tidytlg
A tables-listings-graphs package that reached CRAN and then went quiet.
A side-by-side editorial comparison of riem and tern — release velocity, themes, recent moves, and the top alternatives to consider.
A weather-data client that keeps rewriting its HTTP layer while slowly tightening its API.
riem pulls observations from the Iowa Environmental Mesonet's weather-station network. Its release history is two threads: successive rewrites of the HTTP and test-mocking stack, and a gradual tightening of function arguments that culminated in 1.0.0 removing convenient-but-dangerous defaults. Contributions come partly from IEM's own maintainer.
tern is migrating its entire analysis-function catalogue off make_afun(), one release at a time.
tern builds the clinical-trial tables, listings, and graphs layer on top of rtables — occurrence counts, survival summaries, ANCOVA, incidence rates, subgroup and biomarker tabulations. The visible work across the window is a systematic refactor: dozens of analysis functions rewritten to drop make_afun() and adopt a common analysis-function style driven by rtables' additional_fun_params. Feature additions ride along with it.
riem pulls observations from the Iowa Environmental Mesonet's weather-station network. Its release history is two threads: successive rewrites of the HTTP and test-mocking stack, and a gradual tightening of function arguments that culminated in 1.0.0 removing convenient-but-dangerous defaults. Contributions come partly from IEM's own maintainer.
The HTTP thread has moved through httr to httr2, and mocking from vcr to httptest2 — following the broader rOpenSci HTTP-stack reorganisation rather than any need of its own. The API thread runs the other way: 1.0.0 removed defaults for date_start and station and flipped latlon to FALSE, trading convenience for callers being explicit about what they request. New arguments in the same release widened what a query can ask for.
With the API stabilised at 1.0.0 and the HTTP stack settled on httr2, the next release is more likely to expose additional IEM query parameters than to change plumbing again.
tern builds the clinical-trial tables, listings, and graphs layer on top of rtables — occurrence counts, survival summaries, ANCOVA, incidence rates, subgroup and biomarker tabulations. The visible work across the window is a systematic refactor: dozens of analysis functions rewritten to drop make_afun() and adopt a common analysis-function style driven by rtables' additional_fun_params. Feature additions ride along with it.
This is a multi-release architectural migration, not incremental polish. Each release converts another batch of functions, and the count is large — roughly two dozen in the most recent entry alone, after a comparable batch the release before. Alongside it, the denom parameter is being threaded through counting functions and g_lineplot is accumulating layout control, both patterns of standardising arguments that previously varied per function.
The refactor should continue until the make_afun() dependency is gone entirely, with the remaining tabulate_* and biomarker functions the likely next batch; the entries give no date for completion.
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 riem or tern.
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.
datasetjson rebuilt its object model to track the CDISC Dataset-JSON 1.1 schema.
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
Both compete on the same themes — r-package — within Analytics. riem and tern 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. riem and tern 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 riem alternatives in Analytics are ranked by recent ship velocity. Browse the "riem alternatives" section above for the current picks, or visit /alternatives/riem for the full list with editorial commentary on each.
Top tern alternatives in Analytics are ranked by recent ship velocity. Browse the "tern alternatives" section above for the current picks, or visit /alternatives/tern for the full list with editorial commentary on each.