### Initialize and Start Geoprocessing Job

Source: https://github.com/r-arcgis/arcgisutils/blob/main/README.md

Demonstrates how to create input points, initialize a geoprocessing job using the defined `trace_downstream` function, and start the asynchronous job.

```r
# 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: j89b6513d04f44037b39748e8d931cb31
#> Status: esriJobExecuting
#> Resource: /TraceDownstream
#> Params:
#> • InputPoints
#> • Generalize
#> • f

```

--------------------------------

### Install arcgisutils Package

Source: https://github.com/r-arcgis/arcgisutils/blob/main/README.md

Install the arcgisutils package from CRAN. For most users, installing the 'arcgis' metapackage is recommended.

```r
install.packages("arcgis")
```

```r
install.packages("arcgisutils")
```

--------------------------------

### Install Development Version of arcgisutils

Source: https://github.com/r-arcgis/arcgisutils/blob/main/README.md

Install the development version of the arcgisutils package using the 'pak' package manager.

```r
pak::pak("r-arcgis/arcgisutils")
```

--------------------------------

### Async Geoprocessing Job Workflow

Source: https://github.com/r-arcgis/arcgisutils/blob/main/llms.txt

Manage asynchronous geoprocessing jobs in R. This includes creating a job object, starting the job, polling its status, awaiting completion, and canceling if necessary. It also shows how to reconnect to an existing job using its URL.

```r
# 1. Create a job object
job <- new_gp_job(
  base_url = "https://logistics.arcgis.com/arcgis/rest/services/World/ServiceAreas/GPServer/GenerateServiceAreas",
  params   = list(f = "json", travelMode = "..."),
  token    = arc_token()
)

# 2. Submit
job$start()

# 3. Poll status (makes an HTTP request each access)
job$status

# 4. Wait for completion and return results
results <- job$await(interval = 0.5, verbose = TRUE)

# 5. Cancel if needed
job$cancel()

# Reconnect to an existing job by URL
job <- gp_job_from_url("https://.../GPServer/ToolName/jobs/j1234abcd")
```

--------------------------------

### Get ArcGIS Portal Item Details

Source: https://github.com/r-arcgis/arcgisutils/blob/main/README.md

Retrieve detailed information for a specific ArcGIS portal item using its ID. This function returns a PortalItem object.

```r
# 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
```

--------------------------------

### Parameter Documentation Inheritance

Source: https://github.com/r-arcgis/arcgisutils/blob/main/CLAUDE.md

Use `@inheritParams` to avoid duplicating parameter documentation. Point to a function that already documents the parameters.

```r
# Correct
#' @inheritParams arc_base_req

# Wrong: do not copy-paste param descriptions
#' @param token an object of class `httr2_token`...
```

--------------------------------

### Build Base HTTP Request

Source: https://github.com/r-arcgis/arcgisutils/blob/main/llms.txt

Use `arc_base_req` to initiate an HTTP request, setting the User-Agent header and authorization token. Chain `httr2` verbs for further request modification and execution.

```r
req <- arc_base_req(
  url   = "https://services.arcgis.com/.../FeatureServer/0",
  token = arc_token(),
  path  = c("query"),
  query = list(f = "json")
)

# Chain httr2 verbs, then perform
resp <- req |
  httr2::req_body_form(where = "1=1", outFields = "*") |
  httr2::req_perform()
```

--------------------------------

### Fetch and Download Item Metadata and Data

Source: https://github.com/r-arcgis/arcgisutils/blob/main/llms.txt

Retrieve metadata for a specific content item using its ID, and download the raw data associated with that item.

```r
# Fetch item metadata by ID
item <- arc_item("9df5e769bfe8412b8de36a2e618c7672")
# Returns a PortalItem: item$id, item$title, item$type, item$owner, ...

# Download the raw data backing an item
raw <- arc_item_data(item)
```

--------------------------------

### Access Users, Groups, and Content

Source: https://github.com/r-arcgis/arcgisutils/blob/main/llms.txt

Retrieve information about specific users, groups, their content, and portal-wide resources.

```r
arc_user("esri_en")
arc_group("group_id")
arc_user_content("username")
arc_group_content("group_id")
arc_portal_users()
arc_portal_resources(id = "portal_id")
```

