Welcome to my Divvy Bike Share Analysis project! This repository contains R scripts and notebooks that explore and analyze the Divvy Bike Share data. Through this project, I aim to provide insights into bike usage patterns, user demographics, and other interesting trends related to the Divvy bike-sharing system.
Divvy is a popular bike-sharing system in several cities, offering a sustainable and convenient mode of transportation. This analysis dives into the Divvy bike share data to uncover valuable information and trends.
- Description: Cleaning and preprocessing of raw Divvy Bike Share data to make it suitable for analysis. This includes handling missing values, converting data types, and ensuring data integrity.
- Description: Exploring key statistics, visualizations, and trends in the Divvy data. EDA helps in understanding the distribution of rides, popular routes, and usage patterns.
- Description: Analyzing user demographics, subscription types, and behavior patterns. This project segment focuses on understanding the diversity of Divvy bike users.
- Description: Building predictive models to forecast bike demand, user preferences, or other relevant factors. This step requires more advanced analytics and machine learning techniques.
- Clone this repository to your local machine.
- Open the R scripts or notebooks in R Studio.
- Execute the code chunks to reproduce the analysis or modify as needed.
Make sure you have the following R packages installed:
tidyverseggplot2dplyr- 'lubridate'
The Divvy Bike Share data used in this project can be obtained from the official Divvy Data page.
- LinkedIn: www.linkedin.com/in/shaulamarquezrn
Feel free to reach out for collaboration, questions, or discussions related to Divvy Bike Share analysis.
Happy coding and biking!