Title: Blue Bike Comprehensive Data
Version: 0.0.3
Description: Facilitates the importation of the Boston Blue Bike trip data since 2015. Functions include the computation of trip distances of given trip data. It can also map the location of stations within a given radius and calculate the distance to nearby stations. Data is from https://www.bluebikes.com/system-data.
License: MIT + file LICENSE
Depends: R (≥ 2.10)
Imports: dplyr, janitor, leaflet, lubridate, magrittr, readr, sf, stringr, tidyselect, utils
Suggests: knitr, rmarkdown, testthat (≥ 3.0.0)
VignetteBuilder: knitr
Config/testthat/edition: 3
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.1.2
NeedsCompilation: no
Packaged: 2022-05-04 05:27:12 UTC; ellayoung
Author: Ziyue Yang ORCID iD [aut, cre], Tianshu Zhang ORCID iD [aut]
Maintainer: Ziyue Yang <zyang2k@gmail.com>
Repository: CRAN
Date/Publication: 2022-05-05 06:00:05 UTC

bluebike - A Data Package for Bluebike Users

Description

bluebike includes functions and dataset that aids bluebike users to retrieve data and perform data wrangling and visualizations

Details

This 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. The package also serves as a visualization tool for user to browse for closest stations as well as trip-planning via computing trip distances.

Available functions are:

Available datasets are:

Examples

library(dplyr)
# Find most used stations:
stations <- trip_history_sample %>%
  group_by(`start_station_name`) %>%
  summarize(trips_from = n())
head(stations)

Import monthly data from bluebike system data

Description

This function takes in numeric year/month values and imports data for the specified time

Usage

import_month_data(year, month)

Arguments

year

numeric value of year

month

numeric value of month

Value

A spec_tbl_df object

Examples


# Pull Jan., 2015 data from web
library(dplyr)
jan_2015 <- import_month_data(2015, 1)

# Pull first quarter of 2015 data from web
spring2015 <- c(1, 2, 3)
quarter_1_2015 <- lapply(spring2015, import_month_data, year = 2015)
quarter_1_2015 <- bind_rows(quarter_1_2015)

Blue bike station data

Description

A dataset that includes identification, position, and other basic information about bluebike stations

Usage

station_data

Format

A data frame of 423 rows and 8 columns

number

Station ID

name

Station name

latitude

Latitude of the station

longitude

Longitude of the station

district

District of the station

public

Character vector showing if a station is public

total_docks

The number of docks at each station

deployment_year

The year that the station was put into work

Source

The original source of the data are bluebikes system data retrieved from https://www.bluebikes.com/system-data

Compute the distance from stations given current location

Description

This function returns stations with distance in ascending order given the user's current location

Usage

station_distance(long, lat)

Arguments

long

longtitude of user location

lat

latitude of user location

Value

a tbl_df object showing the distance between the user and top five closest stations with ID, name, number of docks, and position

Examples

# Calculate distance for user at (-71.11467361, 42.34414899) and show the closest five stations
top_5_station <- head(station_distance(-71.11467361, 42.34414899), 5)

Plot bike stations within a given radius

Description

This function plots the position of the stations within walking distance

Usage

station_radius(long, lat, r = 1000)

Arguments

long

numeric value of longitude

lat

numeric value of latitude

r

numeric value of set radius in meters

Value

A leaflet map

Examples

# Show user at (-71.11467, 42.34415) and set the radius to 500 m
station_radius(long = -71.11467, lat = 42.34415, r = 2000)

Compute trip distance for a specific dataset

Description

This function computes the geographical distance between the start and end stations for trips in a given dataset

Usage

trip_distance(data)

Arguments

data

trip data pulled from the Blue Bike System data

Value

a tbl_df object with an additional distance column

Examples

# Calculate distance for sample trip data
sample_distance <- trip_distance(trip_history_sample)$distance

Random 1000 samples from the Blue Bikes System Data website

Description

a random sample of bluebike trip history data from February, 2022

Usage

trip_history_sample

Format

A data frame of 1,000 rows representing each sample of trip history

trip_duration

Trip duration of each trip measured in seconds

start_time

Start time and date of each trip

stop_time

Stop time and date of each trip

start_station_id

The identification variable of the start station

start_station_name

The name of the end station

start_station_latitude

The latitude of the start station

start_station_longitude

The longitude of the start station

end_station_id

The identification variable of the end station

end_station_name

The name of the end station

end_station_latitude

The latitude of the end station

end_station_longitude

The longitude of the start station

bike_id

The identification variable of the bike corresponding to each trip

user_type

Type of user in each trip (Casual = Single Trip or Day Pass user; Member = Annual or Monthly Member)

postal_code

Postal code of the user

Source

The original source of the data are bluebikes system data retrieved from https://www.bluebikes.com/system-data