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fastplyr vs tidypolars

A side-by-side editorial comparison of fastplyr and tidypolars — release velocity, themes, recent moves, and the top alternatives to consider.

fastplyr vs tidypolars: at a glance

Featurefastplyrtidypolars
SectorAnalyticsAnalytics
Velocity score0.00.0
Sparks · 30d00
Top themesdataframe-performance, dplyr-alternative, query-optimization, cran-policypolars, r, dplyr, dataframes
Last editorial update50m ago1h ago
WebsiteVisit →Visit →

What is fastplyr?

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.

Read the full fastplyr trajectory →

What is tidypolars?

tidypolars is grinding toward complete dplyr coverage, one supported function at a time

tidypolars lets you write dplyr and tidyr syntax against Polars DataFrames and LazyFrames. Its releases follow a fixed shape: raise the required polars version, add a handful of newly supported R functions and arguments, fix places where behaviour diverges from dplyr. Recent additions run from %notin% and as.integer() to .before/.after in mutate() and time zone handling in datetime parsing. Cadence is roughly every six to ten weeks and has not varied.

Read the full tidypolars trajectory →

fastplyr vs tidypolars: editorial side-by-side

F
fastplyr
ANALYTICS
0.0

A fast dplyr stand-in that keeps finding new places to skip work entirely.

◆ Current state

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.

◆ Where it's heading

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.

◆ Prediction

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.

T
tidypolars
ANALYTICS
0.0

tidypolars is grinding toward complete dplyr coverage, one supported function at a time

◆ Current state

tidypolars lets you write dplyr and tidyr syntax against Polars DataFrames and LazyFrames. Its releases follow a fixed shape: raise the required polars version, add a handful of newly supported R functions and arguments, fix places where behaviour diverges from dplyr. Recent additions run from %notin% and as.integer() to .before/.after in mutate() and time zone handling in datetime parsing. Cadence is roughly every six to ten weeks and has not varied.

◆ Where it's heading

Coverage is the whole strategy, and the target has been widening from dplyr into tidyr — unnest_longer_polars(), separate_longer_delim_polars() and separate_longer_position_polars() bring list-column and string-splitting verbs that have no Polars-idiomatic equivalent in the tidyverse dialect. The other consistent thread is fidelity: distinct() dropping unselected columns, summarize() dropping the last group, relocate() honouring tidy-select helpers, NULL in mutate() behaving as dplyr does. Each of these is a small breaking change made to match the reference rather than to differ from it.

◆ Prediction

The pattern of tracking the polars floor upward every release and following tidyverse changes closely — .by in fill() arrived when tidyr 1.3.2 shipped it — suggests the next releases continue mirroring new dplyr and tidyr arguments rather than adding a distinct capability.

Alternatives to fastplyr and tidypolars

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 fastplyr or tidypolars.

See all fastplyr alternatives → · See all tidypolars alternatives →

Recent activity from fastplyr and tidypolars

Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.

  1. 1mo agotidypolarstidypolars 0.19.0
  2. 4mo agofastplyrNon-API C functions dropped, R 4.5.0 now required
  3. 4mo agotidypolarstidypolars 0.18.0
  4. 6mo agotidypolarstidypolars 0.17.0
  5. 6mo agotidypolarstidypolars 0.16.0
  6. 8mo agofastplyrIn-place sorting arrives with a C++17 requirement
  7. 9mo agotidypolarstidypolars 0.15.1
  8. 9mo agotidypolarstidypolars 0.15.0
  9. 10mo agofastplyrGroup-unaware expressions evaluated on the whole frame
  10. 1y agofastplyrf_mutate and f_reframe complete the verb set
  11. 1y agofastplyrDynamic argument evaluation and f_pull
  12. 1y agofastplyrf_fill added and grouped joins repaired

Frequently asked questions

What is the difference between fastplyr and tidypolars?

They serve adjacent needs but don't currently overlap on shipped themes. fastplyr and tidypolars 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 fastplyr better than tidypolars?

Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. fastplyr and tidypolars 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 fastplyr?

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.

What are the best alternatives to tidypolars?

Top tidypolars alternatives in Analytics are ranked by recent ship velocity. Browse the "tidypolars alternatives" section above for the current picks, or visit /alternatives/tidypolars for the full list with editorial commentary on each.