Homework #1 - Vis Sketches

Nandana Shibu Elizabeth   -   nshibuel@asu.edu

The cool bar chart that I made!
The cool waffle chart that I made!
The cool scatter plot that I made!

Dataset Summary (Link to dataset): This dataset shows the statistics of 13 football leagues in 2023. The main attributes of the dataset are: League, Country, Longitude, Latitude, Famous Players in 2023, Total Revenue in Dollars (2023), and Projected Revenue for 2024. For purposes of this assignment, the attributes considered accross the 3 data visualizations are League Name, Total Revenue in 2023, and Projected Revenue for 2024.

Sketch #1: Bar Chart - The X-axis represents the leagues (13 data points), the Y-axis represents the total revenue in dollars in 2023 - the scale chosen is in millions (each point is 1000M), since any other range would make it difficult to visualize extremely small/large values (e.g. 1M or 7B). The color of the bars represents the projected revenue for 2024 - lighter blue represents lower projected revenue, medium blue represents the medium range, and the darkest blue represents the highest projected revenue. This is an excellent way to visualize this specific dataset, as using the color gradient makes it easy to draw conclusions intuitively.

Sketch #2: Waffle Chart - In this chart, the color represents the league, the squares represent the projected revenue, i.e., 1 square = 1%, and the total revenue is depicted next to each colored square. This is a 10x10 chart, which is standard for waffle charts. This chart was trickier to create, as it was challenging to represent the total revenue without overcrowding the chart, but since the instructions said be creative, I figured why not!

Sketch #3: Scatter Plot - The X-axis represents the total revenue in dollars in 2023 (scale in millions), the Y-axis represents the projected revenue for 2024 (%), and the colored points represent the leagues. Since there are some extremely small values that are depicted as points, it may be slightly tedious to accurately interpret this plot, when compared to the other charts. Regardless, this is also a cool way to visualize this data and observe the variations.