Title:	ArcGIS Utility Functions
Version:	0.3.3
Description:	Developer oriented utility functions designed to be used as the building blocks of R packages that work with ArcGIS Location Services. It provides functionality for authorization, Esri JSON construction and parsing, as well as other utilities pertaining to geometry and Esri type conversions. To support 'ArcGIS Pro' users, authorization can be done via 'arcgisbinding'. Installation instructions for 'arcgisbinding' can be found at https://developers.arcgis.com/r-bridge/installation/.
License:	Apache License (≥ 2)
URL:	https://github.com/R-ArcGIS/arcgisutils, https://developers.arcgis.com/r-bridge/api-reference/arcgisutils/
BugReports:	https://github.com/R-ArcGIS/arcgisutils/issues
Imports:	cli, dbplyr, httr2 (≥ 1.0.5), RcppSimdJson, rlang, sf, utils
Suggests:	arcgisbinding, collapse (≥ 2.0.0), data.table, vctrs, testthat (≥ 3.0.0), jsonify
Config/testthat/edition:	3
Encoding:	UTF-8
Language:	en
RoxygenNote:	7.3.2
Config/rextendr/version:	0.3.1.9001
SystemRequirements:	Cargo (Rust's package manager), rustc >= 1.67
Depends:	R (≥ 4.2)
NeedsCompilation:	yes
Packaged:	2025-04-10 14:22:38 UTC; josiahparry
Author:	Josiah Parry [aut, cre], Kenneth Vernon [ctb], Martha Bass [ctb]
Maintainer:	Josiah Parry <josiah.parry@gmail.com>
Repository:	CRAN
Date/Publication:	2025-04-10 19:20:32 UTC

Set user-agent for arcgisutils

Description

Override the default user-agent set by httr2 to indicate that a request came from arcgisutils.

Usage

arc_agent(req)

Arguments

Value

an httr2 request object

Examples

req <- httr2::request("http://example.com")
arc_agent(req)

Generate base request

Description

This function takes a url and creates a basic httr2 request that adds the user-agent and adds an authorization token to the X-Esri-Authorization header.

Usage

arc_base_req(
  url,
  token = NULL,
  path = NULL,
  query = NULL,
  error_call = rlang::caller_env()
)

Arguments

Examples

arc_base_req("https://arcgis.com")

Determines Portal Host

Description

Returns a scalar character indicating the host to make requests to.

Usage

arc_host()

Details

By default, the host is ArcGIS Online <⁠https://www.arcgis.com⁠>. If the environment variable ARCGIS_HOST is set, it will be returned.

Value

A scalar character, "https://www.arcgis.com" by default.

Examples

arc_host()

Access the Self Resource

Description

The function returns the ⁠/self⁠ resource from the ArcGIS REST API. The ⁠/self⁠ endpoint returns the view of the portal as seen by the current user, whether anonymous or signed in.

Usage

arc_self_meta(token = arc_token(), error_call = rlang::current_call())

Arguments

Details

See the endpoint documentation for more details.

The Portal Self response can vary based on whether it's called by a user, an app, or both.

The response includes user and appinfo properties, and the variations in responses are primarily related to these two properties. As the names indicate, the user property includes information about the user making the call, and the appinfo property includes information pertaining to the app that made the call.

Value

A named list.

Examples

## Not run: 
set_arc_token(auth_code())
self <- arc_self_meta()
names(self)

## End(Not run)

Manage authorization tokens

Description

These functions are used to set, fetch, and check authorization tokens.

Usage

arc_token(token = "ARCGIS_TOKEN")

set_arc_token(token, ...)

unset_arc_token(token = NULL)

obj_check_token(token, call = rlang::caller_env())

check_token_has_user(token, call = rlang::caller_env())

Arguments

Details

It is possible to have multiple authorization tokens in one session. These functions assist you in managing them.

arc_token() is used to fetch tokens by name. The default token is ARCGIS_TOKEN. However, they can be any valid character scalar. set_arc_token() will create store a token with the name ARCGIS_TOKEN. However, you can alternatively set the tokens by name using a key-value pair. The key is what you would pass to arc_token() to fetch the httr2_token object. To remove a token that has been set, use unset_arc_token().

obj_check_token() is a developer oriented function that can be used to check if an object is indeed an httr2_token. To check if a token has expired, validate_or_refresh_token() will do so.

check_token_has_user() is a developer oriented function that checks to see if a token has a username field associated with it.

