Country Level Data Analysis using Parallel Coordinate Plot

Introduction

We are analyzing country-level indicators using R, focusing on health and economic measures. The aim is to understand how life expectancy, GDP per capita, and population vary across nations and continents.

Objective

The study compares three key indicators:
- Life Expectancy
- GDP per Capita
- Population

This helps us identify global disparities and patterns in health outcomes.

Dataset

We use the Gapminder dataset, which provides historical data on countries across multiple years. For this analysis, we filter the dataset to the latest year available, giving us a snapshot of current conditions.

Bar Chart Analysis

A bar chart of the top 10 countries by life expectancy shows that developed nations dominate the list. Countries like Australia and several European nations consistently achieve high life expectancy, highlighting the gap between developed and developing regions.

Scatter Plot Analysis

The scatter plot of GDP per capita vs. life expectancy reveals a clear positive relationship. Wealthier nations tend to have longer life expectancy, and the regression line confirms this trend. This suggests that economic prosperity is strongly linked to better health outcomes.

Box Plot Analysis

The box plot compares life expectancy by continent.
- Europe shows consistently high values.
- Africa has the lowest median and widest variation.
- Asia and the Americas fall in between.

This emphasizes regional differences in health outcomes.

Parallel Coordinates Plot

The parallel coordinates plot allows us to compare multiple indicators simultaneously. Each country is represented as a line across life expectancy, GDP per capita, and population.
- High GDP and high life expectancy countries cluster together (Europe, North America).
- Low GDP and low life expectancy countries form another cluster (Africa).

This visualization highlights global patterns and disparities across multiple dimensions.

Interpretation

• Countries with higher GDP generally have higher life expectancy.
  
• Europe demonstrates consistently strong health outcomes.
  
• Africa shows lower averages and greater variation.
  
• The parallel coordinates plot reveals clusters of similar-performing countries.

Conclusion

Through these visualizations, we see:
- Clear global inequalities in health and wealth.
- Strong correlation between economic prosperity and life expectancy.
- Regional clusters that highlight disparities.
- The parallel coordinates plot ties everything together, showing how countries align across multiple indicators.