{arcgislayers} is the core data access package in the R-ArcGIS Bridge,
providing a unified interface for working with ArcGIS data services. As
part of the {arcgis} metapackage, it enables seamless integration
between R and the ArcGIS Web GIS ecosystem, including ArcGIS Online,
Enterprise, and Location Platform.
An llms.txt file is
available to provide context for LLMs when working with this package.
sf and terra.{arcgislayers} is part of the {arcgis} metapackage, which provides
the complete R-ArcGIS Bridge toolkit. For most users, installing the
metapackage is recommended:
install.packages("arcgis")
You can also install {arcgislayers} individually from CRAN:
install.packages("arcgislayers")
To install the development version:
# pak::pak("r-arcgis/arcgislayers")
install.packages("arcgislayers", repos = c('https://r-arcgis.r-universe.dev', 'https://cloud.r-project.org'))
The basic workflow is: connect ➡️ query ➡️ analyze ➡️ publish. Here’s how to get started:
library(arcgis)
#> Attaching core arcgis packages:
#> → arcgisutils v0.6.0
#> → arcgislayers v0.6.1
#> → arcgisgeocode v0.4.0
#> → arcgisplaces v0.1.2
#> → arcpbf v0.2.0
arc_open() connects to any ArcGIS data service using its URL or item
ID. This creates a connection to the remote data without downloading
anything yet.
# Connect to a feature service
furl <- "https://services.arcgis.com/P3ePLMYs2RVChkJx/ArcGIS/rest/services/USA_Counties_Generalized_Boundaries/FeatureServer/0"
county_fl <- arc_open(furl)
county_fl
#> <FeatureLayer>
#> Name: USA Counties - Generalized
#> Geometry Type: esriGeometryPolygon
#> CRS: 4326
#> Capabilities: Query,Extract
arc_select() brings data from ArcGIS into R as familiar sf objects.
You can get everything, or be selective:
# Get all data (use with caution on large datasets!)
counties_all <- arc_select(county_fl)
# Get specific columns and rows
large_counties <- arc_select(
county_fl,
fields = c("state_abbr", "population"),
where = "population > 1000000"
)
large_counties
#> Simple feature collection with 49 features and 2 fields
#> Geometry type: POLYGON
#> Dimension: XY
#> Bounding box: xmin: -158.2674 ymin: 21.24986 xmax: -71.02671 ymax: 47.77552
#> Geodetic CRS: WGS 84
#> First 10 features:
#> STATE_ABBR POPULATION geometry
#> 1 OH 1264817 POLYGON ((-81.37707 41.3463...
#> 2 OH 1323807 POLYGON ((-83.24282 39.8044...
#> 3 PA 1250578 POLYGON ((-79.86399 40.2007...
#> 4 PA 1603797 POLYGON ((-75.1429 39.8816,...
#> 5 HI 1016508 POLYGON ((-157.6733 21.2980...
#> 6 IL 5275541 POLYGON ((-88.26711 41.9887...
#> 7 AZ 4420568 POLYGON ((-111.0425 33.4759...
#> 8 AZ 1043433 POLYGON ((-110.4522 31.7360...
#> 9 CA 1682353 POLYGON ((-121.4721 37.4777...
#> 10 CA 1165927 POLYGON ((-122.3076 37.8917...
