Even though the world shares similar ingredients and foods, each continent has own recipes and tastes which made me curious to figure them out. Do people prefer specific ingredients or they use just general ingredients, such as pepper, olive oil, or chicken? Which ingredients are mostly used out of all?
These questions led many people to create a concept for food pairing. Food pairing is a method for identifying which foods go well together from a flavor standpoint. me to make six of networks that contained each ethnic groups’ ingredients based on food pairing database.
TOOLS & METHODS
1. Data Collection
Tools: Supplementary Information Supplementary Dataset 2
An article, Flavor network and the principles of food pairing, is one of the most prominent and popular thesis in food pairing or bridging, especially, their datasets are commonly used by other researchers.
There are most commonly used ingredients among seven ethnic groups by the principles of food pairing. Since Northern America was not able to clean from RStudio due to the reason for the super big size, I decided to exclude these two groups, northern American and Southern American.
2. Data Cleaning
Tools: Introduction to Data Science with R Data Analysis Part 1
It was RStudio which I had to use for the cleaning the data and it was my first experience. Although I’d liked to understand the meaning of the comments, a few hours later, I concluded to memorize them not to understand which I was not able to fully understand. Also, another problem occurred and it was after saving the file from RStudio which was “x”. I supposed to rename the final column to “weight” not “x”.
3. Pre-Design Research
Tools: Gephi Tutorial: How to use Gephi for Network Analysis
The video helps me to get overall understanding of Gephi, especially on Statistics parts, I found that for my data needed at least Network Diameter to analyze how much they are centralized. Average Degree is also good to have which contains average connection out of all nodes so it could see at a glance.
It was very worth to see the above article that it tells me “eccentricity” measurement. “It captures the distance between a node and the node that is furthest from it; so a high eccentricity means that the furthest away node in the network is a long way away, and a low eccentricity means that the furthest away node is actually quite close(Hirst, 2010).” I used the eccentricity into the size of nodes.
Five ethnic groups are all in the center so after I made the node to be scared by node size, they are all placed together. After watching the video, I was able to make less cluster than before.
4. Visualization creation