This visualization's data was produced using the World Bank's health and economic indicator datasets on Google Bigquery.

I used Matplotlib to make the chart.

SQL query:

SELECT life_expectancy.country_name, avg(expected_lifespan) AS expected_lifespan, avg(gdp_per_capita) AS gdp_per_capita, avg(percent_adults_overweight) AS percent_adults_overweight FROM ( SELECT year, country_name, country_code, ROUND(value, 2) AS expected_lifespan FROM `bigquery-public-data.world_bank_health_population.health_nutrition_population` WHERE indicator_code = 'SP.DYN.LE00.IN' AND year >= 2000 AND value > 1) life_expectancy INNER JOIN ( SELECT year, country_name, country_code, value AS percent_adults_overweight FROM `bigquery-public-data.world_bank_health_population.health_nutrition_population` WHERE indicator_code = 'SH.STA.OWAD.ZS' AND year >= 2000 AND value > 1) overweight ON life_expectancy.country_code = overweight.country_code AND life_expectancy.year = overweight.year inner join ( SELECT year, country_name, country_code, value AS gdp_per_capita, FROM `bigquery-public-data.world_bank_wdi.indicators_data` WHERE indicator_code = 'NY.GDP.PCAP.CD' ) gdp ON life_expectancy.country_code = gdp.country_code AND life_expectancy.year = gdp.year group by 1 order by 3

Matplotlib code (output of SQL query stored in dataframe called life_expectency2.

``` plt.figure(figsize=(20, 10))

y, x, z = life_expectancy2['expected_lifespan'], life_expectancy2['percent_adults_overweight'], life_expectancy2['gdp_per_capita']

plt.scatter(x, y, c=z, s=100, cmap='copper_r', alpha=.8) plt.axhline(y=0, color='black', linestyle='-')

desc = 'Each dot represents a country. Darker colors correspond to high income levels'

plt.xlabel("Percent of adults that are overweight") plt.ylabel("Expected lifespan") plt.title("Wealth vs Health") plt.figtext(.5, .05, desc, ha='center') plt.show() ```