This project was developed as part of the Information Visualization course at the Faculty of Computer and Information Sciences (FCIS), Ain Shams University (ASU).

The dataset provided for supply chain analysis is a detailed collection of various columns capturing crucial information related to the company's order and distribution processes. Each column holds significant attributes that contribute to understanding the intricacies of the supply chain, including:

• OrderNumber: Identifies each unique order made within the company.
• Sales Channel: Indicates the specific channel (e.g., online, in-store) through which sales transactions occurred.
• WarehouseCode: Represents the code corresponding to the warehouse responsible for dispatching the product.
• ProcuredDate: Records the date when products were procured or obtained for the supply chain.
• CurrencyCode: Specifies the currency used for the transactions.
• OrderDate, ShipDate, DeliveryDate: Chronological records of the order timeline—placement, shipment, and delivery dates.
• SalesTeamID, CustomerID, StoreID, ProductID: Unique identifiers for sales team, customers, stores, and products, respectively.
• Order Quantity: Quantity of products ordered in each transaction.
• Discount Applied: Amount of discount applied to the order.
• Unit Cost: Cost per unit of the product.
• Unit Price: Price per unit of the product.

The code begins by loading necessary R libraries (tidyverse, lubridate, shiny, shinyjs, plotly).

Reads data from a CSV file named "US_Regional_Sales_Data.csv" into the supplychain_data dataframe and displays its initial rows, summary, and structure.

• Length: Indicates the number of entries or observations in each column (7991 observations for each column).
• Class: Displays the data type or class of the values within each column (all columns are detected as character type).
• Mode: Represents the most frequent value mode within each column.
• Statistical Measures: For numerical columns (X_SalesTeamID, X_CustomerID, X_StoreID, X_ProductID, Order.Quantity, Discount.Applied), the summary provides statistical measures:
  • Min.: Minimum value observed in the column.
  • 1st Qu.: First quartile or 25th percentile.
  • Median: Median or 50th percentile.
  • Mean: Mean or average value.
  • 3rd Qu.: Third quartile or 75th percentile.
  • Max.: Maximum value observed.
• For non-numerical columns (OrderNumber, Sales.Channel, WarehouseCode, ProcuredDate, OrderDate, ShipDate, DeliveryDate, CurrencyCode), the summary provides the length, class, and mode as these columns contain text or categorical data.

• Checks for missing values (na_count) and removes rows with missing data (na.omit).
• Converts date columns from character format to the Date format (as.Date) using a specific date format (format = "%d/%m/%Y").
• Clears dollar signs from cost-related columns and converts them to numeric format using gsub and as.numeric.

Renames columns for improved clarity and consistency within the dataset.

Defines a Shiny app UI with two distinct tabs—'Supply Chain Analysis' and 'Sales Visualization'.

Includes various interactive input elements like dropdown menus (selectInput), checkboxes (checkboxGroupInput), and date range selectors (dateRangeInput) for filtering the dataset.

Offers a selection of visualizations using plotlyOutput and plotOutput for diverse insights.

Defines server functions (server_supply_chain) to render dynamic plotly visualizations:

• Total Sales by Sales Channel
• Total Sales by Warehouse
• Average Delivery Time by Sales Channel
• Discounts Applied
• Top 10 Products by Sales
• Inventory Turnover Analysis
• Total Cost and Total Profit Distribution
• Sales Channels Distribution as a pie chart

Implements a feature allowing users to switch between light and dark modes for improved user experience.

Defines a server function (server_sales_visualization) to display a time series plot showcasing the variation of sales quantity over time.

Runs shinyApp separately for both supply chain analysis and sales visualization, ensuring distinct functionalities.