There is a ton of spatial data on the City of Toronto Open Data Portal. Spatial resources are retrieved the same way as all other resources, by using get_resource(), and may require the sf and geojsonsf packages.
We can look at bicycle parking in Toronto. The result is an sf object with WGS84 projection.
library(opendatatoronto)
library(dplyr)
bike_parking_racks <- search_packages("Bicycle Parking Racks") %>%
list_package_resources() %>%
filter(name == "Bicycle Parking Racks Data") %>%
get_resource()
bike_parking_racks
#> Simple feature collection with 241 features and 30 fields
#> geometry type: POINT
#> dimension: XY
#> bbox: xmin: -79.59575 ymin: 43.60367 xmax: -79.25724 ymax: 43.82303
#> epsg (SRID): 4326
#> proj4string: +proj=longlat +datum=WGS84 +no_defs
#> # A tibble: 241 x 31
#> `_id` ADDRESS_POINT_ID ADDRESS_NUMBER LINEAR_NAME_FULL ADDRESS_FULL
#> <int> <int> <chr> <chr> <chr>
#> 1 4097 10335148 538 Lansdowne Ave 538 Lansdow…
#> 2 3857 51630 5 Bartonville Ave… 5 Bartonvil…
#> 3 3858 310564 150 Borough Dr 150 Borough…
#> 4 3859 367443 71 New Forest Sq 71 New Fore…
#> 5 3860 379258 95 River Grove Dr 95 River Gr…
#> 6 3861 394585 24 Victoria Park A… 24 Victoria…
#> 7 3862 772775 315 Bloor St W 315 Bloor S…
#> 8 3863 772800 398 Bloor St W 398 Bloor S…
#> 9 3864 772833 481 Bloor St W 481 Bloor S…
#> 10 3865 772852 509 Bloor St W 509 Bloor S…
#> # … with 231 more rows, and 26 more variables: POSTAL_CODE <chr>,
#> # MUNICIPALITY <chr>, CITY <chr>, CENTRELINE_ID <int>, LO_NUM <int>,
#> # LO_NUM_SUF <chr>, HI_NUM <int>, HI_NUM_SUF <lgl>,
#> # LINEAR_NAME_ID <int>, WARD_NAME <chr>, X <dbl>, Y <dbl>,
#> # LONGITUDE <dbl>, LATITUDE <dbl>, MI_PRINX <int>, OBJECTID <int>,
#> # CAPACITY <int>, MULTIMODAL <chr>, SEASONAL <chr>, SHELTERED <chr>,
#> # SURFACE <chr>, STATUS <chr>, LOCATION <chr>, NOTES <chr>,
#> # MAP_CLASS <chr>, geometry <POINT [°]>If we want to plot this data on a map of Toronto, data to map the different neighbourhoods of Toronto is also available from the portal!
neighbourhoods <- list_package_resources("https://open.toronto.ca/dataset/neighbourhoods/") %>%
get_resource()
neighbourhoods[c("AREA_NAME", "geometry")]
#> Simple feature collection with 140 features and 1 field
#> geometry type: POLYGON
#> dimension: XY
#> bbox: xmin: -79.63926 ymin: 43.581 xmax: -79.11545 ymax: 43.85546
#> epsg (SRID): 4326
#> proj4string: +proj=longlat +datum=WGS84 +no_defs
#> # A tibble: 140 x 2
#> AREA_NAME geometry
#> <chr> <POLYGON [°]>
#> 1 Wychwood (94) ((-79.43592 43.68015, -79.43492 43.68037, -79.4339…
#> 2 Yonge-Eglinton (100) ((-79.41096 43.70408, -79.40962 43.70436, -79.4085…
#> 3 Yonge-St.Clair (97) ((-79.39119 43.68108, -79.39141 43.68097, -79.3932…
#> 4 York University Hei… ((-79.50529 43.75987, -79.50488 43.75996, -79.5049…
#> 5 Yorkdale-Glen Park … ((-79.43969 43.70561, -79.44011 43.70559, -79.4410…
#> 6 Lambton Baby Point … ((-79.50552 43.66281, -79.50577 43.66291, -79.5061…
#> 7 Lansing-Westgate (3… ((-79.43998 43.76156, -79.44004 43.76177, -79.4404…
#> 8 Lawrence Park North… ((-79.39008 43.72768, -79.39199 43.72726, -79.3939…
#> 9 Lawrence Park South… ((-79.41096 43.70408, -79.41165 43.70394, -79.4120…
#> 10 Leaside-Bennington … ((-79.37749 43.71309, -79.37762 43.71385, -79.3779…
#> # … with 130 more rowsThen, we can plot the bike racks along with a map of Toronto:
library(ggplot2)
ggplot() +
geom_sf(data = neighbourhoods[["geometry"]]) +
geom_sf(data = bike_parking_racks) +
theme_minimal()