rjd3highfreq
rjd3highfreq ships whatever the Java side ships, and only occasionally says what that was.
A side-by-side editorial comparison of audubon and fastplyr — release velocity, themes, recent moves, and the top alternatives to consider.
audubon's release feed is almost entirely Renovate bumping the JavaScript toolchain behind its Japanese text splitter.
An R package for Japanese text processing — normalisation, tokenisation via MeCab and SudachiPy, and phrase splitting through budoux. Ten releases since 2022, but the changelogs are dominated by automated dependency updates to a webpack, babel and prettier toolchain, because the budoux component is JavaScript that has to be bundled. Actual R-facing changes appear in perhaps one release in three.
A fast dplyr stand-in that keeps finding new places to skip work entirely.
fastplyr reimplements the dplyr verbs on a faster backend, exposing f_summarise, f_mutate, f_reframe and a set of group metadata helpers alongside optimized joins and quantiles. The most recent release removes non-API C functions and raises the floor to R 4.5.0, a steep requirement that follows the C++17 requirement introduced a release earlier. The verb surface itself has been stable since 0.9.0.
An R package for Japanese text processing — normalisation, tokenisation via MeCab and SudachiPy, and phrase splitting through budoux. Ten releases since 2022, but the changelogs are dominated by automated dependency updates to a webpack, babel and prettier toolchain, because the budoux component is JavaScript that has to be bundled. Actual R-facing changes appear in perhaps one release in three.
The package appears feature-stable and in maintenance. The last substantive R-level addition visible here is bind_lr() for bigram LR values back in 0.5.0; everything since has been dependency hygiene, a tokeniser refactor, and platform-specific test fixes. That is a reasonable end state for a wrapper whose value is the binding rather than ongoing invention, but it does mean the release feed carries almost no signal about the package itself — a reader watching this feed would learn more about webpack's version history than about Japanese text processing.
Expect the Renovate cadence to continue setting the release rhythm, with R-facing changes arriving only when budoux itself gains capability or a platform breaks.
fastplyr reimplements the dplyr verbs on a faster backend, exposing f_summarise, f_mutate, f_reframe and a set of group metadata helpers alongside optimized joins and quantiles. The most recent release removes non-API C functions and raises the floor to R 4.5.0, a steep requirement that follows the C++17 requirement introduced a release earlier. The verb surface itself has been stable since 0.9.0.
The optimization strategy has shifted from making individual functions fast to reasoning about expressions before evaluating them — 0.9.9 began marking simple operators as group-unaware so expressions built only from them are evaluated across the whole data frame rather than per group. That is a structural bet: the package increasingly inspects what you wrote to decide how much work is actually needed. Running alongside it is a steady tightening of build requirements, with C++17, R 4.5.0 and CRAN's C API rules all landing within a year.
Expect the group-unaware classification to widen to more functions, since each addition compounds across every grouped expression, and expect the dependency floors to keep rising as the package tracks CRAN's compiled-code policy.
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 audubon or fastplyr.
rjd3highfreq ships whatever the Java side ships, and only occasionally says what that was.
affiner is quietly turning a grid transformation helper into a small computational geometry library.
ageproR spent two years chasing a moving file format, then added the recruitment models that justify the effort.
ledger adds a Rust toolchain fallback, so beancount imports work whether or not the Python tooling is installed.
gridpattern keeps widening its catalogue, and the newest patterns finally use the device's own line rendering.
distributional taught + and - to work on any pair of distributions, closing the algebra it started with.
See all audubon alternatives → · See all fastplyr alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
Both compete on the same themes — r-package — within Analytics. audubon and fastplyr 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. audubon and fastplyr 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 audubon alternatives in Analytics are ranked by recent ship velocity. Browse the "audubon alternatives" section above for the current picks, or visit /alternatives/audubon-r for the full list with editorial commentary on each.
Top fastplyr alternatives in Analytics are ranked by recent ship velocity. Browse the "fastplyr alternatives" section above for the current picks, or visit /alternatives/fastplyr for the full list with editorial commentary on each.