tidytlg
A tables-listings-graphs package that reached CRAN and then went quiet.
A side-by-side editorial comparison of datasetjson and tinkr — 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.
Markdown round-tripping through XML, where every release is another thing it learned not to mangle.
tinkr parses Markdown into XML, lets callers manipulate it with XPath, and writes it back out — the yarn R6 class is the whole interface. Its development is defined by a single hard problem: surviving the round trip without corrupting syntax the XML representation does not natively model. Maintenance is shared between two active contributors and release cadence is roughly annual.
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.
tinkr parses Markdown into XML, lets callers manipulate it with XPath, and writes it back out — the yarn R6 class is the whole interface. Its development is defined by a single hard problem: surviving the round trip without corrupting syntax the XML representation does not natively model. Maintenance is shared between two active contributors and release cadence is roughly annual.
Each release extends the set of constructs that get protected across the round trip — curly braces, escaped brackets, inline math, dollar signs used as currency, French-style dollars, fenced divs, frontmatter. The 0.3.0 notes also add get_protected(), append_md(), and prepend_md(), which is the first sign of building a manipulation API on top of the protection machinery rather than only widening it.
Expect the protection list to keep growing toward full Quarto syntax coverage, which the first release named as the long-term goal and which fenced divs and frontmatter both move toward.
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 tinkr.
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 tinkr alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
Both compete on the same themes — r-package — within Analytics. datasetjson and tinkr 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 tinkr 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 tinkr alternatives in Analytics are ranked by recent ship velocity. Browse the "tinkr alternatives" section above for the current picks, or visit /alternatives/tinkr for the full list with editorial commentary on each.