Morning Joe Project, Part 5: The Finished Product

I am concluding my five-part series on a coffee rust and futures project by talking about how I am presenting my results using a webpage article and the D3 library in JavaScript. Since communicating effectively is a key component in the data science process (and life itself), several types of visuals were used in the Morning Joe project. This was my first time coding in D3, a JavaScript library for manipulating web documents.

In order to best tell the story of coffee rust, production and futures to a general audience, I needed to follow principles of good data visualization. Since the topic would probably not be familiar to lots of people, I began the webpage with a slideshow images that explained the background of the project at a high level. Having too many words on the page and not enough white space would make the page uninteresting and ugly so I kept text explanation to a minimum.

Next I added a section with country map images that showed where coffee was grown in that country. Visual encodings of color were added to blank administrative maps of Brasil, Colombia and Papua New Guinea to show coffee growing regions. These maps were presented in a slideshow format.


Finally, a histogram of coffee futures, line plots of rust versus production and rust versus futures, a correlation matrix, and a linear regression plot were added to the webpage. These images were created with the data using Python. These Python images were also added to sections on the website to explain how the variables were related or not and to also explain the process of using visuals to accept or not accept original hypotheses.




I concluded the visuals with an interactive box plot of coffee rust intensity vs. futures created with D3. Hovering over each box plot, a user can see the minimum, mean and maximum value for that rust intensity level.



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