I am starting to use arrow in a workflow where I have a dataset partitioned by a couple variables (like location and year) that leads to > 100,000 parquet files.


I have been using `arrow::open_dataset(sources = FILEPATH, unify_schemas = FALSE)` but found this is taking a couple minutes to run. I can see that almost all the time is spent on this line creating the FileSystemDatasetFactory. https://github.com/apache/arrow/blob/master/r/R/dataset-factory.R#L135


In my use case I know all the partition file paths and I know the schema (and that it is consistent across partitions). Is there any way to use that information to more quickly create the Dataset object with a highly partitioned dataset?


I found this section in the Python docs about creating a dataset from filepaths, is this possible to do from R? https://arrow.apache.org/docs/python/dataset.html#manual-specification-of-the-dataset


Thank you! I’ve been finding arrow/parquet really useful as an alternative to hdf5 and csv.