#Introduction For my final Capstone project, I really wanted to dive into the intersection of economic wealth and public health. We often hear about global inequality, but I wanted to see exactly what that looks like in the data. Using the Gapminder dataset, I put together these eight visualizations to walk through the progress we’ve made, the massive gaps that still exist, and how a country’s wealth actually impacts how long its citizens live.

#1. The Big Picture Figure 1: Average Life Expectancy Trends (1952-2007) I started by looking at the global trend. This line chart tracks the average life expectancy over a 55-year period. It’s encouraging to see that every single continent has made upward progress, though the gaps between the top and bottom remain pretty stark.

#2. Breaking Down the Wealth Gap Figure 2: The Reality of Regional Wealth (2007) Averages can be misleading, so I wanted to look closer at the actual spread of wealth in the most recent year of our data (2007). I used a boxplot with a logarithmic scale here because the wealth gap is so massive that a standard axis just squashes the poorer nations into a flat line. Notice how wide the economic spread is within Asia compared to Europe!

Figure 3: Where Do Most Countries Fall? To get another angle on the wealth data, I faceted a histogram by continent. This makes it really easy to see the “shape” of wealth distribution. Africa’s data piles up on the lower end of the spectrum, while Europe’s shifts heavily to the right.

#3. A Closer Look at Health Figure 4: The Inequality of Longevity Next, I shifted my focus entirely to health outcomes. This density plot shows how life expectancy is distributed globally. I found it fascinating (and a bit sad) that there’s a “double peak” in the global data, which is completely driven by the severe regional divide between places like Africa and Europe.

Figure 5: The Top 10 World Leaders in Health Just out of curiosity, I wanted to pull out the specific countries that are leading the world in life expectancy. Unsurprisingly, Japan takes the top spot, followed heavily by European nations.

Figure 6: Zooming in on the Americas To make this a bit more personal to my own region, I created a Cleveland dot plot to track exactly how much life expectancy improved for each country in the Americas from the start of the dataset (1952) to the end (2007). The length of the line essentially represents “years of life gained.”

#4. Bringing it Together: Health vs. Wealth Figure 7: The Preston Curve Finally, I wanted to see how these two metrics interact. This scatter plot creates what economists call the “Preston Curve.” I used a log scale for the X-axis again, which perfectly illustrates how the first few thousands of dollars in GDP create a massive spike in life expectancy, but after a certain point of wealth, the health benefits start to level off.

Figure 8: Explore the Data Yourself To wrap up the project, I made the previous scatter plot interactive and added population size to the mix. Go ahead and hover over the bubbles! You can see the exact country, its life expectancy, and its precise GDP per capita.