World Happiness Report Data Analysis

I analysed the 2019 World Happiness Report data to explore the importance of factors that contribute to self-reported happiness. This is what the ranked data looks like. I’ve added a column indicating the attribute most important for the happiness score.

The numbers in the metric columns quantify the extent to which that factor affects the respondent’s perception of happiness, on average across the country.

Social support is the most important reason for happiness across 14 of the 15 highest-scoring countries. 137 countries’ dominant attribute for happiness was social support, 18 countries’ was GDP per capita, and 1 country’s was generosity.

These are the 15 lowest-scoring countries.

These are countries ranked by how important GDP per capita is for the country’s people’s happiness.

Below are countries ranked by how important social support is for the country’s people’s happiness.

These are countries ranked by how important healthy life expectancy is for the country’s people’s happiness.

These are countries ranked by how important freedom to make life choices is for the country’s people’s happiness.

These are countries ranked by how important generosity is deemed for the country’s people’s happiness.

Here’s how the various metrics correlate with each other. The number indicates the correlation (-1 to 1) between the row and column metric.

You can find the code I wrote to conduct my analyses here and the data here. The former is a copy of the Python notebook I worked off locally.