Data Visualization on World Happiness Report
The skills the author demoed here can be learned through taking Data Science with Machine Learning bootcamp with NYC Data Science Academy.
Hello! Thank you for taking the time to take a look at my blog post. In this post I am going to be talking about my project on the World Happiness Report. You can find the link to my app below. Now lets start with a little background of the data.
https://adlardee.shinyapps.io/shiny_proj/
Background
The World Happiness Report is a landmark survey of the state of global happiness. The first report was published in 2012, the second in 2013, the third in 2015, and the fourth in the 2016 Update. The World Happiness Report 2017, which ranks 155 countries by their happiness levels, was released at the United Nations during an event celebrating International Day of Happiness on March 20th, 2018.
The report continues to gain global recognition as governments, organizations and civil societies increasingly use happiness indicators to inform their policy-making decisions. Leading experts across fields — economics, psychology, survey analysis, national statistics, health, public policy and more — describe how measurements of well-being can be used effectively to assess the progress of nations.
Data Set
The Happiness Scores and Rankings use data from the Gallup World Poll. The scores are based on answers to the main life evaluation question, known as the Cantril Ladder, which asks respondents to visualize their lives as a ladder and rate their own current lives on a scale of 0-10. The top rung of the ladder represents a score of 10, or the best possible life for the respondent. The bottom rung of the ladder represents a score of 0, or the worst possible life for the respondent.
The scores are from nationally representative samples between the years 2013-2016 and are weighted to correct for sampling errors. The Happiness Score is primarily driven by six factors — economic production, social support, life expectancy, freedom, absence of corruption, and generosity. Each of these factors contribute to making life evaluations higher in each country than they are in Dystopia, a hypothetical country that has scores equal to the world's lowest national averages for each of the six factors. They have no impact on the total score reported for each country, but they do explain why some countries rank higher than others.
Data Findings
Initially I wanted to see the overall happiness rankings overlaid against a world map and then dive into the metrics for the six factors. I was able to achieve that visual, which you can see below, and it was really helpful to see the rankings across the globe and how they played against our own perceived assumptions of the happiest countries in the world.
Map of overall Country Happiness Rankings. Scale shows countries from Red to Green with red indicating a lower ranking and green indicating a higher ranking.
Variables of Happiness Score
Next lets look at the correlation between variables that make up the Happiness Score. In the visual below we see a darkly shaded square in the bottom left corner, which represents the strong correlations between Happiness and Economy..GDP.per.Capita (economic production), Family (social support), Health..Life.Expectancy (life expectancy). High scores among these categories speak to the likelihood of having a high overall Happiness Score. The variable that I found most interesting was Trust.Government.Corruption (absence of corruption).
Below is a picture of scores regarding how citizens felt about the absence of corruption within their countries. The key point here is the majority of countries are red with 130 out of 155 countries coming in below the scale average of 0.232. Governments should be worried that the 'absence of corruption' had very poor scores across the globe. Citizens need to believe that the government has their best interests at heart and only a handful of countries that achieved positive remarks. There were some high scores that did not line up with other corruption indices I researched, primarily Somalia, Turkmenistan, and Uzbekistan. But outside of these outliers, the rankings were in line with other corruption indices
Absence of Corruption
One thing to note about the below visual, which looks at
Economy..GDP.per.Capita (economic production) scores, is that it is extremely similar to the first visual that looks at overall Happiness Scores. The data shows that a good economy has a very strong correlation to overall happiness, which makes sense when you think about it. A thriving economy means that people can take care of themselves and that there is more goodwill to be expressed among citizens.
Economy / GDP Per Capita
As income inequality becomes a bigger topic discussed around the world. I am curious if making this a separate metric would tell a different story. How much income inequality can be tolerated within a strong economy? How does income inequality affect overall happiness in any country?
Conclusion
I like to think of this project as a good building block where we can then start asking more questions. We can already see the correlation among the six primary factors within the report. The next step is to uncover additional variables that could explain a country's Happiness Score, like income inequality for example. As I spend more time with this project I hope I find additional data sets to join with this one and further enhance my analysis.
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Eric Adlard
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