# collapse Documentation and Resources

#### Sebastian Krantz

#### 2025-10-23

collapse is a C/C++ based package for data transformation and statistical computing in R. It’s aims are:

1. To facilitate complex data transformation, exploration and computing tasks in R.
2. To help make R code fast, flexible, parsimonious and programmer friendly.

Documentation comes in 6 different forms:

## Built-In Structured Documentation

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.

## Cheatsheet

An up-to-date (v2.0) cheatsheet compactly summarizes the package.

## Article on arXiv

An article on collapse is forthcoming at Journal of Statistical Software.

## useR 2022 Presentation and Slides

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.

## Vignettes

Updated vignettes are

- collapse for tidyverse Users: A quick introduction to collapse for tidyverse users

- collapse and sf: Shows how collapse can be used to efficiently manipulate sf data frames

- collapse’s Handling of R Objects: A quick view behind the scenes of class-agnostic R programming

- Developing with collapse: How to write efficient statistical packages using R and collapse

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

## Blog

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.