R Project (Merging, cleaning, Analysing, Visualization)

🚲 Cyclistic Bike-Share Capstone Project

📘 About the Project

This analysis was conducted as part of the Google Data Analytics Capstone Project. It explores rider behavior using one year of trip data from Cyclistic — a fictional bike-share company in Chicago — with the goal of recommending strategies to increase annual memberships.

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🗂️ Repository Structure

├── data/                  # 16 CSV files (one per month)
├── visuals/               # 6 PNGs: graphs & EDA visuals
├── cyclistic_analysis.R   # R script with all code
├── cyclistic_report.Rmd   # Full RMarkdown notebook
└── README.md              # You're here

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🧪 Tools & Packages

• Language: R
• Editor: RStudio
• Libraries: tidyverse, dplyr, ggplot2, lubridate, janitor, scales, readr, rmarkdown

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🔍 Analysis Workflow

1. Data Wrangling

   • Merged 12 months of trip data into a single dataframe
   • Removed nulls, duplicates, invalid ride lengths
   • Converted timestamps and engineered new time-related features

2. Exploratory Data Analysis (EDA)

   • Compared casual vs member usage across day of week, ride duration, station start/end
   • Visualized trends over time and across demographics

3. Key Visuals (from /visuals)

   • Plot01 – Percentage of Bike Types
   • Plot02 – Total Rides by Bike Type
   • Plot03 – Pie Chart of Ride Type Distribution
   • Plot04 – Total Rides in 2023
   • Plot05 – Monthly Ride Trends in 2023
   • Plot06 – Total Rides (2023–2024)

4. Insights & Recommendations

   • Members ride more frequently but for shorter durations
   • Casuals prefer weekends and afternoons
   • Suggest using trial memberships, targeted weekend discounts, and app-based ride nudges

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📊 R Markdown Notebook

You can explore the full narrative and code in the R Markdown file:

• cyclistic_report.Rmd — combines code, outputs, and insights
• Knit it in RStudio or from the terminal:

install.packages("rmarkdown")
rmarkdown::render("cyclistic_report.Rmd")

Or click the Knit button in RStudio to generate an HTML or PDF report.

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📊 Visualizations

Plot01 – Percentage of Bike Types

Bar chart showing the percentage breakdown of bike types used (e.g., classic, docked, electric). This highlights rider preferences by bike category.
📁 visuals/bike_type_percentage.png

Plot02 – Total Rides by Bike Type

Displays the total number of rides taken with each bike type. Helps identify demand and usage levels across categories.
📁 visuals/total_rides_by_type.png

Plot03 – Pie Chart of Ride Type Distribution

A pie chart visualizing the ratio between member and casual riders across all trips. Shows how the user base is split.
📁 visuals/ride_type_distribution_pie.png

Plot04 – Total Rides in 2023

Bar or line chart of all rides logged during the 2023 calendar year. Useful for identifying peak ridership months or seasons.
📁 visuals/total_rides_2023.png

Plot05 – Monthly Ride Trends in 2023

Line graph showing how ride counts fluctuated each month in 2023. Clear insight into seasonal and behavioral changes.
📁 visuals/monthly_ride_trends_2023.png

Plot06 – Total Rides (2023–2024)

Combined visualization showing total ride volumes across two full years (2023 and 2024), helping assess overall growth or decline.
📁 visuals/total_rides_2023_2024.png

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📁 Data Source

• 16 months (Jan 2023 to April 2024) of trip data provided by Motivate International Inc.
• Available via: divvy-tripdata

Data is licensed under the Chicago Data License

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🙌 Acknowledgments

Special thanks to the Google Data Analytics Certificate team and the R for Data Science community for resources, guidance, and motivation throughout this capstone journey.

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