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SIMPSONS GUEST STARS: The Artometrics of Simpsons Guest Stars

This report analyzes the TidyTuesday 2019-08-27 release on Simpsons Guest Stars — 1,381 rows after cleaning and merge.

Artometrics Editorial5 min read
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SIMPSONS GUEST STARS: The Artometrics of Simpsons Guest Stars
This report analyzes the TidyTuesday 2019-08-27 release on Simpsons Guest Stars — 1,381 rows after cleaning and merge.

This report analyzes the TidyTuesday 2019-08-27 release on Simpsons Guest Stars1,381 rows after cleaning and merge. Who guest-starred most often — and did celebrity density cluster in golden seasons?

Five charts track record counts across time, category, and named entities — trend, leaders, distribution, tiers, and relationships. Where companion files exist in the repo, they are joined before analysis so reception, geography, or metadata columns are not left on the table.

FAST FACTS

1,381Records in the working dataset
1–30Year span covered in the file

DATASET CONTEXT

The source is the TidyTuesday release from 2019-08-27 (R for Data Science community). This working file contains 1,381 rows and 7 columns after merging all available CSV/XLSX tables in the week folder.

Charts are exported as Plotly JSON with PNG fallbacks. Medians are used for robustness where distributions skew. Index-style fields (row numbers, sequential IDs) are excluded from metric selection.

How to read this report: start with the chart caption, then ask what the metric actually means, what a non-expert should notice first, and what an expert would challenge in the source. The goal is not to memorize every number; it is to leave with a sharper question than the one you arrived with.

Reader path: if you are new to the topic, treat each chart as a guided tour of one question: who leads, how concentrated the field is, what changes over time, and where the outliers sit. If you already know the domain, use the same charts as a challenge: check whether the metric is the right proxy, whether the source omits an important population, and whether the headline survives the limitations section.

CHART 1 — VOLUME

Records By Period

Activity peaks in 24.0 with 65 records.

Period-level counts reveal when the dataset's subject matter intensified.

CHART 2 — LEADERS

Top Guest star

Marcia Wallace appears 175 times — the most recurring name in the file.

The top dozen account for a visible share of all 1,381 rows.

CHART 4 — TIMELINE

Leaders Over Time

The leading names do not move in lockstep — some fade as others surge.

Tracking counts over time separates sustained presence from one-off spikes.

SUPPLEMENT — FREQUENCY

Most guest star entities appear only once; a small head revisits repeatedly

Most guest star entities appear only once; a small head revisits repeatedly.

This power-law shape is typical of guest lists, credits, and catalog-style tables.

SUPPLEMENT — MIX

24 is the most repeated season in the extract

24 is the most repeated season in the extract.

Secondary dimensions add context when the primary table has no numeric score column.

LIMITATIONS

Community-cleaned TidyTuesday snapshots are not live APIs. Missing values, spelling variants, and week-of-export coverage limits apply. Merged tables may fan out or duplicate rows when join keys are imperfect.

Findings describe the file on hand — treat them as structural signals about Simpsons Guest Stars, not exhaustive truth about the full domain.

CONCLUSION

Read as a teaching map, Simpsons Guest Stars shows why one metric is rarely enough: leaders, tails, trends, and relationships each answer a different question about the field.

The best reading is modest: use the chart to sharpen the question, then check the source and limits before turning it into a claim.

REFERENCES

Data Science Learning Community. (2019). TidyTuesday: Simpsons Guest Stars. https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2019/2019-08-27/simpsons-guests.csv

EDITOR'S NOTE

Artometrics data report from the TidyTuesday research pipeline. Charts and aggregates are reproducible from the embedded exhibits and public source files.

View TidyTuesday source on GitHub