bikedata is an R package for downloading and aggregating data from
public bicycle hire, or bike share, systems. Although there are very many
public bicycle hire systems in the world (see this wikipedia
list),
relatively few openly publish data on system usage. The bikedata package aims
to enable ready importing of data from all systems which provide it, and will
be expanded on an ongoing basis as more systems publish open data. Cities and
names of associated public bicycle hire systems currently included in the
bikedata package, along with numbers of bikes and of docking stations, are:
| City | Hire Bicycle System | Number of Bicycles | Number of Docking Stations |
|---|---|---|---|
| London, U.K. | Santander Cycles | 13,600 | 839 |
| New York City NY, U.S.A. | citibike | 7,000 | 458 |
| Chicago IL, U.S.A. | Divvy | 5,837 | 576 |
| Washingon DC, U.S.A. | Capital BikeShare | 4,457 | 406 |
| Boston MA, U.S.A. | Hubway | 1,461 | 158 |
| Los Angeles CA, U.S.A. | Metro | 1,000 | 65 |
| Philadelphia PA, U.S.A. | Indego | 600 | 60 |
All of these systems record and disseminate individual trip data, minimally including the times and places at which every trip starts and ends. Some provide additional anonymised individual data, typically including whether or not a user is registered with the system and if so, additional data including age, gender, and residential postal code.
Cities with extensively developed systems and cultures of public hire bicycles, yet which do not provide (publicly available) data include:
| City | Number of Bicycles | Number of Docking Stations |
|---|---|---|
| Hangzhou, China | 78,000 | 2,965 |
| Paris, France | 14,500 | 1,229 |
| Barcelona, Spain | 6,000 | 424 |
The current version of the bikedata R package can be installed with the
following command:
install.packages ('bikedata')
Or the development version with
devtools::install_github ("mpadge/bikedata")
Once installed, it can be loaded in the usual way:
library (bikedata)
## Data for London, U.K. powered by TfL Open Data:
## Contains OS data Ⓒ Crown copyright and database rights 2016
## Data for New York City provided and owned by:
## NYC Bike Share, LLC and Jersey City Bike Share, LLC ("Bikeshare")
## see https://www.citibikenyc.com/data-sharing-policy
## Data for Washington DC (Captialbikeshare), Chiago (Divvybikes) and Boston (Hubway)
## provided and owned by Motivate International Inc.
## see https://www.capitalbikeshare.com/data-license-agreement
## and https://www.divvybikes.com/data-license-agreement
## and https://www.thehubway.com/data-license-agreement
The bikedata function store_bikedata() downloads individual trip data from
any or all or the above listed systems and stores them in an SQLite3 database.
For example, the following line will download all data from the Metro
system of Los Angeles CA, U.S.A., and store them in a database named 'bikedb',
bikedb <- file.path (tempdir (), "bikedb.sqlite") # or whatever
store_bikedata (city = 'la', bikedb = bikedb, quiet = TRUE)
## [1] 183254
The function returns the number of trips added to the database. Both the
downloaded data and the SQLite3 database are stored by default in the
temporary directory of the current R session. The downloaded data are deleted
after having been loaded into the SQLite3 database, and the database itself
is deleted on termination of the R session. (All of these options may be
overridden as described below.)
Successive calls to store_bikedata() will append additional data to the same
database. For example, the following line will append all data from Chicago's
Divvy bike system from the year 2017 to the database created with the first
call above.
store_bikedata (bikedb = bikedb, city = 'divvy', dates = 2016, quiet = TRUE)
## [1] 3595383
The function again returns the number of trips added to the database, which is now less than the total number of trips stored of:
bike_db_totals (bikedb = bikedb)
## [1] 3726719
Prior to accessing any data from the SQLite3 database, it is recommended to
create database indexes using the function index_bikedata_db():
index_bikedata_db (bikedb = bikedb)
This will speed up subsequent extraction of aggregated data.
