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Thebikedata package aims to enable ready importing of historical tripdata from all public bicycle hire systems which provide data, and willbe expanded on an ongoing basis as more systems publish open data.Cities and names of associated public bicycle systems currentlyincluded, along with numbers of bikes and of docking stations (fromwikipedia),are

These data include the places and times at which all trips start andend. Some systems provide additional demographic data including years ofbirth and genders of cyclists. The list of cities may be obtained withthebike_cities() functions, and details of which include demographicdata withbike_demographic_data().

The following provides a brief overview of package functionality. Formore detail, see thevignette.

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

devtools::install_github("ropensci/bikedata")

and then loaded the usual way

library (bikedata)

Data may downloaded for a particular city and stored in anSQLite3database with the simple command,

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

where thebikedb parameter provides the name for the database, and theoptional argumentdates can be used to specify a particular range ofdates (Jan-March 2016 in this example). Thestore_bikedata functionreturns the total number of trips added to the specified database. Theprimary objects returned by thebikedata 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 tripsbetween 518 bicycle docking stations. Note that the associated databasescan be very large, particularly in the absence ofdates 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        FALSE

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

Trip matrices can be constructed for trips filtered by dates, days ofthe week, times of day, or any combination of these. The temporal extentof abikedata database is given in the abovebike_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 thebike_tripmatfunction includestart_date,end_date,start_time,end_time, andweekday. Dates and times may be specified in almost any format, butlarger units must always precede smaller units (so years before monthsbefore days; hours before minutes before seconds). The followingexamples illustrate the variety of acceptable formats for thesearguments.

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 - 01:00tm<- bike_tripmat ('bikedb',start_date=20160101,end_date="16,02,28",start_time=6,end_time=24)# 06:00 - 23:59tm<- bike_tripmat ('bikedb',weekday=1)# 1 = Sundaytm<- 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 characteristicsthrough specifying the three additional arguments ofmember,gender,andbirth_year.member = 0 is equivalent tomember = FALSE, and1 equivalent toTRUE.gender is specified numerically such thatvalues of2,1, and0 respectively translate to female, male, andunspecified. 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 aContributor Code ofConduct. By contributing to thisproject you agree to abide by its terms.