In the previous section we added a new feature that is the average of our numeric columns and stored the results in the variable nc_avgs. When looking at it, we can see that the AREA AND PERIMETER values are missing. These might be helpful at a later point.
library(arcgis) set_arc_token(auth_code()) nc_url <- "https://services1.arcgis.com/hLJbHVT9ZrDIzK0I/arcgis/rest/services/North%20Carolina%20SIDS/FeatureServer/0" nc <- arc_open(nc_url)
In this section we will use the function update_features() to modify these values. First, let’s create a new object called to_update that has the AREA and PERIMETER computed.
nc_area_perim <- nc_avgs |> mutate( AREA = st_area(geometry) / 1e10, PERIMETER = s2::s2_perimeter(geometry) / 1e5 ) nc_area_perim
Simple feature collection with 1 feature and 15 fields
Geometry type: MULTIPOLYGON
Dimension: XY
Bounding box: xmin: -84.32385 ymin: 33.88199 xmax: -75.45698 ymax: 36.58965
Geodetic CRS: NAD27
object_id AREA PERIMETER CNTY_ CNTY_ID NAME FIPS FIPSNO CRESS_ID BIR74 SID74 NWBIR74 BIR79 SID79 NWBIR79 geometry
1 101 12.70259 [m^2] 33.58819 NA NA Total NA NA NA 3299.62 6.67 1050.81 4223.92 8.36 1352.81 MULTIPOLYGON (((-75.9248 36...
Like add_features(), we need to be able to match columns to their respective fields. The match_on argument is used to specify if the column names match the field name or field alias.
In the case of update_features() we also need to be able to match the features in the sf dataset to the exact feature in the FeatureLayer. We do this by providing the object ID of the feature. This tells ArcGIS which features we are actually going to update.
When using update_features() we should be aware that every column present in the sf object will be updated including the geometry. For this reason, we should select only those columns which we truly wish to update.
to_update <- nc_area_perim |> st_drop_geometry() |> select(object_id, AREA, PERIMETER) to_update
Here we use sf::st_drop_geometry()to remove the geometry of our object since we do not want to update the geometry in our FeatureLayer. We also only select the object_id, AREA, and PERIMETER columns so that we do not make errant updates.
update_res <- update_features(nc, to_update)
$updateResults
objectId uniqueId globalId success
1 101 101 NA TRUE
Our update process was successful! We can repeat our previous query to verify this.
nc |> filter(NAME == "Total") |> collect()
Simple feature collection with 1 feature and 15 fields
Geometry type: MULTIPOLYGON
Dimension: XY
Bounding box: xmin: -84.32385 ymin: 33.88199 xmax: -75.45698 ymax: 36.58965
Geodetic CRS: NAD27
object_id AREA PERIMETER CNTY_ CNTY_ID NAME FIPS FIPSNO CRESS_ID BIR74 SID74 NWBIR74 BIR79 SID79 NWBIR79 geometry
1 101 12.70259 33.58819 NA NA Total NA NA NA 3299.62 6.67 1050.81 4223.92 8.36 1352.81 MULTIPOLYGON (((-75.9248 36...