📊 Retail Data Wrangling & Profit Analysis using R

A complete Retail Sales Data Analysis Project developed using R Programming and the Tidyverse ecosystem. This project focuses on data cleaning, wrangling, exploratory data analysis (EDA), and profit analysis across multiple retail datasets from different regions.

🚀 Project Overview

This project combines and analyzes retail order datasets from:

Central Region Western Region Eastern Region

The datasets contained inconsistencies in:

Column names Date formats Numeric formats Duplicate fields Missing values

Using R, the project standardizes and cleans the data to create a unified dataset for analysis and reporting.

🛠️ Technologies & Libraries Used 🔹 Programming Language R Programming 🔹 Libraries tidyverse dplyr tidyr ggplot2 lubridate readr stringr 📂 Project Features ✅ Data Wrangling Cleaned inconsistent datasets Standardized column formats Converted date formats Removed duplicate columns Parsed numeric values Combined multiple regional datasets ✅ Exploratory Data Analysis (EDA) Sales Analysis Profit Analysis Shipping Time Analysis Regional Performance Comparison Product Category Insights ✅ Data Transformation Created calculated fields Generated shipping duration metrics Unified schema across datasets Built analytical-ready dataset ✅ Visualization Profit Trends Sales Comparisons Regional Distribution Analysis Product Category Charts 📁 Project Structure Retail-Data-Wrangling-Profit-Analysis-R/ │ ├── mid+project.pdf # Project Report & R Code ├── Orders_Central.csv # Central Region Dataset ├── Orders_West.csv # Western Region Dataset ├── Orders_East.txt # Eastern Region Dataset └── README.md # Project Documentation ⚙️ Data Processing Workflow 📌 Step 1 — Import Datasets Loaded CSV and TXT datasets Imported regional retail order data 📌 Step 2 — Data Cleaning Removed unnecessary columns Renamed inconsistent attributes Converted data types 📌 Step 3 — Data Wrangling Standardized datasets Merged datasets into one master dataset 📌 Step 4 — Feature Engineering

Created:

Shipping Duration Year-Based Metrics Profit Calculations 📌 Step 5 — Analysis & Insights

Performed:

Regional Analysis Profitability Analysis Sales Trend Analysis Customer Insights 📊 Key Analysis Areas 💰 Profit Analysis Identified high-profit regions Compared category-level profits Evaluated discount impact on profits 🚚 Shipping Analysis Calculated time-to-ship metrics Compared regional shipping performance 🛒 Sales Insights Product category performance Regional sales comparison Customer purchasing behavior ▶️ How to Run the Project 1️⃣ Install Required Libraries install.packages("tidyverse") install.packages("lubridate") 2️⃣ Open the Project in RStudio

Open:

mid+project.pdf

OR run the R scripts directly.

3️⃣ Execute the Code

Run the code sections step-by-step in:

RStudio Posit Cloud Jupyter with R Kernel 📈 Skills Demonstrated 🔹 Data Cleaning 🔹 Data Wrangling 🔹 Data Transformation 🔹 Exploratory Data Analysis 🔹 Statistical Thinking 🔹 Data Visualization 🔹 Business Insights Generation 🔹 Retail Data Analytics 🎯 Learning Outcomes

This project helped in gaining hands-on experience with:

Real-world messy datasets Retail business analytics Data preprocessing techniques Data integration across multiple sources Analytical storytelling using R 👨‍💻 Developed By Sai Cholik Vempati

Master’s in Information Systems

🌐 GitHub Profile 💼 LinkedIn Profile

⭐ Project Highlights

✅ Multi-region retail dataset integration ✅ Advanced data wrangling using Tidyverse ✅ Business-focused profit analysis ✅ Shipping performance analytics ✅ Clean and structured analytical workflow