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osmnxr is OSMnx for R: a tidyverse-friendly toolkit, inspired by the OSMnx Python library, to download, model, simplify, analyze and visualize street networks and other geospatial features from OpenStreetMap.
It is a reimplementation — not a Python wrapper.
Heavy graph computation (routing, simplification, metrics) runs in a
bundled Rust core via extendr, so there is no Python runtime to
manage. Everything you get back is tidy sf.
osmnxr builds from source and needs the Rust
toolchain (rustup) at install
time.
# install.packages("remotes")
remotes::install_github("StrategicProjects/osmnxr")library(osmnxr)
# Download a drivable street network for a place
g <- ox_graph_from_place("Olinda, Brazil", network_type = "drive")
g
plot(g)
# Route between two points (Rust Dijkstra)
from <- ox_nearest_nodes(g, x = -34.85, y = -8.01)
to <- ox_nearest_nodes(g, x = -34.84, y = -8.00)
ox_shortest_path(g, from, to)
# Urban metrics
ox_basic_stats(g)
ox_orientation_entropy(g) # street-grid order/disorderNo network access? Explore the whole API offline with a synthetic grid:
g <- example_osm_graph()
ox_basic_stats(g)
ox_shortest_path(g, ox_nearest_nodes(g, 0, 0), ox_nearest_nodes(g, 300, 300))osmnxr is split into a tidy R API over a Rust compute
core:
The pipeline, from a place name to an analyzable network:
osmnxr complements the R geospatial stack — it can hand
graphs to sfnetworks,
tidygraph
and dodgr, and
downloads via the same Overpass API used by osmdata.
MIT © Andre Leite and contributors / StrategicProjects.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.