Having stored individual trip data in a database, the primary function of the
bikedata package is bike_tripmat(), which extracts aggregate numbers of
trips between all pairs of stations. The minimal arguments to this function are
the name of the database, and the name of a city for databases holding data from
multiple cities.
tm <- bike_tripmat (bikedb = bikedb, city = 'la')
class (tm); dim (tm); sum (tm)
## [1] "matrix"
## [1] 66 66
## [1] 131336
The Los Angeles Metro system has 66 docking stations, and there were a total of 131,336 individual trips up to April 2017. Trip matrices can also be extracted in long form using
bike_tripmat (bikedb = bikedb, city = 'la', long = TRUE)
## # A tibble: 4,356 x 3
## start_station_id end_station_id numtrips
## <chr> <chr> <dbl>
## 1 la3005 la3005 324
## 2 la3005 la3006 105
## 3 la3005 la3007 31
## 4 la3005 la3008 173
## 5 la3005 la3009 1
## 6 la3005 la3010 9
## 7 la3005 la3011 86
## 8 la3005 la3014 49
## 9 la3005 la3016 12
## 10 la3005 la3018 39
## # ... with 4,346 more rows
Details of the docking stations associated with these trip matrices can be obtained with
bike_stations (bikedb = bikedb)
## # A tibble: 662 x 6
## id city stn_id name longitude latitude
## <int> <chr> <chr> <chr> <dbl> <dbl>
## 1 1 la la3005 -118.2590 34.04855
## 2 2 la la3006 -118.2567 34.04554
## 3 3 la la3007 -118.2546 34.05048
## 4 4 la la3008 -118.2627 34.04661
## 5 5 la la3009 -118.4728 33.98738
## 6 6 la la3010 -118.2549 34.03705
## 7 7 la la3011 -118.2680 34.04113
## 8 8 la la3014 -118.2372 34.05661
## 9 9 la la3016 -118.2416 34.05290
## 10 10 la la3018 -118.2601 34.04373
## # ... with 652 more rows
Stations can also be extracted for particular cities:
st <- bike_stations (bikedb = bikedb, city = 'ch')
For consistency and to avoid potential confusion of function names, most
functions in the bikedata package begin with the prefix bike_ (except for
store_bikedata() and dl_bikedata()).
Databases generated by the bikedata package will generally be very large
(commonly at least several GB), and many functions may take considerable time to
execute. It is nevertheless possible to explore package functionality quickly
through using the additional helper function, bike_write_test_data(). This
function uses the bike_dat data set provided with the package, which contains
details of 200 representative trips for each of the cities listed above. The
function writes these data to disk as .zip files which can then be read by the
store_bikedata() function.
bike_write_test_data ()
store_bikedata (bikedb = 'testdb')
bike_summary_stats (bikedb = 'testdb')
The .zip files generated by bike_write_test_data() are created by default
in the tempdir() of the current R session, and so will be deleted on
session termination. Specifying any alternative bike_dir will create enduring
copies of those files in that location which ought to be deleted when finished.
The remainder of this vignette provides further detail on these three distinct functional aspects of downloading, storage, and extraction of data.
The store_bikedata() function demonstrated above automatically downloads data
and deletes the downloaded files once the data has been loaded into the
SQLite3 database. Enduring copies of the raw data files may be created with
the function dl_bikedata(), and specifying a (non-default) location, such as,
dl_bikedata (city = 'chicago', data_dir = '/data/bikedata/')
Both store_bikedata() and dl_bikedata() accept an additional argument
(dates) specifying ranges of dates for which data should be downloaded and
stored. The format of this argument is quite flexible so that,
dl_bikedata (city = 'dc', dates = 16)
will download data from Washington DC's Capital Bikeshare system for all 12 months of the year 2016, while,
dl_bikedata (city = 'ny', dates = 201604:201608)
will download New York City data from April to August (inclusively) for that
year. (Note that the default data_dir is the tempdir() of the current R
session, with downloaded files being deleted upon session termination.) Dates
can also be entered as character strings, with the following calls producing
results equivalent to the preceding call,
dl_bikedata (city = 'ny', dates = '2016/04:2016/08')
dl_bikedata (city = 'new york', dates = '201604:201608')
dl_bikedata (city = 'n.y.c.', dates = '2016-04:2016-08')
dl_bikedata (city = 'new', dates = '2016 Apr-Aug')
The only strict requirement for the format of dates is that years must be
specified before months, and that some kind of separator must be used between
the two except when formatted as single six-digit numbers or character strings
(YYYYMM). The arguments city = 'new' and city = 'CI' in the final call are
sufficient to uniquely identify New York City's citibike system.
