billboarder
An htmlwidget whose release notes are mostly billboard.js version bumps, quiet since 2021.
A side-by-side editorial comparison of embed and pins — release velocity, themes, recent moves, and the top alternatives to consider.
embed keeps adding encoding steps while shedding its deep-learning dependencies
embed supplies recipes steps that turn categorical predictors into numeric representations — likelihood encoding, UMAP projection, string-distance collapsing. The 1.1.x line made UMAP arguments tunable and moved keras and tensorflow out of hard dependencies; 1.2.0 added analytical likelihood encoding with partial pooling and retired step_feature_hash() in favor of textrecipes.
pins keeps adding a storage backend per release while retiring its original API
pins publishes and versions R objects to a board, where a board is whatever storage you have. The recent releases read as a steady list of new boards - Google Cloud Storage, Google Drive, Databricks Volumes, Connect vanity URLs - alongside serialization changes that track which formats R users actually want: parquet via nanoparquet, and qs replaced by qs2.
embed supplies recipes steps that turn categorical predictors into numeric representations — likelihood encoding, UMAP projection, string-distance collapsing. The 1.1.x line made UMAP arguments tunable and moved keras and tensorflow out of hard dependencies; 1.2.0 added analytical likelihood encoding with partial pooling and retired step_feature_hash() in favor of textrecipes.
Two quiet directions run through these releases. One is making the steps tunable rather than fixed, so they participate properly in tidymodels grids. The other is boundary maintenance: heavy dependencies pushed to Suggests, overlapping steps handed to the package that owns them. Recent releases are thin and fix-driven.
Expect further consolidation with textrecipes over which package owns which encoding step, and continued upkeep against xgboost and uwot releases rather than new step families.
pins publishes and versions R objects to a board, where a board is whatever storage you have. The recent releases read as a steady list of new boards - Google Cloud Storage, Google Drive, Databricks Volumes, Connect vanity URLs - alongside serialization changes that track which formats R users actually want: parquet via nanoparquet, and qs replaced by qs2.
Two long-running processes, neither dramatic. Backend coverage expands toward wherever teams already store artifacts, which increasingly means Databricks and cloud object storage rather than a shared drive. Meanwhile the legacy pin() API from before the board model has been in a staged deprecation across at least three releases, escalated each time rather than removed.
Expect another board or two as storage platforms are requested, and the legacy pin() functions to finally become errors; the format list will keep tracking whichever serializer the R community settles on.
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 embed or pins.
An htmlwidget whose release notes are mostly billboard.js version bumps, quiet since 2021.
bbotk is generalizing from an optimizer toolkit into an evaluation framework.
bayesplot keeps widening its posterior-check catalogue while absorbing each ggplot2 break.
Contribution-driven maintenance on a package whose last structural change was magick support.
tsibble shipped one release in five and a half years - the data structure is finished
yardstick made fairness metrics a first-class part of tidymodels evaluation
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
They serve adjacent needs but don't currently overlap on shipped themes. embed and pins 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.
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. embed and pins 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.
Top embed alternatives in Analytics are ranked by recent ship velocity. Browse the "embed alternatives" section above for the current picks, or visit /alternatives/embed for the full list with editorial commentary on each.
Top pins alternatives in Analytics are ranked by recent ship velocity. Browse the "pins alternatives" section above for the current picks, or visit /alternatives/pins-r for the full list with editorial commentary on each.