Life Expectancy - GNI per capita analysis
The below graph shows yearly analysis of GNI per capita by country from 1990 to 2018.
The data is from World Bank.
The indicator GNI per capita, PPP (current international $) is defined as follows:
PPP GNI is gross national income (GNI) converted to international dollars using purchasing power parity rates. An international dollar has the same purchasing power over GNI as a U.S. dollar has in the United States.
GNI is the sum of value added by all resident producers plus any product taxes (less subsidies) not included in the valuation of output plus net receipts of primary income (compensation of employees and property income) from abroad.
Data are in current international dollars based on the 2011 ICP round.
The indicator Life expectancy at birth is defined as follows:
Life expectancy at birth indicates the number of years a newborn infant would live if prevailing patterns of mortality at the time of its birth were to stay the same throughout its life.
Plotly
For the analysis and plotly visualization, We have used three different variables.
Life Expectancy (y axis)
GNI per capita ($) (x axis)
Population (bubble size)
- Colour and Bubble Size
Colour refers to country and bubble size is according to Population size.
- Animation
Below the graph, we have a “play” button which can be used to play the animated plotly and see the yearly trend.
When we hover over one bubble point, we can see all the information on that specific country.
- Legend - Filter
We can use the legend in the right-hand-side to filter countries within a specific continent.
By default, the graph will show all countries in all continents.
If we click one continent, the font letter from that continent will turn grey which means that all countries within that continent will not display on the graph.
If we double-click one continent, we will only see the countries within that continent.
This is very useful in order to compare yearly trend among different countries.
Code, questions and feedback
The code for this article is published in my github account.
Please feel free to follow me on my Twitter account and ask any question or feedback you might have.
Thank you very much for your time in reading the article.
