Statistical software · The R Foundation

R

Open-source programming language and environment for statistical computing and graphics, the default language of applied statistics education.

Visit R Alternatives

R is a free, open-source language built specifically for statistical modeling, data manipulation, and visualization, maintained by the R Foundation and distributed through CRAN. In analytics work it is used for custom regression and multivariate models, hypothesis testing, time-series analysis, and highly customizable visualization via packages like ggplot2, typically inside an IDE such as RStudio/Posit. Its CRAN ecosystem of over 20,000 packages covers nearly every statistical method in academic use, and R Markdown/Quarto support reproducible, publication-ready reports mixing code, output, and narrative. Compared with Python, R remains the stronger choice where the analysis is statistics-first (academic research, biostatistics, econometrics) rather than software-engineering-first. It runs locally or in hosted notebook environments offered by third parties such as Posit.

At a glance

Vendor The R Foundation
Pricing model Open source + paid options
Free tier Yes
Deployment Self-hosted
Open source Yes (GPL-2.0-or-later)
Best for Statisticians, academic researchers, and analytics engineers doing custom statistical modeling and visualization.

Pricing

Free and open source; no paid tiers for the language itself. Commercial support and hosted environments are sold separately by third parties (e.g. Posit).

Pricing has not been verified yet — see the vendor's site.

Features

  • Extensive statistical modeling functions (linear/generalized/mixed models)
  • CRAN package ecosystem with 20,000+ packages
  • ggplot2 grammar-of-graphics visualization
  • R Markdown/Quarto for reproducible reports
  • Vectorized data manipulation (base R and tidyverse)
  • Shiny for interactive statistical web apps
  • Interfaces to C/C++/Fortran for performance-critical code

Integrations

Profile last reviewed September 21, 2026

Head to head

Alternatives

Julia Open-source, high-performance programming language for numerical and scientific computing, used for statistical simulation at near-C speed. statsmodels Open-source Python library for classical statistical modeling, hypothesis testing, and econometrics alongside pandas and scikit-learn. SciPy Open-source Python library of numerical algorithms — optimization, integration, statistics, linear algebra — underlying the scientific Python stack.

R in the index now

Terms to know

Regression analysis A statistical method for estimating the relationship between one outcome variable and one or more predictor variables. Data visualization Representing data graphically so that patterns, trends and outliers are easier to see than in a table of numbers. Exploratory data analysis An open-ended first pass through a dataset to understand its structure and spot patterns before formal analysis. P-value The probability of observing data at least as extreme as the result, if the null hypothesis were actually true. Hypothesis testing A statistical framework for deciding whether observed data provides enough evidence to reject a default assumption. Time series forecasting Predicting future values of a metric using patterns found in its own historical sequence over time.

Related guides

How to choose statistical software The decisions that matter are code vs menus, general-purpose vs domain specialist, and whether your field already has a de facto standard you can't ignore.