The {duckspatial} package provides fast and memory-efficient functions to analyze and manipulate large spatial vector datasets in R. It allows R users to benefit directly from the analytical power of DuckDB and its spatial extension, while remaining fully compatible with R’s spatial ecosystem, especially {sf}.

At its core, {duckspatial} bridges two worlds:

R spatial workflows based on {sf} objects
Database-backed spatial analytics powered by DuckDB SQL

This design makes {duckspatial} especially well suited for:

Working with large spatial data sets
Speeding up spatial analysis at scale
Workflows where data does not fit comfortably in memory

Importantly, {duckspatial} brings the power of DuckDB spatial to R users while keeping workflows similar to {sf} .

Installation

You can install duckspatial directly from CRAN with:

install.packages("duckspatial")

Or you can install the development version from GitHub with:

# install.packages("pak")
pak::pak("Cidree/duckspatial")

Core idea: flexible spatial workflows

A central design principle of {duckspatial} is that the same spatial operation can be used in different ways, depending on how your data is stored and how you want to manage memory and performance.

Most functions in {duckspatial} support four complementary workflows:

Inputsf → Output sf
Input sf → Output DuckDB table
Input DuckDB table → Output sf
Input DuckDB table → Output DuckDB table

See the “Get Started” vignette for examples.