When first looking at all of the flavors in the data chart, I was surprised to find such extreme flavors. I at first imagined, the flavors would be more basic flavors such as the common ice cream flavors or other sweets. This made me think about what everyone else could be thinking when they were asked for flavors. Were they thinking of their favorite sweets? Or maybe their favorite dinner time meal? All of these were taken into consideration when organizing the data. After wrapping our heads around all the present data, our group was able to sort the flavors into several categories that we saw being the best fit. With all extreme amounts of data, there are always outliers, which we’ve excluded from our graph to give a better representation of our data. We separated our flavors into categories such as Meats/Proteins, Fruits, Vegetables, Spices, Sweets, Dairy, and Beverages. Of course some flavors will roll over into several categories, but that is the point however to figure out the correlation amongst the groups. Chocolate, for example was the most common flavor and can be categorized as both Dairies as well as in Sweets. However within this chocolate flavor, we found many sub categories such as white chocolate, dark chocolate, and milk chocolate, which also had to be separated into the appropriate categories. It was interesting to find that chocolate was more common than vanilla because research shows that vanilla is still a more popular flavor than chocolate. In our graph, we were able to determine the significance of each title by showing the size of the word in regards with the rest of the data. Each flavor is different in size and as we can see, chocolate is the biggest and most popular of our data, whereas salt and beer were very small in size. We use this scale with the rest of our data as well.