overtureR

# CRAN application pending
# install.packages("overtureR")
devtools::install_github("arthurgailes/overtureR")

library(overtureR)
library(dplyr)
library(ggplot2)

counties <- open_curtain("division_area") |>
  # in R, filtering on variables must come before removing them via select
  filter(subtype == "county" & country == "US" & region == "US-PA") |>
  transmute(
    id,
    division_id,
    # STRUCT/MAP columns can be accessed as column[["subcolumn"]] in transmute
    primary = names[["primary"]],
    geometry
  )

# Plot the results
counties |>
  collect_sf() |>
  ggplot() +
  geom_sf(aes(fill = as.numeric(sf::st_area(geometry))))

library(overtureR)
library(dplyr)

# lazily load the full `mountains` dataset
mountains <- open_curtain(type = "*", theme = "places") |>
  transmute(
    id,
    primary_name = names[["primary"]],
    x = bbox[["xmin"]],
    y = bbox[["ymin"]],
    main_category = categories[["primary"]],
    primary_source = sources[[1]][["dataset"]],
    confidence,
    geometry # currently no duckdb spatial implementation
  ) |>
  filter(main_category == "mountain" & confidence > .90)

head(mountains)
#> # Source:   SQL [6 x 8]
#> # Database: DuckDB v1.0.0 [Arthur.Gailes@Windows 10 x64:R 4.2.1/:memory:]
#>   id            primary_name     x     y main_category primary_source confidence
#>   <chr>         <chr>        <dbl> <dbl> <chr>         <chr>               <dbl>
#> 1 08f464e0e312… Kawaikini    -159.  22.1 mountain      meta                0.954
#> 2 08f464e3b1a2… Kalepa       -159.  22.0 mountain      meta                0.938
#> 3 08f464e05984… Sleeping Gi… -159.  22.1 mountain      meta                0.945
#> 4 08f464e3a4d0… Nounou-East… -159.  22.1 mountain      meta                0.945
#> 5 08f464e05514… Makaleha Mo… -159.  22.1 mountain      meta                0.965
#> 6 08f464e03538… Makana       -160.  22.2 mountain      meta                0.938
#> # ℹ 1 more variable: geometry <list>