collapse is a C/C++ based package for data transformation and statistical computing in R. It’s aims are:
Documentation comes in 5 different forms:
After installing collapse, you can call
help("collapse-documentation") which will produce a central
help page providing a broad overview of the entire functionality of the
package, including direct links to all function documentation pages and
links to 13 further topical documentation pages (names in
.COLLAPSE_TOPICS) describing how clusters of related
functions work together.
Thus collapse comes with a fully structured hierarchical documentation which you can browse within R - and that provides everything necessary to fully understand the package. The Documentation is also available online.
The package page under help("collapse-package") provides
some general information about the package and its design philosophy, as
well as a compact set of examples covering important functionality.
Reading help("collapse-package") and
help("collapse-documentation") is the most comprehensive
way to get acquainted with the package.
help("collapse-documentation") is always the most
up-to-date resource.
An up-to-date (v2.0) cheatsheet compactly summarizes the package.
I have presented collapse (v1.8) in some level of detail at useR 2022. A 2h video recording that provides a quite comprehensive introduction is available here. The corresponding slides are available here.
Updated vignettes are
collapse for tidyverse Users: A quick introduction to collapse for tidyverse users
collapse’s Handling of R Objects: A quick view behind the scenes of class-agnostic R programming
The other vignettes (only available online) do not cover major features introduced in versions >= 1.7, but contain much useful information and examples:
Introduction to collapse : Introduces key features in a structured way
collapse and dplyr : Demonstrates the integration of collapse with dplyr / tidyverse workflows and associated performance improvements
collapse and plm: Demonstrates the integration of collapse with plm and shows examples of efficient programming with panel data
collapse and data.table: Shows how collapse and data.table may be used together in a harmonious way
collapse and sf: Shows how collapse can be used to efficiently manipulate sf data frames
I maintain a blog linked to Rbloggers.com where I introduced collapse with some compact posts covering central functionality. Among these, the post about programming with collapse is useful for developers.