API Key

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; however, API requests are limited in volume. 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 options(cancensus.api_key = "your_api_key").

Installing cancensus

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 unneccessarily 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 options(cancensus.cache_path = 'XXX'), this enables better control over the 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.

Setting the API key and the cache path in the .Rprofile facilitates the ability to share code without exposing API keys or relying on the local directory structure.

Census Datasets

cancensus can access Statistics Canada Census data for the 2006 Census, the 2011 Census and National Household Survey, as well as the latest available data from the 2016 Census. 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.

To view available datasets, run list_census_datasets().

list_census_datasets()

As other Census datasets become available via the CensusMapper API, they will be listed as output when calling list_census_datasets().

Census Regions

Census data is aggregated at multiple geographic levels. Census geographies at the national (C), provincial (PR), census metropolitan area (CMA), 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")

The 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"))

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)

Census Geographic Levels

Census data accessible through cancensus comes is available in a number of different aggregation levels including:

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. Working with CT and DA level data significantly increases the size of data downloads and API usage. cancensus relies on 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.

Displaying available Census variables

Run list_census_vectors(dataset) to view all available Census variables for a given dataset.

list_census_vectors("CA16")

Variable characteristics

For each variable (vector) in that Census dataset, this shows:

• Vector: short variable code
• Type: variables are provided as aggregates of female responses, male responses, or total (male+female) responses
• Label: detailed variable name
• Units: provides information about whether the variable represents a count integer, a ratio, a percentage, or a currency figure
• Parent_vecctor: shows the immediate hierarchical parent category for that variable, where appropriate
• Aggregation: indicates how the variable should be aggregated with others, whether it is additive or if it is an average of another variable
• Description: a rough description of a variable based on its hierarchical structure. This is constructed by cancensus by recursively traversing the labels for every variable’s hierarchy, and facilitates searching for specific variables using key terms.

Variable search

Each Census dataset features numerous variables making it a bit of a challenge to find the exact variable you are looking for. To help with that, this package includes a built-in vector search tool to help find specific variables.

# Find the variable indicating the number of people of Austrian ethnic origin
search_census_vectors("Austrian", dataset = 'CA16')

Knowing exactly how to spell the right variable can be tricky, but this search function relies on fuzzy searching so if it is unable to find an exact match of your search term, it will provide some helpful alternatives. In this case, searching for “Austraian” origin will show search terms for the vectors for both Austrian and Australian origins.

# Find the variable indicating the number of people of Austrian ethnic origin
search_census_vectors("Austraian", dataset = 'CA16')

Managing variable hiararchy

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()

Sometimes we want to traverse the hierarchy in the oppposite 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
search_census_vectors("Northern European", dataset = 'CA16')

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)

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.