tidytlg
A tables-listings-graphs package that reached CRAN and then went quiet.
Comparison · Analytics
gigs vs nanoparquet
A side-by-side editorial comparison of gigs and nanoparquet — release velocity, themes, recent moves, and the top alternatives to consider.
gigs vs nanoparquet: at a glance
What is gigs?
gigs redesigned its whole conversion API for rOpenSci, then spent three releases getting the docs to build.
gigs implements international newborn and infant growth standards — INTERGROWTH-21st, WHO — converting anthropometric measurements to z-scores and centiles and classifying growth outcomes. The 0.5.0 release rewrote the public API around rOpenSci review feedback; the two releases after it change no code at all, existing purely to get the documentation site building.
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.
gigs vs nanoparquet: editorial side-by-side
gigs
0.0ANALYTICS
gigs redesigned its whole conversion API for rOpenSci, then spent three releases getting the docs to build.
◆ Current state
gigs implements international newborn and infant growth standards — INTERGROWTH-21st, WHO — converting anthropometric measurements to z-scores and centiles and classifying growth outcomes. The 0.5.0 release rewrote the public API around rOpenSci review feedback; the two releases after it change no code at all, existing purely to get the documentation site building.
◆ Where it's heading
The package has moved from vector-in, vector-out conversion helpers to a data.frame-oriented interface with a single classify_growth() entry point that computes whatever outcomes the supplied columns allow. That is a shift from library to tool — the user describes their data rather than picking the right function. The trailing releases suggest the code is settled and the remaining work is packaging and discoverability.
◆ Prediction
With the API rewrite absorbed and hosting moved to rOpenSci, the next substantive release should add growth standards or outcomes rather than reshape the interface again.
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 gigs 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 gigs or nanoparquet.
Tplyr
Tplyr made clinical summary tables explain where every number came from.
rlistings
Clinical listings that keep inheriting their hardest problem — pagination — from the layer below.
hoardr
A cache-directory helper that has shipped nothing but CRAN-triggered patches for seven years.
riem
A weather-data client that keeps rewriting its HTTP layer while slowly tightening its API.
datasetjson
datasetjson rebuilt its object model to track the CDISC Dataset-JSON 1.1 schema.
See all gigs alternatives → · See all nanoparquet alternatives →
Recent activity from gigs 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
- 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 agogigsDocs and Zenodo archiving
- 1y agogigsDocs-only release; nothing changed internally
- 1y agogigsConversion API rewritten around data frames and classify_growth() ⚡
- 1y agonanoparquetFixes a write_parquet crash
- 2y agogigsDocumentation fixes for autotest compliance
- 2y agogigsINTERGROWTH-21st fetal standards and input validation
- 2y agogigsPatch release with documentation update
Frequently asked questions
What is the difference between gigs and nanoparquet?
They serve adjacent needs but don't currently overlap on shipped themes. gigs 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 gigs better than nanoparquet?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. gigs 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 gigs?
Top gigs alternatives in Analytics are ranked by recent ship velocity. Browse the "gigs alternatives" section above for the current picks, or visit /alternatives/gigs 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.