arc.write error

I am getting the following error while writing an sf object dataframe to a GDB. The dataframe imported is a feature class from a GDB brought in via the sf package. After some data manipulation I am trying to write the dataframe back into the same GDB as a new feature class/layer.

I read in another post that sf does not support writing a feature class to a gdb so I had to use arcgisbinding. However, when I use sf::st_write to write the df as a shapefile, it works just fine.

The arc.write works on the sample nc dataset in the sf package but does not work on my dataset. I don’t know if it matters but my sf object has more than 700,000 rows and 82 columns.

How can I fix this?

Error

Error in .call_proxy("arc_write", path, pairlist(data = data, coords = coords,  : 
  insert row failed

Code

library(sf)
 library(arcgisbinding)

arc.check_product()

product: ArcGIS Pro (12.8.0.29751)
license: Advanced
version: 1.0.1.244 
 
 arc.write("path/GDB.gdb/Feature_Class_Name", data = df, overwrite = TRUE)

[orhuna]

Another solution we can try is if you have shapefile you could do the following:

data.obj <- arc.open("full_path_to_shapefile.shp")
data.arc <- arc.select(data.obj)
arc.write("write_path", data.arc)

The above should work with the caveat that your data will be at the mercy of what we could write to a shapefile and the checks that went into creating that shapefile to start with, or lack thereof.

[orhuna]

@Saadi4469 thank you. As per your question on why shapefile works, shapefiles are not subject to stringent value and geometry checks that feature classes are subject to. This means it is easy to export to a .shp but you may end up with problematic geometries in terms of their description (vertices) and/or relationships (such as coincidence, holes, etc.). Also please double check to make sure that the geometries and the values make sense. For shapefiles, if it cannot output a column it silently drops it whereas the arc object throws the error that you saw.

Thank you for the column types. I do see some issues in the column types. I do not know details of your dataset but some columns that should be the same type are different for instance high_chance_00_year00 is a character type whereas low_chance_15_year00 is a numeric type. Would you spot check some of these columns that are inconsistent? If R converts the whole column to a character that might mean the somewhere there may be an inconsistent row value at that column such as , insteda of . or some other character.

An easy solution here would be casting. You can set the types of the columns explicitly before writing it out.