Read GDAL/OGR via the sf package

Uses the ArrowArrayStream interface to GDAL exposed via the sf package to read GDAL/OGR-based data sources.

sd_read_sf(
  dsn,
  layer = NULL,
  ...,
  query = NA,
  options = NULL,
  drivers = NULL,
  filter = NULL,
  fid_column_name = NULL,
  lazy = FALSE
)

sd_ctx_read_sf(
  ctx,
  dsn,
  layer = NULL,
  ...,
  query = NA,
  options = NULL,
  drivers = NULL,
  filter = NULL,
  fid_column_name = NULL,
  lazy = FALSE
)

Arguments

dsn, layer

Description of datasource and layer. See sf::read_sf() for details.

...

Currently unused and must be empty

query

A SQL query to pass on to GDAL/OGR.

options

A character vector with layer open options in the form "KEY=VALUE".

drivers

A list of drivers to try if the dsn cannot be guessed.

filter

A spatial object that may be used to filter while reading. In the future SedonaDB will automatically calculate this value based on the query. May be any spatial object that can be converted to WKT via wk::as_wkt(). This filter's CRS must match that of the data.

fid_column_name

An optional name for the feature id (FID) column.

lazy

Use TRUE to stream the data from the source rather than collect first. This can be faster for large data sources but can also be confusing because the data may only be scanned exactly once.

ctx

A SedonaDB context created using sd_connect().

Value

A SedonaDB DataFrame.

Examples

nc_gpkg <- system.file("gpkg/nc.gpkg", package = "sf")
sd_read_sf(nc_gpkg)
#> <sedonab_dataframe: ?? x 15>
#> ┌─────────┬───┬─────────┬──────────────────────────────────────────────────────┐
#> │   AREA  ┆ … ┆ NWBIR79 ┆                         geom                         │
#> │ float64 ┆   ┆ float64 ┆                       geometry                       │
#> ╞═════════╪═══╪═════════╪══════════════════════════════════════════════════════╡
#> │   0.114 ┆ … ┆    19.0 ┆ MULTIPOLYGON(((-81.4727554321289 36.23435592651367,… │
#> ├╌╌╌╌╌╌╌╌╌┼╌╌╌┼╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
#> │   0.061 ┆ … ┆    12.0 ┆ MULTIPOLYGON(((-81.2398910522461 36.36536407470703,… │
#> ├╌╌╌╌╌╌╌╌╌┼╌╌╌┼╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
#> │   0.143 ┆ … ┆   260.0 ┆ MULTIPOLYGON(((-80.45634460449219 36.24255752563476… │
#> ├╌╌╌╌╌╌╌╌╌┼╌╌╌┼╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
#> │    0.07 ┆ … ┆   145.0 ┆ MULTIPOLYGON(((-76.00897216796875 36.31959533691406… │
#> ├╌╌╌╌╌╌╌╌╌┼╌╌╌┼╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
#> │   0.153 ┆ … ┆  1197.0 ┆ MULTIPOLYGON(((-77.21766662597656 36.24098205566406… │
#> ├╌╌╌╌╌╌╌╌╌┼╌╌╌┼╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
#> │   0.097 ┆ … ┆  1237.0 ┆ MULTIPOLYGON(((-76.74506378173828 36.23391723632812… │
#> └─────────┴───┴─────────┴──────────────────────────────────────────────────────┘
#> Preview of up to 6 row(s)