Hello, welcome to today’s blog which is going to be my second one covering the tidy Tuesday dataset. This week it was looking at a dataset with life expectancy for every country in the world since 1950. I decided you could do some cluster analysis on this dataset and then once you have the clusters can further analyse to understand trends. We are going to use K-means clustering to put the countries together then look for trends and differences between the clusters. So the dataset has country, year between 1950 and 2015 and the life expectancy of that year. Now in order to do clustering, you need at least two measures, therefore, I created one with the change in life expectancy per year. The other measure is going to be the life expectancy in 2015.

In order to find our value of K, I did the below silhouette plot. Now you’re meant to use the value of K with the highest sil width, in this case, it would be 3. However, with so many different countries I feel that would be unfair and group the countries up too much. There are further spikes at 6 and 10.

I decided to do the below plot for different k values both 6 and 10. The plot for 10 is below

10 seems like a good value as there are not too many clusters to deal with but also good variation between the different clusters. We will take k equal to 10 for further analysis.

The comparison above looks at causes of death and i have grouped it up to get the mean for each for cause for each cluster. Conclusions that can be made:

• Cancer is prevalent across all clusters, however, the higher the life expectancy the more prevalent it is. This could be because your more likely to get cancer at older ages.
• Dementia is another cause which seems to increase with older life expectancy.
• HIV is highest in the two lowest life expectancy clusters the same with neonatal deaths
• Finally, road accident is an interesting cause, by far the highest cluster is cluster 7 which seems to be the cluster with the highest increase in life expectancy over the last 65 years. Could this because these are fast developing nations and have not got the safe road infrastructure in as yet.

That’s it for a little intro into reviewing the data this way. Let me know your thoughts and comments. There are lots of dataset on the World Health Organisations website as well as other datasets such as economic growth i can add to this analysis and develop it further.