# collapse Documentation and Resources

#### Sebastian Krantz

#### 2026-08-29

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 7 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.

## DeepWiki

DeepWiki is an AI-powered platform designed to automatically generate structured, interactive documentation for software repositories, primarily on GitHub. Developed by Cognition AI—the same laboratory behind the autonomous AI engineer Devin—it serves as a dynamic, “Wikipedia-like” encyclopedia for codebases.

While not more comprehensive or accurate than the structured documentation, it is great to learn more about the internal structure of collapse and use a chatbot (Devin) to ask questions about or write code using collapse.

You can access the collapse DeepWiki here.

## JSS Article

The collapse article is published in the Journal of Statistical Software (volume 116, issue 1). If you want to ‘read something concise’ about collapse, this is the best place to start.

## Cheatsheet

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

## 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.

## Presentations 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.

- I have recently presented collapse (v2.1) and the fastverse at a workshop on “Speeding Up Empirical Research: Tools and Techniques for Fast Computing” organized by the Bank of Portugal in December 2025. My 45-minute talk focused on two advanced applications in international trade and spatial network analysis/package development. You can find the materials (slides and recording) here.