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arcgisutils

R-CMD-check CRAN status extendr

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

šŸ” Comprehensive Authentication:

šŸŒ Portal Integration:

āš™ļø Geoprocessing Services:

šŸ“„ Esri JSON Ecosystem:

šŸ› ļø Developer Utilities:

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 Ɨ 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€¦
#> # ā„¹ 40 more rows
#> # ā„¹ 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, sfā€™s 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.

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.
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