How Happy is Your Country? — Happy Planet Index Visualized | by Susan Li | Towards Data Science

The Happy Planet Index (HPI) is an index of human well-being and environmental impact that was introduced by NEF, a UK-based economic think tank promoting social, economic and environmental justice. It ranks 140 countries according to “what matters most — sustainable wellbeing for all”.
This is how HPI is calculated:
It’s tells us “how well nations are doing at achieving long, happy, sustainable lives”. The index is weighted to give progressively higher scores to nations with lower ecological footprints.
I downloaded the 2016 dataset from the HPI website. I am interested to find correlations among happiness, wealth, life expectancy, footprint and so on, and then put these 140 countries into different clusters, according to the above measures. I wonder whether the findings will surprise me.
Note: for those who want to see the results right now, I have set up a Tableau story, that can be accessed from here.
After preprocessing, this is the data we are dealing with: it contains 140 countries and 12 variables: country, region, life_expectancy, wellbeing, happy_years, footprint, inequality_outcomes, adj_life_expectancy, adj_wellbeing, hpi, gdp and population.
The Top 20 Happiest Countries(according to Happy Planet Index)
Costa Rica takes the top spot — followed by Mexico and Colombia. Norway is the highest ranked European country.
The 20 Least Happy Countries(according to Happy Planet Index)
Chad is at the bottom of the list, followed by Luxembourg. Luxembourg is the most extreme example of a wealthy nation scoring very badly — it does well on life expectancy, but has low inequality; and it has one of the highest GDP per capita, but sustains this lifestyle with the largest ecological footprint per capita of any country in the world. “If life in Luxembourg was replicated across every other country, we’d need 9.1 planet Earths to sustain us!” Happy Planet Index claims.
The Relationship between GDP per capita and life expectancy
The correlation between GDP per capita and life expectancy is relatively strong as these two variables are concordant. The Pearson correlation between these two variables is reasonably high, at approximately 0.62.
Pearson's product-moment correlationdata:  hpi$gdp and hpi$life_expectancyt = 9.3042, df = 138, p-value = 2.766e-16alternative hypothesis: true correlation is not equal to 095 percent confidence interval: 0.5072215 0.7133067sample estimates:      cor 0.6208781
The Relationship between life expectancy and Happy Planet Index score
Many countries in Europe and the Americas end up with middle-to-low HPI indices because of their large carbon footprints, despite their long life expectancy.
The Relationship between GDP and Happy Planet Index score
GDP can’t buy happiness. The correlation between GDP and Happy Planet Index score is indeed very low, at about 0.11.
Pearson's product-moment correlationdata:  hpi$gdp and hpi$hpi_indext = 1.3507, df = 138, p-value = 0.179alternative hypothesis: true correlation is not equal to 095 percent confidence interval: -0.05267424  0.27492060sample estimates:      cor 0.1142272
A simple correlation heatmap
After a series of statistical analyses, by applying Pam Clustering Analysis, I grouped these 140 countries by wealth, development, carbon emissions, and happiness into three groups (If you are interested in the details of the clustering analysis, you can find them on R Markdown file on Github).
How do these three clusters appear on a world map?
The Happy Planet index has been criticized for weighting the ecological footprint too heavily; and the ecological footprint is a controversial concept. In addition, the Happy Planet Index has been misunderstood as a measure of personal “happiness”, when in fact, it is a measure of the “happiness” of the planet.
Nevertheless, the Happy Planet Index has been a consideration in the political area. For us, it is useful because it combines well being and environmental aspects, and it is simple and understandable. Also, it is available online, so we can create a story out of it.
The source code that created this post can be found here. Again, you are welcome to dabble with a Tableau story that is created from this data. I would be pleased to receive feedback or questions on any of the above.