US Monthly Retail Trade Analysis and Forecast Jan 1992 - Jan 2023: Shiny, Happy, Interactive Web Apps with R!

Overview: This project encompasses a comprehensive Data Analysis and Visualization endeavor utilizing R and RShiny Web App. The project comprises data processing, exploration, visualization, and modeling, employing techniques such as Time Series HoltWinters forecast. The focal point is the development of an interactive RShiny web application for dynamic data representation.

Data Source: The primary dataset is sourced from the official Census website: US Monthly Retail Trade Data

Project Highlights:

1. Web Application Development:

   • Utilization of RShiny for creating an interactive web application, enhancing user experience and engagement.
   • Integration of dynamic elements for real-time data interaction and visualization.

2. Time Series Analysis and Forecast:

   • Implementation of Time Series analysis techniques, specifically HoltWinters forecast, for predictive modeling.
   • Forecasting trends and patterns in US Monthly Retail Trade data, providing valuable insights for decision-making.

3. Statistical Analysis and Visualization:

   • In-depth statistical analysis using R, revealing underlying patterns and correlations in the retail trade data.
   • Visualization of statistical findings through interactive charts and graphs for intuitive understanding.

Interactive Web App: Explore the developed RShiny web app here. Interact with the data, visualize trends, and gain meaningful insights through user-friendly controls and graphical representations.

Additional Resources: For detailed information about the dataset and the project's methodology, refer to the provided documentation: Data Exploration and Analysis Details.

This project showcases the power of R and RShiny in transforming raw data into actionable insights, providing a visually appealing and interactive platform for exploring US Monthly Retail Trade trends over three decades. Dive into the web app, uncover patterns, and stay informed about the dynamic retail landscape in the United States.