The good news is that you do not need a formal institution to learn R.

R is a programming language designed mainly for working with data and statistics. It allows users to analyse data, run statistical tests, build models, and create graphs. Unlike many software tools that only perform fixed tasks, R lets you write commands that tell the computer exactly what to do with your data. It is widely used in research and data-related work.

Researchers use it for statistical analysis, data cleaning, visualisation, modelling, and even machine learning. It is popular in fields such as statistics, public health, economics, ecology, and social sciences.

To understand how young researchers can grasp the core concepts of R, we interviewed two statisticians currently working as researchers at ICDDR,B.

Among them, Abdur Rahman Sumon explains, “R is useful for many kinds of analysis, including regression, time series, survival analysis, Bayesian analysis, and all analysis steps are saved as code, allowing us to reproduce results exactly.”

People learn R because it helps turn complex data into useful insights. According to Mostafiz, “R converts raw numbers into meaningful insights that support better decision-making.”

It is also efficient. Many tasks that would take hours using manual tools can be automated with code. Researchers can analyse large datasets quickly and produce clear graphs for reports or publications.

In real research work, R is used for many practical tasks. Because of its flexibility and large collection of packages, R can be adapted for many types of projects and research questions.

Learning R can open many career opportunities. Data analysis and data science are growing fields, and many organisations need people who can work with data.

Sumon notes, “Employers value candidates who can analyse and interpret data using R.” Mostafiz also points out, “For careers in data analysis, bioinformatics, statistics, and research, R is a highly valuable skill.”

So, want to learn R but do not have access to a formal course?

While prominent institutions such as Dhaka University offer courses to learn the R language, both statisticians emphasise that self-learning is possible.

Mostafiz says, “With consistent practice and dedication, anyone can successfully learn R without formal classroom training.”

According to Sumon, all you need is some prior theoretical knowledge, such as logic, mathematics, statistics, data structures, etc. He added, “As a student of statistical science, I know the statistical theory behind the topics that R functions often implement, such as means, variances, probabilities, regression models, hypothesis testing, distributions, understanding mathematical formulas, interpreting models, and performing matrix operations used in statistical modelling. So, knowing the theory helps interpret results.”

Here are some simple steps to start learning R on your own:

First, download R and RStudio to set up your working environment. Then start with beginner-friendly, text-based tutorial websites like GeeksforGeeks, which offer structured lessons from basic to advanced concepts. Begin with simple commands such as reading a dataset, calculating averages, or making basic plots. Mostafiz suggests starting small and experimenting with simple datasets.

Watch YouTube tutorials, read beginner guides, and follow online lessons. Additionally, reading good R programming books can strengthen your foundation. Books like R for Data Science by Hadley Wickham and Garrett Grolemund can also help beginners understand the basics. You can also purchase structured courses on Udemy or follow free R programming courses on YouTube.

Programming is learned through practice. Write your own code and try solving small data problems. Errors are part of learning. Sumon advises beginners not to ignore them because error messages usually indicate exactly what is wrong.

Practising with real datasets helps you understand how R is used in research and real-world analysis. As Mostafiz advised, “Find a small, interesting dataset and experiment with simple tasks such as printing text, reading files, calculating summary statistics, and creating basic plots. Hands-on practice with real data is the key to mastering R.”

Learning R may feel challenging at first, especially if you have never coded before. But with patience, curiosity, and regular practice, it becomes easier over time. Many professionals started the same way, learning step by step and improving through practice.

As Abdur Rahman Sumon and Mostafiz both highlight, dedication and consistent learning are the keys.

Let the coding begin.