library(arcgeocoder)
library(dplyr)

# Create a data frame with addresses.
some_addresses <- tribble(
  ~name, ~addr,
  "White House", "1600 Pennsylvania Ave NW, Washington, DC",
  "Transamerica Pyramid", "600 Montgomery St, San Francisco, CA 94111",
  "Willis Tower", "233 S Wacker Dr, Chicago, IL 60606"
)

# Geocode the addresses.
lat_longs <- arc_geo(
  some_addresses$addr,
  lat = "latitude",
  long = "longitude",
  progressbar = FALSE
)

reverse <- arc_reverse_geo(
  x = lat_longs$longitude,
  y = lat_longs$latitude,
  address = "address_found",
  progressbar = FALSE
)

library(ggplot2) # For plotting.

# Step 1: Locate the Eiffel Tower using a multi-field query.

eiffel_tower <- arc_geo_multi(
  address = "Tour Eiffel",
  city = "Paris",
  countrycode = "FR",
  langcode = "FR",
  custom_query = list(outFields = "LongLabel")
)

# Display results.
eiffel_tower |>
  select(lon, lat, LongLabel)
#> # A tibble: 1 × 3
#>     lon   lat LongLabel                                                         
#>   <dbl> <dbl> <chr>                                                             
#> 1  2.29  48.9 Tour Eiffel, 3 Rue de l'Université, 75007, 7e Arrondissement, Par…

# Use `lon` and `lat` to boost the search with `category = "Food"`.
food_eiffel <- arc_geo_categories(
  "Food",
  x = eiffel_tower$lon,
  y = eiffel_tower$lat,
  limit = 50,
  full_results = TRUE
)

# Plot by food type.
ggplot(eiffel_tower, aes(x, y)) +
  geom_point(shape = 17, color = "red", size = 4) +
  geom_point(data = food_eiffel, aes(x, y, color = Type)) +
  labs(
    title = "Food near the Eiffel Tower",
    subtitle = "Using arcgeocoder",
    color = "Type of place",
    x = "",
    y = "",
    caption = "Data from the ArcGIS REST API service"
  )

library(sf)

food_eiffel_sf <- st_as_sf(
  food_eiffel,
  coords = c("lon", "lat"),
  # Set the CRS of the resulting coordinates.
  crs = eiffel_tower$wkid
)

food_eiffel_sf
#> Simple feature collection with 50 features and 85 fields
#> Geometry type: POINT
#> Dimension:     XY
#> Bounding box:  xmin: 2.2899 ymin: 48.8565 xmax: 2.299326 ymax: 48.86134
#> Geodetic CRS:  WGS 84
#> # A tibble: 50 × 86
#>    q_category   q_x   q_y q_bbox_xmin q_bbox_ymin q_bbox_xmax q_bbox_ymax
#>  * <chr>      <dbl> <dbl> <lgl>       <lgl>       <lgl>       <lgl>      
#>  1 Food        2.29  48.9 NA          NA          NA          NA         
#>  2 Food        2.29  48.9 NA          NA          NA          NA         
#>  3 Food        2.29  48.9 NA          NA          NA          NA         
#>  4 Food        2.29  48.9 NA          NA          NA          NA         
#>  5 Food        2.29  48.9 NA          NA          NA          NA         
#>  6 Food        2.29  48.9 NA          NA          NA          NA         
#>  7 Food        2.29  48.9 NA          NA          NA          NA         
#>  8 Food        2.29  48.9 NA          NA          NA          NA         
#>  9 Food        2.29  48.9 NA          NA          NA          NA         
#> 10 Food        2.29  48.9 NA          NA          NA          NA         
#> # ℹ 40 more rows
#> # ℹ 79 more variables: address <chr>, score <int>, x <dbl>, y <dbl>,
#> #   Loc_name <chr>, Status <chr>, Score <int>, Match_addr <chr>,
#> #   LongLabel <chr>, ShortLabel <chr>, Addr_type <chr>, Type <chr>,
#> #   PlaceName <chr>, Place_addr <chr>, Phone <chr>, URL <chr>, Rank <int>,
#> #   AddBldg <chr>, AddNum <chr>, AddNumFrom <chr>, AddNumTo <chr>,
#> #   AddRange <chr>, Side <chr>, StPreDir <chr>, StPreType <chr>, …

ggplot(food_eiffel_sf) +
  geom_sf(aes(color = Type)) +
  coord_sf(crs = 3035)