If files have been previously downloaded to a nominated directory, then calling
the dl_bikedata() function will only download those data files that do not
already exist. This function may thus be used to periodically refresh the
contents of a nominated directory as new data files become available.
Some systems disseminate data on quarterly (Washington DC and Los Angeles) or
bi-annual (Chicago) bases. The dates argument in these cases is translated to
the appropriate quarterly or bi-annual files. These are then downloaded as
single files, and thus the following call
dl_bikedata (city = 'dc', dates = '2016.03-2016.05')
will actually download data for the entire first and second quarters of 2016.
Even though the database constructed with store_bikedata() will then contain
data beyond the specified date ranges, it is nevertheless possible to obtain a
trip matrix corresponding to specific dates and/or times, as described below.
As mentioned above, individual trip data are stored in a single SQLite3
database, created by default in the temporary directory of the current R
session. Specifying a path for the bikedb argument in the store_bikedata()
function will create a database that will remain in that location until
explicitly deleted.
The nominated database is created if it does not already exist, otherwise
additional data are appended to the existing database. As described above, the
same dates argument can be passed to both dl_bikedata() and
store_bikedata() to download data within specified ranges of dates.
Both dl_bikedata() and store_bikedata() are primarily intended to be used to
download data for specified cities. It is possible to use the latter to store
all data for all cities simply by calling store_bikedata (bikedb = bikedb),
however doing so will request confirmation that data from all cities really
ought to be downloaded and/or stored. Intended general usage of the
store_bikedata() function is illustrated in the following line:
ntrips <- store_bikedata (bikedb = bikedb, city = 'ny',
dates = '2014 aug - 2015 Dec')
Or to load data which have been previously downloaded using dl_bikedata():
ntrips <- store_bikedata (bikedb = bikedb, city = 'ny',
data_dir = '/data/stored/here')
As described above, the function dl_bikedata() may be used to periodically
refresh downloaded files when new data become available. The store_bikedata()
function provides a similar capability. When called without specifying
data_dir, the function will download only those files which have not been
previously stored in the database, whereas when called with a specific
data_dir, the function will download any files not present in the nominated
directory and load them in to the database.
In short, the store_bikedata() function may be repeatedly called to load only
those data published since the last time the function was called, while
enduring copies of the raw data files on individual trips may be periodically
refreshed with dl_bikedata(), and the associated directory specified in the
call to store_bikedata() to load only recently added files.
As briefly described in the introduction, the primary function for extracting
aggregate data from the SQLite3 database established with store_bikedata()
is bike_tripmat(). With the single mandatory argument naming the database,
this function returns a matrix of numbers of trips between all pairs of
stations. Trip matrices can be returned either in square form (the default),
with both rows and columns named after the bicycle docking stations and matrix
entries tallying numbers of rides between each pair of stations, or in long form
by requesting bike_tripmat (..., long = TRUE). The latter case will return a
tibble with the three columns of
station_station_id, end_station_id, and number_trips, as demonstrated
above.
