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Johns Hopkins University · on Coursera

R Programming

4.5(20,000) on Coursera·680K enrolled
Beginner 57 hours English Specialization
SkillsRRStudioData analysisFunctionsData structuresStatistical programming

Is this course right for you?

Our take
A solid, rigorous way to learn R for data work, part of Johns Hopkins' well-known Data Science specialization. It is free to audit.

Good for: learners heading into data science or statistics who want to learn R.

Skip if: you prefer Python, or you want a very gentle, hand-held pace.

It teaches R from the fundamentals — vectors, data frames, functions, control structures — with assignment-heavy practice on real datasets, covering both base R and the tidyverse. It expects you to write scripts, not just follow along, which is why the skills stick.

It is less hand-holding than some beginner courses and can feel steep, and it is R-specific, so if you prefer Python or want a gentle pace, start elsewhere. The certificate needs a paid Coursera subscription; the free audit covers the learning. R fundamentals remain relevant.

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About this course

This Johns Hopkins course is part of the Data Science Specialization and covers R from fundamentals: vectors, matrices, frames, lists, functions, and control structures. You work with real datasets throughout, applying R's functional programming model and learning both the tidyverse and base R approaches.

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Instructor

RP
Roger Peng / Jeff Leek
Coursera instructor
680K+ learners10 courses4.5 instructor rating

Taught by Roger Peng, Jeff Leek, and Brian Caffo from Johns Hopkins Bloomberg School of Public Health — pioneers of data science education on Coursera.

Frequently asked questions

For most people heading into data science, Python — it's used by the large majority of practitioners and dominates machine learning and production work. R remains excellent for statistics, research and visualisation. Learn R through this if your field leans statistical or academic; otherwise Python is the more practical first choice, though the data-science thinking transfers either way.

Yes, and for a specific, well-known reason: a difficulty spike. The assignments — especially in weeks 2 and 4 — jump well beyond what the lectures and the swirl exercises prepare you for, sometimes needing functions never shown in the videos. It's not that the lectures are hard; it's the gap to the assignments, so budget extra time and expect to search for help.

The videos are from 2015, so the interface and some tooling look dated, and it predates the modern tidyverse-first style of teaching R. But core R — vectors, functions, the apply family, data frames — hasn't changed, so what you learn still works day to day.

It's an early course in Johns Hopkins' ten-course Data Science Specialization. The specialization is respected but showing its age (2015-era), so many people now take individual courses like this one for the R grounding rather than the whole sequence. Take it alone for R; commit to the full specialization only if the structured path genuinely suits you.

Some general programming familiarity and comfort installing software helps, because you'll set up R and RStudio and work with real assignments early. It's pitched at people heading into data science rather than absolute beginners to computers, so a little prior coding smooths the steep early weeks considerably.
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