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README.md
In arcgisutils: R-ArcGIS Bridge Utility Functions
arcgisutils 
arcgisutils is the foundational infrastructure package that powers the
R-ArcGIS Bridge Data and Location Service ecosystem. It provides
sophisticated, production-ready tools for interacting with ArcGIS
Online, ArcGIS Enterprise, and ArcGIS Platform via their REST APIs.
Key Capabilities
š Comprehensive Authentication:
- Multiple OAuth2 workflows (
auth_code(), auth_client())
- API key and legacy token support
- Automatic token refresh and validation
- Integration with ArcGIS Pro via
arcgisbinding
š Portal Integration:
- Advanced content search with filtering, sorting, and pagination
- User, group, and organization metadata management
- Portal item discovery and content management workflows
āļø Geoprocessing Services:
- Support for the geoprocessing service framework built upon R6 and S7
- Enables users to call their own custom geoprocessing services or build
on top of existing services
- R6-based job management (
arc_gp_job) with real-time status tracking
and built-in result parsing
š Esri JSON Ecosystem:
- Bidirectional conversion between R spatial data and Esri JSON formats
- Support for FeatureSets, geometry objects, field definitions, and
spatial reference systems
- Optimized parsing with automatic
sf integration
š ļø Developer Utilities:
- Standardized HTTP client (
arc_base_req(), arc_paginate_req())
- Robust error detection and user-friendly error messages
- URL parsing, service introspection, and metadata extraction
Installation
{arcgisutils} 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 {arcgisutils} individually from CRAN:
install.packages("arcgisutils")
To install the development version:
pak::pak("r-arcgis/arcgisutils")
Authentication
Authorization tokens are provided through the functions auth_code(),
auth_client(), auth_user(), auth_key(), and auth_binding().
Additional token validation functions are provided via refresh_token()
and validate_or_refresh_token().
auth_code() can be used for integrating into Shiny applications, for
example, to have individual users log in. We recommend using
auth_key() for authenticating in non-interactive environments (for
example scheduled scripts or deployments).
Tokens are managed using set_arc_token() and unset_arc_token(). They
are fetched using arc_token(). set_arc_token() can set the token
globally or set multiple named environments. Here is a minimal example:
library(arcgisutils)
#>
#> Attaching package: 'arcgisutils'
#> The following object is masked from 'package:base':
#>
#> %||%
key <- auth_key()
set_arc_token(key)
Alternatively, tokens can be set based on a key-value pair for multiple
environments:
set_arc_token("production" = prod_token, "development" = dev_token)
And fetched based on their name via
arc_token("production")
Portal Integration
Search and discover content across your ArcGIS organization:
# Search for feature services containing "crime" data
crime_items <- search_items(
query = "crime",
item_type = "Feature Service",
max_pages = 1
)
crime_items
#> # A data frame: 50 Ć 46
#> id owner created modified guid name title type
#> * <chr> <chr> <dttm> <dttm> <lgl> <chr> <chr> <chr>
#> 1 ea0cfeā¦ Toroā¦ 2023-03-28 15:02:39 2025-01-22 18:27:48 NA Neigā¦ Neigā¦ Featā¦
#> 2 0a239aā¦ Toroā¦ 2023-03-27 18:59:00 2025-08-06 14:01:27 NA Majoā¦ Majoā¦ Featā¦
#> 3 5e055dā¦ JASoā¦ 2023-04-04 17:36:59 2023-09-07 19:05:06 NA <NA> Sherā¦ Featā¦
#> 4 64691aā¦ Tempā¦ 2024-01-17 20:01:43 2024-01-17 20:04:45 NA hateā¦ Hateā¦ Featā¦
#> 5 7c2b78ā¦ JASoā¦ 2023-04-04 17:49:30 2023-06-02 22:27:11 NA <NA> Sherā¦ Featā¦
#> 6 e0992dā¦ baltā¦ 2023-07-31 20:27:01 2025-01-22 21:21:01 NA Partā¦ Partā¦ Featā¦
#> 7 c749e3ā¦ openā¦ 2024-02-23 19:36:34 2025-09-04 17:08:47 NA <NA> Crimā¦ Featā¦
#> 8 2cb53dā¦ KASUā¦ 2019-12-10 19:06:39 2019-12-10 19:14:27 NA Violā¦ Violā¦ Featā¦
#> 9 30644dā¦ MyCiā¦ 2025-03-14 14:55:06 2025-08-20 13:55:24 NA HPD_ā¦ HPD ā¦ Featā¦
#> 10 5dc4e6ā¦ iwatā¦ 2023-06-23 22:07:21 2023-08-09 15:33:46 NA <NA> Propā¦ Featā¦
#> # ā¹ 40 more rows
#> # ā¹ 38 more variables: typeKeywords <list>, description <chr>, tags <list>,
