Statistics Project: Exploring Global Economic
Indicators
Objective
Analyze and visualize the relationship between various global economic indicators (e.g.,
GDP per capita, unemployment rate, inflation rate) across different countries and over
time.
Data Sources
● World Bank Open Data: Free and open access to global development data.
● International Monetary Fund (IMF) Database: Economic statistics and indicators
on IMF lending, exchange rates, and other economic and financial indicators.
Steps to Address the Task
1. Data Collection and Preparation:
● Select indicators of interest, such as GDP per capita, unemployment rate,
and inflation rate.
● Download the corresponding datasets for a range of countries over the
past decade.
● Clean and preprocess the data for analysis, ensuring consistency and
filling in missing values as needed.
2. Initial Analysis:
● Conduct a preliminary analysis to understand the distribution and trends
of each indicator.
● Identify any apparent correlations or patterns between indicators.
3. Designing Interactive Visualizations: ● Time Series Analysis: Create interactive line graphs for each indicator,
allowing users to select which countries to display for comparison.
● Correlation Matrix: Use interactive heat maps to explore correlations
between different indicators, with the ability to filter by year or country
group (e.g., income level).
● Geographic Distribution: Implement an interactive map to visualize the
value of each indicator by country, with the capability to adjust the year
and switch between indicators.
● Dynamic Scatter Plots: Allow users to compare two indicators against
each other, with countries represented as points sized by population and
colored by continent. Include options to play the scatter plot over time or
select specific years.
4. Implementation in R or Tableau:
● In R, use packages like ggplot2 for static plots and plotly or shiny for
interactive components. Shiny can be particularly powerful for creating
web apps that allow users to interactively explore the data.
● In Tableau, leverage its drag-and-drop interface to create dashboards
combining different interactive visualizations. Use parameters and filters
to allow users to customize the view.
5. Reporting and Presentation:
● Develop a narrative around the data, guiding users through key insights
and trends revealed by the visualizations.
● Consider creating a dashboard that integrates all visualizations for a
cohesive user experience.
Considerations ● Ensure your visualizations are accessible, with clear labeling and consideration
for colorblindness.
● Test your interactive elements thoroughly to ensure they work as intended and
enhance user understanding.
Conclusion
This project, while requiring access to specific software or programming environments,
offers a hands-on opportunity to apply and showcase data visualization skills. By
making the visualizations interactive, you engage your audience more deeply, allowing
them to explore the data in a way that static charts cannot. Whether in R with shiny and
plotly or in Tableau, the skills you develop through this project are highly valuable in
the field of data analysis and beyond.