Advanced HR Analytics Dashboard

Overview

This project provides a comprehensive suite of tools for analyzing employee attrition, performance, and demographics. It features an enterprise-grade interactive Shiny dashboard for real-time insights and predictive modeling, alongside an R Markdown report for detailed exploratory analysis.

Files

• app.r: The main R Shiny application. Features predictive analytics, interactive visualizations, and department-level insights.
• Hr_Analysis.rmd: R Markdown file for generating a static analysis report (Hr_Analysis.html / Hr_Analysis.pdf).
• dataset.csv: The source dataset containing employee records.

Features

📊 Interactive Dashboard (app.r)

• Executive Summary: Real-time KPIs (Attrition Rate, Satisfaction, Cost) and trend analysis.
• Predictive Analytics: AI-driven risk scoring to identify employees at risk of leaving.
• Workforce Overview: Interactive demographics, age/gender pyramids, and role distribution.
• Compensation Analysis: Salary benchmarks, pay equity analysis, and income vs. experience trends.
• Attrition Analysis: Deep dive into attrition drivers, correlation matrices, and retention factors.
• Data Explorer: Searchable, exportable raw data view.

📝 Analysis Report (Hr_Analysis.rmd)

• Detailed Exploratory Data Analysis (EDA).
• Statistical summaries and data quality checks.
• Visualizations for gender, education, and income distributions.
• Automated data cleaning and preprocessing steps.

Getting Started

Prerequisites

Ensure you have R installed with the following packages:

install.packages(c(
  "shiny", "shinydashboard", "ggplot2", "dplyr", "plotly",
  "DT", "scales", "tidyr", "corrplot", "viridis",
  "shinyWidgets", "shinycssloaders", "lubridate"
))

Running the Dashboard

Open app.r in RStudio and click Run App, or execute the following command in your R console:

shiny::runApp("app.r")

Generating the Report

Open Hr_Analysis.rmd and click Knit, or execute:

rmarkdown::render("Hr_Analysis.rmd")

Dataset

The dataset (dataset.csv) contains employee records with various attributes including:

• Demographics: Age, Gender, Marital Status, Education
• Job Details: Department, Job Role, Job Level, Overtime
• Compensation: Monthly Income, Percent Salary Hike
• Satisfaction: Job Satisfaction, Environment Satisfaction, Work-Life Balance
• Attrition: Employment status (Yes/No)

License

This project is for internal HR analytics and decision support.