Understanding Repositories and Libraries#
Packages live in one of two places: repositories and libraries. Packages get to libraries from repositories by installation.
Think of your data science workbench as a kitchen:
The repository is the grocery store, a central place to get packages.
The library is the pantry, where you keep your own private set of packages.
Installation is the shopping trip to stock your library from the repository.
Like the grocery store, the package repository includes more packages – inert and boxed up – than any one person needs. Common repositories include CRAN, BioConductor, PyPI, private RStudio Package Manager, and public RStudio Package Manager.
And like the pantry in your home, which holds shelf-stable ingredients until they’re needed in the kitchen, the library holds packages specific to your work, ready for you to combine with other raw ingredients for your analysis.
Libraries are needed wherever there’s a running R or Python process, like within the RStudio IDE and Jupyter Notebooks, or alongside apps and reports running on platforms like RStudio Connect.
Unlike your ability to choose among various colors or flavors of the same item in your pantry, R and Python find packages in the library by name alone, so the library that corresponds to your code must include:
- exactly one version of each package needed and
- only package versions that are consistent, so package interdependencies work.
Originally posted on the RStudio blog.