What are the best practices for using R to query GIS databases?

1 Connect to GIS databases

The first step to query GIS databases with R is to establish a connection to the database server. There are different packages and functions that you can use to connect to different types of databases, such as PostgreSQL, SQLite, Oracle, or MongoDB. Some of the most common ones are DBI, RPostgreSQL, RSQLite, ROracle, and mongolite. You can use the dbConnect function from the DBI package to create a connection object, and then use the dbGetQuery function to send SQL queries to the database and return the results as data frames. For example, to connect to a PostgreSQL database with the PostGIS extension, you can use the following code:

library(DBI)
library(RPostgreSQL)
conn <- dbConnect(RPostgreSQL::PostgreSQL(), dbname = "gisdb", user = "user", password = "password")
result <- dbGetQuery(conn, "SELECT * FROM spatial_table")        

3 Handle spatial objects and projections

One of the challenges of querying GIS databases with R is to handle the spatial objects and projections that are returned by the queries. Spatial objects are data structures that store the coordinates, attributes, and metadata of spatial features, such as points, lines, polygons, or raster images. Projections are systems that define how the coordinates of spatial objects are mapped to the earth's surface. To work with spatial objects and projections in R, you need to use packages that can read, write, manipulate, and transform spatial data, such as sf, sp, rgdal, or raster. For example, you can use the st_read function from the sf package to read a spatial object from a query result, and then use the st_transform function to change its projection. For example, to read a spatial object from a query result and transform it to the EPSG:4326 projection, you can use the following code:

library(sf)
sp_obj <- st_read(result, geom_column = "geom")
sp_obj <- st_transform(sp_obj, 4326)        

4 Optimize your queries for performance and efficiency

To optimize queries for GIS databases with R, you should aim to minimize the amount of data transferred from the database to R and maximize the use of the database's spatial capabilities. You can do this by creating spatial indexes on geometry or geography columns using the CREATE INDEX statement with the USING GIST option. Additionally, subqueries can be used to filter data before joining or aggregating, reducing the size of the data that is joined or aggregated. Furthermore, you should use the database's spatial functions and operators instead of R's as they are optimized for handling spatial data and can perform faster and more accurately. For example, you can use the ST_Area function from the database to calculate the area in a query and return it as a column.