Analyzing millions of bike-share trips reveals how travel patterns differ across rider groups, seasons, and neighborhoods. This project uses R to investigate those differences across two years of Chicago bike-share data and translate them into actionable marketing recommendations.
Project Links
I’m a fan of biking, was born outside Chicago, and have long been curious about how bikeshare systems work. I built this project to refresh my R skills and soon realized the value of comparing data across years. For example, between 2019 and 2024, the rise of e-bikes coincided with a notable increase in casual ridership within the bikeshare market. This is where I (finally) stumbled upon the obvious trick of turning my analysis notebooks into easy-to-read web pages—a game changer for presenting data work cleanly.
Project Overview
Completed as the capstone to Google's Data Analytics Certificate, this project analyzes Chicago bike-share data to understand how casual riders and annual members use Cyclistic differently, and how those differences could inform membership growth strategies. Using R, I cleaned and analyzed millions of trips from 2019 and 2024, examining ride duration, timing, seasonality, bicycle type, and geographic patterns.
In 2024, casual riders averaged roughly 20 minutes per trip, compared with 12 minutes for members; in 2019, the gap was wider. Members showed weekday commuting peaks and clustered around downtown business districts, while casual riders took longer trips, rode more on weekends, and favored lakefront attractions and cultural destinations. Ridership also varied seasonally, and electric bike use increased across both groups.
These patterns suggest opportunities for leisure-focused messaging, seasonal promotions, and outreach near popular recreational destinations. R Markdown reports, exploratory visualizations, and interactive maps bring the analysis together, translating observed rider behavior into practical marketing recommendations.
Gallery
Rides per Hour (2024): Members show two peaks during commuting
hours, while casual riders peak in the late afternoon.
Rides by Hour & Day (2024): Members commute on weekdays;
casual riders increase on weekends, reflecting leisure
patterns.
2019 Comparison Map: Casual rider hotspots align with Chicago’s
major tourist destinations and attractions, prime locations for
time-bound passes and seasonal promotions.
Casual Rider Stations (2024): High activity in neighborhoods near
the lakefront, parks, and cultural sites—Millennium Park, Museum
Campus, and Navy Pier.
Member Stations (2024): Concentrated on The Loop and River North,
near transit hubs and dense commercial districts.
Rides by Bike Type & Month (2024): Electric bike usage is
rising. Overall ridership shows clear seasonal trends: higher in
summer, lower in winter.
AI-generated Cyclistic Logo: Branding for the project’s visual
identity.
References
Dataset provided by Google, from Lyft and Divvy's Chicago operations. The data has been made available by Motivate International Inc. under this license.