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spanishoddata vs weird

A side-by-side editorial comparison of spanishoddata and weird — release velocity, themes, recent moves, and the top alternatives to consider.

Shared themes:r-package

spanishoddata vs weird: at a glance

Featurespanishoddataweird
SectorAnalyticsAnalytics
Velocity score0.00.0
Sparks · 30d00
Top themesmobility-data, origin-destination, duckdb, open-dataanomaly-detection, r-package, distributional, robust-statistics
Last editorial update1h ago47m ago
WebsiteVisit →Visit →

What is spanishoddata?

spanishoddata spent a year finding out its 2020-2021 data was quietly incomplete.

spanishoddata provides access to Spain's open mobility origin-destination datasets from the Ministry of Transport, converting them into DuckDB and parquet for analysis at scale. Nearly every release in this window is a data-fidelity fix rather than a feature: district-to-municipal reaggregation was wrong for the 2020-2021 vintage, literal 'NA' strings in the source CSVs broke DuckDB enum casting, and the Amazon S3 metadata bucket turned out to be truncated at March 2021, silently hiding data.

Read the full spanishoddata trajectory →

What is weird?

weird rebuilt itself on distributional objects, and now the anomaly tooling composes with everything else.

An R package for anomaly detection and unusual-observation diagnostics, at four releases with a long gap between the 2024 patch and the 2026 major line. The current shape is set by 2.0.0, which refactored the package onto distributional objects and renamed its central concept from density_scores() to surprisals(). Since then the work has been filling that structure in: surprisals for more model classes, faster bandwidth and probability calculations, and new visual diagnostics.

Read the full weird trajectory →

spanishoddata vs weird: editorial side-by-side

S
spanishoddata
ANALYTICS
0.0

spanishoddata spent a year finding out its 2020-2021 data was quietly incomplete.

◆ Current state

spanishoddata provides access to Spain's open mobility origin-destination datasets from the Ministry of Transport, converting them into DuckDB and parquet for analysis at scale. Nearly every release in this window is a data-fidelity fix rather than a feature: district-to-municipal reaggregation was wrong for the 2020-2021 vintage, literal 'NA' strings in the source CSVs broke DuckDB enum casting, and the Amazon S3 metadata bucket turned out to be truncated at March 2021, silently hiding data.

◆ Where it's heading

The package is in a trust-building phase. The pattern across 0.2.1 through 0.2.6 is the maintainers repeatedly discovering that upstream metadata and the package's own aggregation were misrepresenting what data existed, then fixing it and adding a check so it surfaces next time. That is now backed by infrastructure: comprehensive unit tests plus weekly live-data runs on GitHub workers that alert maintainers when the upstream ministry changes something. The last feature release sits outside the six-entry window, which is itself the story.

◆ Prediction

Expect continued upstream-tracking fixes as the ministry's API and S3 layout shift, with the experimental quick-access and checksum functions the most likely candidates for promotion to stable.

W
weird
ANALYTICS
0.0

weird rebuilt itself on distributional objects, and now the anomaly tooling composes with everything else.

◆ Current state

An R package for anomaly detection and unusual-observation diagnostics, at four releases with a long gap between the 2024 patch and the 2026 major line. The current shape is set by 2.0.0, which refactored the package onto distributional objects and renamed its central concept from density_scores() to surprisals(). Since then the work has been filling that structure in: surprisals for more model classes, faster bandwidth and probability calculations, and new visual diagnostics.

◆ Where it's heading

The refactor onto a shared distribution representation is the decision everything else follows from. It let 2.1.0 add hdr() and parameters() methods for kde objects rather than bespoke accessors, and it let 3.0.0 bring in dist_mclust() to turn a Gaussian mixture model into the same object type — so a mixture, a kernel density estimate and a fitted distribution all flow through one interface. The 3.0.0 additions lean visual and multivariate: outlier maps plotting score distance against orthogonal distance, biplot projections with variable axes overlaid, and an augment() method for robust PCA objects. Dependencies have been shed steadily along the way — lookout, interpolation — while mvscale() moved out and then back in.

◆ Prediction

Expect surprisals() coverage to keep extending to further model classes, and the multivariate and robust-PCA diagnostics introduced in 3.0.0 to gain the same distributional-object treatment as the univariate side.

Alternatives to spanishoddata and weird

Other Analytics products tracked by Sparkpulse, ranked by recent ship velocity. Each card links to a full editorial trajectory and lets you pivot into a head-to-head comparison with either spanishoddata or weird.

See all spanishoddata alternatives → · See all weird alternatives →

Recent activity from spanishoddata and weird

Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.

  1. 1mo agoweirdOutlier maps, biplot projections, and Gaussian mixtures as distributional objects
  2. 2mo agospanishoddataRedirected district files identified in v1 metadata
  3. 2mo agospanishoddataS3 metadata truncation at March 2021 bypassed via XML feed
  4. 3mo agoweirdsurprisals() reaches glm objects; lookout dependency dropped
  5. 4mo agospanishoddataLiteral NA strings no longer break DuckDB casting
  6. 4mo agospanishoddataLarge urban area zones can be reloaded again
  7. 5mo agospanishoddatatime_slot column removed; test coverage goes live-weekly
  8. 6mo agoweirdPackage refactored onto distributional objects; density_scores becomes surprisals
  9. 1y agospanishoddataDistrict-to-municipal reaggregation corrected for 2020-2021
  10. 2y agoweirdWine reviews dataset replaced with a fetch function

Frequently asked questions

What is the difference between spanishoddata and weird?

Both compete on the same themes — r-package — within Analytics. spanishoddata and weird are shipping at a similar cadence (velocity 0.0 vs 0.0, both within Sparkpulse's "active" band). See the at-a-glance table above for a side-by-side breakdown of velocity, recent sparks, and editorial themes.

Is spanishoddata better than weird?

Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. spanishoddata and weird are shipping at a similar cadence (velocity 0.0 vs 0.0, both within Sparkpulse's "active" band). For your specific use case, the alternatives sections above list other Analytics products to evaluate alongside.

What are the best alternatives to spanishoddata?

Top spanishoddata alternatives in Analytics are ranked by recent ship velocity. Browse the "spanishoddata alternatives" section above for the current picks, or visit /alternatives/spanishoddata for the full list with editorial commentary on each.

What are the best alternatives to weird?

Top weird alternatives in Analytics are ranked by recent ship velocity. Browse the "weird alternatives" section above for the current picks, or visit /alternatives/weird-r for the full list with editorial commentary on each.