Github user falaki commented on the issue:
https://github.com/apache/spark/pull/17941
@felixcheung we all know that SparkR (and in general R) API is not perfect
when it comes to ETLing unstructured data. For example we don't have a great
story for nested data, etc. To overcome these limitations many ETL their data
in Python or Scala and then analyze them in R.
With introduction of sessions that workflow is partially broken. You can
still do it but you need to persist the table. The global temp view is to solve
that problem. It exists in PySpark, so I think it deserves to exist in SparkR
as well.
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