#> # snippet <chr>, thumbnail <chr>, documentation <lgl>, extent <list>,
#> # categories <list>, spatialReference <chr>, accessInformation <chr>,
#> # classification <lgl>, licenseInfo <chr>, culture <chr>, properties <list>,
#> # advancedSettings <lgl>, url <chr>, proxyFilter <lgl>, access <chr>,
#> # size <int>, subInfo <int>, appCategories <list>, industries <list>, ā¦
# Get detailed item information for a portal item
arc_item(crime_items$id[1])
#> <PortalItem<Feature Service>>
#> id: ea0cfecdb1de416884e6b0bf08a9e195
#> title: Neighbourhood Crime Rates Open Data
#> owner: TorontoPoliceService
Developer Utilities
Always use arc_base_req() as this will handle setting the user agent
and authorization token. The function creates a standardized httr2
request object:
# defaults to arcgis.com
host <- arc_host()
req <- arc_base_req(host)
req
#> <httr2_request>
#> GET https://www.arcgis.com
#> Body: empty
#> Options:
#> * useragent: "arcgisutils v0.3.3.9000"
To handle paginated services and requests use arc_paginate_req() to
automatically handle fetching pages.
Esri JSON
There are also a number of utility functions for creating and parsing
Esri JSON. For example we can create an Esri FeatureSet json string
using as_esri_featureset() directly from an sf object.
library(sf)
# load the NC SIDS dataset and extract centroids
# of the first few rows
nc <- system.file("shape/nc.shp", package = "sf") |>
st_read(quiet = TRUE) |>
st_centroid()
# convert to json
nc_json <- as_esri_featureset(nc[1:2, 1:3])
jsonify::pretty_json(nc_json)
#> {
#> "geometryType": "esriGeometryPoint",
#> "spatialReference": {
#> "wkid": 4267
#> },
#> "features": [
#> {
#> "geometry": {
#> "x": -81.4982290095261,
#> "y": 36.43139560823758
#> },
#> "attributes": {
#> "AREA": 0.114,
#> "CNTY_": 1825.0,
#> "PERIMETER": 1.442
#> }
#> },
#> {
#> "geometry": {
#> "x": -81.12512977849917,
#> "y": 36.49110847237506
#> },
#> "attributes": {
#> "AREA": 0.061,
#> "CNTY_": 1827.0,
#> "PERIMETER": 1.231
#> }
#> }
#> ]
#> }
Feature set json can also be parsed using parse_esri_json().
parse_esri_json(nc_json)
#> Simple feature collection with 2 features and 3 fields
#> Geometry type: POINT
#> Dimension: XY
#> Bounding box: xmin: -81.49823 ymin: 36.4314 xmax: -81.12513 ymax: 36.49111
#> Geodetic CRS: NAD27
#> AREA CNTY_ PERIMETER geometry
#> 1 0.114 1825 1.442 POINT (-81.49823 36.4314)
#> 2 0.061 1827 1.231 POINT (-81.12513 36.49111)
Additionally, sfās crs object can be converted to a
spatialReference
JSON object using validate_crs(). Convert these to json with yyjsonr
or jsonify.
crs <- validate_crs(27700)
jsonify::pretty_json(crs, unbox = TRUE)
#> {
#> "spatialReference": {
#> "wkid": 27700
#> }
#> }
Geoprocessing Services
The geoprocessing service framework is completely supported in
{arcgisutils}. Here we combine the functionality of the geoprocessing
job framework with utilities such as as_esri_featureset() to call the
Trace DownStream Elevation
Service
trace_downstream <- function(
input_points,
point_id_field = NULL,
resolution = NULL,
generalize = FALSE,
token = arc_token()
) {
# create a list of parameters
params <- compact(list(
InputPoints = as_esri_featureset(input_points),
PointIdField = point_id_field,
DataSourceResolution = resolution,
Generalize = as.character(generalize),
f = "json"
))
service_url <- "https://hydro.arcgis.com/arcgis/rest/services/Tools/Hydrology/GPServer/TraceDownstream"
arc_gp_job$new(
base_url = service_url,
params = params,
result_fn = parse_gp_feature_record_set,
token
)
}
This new function can be called to start a new job:
# create input points
input_points <- st_sfc(
st_point(c(-159.548936, 21.955888)),
crs = 4326
)
# initialze an empty job
job <- trace_downstream(
input_points,
token = auth_user()
)
# start the job
job$start()
#> <arc_gp_job>
#> Job ID: jd9f76de1e62e4d8a877f3e6859fdb7b2
#> Status: not started
#> Resource: /TraceDownstream
#> Params:
#> ā¢ InputPoints
#> ā¢ Generalize
#> ā¢ f
Jobs run asynchronously so we can check the status with job$status
job$status
#> <arcgisutils::arc_job_status>
#> @ status: chr "esriJobSubmitted"
Then, when the job is complete, we can fetch the results applying the
result function which is parse_gp_feature_record_set() in this case.
