overtureR

Installation

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

Key Features

Usage

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,primary=names$primary,geometry  )|>  collect()# Plot the resultsggplot(counties)+  geom_sf(aes(fill= as.numeric(sf::st_area(geometry))),color="white",size=0.2)+viridis::scale_fill_viridis(option="plasma",guide=FALSE)+  labs(title="Pennsylvania Counties by Area",caption="Data: Overture Maps"  )

library(overtureR)library(dplyr)# lazily load the full `mountains` datasetmountains<- 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 <POINT [°]>

Downloading data locally

library(overtureR)library(ggplot2)library(dplyr)library(rayshader)# Define a bounding box for New York Citybroadway<- c(xmin=-73.9901,ymin=40.755488,xmax=-73.98,ymax=40.76206)# Download building data for NYC to a local directorylocal_buildings<- open_curtain("building",broadway)|>   record_overture(output_dir= tempdir(),overwrite=TRUE)# The downloaded data is returned as a `dbplyr` object, same as the original (but faster!)broadway_buildings<-local_buildings|>   filter(!is.na(height))|>   mutate(height= round(height))|>   collect()p<- ggplot(broadway_buildings)+  geom_sf(aes(fill=height))+  scale_fill_distiller(palette="Oranges",direction=1)+# guides(fill = FALSE) +  labs(title="Buildings on Broadway",caption="Data: Overture Maps",fill="")# Convert to 3D and renderplot_gg(p,multicore=TRUE,width=6,height=5,scale=250,windowsize= c(1032,860),zoom=0.55,phi=40,theta=0,solid=FALSE,offset_edges=TRUE,sunangle=75)render_snapshot(clear=TRUE)

Roadmap