The data for the individual stations associated with the trip matrix can be
extracted with bike_stations(), which returns a tibble containing the 6
columns of city, station code, station name, longitude, and latitude. Station
codes are specified by the operators of each system, and pre-pended with a
2-character city identifier (so, for example, the first of the stations shown
above is la3005). The bike_stations() function will generally return all
operational stations within a given system, which bike_tripmat() will return
only those stations in operation during the requested time period. The previous
call stored all data from Chicago's Divvybikes system for the year 2016 only, so
the trip matrix has less entries than the full stations table, which includes
stations added since then.
dim (bike_tripmat (bikedb = bikedb, city = 'ch'))
## [1] 581 581
dim (bike_stations (bikedb = bikedb, city = 'ch'))
## [1] 596 6
Trip matrices can also be extracted for particular dates, times, and days of the week, through specifying one or more of the optional arguments:
start_dateend_datestart_timeend_timeweekdayArguments may in all cases be specified in a range of possible formats as long
as they are unambiguous, and as long as 'larger' units precede 'smaller' units
(so years before months before days, and hours before minutes before seconds).
Acceptable formats may be illustrated through specifying a list of arguments to
be passed to bike_tripmat(). This is done here through passing two lists to
bike_tripmat() via do.call(), enabling the second list (args1) to be
subsequently modified.
args0 <- list (bikedb = bikedb, city = 'ny', args)
args1 <- list (start_date = 16, end_time = 12, weekday = 1)
tm <- do.call (bike_tripmat, c (args0, args1))
In args1, a two-digit start_date (or end_date) is interpreted to represent
a year, while a one- or two-digit _time is interpreted to represent an hour.
A value of end_time = 24 is interpreted as end_time = '23:59:59', while a
value of _time = 0 is interpreted as 00:00:00. The following further
illustrate the variety of acceptable formats,
args1 <- list (start_date = '2016 May', end_time = '12:39', weekday = 2:6)
args1 <- list (end_date = 20160720, end_time = 123915, weekday = c ('mo', 'we'))
args1 <- list (end_date = '2016-07-20', end_time = '12:39:15', weekday = 2:6)
Both _date and _time arguments may be specified in either character or
numeric forms; in the former case with arbitrary (or no) separators.
Regardless of format, larger units must precede smaller units as explained
above.
Weekdays may specified as characters, which must simply be unambiguous and (in
admission of currently inadequate internationalisation) correspond to standard
English names. Minimal character specifications are thus 'so', 'm', 'tu', 'w',
'th', 'f', 'sa'. The value of weekday = 1 denotes Sunday, so weekdays =
2:6 denote the traditional working days, Monday to Friday, while weekends may
be denoted with weekdays = c ('sa', 'so') or weekdays = c (1, 7).
As described at the outset, the bicycle hire systems of several cities provide additional demographic information including whether or not cyclists are registered with the system, and if so, additional information including birth years and genders. Note that the provision of such information is voluntary, and that no providers can or do guarantee the accuracy of their data.
Those systems which provide demographic information are listed with the
function bike_demographic_data(), which also lists the nominal kinds of
demographic data provided by the different systems.
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 la Los Angeles Metro FALSE
## 5 lo London Santander FALSE
## 6 ny New York Citibike TRUE
## 7 ph Philadelphia Indego FALSE
Data can then be filtered by demographic parameters with additional optional
arguments to bike_tripmat() of,
registered (TRUE/FALSE, 'yes'/'no', 0/1)birth_year (as one or more four-digit numbers or character strings)gender ('m/f/.', 'male/female/other')Users are not required to specify genders, and any values of gender other than
character strings beginning with either f or m (case-insensitive) will be
interpreted to request non-specified or alternative values of gender. Note
further than many systems offer a range of potential birth years starting from a
default value of 1900, and there are consequently a significant number of
cyclists who declare this as their birth year.
It is of course possible to combine all of these optional parameters in a single query. For example,
tm <- bike_tripmat (bikedb = bikedb, city = 'ny', start_date = 2016,
start_time = 9, end_time = 24, weekday = 2:6, gender = 'xx',
birth_year = 1900:1950)
The value of gender = 'xx' will be interpreted to request data from all
members with nominal alternative genders. As demographic data are only given
for registered users, the registered parameter is redundant in this query.