For developers:

set_arc_token() uses a package level environment to store the tokens. The tokens are fetched from the environment using arc_token().

Examples

# create fake tokens
token_a <- httr2::oauth_token("1234", arcgis_host = arc_host())
token_b <- httr2::oauth_token("abcd", arcgis_host = arc_host())

# set token to the default location
set_arc_token(token_a)

# fetch token from the default location
arc_token()

# set token by name
set_arc_token(org_a = token_a, org_b = token_b)

# fetch token by name
arc_token("org_a")
arc_token("org_b")

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

Create Esri JSON Geometry Objects

Description

as_esri_geometry() converts an sfg object to a EsriJSON Geometry object as a string.

Usage

as_esri_geometry(x, crs = NULL, call = rlang::caller_env())

Arguments

Details

See as_featureset() and as_features() for converting sfc and sf objects into EsriJSON.

Value

a scalar string

References

API Reference

Examples

library(sf)
# POINT
# create sfg points
xy <- st_point(c(1, 2))
xyz <- st_point(c(1, 2, 3))
xym <- st_point(c(1, 2, 3), dim = "XYM")
xyzm <- st_point(c(1, 2, 3, 4))

as_esri_geometry(xy)
as_esri_geometry(xyz)
as_esri_geometry(xym)
as_esri_geometry(xyzm)

# MULTIPOINT
# vector to create matrix points
set.seed(0)
x <- rnorm(12)

xy <- st_multipoint(matrix(x, ncol = 2))
xyz <- st_multipoint(matrix(x, ncol = 3))
xym <- st_multipoint(matrix(x, ncol = 3), dim = "XYM")
xyzm <- st_multipoint(matrix(x, ncol = 4), dim = "XYM")

as_esri_geometry(xy)
as_esri_geometry(xyz)
as_esri_geometry(xym)
as_esri_geometry(xyzm)

# LINESTRING
xy <- st_linestring(matrix(x, ncol = 2))
xyz <- st_linestring(matrix(x, ncol = 3))
xym <- st_linestring(matrix(x, ncol = 3), dim = "XYM")
xyzm <- st_linestring(matrix(x, ncol = 4), dim = "XYM")

as_esri_geometry(xy)
as_esri_geometry(xyz)
as_esri_geometry(xym)
as_esri_geometry(xyzm)

# MULTILINESTRING
as_esri_geometry(st_multilinestring(list(xy, xy)))
as_esri_geometry(st_multilinestring(list(xyz, xyz)))
as_esri_geometry(st_multilinestring(list(xym, xym)))
as_esri_geometry(st_multilinestring(list(xyzm, xyzm)))

# POLYGON
coords <- rbind(
  c(0, 0, 0, 1),
  c(0, 1, 0, 1),
  c(1, 1, 1, 1),
  c(1, 0, 1, 1),
  c(0, 0, 0, 1)
)

xy <- st_polygon(list(coords[, 1:2]))
xyz <- st_polygon(list(coords[, 1:3]))
xym <- st_polygon(list(coords[, 1:3]), dim = "XYM")
xyzm <- st_polygon(list(coords))

as_esri_geometry(xy)
as_esri_geometry(xyz)
as_esri_geometry(xym)
as_esri_geometry(xyzm)

# MULTIPOLYGON
as_esri_geometry(st_multipolygon(list(xy, xy)))
as_esri_geometry(st_multipolygon(list(xyz, xyz)))
as_esri_geometry(st_multipolygon(list(xym, xym)))
as_esri_geometry(st_multipolygon(list(xyzm, xyzm)))

Convert an object to an extent

Description

Given an sf or sfc object create a list that represents the extent of the object. The result of this function can be parsed directly into json using jsonify::to_json(x, unbox = TRUE) or included into a list as the extent component that will be eventually converted into json using the above function.

Usage

as_extent(x, crs = sf::st_crs(x), call = rlang::caller_env())

Arguments

Value

An extent json object. Use jsonify::to_json(x, unbox = TRUE) to convert to json.

Examples

nc <- sf::st_read(system.file("shape/nc.shp", package = "sf"), quiet = TRUE)
as_extent(nc)

Create Esri Features

Description

These functions create an array of Esri Feature objects. Each feature consists of a geometry and attribute field. The result of as_esri_features() is a JSON array of Features whereas as_features() is a list that represents the same JSON array. Using jsonify::to_json(as_features(x), unbox = TRUE) will result in the same JSON array.

