In Pursuit of Happiness (Part I)
Happy countries are all alike; every unhappy country is unhappy in its own way.
In a world where growth often equates economy, OCED raised the importance of well-being, which put country-level happiness in the spotlight.
Today I’m looking into 2016 World Happiness Report commissioned by UN, which contains global happiness index and a series of social-economic factors such as economy, family, health, freedom, trust and generosity. Countries are increasing adopting Happiness as a measure of social progress. It’s widely aware that Bhutan uses Gross National Happiness (GNH) as a development indicator. To less public awareness, Mar 20 has been designated as International Day of Happiness.
First thing I noticed is a broad pattern where countries rank higher on each factors tend be happier. So after some data cleaning, I looked into how countries in each regions are doing in terms of their social-economic well-being.
- generosity, trust and freedom all tends to be rather low
- some region are more homogenous (such as North America) than others (such as South America).
- there are outliers in the region: trust index is rather low in Southeast Asia, except Singapore; economy index in Eastern Africa is especially high in Mauritius, which is above world median too; Sri Lanka and Bhuta n are much better off in family index compared to their South Asian peers.
This is the first step to visualize the dataset. Coming next I intend to look into how social-economic wellbeings contribute to country-level happiness and how does the trend change year-on-year.
What I learnt today is that in order to loop through charts, one needs to wrap ggplot in print() statement. The charts are simply barcharts projects onto polar coordinates.
You could read more about world happiness report here.
Thanks for reading. It’s #day12 of my #100daysproject on data science and visual storytelling.