Most bicycle hire systems have progressively expanded over time through ongoing
addition of new docking stations. Total numbers of counts within a trip matrix
will thus be generally less for more recently installed stations, and more for
older stations. The bike_tripmat() function has an option, standardise =
FALSE. Setting standardise = TRUE allows trip matrices to be standardised for
durations of station operation, so that numbers of trips between any pair of
stations reflect what they would be if all stations had been in operation for
the same duration.
Standardisation implements a linear scaling of total numbers of trips to and from each station according to total durations of operation, with counts in the final trip matrix scaled to have the same total number of trips as the original matrix. This standardisation has two immediate consequences:
The standardise option nevertheless enables travel patterns between different
(groups of) stations to be statistically compared in a way that is free of the
potentially confounding influence of differing durations of operation.
Data on docking stations may be accessed with the function bike_stations()
as demonstrated above:
bike_stations (bikedb = bikedb)
## # A tibble: 662 x 6
## id city stn_id name longitude latitude
## <int> <chr> <chr> <chr> <dbl> <dbl>
## 1 1 la la3005 -118.2590 34.04855
## 2 2 la la3006 -118.2567 34.04554
## 3 3 la la3007 -118.2546 34.05048
## 4 4 la la3008 -118.2627 34.04661
## 5 5 la la3009 -118.4728 33.98738
## 6 6 la la3010 -118.2549 34.03705
## 7 7 la la3011 -118.2680 34.04113
## 8 8 la la3014 -118.2372 34.05661
## 9 9 la la3016 -118.2416 34.05290
## 10 10 la la3018 -118.2601 34.04373
## # ... with 652 more rows
This function returns a tibble
detailing the names and locations of all bicycle stations present in the
database. Station data for specific cities may be extracted through specifying
an additional city argument.
bike_stations (bikedb = bikedb, city = 'ch')
## # A tibble: 596 x 6
## id city stn_id name longitude latitude
## <dbl> <chr> <chr> <chr> <dbl> <dbl>
## 1 67 ch ch456 2112 W Peterson Ave -87.68359 41.99118
## 2 68 ch ch101 63rd St Beach -87.57612 41.78102
## 3 69 ch ch109 900 W Harrison St -87.65002 41.87468
## 4 70 ch ch21 Aberdeen St & Jackson Blvd -87.65479 41.87773
## 5 71 ch ch80 Aberdeen St & Monroe St -87.65393 41.88046
## 6 72 ch ch346 Ada St & Washington Blvd -87.66121 41.88283
## 7 73 ch ch341 Adler Planetarium -87.60727 41.86610
## 8 74 ch ch444 Albany Ave & 26th St -87.70201 41.84448
## 9 75 ch ch511 Albany Ave & Bloomingdale Ave -87.70513 41.91403
## 10 76 ch ch376 Artesian Ave & Hubbard St -87.68822 41.88949
## # ... with 586 more rows
bikedata provides a number of helper functions for extracting summary
statistics from the SQLite3 database. The function bike_summary_stats
(bikedb) generates an overview table. (This function may take some time to
execute on large databases.)
bike_summary_stats ('bikedb')
## # A tibble: 3 x 5
## num_trips num_stations first_trip last_trip
## <dbl> <dbl> <fctr> <fctr>
## 1 3726719 662 2016-01-01 00:07:00 2017-03-31 23:45:00
## 2 3595383 596 2016-01-01 00:07:00 2016-12-31 23:57:52
## 3 131336 66 2016-07-07 04:17:00 2017-03-31 23:45:00
## # ... with 1 more variables: latest_files <lgl>
Additional helper functions provide individual components from this summary
data, and will generally do so notably faster for large databases than the above
function. The primary individual function is bike_db_totals(), which can be
used to extract total numbers of either trips (the default) or stations (by
specifying trips = FALSE) from the entire database or from specific cities.
