Parker Gustafson

Prof. Thompson

ENGL 102

29 September 2016


Graph #1: U.S. 2016/17 rice crop projected at a near-record 237.1 million cwt

1.      First impressions: graph seems to meet the planted area to the production from the year 2000 and on, while years prior has quite a few disparities. The absence of colors is somewhat of an indicator of the legitimacy of the graph, in my opinion. It has been in my experience that when a person or group creates a graph and keeps that color variety means that they prioritize telling the truth over making it look pretty for you. There’s a definite growth in the overall production whilst the plotted land seems to remain roughly the same throughout the 20-year period.

2.      Though this is simply a projection, the results indicate this year’s yield will be in record amounts, seconded only to 2010’s yield of some 247 billion 100-lbs. The purpose of this chart is to describe what the statisticians working on this think the 2016 yield of rice in 100-lb units will be, but it is only a theoretical yield. Nothing appears abnormal or absent from this graph; it shows what it knows and just utilizes math to predict the future based on the knowledge it has.

3.      I’m satisfied with this graph. It displays the information it wants to be made known in a comprehensive and accessible format. I know this puts a damper on question 4, but I’ll think of something.

4.      So, I think I want to make three changes to this graph: make the units along the y-axis more layman-friendly with regards to the amount produced during the 20-year interval, maybe a larger viewing window or more tabs along the x-axis to show a more detailed listing of how much rice was produced vs. predicted in year X, and make the year colors a repeating rainbow. For the first issue, I want to be able to understand the amount produced and predicted without using a small conversion in my head, so just a minor change to how the graph is measured. For the second idea, I want to just show a more detailed version of the graph, which would be another minor change for a better graphical comprehension of the data. As for the final change, it may sound insipid but I ask you this: who doesn’t like rainbows? More to the point, who hates rainbows and would oppose this on the grounds of it being slightly harder to look at than plain black because they hate rainbows. Checkmate, contrarians and opposing opinions!

Graph #2: Obese adults spend more time watching TV and movies and less time engaged in sports and exercise

  1. Distinct colors and very different lengths between categories. Spaced well enough to clearly distinguish which courses are which.
  2. It wants to convey how much time each groups spend doing such and such activity. There seems to be a lack of underweight people, but I infer the reason is because they are such a minority when comparing normal, over, and obese weight levels.
  3. I enjoy the use of colors. They are a little bland and dull, but that means little to both the information and the format they are presented in because of the range of the colors.
  4. Sharper colors for greater contrast, more concise x-axis to be more specific, and maybe a less banal classification for “eating while doing something else” to give it an air of professionalism.


This is a remade graph of the percent of farms that have potential for carbon sequestration at farms and farmlands. Carbon sequestration means how much carbon dioxide can be extracted from the air, condensed, and stored in a liquid form. What I did was take the original graph, alter the colors for a more vivid and in-your-face scheme, and arrange the X-axis categories of “carbon sequestration potential”, “farm” and “farmlands” into a more set more separated. The color scheme of the original graph was aesthetically pleasing for what they were going for, but I felt that this sharper scheme would stand out more.



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