R is just one of those tools, along with technologies like, Python, SQL, Power BI, and Tableau. These technologies allow professionals – data analysts, data scientists, and data engineers – to make sense of the data, as well as to tell the data’s story.

As data continues to be a more important catalyst for innovation and strategy, the demand for professionals trained in data science has also grown enormously. Now, according to Indeed, data science is among the best highest-paying roles in technology, with an average salary of over $120,000 USD.

R’s growth is linked to this shift, as R provides the analytical capabilities to propel us forward in data-oriented world.

If you know R, you can work in a related area and pursue a successful career as:

• Data Scientist
•  Data Analyst
•  Statistical Engineer
•  R Programmer
•  Data Architect
•  Database Administrator
•  Geo-Statistician
•  Business Intelligence Analyst
•  Machine Learning Engineer
•  Quantitative Analyst
•  Financial Analyst
•  Academic Researcher
•  Statistician

These career titles are diverse and represent roles in education and industry analytics. The number of positions in these fields indicates how data-savvy employees have become valuable assets to organizations.

Industries that utilize R:

Because of R’s inherent capability for handling, analyzing, and visualizing data, it can be used across many industries. Below are a few examples:

Academia and Research:

R is the tool of choice at many universities and research institutes across the globe. In a manner akin to how English functions as a global communication language, R has become the language of statistical computed language used globally. The ability to conduct reproducible research, read in many data types and formats, and use it on virtually any operating system has made R a great asset to academia.

Academic fields such as social sciences, economics, and business – once dominated by tools such as IBM’s SPSS or SAS – are adopting R for its open-source and flexible nature.

• 1% of those using R in an educational setting were coming from economics or business studies.
• Only 10.5% of respondents used R in a computing fashion.

This continues to demonstrate R’s influence and permeation into the academic community.

Both R and Python are essential for data science practitioners because they allow manipulation of structured and unstructured data, and they provide a complete journey from importing and cleaning the data to creating models and training machine learning algorithms.

R is easy with its multitude of libraries through CRAN, so it is generally intuitive to create statistical models and machine learning algorithms. R is also very good at data visualization and allows you to make graphics that will look good in a presentation and makes sense and are easy to share.

For exploratory R programming for data analysis or predictive modeling, or even developing custom analytics, people around the world use R every day!

Statistics

R was a programming language developed by statisticians for statistical analysis, therefore it is still one of most reliable tools used by statisticians. R provides lots of statistical functions from the outset and will provide access to pile of more packages and many (many!) other specialized areas of statistics, such as epidemiology, econometrics, psychometrics, etc.

R can also take this an additional step, to allow developers low level access to build entirely new statistical tools. Joe Cheng from RStudio even mentioned that R can be treated as a general-purpose platform to implement new statistical programming languages!

Finance

R has really taken off in finance for any analytical tasks that have a high data intensity. Some of the major financial institutions such as ANZ and Bank of America have adopted R for:

• Credit risk assessment and modelling
• Financial reporting
• Portfolio management
• Forecasting and trend analysis

There are also R packages – jrvFinance and Rmetrics – providing a suite of functionalities, backed by the same principles as R. So while they all involve some coding, it is often practical for professionals with very little coding experience to use R for their financial calculations. There are also recognised educational platforms providing structured and conditional learning pathways, such as Finance Fundamentals in R, or Applied Finance in R to assist finance professionals moving beyond these practical skills.

Social Media & Tech

Social media channels have become major sources of data collection by tracking every click, view, and interaction to present both personalized content and advertising. The connected networks of companies that own social media platforms like Meta (Facebook and Instagram) and TikTok are heavily reliant on analyzing data sets to optimize their algorithms and advertising initiatives.

R is a great tool here for analyzing human development, segmenting audiences, developing data-based content recommendation models, as well as being able to manage large data sets and distilling clear and concise insights into the data, R has an important place in both the tech and digital marketing world.

R has been firmly established as among the strongest and most flexible tools for data analysis, statistical computing, and data science. R was developed primarily in academia and has developed through a dedicated participant community all over the world and is continually evolving with the needs of the contemporary world, regardless of whether it is in research, finance, healthcare, social media, or R programming machine learning.

What also makes R unique is that it uniquely separates itself with an inherently strong statistic foundation and visualization capabilities while also offering an open-source foundation that is supported by thousands of smart contributors. Whether you are a student starting or have experience as a researcher interested in performing reproducible research, or data professional who specializing in looking for discover transformative manipulation, R is equipped to do this.

Where the need for hiring data professionals to take data-driven decisions will only grow, R will continue relevant. R is not only founded in strong community support, but also has accessible grants and a growing eco-system of packages, and is increasingly and widely adopted in industry making R even more complimentary.

If you pride yourself on developing a career data analytics or developing your understanding of statistics to develop a skill set in R, learning R is not just a smart move, it is really essential to both!