Introduction
Data analysis plays a crucial role in understanding economic and social trends across countries. This project utilizes R programming to analyze key global indicators such as Gross Domestic Product (GDP), unemployment rates, life expectancy, and the Global Reporting Initiative (GRI) scores. Using spatial visualizations, regression models, and correlation analysis, we investigate the relationships between these variables to uncover meaningful insights.
Data Collection and Preparation
The analysis integrates multiple datasets, each representing crucial economic and social indicators:
- GDP Data (2019): Captures economic productivity across countries.
- Unemployment Data (2022): Reflects labor market conditions.
- Life Expectancy (2019): Indicates the general health and living standards of populations.
- GRI Scores: Measure corporate sustainability performance across nations.
After importing the datasets, they were merged to ensure a comprehensive dataset for analysis. The spData and sf libraries were utilized to handle spatial data, while ggplot2 was used for visualization.
Visualizing Economic and Social Indicators
To gain a deeper understanding of global disparities, we created maps to visualize GDP, unemployment, and life expectancy.
- World GDP 2019: A choropleth map displaying GDP distribution, where red indicates higher economic output and blue represents lower GDP values.
- Unemployment Rate 2022: A spatial visualization highlighting variations in joblessness across countries.
- Life Expectancy 2019: A map showcasing health conditions worldwide, with darker colors representing higher life expectancy.
Each of these visualizations provided an intuitive understanding of global economic and social patterns.
Regression Analysis and Correlation Study
To assess the impact of sustainability (GRI scores) on economic and social factors, we conducted regression analysis:
- GDP vs. GRI Scores: Examines whether sustainability practices influence economic performance.
- Unemployment vs. GRI Scores: Tests if stronger corporate responsibility reduces unemployment.
- Life Expectancy vs. GRI Scores: Investigates whether sustainability efforts contribute to improved health outcomes.
The correlation matrix provided additional insights into how these indicators are interconnected, highlighting significant relationships between variables.
Key Findings
- Countries with higher GRI scores generally exhibited higher GDP and longer life expectancy.
- The correlation analysis showed a negative relationship between unemployment and GRI scores, suggesting that sustainable economic practices may foster employment.
- Spatial analysis revealed clear regional disparities, with developed nations having higher GRI scores, GDP, and life expectancy, while developing countries lagged in these indicators.
Conclusion
This study demonstrates the power of R for economic and social data analysis, integrating spatial mapping and regression modeling. The findings emphasize the importance of sustainability in shaping economic prosperity and social well-being. Future research can explore additional variables, such as education levels and environmental factors, to further enhance our understanding of global development trends.