tidytlg
A tables-listings-graphs package that reached CRAN and then went quiet.
A side-by-side editorial comparison of cffr and Tplyr — release velocity, themes, recent moves, and the top alternatives to consider.
cffr keeps CITATION.cff generation in step with the standard and its R sources.
The package generates and validates CITATION.cff files from R package metadata. Recent work is validation and parsing accuracy rather than new outputs: cff_validate() moved onto the ajv engine through jsonvalidate for clearer errors, ROR identifiers act as a website fallback for people and entities, and DOIs are now detected in inst/CITATION url fields including dx.doi.org forms.
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 package generates and validates CITATION.cff files from R package metadata. Recent work is validation and parsing accuracy rather than new outputs: cff_validate() moved onto the ajv engine through jsonvalidate for clearer errors, ROR identifiers act as a website fallback for people and entities, and DOIs are now detected in inst/CITATION url fields including dx.doi.org forms.
The package tracks two moving targets — the Citation File Format schema and R's own person and citation handling, which has broken extraction twice in recent releases. Between those, releases pick up ecosystem details: Codeberg recognised as a repository host, CRAN-to-SPDX licence mappings refreshed, GitHub Action defaults changed to save quota. The most recent release is an internal refactor carried out with AI assistance, with no user-facing change.
With validation migrated and the R 4.5 person changes absorbed, the next release most likely follows a Citation File Format schema update rather than adding capability.
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 cffr 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.
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
They serve adjacent needs but don't currently overlap on shipped themes. cffr 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. cffr 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 cffr alternatives in Analytics are ranked by recent ship velocity. Browse the "cffr alternatives" section above for the current picks, or visit /alternatives/cffr 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.