Filter by location or attributes before bringing data into R:
# Spatial filter: get counties that intersect with North Carolina
nc <- sf::st_read(system.file("shape/nc.shp", package="sf"))
#> Reading layer `nc' from data source
#> `/Users/josiahparry/Library/R/arm64/4.5/library/sf/shape/nc.shp'
#> using driver `ESRI Shapefile'
#> Simple feature collection with 100 features and 14 fields
#> Geometry type: MULTIPOLYGON
#> Dimension: XY
#> Bounding box: xmin: -84.32385 ymin: 33.88199 xmax: -75.45698 ymax: 36.58965
#> Geodetic CRS: NAD27
nc_area_counties <- arc_select(
county_fl,
filter_geom = sf::st_bbox(nc[1,])
)
nc_area_counties
#> Simple feature collection with 6 features and 12 fields
#> Geometry type: POLYGON
#> Dimension: XY
#> Bounding box: xmin: -82.0477 ymin: 35.98946 xmax: -80.83795 ymax: 36.80746
#> Geodetic CRS: WGS 84
#> OBJECTID NAME STATE_NAME STATE_FIPS FIPS SQMI POPULATION
#> 1 467 Johnson County Tennessee 47 47091 302.6644 17948
#> 2 1924 Alleghany County North Carolina 37 37005 236.1822 10888
#> 3 1926 Ashe County North Carolina 37 37009 429.3538 26577
#> 4 2016 Watauga County North Carolina 37 37189 313.3604 54086
#> 5 2018 Wilkes County North Carolina 37 37193 756.5252 65969
#> 6 2995 Grayson County Virginia 51 51077 445.7267 15333
#> POP_SQMI STATE_ABBR COUNTY_FIPS Shape__Area Shape__Length
#> 1 59.3 TN 091 0.07960385 1.290607
#> 2 46.1 NC 005 0.06140165 1.231232
#> 3 61.9 NC 009 0.11428581 1.442112
#> 4 172.6 NC 189 0.08142272 1.287674
#> 5 87.2 NC 193 0.19911944 1.984232
#> 6 34.4 VA 077 0.11578917 1.945424
#> geometry
#> 1 POLYGON ((-81.74091 36.3919...
#> 2 POLYGON ((-81.2397 36.36549...
#> 3 POLYGON ((-81.47258 36.2344...
#> 4 POLYGON ((-81.80605 36.1046...
#> 5 POLYGON ((-81.02037 36.0350...
#> 6 POLYGON ((-81.34512 36.5729...
Use list_fields() to explore available attributes:
list_fields(county_fl)
#> # A data frame: 12 × 10
#> name type alias sqlType nullable editable domain defaultValue length
#> * <chr> <chr> <chr> <chr> <lgl> <lgl> <lgl> <lgl> <int>
#> 1 OBJECTID esri… OBJE… sqlTyp… FALSE FALSE NA NA NA
#> 2 NAME esri… Name sqlTyp… TRUE TRUE NA NA 50
#> 3 STATE_NAME esri… Stat… sqlTyp… TRUE TRUE NA NA 20
#> 4 STATE_FIPS esri… Stat… sqlTyp… TRUE TRUE NA NA 2
#> 5 FIPS esri… FIPS sqlTyp… TRUE TRUE NA NA 5
#> 6 SQMI esri… Area… sqlTyp… TRUE TRUE NA NA NA
#> 7 POPULATION esri… 2020… sqlTyp… TRUE TRUE NA NA NA
#> 8 POP_SQMI esri… Peop… sqlTyp… TRUE TRUE NA NA NA
#> 9 STATE_ABBR esri… Stat… sqlTyp… TRUE TRUE NA NA 2
#> 10 COUNTY_FIPS esri… Coun… sqlTyp… TRUE TRUE NA NA 3
#> 11 Shape__Area esri… Shap… sqlTyp… TRUE FALSE NA NA NA
#> 12 Shape__Leng… esri… Shap… sqlTyp… TRUE FALSE NA NA NA
#> # ℹ 1 more variable: description <chr>
arc_raster() extracts raster data from ArcGIS ImageServers as terra
objects:
# Connect to Landsat imagery
img_url <- "https://landsat2.arcgis.com/arcgis/rest/services/Landsat/MS/ImageServer"
landsat <- arc_open(img_url)
# Extract imagery for a specific area
res <- arc_raster(
landsat,
xmin = -71,
ymin = 43,
xmax = -67,
ymax = 47.5,
bbox_crs = 4326,
width = 250,
height = 250,
)
terra::plot(res)
Turn your R analysis into ArcGIS services that others can access:
# Publish an sf object as a feature service (requires authentication)
my_analysis <- large_counties |>
dplyr::mutate(density_category = ifelse(pop_sqmi > 100, "Dense", "Sparse"))
publish_layer(
my_analysis,
title = "County Population Density Analysis",
description = "Counties categorized by population density"
)
To learn more about about how to most effectively use {arcgislayers}
for your use case review the developer site
documentation.