Superstore Sales Trend Analysis using R

Project Overview

This project analyzes Superstore sales data using R to understand how sales evolve over time. The goal was to clean the data, transform dates correctly, and visualize sales trends through meaningful time-series analysis.

Dataset

• File: Superstore_Sales.csv
• Rows: 9,800
• Variables: 19
• Source: Kaggle Superstore Dataset

Tools & Libraries Used

• RStudio
• readr → for importing data
• dplyr → for data manipulation
• ggplot2 → for data visualization

What I did in this project

1. Loaded the dataset using read_csv()
2. Inspected column structure using colnames()
3. Cleaned and converted Order_Date into proper Date format
4. Aggregated and analyzed sales over time
5. Created a time-series line chart using ggplot2

Final Output — Sales Trend Visualization

Interpretation:

• The chart shows how total sales changed from 2015 to 2019.
• We can observe seasonal fluctuations and overall growth patterns.
• This visualization helps in understanding peak sales periods and long-term trends.

Want to see the full R code?

Sales_Trend_Analysis_R_Notebook.Rmd This contains all the step-by-step R code used for data cleaning, transformation, and visualization.

Files in this Repository

• Superstore_Sales.csv → Raw dataset
• Sales_Trend_Analysis_R_Notebook.Rmd → Main R analysis code (open this to see everything)
• Sales_Trend_Analysis_R_Notebook.nb.html → Rendered HTML report
• sales_trend.png → Final chart preview