Store previously downloaded data (via the dl_bikedata function) in a database for subsequent extraction and analysis.

  dates = NULL,
  latest_lo_stns = TRUE,
  quiet = FALSE



A string containing the path to the SQLite3 database to use. If it doesn't already exist, it will be created, otherwise data will be appended to existing database. If no directory specified, it is presumed to be in tempdir().


One or more cities for which to download and store bike data, or names of corresponding bike systems (see Details below).


A character vector giving the directory containing the data files downloaded with dl_bikedata for one or more cities. Only if this parameter is missing will data be downloaded.


If specified and no data_dir is given, data are downloaded and stored only for these dates specified as vector of YYYYMM values.


If TRUE (default), download latest version of London stations; otherwise use potentially obsolete internal version. (This parameter should not need to be changed, but can be set to FALSE to avoid external calls; for example when not online.)


If FALSE, progress is displayed on screen


Number of trips added to database


Data for different cities may all be stored in the same database, with city identifiers automatically established from the names of downloaded data files. This function can take quite a long time to execute, and may generate an SQLite3 database file several gigabytes in size.


City names are not case sensitive, and must only be long enough to unambiguously designate the desired city. Names of corresponding bike systems can also be given. Currently possible cities (with minimal designations in parentheses) and names of bike hire systems are:

Boston (bo)Hubway
Chicago (ch)Divvy Bikes
Washington, D.C. (dc)Capital Bike Share
Los Angeles (la)Metro Bike Share
London (lo)Santander Cycles
Minnesota (mn)NiceRide
New York City (ny)Citibike
Philadelphia (ph)Indego
San Francisco Bay Area (sf)Ford GoBike


if (FALSE) { data_dir <- tempdir () bike_write_test_data (data_dir = data_dir) # or download some real data! # dl_bikedata (city = 'la', 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) trips <- bike_tripmat (bikedb = bikedb, city = 'LA') # trip matrix stations <- bike_stations (bikedb = bikedb) # station data bike_rm_test_data (data_dir = data_dir) bike_rm_db (bikedb) # don't forget to remove real data! # file.remove (list.files (data_dir, pattern = '.zip')) }