tidytlg
A tables-listings-graphs package that reached CRAN and then went quiet.
A side-by-side editorial comparison of datasetjson and excluder — release velocity, themes, recent moves, and the top alternatives to consider.
datasetjson rebuilt its object model to track the CDISC Dataset-JSON 1.1 schema.
datasetjson reads and writes CDISC Dataset-JSON, the JSON replacement for SAS transport files in clinical-trial submissions. The package went from a thin reader in 2023 to a redesigned interface in 0.3.0 that targets the 1.1.0 schema, uses yyjsonr as its JSON backend, and exposes column metadata as first-class arguments. Development is contributor-driven inside the Atorus and pharmaverse orbit.
A Qualtrics data-cleaning package that has been in maintenance mode since its CRAN acceptance.
excluder marks, checks, and excludes online-survey rows that fail quality criteria — duplicate responses, suspicious IP or geolocation, screen resolution, completion duration, preview rows. The mark_*/check_*/exclude_* verb trio and the column-renaming helpers are the whole public surface. Recent releases are dependency chasing and test robustness rather than new exclusion criteria.
datasetjson reads and writes CDISC Dataset-JSON, the JSON replacement for SAS transport files in clinical-trial submissions. The package went from a thin reader in 2023 to a redesigned interface in 0.3.0 that targets the 1.1.0 schema, uses yyjsonr as its JSON backend, and exposes column metadata as first-class arguments. Development is contributor-driven inside the Atorus and pharmaverse orbit.
The package's roadmap is not its own — it tracks a CDISC standard that is still moving, and 0.3.0 is what happens when the standard revises: object model, read and write paths, and JSON backend all changed together. Performance was addressed in the same pass, which matters because submission datasets are large enough that a slow serialiser is a real constraint.
The next significant release will most likely follow the next Dataset-JSON schema revision rather than an internal roadmap, given that 0.3.0 was driven entirely by the 1.1.0 update.
excluder marks, checks, and excludes online-survey rows that fail quality criteria — duplicate responses, suspicious IP or geolocation, screen resolution, completion duration, preview rows. The mark_*/check_*/exclude_* verb trio and the column-renaming helpers are the whole public surface. Recent releases are dependency chasing and test robustness rather than new exclusion criteria.
The package is stable and its maintenance load comes from things it does not control: the {iptools} package leaving CRAN, {tidyselect} deprecating the .data pronoun, IP-geolocation tests breaking when the underlying address data shifts. Much of that work is about staying installable, not about better exclusions. Note that several of these entries were backfilled into the feed within the same two-minute window and are not in version order.
The next release will most likely be another dependency or CRAN-check response rather than a new exclusion criterion, following the pattern of the last three.
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 datasetjson or excluder.
A tables-listings-graphs package that reached CRAN and then went quiet.
Tplyr made clinical summary tables explain where every number came from.
Clinical listings that keep inheriting their hardest problem — pagination — from the layer below.
A cache-directory helper that has shipped nothing but CRAN-triggered patches for seven years.
gigs redesigned its whole conversion API for rOpenSci, then spent three releases getting the docs to build.
A weather-data client that keeps rewriting its HTTP layer while slowly tightening its API.
See all datasetjson alternatives → · See all excluder alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
Both compete on the same themes — r-package — within Analytics. datasetjson and excluder 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. datasetjson and excluder 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 datasetjson alternatives in Analytics are ranked by recent ship velocity. Browse the "datasetjson alternatives" section above for the current picks, or visit /alternatives/datasetjson for the full list with editorial commentary on each.
Top excluder alternatives in Analytics are ranked by recent ship velocity. Browse the "excluder alternatives" section above for the current picks, or visit /alternatives/excluder for the full list with editorial commentary on each.