Usage

as_features(x, crs = sf::st_crs(x), call = rlang::caller_env())

as_esri_features(x, crs = sf::st_crs(x), call = rlang::caller_env())

Arguments

Value

Either a scalar string or a named list.

References

API Reference

Examples

library(sf)
# POINT
# create sfg points
xy <- st_sfc(st_point(c(1, 2)))
xyz <- st_sfc(st_point(c(1, 2, 3)))
xym <- st_sfc(st_point(c(1, 2, 3), dim = "XYM"))

as_esri_features(xy)
as_esri_features(xyz)
as_esri_features(xym)

# MULTIPOINT
# vector to create matrix points
set.seed(0)
x <- rnorm(12)

xy <- st_sfc(st_multipoint(matrix(x, ncol = 2)))
xyz <- st_sfc(st_multipoint(matrix(x, ncol = 3)))
xym <- st_sfc(st_multipoint(matrix(x, ncol = 3), dim = "XYM"))

as_esri_features(xy)
as_esri_features(xyz)
as_esri_features(xym)

# LINESTRING
xy <- st_sfc(st_linestring(matrix(x, ncol = 2)))
xyz <- st_sfc(st_linestring(matrix(x, ncol = 3)))
xym <- st_sfc(st_linestring(matrix(x, ncol = 3), dim = "XYM"))

as_esri_features(xy)
as_esri_features(xyz)
as_esri_features(xym)

# MULTILINESTRING
as_esri_features(st_sfc(st_multilinestring(list(xy[[1]], xy[[1]]))))
as_esri_features(st_sfc(st_multilinestring(list(xyz[[1]], xyz[[1]]))))
as_esri_features(st_sfc(st_multilinestring(list(xym[[1]], xym[[1]]))))

# POLYGON
coords <- rbind(
  c(0, 0, 0, 1),
  c(0, 1, 0, 1),
  c(1, 1, 1, 1),
  c(1, 0, 1, 1),
  c(0, 0, 0, 1)
)

xy <- st_sfc(st_polygon(list(coords[, 1:2])))
xyz <- st_sfc(st_polygon(list(coords[, 1:3])))
xym <- st_sfc(st_polygon(list(coords[, 1:3]), dim = "XYM"))

as_esri_features(xy)
as_esri_features(xyz)
as_esri_features(xym)

# MULTIPOLYGON
as_esri_features(st_sfc(st_multipolygon(list(xy[[1]], xy[[1]]))))
as_esri_features(st_sfc(st_multipolygon(list(xyz[[1]], xyz[[1]]))))
as_esri_features(st_sfc(st_multipolygon(list(xym[[1]], xym[[1]]))))

Create Esri FeatureSet Objects

Description

These functions create an Esri FeatureSet object. A FeatureSet contains an inner array of features as well as additional metadata about the the collection such as the geometry type, spatial reference, and object ID field.

Usage

as_featureset(x, crs = sf::st_crs(x), call = rlang::caller_env())

as_esri_featureset(x, crs = sf::st_crs(x), call = rlang::caller_env())

Arguments

References

API Reference

Examples

library(sf)
# POINT
# create sfg points
xy <- st_sfc(st_point(c(1, 2)))
xyz <- st_sfc(st_point(c(1, 2, 3)))
xym <- st_sfc(st_point(c(1, 2, 3), dim = "XYM"))

as_esri_featureset(xy)
as_esri_featureset(xyz)
as_esri_featureset(xym)

# MULTIPOINT
# vector to create matrix points
set.seed(0)
x <- rnorm(12)

xy <- st_sfc(st_multipoint(matrix(x, ncol = 2)))
xyz <- st_sfc(st_multipoint(matrix(x, ncol = 3)))
xym <- st_sfc(st_multipoint(matrix(x, ncol = 3), dim = "XYM"))

as_esri_featureset(xy)
as_esri_featureset(xyz)
as_esri_featureset(xym)

# LINESTRING
xy <- st_sfc(st_linestring(matrix(x, ncol = 2)))
xyz <- st_sfc(st_linestring(matrix(x, ncol = 3)))
xym <- st_sfc(st_linestring(matrix(x, ncol = 3), dim = "XYM"))

as_esri_featureset(xy)
as_esri_featureset(xyz)
as_esri_featureset(xym)