--------------------------------

### Error Message Formatting with cli

Source: https://github.com/r-arcgis/arcgisutils/blob/main/CLAUDE.md

Employ `cli::cli_abort()` for error messages, using `call = error_call` or `call = rlang::caller_env()`. Utilize cli inline markup for clarity.

```r
Use `cli::cli_abort()` with `call = error_call` (or `call = rlang::caller_env()`). Use cli inline markup: `{.arg x}`, `{.cls ClassName}`, `{.fn function_name}`, `{.val value}`, `{.code expr}`.
```

--------------------------------

### Set Multiple Named ArcGIS Tokens

Source: https://github.com/r-arcgis/arcgisutils/blob/main/README.md

Set ArcGIS tokens for multiple environments using key-value pairs. Fetch tokens by name using arc_token().

```r
set_arc_token("production" = prod_token, "development" = dev_token)
```

```r
arc_token("production")
```

--------------------------------

### Content Items

Source: https://github.com/r-arcgis/arcgisutils/blob/main/llms.txt

Fetch metadata for content items by their ID and download the raw data backing an item.

```APIDOC
### Content items

Fetch item metadata by ID:
```r
# Fetch item metadata by ID
item <- arc_item("9df5e769bfe8412b8de36a2e618c7672")
# Returns a PortalItem: item$id, item$title, item$type, item$owner, ...
```

Download the raw data backing an item:
```r
# Download the raw data backing an item
raw <- arc_item_data(item)
```
```

--------------------------------

### Authenticate with ArcGIS using a Key

Source: https://github.com/r-arcgis/arcgisutils/blob/main/README.md

Authenticate using a key and set the ArcGIS token globally. This is recommended for non-interactive environments.

```r
library(arcgisutils)
#> 
#> Attaching package: 'arcgisutils'
#> The following object is masked from 'package:base':
#> 
#>     %||

```

```r
key <- auth_key()
set_arc_token(key)
```

--------------------------------

### Users, Groups, and Content

Source: https://github.com/r-arcgis/arcgisutils/blob/main/llms.txt

Retrieve information about users, groups, and their associated content within the portal.

```APIDOC
### Users, groups, and content

Get information about a specific user:
```r
arc_user("esri_en")
```

Get information about a specific group:
```r
arc_group("group_id")
```

Get a user's content:
```r
arc_user_content("username")
```

Get a group's content:
```r
arc_group_content("group_id")
```

Get all users in the portal:
```r
arc_portal_users()
```

Get portal resources by ID:
```r
arc_portal_resources(id = "portal_id")
```
```

--------------------------------

### View Portal Self Information

Source: https://github.com/r-arcgis/arcgisutils/blob/main/llms.txt

Use these functions to view metadata about the portal, the authenticated user, registered service URLs, and federated servers.

```r
# View the portal as the current user/app
arc_portal_self()

# Authenticated user's own metadata (credits, groups, email, etc.)
arc_user_self()

# Organization's registered service URLs
arc_portal_urls()

# Federated ArcGIS Enterprise servers
arc_portal_servers()
```

--------------------------------

### Search for Content Items

Source: https://github.com/r-arcgis/arcgisutils/blob/main/llms.txt

Search for items within the portal based on various criteria such as query, item type, owner, and tags. Results can be sorted and paginated.

```r
results <- search_items(
  query      = "crime",
  item_type  = "Feature Service",
  owner      = "esri",
  tags       = c("police", "public safety"),
  sort_field = "numviews",
  sort_order = "desc",
  page_size  = 50,
  max_pages  = 3
)
# Returns a data.frame of matching items
```

--------------------------------

### Create Feature Collections for Web Maps

Source: https://github.com/r-arcgis/arcgisutils/blob/main/llms.txt

Convert R spatial data (sf objects) into the JSON structures required to add them as feature collections to a web map.

```r
layer_def <- as_layer_definition(my_sf, name = "my_layer", object_id_field = "OBJECTID")
layer     <- as_layer(my_sf, name = "my_layer", title = "My Layer Title")
fc        <- as_feature_collection(layers = list(layer))
```

--------------------------------

### Create Prototype Data Frame from Fields List

Source: https://github.com/r-arcgis/arcgisutils/blob/main/llms.txt

Use `fields_as_ptype_df` to create an empty prototype data.frame from a list of ArcGIS field definitions.

```r
# Create an empty prototype data.frame from a fields list
fields_as_ptype_df(fields)
```

