“Visualization is what happens when you make the jump from raw data to bar graphs, line charts, and dot plots…It’s easy to think that this process is instant because software enables you to plug data in, and you get something back instantly, but there are steps and choices in between…”– Nathan Yau, Data Points
After going over our survey results with RAW in class Wednesday, I felt pretty competent in using the data visualization tool. It was my first choice for the assignment because of this but also because of the pop in color and graphics it offers. In Data Points, Nathan Yau (Statistician and fellow Bruin) advises the ingredients to an effective data visualization are visual cues, coordinate system, scale, and context. I thought I could use RAW to communicate this but after my first half a dozen tries,I didn’t know what I was looking at. They key was choosing the appropriate graph for my data and finding relevant points to correlate.
The RAW graph above shows the closing stock market prices for Amazon.com from 2004-2014. I used the Hexagonal Binning that visually clusters the most populated areas on a scatterplot.
The prices ranged from 284 to 358 a share. The Dr. John Rasp’s Statistics Website data set was based on Yahoo Finance and showed the daily returns of both Amazon and Coca Cola. Initially, I tried to create a visualization that compared the two companies returns. I found it more effective to follow only Amazon because the visualization was too cluttered and hard to decipher. Everything Nathan Yau goes against in Data Points.
Ultimately, I have a data visualization that mimics the one from Google above . I wanted to represent the market progression of Amazon accurately and I did that by making the steps and choices Yau advised. The visualization is more Amazon focused than the raw excel sheet. It also shows the price progression in an order and actualization that cant be seen or compared with just the numbers.