bike_db_totals (bikedb = bikedb)
## [1] 3726719
bike_db_totals (bikedb = bikedb, city = "ch")
## [1] 3595383
bike_db_totals (bikedb = bikedb, city = "la")
## [1] 131336
bike_db_totals (bikedb = bikedb, trips = FALSE)
## [1] 662
bike_db_totals (bikedb = bikedb, trips = FALSE, city = "ch")
## [1] 596
bike_db_totals (bikedb = bikedb, trips = FALSE, city = "la")
## [1] 66
The other primary components of bike_summary_stats() are the dates of first
and last trips for the entire database and for individual cities. These dates
can be obtained directly with the function bike_datelimits():
bike_datelimits (bikedb = bikedb)
## first last
## "2016-01-01 00:07:00" "2017-03-31 23:45:00"
bike_datelimits (bikedb = bikedb, city = 'ch')
c ('first' = "2016-01-01 00:07:00", 'last' = "2016-12-31 23:57:52")
## first last
## "2016-01-01 00:07:00" "2016-12-31 23:57:52"
A helper function is also provided to determine whether the files stored in the database represent the latest available files.
bike_latest_files (bikedb = bikedb)
c ('la' = TRUE, 'ch' = TRUE)
## la ch
## TRUE TRUE
(At the time of this vignette, Chicago data for the first quarter of 2017 have not been released, and thus 2016 data are the latest.)
The bike_tripmat() function provides a spatial aggregation of data. An
equivalent temporal aggregation is provided by the function
bike_daily_trips(), which aggregates trips for individual days.
bike_daily_trips (bikedb = bikedb, city = 'ch')
## # A tibble: 366 x 2
## date numtrips
## <chr> <dbl>
## 1 2016-01-01 935
## 2 2016-01-02 1421
## 3 2016-01-03 1399
## 4 2016-01-04 3833
## 5 2016-01-05 4189
## 6 2016-01-06 4608
## 7 2016-01-07 5028
## 8 2016-01-08 3425
## 9 2016-01-09 1733
## 10 2016-01-10 993
## # ... with 356 more rows
Daily trip counts can also be standardised to account for differences in numbers of stations within a system as for trip matrix standardisation described above. Such standardisation is helpful because daily numbers of trips will generally increase with increasing numbers of stations. Standardisation returns a time series of daily trips reflecting what they would be if all system stations had been in operation throughout the entire time.
bike_daily_trips (bikedb = bikedb, city = 'ch', standardise = TRUE)
## # A tibble: 366 x 2
## date numtrips
## <chr> <dbl>
## 1 2016-01-01 2468.925
## 2 2016-01-02 2481.939
## 3 2016-01-03 2200.766
## 4 2016-01-04 5509.787
## 5 2016-01-05 5884.207
## 6 2016-01-06 6298.229
## 7 2016-01-07 6630.111
## 8 2016-01-08 4476.455
## 9 2016-01-09 2265.021
## 10 2016-01-10 1297.845
## # ... with 356 more rows
This tibble reveals two points of
immediate note:
Although the bikedata package aims to circumvent any need to access the
database directly, through providing ready extraction of trip data for most
analytical or visualisation needs, direct access may be achieved either using
the convenient dplyr functions, or the more powerful functionality provided
by the RSQLite package.
