Introduction

For this lab, I chose to analyze the relationship between a country’s happiness and economic and social factors impacting the citizens. I found a dataset called “World Happiness Index and Inflation Dataset” on Kaggle and was originally interested in its topic because I wanted to see whether the heightened tensions felt worldwide in recent years would be visible in the result. I realized quickly this would result in basing my visualizations on speculations, so I instead pivoted to finding correlations between social or economic parameters and the ‘Happiness Index’. In order to make the data relevant and manageable to display, I chose to only show data from the most recent year, 2023. I felt some value would be lost in combining data from different years, since the global climate can vary so much over time and these economic and social changes could distort the trend if combined into singular plots.

Visualizations

The first thing I wanted to do was highlight the happiest and least happy countries in 2023 and their respective continents/regions. This was going to give the readers a base for recognizing the regions later on when looking at social factors by region. The ‘happiest’ regions were given yellow colors, the ‘least happiest’ regions were given blues, while the overlapping region was given a deep purple. I went back and forth on the coloring for the overlapping region because there was not a large enough difference in count of countries to color it in the blue or yellow family (even though, as we will see later, the correlations align with the blue regions), but ultimately ended with a color in between the two ranges. In following plots, a grey color is used to show regions that did not appear in the top and bottom countries, as a basis for the ‘middle of the pack’.

Based on the plotted data, countries with higher inflation tended to be countries with lower happiness scores.  Out of the 15 countries at either end of the spectrum, there is only one exception – Israel had, despite being part of the region with the highest average inflation rate, the fourth highest happiness score in 2023. I wasn’t surprised to see the negative correlation between the two variables but still it was interesting to see – and though correlation, of course, is not causation, this made me wonder what other factors would look like in a similar visual.

Social Factors

Luckily for me, the dataset also included different factors related to the social aspects of life in each country. I chose to plot some of these variables similarly by grouping them based on regions and keeping with the same color theming. I chose ‘social support’, ‘freedom to make life choices’ and ‘healthy life expectancy at birth’ as the three variables to focus on. As expected there was a positive correlation between all three variables and the happiness score. There is a bias in the selection of variables shown because I chose these three because of the strong correlation and purposefully did not select the variable I found with no obvious correlation.

Correlations

Lastly, I wanted to highlight the correlation at a country level using scatterplots to make the trend line even more apparent to the users. In keeping with the same color theme, I wanted users to be able to recognize the blue regions and yellow regions and see where they land on the trend line of the social variables vs the happiness score. This felt like an important addition because the previous plot diluted the data to a certain degree because of the grouping by region rather than by country.

Reflection

I would be interested to see a further iteration of this project by further refining or redefining of the regions. I would think there would be a stronger correlation if regions were defined not just on geography but also similar political climates, social movements, etc. I also would be open to representing some of the data in a different format. Though I like the simplicity and familiarity of using horizontal bar charts throughout most of the project, I think there is room for improvement in showcasing the data in a more creative way.

In gathering feedback, I spoke with users after giving them a brief introduction to the project and asked them to tell me their main takeaways and any questions they had while looking through the slides. In general they were able to draw the same conclusions that I intended but had a bit of confusion around the metrics and regions. One suggestion was to change the color given to Middle East and North Africa because it made them think that was supposed to be the focus since it appeared ‘bolder’. They also suggested adding descriptions to the metrics used to better understand how the social factors were measured.

Sources

Fintech, Agra. “World Happiness Index and Inflation Dataset.” Kaggle, www.kaggle.com/datasets/agrafintech/world-happiness-index-and-inflation-dataset/data. Accessed Mar. 2025.