job$results
#> $param_name
#> [1] "OutputTraceLine"
#>
#> $data_type
#> [1] "GPFeatureRecordSetLayer"
#>
#> $geometry
#> Simple feature collection with 1 feature and 6 fields
#> Geometry type: MULTILINESTRING
#> Dimension: XY
#> Bounding box: xmin: 438895 ymin: 2422310 xmax: 443325 ymax: 2428045
#> Projected CRS: NAD83 / UTM zone 4N + Unknown VCS
#> OBJECTID PourPtID Description DataResolution LengthKm
#> 1 1 1 NED 10m processed by Esri 10.0 9.489823
#> Shape_Length geometry
#> 1 9489.823 MULTILINESTRING ((443325 24...
# store and view the results
res <- job$results
res
#> $param_name
#> [1] "OutputTraceLine"
#>
#> $data_type
#> [1] "GPFeatureRecordSetLayer"
#>
#> $geometry
#> Simple feature collection with 1 feature and 6 fields
#> Geometry type: MULTILINESTRING
#> Dimension: XY
#> Bounding box: xmin: 438895 ymin: 2422310 xmax: 443325 ymax: 2428045
#> Projected CRS: NAD83 / UTM zone 4N + Unknown VCS
#> OBJECTID PourPtID Description DataResolution LengthKm
#> 1 1 1 NED 10m processed by Esri 10.0 9.489823
#> Shape_Length geometry
#> 1 9489.823 MULTILINESTRING ((443325 24...
# plot the resultant geometry
plot(st_geometry(res$geometry))
Learn More
To learn more about the R-ArcGIS Bridge project visit the developer
documentation.
Try the arcgisutils package in your browser
Any scripts or data that you put into this service are public.
arcgisutils documentation built on March 4, 2026, 9:06 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
arcgisutils
arcgisutils is the foundational infrastructure package that powers the R-ArcGIS Bridge Data and Location Service ecosystem. It provides sophisticated, production-ready tools for interacting with ArcGIS Online, ArcGIS Enterprise, and ArcGIS Platform via their REST APIs.
Key Capabilities
š Comprehensive Authentication:
- Multiple OAuth2 workflows (
auth_code(),auth_client()) - API key and legacy token support
- Automatic token refresh and validation
- Integration with ArcGIS Pro via
arcgisbinding
š Portal Integration:
- Advanced content search with filtering, sorting, and pagination
- User, group, and organization metadata management
- Portal item discovery and content management workflows
āļø Geoprocessing Services:
- Support for the geoprocessing service framework built upon R6 and S7
- Enables users to call their own custom geoprocessing services or build on top of existing services
- R6-based job management (
arc_gp_job) with real-time status tracking and built-in result parsing
š Esri JSON Ecosystem:
- Bidirectional conversion between R spatial data and Esri JSON formats
- Support for FeatureSets, geometry objects, field definitions, and spatial reference systems
- Optimized parsing with automatic
sfintegration
š ļø Developer Utilities:
- Standardized HTTP client (
arc_base_req(),arc_paginate_req()) - Robust error detection and user-friendly error messages
- URL parsing, service introspection, and metadata extraction
Installation
{arcgisutils} 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 {arcgisutils} individually from CRAN:
install.packages("arcgisutils")
To install the development version:
pak::pak("r-arcgis/arcgisutils")
Authentication
Authorization tokens are provided through the functions auth_code(),
auth_client(), auth_user(), auth_key(), and auth_binding().
Additional token validation functions are provided via refresh_token()
and validate_or_refresh_token().
auth_code() can be used for integrating into Shiny applications, for
example, to have individual users log in. We recommend using
auth_key() for authenticating in non-interactive environments (for
example scheduled scripts or deployments).
