tidytlg
A tables-listings-graphs package that reached CRAN and then went quiet.
A side-by-side editorial comparison of datasetjson and sass — 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.
Nine releases of compiler warnings and CRAN checks — the Sass binding is in pure upkeep.
sass compiles Sass to CSS for R, and its recent history is almost entirely about staying installable. The last ten releases are dominated by compilation warnings on new toolchains — Apple Clang 15, gcc-12, Windows — plus R CMD check fixes for r-devel and one LibSass version bump. The only user-visible changes in the set are the switch to woff2 font files in font_google(local = TRUE) and clearer output when Google fonts are downloaded.
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.
sass compiles Sass to CSS for R, and its recent history is almost entirely about staying installable. The last ten releases are dominated by compilation warnings on new toolchains — Apple Clang 15, gcc-12, Windows — plus R CMD check fixes for r-devel and one LibSass version bump. The only user-visible changes in the set are the switch to woff2 font files in font_google(local = TRUE) and clearer output when Google fonts are downloaded.
This is a stable binding to a C++ library that is itself no longer moving, so the package's work is defined by the compilers and CRAN policies around it rather than by Sass features. Nothing in these entries suggests active development; the maintenance is competent and prompt, but it is maintenance.
The next release will most likely be triggered by a new compiler warning class or an R CMD check requirement rather than by anything in the Sass language. The entries give no signal of planned feature work.
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 sass.
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 sass alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
Both compete on the same themes — r-package — within Analytics. datasetjson and sass 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 sass 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 sass alternatives in Analytics are ranked by recent ship velocity. Browse the "sass alternatives" section above for the current picks, or visit /alternatives/sass for the full list with editorial commentary on each.