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ALL THE PIZZA: The Artometrics of All The Pizza

This report analyzes the TidyTuesday 2019-10-01 release on All The Pizza — 10,000 rows after cleaning and merge.

Artometrics Editorial5 min read
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ALL THE PIZZA: The Artometrics of All The Pizza
This report analyzes the TidyTuesday 2019-10-01 release on All The Pizza — 10,000 rows after cleaning and merge.

This report analyzes the TidyTuesday 2019-10-01 release on All The Pizza10,000 rows after cleaning and merge. What does pizza cost — and what do you pay in calories?

Five charts track Price range min 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

10,000Records in the working dataset
0.00Median Price range min
50.0Highest observed Price range min
OreganoTop Name by Price range min

DATASET CONTEXT

The source is the TidyTuesday release from 2019-10-01 (R for Data Science community). This working file contains 10,000 rows and 10 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 — BREAKDOWN

Price range min by Name

Crust Stone Oven Pizza leads at 50.0; Scuola Vecchia Pizza E Vino anchors the low end at 25.0.

Grouping by name exposes how the metric varies across the catalog's major entities.

CHART 2 — LEADERS

Crust Stone Oven Pizza leads at 50.0 — 40.0 marks the median among the top dozen

Crust Stone Oven Pizza leads at 50.040.0 marks the median among the top dozen.

Head-of-field concentration is where quality, scale, or brand visibly separates from the pack.

CHART 3 — DISTRIBUTION

Price range min Distribution

Median 0.00 vs mean 4.66 — the shape is right-skewed.

The top decile begins at 25.0; that tail is where defining cases live.

CHART 4 — CONCENTRATION

Cumulative Price range min

The top 5 name entries account for 43% of the aggregate price range min.

Steep Pareto curves mean a small head drives most of the signal — the long tail is noise until it isn't.

SUPPLEMENT — RELATIONSHIP

Price range min vs Price range max

Joint plot of price range min and price range max surfaces clusters the averages erase.

Bubble size tracks repeat presence — outliers are archetypes, not noise.

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 All The Pizza, not exhaustive truth about the full domain.

CONCLUSION

Read as a teaching map, All The Pizza shows why one metric is rarely enough: leaders, tails, trends, and relationships each answer a different question about price range min.

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: All The Pizza. https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2019/2019-10-01/pizza_datafiniti.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