--------------------------------

### Handle Paginated Requests

Source: https://github.com/r-arcgis/arcgisutils/blob/main/llms.txt

Employ `arc_paginate_req` to automatically handle cursor-based pagination for portal endpoints. This function simplifies retrieving all pages of results.

```r
req <- arc_base_req(host, token, path = c("sharing", "rest", "search"), query = c(f = "json", q = "crime"))

responses <- arc_paginate_req(
  req,
  page_size = 50,
  max_pages = Inf,
  .progress = TRUE
)
# Returns a list of httr2_response objects
```

--------------------------------

### Displaying Parsed Date Fields

Source: https://github.com/r-arcgis/arcgisutils/blob/main/tests/testthat/_snaps/date-parsing.md

Shows the output of a spatial data object after date fields have been parsed. This is useful for verifying date formats and values.

```text
Code
  res
Output
  Simple feature collection with 120 features and 2 fields
  Geometry type: POINT
  Dimension:     XY
  Bounding box:  xmin: 3218461 ymin: 1336355 xmax: 3221347 ymax: 1339741
  Projected CRS: NAD83 / Colorado Central (ftUS)
  First 10 features:
     datemodified datecreated                geometry
  1    2003-12-05  2002-02-11 POINT (3218466 1337879)
  2    2003-12-05  2002-02-11 POINT (3218461 1337450)
  3    2003-12-05  2002-02-11 POINT (3219487 1336481)
  4    2012-01-11  2002-02-11 POINT (3219289 1336355)
  5    2011-12-08  2002-02-11 POINT (3220772 1339674)
  6    2011-12-08  2002-02-11 POINT (3221029 1339672)
  7    2011-12-06  2002-02-11 POINT (3218986 1339649)
  8    2011-12-08  2002-02-11 POINT (3221324 1339617)
  9    2011-12-06  2002-02-11 POINT (3218830 1339545)
  10   2011-12-06  2002-02-11 POINT (3218597 1339519)
```

--------------------------------

### Search Items

Source: https://github.com/r-arcgis/arcgisutils/blob/main/llms.txt

Search for content items within the portal based on various criteria such as query, item type, owner, and tags. Results can be sorted and paginated.

```APIDOC
### Search

Search for items with specified criteria:
```r
results <- search_items(
  query      = "crime",
  item_type  = "Feature Service",
  owner      = "esri",
  tags       = c("police", "public safety"),
  sort_field = "numviews",
  sort_order = "desc",
  page_size  = 50,
  max_pages  = 3
)
# Returns a data.frame of matching items
```
```

--------------------------------

### Convert Esri Envelope to sf bbox

Source: https://github.com/r-arcgis/arcgisutils/blob/main/llms.txt

Converts an Esri Envelope list object to an sf bounding box representation.

```r
# Convert Esri Envelope list to sf bbox
from_envelope(list(xmin = -122.4, ymin = 37.3, xmax = -122.0, ymax = 37.8,
                   spatialReference = list(wkid = 4326L)))

```

--------------------------------

### R Authentication Flows

Source: https://github.com/r-arcgis/arcgisutils/blob/main/llms.txt

Demonstrates various authentication methods for ArcGIS services, including interactive and non-interactive OAuth2, API keys, username/password, and ArcGIS Pro integration. Credentials are read from environment variables by default.

```r
# Interactive OAuth2: opens browser, prompts for code
token <- auth_code()

# Non-interactive OAuth2 client credentials
token <- auth_client(expiration = 120)  # minutes

# Static API key
token <- auth_key()

# Legacy username/password (generateToken endpoint)
token <- auth_user()

# ArcGIS Pro via arcgisbinding
token <- auth_binding()

# Shiny application OAuth2 (requires shinyOAuth package)
client <- auth_shiny()
```

--------------------------------

### Create Esri JSON FeatureSet from sf object

Source: https://github.com/r-arcgis/arcgisutils/blob/main/README.md

Use `as_esri_featureset()` to convert an `sf` object into an Esri JSON FeatureSet string. Ensure the `sf` object is loaded and processed before conversion.

```r
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)
```

--------------------------------

### Check Geoprocessing Job Status

Source: https://github.com/r-arcgis/arcgisutils/blob/main/README.md

Shows how to check the current status of an asynchronous geoprocessing job using the `job$status` attribute.

```r
job$status
#> <arcgisutils::arc_job_status>
#>  @ status: chr "esriJobExecuting"