Tokens are managed using set_arc_token() and unset_arc_token(). They
are fetched using arc_token(). set_arc_token() can set the token
globally or set multiple named environments. Here is a minimal example:
library(arcgisutils)
#>
#> Attaching package: 'arcgisutils'
#> The following object is masked from 'package:base':
#>
#> %||%
key <- auth_key()
set_arc_token(key)
Alternatively, tokens can be set based on a key-value pair for multiple environments:
set_arc_token("production" = prod_token, "development" = dev_token)
And fetched based on their name via
arc_token("production")
Portal Integration
Search and discover content across your ArcGIS organization:
# Search for feature services containing "crime" data
crime_items <- search_items(
query = "crime",
item_type = "Feature Service",
max_pages = 1
)
crime_items
#> # A data frame: 50 Ć 46
#> id owner created modified guid name title type
#> * <chr> <chr> <dttm> <dttm> <lgl> <chr> <chr> <chr>
#> 1 ea0cfeā¦ Toroā¦ 2023-03-28 15:02:39 2025-01-22 18:27:48 NA Neigā¦ Neigā¦ Featā¦
#> 2 0a239aā¦ Toroā¦ 2023-03-27 18:59:00 2025-08-06 14:01:27 NA Majoā¦ Majoā¦ Featā¦
#> 3 5e055dā¦ JASoā¦ 2023-04-04 17:36:59 2023-09-07 19:05:06 NA <NA> Sherā¦ Featā¦
#> 4 64691aā¦ Tempā¦ 2024-01-17 20:01:43 2024-01-17 20:04:45 NA hateā¦ Hateā¦ Featā¦
#> 5 7c2b78ā¦ JASoā¦ 2023-04-04 17:49:30 2023-06-02 22:27:11 NA <NA> Sherā¦ Featā¦
#> 6 e0992dā¦ baltā¦ 2023-07-31 20:27:01 2025-01-22 21:21:01 NA Partā¦ Partā¦ Featā¦
#> 7 c749e3ā¦ openā¦ 2024-02-23 19:36:34 2025-09-04 17:08:47 NA <NA> Crimā¦ Featā¦
#> 8 2cb53dā¦ KASUā¦ 2019-12-10 19:06:39 2019-12-10 19:14:27 NA Violā¦ Violā¦ Featā¦
#> 9 30644dā¦ MyCiā¦ 2025-03-14 14:55:06 2025-08-20 13:55:24 NA HPD_ā¦ HPD ā¦ Featā¦
#> 10 5dc4e6ā¦ iwatā¦ 2023-06-23 22:07:21 2023-08-09 15:33:46 NA <NA> Propā¦ Featā¦
#> # ā¹ 40 more rows
#> # ā¹ 38 more variables: typeKeywords <list>, description <chr>, tags <list>,
#> # snippet <chr>, thumbnail <chr>, documentation <lgl>, extent <list>,
#> # categories <list>, spatialReference <chr>, accessInformation <chr>,
#> # classification <lgl>, licenseInfo <chr>, culture <chr>, properties <list>,
#> # advancedSettings <lgl>, url <chr>, proxyFilter <lgl>, access <chr>,
#> # size <int>, subInfo <int>, appCategories <list>, industries <list>, ā¦
# Get detailed item information for a portal item
arc_item(crime_items$id[1])
#> <PortalItem<Feature Service>>
#> id: ea0cfecdb1de416884e6b0bf08a9e195
#> title: Neighbourhood Crime Rates Open Data
#> owner: TorontoPoliceService
Developer Utilities
Always use arc_base_req() as this will handle setting the user agent
and authorization token. The function creates a standardized httr2
request object:
# defaults to arcgis.com
host <- arc_host()
req <- arc_base_req(host)
req
#> <httr2_request>
#> GET https://www.arcgis.com
#> Body: empty
#> Options:
#> * useragent: "arcgisutils v0.3.3.9000"
To handle paginated services and requests use arc_paginate_req() to
automatically handle fetching pages.
Esri JSON
There are also a number of utility functions for creating and parsing
Esri JSON. For example we can create an Esri FeatureSet json string
using as_esri_featureset() directly from an sf object.
library(sf)
# load the NC SIDS dataset and extract centroids
# of the first few rows
nc <- system.file("shape/nc.shp", package = "sf") |>
st_read(quiet = TRUE) |>
st_centroid()
# convert to json
nc_json <- as_esri_featureset(nc[1:2, 1:3])
jsonify::pretty_json(nc_json)
#> {
#> "geometryType": "esriGeometryPoint",
#> "spatialReference": {
#> "wkid": 4267
#> },
#> "features": [
#> {
#> "geometry": {
#> "x": -81.4982290095261,
#> "y": 36.43139560823758
#> },
#> "attributes": {
#> "AREA": 0.114,
#> "CNTY_": 1825.0,
#> "PERIMETER": 1.442
#> }
#> },
#> {
#> "geometry": {
#> "x": -81.12512977849917,
#> "y": 36.49110847237506
#> },
#> "attributes": {
#> "AREA": 0.061,
#> "CNTY_": 1827.0,
#> "PERIMETER": 1.231
#> }
#> }
#> ]
#> }
Feature set json can also be parsed using parse_esri_json().
