Hi, welcome back! In this project I will explore the data visualizations that Excel offers. The dataset I used was found at data.gov, it will explore chronic absenteeism across all fifty states in the United States. I will also be using the checklist for visualizations which is listed below.
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 is 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.
This is the dataset I will be reviewing:
The data here is discrete, and the scale is appropriate for the data collected, however for the data that measures the United States as a whole, a break is needed so the information from the individual states can be read more clearly. I will label the data by state and organize it alphabetically to more easily locate data for a specific state. The data that is most essential is total students that are chronic absentees and what states they belong to. The information about race, disability status, and students who are english language learners is important but it is also important to look at total state demographics when considering this data.
The first data visualization that I made is below, however once I created this visualization I realized that the total number of students for all of the United States was skewing the data, so I decided to make another visualization without the United States as one of the data points and that is what is pictured in the second visualization.
This visualization enhances the colors on the map to more accurately visualize the data. While the data is hard to understand by just looking at a map, you can easily see the specific number of chronic absentee students by state by hovering your curser above the state. Above you can see the numbers for Michigan.
I also created another data visualization which took a deeper look at specific racial demographics of chronic absenteeism.
In the first graph the United States total data was again skewing the results and making the individual state data more difficult to read so I remade the graph but without the total United States data included.
In this visualization we are able to much more easily see the differences between the demographics. Colors really help in this way as each smaller group of student demographics is able to be seen here. From both visualizations we can see that Texas and California have higher rates of chronic absenteeism, but we also need to consider that these states are the most populated in the whole country.
These two data visualizations are different in what data they use and how they present information. Their usefulness is dependent on what one would want to gather from the data.
From these data visualizations we can gather that Excel has many useful data visualization tools but it is important that we look at these datasets with a critical eye and make alterations to make the data more useful.
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