Vector Operations and Utilities

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.

Last updated: August 2026 · 53 examples · All platforms

Quick Start

  1. Select a topic from the sidebar
  2. Study the code and expected output
  3. Run with Rscript file.R or paste into RStudio
  4. Modify inputs to experiment and learn
Sum of Natural Numbers Using Recursion
Output — Sums 1..N recursively.
[1] 55
R Program to Add Two Vectors
Output — Adds two numeric vectors element-wise.
[1] 5 7 9
Find Sum, Mean and Product of Vector in R Programming
Output — Computes sum, mean, and product of a numeric vector.
Sum: 10
Mean: 2.5
Product: 24
R Program to Find Minimum and Maximum
Output — Uses min() and max() on a numeric vector.
Min: 1
Max: 8
R Program to Sort a Vector
Output — Sorts a numeric vector in ascending order.
[1] 1 3 5 8
R Program to Find the Sum of Natural Numbers
Output — Sums the first N natural numbers.
[1] 5050
R Program to Check if a Vector Contains the Given Element
Output — Tests membership using %in%.
[1] FALSE
R Program to Count the Number of Elements in a Vector
Output — Counts elements using length.
[1] 4
R Program to Find Index of an Element in a Vector
Output — Finds index using which.
[1] 2
R Program to Access Values in a Vector
Output — Accesses elements via indexing.
[1] 10 30
R Program to Find the Statistical Mode
Output — Computes mode via table and which.max.
[1] 3
R Program to Add Leading Zeros to Vector
Output — Formats integers with fixed width using sprintf.
[1] "005" "042" "300"

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