/Regression-Analysis-Weather

This R Markdown project conducts detailed weather-related sales analysis. It includes data loading, cleansing, integration of sales with weather data, trend analysis, and correlation studies, all wrapped in a modular, reusable code structure with comprehensive documentation and an MIT license.

Weather Analysis Project

Overview

This R Markdown project is designed for conducting a comprehensive weather analysis related to sales data. The script encompasses various data processing steps, including sales and weather data manipulation, trend analysis, and correlations between weather conditions and sales performance.

Features

  • Sales Data Processing: Functions to clean and prepare sales data for analysis.
  • Weather Data Integration: Tools for loading and processing weather data, and aligning it with sales data.
  • Analytical Functions: Custom functions for systemwide sales analysis and weather trend analysis.
  • Modular Design: Each major step is encapsulated in a function for better organization and reusability.
  • Data Exporting: Capabilities to output processed data and analysis results.

Prerequisites

The following R packages should be installed for the script to function correctly:

  • tidyr
  • dplyr
  • reshape2
  • lubridate
  • geosphere
  • zipcode
  • RCurl
  • RJSONIO
  • corrplot

Usage

To use the script, follow these steps:

  1. Set Up File Paths: Replace the placeholder paths in the run_analysis function call with the paths to your actual data files.
  2. Run Analysis: Execute the run_analysis function to process the data and perform the analysis.
  3. Review Results: Check the output files for insights and data visualizations.

Example

run_analysis("path/to/Flash Sales by Order Date.csv", 
             "path/to/store list.csv", 
             "path/to/weather_2018.csv", 
             "path/to/weather_2019.csv", 
             "path/to/weather_station.csv")

Contributing

Contributions to enhance the script's functionality or to extend its capabilities are welcome. Please ensure to follow coding standards and add appropriate documentation for new features.

Authors

  • Lisa Li - Initial work

License

This project is licensed under the MIT License.

Acknowledgments

Special thanks to all contributors and collaborators who have provided data, insights, and expertise to the project.