Static summary of which systems provide demographic data

bike_demographic_data()

Value

A data.frame detailing the kinds of demographic data provided by the different systems

Examples

bike_demographic_data ()
#> city city_name bike_system demographic_data #> 1 bo Boston Hubway TRUE #> 2 ch Chicago Divvy TRUE #> 3 dc Washington DC CapitalBikeShare FALSE #> 4 gu Guadalajara mibici TRUE #> 5 la Los Angeles Metro FALSE #> 6 lo London Santander FALSE #> 7 mo Montreal Bixi FALSE #> 8 mn Minneapolis NiceRide TRUE #> 9 ny New York Citibike TRUE #> 10 ph Philadelphia Indego FALSE #> 11 sf Bay Area FordGoBike TRUE
# Examples of filtering data by demographic parameters:
# NOT RUN { data_dir <- tempdir () bike_write_test_data (data_dir = data_dir) bikedb <- file.path (data_dir, 'testdb') store_bikedata (data_dir = data_dir, bikedb = bikedb) # create database indexes for quicker access: index_bikedata_db (bikedb = bikedb) sum (bike_tripmat (bikedb = bikedb, city = 'bo')) # 200 trips sum (bike_tripmat (bikedb = bikedb, city = 'bo', birth_year = 1990)) # 9 sum (bike_tripmat (bikedb = bikedb, city = 'bo', gender = 'f')) # 22 sum (bike_tripmat (bikedb = bikedb, city = 'bo', gender = 2)) # 22 sum (bike_tripmat (bikedb = bikedb, city = 'bo', gender = 1)) # = m; 68 sum (bike_tripmat (bikedb = bikedb, city = 'bo', gender = 0)) # = n; 9 # Sum of gender-filtered trips is less than total because \code{gender = 0} # extracts all registered users with unspecified genders, while without gender # filtering extracts all trips for registered and non-registered users. # The following generates an error because Washinton DC's DivvyBike system does # not provide demographic data sum (bike_tripmat (bikedb = bikedb, city = 'dc', birth_year = 1990)) bike_rm_test_data (data_dir = data_dir) bike_rm_db (bikedb) # }