Simple Features for R
A package that provides simple features access for R.
Blogs, links • Cheatsheet • Installing
• Contributing • Acknowledgment • How to cite
Package sf:
data.frame
or tibble
with a geometry list-columnDate
and POSIXct
and list-columns(Illustration © 2018 by Allison Horst)
Install either from CRAN with:
install.packages("sf")
This will install binary packages on Windows and MacOS, unless you configured R such that it tries to install source packages; in that case, see below.
Install development versions from GitHub with:
library(remotes)
install_github("r-spatial/sf")
Installing sf from source works under Windows when Rtools is installed.
MacOS users are strongly encouraged to install the sf
binary packages from CRAN, unless they are familiar with compilers, linking, C++ source code, and homebrew.
The easiest way to install gdal
is using Homebrew. Recent versions of Homebrew include a full-featured up-to-date gdal formula, which installs proj
and gdal
at the same time:
brew install pkg-config
brew install gdal
Once gdal is installed, you will be able to install sf
package from source in R. With the current version of proj
on homebrew, installation requires additional configuration:
install.packages("sf", type = "source", configure.args = "--with-proj-lib=$(brew --prefix)/lib/")
Or the development version:
library(remotes)
install_github("r-spatial/sf", configure.args = "--with-proj-lib=$(brew --prefix)/lib/")
If you are using sf
and rgdal
together, it is necessary to install rgdal
from source using this configuration:
install.packages("rgdal", type = "source", configure.args = c("--with-proj-lib=$(brew --prefix)/lib/", "--with-proj-include=$(brew --prefix)/include/"))
Alternatively, these instructions explain how to install gdal using kyngchaos frameworks.
For Mac OS 11 Big Sur source install instruction, see here
For Unix-alikes, GDAL (>= 2.0.1), GEOS (>= 3.4.0) and Proj.4 (>= 4.8.0) are required.
Dependencies for recent versions of Ubuntu (18.04 and later) are available in the official repositories; you can install them with:
apt-get -y update && apt-get install -y libudunits2-dev libgdal-dev libgeos-dev libproj-dev
However, to get more up-to-date versions of dependencies such as GDAL, we recommend adding the ubuntugis-unstable PPA to the package repositories and installing them as follows:
sudo add-apt-repository ppa:ubuntugis/ubuntugis-unstable
sudo apt-get update
sudo apt-get install libudunits2-dev libgdal-dev libgeos-dev libproj-dev libsqlite0-dev
Adding this PPA is required for installing sf
on older versions of Ubuntu (e.g. Xenial).
Another option, for advanced users, is to install dependencies from source; see e.g. an older Travis config file for hints.
The following command installs all required dependencies:
sudo dnf install gdal-devel proj-devel geos-devel sqlite-devel udunits2-devel
Get gdal, proj, geos and podofo from the main repos, and udunits from the AUR:
pacman -S gdal proj geos arrow podofo
yay/pacaur/yaourt/whatever -S udunits
renv
or conda
There are several reports that sf
fails to install as a source package when R is used with renv
, or when R is installed in a conda
environment. If you experience this, please do not raise an issue here, but
renv
developers or the conda
maintainers, orsf
package, e.g. from r2u, or the Posit package managerTo install on Debian, the rocker geospatial Dockerfiles may be helpful. Ubuntu Dockerfiles are found here.
If you use dynamic linking (installation from source) and have multiple versions of these libraries installed (e.g. one from ubuntugis-unstable, another installed from source in /usr/local/lib
) then this will in general not work, even when setting LD_LIBRARY_PATH
manually. See here for the reason why.
Functions and methods that require liblwgeom
, including ellipsoidal (not spherical or Euclidean) metrics (area, distances), are provide by and used from lwgeom, which is also on CRAN.
=
rather than <-
; don’t change indentation; tab stops of 4 spaces are preferred).Package sf
can be cited as:
Edzer Pebesma, 2018. Simple Features for R: Standardized Support
for Spatial Vector Data. The R Journal 10:1, 439-446.
Pebesma, E.; Bivand, R. (2023). Spatial Data Science: With Applications in R
(1st ed.). 314 pages. Chapman and Hall/CRC.
This project gratefully acknowledges financial support from the