Google BigQuery

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:

# install.packages('devtools')


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 .httr-oauth.

Note that 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 delete_table() or insert_upload_job()).

Sample data and a billing project

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:

  1. Open
  2. Click “Create Project” at the top
  3. Select a name and project ID, and click “Create”
  4. Turn on the BigQuery API by clicking “APIs & Auth” on the left, scrolling down to “BigQuery API”, and clicking the button at the right from “OFF” to “ON”.
  5. Click on “Overview” at the left
  6. Use the Project ID or Project Number to identify your project with bigrquery. (You can also use the project number, though it’s harder to remember.)

To run your first query:

project <- "fantastic-voyage-389" # put your project ID here
sql <- "SELECT year, month, day, weight_pounds FROM [publicdata:samples.natality] LIMIT 5"
query_exec(sql, project = project)

Using DBI and dplyr

For a more traditional database connection using dbConnect(), the driver to use is bigrquery::dbi_driver().

The arguments passed to Big Query connections differ a bit from others, there are 3 specific arguments:

  • dataset
  • project
  • billing

If you plan to access a Public Dataset, then very specific information will have to be passed to the connection. For example, the examples in the NOAA GSOD Weather page, found here:, the code snippets address the tables in a very specific way. To access the list of Weather Stations, the code calls this table: [bigquery-public-data:noaa_gsod.stations]. These values can be parsed to create the correct dbConnect() call:

con <- DBI::dbConnect(bigrquery::dbi_driver(),
                       dataset = "noaa_gsod",
                       project = "bigquery-public-data",
                       billing = project)

The stations table can then be referred to using tbl()

tbl(con, "stations")