rstudio.github.io

What is rstudio.github.io?

rstudio.github.io is GitHub Pages hosting for projects maintained by Posit (formerly RStudio). It is not a single product site; it is a collection of documentation and package websites published under one GitHub organization domain.

What you'll find there

• Documentation sites for R packages and tools developed by Posit, such as tidyverse packages, Shiny, and Quarto-related projects.
• Package reference pages, vignettes, and getting-started guides.
• Source links back to the corresponding GitHub repositories.

Who it's for

• R users looking up function reference or tutorials for a specific package.
• Developers checking version notes, changelogs, or contribution guidelines.
• Instructors and analysts who need stable, citable documentation for teaching or reports.

How to use it

Because the domain hosts many separate sites, the practical step is to navigate to the specific project rather than browse the root. If you know the package name, the pattern is usually rstudio.github.io/<package-name>/. For example, a Shiny user would go to the Shiny documentation site, while a tidyverse user would look for the relevant package page.

For the broadest entry points, see Posit for company-level resources and CRAN for package downloads and canonical reference material. If you are unsure which package you need, searching CRAN first and then following the link to the project's documentation is often the fastest route.

How do I install packages from rstudio.github.io?

You don't install packages from rstudio.github.io directly — it's a documentation hub for RStudio-related projects. Installation happens in R, using the package name and the repository where it lives.

Common installation routes

• CRAN (standard): install.packages("packagename") — works for most packages you'll find documented on rstudio.github.io.
• GitHub (development versions): remotes::install_github("rstudio/packagename") or devtools::install_github("rstudio/packagename"). This pulls the source repository, not the documentation site.
• RStudio IDE: Tools → Install Packages, or the Packages pane's Install button, if you prefer a GUI.

How to decide

Practical next step

Find the package's page on rstudio.github.io, check its README for the installation line — many projects list both the CRAN and GitHub commands. If it's on CRAN, prefer that version for reproducibility; reach for GitHub only when you specifically need unreleased changes.

For RStudio IDE itself, download from Posit rather than the GitHub docs site.

What is the difference between rstudio.github.io and CRAN?

rstudio.github.io is the GitHub Pages home for RStudio's open-source project documentation, while CRAN (the Comprehensive R Archive Network) is the official repository for published R packages. In practice, rstudio.github.io hosts documentation sites for individual packages and tools—things like package reference pages, vignettes, and project guides—whereas CRAN is where you download and install the actual released package code.

What each one is for

• rstudio.github.io: A documentation hub. You go here to read how a package works, browse function references, and follow tutorials. It is a website, not an installation source.
• CRAN: A package distribution network. You go here to install packages, check version histories, and review package policies and checks. It is the canonical source for stable, peer-reviewed R packages.

How they relate

Many packages documented on rstudio.github.io are also published on CRAN. The GitHub Pages site typically reflects the development or current documentation, while CRAN hosts the released version you install. A package might appear on rstudio.github.io before or independently of a CRAN release, especially for in-development tools.

A practical example

Suppose you want to use a package for data manipulation. You would install it from CRAN with install.packages("packagename"), then visit its rstudio.github.io documentation to learn the functions and see examples. If the documentation shows features not yet on CRAN, that signals a development version you would install from GitHub instead.

Decision criterion

Use CRAN when you need a stable, installable package. Use rstudio.github.io when you need to understand how a package works or find its latest documentation. For official R resources, see CRAN and The R Project.

How do I deploy a Shiny app from rstudio.github.io?

Shiny apps are deployed by pushing your app code to a hosting service, not by publishing the rstudio.github.io documentation site itself. The rstudio.github.io domain is where the Shiny documentation lives, so you use it to learn the deployment workflow, then run that workflow against your own app.

The standard deployment paths

Typical workflow

1. Build and test the app locally in RStudio or VS Code by running it.
2. Make sure all dependencies are declared (for example, with renv or by listing packages).
3. Choose a target: rsconnect is the R package used to publish to shinyapps.io and Posit Connect.
4. Authenticate once, then deploy from the IDE's publish button or from the console with rsconnect::deployApp().
5. Re-deploy after changes; the service rebuilds the app from your uploaded files.

