COVID Dashboard Project

This project provides a Shiny dashboard to explore COVID-19 deaths by condition, age group, and state using cleaned national and state-level datasets. It includes preprocessing scripts to handle large raw datasets efficiently.

Project Overview

• Explore COVID-19 deaths by condition, age group, and state
• Clean and preprocess large datasets (~500 MB)
• Generate national and state-level datasets for analysis
• Interactive Shiny dashboard for visualization and exploration

Project Structure

CovidDashboard/
│
├── data/
│   ├── covid_cleaned.csv  # Cleaned full dataset
│   ├── national_data.csv  # Cleaned national-level dataset
│   └── state_data.csv     # Cleaned state-level dataset
│
├── DB_code.R              # Script in R for cleaning CSV and saving cleaned data
│
├── LICENSE
│
├── app(1).R               # UI file for R shiny Dashboard
│
├── README.md
│
└── .gitignore

Setup Instructions

1. Clone the repository

git clone https://github.com/yourusername/ff-covid.git
cd ff-covid

2. Set working directory in R or RStudio

setwd("path_to_project/ff-covid")

3. Install required packages

install.packages(c(
  "tidyverse",
  "janitor",
  "skimr",
  "lubridate",
  "naniar",
  "shiny",
  "shinydashboard",
  "plotly",
  "DT",
  "scales",
  "arules"
))

4. Run the data cleaning script

source("DB_code.R")

This script will:

• Load covid.csv

• Inspect and clean the dataset

• Remove rows with missing or zero deaths

• Create derived variables like mention_ratio and age_category

• Save the cleaned datasets in the data/ folder:

  • covid_cleaned.csv (all cleaned data)
  • national_data.csv (national-level data only)
  • state_data.csv (state-level data only)

---

Run the Shiny Dashboard

After cleaning the data, run the Shiny app:

shiny::runApp("app")

Features

• Explore COVID-19 deaths by condition
• Visualize deaths by age group
• Compare deaths across states
• Interactive charts and tables using Plotly and DT

---

Notes

• The data cleaning script must be run first to generate cleaned datasets.

• All datasets and outputs are stored in the data/ folder.

• Large CSV files (~500 MB) are processed efficiently (~3-5 minutes depending on system).

• Age groups are categorized as:

  • Young (0-34)
  • Middle-aged (35-64)
  • Elderly (65+)

---

Dependencies

• R (version >= 4.0 recommended)

• Packages:

  • tidyverse
  • janitor
  • skimr
  • lubridate
  • naniar
  • shiny
  • shinydashboard
  • plotly
  • DT
  • scales
  • arules

---

License

MIT License