Excel built-in visualizations
Welcome, everyone! In this project, I will explore Excel’s built-in data visualization tools. Excel offers some excellent and useful features that are often overlooked. I’m here to review a few of them and demonstrate their use with a dataset on harassment and bullying based on race.
The data set I will be reviewing is located here.
I am also going to go through a checklist of questions for the visualization.
Let’s get started!
Checklist for visualizations
· Assess your data: discrete or continuous?
· Appropriate scale: Too big? Too small? Need a break?
· How will you label the data? What order? What data most
essential?
· Use graphic variables carefully: shape, tone, texture, and color
convey meanings
· Proximity of labels to values is optimal for reducing cognitive
load; make it easy for the viewer
· Never use changes in area to show a simple increase in value.
· Review the graph to see if it contains elements that are
“incidental” artifacts of production rather than meaningful ones.
· While illustrations, images, or exaggerated forms may be
considered “junk,” they can also help set a theme or tone when
used effectively.
Here is the data set that we are reviewing.
After reviewing our data set let’s jump right into answering our questions.
- This is a discrete data set. Discrete data means distinct values that can be counted.
- Scale: This scale is appropriate for the data. However, a vertical bar graph could add more depth. Here’s an example using data from the State of Illinois.
Here is a line graph representing the State of Michigan. You can compare how the data is displayed side by side. Which presentation do you prefer? Moving into another one of our questions, How will you label the data? What order? What data is most essential? It makes sense to label the data by the state you are focusing on. As shown in the Excel file, the data is organized alphabetically by state, which makes sense. The most important data point here is the percentage of schools reporting harassment and bullying, found in column Y. Without schools reporting this data, it would not exist.
It is best practice to use shape, tone, texture, and color thoughtfully to highlight key insights. Keep labels close to values for easier interpretation and processing. For example, see from the two graphs I created how simple the layout, shape, and color are representing the data? Let’s review the graphs. Do you notice any extra lines or shading that seem out of place? Are there any incidental artifacts? I think both graphs are well-designed and clearly represent the data for each state.
For these two graphs, I selected a clear text font to ensure readability and used blue to represent the data set, which adds a professional and trustworthy feel. This choice helps enhance clarity and makes it easier for viewers to understand the information.
Just for context, here is a line graph that poorly represents the data from the States of Illinois and Michigan.
The dark background combined with dark colors for the data makes it hard to read. The overlapping numbers further obscure the information. To maintain clarity and readability, it’s important to present data in a way that is easy to view. I believe using a dark background with dark red and blue colors for the data is not a good choice.
Here’s another example where the same color represents both states, with Michigan in a lighter shade and Illinois in a darker shade. This still maintains a clean look, with bold colors that create a dramatic tone for Illinois, which has a higher report of students being harassed.
As you can see, Excel offers many options for creating data visualizations, but it’s essential to consider your audience. When building visualizations for presentations, it’s best to keep them clean, easy to read, and eye-catching to effectively represent the data.
Thank you for reviewing Excel visualizations with me!
Cheers!
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