A concrete scenario: you have a small internal dashboard that three colleagues use weekly. Deploying to shinyapps.io takes minutes and avoids server maintenance. If instead the app must sit behind your company's single sign-on and read from a private database, a self-managed or hosted Connect-style platform is the more realistic choice.

Decision criteria

• Audience size and access control: public demo vs. authenticated internal users.
• Environment control: do you need specific system libraries or pinned R versions?
• Operational burden: who patches the server and monitors uptime?
• Cost model: free tiers usually limit active hours or concurrent users.

Next step

Read the deployment section of the Shiny documentation at Shiny and the rsconnect package docs, then run one deployment to a free or test account before committing to a production target.

Can I use rstudio.github.io packages with Python?

No, not directly. rstudio.github.io is the GitHub Pages domain that hosts documentation and package sites for R-focused projects, most notably the Posit (formerly RStudio) ecosystem—things like the tidyverse, shiny, and rmarkdown. These are R packages, written in R and distributed for R, so you cannot import them into a Python program the way you would a Python library.

What "use with Python" can actually mean

There are a few realistic interpretations, and each has a different answer:

Bridging R from Python

The rpy2 library lets Python start an R session and call R functions, so you can technically invoke an R package's functions from a Python script. In practice this is workable for occasional calls but adds real friction: you need a working R installation, version alignment between R, the package, and rpy2, and you lose Python's normal packaging and debugging flow. It suits analysts who already have R code they trust and want to reuse it, not teams building a Python-first application.

The cleaner option: Python equivalents

If your reason for looking at these packages is a specific capability, Python usually has a well-supported counterpart:

• Data wrangling (tidyverse/dplyr) → pandas or polars
• Plotting (ggplot2) → plotly, seaborn, or the plotnine library, which deliberately mirrors ggplot2's grammar of graphics
• Interactive dashboards (shiny) → Shiny for Python, Dash, or Streamlit
• Reproducible reports (rmarkdown) → Jupyter notebooks, Quarto, or nbconvert

plotnine is often the least painful switch for R users, because it keeps the same "layers and aesthetics" mental model.

A decision rule

Ask what you actually need. If you have existing R code or a method that only exists in R, bridge it with rpy2 and accept the setup cost. If you are starting fresh in Python and just want similar results, pick the Python-native library—you will get better tooling, easier deployment, and a larger pool of help. Mixing the two ecosystems in one codebase is possible, but the maintenance burden rarely pays off unless the R dependency is genuinely irreplaceable.

For the R-side documentation itself, see rstudio.github.io; for the Python equivalents, the official project sites for pandas, plotnine, and Shiny for Python are the best starting points.

How do I contribute to a repository on rstudio.github.io?

The site at rstudio.github.io is the GitHub Pages host for RStudio's open-source project documentation and package sites, not a single repository with one contribution process. Each project published there—such as Shiny, tidyverse packages, or R Markdown—lives in its own GitHub repository, and each has its own contributing guidelines.

Find the right repository

Start from the page you want to improve. Most sites hosted under this domain include a link back to the source repository, often in the navigation bar, footer, or a "View source" or "Edit this page" link. That link takes you to the GitHub repo where the site content and code actually live.

Once you are in the repository:

• Read CONTRIBUTING.md if it exists; it states the maintainers' preferred workflow.
• Check the README for build and test instructions.
• Look at open issues and pull requests to see what is already in progress.

Typical contribution path

For documentation-only fixes, the simplest route is to edit the page source directly on GitHub and open a pull request. For code changes, the usual flow is:

1. Fork the repository to your own GitHub account.
2. Clone your fork locally and create a branch for your change.
3. Make the edit, following the project's style guide.
4. Run any tests or build steps the project documents.
5. Push the branch and open a pull request against the upstream repository.
6. Respond to review comments; maintainers may ask for changes before merging.

Practical considerations

• Small, focused changes are easier to review and merge than large mixed pull requests.
• Documentation fixes are often a good first contribution because they rarely require deep familiarity with the codebase.
• R package repositories usually expect you to run R CMD check or the project's test suite before submitting.
• License and contributor agreements vary by repository; check before investing significant effort.

If you are unsure where to start, open an issue describing the problem or improvement you have in mind, and a maintainer can point you to the correct repository and process.