# MULTILINESTRING
as_esri_featureset(st_sfc(st_multilinestring(list(xy[[1]], xy[[1]]))))
as_esri_featureset(st_sfc(st_multilinestring(list(xyz[[1]], xyz[[1]]))))
as_esri_featureset(st_sfc(st_multilinestring(list(xym[[1]], xym[[1]]))))

# POLYGON
coords <- rbind(
  c(0, 0, 0, 1),
  c(0, 1, 0, 1),
  c(1, 1, 1, 1),
  c(1, 0, 1, 1),
  c(0, 0, 0, 1)
)

xy <- st_sfc(st_polygon(list(coords[, 1:2])))
xyz <- st_sfc(st_polygon(list(coords[, 1:3])))
xym <- st_sfc(st_polygon(list(coords[, 1:3]), dim = "XYM"))

as_esri_featureset(xy)
as_esri_featureset(xyz)
as_esri_featureset(xym)

# MULTIPOLYGON
as_esri_featureset(st_sfc(st_multipolygon(list(xy[[1]], xy[[1]]))))
as_esri_featureset(st_sfc(st_multipolygon(list(xyz[[1]], xyz[[1]]))))
as_esri_featureset(st_sfc(st_multipolygon(list(xym[[1]], xym[[1]]))))

Create Esri layer objects

Description

These functions are used to generate list objects that can be converted into json objects that are used in REST API requests. Notably they are used for adding R objects as items to a portal.

Usage

as_layer(
  x,
  name,
  title,
  layer_definition = as_layer_definition(x, name, "object_id", infer_esri_type(x)),
  id = NULL,
  layer_url = NULL,
  legend_url = NULL,
  popup_info = NULL,
  call = rlang::caller_env()
)

as_layer_definition(
  x,
  name,
  object_id_field,
  fields = infer_esri_type(x),
  display_field = NULL,
  drawing_info = NULL,
  has_attachments = FALSE,
  max_scale = 0,
  min_scale = 0,
  templates = NULL,
  type_id_field = NULL,
  types = NULL,
  call = rlang::caller_env()
)

as_feature_collection(
  layers = list(),
  show_legend = TRUE,
  call = rlang::caller_env()
)

Arguments

Details

A featureCollection defines a layer of features that will be stored on a web map. It consists of an array of layers. The layer contains the features (attributes and geometries) as a featureSet (see as_esri_featureset()) and additional metadata which is stored in the layerDefinitionobject. The layerDefinition most importantly documents the fields in the object, the object ID, and additional metadata such as name, title, and display scale.

Additional documentation for these json object:

• layer

• layerDefinition

• featureCollection

Value

A list object containing the required fields for each respective json type. The results can be converted to json using jsonify::to_json(x, unbox = TRUE)

Examples

ld <- as_layer_definition(iris, "iris", "objectID")
l <- as_layer(iris, "iris name", "Iris Title")
fc <- as_feature_collection(layers = list(l))

Authorization

Description

Authorize your R session to connect to an ArcGIS Portal. See details.

Usage

auth_code(client = Sys.getenv("ARCGIS_CLIENT"), host = arc_host())

auth_client(
  client = Sys.getenv("ARCGIS_CLIENT"),
  secret = Sys.getenv("ARCGIS_SECRET"),
  host = arc_host(),
  expiration = 120
)

auth_binding()

auth_user(
  username = Sys.getenv("ARCGIS_USER"),
  password = Sys.getenv("ARCGIS_PASSWORD"),
  host = arc_host(),
  expiration = 60
)

auth_key(api_key = Sys.getenv("ARCGIS_API_KEY"), host = arc_host())

refresh_token(token, client = Sys.getenv("ARCGIS_CLIENT"), host = arc_host())

validate_or_refresh_token(
  token,
  client = Sys.getenv("ARCGIS_CLIENT"),
  host = arc_host(),
  refresh_threshold = 0,
  call = rlang::caller_env()
)

Arguments

Details

ArcGIS Online and Enterprise Portals utilize OAuth2 authorization via their REST APIs.

• auth_code() is the recommend OAuth2 workflow for interactive sessions

• auth_client() is the recommended OAuth2 workflow for non-interactive sessions

• auth_user() uses legacy username and password authorization using the generateToken endpoint. It is only recommended for legacy systems that do not implement OAuth2.

