Introduction

Since 2012, 156 countries have been ranked by how happy their citizens perceive themselves to be by using the data from Gallup World Poll. The report gains global recognition as governments, organizations and civil society increasingly use these happiness indicators to define their policies. Happiness and citizens’ well-being is defined by key variables including healthy life expectancy, social support, freedom to make life choices and GDP. 

It has been more than a year since COVID-19 was declared a pandemic by the World Health Organization. COVID-19 has challenged the world’s happiness through death, illness, instability and stress which is why for this lab report, I decided to visualize the state of happiness in the pre-covid world along with the factors that influence the happiness score, the world ranking of these countries and reviews the state of happiness in the years 2018 and 2019. Some of the areas of this visualization highlights the major differences between the 10 most and the 10 least happy nations according to the World Happiness Report. 

Inspiration

One of the visualizations that I found initially on Tableau captures the Water levels of the Yangtze River and its major tributaries in China. I liked how the colors and shapes are used to provide different details on water levels and the days that the water level is above warning level. What could have been better though is the division of the colors and its accessibility and visibility for all the users.

Even though I looked at sample dashboards on Tableau’s official website, my inspiration for this project was primarily derived from the datasets available on Kaggle.  After browsing through several topics such as spotify music analysis and personality types, I decided to work on the World Happiness Report. 

Methodology

I have used two main tools to create the final visualization for this project. These include:

Step 1 – Kaggle

For this project, my data is derived from Kaggle which offers a public data platform, a cloud-based workbench for data science, and artificial intelligence education. The data provides happiness scores which are based on answers to the main life evaluation question asked in the poll. The factors used in this dataset have some impact on the total score but it is majorly used to explain why some countries rank higher than others. 

Step 2 – Tableau

After finalizing the dataset, I imported it on Tableau. There are a total of 6 graphs/maps created to represent the data out of which some only cover 10 most and least happy countries to show the major differences between them. These countries are:

Most happy countries in the World: Finland, Denmark, Norway, Iceland, Netherlands, Switzerland, Sweden, Canada, New Zealand, Australia.

Least happy countries: South Sudan, Burundi, Central African Republic, Afghanistan, Tanzania, Rwanda, Yemen, Malawi, Syria, Botswana.

Selecting the visuals

To represent the data in a versatile yet understandable manner and to translate it into a visual context, I used different means such as stacked bar, maps, treemaps, scatter plots, packed bubbles and box- and- whisker plots. I wanted to make use of the different elements of visual design such as shape, color, balance, contrast, etc. to convey the data with more clarity. 

Color Palette

As the data is about world happiness, I used color psychology to define the most to least happy countries using the colors yellow and blue first. Yellow is a warm color and can evoke feelings of happiness along with having an attention grabbing effect. Blue on the other hand is a cool color, usually used for calming and soothing but can also express sadness. 

Making visualization accessible

Since color is the most relative medium in representing data, I later worked on selecting a different color palette and making sure that the colors are accessible to all with a good amount of contrast. Yellow may sometimes be hard to identify for some people as it may look violet/grey or pink. Blue-yellow color vision deficiency is also very rare but is still a possibility which is why I decided to replace yellow with orange as it is also a part of warm colors. 

Results and Discussion

Rather than presenting a new finding, this data visualization focuses more on highlighting the factors that determine happiness in different countries and clarifying the major differences between the world’s most and least happy countries. Majorly it focuses on asking this question: what makes us happy as a nation? 

This visualization explores the following points:

Reflection

Overall, I really liked working on Tableau but it took me a while in the beginning to finalize the data that I wanted to work on. 

The next step for me would be to explore more ways to represent the data and find ways to improve the visual layout. I would also like to explore Tableau more to be able to improve and enhance the quality of visualization for future work.