Why Learning R is a Good Career Move in 2026

Over the course of my career as a Data Scientist, I’ve worked on projects ranging from simple code reviews, to large application builds. For the most part, I have used R to do this.

If you’re getting into coding or data science, one question you’re probably asking yourself is “Which language should I learn?”

This blog aims to show you why R might be a good decision.

R was built for data (not just programming)

Unlike general purpose languages (such as Python), R was designed specifically for statistics and data analysis.

That means:

• Built in statistical tools
• Powerful visualisation capabilities
• Research level methods available immediately

With packages like the tidyverse, you can clean, analyse, and visualise data with surprisingly little code.

High demand in analytics, research, and healthcare

R is especially popular in many sectors such as:

• Healthcare & biostats
• Academic research
• Government departments
• Finance & risk modeling
• Pharmaceutical companies

Here are some examples of R in production use:

• The {bbplot} R package. Yes, the BBC use R to create graphics for their website!
• Health and wellbeing profiling app for the NHS
• During the Covid-19 pandemic, the financial times had a stats tracker in which the graphs were built with R.

Knowing some R will give you a competitive edge if you’re looking at working within these sectors.

Open source with the backing of Posit

R is open source. This means that:

• It’s free, and always will be!
• Anyone can view the source code the makes up R, there are.
• Each R package (a folder containing code) has to live on GitHub.com, for everyone to see.
• It has a large community of contributors. There are great forums to get help such as Stack Overflow, Posit Community and the R weekly newsletter and tonnes more.
• There are thousands more available functionalities compared to paid softwares such as SPSS, SAS or Excel.

Posit, who maintain the free to use RStudio and Positron IDEs (integrated development environment), have many full time staff working solely on maintaining and creating new functionality within R. This means we get:

• Defined accountability
• Predictable release cycles
• Bugs can be solved quicker

Incredible data visualisation possibilities

Being able to communicate your findings with stakeholders is very important in data science, and one of R’s biggest strengths is visualisation and reporting.

With the {ggplot2} package, you can create publication ready charts with very little code. The R Graph Gallery has some amazing examples of what is possible with {ggplot2}.

With the {quarto} and {shiny} packages, you are able to build reproducible reports and interactive dashboards. All this without needing to know any HTML, CSS or JavaScript.

Beginner friendly learning curve

This is very much my own opinion. Compared to other languages, I think R is fairly intuitive and feels rewarding much earlier on in the journey. It also has (in my opinion), the most beginner friendly programme to code in, called RStudio.

Most people attend only two days worth of training with Jumping Rivers, and say they feel ready to start tackling their own data problems.

So… is R worth learning in 2026?

I think so. If you want pure software engineering or large-scale production systems, you may need Python. But for becoming a strong data thinker, and giving you an edge in your analysis, R is one of the best starting points.