mirror of
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Claude
761e048811
The source-request loop paid for itself immediately — two of the five corrected something rather than confirming it. The Economist piece settles V1, the riskiest live claim, but our note had over-claimed: truncating the scale is its *first* example, not one of "several." Note rewritten to quote her. The same article turned out to contain a dual-axis chart she caught herself on, now cited. Datawrapper's color-scale part 1 refuted the assumption behind a queued sin outright: it makes no argument against rainbow scales and endorses multi-hue sequential gradients. The rainbow entry is now marked as having no verified source at all, rather than ample backing. The dual-axis post independently draws the same line we drew last turn — of four uses only the alternative-scale case survives, their example being F against C — and led to two references worth more than the blog post: the Isenberg et al. study that tested dual-scale charts, and Few's article working through the cases. Both ship without full metadata rather than guessed metadata; queued. Pie and stacked posts gave verbatim support, plus one correction: a 100%-stacked chart has two readable baselines, not one. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01ULE5RRxdQE1ebwefEd2eCM
72 lines
2.7 KiB
Markdown
72 lines
2.7 KiB
Markdown
# Datawrapper — pie charts, and stacked column charts
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Two "Data vis do's & don'ts" posts by Lisa Charlotte Muth, supplied as PDFs and
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read 2026-08-05. Answers queue rows 1 and 6.
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## "What to consider when creating pie charts" (3 Jan 2018)
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`blog.datawrapper.de/pie-charts/`
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Opening line, which is the useful one for us:
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> Pie charts are great to show how 100% divide up into a few shares.
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And the positive case, which we should keep quoting when people accuse us of
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banning the form:
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> Pie charts work best for values around 25%, 50% or 75%. It's easier for
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> readers to spot these percentages in a pie chart than in a stacked bar or
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> column chart.
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Then the limits, both of which are our published sin:
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> Pie charts are not the best choice if you want readers to compare the size of
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> shares. That's especially true if the differences between the shares are
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> small.
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> Pie charts work best if you only have a few values – five max.
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Our sample pie has eight. Cited on `too-many-pie-slices`.
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**Caveat for backlog entries 2 and 3.** This post does *not* say in so many
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words that a pie's parts must be mutually exclusive and sum to 100% — so it
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can't be cited for the multi-select survey case as I'd hoped. What it does give
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is the premise those sins rest on:
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> One pie chart can only show one total and its shares.
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That's enough to support "there must be a whole," and nothing more. The backlog
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notes now say exactly that rather than implying a stronger claim.
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Also flags Robert Kosara's "Understanding Pie Charts" as a "great research-based
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explanation of how people read pie charts" — independent support for our
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`eagereyes` note (claim V7). URL not captured; queued.
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## "What to consider when creating stacked column charts" (13 Feb 2018)
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`blog.datawrapper.de/stacked-column-charts/`
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The sentence backlog entry 6 is built on:
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> It's hard for readers to compare columns that don't start at the same
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> baseline.
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And the design guidance:
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> Bring the most important value to the bottom of the chart and use color to
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> make it stand out. Your readers can compare values easier with each other if
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> they have the same baseline.
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Useful nuance I hadn't accounted for: a 100%-stacked chart has **two** usable
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baselines, not one —
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> You will gain a second baseline at the top of your chart where you can place
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> the second most important category in your data.
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So the sin is sharper than "only the bottom is readable": everything *between*
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the two baselines floats. Worth writing the page that way.
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Also: "Make sure that you include all parts of the total in your charts – and
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only parts of the total." Pairs with the Economist's rainbow-stack example,
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where a partial selection of countries was stacked as if it were the whole.
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