Chart Atlas

Use: Show the shape of one numeric variable with counts.

Question: Are most store-months small, or do a few stores dominate volume?

Trap: Do not compare many groups with overlapping histograms.

Soup: Progresso store-month volume is long-tailed.

Use: Show a smooth distribution shape when exact bins would distract.

Question: Where is the typical county in the vote distribution?

Trap: Do not imply precision in thin tails.

County: Trump vote share clusters heavily above 50 percent across counties.

Use: Compare distributions across groups compactly.

Question: Which regions have higher or lower typical county vote shares?

Trap: Do not use it when stakeholders need individual values.

County: regional boxes reveal different medians and spreads.

Use: Show every observation when the distribution itself is the point and groups are few.

Question: How spread out are store-month volumes within each region, not just the average?

Trap: Do not jitter thousands of points into an ink-blob; sample or switch to a box plot at scale.

Soup: a regional sample of store-month volume, one dot per store-month.

Use: Rank a small set of categories by one metric.

Question: Which region has the highest average county vote share?

Trap: Do not use unsorted bars when rank is the message.

County: regions sorted by average Trump vote.

Use: Compare values precisely without heavy bars.

Question: Which states had the largest home-value increase since 2020?

Trap: Do not use it for continuous time paths.

Zillow: state home-value change from January 2020 to the latest month.

Use: Show part-to-whole composition across a few ordered groups.

Question: Does the soup category mix shift across quarters, in units or in share?

Trap: Do not stack many categories; only the bottom segment is easy to compare across bars.

Soup: category units by quarter, toggled between absolute and 100% share.

Use: Compare a few categories within each group side by side.

Question: How do core, premium, and broth units compare within each quarter?

Trap: Do not group so many series that bars become too thin to read.

Soup: the same quarterly category data shown as clustered bars instead of stacked.

Use: Compare two points in time across categories and emphasize who moved.

Question: Which regions shifted between the two elections, and in which direction?

Trap: Do not use it for more than two time points; it stops being a slope.

County: average regional vote share in two elections, connected by a line per region.

Use: Show how a metric evolves over ordered time.

Question: How did state home values move through the housing cycle?

Trap: Do not use it for unordered categories.

Zillow: California, Texas, Florida, and New York home value paths.

Use: Compare growth paths with different starting levels.

Question: Which housing markets grew faster after 2020?

Trap: Do not read index values as dollars.

Zillow: each state equals 100 in January 2020.

Use: Repeat the same chart across groups with common scales.

Question: Do housing cycles look similar across states?

Trap: Do not vary scales silently when magnitude matters.

Zillow: four state panels reveal timing and amplitude.

Use: Emphasize magnitude over time, or composition over time when totals matter.

Question: How large did each state market get, and how do they sum over time?

Trap: Do not stack area when readers need each series read precisely; only the bottom is honest.

Zillow: California alone as a filled area, then four states stacked.

Use: Show the relationship between two numeric variables.

Question: Do counties with more college graduates vote differently?

Trap: Do not call a slope causal without a design.

County: education share and Trump vote share.

Use: Add magnitude to a two-variable relationship.

Question: Do the largest-vote counties sit in different density/vote space?

Trap: Do not let bubble area overwhelm the x-y comparison.

County: vote count sizes each point.

Use: Location is part of the decision.

Question: Where is the regional pattern strongest?

Trap: Do not map data just because a place field exists.

County: state tile map summarizes county vote patterns.

Use: Location is part of the decision and the geographic shape itself carries meaning.

Question: Where did home values grow fastest since 2020, on a real US map?

Trap: Do not map raw counts on a choropleth; area distorts them — map rates or changes.

Zillow: state home-value growth since 2020 on an albers-usa projection.

Use: Scan many pairwise relationships at once.

Question: Which demographic variables move together before modeling?

Trap: Do not treat correlation as effect size or causality.

County: demographics, density, income, and vote share.

Use: Show a matrix of values across two ordered dimensions.

Question: Which states and years show the biggest housing jumps?

Trap: Do not use color when exact values are the decision.

Zillow: annual home-value changes by state and year.

Use: Show part-to-whole for many categories where rank and rough share matter.

Question: How is revenue split across product families at a glance?

Trap: Do not expect precise comparisons; area is read far less accurately than length.

Teaching data: revenue share across six product families as nested rectangles.

Use: Show estimates with uncertainty in the same visual.

Question: Does the density-vote pattern look stable enough to discuss?

Trap: Do not hide the denominator behind a precise dot.

County: mean Trump vote by population-density decile.

Use: Summarize model estimates and intervals.

Question: Which predictors remain visually important after adjustment?

Trap: Do not show coefficients before explaining units and controls.

County: standardized regression preview.

Use: Show which categories contribute most of a total.

Question: Which product families explain most revenue?

Trap: Do not use it when categories are mutually ordered by time.

Teaching data: revenue concentration across product families.

Use: Explain how components bridge from start to finish.

Question: What moved gross price to net revenue?

Trap: Do not use it for unrelated categories.

Teaching data: price bridge from list price to net revenue.

Use: Show sequential drop-off through an ordered pipeline.

Question: Where does the pricing-test pipeline lose the most stores?

Trap: Do not use a funnel for non-sequential categories; the narrowing implies an order.

Teaching data: stores moving from review to a confirmed price rollout.

Use: Almost never for analysis; at most a single two-to-three slice part-to-whole.

Question: Which product family is biggest — and can you rank the rest by eye?

Trap: Do not ask readers to compare angles; rank and small differences are nearly unreadable.

Teaching data: the same revenue mix as the treemap, shown as six pie slices.