Today, we will be exploring a fascinating topic that has garnered attention in recent years – the correlation between happiness and a chosen index. For this particular discussion, I have chosen to focus on the fertility index, due to the significant decrease in global fertility rates that have been reported in the news lately. This decline has resulted in a shrinking population in many countries, which has caused concern among policymakers and researchers alike.

There are several reasons why this trend is occurring, and it is important to explore them to gain a better understanding of the situation. For example, societal changes and shifting cultural norms may be playing a role in the declining fertility rates. Additionally, access to family planning and birth control has become more widely available in recent years, which may also be contributing to the trend. Economic factors, such as high costs associated with raising children, may also be influencing people’s decisions regarding family size.

In this blog, I will be creating a dashboard that showcases the correlation between the fertility index and happiness levels in different countries. By examining this relationship, we can gain insights into how the decline in fertility rates is affecting people’s happiness and overall well-being. The dashboard will include data from a range of countries, and we will explore the trends and patterns that emerge from the data.

Data Understanding and data prep

To prepare my data for visualization in my blog post, I utilized Alteryx. Firstly, I joined the fertility table with the happiness index table and grouped countries by continent. Additionally, I incorporated supplementary data using country codes. As the data was already clean, I didn’t need to spend much time on data preparation. Finally, I exported the data to Power BI to create my visualization.


I admit I struggled with Power BI at first because my muscle memory was accustomed to using Tableau. However, I soon discovered some of the strengths of Power BI that are worth mentioning. For instance, its embedded visual types make creating complex visualizations a breeze. With Power BI, you no longer need to create a donut chart using dual-axis charts or other complicated techniques. Instead, you can easily access a wide variety of visual types and customize them to suit your needs.

However, while working on the dashboard, I found some aspects of Power BI a bit annoying. Specifically, I had some trouble with the bookmarks feature. Compared to Tableau’s parameters, bookmarks in Power BI didn’t seem to offer the same level of dynamic control over visuals. Nonetheless, I was able to work around these limitations and create a dashboard that met my needs.

The Data School
Author: The Data School