Module 5. Assignment: Create Your Own Visualizations assignment Plotly vs. Datawrapper

The dataset can be seen below:


Ranking Chart using an ordered bar chart:




Part-to-Whole Chart using a donut chart:




  • Why you chose this dataset.
I chose this dataset because it has two straightforward but meaningful variables: Time on the x-axis and Average Position on the y-axis. This setup is applicable in many real-world contexts. For example, “Time” could represent days, months, or trials in an experiment, while “Average Position” could represent a performance ranking, a score, or some measure of quality. Many real datasets look like this: something is being tracked over time to see whether it improves or declines. Practicing with this data can assist in getting used to analyzing patterns that are very relevant in fields like business (tracking customer satisfaction), sports (tracking performance stats), or technology (tracking algorithm accuracy). 
  • The story or insights your visualizations tell.
The ordered bar chart revealed that as Time increased, the Average Position steadily rose. This indicates that performance worsened over time, since a higher average position means being ranked further down (less desirable). The donut chart grouped these values into performance bands and revealed that roughly 35% of the times were “best,” 30% were “middle,” and 35% were “worst.” This made the distribution easy to understand at a glance. The stacked bar chart added another dimension by showing when those shifts happened, not just how much of the dataset fell into each category. It clearly illustrated a transition: early time periods were mostly in the “Best” band, middle periods in “Middle,” and later periods in “Worst.”
  • A quick reflection: What are the strengths and limitations of Part-to-Whole as a design framework?
The Part-to-Whole design was useful because it let me simplify continuous values into meaningful categories, making proportions easier to understand. The strength of this approach is that it highlights overall distributions and makes quick comparisons across categories clear. However, the limitation is that it hides the finer details of the trend and can oversimplify complex data. For example, while the donut chart tells me “how much” time fell into each band, it doesn’t show when those changes happened—that required the stacked bar and line visualizations. When reflecting on which chart I personally prefer for analyzing data, I find the Ranking Chart more powerful. The Ranking Chart provides a more detailed overview because it keeps each data point visible and ordered, letting me see not only the best and worst points but also the full gradient in between. Unlike the donut chart, which condenses the dataset into only three categories, the Ranking Chart preserves the precision of the original values while still making the story clear by ordering from best to worst. This makes it easier to identify outliers, spot subtle patterns, and draw stronger conclusions. In many cases, this level of detail is more useful for analysis and decision-making, whereas Part-to-Whole designs are better suited for quick summaries or presentations to a broader audience.




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