R code and markdown used to complete an analysis project examining bike sharing trends between casual riders and subscribed members over a 12-month period.
One of Cyclistic’s main goals is to increase profits by converting casual riders into subscribed members. This analysis examines twelve months of rider trip data to determine key differences between how casual riders and members utilize Cyclistic’s services to provide recommendations that can help Cyclistic reach its business goals. Casual riders are more likely to utilize Cyclistic’s services for pleasure over commuting, thus Cyclistic should diversify membership options, such as shorter term subscriptions and tourist passes, to appeal to a wider audience. Marketing strategies should prioritize the seasonal summer peak and be targeted at popular stations among their casual users to increase odds of subscription conversion.
This analysis serves as the capstone project of the Google Data Analytics Certification and is a simulated exercise. Cyclistic is a fictional company, but all data gathered and analyzed come from the real-world company Divvy, a bike-sharing company owned by Lyft Bikes and Scooters, LLC that makes its usage data available online. Google provided fictional background information about Cyclistic and its business goals as part of the course corresponding to this capstone project. The conclusions of this report apply to the fictional company Cyclistic only and do not apply to or reflect on Divvy in any way.
How to replicate this project:
- Go to Divvy's data repository (https://divvy-tripdata.s3.amazonaws.com/index.html) to acquire the datasets used in this simulated project.
- Download datasets 202108-divvy-tripdata (August 2021) through 202207-divvy-tripdata (July 2022) for a total of 12 datasets.
- Download the R scripts and RMarkdown provided here to replicate the cleaning and analysis I performed.