The following code illustrates access using the dplyr package:
db <- dplyr::src_sqlite (bikedb, create=F)
dplyr::src_tbls (db)
c ("datafiles", "stations", "trips")
## [1] "datafiles" "stations" "trips"
dplyr::collect (dplyr::tbl (db, 'datafiles'))
## # A tibble: 5 x 3
## id city name
## <int> <chr> <chr>
## 1 0 la la_metro_gbfs_trips_Q1_2017.zip
## 2 1 la MetroBikeShare_2016_Q3_trips.zip
## 3 2 la Metro_trips_Q4_2016.zip
## 4 3 ch Divvy_Trips_2016_Q1Q2.zip
## 5 4 ch Divvy_Trips_2016_Q3Q4.zip
dplyr::collect (dplyr::tbl (db, 'stations'))
## # A tibble: 662 x 6
## id city stn_id name longitude latitude
## <int> <chr> <chr> <chr> <dbl> <dbl>
## 1 1 la la3005 -118.2590 34.04855
## 2 2 la la3006 -118.2567 34.04554
## 3 3 la la3007 -118.2546 34.05048
## 4 4 la la3008 -118.2627 34.04661
## 5 5 la la3009 -118.4728 33.98738
## 6 6 la la3010 -118.2549 34.03705
## 7 7 la la3011 -118.2680 34.04113
## 8 8 la la3014 -118.2372 34.05661
## 9 9 la la3016 -118.2416 34.05290
## 10 10 la la3018 -118.2601 34.04373
## # ... with 652 more rows
dplyr::collect (dplyr::tbl (db, 'trips'))
## # A tibble: 100,000 x 11
## id city trip_duration start_time stop_time
## <int> <chr> <dbl> <chr> <chr>
## 1 1 la 480 2017-01-01 00:15:00 2017-01-01 00:23:00
## 2 2 la 720 2017-01-01 00:24:00 2017-01-01 00:36:00
## 3 3 la 1020 2017-01-01 00:28:00 2017-01-01 00:45:00
## 4 4 la 300 2017-01-01 00:38:00 2017-01-01 00:43:00
## 5 5 la 300 2017-01-01 00:38:00 2017-01-01 00:43:00
## 6 6 la 1200 2017-01-01 00:39:00 2017-01-01 00:59:00
## 7 7 la 720 2017-01-01 00:43:00 2017-01-01 00:55:00
## 8 8 la 2880 2017-01-01 00:56:00 2017-01-01 01:44:00
## 9 9 la 2820 2017-01-01 00:57:00 2017-01-01 01:44:00
## 10 10 la 1500 2017-01-01 01:54:00 2017-01-01 02:19:00
## # ... with 99,990 more rows, and 6 more variables: start_station_id <chr>,
## # end_station_id <chr>, bike_id <chr>, user_type <chr>,
## # birth_year <int>, gender <int>
## Warning: Only first 100,000 results retrieved. Use n = Inf to retrieve all.
The RSQLite package enables more
complex queries to be constructed. The names of stations, for example, could be
extracted using the following code
db <- RSQLite::dbConnect(SQLite(), bikedb, create = FALSE)
qry <- "SELECT stn_id, name FROM stations WHERE city = 'ch'"
stns <- RSQLite::dbGetQuery(db, qry)
RSQLite::dbDisconnect(db)
head (stns)
## stn_id name
## 1 ch456 2112 W Peterson Ave
## 2 ch101 63rd St Beach
## 3 ch109 900 W Harrison St
## 4 ch21 Aberdeen St & Jackson Blvd
## 5 ch80 Aberdeen St & Monroe St
## 6 ch346 Ada St & Washington Blvd
Many of the queries used in the bikedata package are constructed in this way
using the RSQLite interface.
The bikedata package does not provide any functions enabling visualisation of
aggregate trip data, both because of the primary focus on enabling access and
aggregation in the simplest practicable way, and because of the myriad
different ways users of the package are likely to want to visualise the data.
This section therefore relies on other packages to illustrate some of the ways
in which trip matrices may be visualised.
