opendatatoronto is an R interface to the City of Toronto Open Data Portal.
The goal of the package is to help read data directly into R without
needing to manually download it via the portal.
In the portal, datasets are called packages. You can
see a list of available packages by using list_packages().
This will show metadata about the package, including what topics
(i.e. tags) the package covers, a description of it, any civic issues it
addresses, how many resources there are (and their formats), how often
it is is refreshed and when it was last refreshed.
library(opendatatoronto)
packages <- list_packages(limit = 10)
packages
#> # A tibble: 10 × 11
#> title id topics civic_issues publisher excerpt dataset_category
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 Toronto Island … toro… "Tran… NULL Parks, F… "This … Table
#> 2 Licensed Dogs a… lice… "Comm… NULL Municipa… "The r… Table
#> 3 Multi-Tenant (R… mult… "Perm… NULL Municipa… "This … Table
#> 4 Polls conducted… 7bce… "City… NULL City Cle… "Polls… Table
#> 5 Rain Gauge Loca… f293… "c(\"… NULL Toronto … "This … Document
#> 6 Sidewalk Constr… side… "Tran… NULL Transpor… "The C… Map
#> 7 Traffic Signal … 7dda… "Tran… Mobility Transpor… "This … Document
#> 8 Daily Shelter &… 21c8… "c(\"… NULL Toronto … "Daily… Table
#> 9 Traffic Volumes… traf… "Tran… Mobility Transpor… "This … Table
#> 10 Toronto Open Da… open… "City… NULL Informat… "This … Table
#> # ℹ 4 more variables: num_resources <int>, formats <chr>, refresh_rate <chr>,
#> # last_refreshed <date>Or, you can search packages by title using
search_packages():
apartment_packages <- search_packages("Apartment")
apartment_packages
#> # A tibble: 2 × 11
#> title id topics civic_issues publisher excerpt dataset_category
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 Apartment Buildi… 4ef8… "c(\"… "NULL" Municipa… This d… Table
#> 2 Apartment Buildi… 2b98… "c(\"… "c(\"Afford… Municipa… This d… Table
#> # ℹ 4 more variables: num_resources <int>, formats <chr>, refresh_rate <chr>,
#> # last_refreshed <date>You can also see metadata for one specific package using
show_package():
show_package("996cfe8d-fb35-40ce-b569-698d51fc683b")
#> # A tibble: 4 × 11
#> title id topics civic_issues publisher excerpt dataset_category
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 TTC Subway Delay… 996c… Trans… NA Toronto … TTC Su… Document
#> 2 TTC Subway Delay… 996c… Trans… NA Toronto … TTC Su… Document
#> 3 TTC Subway Delay… 996c… Trans… NA Toronto … TTC Su… Document
#> 4 TTC Subway Delay… 996c… Trans… NA Toronto … TTC Su… Document
#> # ℹ 4 more variables: num_resources <int>, formats <chr>, refresh_rate <chr>,
#> # last_refreshed <date>Within a package, there are a number of resources - e.g. CSV, XSLX, JSON, SHP files, and more. Resources are the actual “data”.
For a given package, you can get a list of resources using
list_package_resources(), either by using a package found
via search_packages() or list_packages():
apartment_building_registration_package <- search_packages("Apartment Building Registration")
apartment_building_registration_resources <- apartment_building_registration_package %>%
list_package_resources()
apartment_building_registration_resources
#> # A tibble: 4 × 4
#> name id format last_modified
#> <chr> <chr> <chr> <date>
#> 1 Apartment Building Registration Data 3ad76a8c-0518-… CSV 2025-03-05
#> 2 Apartment Building Registration Data.csv 97b8b7a4-baca-… CSV 2025-03-05
#> 3 Apartment Building Registration Data.xml b1b6df2c-2c7d-… XML 2025-03-05
#> 4 Apartment Building Registration Data.json 005b39d2-4503-… JSON 2025-03-05or by passing the package’s portal URL directly:
list_package_resources("https://open.toronto.ca/dataset/apartment-building-registration/")
#> # A tibble: 4 × 4
#> name id format last_modified
#> <chr> <chr> <chr> <date>
#> 1 Apartment Building Registration Data 3ad76a8c-0518-… CSV 2025-03-05
#> 2 Apartment Building Registration Data.csv 97b8b7a4-baca-… CSV 2025-03-05
#> 3 Apartment Building Registration Data.xml b1b6df2c-2c7d-… XML 2025-03-05
#> 4 Apartment Building Registration Data.json 005b39d2-4503-… JSON 2025-03-05Finally (and most usefully!), you can download the resource (i.e.,
the actual data) directly into R using get_resource():
library(dplyr)
apartment_building_registration_data <- apartment_building_registration_resources %>%
filter(name == "Apartment Building Registration Data") %>%
get_resource()
apartment_building_registration_data
#> # A tibble: 3,597 × 70
#> `_id` AIR_CONDITIONING_TYPE AMENITIES_AVAILABLE ANNUAL_FIRE_ALARM_TEST_…¹
#> <int> <chr> <chr> <chr>
#> 1 134649 NONE NA YES
#> 2 134650 NONE NA YES
#> 3 134651 NONE NA YES
#> 4 134652 NONE NA YES
#> 5 134653 NONE NA YES
#> 6 134654 INDIVIDUAL UNITS NA NO
#> 7 134655 NONE NA YES
#> 8 134656 NONE NA YES
#> 9 134657 NONE NA YES
#> 10 134658 INDIVIDUAL UNITS Indoor recreation room YES
#> # ℹ 3,587 more rows
#> # ℹ abbreviated name: ¹ANNUAL_FIRE_ALARM_TEST_RECORDS
#> # ℹ 66 more variables: ANNUAL_FIRE_PUMP_FLOW_TEST_RECORDS <chr>,
#> # APPROVED_FIRE_SAFETY_PLAN <chr>, BALCONIES <chr>,
#> # BARRIER_FREE_ACCESSIBILTY_ENTR <chr>, BIKE_PARKING <chr>,
#> # CONFIRMED_STOREYS <int>, CONFIRMED_UNITS <int>,
#> # DATE_OF_LAST_INSPECTION_BY_TSSA <chr>, …The opendatatoronto package can currently handle the
download of CSV, XLS/XLSX, XML, JSON, SHP, and GeoJSON resources, as
well as ZIP resources that contain multiple files. For more information,
see the following vignettes: