The cancensus package was developed to provide users with a way to access Canadian Census in a programmatic way following good tidy data practices. While the structure and data in cancensus is unique to Canadian Census data, this package is inspired in part by tidycensus, a package to interface with the US Census Bureau data APIs.
As Statistics Canada does not provide direct API access to Census data, cancensus retrieves Census data indirectly through the CensusMapper API. CensusMapper is a project by Jens von Bergmann, one of the authors of cancensus, to provide interactive geographic visualizations of Canadian Census data. CensusMapper databases store all publicly available data from Statistics Canada for the 2006, 2011, and 2016 Censuses. Censusmapper data can be accessed via an API and cancensus is built to interface directly with it.
cancensus requires a valid CensusMapper API key to use. You can obtain a free API key by signing up for a CensusMapper account. CensusMapper API keys are free and public API quotas are generous; however, due to incremental costs of serving large quantities of data, there limits to API usage in place. For most use cases, these API limits should not be an issue. Production uses with large extracts of fine grained geographies may run into API quota limits. For larger quotas, please get in touch with Jens directly.
To check your API key, just go to “Edit Profile” (in the top-right of the CensusMapper menu bar). Once you have your key, you can store it in your system environment so it is automatically used in API calls. To do so just enter set_api_key(<your_api_key>, install = TRUE)
The stable version of cancensus can be easily installed from CRAN.
install.packages("cancensus")
library(cancensus)
options(cancensus.api_key = "your_api_key")
options(cancensus.cache_path = "custom cache path")Alternatively, the latest development version can be installed from Github using devtools.
# install.packages("devtools")
devtools::install_github("mountainmath/cancensus")
library(cancensus)
options(cancensus.api_key = "your_api_key")
options(cancensus.cache_path = "custom cache path")For performance reasons, and to avoid unnecessarily drawing down API quotas, cancensus caches data queries under the hood. By default, cancensus caches in R’s temporary directory, but this cache is not persistent across sessions. In order to speed up performance, reduce quota usage, and reduce the need for unnecessary network calls, we recommend assigning a persistent local cache using set_cache_path(<local cache path>, install = TRUE), this enables more efficient loading and reuse of downloaded data.. Users will be prompted with a suggestion to change their default cache location when making API calls if one has not been set yet.
cancensus provides three different functions for retrieving Census data: * get_census to retrieve Census data and geography as a spatial dataset * get_census_data to retrieve Census data only as a flat data frame * get_census_geometry to retrieve Census geography only as a collection of spatial polygons.
get_census takes as inputs a dataset parameter, a list of specified regions, a vector of Census variables, and a Census geography level. You can specify one of three options for spatial formats: NA to return data only, sf to return an sf-class data frame, or sp to return a SpatialPolygonsDataFrame object.
# Returns a data frame with data only
census_data <- get_census(dataset='CA16', regions=list(CMA="59933"),
vectors=c("v_CA16_408","v_CA16_409","v_CA16_410"),
level='CSD', use_cache = FALSE, geo_format = NA, quiet = TRUE)
# Returns data and geography as an sf-class data frame
census_data <- get_census(dataset='CA16', regions=list(CMA="59933"),
vectors=c("v_CA16_408","v_CA16_409","v_CA16_410"),
level='CSD', use_cache = FALSE, geo_format = 'sf', quiet = TRUE)
# Returns a SpatialPolygonsDataFrame object with data and geography
census_data <- get_census(dataset='CA16', regions=list(CMA="59933"),
vectors=c("v_CA16_408","v_CA16_409","v_CA16_410"),
level='CSD', use_cache = FALSE, geo_format = 'sp', quiet = TRUE)cancensus utilizes caching to increase speed, minimize API token usage, and to make data available offline. Downloaded data is hashed and stored locally so if a call is made to access the same data, cancensus will read the local version instead. To force cancensus to refresh the data, specify use_cache = FALSE as a parameter for get_census.
Additional parameters for advanced options can be viewed by running ?get_census.
cancensus can access Statistics Canada Census data for Census years 1996, 2001, 2006, 2011, and 2016 . You can run list_census_datasets to check what datasets are currently available for access through the CensusMapper API. Additional data for the 2016 Census will be included in Censusmapper within a day or two after public release by Statistics Canada. Statistics Canada maintains a release schedule for the Census 2016 Program which can be viewed on their website.
Thanks to contributions by the Canada Mortgage and Housing Corporation (CMHC), cancensus now includes additional Census-linked datasets as open-data releases. These include annual taxfiler data at the census tract level for tax years 2000 through 2017, which includes data on incomes and demographics, as well as specialized crosstabs for Structural type of dwelling by Document type, which details occupancy status for residences. These crosstabs are available for the 2001, 2006, 2011, and 2016 Census years at all levels starting with census tract.
The function list_census_datasets() will show all available datasets alongside their metadata.
list_census_datasets()
#> # A tibble: 28 × 6
#> dataset description geo_dataset attribution reference reference_url
#> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 CA1996 1996 Canada Ce… CA1996 StatCan 1996… 92-351-U https://www150.s…
#> 2 CA01 2001 Canada Ce… CA01 StatCan 2001… 92-378-X https://www150.s…
#> 3 CA06 2006 Canada Ce… CA06 StatCan 2006… 92-566-X https://www150.s…
#> 4 CA11 2011 Canada Ce… CA11 StatCan 2011… 98-301-X… https://www12.st…
#> 5 CA16 2016 Canada Ce… CA16 StatCan 2016… 98-301-X https://www150.s…
#> 6 CA01xSD 2001 Canada Ce… CA01 StatCan 2001… 92-378-X https://www150.s…
#> 7 CA06xSD 2006 Canada Ce… CA06 StatCan 2006… 92-566-X https://www150.s…
#> 8 CA11xSD 2011 Canada Ce… CA11 StatCan 2011… 98-301-X https://www150.s…
#> 9 CA16xSD 2016 Canada Ce… CA16 StatCan 2016… 98-301-X https://www150.s…
#> 10 TX2000 2000 T1FF taxf… CA1996 StatCan 2000… 72-212-X https://www150.s…
#> # … with 18 more rowsAs other Census datasets become available via the CensusMapper API, they will be listed as output when calling list_census_datasets().
Census data is aggregated at multiple geographic levels. Census geographies at the national (C), provincial (PR), census metropolitan area (CMA), census agglomeration (CA), census division (CD), and census subdivision (CSD) are defined as named census regions.
Canadian Census geography can change in between Census periods. cancensus provides a function, list_census_regions(dataset), to display all named census regions and their corresponding id for a given census dataset.
list_census_regions("CA16")
#> # A tibble: 5,518 × 8
#> region name level pop municipal_status CMA_UID CD_UID PR_UID
#> <chr> <chr> <chr> <int> <chr> <chr> <chr> <chr>
#> 1 01 Canada C 3.52e7 <NA> <NA> <NA> <NA>
#> 2 35 Ontario PR 1.34e7 <NA> <NA> <NA> <NA>
#> 3 24 Quebec PR 8.16e6 <NA> <NA> <NA> <NA>
#> 4 59 British Columbia PR 4.65e6 <NA> <NA> <NA> <NA>
#> 5 48 Alberta PR 4.07e6 <NA> <NA> <NA> <NA>
#> 6 46 Manitoba PR 1.28e6 <NA> <NA> <NA> <NA>
#> 7 47 Saskatchewan PR 1.10e6 <NA> <NA> <NA> <NA>
#> 8 12 Nova Scotia PR 9.24e5 <NA> <NA> <NA> <NA>
#> 9 13 New Brunswick PR 7.47e5 <NA> <NA> <NA> <NA>
#> 10 10 Newfoundland and… PR 5.20e5 <NA> <NA> <NA> <NA>
#> # … with 5,508 more rowsThe regions parameter in get_census requires as input a list of region id strings that correspond to that regions geoid. You can combine different regions together into region lists.
# Retrieves Vancouver and Toronto
list_census_regions('CA16') %>%
filter(level == "CMA", name %in% c("Vancouver","Toronto"))
#> # A tibble: 2 × 8
#> region name level pop municipal_status CMA_UID CD_UID PR_UID
#> <chr> <chr> <chr> <int> <chr> <chr> <chr> <chr>
#> 1 35535 Toronto CMA 5928040 B <NA> <NA> 35
#> 2 59933 Vancouver CMA 2463431 B <NA> <NA> 59
census_data <- get_census(dataset='CA16', regions=list(CMA=c("59933","35535")),
vectors=c("v_CA16_408","v_CA16_409","v_CA16_410"),
level='CSD', use_cache = FALSE, quiet = TRUE)Census data accessible through cancensus comes is available in a number of different aggregation levels including:
| Code | Description | Count in Census 2016 |
|---|---|---|
| C | Canada (total) | 1 |
| PR | Provinces/Territories | 13 |
| CMA | Census Metropolitan Area | 35 |
| CA | Census Agglomeration | 14 |
| CD | Census Division | 287 |
| CSD | Census Subdivision | 713 |
| CT | Census Tracts | 5621 |
| DA | Dissemination Area | 56589 |
| EA | Enumeration Area (1996 only) | - |
| DB | Dissemination Block (2001-2016) | 489676 |
| Regions | Named Census Region |
Selecting regions = "59933" and level = "CT" will return data for all 478 census tracts in the Vancouver Census Metropolitan Area. Selecting level = "DA" will return data for all 3450 dissemination areas and selecting level = "DB" will retrieve data for 15,197 dissemination block. Working with CT, DA, EA, and especially DB level data significantly increases the size of data downloads and API usage. To help minimize additional overhead, cancensus supports local data caching to reduce usage and load times.
Setting level = "Regions" will produce data strictly for the selected region without any tiling of data at lower census aggregation levels levels.
Census data contains thousands of different geographic regions as well as thousands of unique variables. In addition to enabling programmatic and reproducible access to Census data, cancensus has a number of tools to help users find the data they are looking for.
Run list_census_vectors(dataset) to view all available Census variables for a given dataset.
list_census_vectors("CA16")
#> # A tibble: 6,623 × 7
#> vector type label units parent_vector aggregation details
#> <chr> <fct> <chr> <fct> <chr> <chr> <chr>
#> 1 v_CA16_401 Total Populatio… Numb… <NA> Additive CA 2016 Census…
#> 2 v_CA16_402 Total Populatio… Numb… <NA> Additive CA 2016 Census…
#> 3 v_CA16_403 Total Populatio… Numb… <NA> Average of … CA 2016 Census…
#> 4 v_CA16_404 Total Total pri… Numb… <NA> Additive CA 2016 Census…
#> 5 v_CA16_405 Total Private d… Numb… v_CA16_404 Additive CA 2016 Census…
#> 6 v_CA16_406 Total Populatio… Ratio <NA> Average of … CA 2016 Census…
#> 7 v_CA16_407 Total Land area… Numb… <NA> Additive CA 2016 Census…
#> 8 v_CA16_1 Total Total - A… Numb… <NA> Additive CA 2016 Census…
#> 9 v_CA16_2 Male Total - A… Numb… <NA> Additive CA 2016 Census…
#> 10 v_CA16_3 Female Total - A… Numb… <NA> Additive CA 2016 Census…
#> # … with 6,613 more rowsFor each variable (vector) in that Census dataset, this shows:
Each Census dataset features numerous variables making it a bit of a challenge to find the exact variable you are looking for. There is a function, find_census_vectors(), for searching through Census variable metadata in a few different ways. There are three types of searches possible using this function: exact search, which simply looks for exact string matches for a given query against the vector dataset; keyword search, which breaks vector metadata into unigram tokens and then tries to find the vectors with the greatest number of unique matches; and, semantic search which works better with search phrases and has tolerance for inexact searches. Switching between search modes is done using the query_type argument when calling find_census_variables() function.
# Find the variable indicating the number of people of Austrian ethnic origin
find_census_vectors("Australia", dataset = "CA16", type = "total", query_type = "exact")
#> # A tibble: 2 × 4
#> vector type label details
#> <chr> <fct> <chr> <chr>
#> 1 v_CA16_3813 Total Australia 25% Data; Citizenship and Immigration; Total - S…
#> 2 v_CA16_4809 Total Australian 25% Data; Minority / Origin; Total - Ethnic orig…
find_census_vectors("Australia origin", dataset = "CA16", type = "total", query_type = "semantic")
#> # A tibble: 1 × 4
#> vector type label details
#> <chr> <fct> <chr> <chr>
#> 1 v_CA16_4809 Total Australian 25% Data; Minority / Origin; Total - Ethnic orig…
find_census_vectors("Australian ethnic", dataset = "CA16", type = "total", query_type = "keyword", interactive = FALSE)
#> # A tibble: 1 × 4
#> vector type label details
#> <chr> <fct> <chr> <chr>
#> 1 v_CA16_4809 Total Australian 25% Data; Minority / Origin; Total - Ethnic orig…Census variables are frequently hierarchical. As an example, consider the variable for the number of people of Austrian ethnic background. We can select that vector and quickly look up its entire hierarchy using parent_census_vectors on a vector list.
list_census_vectors("CA16") %>%
filter(vector == "v_CA16_4092") %>%
parent_census_vectors()
#> # A tibble: 3 × 7
#> vector type label units parent_vector aggregation details
#> <chr> <fct> <chr> <fct> <chr> <chr> <chr>
#> 1 v_CA16_4089 Total Western Euro… Numb… v_CA16_4044 Additive CA 2016 Censu…
#> 2 v_CA16_4044 Total European ori… Numb… v_CA16_3999 Additive CA 2016 Censu…
#> 3 v_CA16_3999 Total Total - Ethn… Numb… <NA> Additive CA 2016 Censu…Sometimes we want to traverse the hierarchy in the opposite direction. This is frequently required when looking to compare different variable stems that share the same aggregate variable. As an example, if we want to look the total count of Northern European ethnic origin respondents disaggregated by individual countries, it is pretty easy to do so.
# Find the variable indicating the Northern European aggregate
find_census_vectors("Northern European", dataset = "CA16", type = "Total")
#> # A tibble: 7 × 4
#> vector type label details
#> <chr> <fct> <chr> <chr>
#> 1 v_CA16_4122 Total Northern European origin… 25% Data; Minority / Origin; Tota…
#> 2 v_CA16_4125 Total Danish 25% Data; Minority / Origin; Tota…
#> 3 v_CA16_4128 Total Finnish 25% Data; Minority / Origin; Tota…
#> 4 v_CA16_4131 Total Icelandic 25% Data; Minority / Origin; Tota…
#> 5 v_CA16_4134 Total Norwegian 25% Data; Minority / Origin; Tota…
#> 6 v_CA16_4137 Total Swedish 25% Data; Minority / Origin; Tota…
#> 7 v_CA16_4140 Total Northern European origin… 25% Data; Minority / Origin; Tota…The search result shows that the vector v_CA16_4092 represents the aggregate for all Northern European origins. The child_census_vectors function can return a list of its constituent underlying variables.
# Show all child variable leaves
list_census_vectors("CA16") %>%
filter(vector == "v_CA16_4122") %>% child_census_vectors(leaves = TRUE)
#> # A tibble: 6 × 7
#> vector type label units parent_vector aggregation details
#> <chr> <fct> <chr> <fct> <chr> <chr> <chr>
#> 1 v_CA16_4125 Total Danish Numb… v_CA16_4122 Additive CA 2016 Census; …
#> 2 v_CA16_4128 Total Finnish Numb… v_CA16_4122 Additive CA 2016 Census; …
#> 3 v_CA16_4131 Total Icelandic Numb… v_CA16_4122 Additive CA 2016 Census; …
#> 4 v_CA16_4134 Total Norwegian Numb… v_CA16_4122 Additive CA 2016 Census; …
#> 5 v_CA16_4137 Total Swedish Numb… v_CA16_4122 Additive CA 2016 Census; …
#> 6 v_CA16_4140 Total Northern … Numb… v_CA16_4122 Additive CA 2016 Census; …The leaves = TRUE parameter specifies whether intermediate aggregates are included or not. If TRUE then only the lowest level variables are returns - the “leaves” of the hierarchical tree.