```

--------------------------------

### Create Base ArcGIS Request

Source: https://github.com/r-arcgis/arcgisutils/blob/main/README.md

Create a standardized httr2 request object using arc_base_req(). This function handles setting the user agent and authorization token. Defaults to arcgis.com.

```r
# 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 v0.4.0.9001"
```

--------------------------------

### Define Trace Downstream Geoprocessing Function

Source: https://github.com/r-arcgis/arcgisutils/blob/main/README.md

Defines a function to call the Trace Downstream geoprocessing service. It prepares parameters using `as_esri_featureset` and creates an `arc_gp_job` object.

```r
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
  )
}
```

--------------------------------

### Fetch Layer Metadata

Source: https://github.com/r-arcgis/arcgisutils/blob/main/llms.txt

Fetches metadata for a specific layer from an ArcGIS service URL. Requires an authentication token if the service is secured.

```r
# Fetch layer metadata from a service URL
meta <- fetch_layer_metadata("https://.../FeatureServer/0", token = arc_token())

```

--------------------------------

### Feature Collections

Source: https://github.com/r-arcgis/arcgisutils/blob/main/llms.txt

Build JSON structures to add R data to a web map as a feature collection. This includes creating layer definitions and feature collection objects.

```APIDOC
## Feature Collections (Web Map layers)

Build the JSON structures required to add R data to a web map as a feature collection.

Create a layer definition:
```r
layer_def <- as_layer_definition(my_sf, name = "my_layer", object_id_field = "OBJECTID")
```

Create a layer object:
```r
layer     <- as_layer(my_sf, name = "my_layer", title = "My Layer Title")
```

Create a feature collection:
```r
fc        <- as_feature_collection(layers = list(layer))
```
```

--------------------------------

### Convert R Spatial Data to EsriJSON FeatureSet

Source: https://github.com/r-arcgis/arcgisutils/blob/main/llms.txt

Generate a complete EsriJSON FeatureSet, including features, fields, and spatialReference, from R spatial objects using `as_featureset` or its equivalent `as_esri_featureset`. The output is a JSON string ready for API request bodies.

```r
# Returns a JSON string ready for use in a request body
as_featureset(my_sf)
as_esri_featureset(my_sf)  # equivalent
```

--------------------------------

### Check if a String is a URL

Source: https://github.com/r-arcgis/arcgisutils/blob/main/llms.txt

Checks if a given string is a valid URL.

```r
# Check if a string is a URL
is_url("https://example.com")  # TRUE

```

--------------------------------

### NULL Coalescing Operator

Source: https://github.com/r-arcgis/arcgisutils/blob/main/llms.txt

Provides a NULL coalescing operator (`%||%`) to return a default value if the left-hand side is NULL.

```r
# NULL coalescing
NULL %||% "default"

```

--------------------------------

### Convert R Spatial Data to EsriJSON Features

Source: https://github.com/r-arcgis/arcgisutils/blob/main/llms.txt

Convert R spatial objects (sf or data.frame) into a list of EsriJSON feature objects using `as_features`. For sfc objects, `as_esri_features` creates a JSON string of feature array.

```r
# sf or data.frame to list of feature objects
as_features(my_sf)

# sf or sfc to JSON string of feature array
as_esri_features(my_sfc)
```

--------------------------------

### Retrieve and Plot Geoprocessing Job Results

Source: https://github.com/r-arcgis/arcgisutils/blob/main/README.md

Retrieves the results of a completed geoprocessing job and plots the resulting geometry. The results are stored in `job$results`.

```r
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))

```

--------------------------------

### Search for ArcGIS Portal Items

Source: https://github.com/r-arcgis/arcgisutils/blob/main/README.md

Search for feature services containing 'crime' data across your ArcGIS organization. Results are returned as a data frame.

```r
# 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 × 46
#>    id      owner created             modified            guid  name  title type 
#>  * <chr>   <chr> <dttm>              <dttm>              <lgl> <chr> <chr> <chr>
#>  1 ea0cfe… Toro… 2023-03-28 15:02:39 2026-02-02 17:45:21 NA    Neig… Neig… Feat…
#>  2 64691a… Temp… 2024-01-17 20:01:43 2024-01-17 20:04:45 NA    hate… Hate… Feat…
#>  3 5e055d… JASo… 2023-04-04 17:36:59 2023-09-07 19:05:06 NA    <NA>  Sher… Feat…
#>  4 30644d… MyCi… 2025-03-14 14:55:06 2025-08-20 13:55:24 NA    HPD_… HPD … Feat…
#>  5 c749e3… open… 2024-02-23 19:36:34 2026-01-09 18:36:21 NA    <NA>  Crim… Feat…
#>  6 e0992d… balt… 2023-07-31 20:27:01 2025-01-22 21:21:01 NA    Part… Part… Feat…
#>  7 2cb53d… KASU… 2019-12-10 19:06:39 2019-12-10 19:14:27 NA    Viol… Viol… Feat…
#>  8 5dc4e6… iwat… 2023-06-23 22:07:21 2023-08-09 15:33:46 NA    <NA>  Prop… Feat…
#>  9 ab92f5… KASU… 2019-12-09 16:16:13 2019-12-10 18:55:20 NA    Prop… Prop… Feat…
#> 10 94bc33… admi… 2023-08-11 20:09:35 2026-01-09 17:03:25 NA    <NA>  Crim… Feat…
#> # ℹ 40 more rows
#> # ℹ 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>, …

```

--------------------------------

### Remove NULL List Elements

Source: https://github.com/r-arcgis/arcgisutils/blob/main/llms.txt

Removes elements that are NULL from a list.

```r
# Remove NULL list elements
compact(list(a = 1, b = NULL, c = 3))

```

--------------------------------

### R Token Management

Source: https://github.com/r-arcgis/arcgisutils/blob/main/llms.txt

Provides functions to manage authentication tokens for ArcGIS services. Tokens can be set for the current session, retrieved by name, unset, and validated or refreshed before use.

```r
# Store a token in the session (default name: "ARCGIS_TOKEN")
set_arc_token(token)

# Store multiple named tokens
set_arc_token(org_a = token_a, org_b = token_b)

# Retrieve the default token
arc_token()

# Retrieve a named token
arc_token("org_a")

# Remove tokens
unset_arc_token()
unset_arc_token(c("org_a", "org_b"))

# Validate or refresh before use
validate_or_refresh_token(token)
```

--------------------------------

### Standalone Parameter Validation Checks

Source: https://github.com/r-arcgis/arcgisutils/blob/main/CLAUDE.md

Utilize rlang standalone checks for parameter validation in functions. These checks ensure data integrity and type correctness.

```r
check_string(x, allow_empty = FALSE)
check_bool(x)
check_number_whole(x, min = 1, max = 100)
check_number_decimal(x)
check_character(x, allow_null = TRUE)
check_data_frame(x)
check_function(x)
obj_check_token(token)        # validates httr2_token with arcgis_host
check_token_has_user(token)   # additionally checks for username field
```

--------------------------------

### Parse ArcGIS Service URL

Source: https://github.com/r-arcgis/arcgisutils/blob/main/llms.txt

Parses a given ArcGIS service URL into its constituent components, such as scheme, hostname, path, and service type.

```r
# Parse an ArcGIS service URL into components
arc_url_parse("https://services.arcgisonline.com/arcgis/rest/services/USA_Topo_Maps/MapServer/0")
# Returns a list with: scheme, hostname, path, url, type ("MapServer"), layer ("0")

```

--------------------------------

### Convert Esri SpatialReference to sf CRS

Source: https://github.com/r-arcgis/arcgisutils/blob/main/llms.txt

Converts an Esri SpatialReference list object to an sf Coordinate Reference System (CRS) object.

```r
# Convert Esri SpatialReference list to sf CRS
from_spatial_reference(list(wkid = 4326L))

```

--------------------------------

### Detect ArcGIS Service Type from URL

Source: https://github.com/r-arcgis/arcgisutils/blob/main/llms.txt

Detects the service type (e.g., FeatureServer, MapServer) from an ArcGIS service URL.

```r
# Detect service type from URL
arc_url_type("https://.../FeatureServer/0")  # "FeatureServer"

```

--------------------------------

### Coerce to tbl Class

Source: https://github.com/r-arcgis/arcgisutils/blob/main/llms.txt

Coerces data to a tibble-style data frame for printing, without requiring the 'tibble' package.

```r
# Coerce to tbl class (tibble-style printing without tibble dependency)
data_frame(x)

