Static summary of which systems provide demographic data
Source:R/database-stats.R
bike_demographic_data.RdStatic summary of which systems provide demographic data
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:
if (FALSE) {
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)
}