tidytlg
A tables-listings-graphs package that reached CRAN and then went quiet.
A side-by-side editorial comparison of Tplyr and xportr — release velocity, themes, recent moves, and the top alternatives to consider.
Tplyr made clinical summary tables explain where every number came from.
Tplyr builds clinical summary tables through a layered grammar — count, descriptive statistics, and shift layers assembled onto a table object. The 1.0.0 release added a traceability metadata framework that lets a user ask which source rows produced any given cell, and later releases extended it to cases the first pass missed. The package is maintained by Atorus within the pharmaverse ecosystem.
The CDISC transport writer collapsed six pipeline calls into one, then spent two years hardening it
xportr applies CDISC metadata — variable types, lengths, labels, formats, ordering — to R data frames and writes the SAS transport files that go into regulatory submissions. Since v0.4.0 the package has had a single entry point, xportr_process(), that runs the whole chain and writes, and metadata arrives as a plain specification rather than a metacore object. The v0.5.0 release in January 2026 finished the cleanup by deleting every deprecated argument left over from that redesign.
Tplyr builds clinical summary tables through a layered grammar — count, descriptive statistics, and shift layers assembled onto a table object. The 1.0.0 release added a traceability metadata framework that lets a user ask which source rows produced any given cell, and later releases extended it to cases the first pass missed. The package is maintained by Atorus within the pharmaverse ecosystem.
Post-1.0 work has been about completing the metadata story and filling gaps in layer composition rather than adding table types — metadata for missing subjects, add_anti_join(), missing-subject rows, data limiting, and fixes to nested count layers where an inner value appears under several outer groups. Releases cluster tightly after a major version, then go quiet, and the window ends with a patch issued days after the release it corrects.
Further releases will most likely continue closing traceability and nested-layer edge cases rather than introducing new layer types, following the pattern of both post-1.0 feature releases.
xportr applies CDISC metadata — variable types, lengths, labels, formats, ordering — to R data frames and writes the SAS transport files that go into regulatory submissions. Since v0.4.0 the package has had a single entry point, xportr_process(), that runs the whole chain and writes, and metadata arrives as a plain specification rather than a metacore object. The v0.5.0 release in January 2026 finished the cleanup by deleting every deprecated argument left over from that redesign.
The work has moved from building the pipeline to defending it against the ways submission data actually arrives: grouped data frames now raise a warning, date and time variables get class checks, illegal characters are resolved rather than erroring, and xportr_write() warns before a file crosses 5GB instead of producing an unusable artifact. Contributor volume is high and spread across sponsors — Atorus, Roche, GSK and others show up in the PR lists — which is what keeps a validated-context package moving without a single owner.
With deprecations cleared in 0.5.0, the next cycle likely targets more input-shape validation of the kind 0.5.0 started — the grouped-data and datetime-class checks read as the first two of a series. Nothing in these entries points to a new output format.
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 Tplyr or xportr.
A tables-listings-graphs package that reached CRAN and then went quiet.
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.
datasetjson rebuilt its object model to track the CDISC Dataset-JSON 1.1 schema.
See all Tplyr alternatives → · See all xportr alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
Both compete on the same themes — pharmaverse — within Analytics. Tplyr and xportr 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. Tplyr and xportr 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 Tplyr alternatives in Analytics are ranked by recent ship velocity. Browse the "Tplyr alternatives" section above for the current picks, or visit /alternatives/tplyr for the full list with editorial commentary on each.
Top xportr alternatives in Analytics are ranked by recent ship velocity. Browse the "xportr alternatives" section above for the current picks, or visit /alternatives/xportr for the full list with editorial commentary on each.