Does this run real R in the browser?

Yes. It runs real R compiled to WebAssembly via webR (the r-wasm project) — not an emulation. Vectors, data frames, statistical functions and base graphics execute exactly as they would in a desktop R session.

Can I create plots and charts in R online?

Yes. Base R graphics — barplot(), hist(), plot() and friends — render as images directly in the output console, each plot appearing below the text output. No setup or device configuration needed.

Which R version and packages are available?

webR ships a recent R with the base packages (stats, graphics, utils, datasets). Additional CRAN packages can be installed at runtime from the webR binary repository; packages that need external system libraries may not be available in the browser.

Does the R editor work offline?

After the first run downloads the R runtime (about 30 MB), a service worker caches it on your machine. Later visits — and full offline use — run with no network connection.

Is my R code private?

Yes — 100%. R runs entirely in your browser through WebAssembly. No code or data is uploaded to a server, there is no sign-up, and all execution happens locally on your device.

R Online Compiler - Run R Code & Plots in Browser

📊 Free Online R Editor with Live Execution

Execute R Code Instantly for Statistical Computing & Data Analysis. Write and execute R code directly in your browser using webR. Full R language support with ggplot2, dplyr, data.table, and the complete Tidyverse. Perfect for statistical analysis, data visualization, and scientific computing.

🚀 Why Choose Our R Editor?

Powered by webR - full R language execution with graphics rendering and statistical packages. 100% free, no registration required, and complete privacy. Your code never touches our servers - everything runs locally in your browser.

Key Features

📊Statistical Computing

Full R language with hypothesis testing, regression, ANOVA, and advanced statistics.

🎨Data Visualization

Create stunning plots with ggplot2, base R, and other visualization packages. See graphics instantly.

🛠️Data Wrangling

Master dplyr, tidyr, and Tidyverse tools for data cleaning and transformation.

🚀Instant Execution

Run R code immediately. See results, plots, and output in real-time console.

Popular Use Cases

Statistical Analysis

• Descriptive statistics
• Hypothesis testing
• Regression analysis
• ANOVA studies
• Correlation analysis
• Time series analysis

Data Visualization

• ggplot2 plots
• Base R graphics
• Interactive dashboards
• Publication plots
• Exploratory plots
• Statistical graphics

Data Science

• Data wrangling
• Data cleaning
• Feature engineering
• Machine learning
• Predictive modeling
• Classification tasks

Learning R

• Practice R syntax
• Master data frames
• Learn dplyr
• ggplot2 essentials
• Statistical functions
• Programming concepts

Academic Work

• Statistics coursework
• Research analysis
• Thesis data analysis
• Lab assignments
• Homework exercises
• Research papers

Teaching & Demos

• Live teaching demos
• Student exercises
• Code examples
• Tutorial creation
• Workshop materials
• Course content

R Features Supported

Core R Language

✓ Variables & data types
✓ Functions & control flow
✓ Vectorized operations
✓ Data frames & lists
✓ S3 & S4 objects
✓ Formula interface
✓ Factor handling
✓ String operations

Data Manipulation

✓ dplyr verbs
✓ Tidyr reshaping
✓ Join operations
✓ Grouping & summarizing
✓ Regular expressions
✓ String parsing
✓ Dates & times
✓ Missing value handling

Statistics & Graphics

✓ Statistical tests
✓ Regression modeling
✓ ggplot2 layering
✓ Base graphics
✓ Color palettes
✓ Faceting
✓ Themes & styling
✓ Publication formatting

Related Code Editors

Python Editor →

NumPy/Pandas for data science

SQL Editor →

Database querying for data

JavaScript Editor →

Data visualization in browser

TypeScript Editor →

Type-safe web development

Go Editor →

Backend data pipelines

Universal Editor →

25+ programming languages

Frequently Asked Questions

How do I run R code online without installing R or RStudio?

Simply paste or write your R code in our editor and click "Run Code". The code executes directly in your browser using webR (R compiled to WebAssembly). No R installation, RStudio, or server needed. Output and plots appear instantly. Perfect for learning and quick statistical analysis.

Does this support ggplot2, dplyr, and the Tidyverse?

Yes! Our editor includes the core Tidyverse packages: ggplot2 for data visualization, dplyr for data manipulation, tidyr for reshaping, and more. You can create publication-quality graphics, perform data wrangling, and write modern R code all in your browser.

Can I create plots and visualizations in R?

Absolutely! Create plots using base R graphics, ggplot2, lattice, and other visualization packages. Plots render directly in the output panel as images. Perfect for exploratory data analysis, data visualization practice, and creating statistical graphics.

Is this suitable for statistical analysis and modeling?

Perfect! Perform statistical tests (t-test, ANOVA, correlation), build regression models (lm, glm), conduct hypothesis testing, and more. Use base R stats package, or specialized packages like lme4 for mixed models, or caret for machine learning workflows.

Can I use data frames and work with data analysis?

Yes! Create and manipulate data frames, use dplyr for data manipulation (filter, select, mutate, summarize), reshape data with tidyr, perform joins, and apply vectorized operations. Perfect for learning data analysis and mastering R's data manipulation tools.

Is this suitable for learning R for data science?

Excellent for learning! Practice R syntax, master data manipulation, create visualizations, perform statistical analysis, and build simple models. No setup overhead—just start coding. Ideal for beginners and experienced statisticians alike.

Can I use R packages and libraries?

Yes! webR includes base R and many popular packages like ggplot2, dplyr, tidyr, forcats, stringr, readr, and more. Some specialized packages may not be available yet. Use library() or require() to load installed packages in your code.

Does it work for statistics homework and coursework?

Perfect! Practice statistical concepts, complete assignments, verify calculations, create visualizations for reports, and test code before final submission. Commonly used in statistics courses, data science bootcamps, and academic research learning.

🎓 Pro Tips for R Development

Tip 1: Use dplyr's pipe %>% for readable data transformation chains
Tip 2: Master ggplot2 layering: ggplot() + geom_*() + theme() for plots
Tip 3: Use vectorized operations instead of loops for better performance
Tip 4: Always check your data with str(), head(), and summary() first
Tip 5: Use <- for assignment instead of = (R convention)
Tip 6: Leverage factors for categorical data and better plot legends
Tip 7: Practice tidyverse workflow: import → tidy → transform → visualize → model
Tip 8: Use RMarkdown concepts to document analyses alongside code

R Editor OnlineR EditorR

Free R Compiler with Live Execution & Plots — runs in your browser