r2rtf
The clinical-report table engine learned Chinese, then learned to leave RTF entirely
Comparison · Analytics
forestly vs nanoparquet
A side-by-side editorial comparison of forestly and nanoparquet — release velocity, themes, recent moves, and the top alternatives to consider.
Shared themes:r-language
forestly vs nanoparquet: at a glance
What is forestly?
forestly built an interactive safety review tool, then taught it to produce submission-ready RTF.
forestly renders adverse-event forest plots as interactive reactable widgets — filterable by AE category, with sliders for incidence thresholds and a toggle for the risk-difference column. Version 0.1.3 added `rtf_static_forestly()` for static RTF output, and 0.1.4 has been about giving the display owner control over what reviewers see: the CSV download button, the AE filter label, and the diff toggle can each be switched off.
What is nanoparquet?
nanoparquet is chasing byte-level agreement with the Java and Rust Parquet readers, not feature count.
nanoparquet reads and writes Parquet from R with no Arrow dependency, which is its entire reason to exist. The 0.4.0 line renamed the reader API and added schema authoring plus `append_parquet()`, and the 0.5.x releases have gone after interoperability: definition and repetition level encodings the Apache Parquet Java library expects, flatbuffer alignment the Rust arrow-rs reader expects, 128-bit decimals, and Polars-written files that omit the dictionary page offset. The newest release adds `bit64::integer64` columns and writing to stdout.
forestly vs nanoparquet: editorial side-by-side
forestly
0.0ANALYTICS
forestly built an interactive safety review tool, then taught it to produce submission-ready RTF.
◆ Current state
forestly renders adverse-event forest plots as interactive reactable widgets — filterable by AE category, with sliders for incidence thresholds and a toggle for the risk-difference column. Version 0.1.3 added `rtf_static_forestly()` for static RTF output, and 0.1.4 has been about giving the display owner control over what reviewers see: the CSV download button, the AE filter label, and the diff toggle can each be switched off.
◆ Where it's heading
The arc runs from a fixed interactive widget toward a configurable one with two output modes. Nearly every new argument in the last two releases exists to remove something from the display or relabel it, which suggests the users driving development are producing outputs for others to review under conventions they do not control. The x-axis range, column header, figure header and slider range arguments point the same way — this is a tool being fitted into standardised reporting rather than used ad hoc.
◆ Prediction
Given that the last two releases have consisted almost entirely of display-control arguments, the next is likely more of the same, applied to whichever parts of the interactive layout are still fixed.
nanoparquet
0.0ANALYTICS
nanoparquet is chasing byte-level agreement with the Java and Rust Parquet readers, not feature count.
◆ Current state
nanoparquet reads and writes Parquet from R with no Arrow dependency, which is its entire reason to exist. The 0.4.0 line renamed the reader API and added schema authoring plus `append_parquet()`, and the 0.5.x releases have gone after interoperability: definition and repetition level encodings the Apache Parquet Java library expects, flatbuffer alignment the Rust arrow-rs reader expects, 128-bit decimals, and Polars-written files that omit the dictionary page offset. The newest release adds `bit64::integer64` columns and writing to stdout.
◆ Where it's heading
Almost every entry since 0.4.0 names another engine — Java, arrow-rs, Polars, Arrow schema metadata — which tells you the maintainers are treating cross-reader fidelity as the product rather than R-side ergonomics. The type system is filling in from the edges: DECIMAL beyond 8 bytes, UUID, FLOAT16 and INTERVAL as raw lists, and now 64-bit integers with an explicit read-type option instead of a silent cast to double. Writing to `:stdout:` points at a second audience, shell pipelines rather than interactive R.
◆ Prediction
The remaining unmapped Parquet types the changelog has been parking in raw-vector lists — FLOAT16 and INTERVAL — are the obvious next targets, following the same pattern by which DECIMAL and UUID graduated to real R types.
Alternatives to forestly and nanoparquet
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 forestly or nanoparquet.
DoseFinding
New stewardship at openpharma, then two releases adding the methods MCP-Mod was missing
webmockr
The stubbing library added httr2 support, then spent a year cutting itself free of everything else
crul
crul took mocking back from webmockr and made it a property of the client itself
dendroNetwork
Six releases, six identical bodies — the feed carries the package abstract instead of release notes
chattr
chattr deleted every LLM integration it had written and outsourced the lot to ellmer
See all forestly alternatives → · See all nanoparquet alternatives →
Recent activity from forestly and nanoparquet
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 3mo agonanoparquet64-bit integer columns and writing Parquet to stdout
- 4mo agonanoparquetFiles now readable by the Java and Rust Parquet libraries
- 5mo agoforestlyDownload button, AE filter label and diff toggle become optional
- 11mo agoforestlyStatic RTF forest plots join the interactive output ⚡
- 1y agonanoparquetReads Polars files that omit the dictionary page offset
- 1y agonanoparquetDate, FLOAT, and mixed-encoding read fixes
- 1y agonanoparquetSchema authoring and append_parquet arrive with a renamed API ⚡
- 1y agoforestlyreactR 0.6.0 rendering fix and slider label control
- 1y agonanoparquetFixes a write_parquet crash
- 2y agoforestlyTreatment group selection and rough-edge fixes
- 3y agoforestlyFirst release on GitHub and CRAN
Frequently asked questions
What is the difference between forestly and nanoparquet?
Both compete on the same themes — r-language — within Analytics. forestly and nanoparquet 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 forestly better than nanoparquet?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. forestly and nanoparquet 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 forestly?
Top forestly alternatives in Analytics are ranked by recent ship velocity. Browse the "forestly alternatives" section above for the current picks, or visit /alternatives/forestly for the full list with editorial commentary on each.
What are the best alternatives to nanoparquet?
Top nanoparquet alternatives in Analytics are ranked by recent ship velocity. Browse the "nanoparquet alternatives" section above for the current picks, or visit /alternatives/nanoparquet for the full list with editorial commentary on each.