How do I run these R programs?

Install R from CRAN, save as main.R, run Rscript main.R from terminal.

Do these examples need external packages?

No. All examples use base R for maximum portability.

Can I copy and modify these examples?

Yes. Copy, tweak inputs, and observe outputs to learn.

Do these work on all platforms?

Yes. R runs on Windows, macOS, and Linux with R 3.6+.

File I/O Utilities in R

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 Write to a File

Writes lines to a text file and confirms existence.

Program (R)

writeLines(c("line1","line2"), "out.txt")
print(file.exists("out.txt"))

Expected Output

[1] TRUE

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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