Online R Compiler

Write and run R code directly in your browser with this free online compiler. R is the leading language for statistical computing, data analysis, and visualization. Used by data scientists, researchers, and analysts worldwide, R provides powerful tools for working with data. This compiler lets you execute R scripts instantly without installing R or RStudio.

What is R?

R is a language and environment for statistical computing and graphics, developed by Ross Ihaka and Robert Gentleman at the University of Auckland in 1993. It is a dialect of the S language and provides a wide variety of statistical and graphical techniques. R is highly extensible and has a large community of users who contribute packages through CRAN (Comprehensive R Archive Network). It is widely used in academia, data science, bioinformatics, and financial analysis.

Key Features

• Vectorized Operations: Operations work on entire vectors without explicit loops.
• Statistical Functions: Built-in functions for probability distributions, hypothesis testing, and regression.
• Rich Visualization: ggplot2 and base R graphics for publication-quality plots.
• CRAN Packages: Over 19,000 packages for every analytical task imaginable.
• Reproducible Research: R Markdown and Quarto for combining code, results, and narrative.

Basic R Syntax

# Variables
site <- "LabsICT"
year <- 2025

# Vectors and operations
languages <- c("R", "Python", "Julia")
for (lang in languages) {
  cat("Learn", lang, "at", site, "\n")
}

# Functions
greet <- function(user) {
  paste("Welcome,", user, "!")
}
cat(greet("Developer"), "\n")

# Data frame
developers <- data.frame(
  name = c("Bilal", "Chukwuemeka", "Fatima"),
  role = c("Founder", "Student", "Developer"),
  experience = c(5, 2, 3)
)
print(developers)

# Statistics
cat("Average experience:", mean(developers$experience), "years\n")

What Can You Build with R?

R is the go-to language for data analysis and statistics. Data scientists use R for exploratory data analysis, machine learning, and predictive modeling. Researchers use it for bioinformatics, epidemiology, and social science studies. Financial analysts use R for quantitative finance and risk modeling. R Markdown and Shiny enable building interactive dashboards and reports. The tidyverse ecosystem provides a consistent, powerful workflow for data wrangling and visualization.

Try It Yourself

Type your R code in the editor and click Run. The compiler supports R's vectorized operations, data frames, functions, and statistical functions. Use Copy to save, Reset to restore the example, and Share to generate a link.

Related Resources

Continue with our R learning track, try more online compilers, or browse tutorials for step-by-step guides.