cancensus (version 0.4.8)

find_census_vectors: Query the CensusMapper API for vectors using exact, semantic, or keyword search

Description

Query the available list of Census vectors based on their label and return details including vector code. Default search behaviour expects an exact match, but keyword or semantic searches can be used instead by setting query_type='keyword' or query_type = 'semantic' instead. Keyword search is useful when looking to explore Census vectors based on broad themes like "income" or "language". Keyword search seperates the query into unigrams and returns Census vectors with matching words, ranked by incidence of matches. Semantic search is designed for more precise searches while allowing room for error for spelling or phrasing, as well as for finding closely related vector matches. Semantic search separates the query into n-grams and relies on string distance measurement using a generalized Levenshtein distance approach.

Some census vectors return population counts segmented by Female and Male populations, in addition to a total aggregate. By default, query matches will return matches for the Total aggregation, but can optionally return only the Female or Male aggregations by adding type = 'female' or type = 'male' as a parameter.

Usage

find_census_vectors(query, dataset, type = "all", query_type = "exact", ...)

Arguments

query

The term or phrase to search for e.g. 'Oji-cree'. Search queries are case insensitive.

dataset

The dataset to query for available vectors, e.g. 'CA16'. To see a list of available datasets: list_census_datasets()

type

One of 'all', 'total', 'male' or 'female'. If specified, only return aggregations of specified `type`. By default, only the 'total' aggregation will be returned.

query_type

One of exact, 'semantic' or 'keyword'. By default, assumes exact string matching, but the alternatives may be better options in some cases. See description section for more details on query types.

...

Other arguments passed to internal functions.

Examples

# NOT RUN {
# }
# NOT RUN {
find_census_vectors('Oji-cree', dataset = 'CA16', type = 'total', query_type = 'exact')

find_census_vectors('commuting duration', dataset = 'CA11', type = 'female', query_type = 'keyword')

find_census_vectors('after tax income', dataset = 'CA16', type = 'total', query_type = 'semantic')

# This incorrect spelling will return a warning that no match was found,
# but will suggest trying semantic or keyword search.
find_census_vectors('Ojibwey', dataset = 'CA16', type = 'total')

# This will find near matches as well
find_census_vectors('Ojibwey', dataset = 'CA16', type = 'total', query_type = "semantic")

find_census_vectors('commute duration', dataset = 'CA16', type = 'female', query_type = 'keyword')

find_census_vectors('commute duration', dataset = 'CA11', type = 'all', query_type = 'keyword')

find_census_vectors('ukrainian origin', dataset = 'CA16', type = 'total', query_type = 'keyword')

# }