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rstudio/cheatsheets

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Cheatsheets

This repository is the source for a curated collection of printable reference sheets for R and Python packages. It provides quick-reference guides organized by topic, available as both PDF files for offline printing and interactive HTML versions for online browsing. The collection also includes community-contributed translations of these reference sheets into multiple languages, expanding accessibility for non-English speakers.

The project is built around a reproducible rendering pipeline that generates both PDF and HTML formats from source documents, using a lockfile to guarantee identical build environments across different machines. It manages contributions through a Git-based workflow with automated checks for HTML accessibility standards, and organizes cheatsheets by language variants using a directory-based naming convention. The entire collection can be rendered as a static Quarto website with managed dependencies and per-project package isolation.

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Features

  • Printable Reference Sheets - Provides searchable printable PDF or HTML references for R and Python packages organized by topic.
  • Language-Specific Reference Sheets - Serves as the central curated collection of printable PDF and HTML reference sheets for R and Python packages.
  • Reference Cheatsheets - Offers interactive HTML versions of reference sheets for online browsing without downloading.
  • Community Translation Contributions - Provides community-contributed translations of reference sheets into multiple languages.
  • Multi-Format Document Rendering - Generates both PDF and HTML versions from source documents using a unified rendering pipeline.
  • Quarto-Powered Generations - Renders the cheatsheet collection as a static website using Quarto's document conversion pipeline.
  • Quarto-Powered Sites - Builds and deploys the cheatsheet collection as a Quarto website with reproducible rendering and dependency management.
  • Per-Project Library Isolations - Creates per-project package libraries to prevent dependency conflicts between projects.
  • Quick-Reference Guides - Provides printable quick-reference guides summarizing key functions for popular Python packages.
  • Build Reproducibility Lockfiles - Uses lockfiles to pin exact package versions for reproducible rendering across environments.
  • Exact Version Pinning - Snapshots exact package versions and sources into a lockfile for reproducible builds.
  • PDF Downloads - Provides printable PDF versions of each reference sheet for offline use and distribution.
  • Quick-Reference Guides - Provides printable quick-reference guides summarizing key functions for popular R packages.
  • Reference Sheets - Provides printable PDF or HTML reference sheets for R and Python packages organized by topic.
  • Contribution Workflows - Manages community contributions through pull requests with automated validation checks.
  • Project Isolation Managers - Creates private per-project libraries so packages installed for one project do not affect others.
  • Reproducible Build Sources - Provides source files and configuration to render the cheatsheet collection as a reproducible Quarto website.
  • Accessible HTML Contributions - Accepts community pull requests for accessible, text-based HTML cheatsheets with alt text and no images.
  • Community-Translated Documentation - Provides community-contributed translations of reference sheets in multiple languages.
  • Accessible HTML Contributions - Accepts community pull requests for accessible, text-based HTML cheatsheets with alt text and no images.
  • Directory-Based Locale Organizations - Organizes cheatsheets by language variants using directory-based naming conventions.
  • Deep Learning - Reference guides for deep learning architectures and model training.
  • Machine Learning - Cheat sheets for machine learning workflows and model implementation.
  • Data Science Cheat Sheets - Official collection of reference guides for data science tools.
  • Big Data - Reference guides for big data integration within R environments.
  • Data Visualization - Cheat sheets for building visualizations in R and Python.
  • General Data Science - General reference guides for data science tools and productivity.
  • R Programming - Official reference sheets for R packages and data science workflows.
6,360 stars·1,944 forks·TeX·CC-BY-4.0·25 views

Star history

Star history chart for rstudio/cheatsheetsStar history chart for rstudio/cheatsheets

How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.

Projects sharing features with Cheatsheets

These projects share indexed features with Cheatsheets. Shared tags can include platform or build tooling; verify the primary use case before treating a result as a replacement.
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  • dmlc/mxnetdmlc avatar

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    MXNet is a deep learning framework and distributed machine learning engine designed for training and deploying neural networks. It functions as a hardware-agnostic backend that allows for the development of deep learning models through a hybrid of symbolic and imperative programming. The system distinguishes itself through automatic distributed parallelism, which scales training workloads across multiple GPUs and machines. It features an extensible hardware backend interface that enables the integration of custom accelerators and proprietary libraries without modifying the core source code.

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    Caffe is a high-performance deep learning framework designed for training and deploying deep neural networks. It functions as a machine learning engine and a convolutional neural network library, providing a C++ backend to accelerate computations on both GPUs and CPUs. The system includes a specialized toolset for computer vision, enabling tasks such as object detection, semantic segmentation, and large-scale image retrieval. It supports the deployment of pre-trained models for image and scene recognition, as well as the ability to fine-tune neural network weights for specialized tasks. The

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Frequently asked questions

What does rstudio/cheatsheets do?

This repository is the source for a curated collection of printable reference sheets for R and Python packages. It provides quick-reference guides organized by topic, available as both PDF files for offline printing and interactive HTML versions for online browsing. The collection also includes community-contributed translations of these reference sheets into multiple languages, expanding accessibility for non-English speakers.

What are the main features of rstudio/cheatsheets?

The main features of rstudio/cheatsheets are: Printable Reference Sheets, Language-Specific Reference Sheets, Reference Cheatsheets, Community Translation Contributions, Multi-Format Document Rendering, Quarto-Powered Generations, Quarto-Powered Sites, Per-Project Library Isolations.

Which projects share features with rstudio/cheatsheets?

Projects with overlapping indexed features include: quarto-dev/quarto-cli — Quarto is an open-source scientific and technical publishing system built on Pandoc that converts Markdown and Jupyter… dmlc/mxnet — MXNet is a deep learning framework and distributed machine learning engine designed for training and deploying neural… fengwang/ceras — Ceras is yet another tiny deep learning engine, in pure c++ and header only. bvlc/caffe — Caffe is a high-performance deep learning framework designed for training and deploying deep neural networks. It… aria42/flare — Dynamic Tensor Graph library in Clojure (think PyTorch, DynNet, etc.). christoschristofidis/awesome-deep-learning — This project is a curated directory of resources, libraries, and frameworks designed to support the development,…