In mpadge/bikedata: Download and Aggregate Data from Public Hire Bicycle Systems

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "README-"
)

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.

1 Installation

Currently a development version only which can be installed with the following command,

devtools::install_github("ropensci/bikedata")

devtools::load_all (".")
#devtools::load_all (".", recompile=TRUE)
#devtools::document (".")
#goodpractice::gp ("bikedata")
#devtools::check (".")
#testthat::test_dir ("./tests/")
#Rcpp::compileAttributes()

and then loaded the usual way

library (bikedata)

options(width = 120)

2 Usage

Data may downloaded for a particular city and stored in an SQLite3 database with the simple command,

dl_bikedata (city = 'ny', data_dir = '/data/data/bikes/nyc-temp/',
             dates = 201601:201603)
store_bikedata (bikedb = 'bikedb', data_dir = '/data/data/bikes/nyc-temp/')

store_bikedata (city = 'nyc', bikedb = 'bikedb', dates = 201601:201603)
# [1] 2019513

where 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 = ',')

c (518, 518)
"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')

n <- 87
dates <- c ('2016-01-01', '2016-01-02', '2016-01-03', '2016-01-04',
            '2016-01-05', '2016-01-06', '2016-01-07', '2016-01-08',
            '2016-01-08', '2016-01-10', rep (NA, n - 10))
nt <- c (11172, 14794, 15775, 19879, 18326, 24922, 28215, 29131, 21140, 14481,
         rep (NA, n - 10))
tibble::tibble (date = dates, numtrips = nt)

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        FALSE

The final field, latest_files, indicates whether the files in the database are up to date with the latest published files.

2.1 Filtering trips by dates, times, and weekdays

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')

res <- c ("2016-01-01 00:00", "2016-03-31 23:59")
names (res) <- c ("first", "last")
res

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")

2.2 Filtering trips by demographic characteristics

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'))