This report analyzes the TidyTuesday 2018-06-26 release on Alcohol Consumption — 193 rows after cleaning and merge. Who drinks the most per capita — and how did consumption move?
Five charts track Beer servings 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
DATASET CONTEXT
The source is the TidyTuesday release from 2018-06-26 (R for Data Science community). This working file contains 193 rows and 5 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
Namibia leads at 376; Panama anchors the low end at 285.
Grouping by country exposes how the metric varies across the catalog's major entities.
CHART 2 — LEADERS
Namibia leads at 376 — 338 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
Median 76.0 vs mean 106 — the shape is right-skewed.
The top decile begins at 259; that tail is where defining cases live.
CHART 4 — CONCENTRATION
The top 5 country entries account for 37% of the aggregate beer servings.
Steep Pareto curves mean a small head drives most of the signal — the long tail is noise until it isn't.
SUPPLEMENT — RELATIONSHIP
Joint plot of beer servings and spirit servings 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 Alcohol Consumption, not exhaustive truth about the full domain.
CONCLUSION
Read as a teaching map, Alcohol Consumption shows why one metric is rarely enough: leaders, tails, trends, and relationships each answer a different question about beer servings.
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. (2018). TidyTuesday: Alcohol Consumption. https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2018/2018-06-26/week13_alcohol_global.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.