The bikedata package aims to enable ready importing of historical trip
data from all public bicycle hire systems which provide data, and will
be expanded on an ongoing basis as more systems publish open data.
Cities and names of associated public bicycle systems currently
included, along with numbers of bikes and of docking stations (from
wikipedia),
are
| City | Hire Bicycle System | Number of Bicycles | Number of Docking Stations |
|---|---|---|---|
| London, U.K. | Santander Cycles | 13,600 | 839 |
| San Francisco Bay Area, U.S.A. | Ford GoBike | 7,000 | 540 |
| New York City NY, U.S.A. | citibike | 7,000 | 458 |
| Chicago IL, U.S.A. | Divvy | 5,837 | 576 |
| Montreal, Canada | Bixi | 5,220 | 452 |
| Washingon DC, U.S.A. | Capital BikeShare | 4,457 | 406 |
| Guadalajara, Mexico | mibici | 2,116 | 242 |
| Minneapolis/St Paul MN, U.S.A. | Nice Ride | 1,833 | 171 |
| Boston MA, U.S.A. | Hubway | 1,461 | 158 |
| Philadelphia PA, U.S.A. | Indego | 1,000 | 105 |
| Los Angeles CA, U.S.A. | Metro | 1,000 | 65 |
These data include the places and times at which all trips start and
end. Some systems provide additional demographic data including years of
birth and genders of cyclists. The list of cities may be obtained with
the bike_cities() functions, and details of which include demographic
data with bike_demographic_data().
The following provides a brief overview of package functionality. For more detail, see the vignette.
Currently a development version only which can be installed with the following command,
devtools::install_github("ropensci/bikedata")and then loaded the usual way
library (bikedata)Data may downloaded for a particular city and stored in an SQLite3
database with the simple command,
store_bikedata (city = 'nyc', bikedb = 'bikedb', dates = 201601:201603)
# [1] 2019513where the bikedb parameter provides the name for the database, and the
optional argument dates can be used to specify a particular range of
dates (Jan-March 2016 in this example). The store_bikedata function
returns the total number of trips added to the specified database. The
primary objects returned by the bikedata packages are ‘trip matrices’
which contain aggregate numbers of trips between each pair of stations.
These are extracted from the database with:
tm <- bike_tripmat (bikedb = 'bikedb')
dim (tm); format (sum (tm), big.mark = ',')#> [1] 518 518
#> [1] "2,019,513"
During the specified time period there were just over 2 million trips
between 518 bicycle docking stations. Note that the associated databases
can be very large, particularly in the absence of dates restrictions,
and extracting these data can take quite some time.
Data can also be aggregated as daily time series with
bike_daily_trips (bikedb = 'bikedb')#> # A tibble: 87 x 2
#> date numtrips
#> <chr> <dbl>
#> 1 2016-01-01 11172
#> 2 2016-01-02 14794
#> 3 2016-01-03 15775
#> 4 2016-01-04 19879
#> 5 2016-01-05 18326
#> 6 2016-01-06 24922
#> 7 2016-01-07 28215
#> 8 2016-01-08 29131
#> 9 2016-01-08 21140
#> 10 2016-01-10 14481
#> # … with 77 more rows
A summary of all data contained in a given database can be produced as
bike_summary_stats (bikedb = 'bikedb')
#> num_trips num_stations first_trip last_trip latest_files
#> ny 2019513 518 2016-01-01 00:00 2016-03-31 23:59 FALSEThe final field, latest_files, indicates whether the files in the
database are up to date with the latest published files.
Trip matrices can be constructed for trips filtered by dates, days of
the week, times of day, or any combination of these. The temporal extent
of a bikedata database is given in the above bike_summary_stats()
function, or can be directly viewed with
bike_datelimits (bikedb = 'bikedb')#> first last
#> "2016-01-01 00:00" "2016-03-31 23:59"
Additional temporal arguments which may be passed to the bike_tripmat
function include start_date, end_date, start_time, end_time, and
weekday. Dates and times may be specified in almost any format, but
larger units must always precede smaller units (so years before months
before days; hours before minutes before seconds). The following
examples illustrate the variety of acceptable formats for these
arguments.
tm <- bike_tripmat ('bikedb', start_date = "20160102")
tm <- bike_tripmat ('bikedb', start_date = 20160102, end_date = "16/02/28")
tm <- bike_tripmat ('bikedb', start_time = 0, end_time = 1) # 00:00 - 01:00
tm <- bike_tripmat ('bikedb', start_date = 20160101, end_date = "16,02,28",
start_time = 6, end_time = 24) # 06:00 - 23:59
tm <- bike_tripmat ('bikedb', weekday = 1) # 1 = Sunday
tm <- bike_tripmat ('bikedb', weekday = c('m', 'Th'))
tm <- bike_tripmat ('bikedb', weekday = 2:6,
start_time = "6:30", end_time = "10:15:25")Trip matrices can also be filtered by demographic characteristics
through specifying the three additional arguments of member, gender,
and birth_year. member = 0 is equivalent to member = FALSE, and
1 equivalent to TRUE. gender is specified numerically such that
values of 2, 1, and 0 respectively translate to female, male, and
unspecified. The following lines demonstrate this functionality
sum (bike_tripmat ('bikedb', member = 0))
sum (bike_tripmat ('bikedb', gender = 'female'))
sum (bike_tripmat ('bikedb', weekday = 'sat', birth_year = 1980:1990,
gender = 'unspecified'))citation ("bikedata")
#>
#> To cite bikedata in publications use:
#>
#> Mark Padgham, Richard Ellison (2017). bikedata Journal of Open Source Software, 2(20). URL
#> https://doi.org/10.21105/joss.00471
#>
#> A BibTeX entry for LaTeX users is
#>
#> @Article{,
#> title = {bikedata},
#> author = {Mark Padgham and Richard Ellison},
#> journal = {The Journal of Open Source Software},
#> year = {2017},
#> volume = {2},
#> number = {20},
#> month = {Dec},
#> publisher = {The Open Journal},
#> url = {https://doi.org/10.21105/joss.00471},
#> doi = {10.21105/joss.00471},
#> }Please note that this project is released with a Contributor Code of Conduct. By contributing to this project you agree to abide by its terms.
