tidytlg
A tables-listings-graphs package that reached CRAN and then went quiet.
Comparison · Analytics
excluder vs pysparklyr
A side-by-side editorial comparison of excluder and pysparklyr — release velocity, themes, recent moves, and the top alternatives to consider.
excluder vs pysparklyr: at a glance
What is excluder?
A Qualtrics data-cleaning package that has been in maintenance mode since its CRAN acceptance.
excluder marks, checks, and excludes online-survey rows that fail quality criteria — duplicate responses, suspicious IP or geolocation, screen resolution, completion duration, preview rows. The mark_*/check_*/exclude_* verb trio and the column-renaming helpers are the whole public surface. Recent releases are dependency chasing and test robustness rather than new exclusion criteria.
What is pysparklyr?
Posit's Spark Connect bridge keeps adding backends — and now runs tidymodels tuning on the cluster.
pysparklyr is the Python-backed backend that lets sparklyr talk to Spark Connect, Databricks Connect, and now Snowflake, handling the reticulate environment, authentication, and Arrow configuration so R users mostly do not have to. The 0.2.x line has widened it well past a connectivity shim: 0.2.0 brought the Spark 4.0 ML function family and Snowpark Connect, and 0.2.2 added tune_grid_spark() so a tidymodels tuning grid executes inside a Spark Connect cluster. Authentication has become a first-class concern, with Snowflake's native authenticators, connections.toml discovery, and Posit Connect viewer credentials all supported.
excluder vs pysparklyr: editorial side-by-side
excluder
0.0ANALYTICS
A Qualtrics data-cleaning package that has been in maintenance mode since its CRAN acceptance.
◆ Current state
excluder marks, checks, and excludes online-survey rows that fail quality criteria — duplicate responses, suspicious IP or geolocation, screen resolution, completion duration, preview rows. The mark_*/check_*/exclude_* verb trio and the column-renaming helpers are the whole public surface. Recent releases are dependency chasing and test robustness rather than new exclusion criteria.
◆ Where it's heading
The package is stable and its maintenance load comes from things it does not control: the {iptools} package leaving CRAN, {tidyselect} deprecating the .data pronoun, IP-geolocation tests breaking when the underlying address data shifts. Much of that work is about staying installable, not about better exclusions. Note that several of these entries were backfilled into the feed within the same two-minute window and are not in version order.
◆ Prediction
The next release will most likely be another dependency or CRAN-check response rather than a new exclusion criterion, following the pattern of the last three.
pysparklyr
3.8ANALYTICS
Posit's Spark Connect bridge keeps adding backends — and now runs tidymodels tuning on the cluster.
◆ Current state
pysparklyr is the Python-backed backend that lets sparklyr talk to Spark Connect, Databricks Connect, and now Snowflake, handling the reticulate environment, authentication, and Arrow configuration so R users mostly do not have to. The 0.2.x line has widened it well past a connectivity shim: 0.2.0 brought the Spark 4.0 ML function family and Snowpark Connect, and 0.2.2 added tune_grid_spark() so a tidymodels tuning grid executes inside a Spark Connect cluster. Authentication has become a first-class concern, with Snowflake's native authenticators, connections.toml discovery, and Posit Connect viewer credentials all supported.
◆ Where it's heading
Two directions are running at once. Horizontally, the package is becoming backend-plural — what started as Databricks-and-Spark now covers Snowflake through Snowpark Connect, with credential handling generalized per platform rather than special-cased. Vertically, it is climbing from data manipulation toward modeling: distributed ML functions in 0.2.0, distributed tuning in 0.2.2. A persistent third thread is absorbing upstream churn — Pandas 3.0 conversion, sparklyr 1.9.5 and dbplyr 2.6.0 restructuring the tbl source slot, reticulate's changing environment management.
◆ Prediction
With tuning distributed and the Spark 4.0 ML surface in place, the unfinished edge is the rest of the tidymodels workflow — expect fitting and resampling paths to follow tune_grid_spark() onto the cluster.
Alternatives to excluder and pysparklyr
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 excluder or pysparklyr.
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.
gigs
gigs redesigned its whole conversion API for rOpenSci, then spent three releases getting the docs to build.
riem
A weather-data client that keeps rewriting its HTTP layer while slowly tightening its API.
See all excluder alternatives → · See all pysparklyr alternatives →
Recent activity from excluder and pysparklyr
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 28d agopysparklyrtune_grid_spark() runs tidymodels tuning on Spark Connect ⚡
- 6mo agopysparklyrSpark 4.0 ML functions and Snowpark Connect support ⚡
- 10mo agopysparklyrDelta writes and a more flexible Python environment picker
- 1y agopysparklyrrpy2 install deferred to first spark_apply() call
- 1y agoexcluderInternet-dependent tests and examples made conditional
- 1y agopysparklyrDatabricks serverless compute and SDK-deferred authentication
- 1y agopysparklyrPositron IDE detection and connection-pane fixes
- 2y agoexcluderqualtrics_fetch2 dataset and tidyselect deprecation fixes
- 3y agoexcluderCRAN acceptance and graceful IP-lookup failure
- 3y agoexcluderdplyr 1.0.8 across()/is.na() compatibility fix
- 3y agoexcluderuse_labels(), rename_columns(), and orientation-agnostic resolution
- 3y agoexcluderSwaps iptools for ipaddress
Frequently asked questions
What is the difference between excluder and pysparklyr?
They serve adjacent needs but don't currently overlap on shipped themes. pysparklyr is currently shipping more aggressively (velocity 3.8 vs 0.0), with 1 editorial sparks in the last 30 days against 0. See the at-a-glance table above for a side-by-side breakdown of velocity, recent sparks, and editorial themes.
Is excluder better than pysparklyr?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. pysparklyr is currently shipping more aggressively (velocity 3.8 vs 0.0), with 1 editorial sparks in the last 30 days against 0. For your specific use case, the alternatives sections above list other Analytics products to evaluate alongside.
What are the best alternatives to excluder?
Top excluder alternatives in Analytics are ranked by recent ship velocity. Browse the "excluder alternatives" section above for the current picks, or visit /alternatives/excluder for the full list with editorial commentary on each.
What are the best alternatives to pysparklyr?
Top pysparklyr alternatives in Analytics are ranked by recent ship velocity. Browse the "pysparklyr alternatives" section above for the current picks, or visit /alternatives/pysparklyr for the full list with editorial commentary on each.