The simplest spatial visualisation involves connecting the geographical coordinates of stations with straight lines, with numbers of trips represented by some characteristics of the lines connecting pairs of stations, such as thickness or colours. This can be achieved with the following code, which also illustrates that it is generally more useful for visualisation purposes to extract trip matrices in long rather than square form.
stns <- bike_stations (bikedb = bikedb, city = 'la')
ntrips <- bike_tripmat (bikedb = bikedb, city = 'la', long = TRUE)
x1 <- stns$longitude [match (ntrips$start_station_id, stns$stn_id)]
y1 <- stns$latitude [match (ntrips$start_station_id, stns$stn_id)]
x2 <- stns$longitude [match (ntrips$end_station_id, stns$stn_id)]
y2 <- stns$latitude [match (ntrips$end_station_id, stns$stn_id)]
# Set plot area to central region of bike system
xlims <- c (-118.27, -118.23)
ylims <- c (34.02, 34.07)
plot (stns$longitude, stns$latitude, xlim = xlims, ylim = ylims)
cols <- rainbow (100)
nt <- ceiling (ntrips$numtrips * 100 / max (ntrips$numtrips))
for (i in seq (x1))
lines (c (x1 [i], x2 [i]), c (y1 [i], y2 [i]), col = cols [nt [i]],
lwd = ntrips$numtrips [i] * 10 / max (ntrips$numtrips))
The following code illustrates a more sophisticated approach to plotting such
data, using routines from the packages osmdata, stplanr, and tmap. Begin
by extracting the street network for Los Angeles using the osmdata package.
Current stplanr routines require spatial objects of class
sp rather than
sf.
library (magrittr)
xlims_la <- range (stns$longitude, na.rm = TRUE)
ylims_la <- range (stns$latitude, na.rm = TRUE)
# expand those limits slightly
ex <- 0.1
xlims_la <- xlims_la + c (-ex, ex) * diff (xlims_la)
ylims_la <- ylims_la + c (-ex, ex) * diff (ylims_la)
bbox <- c (xlims_la [1], ylims_la [1], xlims_la [2], ylims_la [2])
bbox <- c (xlims [1], ylims [1], xlims [2], ylims [2])
# Then the actual osmdata query to extract all OpenStreetMap highways
highways <- osmdata::opq (bbox = bbox) %>%
osmdata::add_osm_feature (key = 'highway') %>%
osmdata::osmdata_sp (quiet = FALSE)
For compatibility with current stplanr code, the stns table also needs to be
converted to a SpatialPointsDataFrame and re-projected.
stns_tbl <- bike_stations (bikedb = bikedb)
stns <- sp::SpatialPointsDataFrame (coords = stns_tbl[,c('longitude','latitude')],
proj4string = sp::CRS("+init=epsg:4326"),
data = stns_tbl)
stns <- sp::spTransform (stns, highways$osm_lines@proj4string)
These data can then be used to create an stplanr::SpatialLinesNetwork which
can be used to trace the routes between bicycle stations along the street
network. This first requires mapping the bicycle station locations to the
nearest nodes in the street network, and converting the start and end stations
of the ntrips table to corresponding rows in the street network data frame.
la_net <- stplanr::SpatialLinesNetwork (sl = highways$osm_lines)
# Find the closest node to each station
nodeid <- stplanr::find_network_nodes (la_net, stns$longitude, stns$latitude)
# Convert start and end station IDs in trips table to node IDs in `la_net`
startid <- nodeid [match (ntrips$start_station_id, stns$stn_id)]
endid <- nodeid [match (ntrips$end_station_id, stns$stn_id)]
ntrips$start_station_id <- startid
ntrips$end_station_id <- endid
The aggregate trips on each part of the network using the sum_network_lines()
function which is part of the current development version of stplanr.
bike_usage <- stplanr::sum_network_links (la_net, data.frame (ntrips))
Then finally plot it with tmap, again trimming the plot using the previous
limits to exclude a very few isolated stations
tmap::tm_shape (bike_usage, xlim = xlims, ylim = ylims, is.master=TRUE) +
tmap::tm_lines (col="numtrips", lwd="numtrips", title.col = "Number of trips",
breaks = c(0, 200, 400, 600, 800, 1000, Inf),
legend.lwd.show = FALSE, scale = 5) +
tmap::tm_layout (bg.color="gray95", legend.position = c ("right", "bottom"),
legend.bg.color = "white", legend.bg.alpha = 0.5)
#tmap::save_tmap (filename = "la_map.png")
