In zyang2k/bluebike: Blue Bike Comprehensive Data

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.width = 5
)

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.

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)

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)

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

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

Data Visualization via Leaflet

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

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

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