parse_esri_json(nc_json)
#> Simple feature collection with 2 features and 3 fields
#> Geometry type: POINT
#> Dimension: XY
#> Bounding box: xmin: -81.49823 ymin: 36.4314 xmax: -81.12513 ymax: 36.49111
#> Geodetic CRS: NAD27
#> AREA CNTY_ PERIMETER geometry
#> 1 0.114 1825 1.442 POINT (-81.49823 36.4314)
#> 2 0.061 1827 1.231 POINT (-81.12513 36.49111)
Additionally, sfās crs object can be converted to a
spatialReference
JSON object using validate_crs(). Convert these to json with yyjsonr
or jsonify.
crs <- validate_crs(27700)
jsonify::pretty_json(crs, unbox = TRUE)
#> {
#> "spatialReference": {
#> "wkid": 27700
#> }
#> }
Geoprocessing Services
The geoprocessing service framework is completely supported in
{arcgisutils}. Here we combine the functionality of the geoprocessing
job framework with utilities such as as_esri_featureset() to call the
Trace DownStream Elevation
Service
trace_downstream <- function(
input_points,
point_id_field = NULL,
resolution = NULL,
generalize = FALSE,
token = arc_token()
) {
# create a list of parameters
params <- compact(list(
InputPoints = as_esri_featureset(input_points),
PointIdField = point_id_field,
DataSourceResolution = resolution,
Generalize = as.character(generalize),
f = "json"
))
service_url <- "https://hydro.arcgis.com/arcgis/rest/services/Tools/Hydrology/GPServer/TraceDownstream"
arc_gp_job$new(
base_url = service_url,
params = params,
result_fn = parse_gp_feature_record_set,
token
)
}
This new function can be called to start a new job:
# create input points
input_points <- st_sfc(
st_point(c(-159.548936, 21.955888)),
crs = 4326
)
# initialze an empty job
job <- trace_downstream(
input_points,
token = auth_user()
)
# start the job
job$start()
#> <arc_gp_job>
#> Job ID: jd9f76de1e62e4d8a877f3e6859fdb7b2
#> Status: not started
#> Resource: /TraceDownstream
#> Params:
#> ā¢ InputPoints
#> ā¢ Generalize
#> ā¢ f
Jobs run asynchronously so we can check the status with job$status
job$status
#> <arcgisutils::arc_job_status>
#> @ status: chr "esriJobSubmitted"
Then, when the job is complete, we can fetch the results applying the
result function which is parse_gp_feature_record_set() in this case.
job$results
#> $param_name
#> [1] "OutputTraceLine"
#>
#> $data_type
#> [1] "GPFeatureRecordSetLayer"
#>
#> $geometry
#> Simple feature collection with 1 feature and 6 fields
#> Geometry type: MULTILINESTRING
#> Dimension: XY
#> Bounding box: xmin: 438895 ymin: 2422310 xmax: 443325 ymax: 2428045
#> Projected CRS: NAD83 / UTM zone 4N + Unknown VCS
#> OBJECTID PourPtID Description DataResolution LengthKm
#> 1 1 1 NED 10m processed by Esri 10.0 9.489823
#> Shape_Length geometry
#> 1 9489.823 MULTILINESTRING ((443325 24...
# store and view the results
res <- job$results
res
#> $param_name
#> [1] "OutputTraceLine"
#>
#> $data_type
#> [1] "GPFeatureRecordSetLayer"
#>
#> $geometry
#> Simple feature collection with 1 feature and 6 fields
#> Geometry type: MULTILINESTRING
#> Dimension: XY
#> Bounding box: xmin: 438895 ymin: 2422310 xmax: 443325 ymax: 2428045
#> Projected CRS: NAD83 / UTM zone 4N + Unknown VCS
#> OBJECTID PourPtID Description DataResolution LengthKm
#> 1 1 1 NED 10m processed by Esri 10.0 9.489823
#> Shape_Length geometry
#> 1 9489.823 MULTILINESTRING ((443325 24...
# plot the resultant geometry
plot(st_geometry(res$geometry))
Learn More
To learn more about the R-ArcGIS Bridge project visit the developer documentation.
Try the arcgisutils package in your browser
Any scripts or data that you put into this service are public.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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