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Carl Boettiger commented on ARROW-15879: ---------------------------------------- Sorry my minimal example was too minimal. Yes, I had noticed dropping the partition works, but I cannot then `filter()` on the partition column _before_ collect. Continuing from your reprex, try: {code:java} > df %>% filter(gear < 3) %>% collect() Error in lapply(args, function(x) { : object 'gear' not found {code} The primary incentive to hive-partition I thought was to benefit from `arrow`'s ability not to even need to parse those files excluded by the filter. (though admittedly hive-partition is more of a parquet concept I guess, I was initially very pleasantly surprised that write_dataset() would even partition in this way with format="csv", so very cool!) > [R] passing a schema calls open_dataset to fail on hive-partitioned csv files > ----------------------------------------------------------------------------- > > Key: ARROW-15879 > URL: https://issues.apache.org/jira/browse/ARROW-15879 > Project: Apache Arrow > Issue Type: Improvement > Components: R > Affects Versions: 7.0.0, 7.0.1 > Reporter: Carl Boettiger > Priority: Major > > Consider this reprex: > > Create a dataset with hive partitions in csv format with write_dataset() (so > cool!): > > {code:java} > library(arrow) > library(dplyr) > path <- fs::dir_create("tmp") > mtcars %>% group_by(gear) %>% write_dataset(path, format="csv")## works fine, > even with 'collect()' > ds <- open_dataset(path, format="csv")## but pass a schema, and things fail > df <- open_dataset(path, format="csv", schema = ds$schema, skip_rows=1) > df %>% collect() > {code} > In the first call to open_dataset, we don't pass a schema and things work as > expected. > However, csv files often need a schema to be read in correctly, particularly > with partitioned data where it is easy to 'guess' the wrong type. Passing > the schema though confuses open_dataset, because the grouping column > (partition column) isn't found on the individual files even though it is > mentioned in the schema! > Nor can we just omit the grouping column from the schema, since then it is > effectively lost from the data. -- This message was sent by Atlassian Jira (v8.20.1#820001)