This project analyzes Cyclistic (Divvy) bike-share data to understand behavioral differences between annual members and casual riders. The goal is to generate data-driven insights and recommendations to help Cyclistic increase annual memberships.
This case study follows the Ask → Prepare → Process → Analyze → Share → Act data analysis framework.
How do annual members and casual riders use Cyclistic bikes differently, and how can these insights inform a strategy to convert casual riders into annual members?
- Divvy Trips Q1 2019
- Divvy Trips Q1 2020
The data includes ride-level information such as start/end times, station names, and rider type.
The datasets were provided through the Google Data Analytics Professional Certificate curriculum.
Data license: Divvy Data License Agreement
- R
- RStudio
- tidyverse
- lubridate
- R Markdown
- GitHub
cyclistic-analysis/
│
├── data/
│ └── raw/
│ ├── Divvy_Trips_2019_Q1.csv
│ └── Divvy_Trips_2020_Q1.csv
│
├── outputs/
│ └── cyclistic_analysis_report.html
│
├── scripts/
│ ├── cyclistic_analysis.R
│ └── cyclistic_analysis_report.Rmd
│
├── visuals/
│ └── Customer type usage behaviour.png
│ └── Number of rides by days of week.png
│ └── Ride length comparison.png
│
├── .gitignore
└── README.md
Key preparation steps include:
- Standardizing column names across datasets
- Converting timestamps to POSIXct format
- Unifying rider types into
memberandcasual - Removing invalid and zero-length rides
- Calculating ride length in minutes
- Removing extreme outliers (rides longer than 24 hours)
- Casual riders take significantly longer trips than annual members
- Members ride more frequently, especially on weekdays
- Casual riders peak on weekends, indicating leisure-focused usage
- Target high-engagement casual riders with membership promotions
- Run weekend-focused marketing campaigns
- Position annual membership as a cost-effective lifestyle upgrade
The full technical analysis is documented in a reproducible R Markdown report:
📄outputs/cyclistic_analysis_report.html
This project uses Divvy bike-share data provided by Lyft Bikes and Scooters, LLC and the City of Chicago under a public data license.
The data is used strictly for non-commercial, analytical, and educational purposes.
Raw data files are not redistributed in this repository.
Original data source:
https://www.divvybikes.com/system-data
Pranav M S Krishnan
Aspiring Data Analyst
Note:
This is a practice case study completed as part of the Google Data Analytics Professional Certificate to apply R programming and data analysis concepts in a real-world scenario.