ModernDive

The ModernDive organization on GitHub

Dive into statistics through data science

ModernDive is home to an open-source textbook, tidyverse-friendly R and Python packages, and ready-to-teach datasets — everything you need to learn or teach statistical inference by working with real data.

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ModernDive logo: a snorkel mask under a wave with a rising bar chart
800+GitHub stars
530+Forks
12Open repositories
2Languages: R & Python

The book

Statistical Inference via Data Science

A free, open-source textbook by Chester Ismay, Albert Y. Kim, and Arturo Valdivia that teaches statistical inference the way it is practiced: starting from real data, wrangling and visualizing it, then building up to sampling, confidence intervals, hypothesis testing, and regression.

ModernDive, Second Edition

A ModernDive into R and the Tidyverse

The current edition, fully revised and freely readable online. Built around the tidyverse, infer, and moderndive packages, with exercises and real datasets throughout — also available in print from CRC Press.

Read v2 online Source on GitHub

First edition

The original ModernDive, still online for courses that use it.

Read v1

Open development

The book is written in the open — file issues, suggest edits, or fork it.

Issue tracker

Software

Companion packages for R and Python

The same tidy, simulation-first approach to inference and regression, in whichever language your course runs.

RCRAN
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moderndive

Tidyverse-friendly introductory linear regression: get_regression_table(), get_regression_points(), wrapper functions designed for first courses, plus all datasets used in the book. Version 0.8.0 adds logistic regression support, correlations across several predictors at once, an interactive 3D regression plot, and a View() that works in Quarto and in the browser.

Documentation CRAN GitHub
PythonPyPI
moderndive Python package hex sticker: a python wearing a dive mask

moderndive for Python

The Python companion: a tidy simulation-based inference grammar modeled on infer, regression helpers, the book's datasets, and dual-engine plotting with plotly and plotnine.

Documentation PyPI GitHub

Data packages

Real datasets, ready to teach with

Tidy, documented R data packages in the spirit of nycflights13 — current enough that students recognize the world in them.

R dataCRAN

nycflights23

All outbound flights from New York City in 2023, with weather, planes, airports, and airline metadata.

Docs CRAN GitHub
R data

nycflights22

All outbound flights from New York City in 2022 — pair with 2023 to study year-over-year change.

Docs GitHub
R data

pnwflights22

Outbound flights from Seattle and Portland in 2022, for a Pacific Northwest take on the flights data.

Docs GitHub
R data

azflights24

Outbound commercial flights from Arizona's primary airports in 2024, plus supporting metadata.

Docs GitHub
R dataCRAN
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olympicAthletes

Every athlete-event in the modern Olympics, Athens 1896 through Milano-Cortina 2026 — about 315,000 rows.

Docs CRAN GitHub
R dataCRAN
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steves

A tidy snapshot of every Rick Steves' Europe episode from 2000 to 2025, for teaching data analysis with a passport.

Docs CRAN GitHub
R dataCRAN
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exoplanetdata

Confirmed exoplanets and their host stars from NASA's Exoplanet Archive, tidied for the classroom.

Docs CRAN GitHub
R dataCRAN
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volcanoes

Around 1,200 Holocene volcanoes with their eruptions and events, from the Smithsonian Global Volcanism Program.

Docs CRAN GitHub

For instructors

Teach a course with ModernDive

Labs, problem sets, and instructor resources built to pair with the book — used in intro statistics and data science courses worldwide.

Labs & course materials

Ready-to-adapt lab exercises and resources for running a ModernDive-based course, openly available.

Browse the labs GitHub

Instructor resources

Additional instructor-only materials for ModernDive v2 are available to verified instructors — sign up to request access.

Request instructor access