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Yuvijen · Hands-on, run-it-live companion

R Programming

Learn R from the ground up, understand why each idea works, and run real R and Python right in your browser as you read.

Start reading Browse the syllabus
4
Modules, full fundamentals
26
Topic-wise chapters
R + Py
Run code in the browser
Free
Open-source throughout

What’s inside every chapter

Concepts, the why

Each idea is built up from first principles in plain language, so you understand what R is doing and why.

Run it live

Editable R and Python code blocks run in the browser, so you can experiment as you read, no install needed.

Worked examples

Step-by-step examples for every operation, from vectors and lists to data frames and functions.

Data structures mastery

Vectors, lists, matrices, arrays, strings, data frames and factors, and the operations that bring them to life.

Real workflows with dplyr

Manipulate and reshape data the modern way, the same patterns analysts use every day.

Build working programs

Conditionals, loops and functions, the building blocks you need to solve real problems through code.

Browse the modules

The Four-Module Path
1Introduction to R ProgrammingInstall R & RStudio, syntax, variables, operators, data types, and the role of data structures 2Data Structures & Operations in RVectors, lists, matrices and arrays, creation, indexing and operations 3Descriptive Analytics with RStrings, data frames and factors, with modern manipulation using dplyr 4Conditional Statements, Loops & FunctionsIf/else and switch, for/while/repeat loops, and writing your own functions
Tools
Full SyllabusThe complete module-and-topic map of the book Live AnalysisRun and edit R & Python right in the page

Try it now

The same mean, computed in R and in Python, both runnable right here:

How to use this book

Read a chapter top to bottom the first time: each builds an idea from the ground up, shows it in a worked example, and lets you run the code live to see the result. Type into the editable blocks and re-run them, that is where the learning sticks. When you need a refresher, the Syllabus page is your map to every module and topic.

About the authors

Photo of Vijayakumar P

Vijayakumar P is an Educator & Data Analytics Professional with 8+ years in Analytics, AI and HR. UGC-JRF-NET in Management.

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Photo of Rani C

Rani C is an Educator & HR Business Intelligence Professional with 8+ years in HRM and HR Analytics. UGC-NET in Management.

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References

TipText Books
  • R for Data Science: Import, Tidy, Transform, Visualize, and Model Data. Wickham, H., & Grolemund, G. (2017). O’Reilly.
  • The Art of R Programming: A Tour of Statistical Software Design. Matloff, N. (2011). No Starch Press.
  • Advanced R. Wickham, H. (2019). (2nd ed.). Chapman and Hall/CRC.
  • R in Action: Data Analysis and Graphics with R. Kabacoff, R. I. (2015). (2nd ed.). Manning Publications.
  • Discovering Statistics Using R. Field, A., Miles, J., & Field, Z. (2012). SAGE Publications.
  • An Introduction to Statistical Learning: With Applications in R. James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). Springer.
  • Hands-On Programming with R. Grolemund, G. (2nd ed.). O’Reilly.

Reference Books

  • R Programming for Beginners. Arora, S., & Malik, L. Universities Press.
  • The R Book. Crawley, M. J. Wiley.
  • Learning R. Cotton, R. O’Reilly.
  • R Programming for Beginners. Rakshit, S. McGraw Hill Education.