R Program to Sample from a Population
Draws a random sample without replacement.
Program (R)
set.seed(1) pop <- 1:10 samp <- sample(pop, size = 5, replace = FALSE) print(samp)
Expected Output
[1] 3 4 10 6 2
Random Sampling and Standard Distributions
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: October 2026 · 53 examples · All platforms
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R Program to Generate Random Number from Standard Distributions
Generates random values from Normal, Uniform, and Poisson distributions.
Program (R)
set.seed(42)
cat("rnorm:", paste(round(rnorm(5), 3), collapse=", "), "\n")
cat("runif:", paste(round(runif(3), 3), collapse=", "), "\n")
cat("rpois:", paste(rpois(3, lambda=2), collapse=", "), "\n")Expected Output
rnorm: 1.371, -0.564, 0.363, -0.106, 1.513 runif: 0.914, 0.940, 0.288 rpois: 2, 1, 2
R Program to Concatenate Two Strings
Concatenates strings using paste0.
Program (R)
a <- "Hello" b <- "World" print(paste0(a, " ", b))
Expected Output
[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
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