dplyr methods for duckspatial_df

These methods use dplyr's extension mechanism (dplyr_reconstruct) to properly preserve spatial metadata through operations.

Executes the accumulated query and stores the result in a DuckDB temporary table. The result remains lazy (a duckspatial_df) but points to the materialized data, avoiding repeated computation of complex query plans.

Usage

# S3 method for class 'duckspatial_df'
dplyr_reconstruct(data, template)

# S3 method for class 'duckspatial_df'
left_join(
  x,
  y,
  by = NULL,
  copy = FALSE,
  suffix = c(".x", ".y"),
  ...,
  keep = NULL,
  na_matches = c("na", "never"),
  relationship = NULL
)

# S3 method for class 'duckspatial_df'
inner_join(
  x,
  y,
  by = NULL,
  copy = FALSE,
  suffix = c(".x", ".y"),
  ...,
  keep = NULL,
  na_matches = c("na", "never"),
  relationship = NULL
)

# S3 method for class 'duckspatial_df'
head(x, n = 6L, ...)

# S3 method for class 'duckspatial_df'
count(x, ..., wt = NULL, sort = FALSE, name = NULL)

# S3 method for class 'duckspatial_df'
glimpse(x, width = NULL, ...)

# S3 method for class 'duckspatial_df'
compute(x, name = NULL, temporary = TRUE, ...)

# S3 method for class 'duckspatial_df'
distinct(.data, ..., .keep_all = FALSE)

Arguments

x

A duckspatial_df object

...

Additional arguments passed to dplyr::compute()

name

Optional name for the result table. If NULL, a unique temporary name is generated.

temporary

If TRUE (default), creates a temporary table that is automatically cleaned up when the connection closes.

Value

A new duckspatial_df pointing to the materialized table, with spatial metadata (CRS, geometry column) preserved.

Details

This is useful when you want to:

• Cache intermediate results for reuse across multiple subsequent operations

• Simplify complex query plans before heavy operations like spatial joins

• Force execution at a specific point without pulling data into R memory

Examples

if (FALSE) { # \dontrun{
library(dplyr)

# Complex pipeline - compute() caches intermediate result
result <- countries |>
  filter(POP_EST > 50000000) |>
  ddbs_filter(argentina, predicate = "touches") |>
  compute() |>  # Execute and cache here
  select(NAME_ENGL, POP_EST) |>
  ddbs_join(rivers, join = "intersects")

# Check query plan - should reference the cached table
show_query(result)
} # }