Assignment 2 Completed - #132
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What changes are you trying to make? (e.g. Adding or removing code, refactoring existing code, adding reports)
Adding. Populating assignment_2.md with my completed analysis of two public data visualizations — one classified as good (Minard's 1812 flow map of Napoleon's Russian campaign) and one as bad (the 2014 Fox News ACA enrollment bar chart) — including classification justifications and suggested improvements for each.
What did you learn from the changes you have made?
Working through these two cases reinforced that the same evaluative principles apply in both directions. Tufte's data-ink ratio and Lie Factor, Cleveland and McGill's ranking of perceptual channels, and Cairo's integrity standard explain why Minard's map works and why the Fox News chart fails — the bad example is essentially the good one's principles violated. I also got clearer on the distinction between encoding choices that mislead (truncated baselines, disproportionate bars) versus accessibility gaps that merely limit reach (single-channel colour cues, unexplained units).
Was there another approach you were thinking about making? If so, what approach(es) were you thinking of?
Were there any challenges? If so, what issue(s) did you face? How did you overcome it?
The main challenge was the word limit — keeping three supported reasons plus two improvements per visualization tight enough to stay under the caps (300 words good, 500 bad). I overcame it by leading each point with the named principle and its source, then stating the evidence in one or two sentences rather than expanding on each.
How were these changes tested?
I verified the word counts stayed within limits, confirmed the markdown renders correctly (headings, code blocks, and answer formatting intact), and checked that each section meets the rubric: clear good/bad classification, at least three supported reasons each, and at least two supported improvement suggestions each.
A reference to a related issue in your repository (if applicable)
N/A
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