Data Set: census$2000$

This dataset is available in Module $2.$

Every $10$ years, the census bureau documents the demographic make$-$up of the United States, influencing everything from congressional districting to social services. This dataset contains a high$-$level summary of census data for two years a century apart: $1900$ and $2000.$ The data is a CSV $($comma$-$separated values$)$ file that describes the U$.$S$.$ population in terms of year, reported sex $(1$: male, $2$: female$),$ age group $($binned into $5-$year segments from $0-4$ years old up to $90+$ years old$),$ and the total count of people per group.

Your Tasks

Start by choosing a question you'd like a visualization to answer.

Design a static visualization $($i$.$e$.,$ a single image$)$ that you believe effectively answers that question and use the question as the title of your graphic.

Provide a short write$-$up $($no more than $4$ paragraphs$)$ describing your design.

While you must use the data set given, you are free to transform the data as you see fit. Such transformations may include $($but are not limited to$)$ log transformation, computing percentages or averages, grouping elements into new categories, or removing unnecessary variables or records. Your chart image should be interpretable without recourse to your short write$-$up$.$ Do not forget to include titles, axis labels or legends as needed!

As different visualizations can emphasize different aspects of a data set, you should document what aspects of the data you are attempting to communicate most effectively. In short, what story are you trying to tell? Just as important, also note which aspects of the data might be obscured or downplayed due to your visualization design.

In your write$-$up$,$ you should provide a rigorous rationale for your design decisions. Document the visual you used and why they are appropriate for the data and your specific question. These decisions include the choice of visualization type, size, color, scale, and other visual elements, as well as the use of sorting or other data transformations. How do these decisions facilitate effective communication?