mlr3mbo
mlr3mbo picked its defaults from a benchmark study, not from taste
Comparison · Analytics
dtplyr vs feasts
A side-by-side editorial comparison of dtplyr and feasts — release velocity, themes, recent moves, and the top alternatives to consider.
dtplyr vs feasts: at a glance
What is dtplyr?
dtplyr stopped hijacking data.table objects and became an opt-in translator
dtplyr converts dplyr and tidyr code into data.table syntax, and 1.3.0 redrew its boundary: verbs no longer dispatch to dtplyr translations just because dtplyr is loaded, so lazy_dt() has to be called explicitly. Since then the work has been translation coverage — reframe(), case_match(), consecutive_id() — plus a long tail of correctness fixes in grouping and .by.
What is feasts?
feasts is splitting itself in two, moving every plot into ggtime
feasts provides feature extraction and statistics for tsibble time series. The last two releases are dominated by one decision: all of its gg_*() plotting functions are being moved out into a separate ggtime package. 0.4.2 announced the deprecation and 0.5.0 makes ggtime a dependency with soft-deprecation messages on every re-export.
dtplyr vs feasts: editorial side-by-side
dtplyr
0.0ANALYTICS
dtplyr stopped hijacking data.table objects and became an opt-in translator
◆ Current state
dtplyr converts dplyr and tidyr code into data.table syntax, and 1.3.0 redrew its boundary: verbs no longer dispatch to dtplyr translations just because dtplyr is loaded, so lazy_dt() has to be called explicitly. Since then the work has been translation coverage — reframe(), case_match(), consecutive_id() — plus a long tail of correctness fixes in grouping and .by.
◆ Where it's heading
The package is trailing dplyr's own feature releases rather than leading them, adding each new verb once it settles upstream. Performance work is targeted at specific verbs where data.table has a faster primitive: setorder() for arrange(), reference drops for select(), rleid() for consecutive_id(). Release cadence has thinned considerably since 2023.
◆ Prediction
Expect further one-for-one translations as dplyr adds verbs, and continued fixes around .by and non-standard column names; the entries show no sign of a broader redesign.
feasts
0.0ANALYTICS
feasts is splitting itself in two, moving every plot into ggtime
◆ Current state
feasts provides feature extraction and statistics for tsibble time series. The last two releases are dominated by one decision: all of its gg_*() plotting functions are being moved out into a separate ggtime package. 0.4.2 announced the deprecation and 0.5.0 makes ggtime a dependency with soft-deprecation messages on every re-export.
◆ Where it's heading
The package is narrowing to its stated purpose — features and statistics — and shedding graphics entirely over a deliberately slow two-year window. Everything else in the recent history is ggplot2 compatibility work and narrow seasonal-plot bug fixes, which is consistent with a maintainer trimming surface area rather than growing it.
◆ Prediction
The next releases should be compatibility upkeep while the ggtime deprecation runs its course; the re-exports stay until the announced window closes.
Alternatives to dtplyr and feasts
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 dtplyr or feasts.
loo
loo keeps rewriting the diagnostics Bayesian modellers read off model comparison
mlr3fselect
mlr3fselect turned feature selection into an asynchronous, distributable job
lime
lime survives on compatibility patches years after its research moment
mlr3measures
mlr3measures is systematically retrofitting sample weights across every metric
mlr3cluster
mlr3cluster went from a handful of clusterers to covering the field
See all dtplyr alternatives → · See all feasts alternatives →
Recent activity from dtplyr and feasts
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 6mo agofeastsfeasts moves its plots to ggtime behind a 2-year deprecation ⚡
- 11mo agodtplyrreframe(), case_match() and consecutive_id() gain translations
- 11mo agofeastsggplot2 4.0.0 compatibility and the ggtime deprecation notice
- 1y agofeastsgg_season() fix for sub-weekly daily data
- 1y agofeastsImpulse-response plots and Johansen cointegration tests
- 2y agofeastsPatch for ggplot2 3.5.0 breaking changes
- 3y agodtplyrcrayon dependency dropped
- 3y agofeastsCRAN patch for S3 method consistency
- 3y agodtplyrVerbs stop auto-dispatching; lazy_dt() now required ⚡
- 3y agodtplyrdtplyr 1.2.2
- 4y agodtplyrdtplyr 1.2.1
- 4y agodtplyrEight tidyr verbs gain data.table translations
Frequently asked questions
What is the difference between dtplyr and feasts?
They serve adjacent needs but don't currently overlap on shipped themes. dtplyr and feasts 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.
Is dtplyr better than feasts?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. dtplyr and feasts 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.
What are the best alternatives to dtplyr?
Top dtplyr alternatives in Analytics are ranked by recent ship velocity. Browse the "dtplyr alternatives" section above for the current picks, or visit /alternatives/dtplyr for the full list with editorial commentary on each.
What are the best alternatives to feasts?
Top feasts alternatives in Analytics are ranked by recent ship velocity. Browse the "feasts alternatives" section above for the current picks, or visit /alternatives/feasts for the full list with editorial commentary on each.