tidytlg
A tables-listings-graphs package that reached CRAN and then went quiet.
A side-by-side editorial comparison of sdtm.oak and Tplyr — release velocity, themes, recent moves, and the top alternatives to consider.
Two releases in, the open-source SDTM toolkit now covers the domains it originally excluded.
sdtm.oak builds SDTM datasets — the tabulation standard clinical trial submissions are filed in — from raw collected data. The 0.1.0 release shipped the mapping algorithm functions and derived-variable helpers but explicitly excluded DM, trial design domains, and several others. Version 0.2.0 closes the largest of those gaps, adding DM domain support via calc_min_max_date() and oak_calc_ref_dates(), plus generate_sdtm_supp() for supplemental qualifier domains.
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.
sdtm.oak builds SDTM datasets — the tabulation standard clinical trial submissions are filed in — from raw collected data. The 0.1.0 release shipped the mapping algorithm functions and derived-variable helpers but explicitly excluded DM, trial design domains, and several others. Version 0.2.0 closes the largest of those gaps, adding DM domain support via calc_min_max_date() and oak_calc_ref_dates(), plus generate_sdtm_supp() for supplemental qualifier domains.
This is the pharmaverse pattern of building submission tooling in the open, one domain class at a time, with the release history running through GitHub release-candidate tags before each CRAN submission. The direction is clear from the domain checklist: start with the mechanically simple Findings and Events domains, then work toward the ones with cross-dataset dependencies. DM and SUPP were the two that most often forced teams back to bespoke code.
The remaining exclusions from the 0.1.0 scope — trial design domains, SV, SE, RELREC and the EPOCH variable — are the obvious next targets, with EPOCH likely first since it depends on the reference dates 0.2.0 just added. Expect the same rhythm of release candidates ahead of each CRAN submission.
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.
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 sdtm.oak or Tplyr.
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 sdtm.oak alternatives → · See all Tplyr alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
Both compete on the same themes — clinical-trials, pharmaverse, r-package — within Analytics. sdtm.oak and Tplyr 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. sdtm.oak and Tplyr 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 sdtm.oak alternatives in Analytics are ranked by recent ship velocity. Browse the "sdtm.oak alternatives" section above for the current picks, or visit /alternatives/sdtm-oak for the full list with editorial commentary on each.
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.