pins
pins keeps adding a storage backend per release while retiring its original API
Comparison · Analytics
readxl vs sparklyr
A side-by-side editorial comparison of readxl and sparklyr — release velocity, themes, recent moves, and the top alternatives to consider.
Shared themes:maintenance
readxl vs sparklyr: at a glance
What is readxl?
readxl has shipped almost nothing but vendored-dependency upkeep since 2022.
readxl reads Excel files into R without requiring Excel, bundling the libxls and RapidXML C libraries. Four of its last six releases state outright that they contain no user-facing changes, existing instead to satisfy CRAN, silence a sanitizer warning, or re-embed a patched libxls.
What is sparklyr?
sparklyr now spends its releases absorbing dbplyr changes and feeding pysparklyr
sparklyr connects R to Spark, and almost nothing in this window originates inside the package. Releases restore compatibility after dbplyr changes its SQL generation, adapt to Spark 4.0 and to R 4.4's version-comparison changes, and convert functions into S3 methods so pysparklyr can supply its own implementations.
readxl vs sparklyr: editorial side-by-side
readxl
0.0ANALYTICS
readxl has shipped almost nothing but vendored-dependency upkeep since 2022.
◆ Current state
readxl reads Excel files into R without requiring Excel, bundling the libxls and RapidXML C libraries. Four of its last six releases state outright that they contain no user-facing changes, existing instead to satisfy CRAN, silence a sanitizer warning, or re-embed a patched libxls.
◆ Where it's heading
The real work has become stewardship of vendored C code: absorbing libxls security fixes and keeping the package compiling across Alpine, UBSAN and successive cpp11 versions. 1.5.0 breaks the streak only slightly, with a network-drive permission warning fix and a cpp11 floor raised to dodge a segfault.
◆ Prediction
Expect the same rhythm — releases triggered by toolchain breakage or an upstream libxls patch rather than new spreadsheet-reading capability.
sparklyr
0.0ANALYTICS
sparklyr now spends its releases absorbing dbplyr changes and feeding pysparklyr
◆ Current state
sparklyr connects R to Spark, and almost nothing in this window originates inside the package. Releases restore compatibility after dbplyr changes its SQL generation, adapt to Spark 4.0 and to R 4.4's version-comparison changes, and convert functions into S3 methods so pysparklyr can supply its own implementations.
◆ Where it's heading
Two dependencies set the agenda. dbplyr repeatedly changes identifier quoting and lazy-table internals, and each change costs sparklyr a release. Meanwhile the package is being hollowed into a backend: ml_fit(), spark_apply(), spark_write_delta() and now tune_grid_spark() exist as methods so that pysparklyr, the Databricks Connect path, can override them. Dependency removal - tibble, rappdirs, digest - runs alongside as the package slims down.
◆ Prediction
Expect the next releases to continue tracking dbplyr and Spark versions, and more functions to be converted to methods as functionality shifts toward pysparklyr; new capability arriving in sparklyr itself looks unlikely.
Alternatives to readxl and sparklyr
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 readxl or sparklyr.
tsibble
tsibble shipped one release in five and a half years - the data structure is finished
yardstick
yardstick made fairness metrics a first-class part of tidymodels evaluation
tune
tune extends tuning past the model itself to postprocessors, and adds a second parallel backend
leaflet
leaflet relicensed to MIT and finished migrating off R's retired spatial stack
ggpubr
ggpubr reached 1.0.0 with p-value formatting presets for specific journals
See all readxl alternatives → · See all sparklyr alternatives →
Recent activity from readxl and sparklyr
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 1mo agosparklyrRestores compatibility after dbplyr changed Hive quoting
- 2mo agoreadxlreadxl 1.5.0 stops false access-denied warnings on network drives
- 3mo agosparklyrAdds tune_grid_spark() for pysparklyr to implement
- 10mo agosparklyrFixes lazy-table field lookup and a name collision
- 1y agosparklyrCatches up with released Spark 4.0; ml_load() reads via Spark
- 1y agoreadxlreadxl 1.4.5 clears a gcc UBSAN warning
- 1y agoreadxlreadxl 1.4.4 embeds libxls 1.6.3 with vulnerability fixes
- 2y agosparklyrDatabricks autoloader streaming ingestion; R 4.4 fixes
- 2y agosparklyrDrops tibble and rappdirs; retires Spark 2.3 JARs
- 3y agoreadxlreadxl 1.4.3 ships with no user-facing changes
- 3y agoreadxlreadxl 1.4.2 embeds a libxls build fixing CVE-2021-27836
- 3y agoreadxlreadxl 1.4.1 regenerates help files for valid HTML5
Frequently asked questions
What is the difference between readxl and sparklyr?
Both compete on the same themes — maintenance — within Analytics. readxl and sparklyr 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 readxl better than sparklyr?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. readxl and sparklyr 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 readxl?
Top readxl alternatives in Analytics are ranked by recent ship velocity. Browse the "readxl alternatives" section above for the current picks, or visit /alternatives/readxl for the full list with editorial commentary on each.
What are the best alternatives to sparklyr?
Top sparklyr alternatives in Analytics are ranked by recent ship velocity. Browse the "sparklyr alternatives" section above for the current picks, or visit /alternatives/sparklyr for the full list with editorial commentary on each.