Assignment for 3/10

1) One person from your group should email me by noon Thursday with a report on your project. Whoever emails me should copy the other two group members. While informal, report should include specific inquiries you’ve made, databases you’ve found, correlations you’ve considered (if any) and other models you’ve discovered. What do you like/not like about those models? How might your story/approach be similar/differ?

2) Using Google Sheets and Fusion Tables, figure out an interesting way to visualize this spreadsheet of people who supported this Kickstarter project. Then, embed the visualization in a blog post on jn430.ua.edu. Publish the post and email me the link. Do this before noon on Thursday.

3) Read Jeff Shaffer’s post on some finer points of data visualization and then publish a comment on this blog post in which you highlight something interesting/helpful from Jeff’s post.

12 thoughts on “Assignment for 3/10”

  1. I think the last modification in Shaffer’s chart remake is really interesting. The difference that moving the bar labels could make seemed really silly until I looked back up and realized it actually did make a huge difference. I appreciate his attention to detail throughout the post, and ability to show his steps with updated charts. With visualization being the main focus, I really enjoyed being able to see what he was talking about as support for the reading.

  2. I thought his version of the 4 C’s was really interesting. I know we have used the inverted pyramid of data journalism (Compile-Clean-Context-Combine) in the past, so maybe that’s why the 4 C’s stuck out to me. There seems to be some overlap between the two, which I think demonstrates the importance of getting clean data. The Shaffer 4 C’s of Data Visualization are a bit more in depth in terms of making sure that the data you have looks good. I think that is probably one of the most important parts of data journalism. Data is such a useful tool to use when writing a story, but if the data isn’t visually pleasing then the readers won’t care what it says.

  3. Overall, I thought that the modifications Shaffer made to the graph were very good and I would be interested in figuring out how to do them. The final chart looks a lot better than the one shown in the original example. Showing the process by which he did this helped a great deal as well. One could do something like that in Excel, but it would be a somewhat complicated process.

  4. I think Shaffer did an excellent job showing that in data journalism, a little can go a long way. I looked at the first chart and was not interested at all in trying to figure out what it was about, but with his modifications, I actually found it kind of interesting. It just goes to show how visual human beings really are, and that it is important to make your information appealing in that aspect.

  5. Looking at the first chart of the University of Cincinnati health website example, I agreed with Shaffer. I thought it was a really good chart and I understood the information. I begrudgingly read on, because I thought that it was going to be a lot of know-it-all, nitpicky critiques. As I read on, I was surprised to find that I was coming around to his redesigns of the chart and his reasonings. Every change he and his student made, made the chart that much more useful and therefore a better tool. I really enjoyed being shown how using his 4 Cs, really made for a better product.

  6. I enjoyed reading the description of the 4 C’s. It’s a really good guideline to figuring out how to synthesize data sets. It can help to look at a set of data and not get bogged down in the information when explaining. It could help make explaining the information in a simple, more concise way.

  7. Shaffer’s 4 C’s of data visualization was interesting and a much easier way for me to interpret a way to attack data sets. One point that he made that I had never considered when making a chart was what colors to use and how some might be too close to one another making it difficult to read or better yet the person interepretting the table might be color blind and not be able to tell a difference at all. I am always one to go for color and would have probably made a chart similar to the “Pac Man” one he showed us. I will now be more conscience when using color in my data visualizations.

  8. I think this article was helpful to read because I have never really thought about graphs this in depth before. I always thought graphs were easy and you could basically use any type of chart for a story. Shaffer’s 4 C’s of Data Visualization is the perfect way to explain the importance of data visualization. I didn’t even think about how a red/green chart wouldn’t be easy to read for someone who is color blind. Also, he didn’t make huge changes to the chart that he fixed in the article, bu the simple changes and things he added made the chart easier and more interesting to read. I will definitely keep his 4 C’s in mind when I make data visualizations.

  9. I really enjoyed this blog post. What hit me the hardest was when he said clarity is more important than aesthetics. Which you would think, well of course it is, but for my brain, I spend much longer focusing on colors and perfect alignment than I sometimes do on content. So that was helpful. It seems like with his 4 Cs, the overall idea was balance. Don’t be too minimalist but don’t be too verbose, not to aesthetic minded but not to plain jane, etc. Also, something I had never thought of before was the idea of rotated text. Once he broke it down, I will never look at rotated text on X axis the same again.

  10. I personally thought that Jeff’s blog post was very interesting and informative. For example, I didn’t know that for graphs you should rotate the x-axis words to 45 degrees to make it easier to read. Additionally, I thought it was interesting how he said that there is no reason to label the y-axis because I have always been told in school to label both the x-axis and y-axis.

  11. The 4 C’s are definitely the most helpful tool that Jeff mentioned. I really like how he incorporated the steps in his post because I feel like they are the most clear and concise easy steps to follow when trying to figure out data visualization. It’s an easy way to make sure you are on track with making sure the data is the easiest to understand for your readers.

  12. The 4 C’s for visualization are vital for interpreting data visualization. It is important that the data is clear, clean, concise, and captivating. Along with these values, developing an equilibrium between minimalism and being verbose is also important. While these four areas have there own separate characteristics, they also can affect more areas than one.

Comments are closed.