Coffee scores are displayed as a spider graph. I wanted to pull from a dataset that had 300+ coffees scored, but all the data was in a spider graph. I explain here how I extract the data and measure the potential error rate per metric.
Typically, when people publish research, they don’t share the raw data sets. Usually, those data sets have more restrictive access. I’ve run into this problem before where I wanted to re-organize some data, so I made a semi-manual process to extract data from published graphs. The issue has come up for a more complicated graph, the spider graph.
Coffee scores are displayed as a spider graph. I wanted to pull from a dataset that had 300+ coffees scored, but all the data was in a spider graph. This dataset is the archive of all of the coffees previously sold by Sweet Maria’s, and I usually buy my green coffee from them. They didn’t have the data saved in a database, and I was quite interested in digging into the data. I could have manually entered the data in probably less time than writing the code to extract it from the graph, but I was worried about being bored and making mistakes.
I explain here how I extract the data and measure the potential error rate per metric. I was able to get pretty accurate within 0.05 of the actual number, so around 0.6% error for a score of 8.
I took the image, and I indexed it to a small color map of 16 colors. This made certain that the line of data colored blue was easy to segment out. Then the other lines of the graph were a similar gray, and I was able to split those apart. I also made sure to use the same color map for all images to reduce errors due to slight shifts in color.
The agenda of the talk included an introduction to 3D data, its applications and case studies, 3D data alignment and more.
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