README for User Behavior and Citi Bike - A Study of Behavioral Economics and Data Analytics (BUS 446 Final Project)

Data Section The data used for this project is sourced from the NYC OpenData repository [Citi Bike System Data] (https://data.cityofnewyork.us/NYC-BigApps/Citi-Bike-System-Data/vsnr-94wk). The study focuses on ride history data from May 2016 to 2023, with specific files listed in the README. Data cleaning steps are detailed, and the cleaned data is available in the "CitiBike Data Cleaned Data Sheet V2" Excel file.

Visualization with Final Findings Section This section contains two versions of the presentation: an abridged version focusing on key findings and visualizations, and a complete version with duplicated slides for live presentations. The presentation covers Citi Bike pricing, hypotheses formulation, distance and duration disparities, station selection, and business recommendations.

Code Section The R Markdown (Rmd) file in this section analyzes user behavior using CitiBike data. Steps to run the analysis, including R package installation, file path specification, and sequential code execution, are provided. The analysis covers trip distances, durations, late fees, station preferences, and usage distribution.

Key Findings

• Trip Distances: Haversine formula used, t-test conducted.
• Trip Durations: T-test performed for subscribers and customers.
• Late Fee Analysis: Percentage of users incurring late fees and average late fees calculated.
• Station Preferences: Top 5 start and end locations identified, Chi-square tests conducted.
• Station Usage Distribution: Stacked bar plots visualize station usage distribution.
• Station Analysis: Stations with fewer than 50 trip starts or ends identified.

Business Case Report Section

This section presents the research paper "User Behavior and Citi Bike: A Study of Behavioral Economics and Data Analytics" by Daniel Elmore. The paper explores Citi Bike user behavior using behavioral economics and data analytics from 2016 to 2023. It includes an introduction, pricing model and options, A/B test proposal, methodology and results, business recommendations, references, figures, and instructions on how to use the repository.

How to Use This Repository

• Accessing the Paper: The full research paper is available in the accompanying PDF file.
• Figures: Visual representations of data analysis results are in the Figures directory.
• Citations: Relevant references are listed in the References section.

Additional Information

• The paper is based on data available up to the year 2023.
• For inquiries, contact the author, Daniel Elmore.

Note: This README file provides an overview. Refer to the respective sections for detailed information.