Random Sampling and Standard Distributions

Generate random values from normal, uniform, and Poisson distributions; sample from populations. Useful for simulation and experimentation.

R Program to Sample from a Population

Program (R)
Example & Expected Output

Draws a random sample without replacement.

[1] 3 4 10 6 2

R Program to Generate Random Number from Standard Distributions

Program (R)
Example & Expected Output

Generates random values from Normal, Uniform, and Poisson distributions.

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

Program (R)
Example & Expected Output

Concatenates strings using paste0.

[1] "Hello World"

Frequently Asked Questions

Install R (from CRAN). Save the code in a file like main.R, then run Rscript main.R from your terminal. Alternatively, use an IDE such as RStudio.

No. All examples rely on base R functions to ensure compatibility across systems. Where packages are useful, they’ll be explicitly mentioned.

Yes. Copy the code, tweak inputs, and observe outputs. Experimentation is the fastest way to build intuition and mastery.

Yes. R is cross-platform. The examples run on Windows, macOS, and Linux with a standard R installation.

Learn R the Practical Way

R is a powerful language for data analysis, visualization, and statistical computing. Practicing small, focused programs builds fluency with vectors, data frames, lists, functions, control flow, and string manipulation.

On this page, you’ll find clean, well-formatted examples that run across platforms. Each example includes a short description and expected output so you can quickly verify your understanding. Explore the topics via the sidebar and extend the code to experiment further.

Whether you’re preparing for coursework, handling data tasks at work, or learning R for research, these examples offer a practical path to mastery. Bookmark the page and revisit to keep sharpening your skills.