The data

 

Today we were given the Olympic Games data set which includes data per participant since 1896. The data set included information on the activities and also some demographic data on the participants such as their age, weight and height. In total, there were 66 different sport types, 765 events, 1175 teams, 42 host cities and 135 571 distinct participants. Even though the data had more than enough rows to make a large enough sample size, it did lack interesting columns. The data was pretty straight forward and there was not much to explore.

Yesterday I struggled with the data and spent way too much time figuring out what to actually show. I decided not to fall into the same trap today. Even though the data was not rich with information, instead of spending hours searching for supplementary data, I time-boxed my search. After an hour of searching for supplementary data and no success, I decided to move on and use the data as is.

The data was pretty clean to start out with, thus it was not really necessary to use Alteryx to manipulate the data. I did use Alteryx to explore the data for a bit and to get to know the data (like field types and grain of the data).

 

 

The viz

 

I am very fond of exploratory visualisations, where the user can explore different aspects of the data in a visual fashion. I decided to show two dashboards, one for the overview of the Olympic Games. To set the stage, I made a few large numbers to display important figures. Next, I included a map that contains the locations of the countries participating in the games as well as the locations where the Olympics are held. I then made three charts (# participants, # sports and # countries over time) that swap each other out with the use of a parameter. Lastly, I have a chart showing the longevity of the different sport types (when they started and how long they have been part of the Olympics). The list of sport types is quite long so I included a top N parameter so the user can choose how many results to display based on the number of participants that sport has had.

The second dashboard focused on the participants. Again, I started with a few big numbers that set the stage for the user to have a good understanding of the data. This dashboard includes a filter so the user can pick a sport and the whole dashboard will update to display the results for that sport only. The information the user will get from this dashboard includes the countries involved, the height/weight of the participants, the age of the participants and the proportion of males and females for each sport type. I don’t like too many charts on one dashboard ( too cluttered), so again I used a parameter to swap out two similar charts (height/weight). The user can then flip between the charts they are interested in.

Additionally, I added a button to each of the dashboards so the user can go back and forth between the two dashboards. Below are screenshots of the two dashboards but click here for the interactive dashboards posted on Tableau Public.

 

 

Interesting find

 

Flipping through the dashboard to make sure everything is working the way it should I found something quite interesting on the Participants Dashboard. Clicking on the age bins from oldest to youngest, I noticed that the proportion of males to females is significantly declining. The 60-65 bin consists only of 9.8% females where the 10-15 age group consists of 80.8% of females. Also, the 60-65 age group consists mostly of Americans and European countries where the 10-15 age group contains a large variety of nationalities.

 

Lessons learned

 

Today I time-boxed my efforts so I could finish my tasks at a reasonable time. If I spent more time searching for supplementary data I could have built something way better for sure, but I wanted to give myself a time limit and see if I could get something decent done. Receiving the data at 11 AM, I worked a solid 8 hours on this challenge, including searching for extra data that I couldn’t find. Overall, I am not crazy about the viz but I am proud of what I could put together given the time limitations.

I am continuously learning and improving my time management skills. Every day is an opportunity for improvement. I am looking forward to the challenge of tomorrow!

 

Keep an eye out for my next blog on DAY 3 of Dashboard Week!

 

In the meantime… happy vizzing!

 

 

Charisma Adlem
Author: Charisma Adlem

Charisma has an interesting background in animal science, having completed a Master’s degree (MSc) in Zoology at the University of Pretoria, South Africa. She found her passion for data analytics through her scientific studies. She was delighted to discover that The Data School provides a means to follow her heart and enter a career in data analytics. Charisma is a loving mother of two ferrets and has discovered a talent for abstract and realism painting in her spare time. If Charisma had to choose only one food type to eat for the rest of her life, it would be sushi. Charisma also enjoys outdoor activities including fishing, camping and hiking.