R projects on GitHub

The R language for statistics and data analysis.

As of October 17, 2026, the most-starred R projects on GitHub are ML-For-Beginners (91.5K stars), awesome-machine-learning (74.6K stars) and spark (44.2K stars). GitHub.Locker tracks 14 R repositories with 409.7K stars combined. The fastest-growing on October 16 was open-science, with +182 stars in a day.

14Projects

409.7KStars in total

+264Stars in a day

Browse in the catalog →github.com/topics/r ↗

Hot right now

Most stars gained in a day · Oct 16

+182

open-science

aipoch/open-science · ★ 5.1K

AIPOCH Open-Science is an open-source, local-first, model-agnostic AI research workbench that runs scientific AI agents to help with literature review, data analysis, code execution, and producing traceable research reports, tables, and figures.

+34

ML-For-Beginners

microsoft/ML-For-Beginners · ★ 91.5K

A free 12-week, 26-lesson curriculum from Microsoft that teaches classic machine learning (not deep learning) using Scikit-learn, with each lesson tied to real-world data from different world cultures.

+12

Auto-Empirical-Research-Skills

brycewang-stanford/Auto-Empirical-Research-Skills · ★ 4.7K

Auto-Empirical Research Skills (AERS) is a curated library of 1,096 AI agent skills across 76 collections for doing empirical social-science research (economics, political science, sociology, psychology, etc.), maintained by Stanford REAP and CoPaper.AI.

+10

awesome-machine-learning

josephmisiti/awesome-machine-learning · ★ 74.6K

A curated, community-maintained list of machine learning frameworks, libraries, and software, organized by programming language.

The all-time top

14 projects · by stars

#1 · microsoft/ML-For-Beginners
ML-For-Beginners
A free 12-week, 26-lesson curriculum from Microsoft that teaches classic machine learning (not deep learning) using Scikit-learn, with each lesson tied to real-world data from different world cultures.
91.5K
#2 · josephmisiti/awesome-machine-learning
awesome-machine-learning
A curated, community-maintained list of machine learning frameworks, libraries, and software, organized by programming language.
74.6K
#3 · apache/spark
spark
Apache Spark is a unified analytics engine for large-scale data processing, offering high-level APIs in Scala, Java, Python, and R on top of an optimized execution engine.
44.2K

4
xgboost
28.9Kdmlc/xgboost · C++
XGBoost is an optimized distributed gradient boosting library designed to be highly efficient, flexible and portable — it implements machine learning algorithms under the Gradient Boosting framework.
5
machine-learning-for-software-engineers
28.9KZuzooVn/machine-learning-for-software-engineers
A free multi-month self-study curriculum that takes a working software engineer from mobile development to a machine learning engineer job, built as a GitHub checklist of articles, books, courses and competitions.
6
go
26.3Kdatasciencemasters/go
The Open Source Data Science Masters (OSDSM) is a free, self-guided curriculum that collects the best open university courses, books, and tutorials to prepare someone for an entry-level Data Scientist role.
7
mxnet
20.8Kapache/mxnet · C++
Apache MXNet is a deep learning framework designed for both efficiency and flexibility: it lets you mix symbolic and imperative programming, and a dynamic dependency scheduler automatically parallelizes operations on the fly.
8
prophet
20.4Kfacebook/prophet · Python
Prophet is an open-source forecasting procedure for time series: an additive model fits non-linear trends with yearly, weekly and daily seasonality plus holiday effects. It is aimed at series with strong seasonal effects and several seasons of history, and handles missing data, trend shifts and outliers well.
9
LightGBM
18.9Klightgbm-org/LightGBM · C++
LightGBM is a gradient boosting framework that uses tree-based learning algorithms, used for ranking, classification and many other machine learning tasks.
10
Machine-Learning-Tutorials
18.3Kujjwalkarn/Machine-Learning-Tutorials
A topic-wise curated list of machine learning and deep learning tutorials, articles and other resources, maintained as a single GitHub README.
11
ds-cheatsheets
16.4KFavioVazquez/ds-cheatsheets
A curated list of data science cheatsheets covering Python, R, math, big data, machine learning, deep learning, SQL, and data visualization, meant to be a quick-reference collection rather than a tutorial.
12
mal
10.7Kkanaka/mal · Assembly
Mal (Make a Lisp) is a Clojure-inspired Lisp interpreter built as a teaching tool: you implement the same interpreter in 11 incremental steps, and the project provides 95+ reference implementations across 89 languages to compare against.
13
open-science
5.1Kaipoch/open-science · TypeScript
AIPOCH Open-Science is an open-source, local-first, model-agnostic AI research workbench that runs scientific AI agents to help with literature review, data analysis, code execution, and producing traceable research reports, tables, and figures.
14
Auto-Empirical-Research-Skills
4.7Kbrycewang-stanford/Auto-Empirical-Research-Skills · Stata
Auto-Empirical Research Skills (AERS) is a curated library of 1,096 AI agent skills across 76 collections for doing empirical social-science research (economics, political science, sociology, psychology, etc.), maintained by Stanford REAP and CoPaper.AI.