This project involves the analysis of reorder data in a retail context. The primary objective is to process and analyze sales data, inventory reports, and SKU details to inform restocking decisions. The analysis pipeline is built using R and connects to a SQL Server database to fetch required data. Key operations include data cleaning, transformation, aggregation, and visualization.
- R and RStudio
- Access to SQL Server with necessary permissions
- Required R packages:
RPostgreSQL,dplyr,dbplyr,data.table,lubridate,reshape2,stringr,readxl,writexl,openxlsx,tidyverse,odbc
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Install R and RStudio: Download and install R from CRAN and RStudio from RStudio Download.
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Install Required Packages: Open RStudio and install the required packages by running the following command in the console:
install.packages(c("RPostgreSQL", "dplyr", "dbplyr", "data.table", "lubridate", "reshape2", "stringr", "readxl", "writexl", "openxlsx", "tidyverse", "odbc"))
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Database Connection: Ensure you have the necessary credentials and network access to connect to the SQL Server.
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Configure Database Connection: Modify the database connection details in the script with your SQL Server information (host, database, user ID, and password).
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Data Files: Place any required CSV files in the specified directory and update the file paths in the script accordingly.
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Run the Script: Open the R project and execute the scripts in the RStudio environment. The scripts are organized sequentially from data loading to final data writing.
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Output: The final output will be an Excel file containing the aggregated and analyzed reorder data.
- Ensure that the SQL queries used in the script match the schema and tables present in your SQL Server database.
- The script includes data cleaning and transformation steps tailored to the specific structure of the input data. Adjust these as necessary for your dataset.
- The analysis parameters like time frames, SKU details, and inventory levels can be modified to fit different analytical needs.