The topic R is niche, but it’s the language every statistician actually uses is currently the subject of lively discussion — readers and analysts are keeping a close eye on developments.
This is taking place in a dynamic environment: companies’ decisions and competitors’ reactions can quickly change the picture.
While Python is my primary programming language for statistical analysis, I’m picking the other major language, R, even though it’s considered niche. Here are the reasons why.
One thing I love about Python is the large number of packages available. My favorite packages have to do with statistics, such as NumPy, SciPy, and statsmodels. Since R is so widely used in statistical research, there are lots of packages I can install.
Browsing through CRAN, or the Comprehensive R Archive Network, I can find thousands of packages to install. The “task views” also let me install packages through themes.
I can install packages for tasks ranging from agriculture (statistical research is used heavily there), to actuarial science (insurance runs on probability), to econometrics, to machine learning, and natural language processing.
One set of packages, which I’ll mention later, taking the R world by storm is the Tidyverse, offering advanced visualization and data wrangling tools.
The open-access Journal of Statistical Software has articles detailing new statistical packages. While there are more Python articles, the vast majority of them mention R packages.
While Python is popular in applied stats because it’s widely used overall, R is still probably the most widely used language for statistics in academia and industry.
If I ever went back to school to get deeper into statistics in some fashion, I would be well-positioned with basic R knowledge. Statistics, if you consider it just a field of math, has embraced technologies more than any other mathematical discipline. And its primary tool is R. Pick up advanced texts in statistics, and that’s the language you’ll find. Read the journal articles on statistical methods, and they’ll also have R code in them.
And R is not just an academic toy. Real organizations depend on it. The BBC uses it to create its infographics, and has published a manual that it also uses internally. Guess which programming language it uses.
As of April 2026, R was rated the 9th most popular programming language in the TIOBE index.
Even though R is a niche language compared to other languages, it’s an important one. Learning R will pay off in being able to follow the statistics community.
As I mentioned earlier, there are plenty of books and articles I can use to learn about R. As I mentioned earlier, the Journal of Statistical Software already has lots of articles covering t. The R Journal is an open-access, peer-reviewed journal covering R specifically.
If I wanted to look for textbooks covering R, I could find plenty. There are plenty of open-access texts covering aspects of R from basic introductory statistics to sophisticated advanced methods. OpenIntro’s Introduction to Modern Statistics is a popular approach that ditches the stuffy old formulas and tables in a traditional introduction to statistics class in favor of R and examining real data. R for Data Science is the standard introduction to the Tidyverse.
Knowing R will let me unlock more advanced statistics knowledge with code.
One thing that has made R continually popular is the advent of some sophisticated tools. The Tidyverse is a collection of libraries that take advantage of R’s extensibility.
ggplot is a graphics library that lets you make some sophisticated plots using a “grammar of graphics” to build up a plot out of basic elements. dplyr lets you wrangle data, tidyr “tidies” data into one column per variable.
While I’ve been skeptical of IDEs in the past, RStudio has been pleasant to work with. Working with data lends itself to interactive use, and something like RStudio seems like an ideal. I’m flexible enough as an amateur programmer to make exceptions to the way I work if there’s a good payoff. RStudio lets me make plots easily with R and ggplot, as well as examine data interactively. Combined with the TidyVers, it feels sophisticated.
The YouTube channel R Programming 101 has a brief screencast and tutorial:
It would probably be easier for someone without any programming experience to set up an environment like this than to learn WSL or the macOS terminal, which is what I expect most people interested in something like this to be using.
With Tidyverse and RStudio, you can explore data and save your work in R scripts. It’s similar to the rationale for using Jupyter Notebooks for stats with Python. You can easily examine data and try things out. You can save your work to come back to it later and have some idea of what you did. You can share your work with other people.
While other forms of engineering have moved to Windows, programmers and computer scientists have been the staunchest supporters of Unix and Linux. Like Linux, R is based on the ideas developed at Bell Labs. R is inspired by the S language developed at Bell Labs to implement legendary statistician John Tukey’s ideas about exploratory data analysis.
If Unix can be described as “by programmers, for programmers,” R’s appeal is that it’s “by statisticians, for statisticians.”
We live in a data-driven world. Data analysis helps us make sense of the world, and statistical programming makes it much easier. Since R is so widely used in academic and industrial statistics and data science, it makes sense to learn it, even if it’s less popular than languages like Python.
If you mainly just want a great laptop for everyday use with a Linux OS, this is the best option for most people.