• auth_binding() fetches a token from the active portal set by arcgisbinding. Uses arcgisbinding::arc.check_portal() to extract the authorization token. Recommended if using arcgisbinding.

Value

an httr2_token

Examples

## Not run: 
auth_code()
auth_client()
auth_user()
auth_key()
auth_binding()

## End(Not run)

General utility functions

Description

General utility functions

Usage

compact(.x)

a %||% b

check_dots_named(dots, call = rlang::caller_env())

Arguments

Details

• compact() removes any NULL list elements

• ⁠%||%⁠ is a special pipe operator that returns b if a is NULL

Value

• compact() a list

• ⁠%||%⁠ the first non-null item or NULL if both are NULL

Examples

# remove null elements
compact(list(a = NULL, b = 1))

# if NULL return rhs
NULL %||% 123

# if not NULL return lhs
123 %||% NULL

Detect errors in parsed json response

Description

The requests responses from ArcGIS don't return the status code in the response itself but rather from the body in the json. This function checks for the existence of an error. If an error is found, the contents of the error message are bubbled up.

Usage

detect_errors(response, error_call = rlang::caller_env())

catch_error(response, error_call = rlang::caller_env())

Arguments

Value

Nothing. Used for it's side effect. If an error code is encountered in the response an error is thrown with the error code and the error message.

Examples

## Not run: 
response <- list(
  error = list(
    code = 400L,
    message = "Unable to generate token.",
    details = "Invalid username or password."
  )
)

detect_errors(response)

## End(Not run)

Determine the dimensions of a geometry object

Description

Given an sfc or sfg object determine what dimensions are represented.

Usage

determine_dims(x)

has_m(x)

has_z(x)

Arguments

Value

determine_dims() returns a scalar character of the value "xy", "xyz", or "xyzm" depending on what dimensions are represented.

has_m() and has_z() returns a logical scalar of TRUE or FALSE if the geometry has a Z or M dimension.

Examples

geo <- sf::st_read(system.file("shape/nc.shp", package="sf"), quiet = TRUE)[["geometry"]]

determine_dims(geo)
has_z(geo)
has_m(geo)

Determine Esri Geometry type

Description

Takes an sf or sfc object and returns the appropriate Esri geometry type.

Usage

determine_esri_geo_type(x, call = rlang::caller_env())

Arguments

Details

Geometry type mapping

• POINT: esriGeometryPoint

• MULTIPOINT: esriGeometryMultipoint

• LINESTRING: esriGeometryPolyline

• MULTILINESTRING: esriGeometryPolyline

• POLYGON: esriGeometryPolygon

• MULTIPOLYGON: esriGeometryPolygon

Value

returns a character scalar of the corresponding Esri geometry type

Examples

determine_esri_geo_type(sf::st_point(c(0, 0)))

Retrieve metadata

Description

Utility functions for feature service metadata.

Usage

fetch_layer_metadata(url, token = NULL, call = rlang::caller_env())

Arguments

Details

• fetch_layer_metadata() given a request, fetches the metadata by setting the query parameter f=json

Value

returns a list object

Examples

# url is broken into parts to fit within 100 characters to avoid CRAN notes
url_parts <- c(
  "https://services.arcgis.com/P3ePLMYs2RVChkJx/ArcGIS/rest/services",
  "/USA_Counties_Generalized_Boundaries/FeatureServer/0"
)

furl <- paste0(url_parts, collapse = "")
meta <- fetch_layer_metadata(furl)
head(names(meta))

Esri field type mapping

Description

Infers Esri field types from R objects.

Usage

infer_esri_type(
  .data,
  arg = rlang::caller_arg(.data),
  call = rlang::caller_env()
)

get_ptype(field_type, n = 1, call = rlang::caller_env())

ptype_tbl(fields, n = 0, call = rlang::caller_env())

remote_ptype_tbl(fields, call = rlang::caller_env())

Arguments

Details

• get_ptype() takes a scalar character containing the Esri field type and returns a prototype of the pertinent R type

• infer_esri_type() takes a data frame-like object and infers the Esri field type from it.