```

--------------------------------

### Convert R Point to EsriJSON Geometry

Source: https://github.com/r-arcgis/arcgisutils/blob/main/llms.txt

Use `as_esri_geometry` to convert an R spatial point object into EsriJSON geometry format, specifying the Coordinate Reference System (CRS).

```r
pt <- sf::st_point(c(-122.4, 37.8))
as_esri_geometry(pt, crs = sf::st_crs(4326))
```

--------------------------------

### Portal Self Information

Source: https://github.com/r-arcgis/arcgisutils/blob/main/llms.txt

Retrieve information about the ArcGIS portal and the authenticated user. This includes portal details, user metadata, registered service URLs, and federated servers.

```APIDOC
## Portal API

All functions default to `host = arc_host()` and `token = arc_token()`.

### Portal self

View the portal as the current user/app:
```r
arc_portal_self()
```

Get authenticated user's own metadata (credits, groups, email, etc.):
```r
arc_user_self()
```

Get organization's registered service URLs:
```r
arc_portal_urls()
```

Get federated ArcGIS Enterprise servers:
```r
arc_portal_servers()
```
```

--------------------------------

### Geoprocessing Parameter Type Conversions

Source: https://github.com/r-arcgis/arcgisutils/blob/main/llms.txt

Convert R data types to ArcGIS geoprocessing parameter types. This includes linear units, areas, feature record sets, record sets, dates, and spatial references. It also covers parsing GP responses for these types.

```r
# Linear distance
dist <- gp_linear_unit(distance = 100, units = "esriMeters")
as_gp_linear_unit(dist)

# Area
area <- gp_areal_unit(area = 500, units = "esriSquareMeters")
as_gp_areal_unit(area)

# Feature record set (accepts sf, data.frame, PortalItem, FeatureLayer)
as_gp_feature_record_set(my_sf)

# Record set (tabular, no geometry)
as_record_set(my_data_frame)

# Date (converts to milliseconds since epoch)
as_gp_date(Sys.time())

# Spatial reference
as_spatial_reference(sf::st_crs(4326))

# Parsing GP responses
parse_gp_feature_record_set(json)
parse_gp_linear_unit(json)
parse_gp_areal_unit(json)
parse_gp_date(json)
parse_spatial_reference(json)
```

--------------------------------

### Parse EsriJSON to R Spatial Object or Data Frame

Source: https://github.com/r-arcgis/arcgisutils/blob/main/llms.txt

Use `parse_esri_json` to convert an EsriJSON string into an R object. It returns an sf object if geometry is present, otherwise a data.frame.

```r
# Converts featureSet JSON string to data.frame or sf
result <- parse_esri_json(json_string)
# Returns sf when geometry is present, data.frame otherwise
```

--------------------------------

### Infer Esri Field Definitions from Data Frame

Source: https://github.com/r-arcgis/arcgisutils/blob/main/llms.txt

Use `as_fields` to infer ArcGIS field definitions from an R data.frame. The function returns a data.frame with columns for name, type, alias, nullable, and editable.

```r
# Infer Esri field definitions from a data.frame
fields <- as_fields(my_data)
# Returns data.frame with columns: name, type, alias, nullable, editable
```

--------------------------------

### Parse Esri JSON FeatureSet to sf object

Source: https://github.com/r-arcgis/arcgisutils/blob/main/README.md

Use `parse_esri_json()` to convert an Esri JSON FeatureSet string back into an `sf` object. This is useful for reading data from ArcGIS services.

```r
parse_esri_json(nc_json)
```

--------------------------------

### Parse ArcGIS REST API Response with Error Detection

Source: https://github.com/r-arcgis/arcgisutils/blob/main/llms.txt

After parsing the response body string, use `detect_errors` to check for errors embedded within the JSON response, as ArcGIS REST API often returns HTTP 200 even for errors.

```r
result <- httr2::resp_body_string(resp) |
  RcppSimdJson::fparse() |
  detect_errors()
```

--------------------------------

### Convert CRS to ArcGIS spatialReference JSON

Source: https://github.com/r-arcgis/arcgisutils/blob/main/README.md

Use `validate_crs()` to convert an R CRS object into an ArcGIS `spatialReference` JSON object. This JSON can then be further processed with libraries like `jsonify`.

```r
crs <- validate_crs(27700)
jsonify::pretty_json(crs, unbox = TRUE)
```

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