World Happiness Report

Import:

After downloading the WorldHappinessData.zip file, I unzipped and connected the 2019.csv to Tableau Public. I used a union to add the other csv files for the other years. I finally told Tableau that these files are related by Country or Region.

Data Cleaning:

I observed in the displayed data table that columns were named slightly different each year. So I changed the sort fields to "A to Z ascending" in order to have the columns return in alphabetical order. This made it easier for me to see columns named similarly to each other, for example "Country" and "Country and region (union)". I merged these mismatched fields.

Visualization:

Since the data is from 2015-2019, I created a calculated field for Year using SQL:

DATE(DATEPARSE ("yyyy", LEFT(STR([Table Name]),4) ))

I then created a scatterplot graph.

Based on the graph, Myanmar had the highest generosity rating at 3.618, but not the highest happiness score - being 21.92. On the other hand, Denmark had the highest happiness score at 37.73, but had a generosity rating of 1.594. So high generosity doesn't necessarily mean that the country's happiness will be the highest. However, I found that in general the more generous a country was, the more happiness they had.