openrouteservice R client

openrouteservice R package provides easy access to the openrouteservice (ORS) API from R. It allows you to painlessly consume the following services:

• directions (routing)
• geocoding powered by Pelias
• isochrones (accessibility)
• time-distance matrices
• snapping to ways
• pois (points of interest)
• SRTM elevation for point and lines geometries
• routing optimization based on Vroom

Disclaimer

By using this package, you agree to the ORS terms and conditions.

Installation

The package is not yet available from CRAN, but you can install the development version directly from GitHub.

# install.packages("pak")
pak::pak("GIScience/openrouteservice-r")

Get started

See the package vignette for an overview of the offered functionality.

Local ORS instance

The default is to fire any requests against the free public services at <api.openrouteservice.org>. In order to query a different openrouteservice instance, say a local one, set

options(openrouteservice.url = "http://localhost:8080/ors")

If necessary, endpoint configuration can be further customized through openrouteservice.paths which specifies a named list of paths. The defaults are equivalent of having

options(openrouteservice.paths = list(directions = "v2/directions",
                                      isochrones = "v2/isochrones",
                                      matrix = "v2/matrix",
                                      geocode = "geocode",
                                      pois = "pois",
                                      elevation = "elevation",
                                      optimization = "optimization",
                                      snap = "v2/snap"))

Recent package news

version 0.5.2

NEW FEATURES

• sf output for snapping.

version 0.5.1

BUG FIXES

• sf output for POIs endpoint (#81)

version 0.5.0

NEW FEATURES

• Enable snap endpoint.

Publications citing openrouteservice R package

Please feel free to reach out if you would like to have your work added to the list below.

1. Baumer BS, Kaplan DT, Horton NJ. Modern data science with r. Chapman; Hall/CRC; 2017.

2. Shields N, Willis C, Imms C, McKenzie G, Van Dorsselaer B, Bruder AM, et al. Feasibility of scaling-up a community-based exercise program for young people with disability. Disability and Rehabilitation. 2022;44(9):1669–81.

3. Cubells J, Miralles-Guasch C, Marquet O. E-scooter and bike-share route choice and detours: Modelling the influence of built environment and sociodemographic factors. Journal of transport geography. 2023;111:103664.

4. Bhowon Y, Prendergast LA, Taylor NF, Shields N. Using geospatial analysis to determine the proximity of community gyms for a population-based cohort of young people with cerebral palsy. Physiotherapy Canada. 2023;e20220064.

5. Jain A, LaValley M, Dukes K, Lane K, Winter M, Spangler KR, et al. Modeling health and well-being measures using ZIP code spatial neighborhood patterns. Scientific Reports. 2024;14(1):9180.