Introduction
When I hear the country Finland, the first thing that comes to mind is that it is known to be the happiest country in the world. This has been determined year after year based on the World Happiness Report that is released every year. This report is based off of global surveys that ask participants about various aspects of their life. I was initially inspired by the visualization shown in Figure 1 which is an interactive visualization that depicts each countries happiness score along with the factors that contribute to it.
Separate from this, I had recently come across a very interesting article about how people around the world spend their time. Since everyone experiences the same amount of time in a day, the differences in how this time is allocated can tell us a lot about someone’s life.
As someone who has always been fascinated by differences in the way of life of people around the world, I was intrigued by the opportunity presented by these two datasets and was inclined to see if there were any interesting correlations between happiness and the way people spend their time.
Materials
Dataset
I found the world happiness report dataset on Kaggle. This dataset contains information about the happiness score of 153 countries in 2022. This data is based on information collected through the Gallup World Survey. Respondents were asked a series of questions about their life for which they had to rank their current life on a scale from 1 (most exceedingly bad conceivable life) to 10 (most excellent conceivable life). The happiness score is computed as the national average of the responses to the survey per country. This dataset also includes 6 other factors that are used to explain the overall happiness score. The following columns are included in the data:
| Rank |
| Country |
| Happiness Score |
| Whisker-high |
| Whisker-low |
| Dystopia + Residual |
| Explained by: GPD per capita |
| Explained by: Emotional Support |
| Explained by: Healthy Life Expectancy |
| Explained by: Freedom to make life choices |
| Explained by: Generosity |
| Explained by: Perceptions of Corruption |
I found a second dataset which contained information about the average time spent (in minutes) on various activities throughout the day per country. This dataset comes from OECD.stat and contains information that has been pulled from various questionnaires where people recall how they spend their time in a day. The data set contains information for 33 countries and contains the following columns:
| Country |
| Paid Work |
| Education |
| Care for Household Members |
| Housework |
| Shopping |
| Other Unpaid Work & Volunteering |
| Sleep |
| Eating & Drinking |
| Personal Care |
| Sports |
| Attending Events |
| Seeing Friends |
| TV & Radio |
| Other Leisure Activities |
Both datasets were clean and nicely formatted, so I had to make minimal changes before creating the visualization. To ensure an effective joining of the data I added some aliases for the country names to standardize the countries between the two datasets. For example, I changed “USA” to “United States” and “UK” to “United Kingdom”.
Tools
Once the datasets were ready, I imported them into Tableau and joined the datasets together as shown in Figure 2. Since the happiness dataset contained 153 countries and the time spent dataset only consisted of 33 countries, I decided to do a left join since the time spent data was simply there to supplement the happiness scores for the available countries. Each row was joined based on a match between the country names from the two datasets.
Once the data was joined, the visualization was ready to be created.
Process
Since Tableau can easily interpret country names, creating the map using this software was very easy. Having a world map to represent all the data seemed to be the best way to portray this data. Tableau was able to display a map as soon as I dragged the country field into the workspace.
Once the map (Figure 3) was created, I decided to assign each country a color to represent how high or low that country’s happiness score is. Since people usually associate the color green positively, I decided to use the green gradient as the color for the map. To ensure understandability, information about the happiness score is included in the tooltip that is displayed on hover for each country. Since data about how people spend their days was also available for many countries, I decided to include information about how long people work, sleep, and do leisure activities as well. Since I am exploring how time spent on various activities throughout the day can impact a country’s overall happiness score, including this information in the tooltip is an easy way to get a base level understanding of how these three basic activities are distributed in each of the countries.
To supplement this map, I decided to add a few charts in order to highlight specific aspects of this data. The first chart, shown in Figure 4, is a stacked bar chart that displays the breakdown of the happiness score by how much each of the six factors have contributed to that score. Since each of these factors make up a part of the whole happiness score, I thought a stacked bar chart would work well for this sort of visualization. I also decided to order this chart based on the happiness score so that the highest scoring country would be first and the lowest scoring country would last when viewing the chart from left to right. Since people would likely be interested to see the ranking of countries based on the happiness score, I thought this chart supplements the map well.
Additionally, I decided to use a horizontal bar chart (Figure 5) to visualize a breakdown of the time spent in a day per country. Each columns represents the amount of time in minutes that a certain activity is done for within the 24 hour day. Since each country is displayed in a separate row, it’s easy to compare the minutes spent doing an activity for each country by looking down the column.
I combined all three visualizations in a dashboard. The biggest visualization is the map since this is the one that displays the happiness score information. The other two bar charts are used to supplement the happiness score data and provide more insight to explain factors that may impact this score. For this reason these two visualizations are smaller in size and intentionally not emphasized on the dashboard.
Results
The resulting dashboard can be viewed here on Tableau Public and seen below in Figure 6.
As you can see, Northern Europe – particularly Scandinavia – seems to be the darkest on the map. These countries do in fact have the highest happiness scores compared to the rest of the world. The United States, Canada, Australia, and New Zealand are close behind the Northern European countries. It’s interesting to see how the countries with the highest happiness scores compare against the countries with the lowest happiness scores in the breakdowns depicted on the other two charts.
For example, in the breakdown of happiness scores based on the six different factors, it can be seen that the happiness score of the majority of countries is most impacted by GDP per capita. Figure 7 shows the breakdown for the happiest countries compared to the least happy countries. In this chart, the orange portion of the stacked bar represents the portion of the happiness score that can be explained by the GDP per capita factor. Amongst most countries, this factor contributes the most to the overall score aside from the purple portion. The purple portion of each bar represents the Dystopia + Residual factor for each country’s happiness score which accounts for any unexplainable factors that contribute to the overall score.
Another interesting analysis can be made by comparing the time spent in a day breakdowns between the happiest and least happiest countries. People in both Finland and Norway spend a considerably less amount of time working during the day when compared to countries such as Japan and China as shown above in Figure 8.
The data highlighted in both Figures 7 and 8 indicate that the economic differences between countries greatly impacts the happiness of its people. In countries with a higher GDP per capita people are generally able to afford to work less which allows for more time for leisure activities, sleep, and personal care which contribute to higher overall happiness scores. All three visualizations work together to support this finding which provides some explanation for the difference in happiness scores around the world. Ultimately the data indicates that how people spend their time can contribute to a higher or lower feeling of happiness.
Reflection
It was very interesting to see how the two datasets used in this project were able to be combined and further explain the differences in happiness levels around the world. I think there is a great opportunity to continue to dive deeper into this data to attempt to understand it better. First I would try to find information on how people spend their time for the rest of the countries which were not included in the original time spent dataset used in this project. Second, I think it would be interesting to see how happiness levels have changed over time especially during the peak of the COVID-19 pandemic.
Also, in the future I think it would be very interesting to add a third dataset with weather information to see if there is any correlation between happiness, how people spend their time, and the weather in the various locations around the world. There are studies that indicate that weather can greatly impact people’s moods, so I would like to explore if the data supports this claim.
References
- https://ourworldindata.org/time-use#how-do-people-across-the-world-spend-their-time-and-what-does-this-tell-us-about-living-conditions
- https://www.kaggle.com/datasets/mathurinache/world-happiness-report?select=2022.csv
- https://www.natureindex.com/news-blog/data-visualization-these-are-the-happiest-countries-world-happiness-report-twenty-nineteen
- https://worldhappiness.report//