Types, Questions, and Perception
By: Charisse Serrano
Tags: Charts, Encoding, Data Visualization

Types, Questions, and Perception

Quite honestly, I really struggled this week. Since this week was about incorporating a variety of visual encodings and I did not want to limit the scope of my imagination to Infogram, I tried my hand at Adobe Illustrator and didn’t get very far (given I could have also given myself more time)! I only managed to get through 2/3 of the charts I had meant to create. And I’m almost ashamed to say, I went with using Canva (we do what we can with what we’ve got right?).

I chose the cereal data set. I cleaned it up in Excel first, separating the columns and sitting with the raw data to ask myself some initial questions. What is MFR? Where did this data come from? And through a Google search found its source here. Thankfully, there was a key provided at this link, explaining each field.

I then went on to ask:

Who is this data useful for? How would they use this data? What is rating system based on and how might I simplify it if needed? Which MFR has the highest and lowest ratings overall?

I surmised the data would be useful for a health-conscious audience, such as health food stores purchasing wholesale or individual consumers such as parents to kids who eat refuse to eat anything but cereal in the morning. After mining through data using a pivot table, it was clear that the ratings were nutritional/health ratings, as they correlated with the contents.

Here is a preview of the back-end pivot table.

I decided on the following questions to create my 3 charts:

Which cereals have the highest sugar content? Which companies are serving the most and the least nutrition for breakfast? What goes in a spoonful of average American cereal?

Chart 1:

I spent the most time with chart 1 of course (which is why I never got to 3), doing battle with Illustrator on Photoshop before settling on Canva.

In this chart, I combined the data with another source searching for the most popular cereal brands in the US, which I found on Statista indicated through sales.

I utilize length, using a pictorial unit chart, using cropping to indicate fractions. I kept color minimal here, using color only for the familiar mascots and logos which correspond to the most popular cereal brands. You might argue that the sugar cube is 3-D however there is no functional 3-D use for it in this chart, so I wouldn’t say this counts.

Chart 2:

I struggled here as well with my illustrating limitations, but had simpler ideas on how to execute it. I experimented first with how I could arrange a gauge chart on Canva, also resorting to Infogram for a bit, before I discovered a workaround combining Google sheets charting with the Canva graphic for a cereal bowl.

I chose to focus on the ratings, taken in an aggregate average for each manufacturer as well as the count of cereal brands underneath each. In the chart, I experimented with a color hue, adding a light brown (close to the color of a Honeynut cheerio which is the #1 cereal brand and matches the illustration). The concept is like a pie chart, experimenting with angle rather than showing a whole pie.

Hoping to improve with illustrating to experiment with aesthetics all the more in the coming weeks!