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
some general information about the package and its design philosophy, as
well as a compact set of examples covering important functionality.
help("collapse-documentation") is the most comprehensive
way to get acquainted with the package.
help("collapse-documentation") is always the most
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