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dplyr support has been updated to require dplyr 0.7.0 and use dbplyr. This
means that you can now more naturally work directly with DBI connections.
dplyr now also uses modern BigQuery SQL which supports a broader set of
translations. Along the way I've also fixed some SQL generation bugs (#48).
The DBI driver gets a new name:
insert_table()allows you to insert empty tables into a dataset.
All POST requests (inserts, updates, copies and
.... This allows you to add arbitrary additional data to the
request body making it possible to use parts of the BigQuery API
that are otherwise not exposed (#149).
snake_caseargument names are
automatically converted to
camelCaseso you can stick consistently
to snake case in your R code.
Full support for DATE, TIME, and DATETIME types (#128).
Big fixes and minor improvements
All bigrquery requests now have a custom user agent that specifies the
versions of bigrquery and httr that are used (#151).
that are passed onto
query_exec(). These allow you to control query options
at the connection level.
insert_upload_job()now sends data in newline-delimited JSON instead
of csv (#97). This should be considerably faster and avoids character
encoding issues (#45).
POSIXltcolumns are now also correctly
coerced to TIMESTAMPS (#98).
query_exec()gain new arguments:
query_exec()now give a nicer progress bar,
including estimated time remaining (#100).
query_exec()should be considerably faster because profiling revealed that
~40% of the time taken by was a single line inside a function that helps
parse BigQuery's json into an R data frame. I replaced the slow R code with
a faster C function.
wait_for()uses now reports the query total bytes billed, which is
more accurate because it takes into account caching and other factors.