This visualization shows the top countries that consume the most alcohol per person (capita) from 2001 to 2018. What’s interesting is that the top countries throughout this time are mainly composed of countries from East Africa and Europe.
If you want to learn more about alcohol consumption globally, check out Oxford’s report on Global Alcohol Consumption.
Just in case you’re interested, this type of data visualization is called a bar chart race. I’m sure you’ve seen many of these all over YouTube and Reddit. If you want to build one yourself, here’s a tutorial that you can check out.
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Usually less is more, but this is my favorite visualization because it communicates so much information in a clear manner and it’s quite positive!
This visualization shows how each country’s wealth (GDP per capita) and health (average life expectancy) has changed over time. The size of the circles represent the population of each country and the color represents which continent each country is associated with.
Pretty amazing to see how far we’ve come as a species huh?
This visualization, created by Tyler Morgan, is a 3-D map of the world’s fiber optic cable network. This network is used to transmit telephone signals, internet communications, and television signals.
It’s pretty insane to really see how interconnected we are globally, isn’t it?
This was created in R using the rayrender and geojsonsf packages. If you want to see the full code, you can check it out here.
I’m not particularly a fan of fancy data visualizations because they generally don’t communicate information as effectively as simple graphs, like a line graph. BUT, because this animation was so unique (and somewhat disturbing), I felt the need to add this to the top 10 visualizations.
While there are no axes that tell us absolute numbers, it is a neat visualization that shows us how fast the number of COVID cases grew relative to the beginning of the year in 2020.
This was created using d3, and the full code can be found here.
This visualization tells us how COVID cases grew in the US from the beginning of February 2020 to October 2021. What’s interesting about this is that you can clearly see the “waves” of COVID over this period.
This type of visualization is called a choropleth map, and it’s incredibly powerful at comparing a particular variable across different regions (states, countries, continents, etc.) over time.
If you want to learn how to build one in Python, I wrote a step-by-step guide to create a Choropleth map here.
As a Canadian myself who has yet to buy a house, this is an utterly depressing visualization, but I still thought it was interesting to share!
This visualization shows every mental disorder in the DSM-5, which stands for the Diagnostic and Statistical Manual of Mental Disorders. It is the standard classification of mental disorders in the United States.
With over 20 categories and hundreds of disorders, this visualization can consume more time than you think once you start to get into it.
This visualization shows the amount of C02 emissions that China emits compared to the rest of the G7 combined from 1960 to 2019. What’s starkly concerning is how fast China’s growth in CO2 emissions accelerated from 2002 and on.
What would be interesting to see is the CO2 emissions per capita, as it would make sense that China, with over 1.2 billion people, would emit more than other countries.
This dataset was taken from Friedlingstein et al. 2020, The Global Carbon Budget 2020.
This animation shows the lifecycle of plastic, and where most of it ends up. Sadly, it’s evident that we don’t recycle nearly as much plastic as we should and a staggering 60% ends up in landfills or the ocean.
This type of visualization is an animated Sankey diagram, which is similar to tree maps as it breaks down data into several subgroups and represents values proportionally.
Check out this link if you want to learn how to build Sankey diagrams in Python.
Lastly, this visualization shows Billboard’s top 100 artists from 1960 to 2020. I personally love this diagram because it provides so much information: you can see who the top artists were, when they were the most prevalent, and how many songs they had that were dominating the charts!
How many artists do you recognize?