LIS 4930 - Visual Analytics

    I used Plotly to design this graph and I think that it turned out pretty well. It clearly shows the distribution over time by showing the rise, peak, and fall of the distribution. The colors I chose ended up looking very nice together and can be clearly differentiated. Overall, I think this design represents the data well. 

    When comparing the two graphs you can clearly see the deviation between the two of them. Below is a representation of the US price of palm oil.     And after that, here is the representation of the Global price of palm oil.     When you put the two of the data sets together, that is when you can really see the vast amount of between them.     The Price of the US Palm Oil is so far below the Global price, it is almost hard to tell we are comparing the two. You see, without the deviation analytic design, the data sets seem similar. However, when put together you can just see how different they are.

   Looking around my room, I saw a poster of the Periodic Table that my roommate has. Gaining inspiration from that, I then decided to base my visualization off of something that had to do with it. Then, after a bit of research, I decided to do a pie chart on the different types of elements in the air. Many people believe it is made up mostly of oxygen so it is very unique to see that the atmosphere we are in is over 3/4 nitrogen. I looked up a spreadsheet on Google Fusion Tables, then created this pie chart. Some of the percentages are so small, it is very hard to see, but it represents the information quite nicely. 

    I decided to keep my visualization simple and clean. There wasn't much variation so the line stayed pretty consistent to represent the gradual increase in time. Below is a depiction of my visualization.     Below is a time series graph I found on plot.ly of the amount of rainfall in different cities in New Zealand between 2000-2012.     The Few recommendation I chose to critique on was stacking line graphs to compare multiple variables. This person did a pretty good job with displaying the multiple cities and you can clearly see where the rainfall peaked and was at its lowest. The colors chosen were very nice as they differentiate each city so you can clearly tell which city is which. I would have liked for the graph to be spread out a little more so you can see it more clearly, but this version is good. Overall, this is a great example of a time series graph in my opinion.