R Basics Examples
Key takeaway: Practice R with 53+ runnable code examples — vectors, strings, math, data frames, recursion, and sampling — with expected output to verify instantly.
R is a programming language for statistical computing and data analysis. These examples are for data science students, researchers, analysts, and anyone learning R for coursework or work projects.
Quick Start
- Select a topic from the sidebar
- Study the code and expected output
- Run with
Rscript file.Ror paste into RStudio - Modify inputs to experiment and learn
Related:All R Examples · Python Examples · Java Examples · C Examples
Check if a Number is Odd or Even in R Programming
Output — Uses modulo to determine parity.
odd
R Program to Check if a Number is Positive, Negative or Zero
Output — Compares sign and prints result.
zero
R Program to Find the Factors of a Number
Output — Lists all positive divisors of a number.
[1] 1 2 4 7 14 28
R Program to Make a Simple Calculator
Output — Uses switch to apply an operation.
[1] 13
R Program to Format Decimal Places
Output — Formats numbers with fixed decimals using sprintf.
[1] "3.14"
R Program to Concatenate Two Strings
Output — Concatenates strings using paste0.
[1] "Hello World"
Frequently Asked Questions
Use Rscript from your terminal. Install R from CRAN, save as
main.R, run Rscript main.R. Or use RStudio.No, base R only. All examples use built-in functions for maximum portability.
Yes, absolutely. Copy, tweak inputs, observe outputs — experimentation is the fastest way to learn.
Yes, fully cross-platform. R runs on Windows, macOS, and Linux. Works with R 3.6+.
Learn R the Practical Way
R ranks among the top 20 languages on the TIOBE Index, dominant in academia and data science.
Topics Covered
- Vectors, lists, and data frames
- String manipulation and text processing
- Math, statistics, and random sampling
- Control flow — if/else, for, while
- Recursion and custom functions
- File I/O and formatted output
Who Is This For?
- Data science students — coursework and R-based exams
- Researchers — statistical analysis for papers
- Analysts — data wrangling skills for work
- Career switchers — transitioning into data roles