Easily talk to Google's 'BigQuery' database from R.
The bigrquery packages provides an R interface to Google BigQuery. It makes it easy to retrieve metadata about your projects, datasets, tables and jobs, and provides a convenient wrapper for working with bigquery from R.
The current bigrquery release can be installed from CRAN:
The newest development release can be installed from github:
The first time you use bigrquery in a session, it will ask you to
authorize bigrquery in
the browser. This gives bigrquery the credentials to access data on your
behalf. By default, bigrquery picks up httr's
policy of caching per-working-directory credentials in
bigrquery requests permission to modify your data; in general, the
only data created or modified by
bigrquery are the temporary tables created
as query results, unless you explicitly modify your own data (say by calling
If you just want to play around with the bigquery API, it's easiest to start with the Google's free sample data. To do that, you'll also need to create your own project for billing purposes. If you're just playing around, it's unlikely that you'll go over the 10,000 request/day free limit, but google still needs a project that it can bill (you don't even need to provide a credit card).
To create a project:
Project Numberto identify your project with
bigrquery. (You can also use the project number, though it's harder to remember.)
To run your first query:
library(bigrquery)project <- "fantastic-voyage-389" # put your project ID heresql <- "SELECT year, month, day, weight_pounds FROM [publicdata:samples.natality] LIMIT 5"query_exec(sql, project = project)
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_extract_job() make it possible to extract data and save in
google storage (@realAkhmed, #119).
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_case argument names are
automatically converted to
camelCase so you can stick consistently
to snake case in your R code.
Full support for DATE, TIME, and DATETIME types (#128).
All bigrquery requests now have a custom user agent that specifies the versions of bigrquery and httr that are used (#151).
dbConnect() gains new
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).
POSIXlt columns are now also correctly
coerced to TIMESTAMPS (#98).
query_exec() gain new arguments:
quiet = TRUEwill suppress the progress bars if needed.
use_legacy_sql = FALSEoption allows you to opt-out of the legacy SQL system (#124, @backlin)
list_tables() (#108) and
list_datasets() (#141) are now paginated.
By default they retrieve 50 items per page, and will iterate until they
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.
set_oauth2.0_cred() allows user to supply their own Google OAuth
application when setting credentials (#130, @jarodmeng)
wait_for() uses now reports the query total bytes billed, which is
more accurate because it takes into account caching and other factors.
set_service_token() allows you to use OAuth service token instead of
^ is correctly translated to
Provide full DBI compliant interface (@krlmlr).
Backend now translates
IF (@realAkhmed, #53).
Compatiable with latest httr.
Computation of the SQL data type that corresponds to a given R object is now more robust against unknown classes. (#95, @krlmlr)
A data frame with full schema information is returned for zero-row results. (#88, @krlmlr)
exists_table(). (#91, @krlmlr)
insert_upload_job(). (#92, @krlmlr)
bigrquery.quiet. (#89, @krlmlr)
format_table(). (#81, @krlmlr)
list_tabledata_iter() that allows fetching a table in chunks of
varying size. (#77, #87, @krlmlr)
Add support for API keys via the
BIGRQUERY_API_KEY environment variable.