On Day 4 of Dashboard Week we were tasked with exploring data from the World Happiness Index to find correlations that might explain a country’s happiness score.

As usual, there was a catch: we had to use PowerBI, which some of us were less familiar with. But that’s all part of the learning process, and I was excited to dive in and see what I could create.

To start, I decided to look at average working hours as a potential factor that might impact a country’s happiness score. After all, as Jack Nicholson famously said in The Shining, “All work and no play makes Jack a dull boy.”

Using data from the World Bank, I cleaned the data in Alteryx and imported it into PowerBI. From there, I created a visualization that compared a country’s happiness score to its average minimum, average, and maximum weekly working hours.

Due to the sparse nature of the data, many of the lower ranking countries were filtered out after the join – there simply was no data available about full time workers in those countries. Given more time, I’d try doing this exercise with different data to gain a fuller picture of what is going on, as this only really captures countries within a happiness rank of 1-80.

What did I find? Countries with higher happiness scores tended to have lower minimum, average, and maximum hours worked per week. This trend was particularly evident in Scandinavian countries like Norway and Finland, known for their work-life balance.

On the other hand, countries with lower happiness scores, such as Greece and Estonia, worked between 40 and 51 hours per week on average. Of course, this doesn’t necessarily mean that working hours directly impact a country’s happiness score. Greece, for example, has faced economic challenges in recent years that could be a contributing factor to their high working hours.

Overall, Day 4 was a great opportunity to explore time series data in PowerBI and discover new ways to visualize our findings. While I’m more familiar with Tableau, it was valuable to spend more time with a relatively new tool and see what insights I could uncover.

 

 

 

The Data School
Author: The Data School