• remote_ptype_tbl() takes a data frame of fields as derived from list_fields() and creates a lazy table proto type intended to be used with dbplyr integration

Field type mapping:

Esri field types are mapped as

• esriFieldTypeSmallInteger: integer

• esriFieldTypeSingle: double

• esriFieldTypeGUID: integer

• esriFieldTypeOID: integer

• esriFieldTypeInteger: integer

• esriFieldTypeBigInteger: double

• esriFieldTypeDouble: double

• esriFieldTypeString: character

• esriFieldTypeDate: date

R types are mapped as

• double: esriFieldTypeDouble

• integer: esriFieldTypeInteger

• character: esriFieldTypeString

• date: esriFieldTypeDate

• raw: esriFieldTypeBlob

Value

• get_pytpe() returns an object of the class of the prototype.

• ptype_tbl() takes a data.frame with columns name and type and creates an empty data.frame with the corresponding columns and R types

• remote_ptype_tbl() provides the results of ptype_tbl() as a lazy data frame from the dbplyr package.

• infer_esri_ptype() returns a data.frame with columns name, type, alias, nullable, and editable columns

  • This resembles that of the fields returned by a FeatureService

Examples

get_ptype("esriFieldTypeDouble")
inferred <- infer_esri_type(iris)
ptype_tbl(inferred)

Date handling

Description

Esri date fields are represented as milliseconds from the Unix Epoch.

Usage

is_date(x, tz)

date_to_ms(x, tz = "UTC")

from_esri_date(x)

Arguments

Details

• is_date(): checks if an object is a Date or POSIXt class object.

• date_to_ms() converts a date object to milliseconds from the Unix Epoch in the specified time zone.

Value

• is_date() returns a logical scalar

• date_to_ms() returns a numeric vector of times in milliseconds from the Unix Epoch in the specified time zone.

Examples

today <- Sys.Date()

is_date(today)

date_to_ms(today)

Parse Esri JSON

Description

Parses an Esri FeatureSet JSON object into an R object. If there is no geometry present, a data.frame is returned. If there is geometry, an sf object is returned.

Usage

parse_esri_json(string, ..., call = rlang::caller_env())

Arguments

Value

A data.frame. If geometry is found, returns an sf object.

Examples

esri_json <- '{
    "geometryType": "esriGeometryPolygon",
    "spatialReference": {
        "wkid": 4326
    },
    "hasZ": false,
    "hasM": false,
    "features": [
        {
            "attributes": {
                "id": 1
            },
            "geometry": {
                "rings": [
                    [
                        [0.0, 0.0],
                        [1.0, 0.0],
                        [1.0, 1.0],
                        [0.0, 1.0],
                        [0.0, 0.0]
                    ]
                ]
            }
        }
    ]
}'

parse_esri_json(esri_json)

Combine multiple data.frames

Description

A general function that takes a list of data.frames and returns a single and combines them into a single object. It will use the fastest method available. In order this is collapse::rowbind(), data.table::rbindlist(), vctrs::list_unchop(), then do.call(rbind.data.frame, x).

Usage

rbind_results(x, call = rlang::current_env(), .ptype = data.frame())

Arguments

Details

If all items in the list are data.frames, then the result will be a data.frame. If all elements are an sf object, then the result will be an sf object. If the items are mixed, the result will be a data.frame.

If any items are NULL, then an attribute null_elements will be attached to the result. The attribute is an integer vector of the indices that were NULL.

Value

see details.

Examples

x <- head(iris)
res <- rbind_results(list(x, NULL, x))
attr(res, "null_elements")

Validate CRS object

Description

Takes a representation of a CRS and ensures that it is a valid one. The CRS is validated using sf::st_crs() if it cannot be validated, a null CRS is returned.

Usage

validate_crs(crs, arg = rlang::caller_arg(crs), call = rlang::caller_env())

Arguments

Details

See sf::st_crs() for more details on valid representations.

Value

Returns a list of length 1 with an element named spatialReference which is itself a named list.

If the provided CRS returns a valid well-known ID (WKID) spatialReference contains a named element called wkid which is the integer value of the WKID. If the WKID is not known but the CRS returned is a valid well-known text representation the wkid field is NA and another field wkt contains the valid wkt.

Examples

# using epsg code integer or string representation
validate_crs(3857)
validate_crs("EPSG:4326")

# using a custom proj4 string
proj4string <- "+proj=longlat +datum=WGS84 +no_defs"

crs <- validate_crs(proj4string)

# using wkt2 (from above result)
crs <- validate_crs(crs$spatialReference$wkt)