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bluebike R package

Summary

Our package includes data from the Boston Blue Bike trip history data acquired from the Blue Bikes System Data. The users can import all monthly trip history data from 2020 to 2022 into a cleaned data set that can easily be used for data analysis.

The package also includes a sample data set that includes 1000 sampled trip history from Feb. 2022, and a full data set that contains information about all available stations. Functions inside the package:

import_month_data: takes in numeric year/month values and imports data for the specified time
station_distance: returns stations with distance in ascending order given the user’s current location
station_radius: plots the position of the stations within walking distance (500 m), and present the basic information about the stations via leaflet
trip_distance: computes the geographical distance between the start and end stations

The package would be a useful tool for the Blue Bike operations to analyze the trip data and help improve the shared bike service based on user data. It is also an easy-to-use tool for data analysis and visualization for anyone interested in the Blue Bike trip data.

Installation

You can install the development version of bluebike from GitHub with:

# install.packages("devtools")
devtools::install_github("zyang2k/bluebike")

Data Sets Included

trip_history_sample: a sample of 1000 trip data entries from February 2022.
station_data: A dataset that includes identification, position, and other basic information about bluebike stations

Basic Usage

library(bluebike)
library(dplyr)

Retrieve data online

import_month_data enables users to retrieve monthly data from Bluebike System Data website.

jan2015 <- import_month_data(2015, 1)
#> Rows: 7840 Columns: 15
#> ── Column specification ────────────────────────────────────────────────────────
#> Delimiter: ","
#> chr  (4): start station name, end station name, usertype, birth year
#> dbl  (9): tripduration, start station id, start station latitude, start stat...
#> dttm (2): starttime, stoptime
#> 
#> ℹ Use `spec()` to retrieve the full column specification for this data.
#> ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

Data Wrangling

Using the cleaned dataset trip_history_sample included in the package, the user can easily find out the most popular station in Feb. 2022:

stations <- trip_history_sample %>% 
  group_by(start_station_name) %>% 
  summarize(trips_from = n())
head(stations)
#> # A tibble: 6 × 2
#>   start_station_name                        trips_from
#>   <chr>                                          <int>
#> 1 175 N Harvard St                                   8
#> 2 191 Beacon St                                      3
#> 3 30 Dane St                                         7
#> 4 359 Broadway - Broadway at Fayette Street          4
#> 5 606 American Legion Hwy at Canterbury St           1
#> 6 699 Mt Auburn St                                   5

Via trip_distance, the user can compute the the average distance that user traveled in Jan. 2015

jan_distance <- jan2015 %>% 
  sample_n(1000) %>% 
  trip_distance()
mean_jan_distance <- mean(jan_distance$distance)

mean_jan_distance
#> 3215.401 [m]

The function station_distance() helps the user to find the closest stations nearby.

top_5_station <- station_distance(-71.13, 42.36) %>%
  head(5)

top_5_station
#>         distance station_ID                                   station_name
#> 210 124.9942 [m]     A32040                                  Honan Library
#> 3   427.6489 [m]     A32019                               175 N Harvard St
#> 221 606.1752 [m]     A32011 Innovation Lab - 125 Western Ave at Batten Way
#> 74  660.5163 [m]     A32005               Brighton Mills - 370 Western Ave
#> 380 954.2026 [m]     A32001    Union Square - Brighton Ave at Cambridge St
#>               station_position docks
#> 210 POINT (-71.12852 42.36027)    15
#> 3    POINT (-71.12916 42.3638)    18
#> 221  POINT (-71.1246 42.36371)    19
#> 74  POINT (-71.13776 42.36155)    15
#> 380 POINT (-71.13731 42.35333)    19

Data Visualization via Leaflet

Incorporated with the interactive map package leaflet, the position of the stations can be displayed:

library(leaflet)

BostonMap <- leaflet(data = station_data) %>% 
  addTiles() %>% 
  addCircleMarkers(lng = station_data$longitude, 
                   lat = station_data$latitude, 
                   radius = 0.1, 
                   color = "blue")

BostonMap

The function station_radius() plots the positions of stations within a certain user defined radius and display basic information about stations available.

station_500 <- station_radius(-71.13, 42.36, r = 500)

station_500

Contributors

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.