ILA 5

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  1. The first thing is see when looking at this graph is that the yellow, light blue and dark blue wedges are the largest.
  2. That corn brings in the most money ($47.7 billion) than all other crops. Also, that cotton is the smallest only bringing in ($5.0 billion) which surprised me.
  3. I like how each segment of the chart has a different color and that the money it makes is included within each piece. I wish I saw what the percentage of revenue each crop brought in, alongside the amount of money each crop received.
  4. I would change:
    • I would add the percentage to the graph so you could see what percentage of revenue each crop was bringing in out of the whole pie chart.
    • Possibly change it into a bar graph because I don’t think that this is a great way to represent the data at hand.
    • Change the ‘All other crops’ to more specific crops in order to make that piece of the chart smaller and more detailed.


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  1. One of the first things that stand out to me is that the colors depict what resource region they are talking about and that there is no patter to the regions.
  2. Basin and Range has largest difference between 2016 and 2015 with -9.9%, while the Southern Seaboard has the smallest difference with -0.6%.
  3. I like how the map shows you where the regions are in the United States, but I don’t like how there is no pattern to the regions. I wish I saw numbers that were produced in 2015 and 2016 instead of just the differences on the map.
  4. I would change:
  • I would not represent this data on a map. I would most likely use a bar graph with the numbers from 2015 on one, the numbers from 2016 on the other, and then the differences on another separate bar.
  • This graph is also just a forecast of what they predict the 2016 percentages to be, to be a more reliable graph they could’ve done the difference between 2015 and 2016.
  • The colors for the ‘Fruitful Rim’ and ‘Northern Great Plains’ are very similar. It would not be that hard to change the colors on the graph in order to make it less confusing and more clear.

 

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In this graph, I am mapping the number of herbicide tolerant Soy Bean plants in certain states and the U.S. While using plot.ly, I chose a line graph to represent this set of data because it was the easiest to read while still being informative. I also chose to represent it with a line graph because it best showed the change over time in the herbicide tolerant plants. One design choice I made was to have each state and the U.S. be represented by a different color so it is easier to spot the change in a specific state or in the overall U.S.

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