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CHRISTMAS SONGS: The Artometrics of Christmas Songs

This report analyzes the TidyTuesday 2019-12-24 release on Christmas Songs — 387 rows after cleaning and merge.

Artometrics Editorial5 min read
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CHRISTMAS SONGS: The Artometrics of Christmas Songs
This report analyzes the TidyTuesday 2019-12-24 release on Christmas Songs — 387 rows after cleaning and merge.

This report analyzes the TidyTuesday 2019-12-24 release on Christmas Songs387 rows after cleaning and merge. Which holiday standards refuse to leave the chart?

Five charts track Weeks on chart 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

387Records in the working dataset
8.00Median Weeks on chart
20.0Highest observed Weeks on chart
BETTER DAYSTop Song by Weeks on chart
1958–2017Year span covered in the file

DATASET CONTEXT

The source is the TidyTuesday release from 2019-12-24 (R for Data Science community). This working file contains 387 rows and 14 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 — TREND

Median Weeks on chart Over Time

Median weeks on chart is falling from 8.00 in the opening period to 5.00 at the close.

Annual medians filter one-off spikes so the structural slope — not viral outliers — drives the story.

CHART 2 — LEADERS

BETTER DAYS leads at 20.0 — 17.0 marks the median among the top dozen

BETTER DAYS leads at 20.017.0 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

Weeks on chart Distribution

Median 8.00 vs mean 9.65 — the shape is right-skewed.

The top decile begins at 19.0; that tail is where defining cases live.

Top Song Over Time

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

Tracking medians over time separates sustained dominance from one-off spikes.

SUPPLEMENT — RELATIONSHIP

Weeks on chart vs Instance

Joint plot of weeks on chart and instance 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 Christmas Songs, not exhaustive truth about the full domain.

CONCLUSION

Read as a teaching map, Christmas Songs shows why one metric is rarely enough: leaders, tails, trends, and relationships each answer a different question about weeks on chart.

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: Christmas Songs. https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2019/2019-12-24/christmas_songs.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