COVID-19 Mortality Data Analysis in R

This repository presents a simple but insightful analysis of COVID-19 mortality data using R.
The goal is to explore how age and gender influence mortality rates, and to visualize these relationships through basic statistical tests and plots.

Project Structure

File	Description
covid_analysis.R	Main R script that performs the full analysis.
COVID19_line_list_data.csv	The dataset used in the analysis.
boxplot_age_by_death.png	Visualization of age distribution by death status.
barplot_death_rate_by_age_group.png	Death rate by age group as a bar plot.

What This Analysis Covers

Data Summary

• Using the Hmisc::describe() function to get a general overview of the dataset.

Data Cleaning

• A new binary column death_dummy is created:
  • 1 for death ≠ 0
  • 0 otherwise

Death Rate

• Calculates the overall mortality rate.

Age Analysis

• Compares the mean age of deceased vs. alive individuals.
• Performs a t-test to check if the difference is statistically significant.
• Plots a boxplot comparing age distribution by death status.

Gender Analysis

• Compares mortality rate for males and females.
• Performs a t-test to assess statistical significance.

Age Group Mortality

• Age is grouped into ranges: 0–20, 21–40, 41–60, 61–80, 81–100, 100+.
• Calculates the death rate in each age group.
• Displays the results as a bar plot.

Insights

• Older age groups show significantly higher mortality rates.

• Male individuals tend to have slightly higher death rates compared to females.

• Visualizations help in quickly identifying high-risk groups.

Contributing

Contributions are welcome! Feel free to fork the repository, make improvements, and submit a pull request.

Dependencies

Make sure the following R packages are installed:

install.packages("Hmisc")