Graph 3: You Must Select A Different Graph Than One That You
Graph 3 You Must Select A Different Graph Than One That You Have Prev
Note: My last discussion is on the time-series graph. Select a data presentation from Chapter 6: Data Representation of the text (Grey Section). Answer the following: What is the visual that you selected? What is the purpose of the visual? What kind of data should be compiled in the selected visual? What kinds of data should not be compiled in the selected visual? How can you avoid making the visual misleading?
Paper For Above instruction
In this paper, I have chosen to analyze a pie chart as the data presentation visual, as outlined in Chapter 6: Data Representation (Grey Section). The pie chart is a circular statistical graphic that is divided into sectors, each representing a proportion of the whole. Its primary purpose is to illustrate the relative sizes or percentages of different categories within a dataset, providing a clear visual comparison of parts to a whole.
The purpose of employing a pie chart is to facilitate quick and intuitive understanding of the proportional relationships among categories in a dataset. It is particularly useful when the objective is to highlight composition or to display how individual parts contribute to a total. For instance, a pie chart can effectively display the market share of different companies within an industry or the distribution of expenditure over various categories in a budget.
When compiling data for a pie chart, it is essential to select categorical data that collectively sum to a meaningful whole, usually 100%, to accurately depict parts of a total. The data should be classified into mutually exclusive categories, each representing a specific segment of the overall dataset. The values assigned to each category should be proportions, percentages, or counts that reflect their contribution to the total. For example, if representing voting preferences, each slice could indicate the percentage of votes received by each candidate or party.
Conversely, data that do not sum to a meaningful whole or are continuous and not naturally divided into categories should not be compiled into a pie chart. For example, raw numerical data like stock prices over time or temperature readings are unsuitable because they lack the part-to-whole relationship necessary for this visualization. Using a pie chart for such data could mislead viewers by exaggerating differences or implying unearned proportionality.
To avoid misleading representations with pie charts, several measures should be undertaken. First, ensure that the categories represent mutually exclusive and collectively exhaustive groups that sum accurately to 100%. Second, limit the number of categories to prevent clutter; ideally, no more than six slices, as more can obscure understanding. Third, use consistent and meaningful color schemes that distinguish categories clearly, avoiding the use of similar shades that may cause confusion. Fourth, include numerical labels directly on the slices or provide a clear legend to specify the exact proportions, ensuring viewers interpret the data correctly. Lastly, avoid manipulating the scale or perspective to exaggerate differences; the pie chart should convey a truthful and proportionate depiction of the data.
References
- Few, S. (2009). Now you see it: Simple visualization techniques for quantitative analysis. Analytics Press.
- Knaflic, C. N. (2015). Storytelling with data: A data visualization guide for business professionals. Wiley.
- Tufte, E. R. (2001). The visual display of quantitative information. Graphics Press.
The truthful art: Data, charts, and maps for communication. New Riders. Information is beautiful. Walter Foster Publishing. Ggplot2: Elegant graphics for data analysis. Springer. Data points: Visualization that means something. Wiley. Informatics, 1(2), 108-129. Journal of Data Science, 8(4), 345-356.