This week we have a fun and relevant data analysis to get you ready for the 2021 Olympics. Yes, we obviously built this with Hal9!
We should mention, instead of using Javier's email, we will be sending you subsequent emails from firstname.lastname@example.org. We also have a new TikTok Hal9 channel and a Twitter Hal9 account, worth following to learn more about Artificial Intelligence, visualizations, and data analysis.
In this week’s post, we are going to showcase how Hal9 can be used to process athlete information from the Olympics and visualize which countries have the most medal-winning athletes. You can open this visualization in Hal9 directly from here.
How does Hal9 do it?
A CSV file archiving the results of the 2016 Olympics posted on GitHub was pulled for this visualization. You can open this link to view the Hal9 pipeline we used to create these visualizations.
Once the CSV file is loaded into Hal9, the ‘Map’ function is utilized to clean the data so that it can be properly visualized. For the Olympics, we needed to get the medal count of various athletes, and what country they competed for.
Using the ‘Summarize’ and ‘Filter’ functions, you can sift through the data and rank the countries that received the medals. For the sake of this visualization, the medals for every individual who received a medal are weighted equally. So for a 4x4 relay winning team 4 medals are awarded. This overweights countries that performed well in team-based events (such as the United States and Great Britain) for the visualization.
At this stage, using the ‘Bubbles’ function, we are able to create the visualization of countries.
To create the final visualization with the bubbles on a map, a new web table which lists country names along with longitude and latitude needed to be uploaded. The two web tables need to be joined together so that the total of athletes who won medals in each country can be visualized across the longitude and latitude map. By using the ‘Join’, ‘Filter’, and ‘Map’ settings, we are able to create the final visualization below (the same image as the beginning of this post).
What can you do?
Using Hal9, we are able to pull, filter, and visualize data on the medal count of Olympic medal receiving countries! Users of Hal9 would similarly be able to easily visualize data on product sales, active users, or other preferred search terms across nations. Visualizations tracking complex interactions between sources and locations can easily be dragged and dropped and visualized with this system.
If you’re ready for a bigger challenge, you can create entirely new data sources, transformations, visualizations or predictive models, and contribute them to our open source GitHub repo.
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