In the previous paper, we compared graphs with the same number of categories but different data quantities. In this paper, I would like to show some methods that I found to make graphs more readable.

Graph #1

Here, I am using the same graph from the previous paper. As mentioned, the significant differences in data values make it difficult to distinguish the categories in each graph. This can lead to misinterpretation or an incomplete understanding of the data. To address this issue, I have created another graph that focuses on proportions, allowing for a more effective analysis of the data by highlighting relative differences rather than absolute values. When presenting the findings, it’s important to combine both graphs to provide a complete and clear picture.

By presenting both graphs together, we can leverage their strengths: one provides a detailed view of the actual data values, while the other shows the proportional relationships. Combining the graphs shows a clear representation of the findings, making it easier for readers to grasp the complete story behind the data.

Graph #2

Let’s look at another graph with a large dataset as given below, which includes a greater number of categories. Even though the graph represents the data successfully, there are too many bars to look at and it is confusing to read for the user.

To address the readability issue, I tried a different approach by dividing the graph into two halves. This method provides a clearer view of each stacked bar, making it easier to interpret the data. However, it’s important for users to note that the scales on the two halves are different. For this representation to be effective, both graphs must be presented together as a single unit to ensure a complete and accurate understanding of the data. They should not be shown separately, as that could lead to confusion or misinterpretation.