Getting Started

Introduction

This vignette demonstrates how to use the {dbSpatial} package to create a DuckDB database with spatial points and polygons starting from various data sources.

All code chunks below are evaluated only when the duckdb package is available in the check environment.

Creating a DuckDB connection

# create db connection in memory
duckdb_conn = DBI::dbConnect(duckdb::duckdb(), ":memory:")
DBI::dbExecute(duckdb_conn, "SET threads = 1")

Reading in spatial data from various sources

From data.frames

# test data
test_data = data.frame(x = 1:10, y = 1:10, id = 1:10)

# df, tbl
# specify x and y column names to cast to a point geometry
a <- dbSpatial(conn = duckdb_conn,
               name = "test_points",
               value = test_data,
               x_colName = "x",
               y_colName = "y",
               overwrite = TRUE)
a

From .csv file

# test data
test_data = data.frame(x = 1:10, y = 1:10, id = 1:10)

# write to file
test_file <- tempfile(fileext = ".csv")
write.csv(test_data, test_file, row.names = FALSE)

# load file in db
a <- dbSpatial(conn = duckdb_conn,
               name = "test_points",
               value = test_file,
               x_colName = "x",
               y_colName = "y",
               overwrite = TRUE)
a

From {terra} objects: SpatVector

# load terra package
library(terra)

# Create a SpatVector from the data.frame
dummy_spatvector <- terra::vect(test_data, geom = c("x", "y"))

# Load SpatVector in db
dbSpatial(conn = duckdb_conn,
          name = "spatVector_proxy",
          value = dummy_spatvector,
          overwrite = TRUE)

Need a high-speed mirror for your open-source project?
Contact our mirror admin team at info@clientvps.com.

This archive is provided as a free public service to the community.
Proudly supported by infrastructure from VPSPulse , RxServers , BuyNumber , UnitVPS , OffshoreName and secure payment technology by ArionPay.