In this repository, I analyze bike-share data for the fictional company Cyclistic to determine differences between riders with an annual membership (“members”) and riders who purchase single-use or day passes (“casual riders”), with the aim of discovering insights that can help Cyclistic convert casual riders into members.
My summary report can be viewed at this page, a rendering of casual-riders-vs-members_summary.html. The report introduces the project, visualizes the data, and presents observations and recommendations based on the data. It was produced by running casual-riders-vs-members_summary.Rmd.
A more thorough report that goes through the analysis process in depth can be viewed at this page, a rendering of casual-riders-vs-members.html. It was produced by running casual-riders-vs-members.Rmd.
I adapted the R scripts from this one, which in turn is based on "'Sophisticated, Clear, and Polished': Divvy and Data Visualization," written by Kevin Hartman.
The task was provided by Google as part of the Coursera course "Google Data Analytics Capstone: Complete a Case Study," part of the Google Data Analytics Professional Certificate.
The data was provided by Divvy, a real-world bike share system in the Chicago area that is owned by the Chicago Department of Transportation (CDOT) and managed by Lyft, Inc., and made available under this license.
Before switching to R Markdown, I initially used a simple R script, cyclistic.R. This script produced 10 dataframes summarizing the data. These dataframes were exported to CSV files, which have been uploaded to the r-output folder. I combined the CSV files into an Excel workbook, where I did further analysis and visualization, and created a PowerPoint presentation summarizing key findings. I created the R Markdown files afterward to combine the entire process into a single program.