The bigrquery package makes it easy to work with data stored in Google BigQuery by allowing you to query BigQuery tables and retrieve metadata about your projects, datasets, tables, and jobs. The bigrquery package provides three levels of abstraction on top of BigQuery:
The low-level API provides thin wrappers over the underlying REST API. All the low-level functions start with bq_, and mostly have the form bq_noun_verb(). This level of abstraction is most appropriate if youâre familiar with the REST API and you want do something not supported in the higher-level APIs.
The DBI interface wraps the low-level API and makes working with BigQuery like working with any other database system. This is most convenient layer if you want to execute SQL queries in BigQuery or upload smaller amounts (i.e. <100 MB) of data.
The dplyr interface lets you treat BigQuery tables as if they are in-memory data frames. This is the most convenient layer if you donât want to write SQL, but instead want dbplyr to write it for you.
The current bigrquery release can be installed from CRAN:
The newest development release can be installed from GitHub:
Usage Low-level API DBI dplyr Important details BigQuery accountTo use bigrquery, youâll need a BigQuery project. Fortunately, if you just want to play around with the BigQuery API, itâs easy to start with Googleâs free public data and the BigQuery sandbox. This gives you some fun data to play with along with enough free compute (1 TB of queries & 10 GB of storage per month) to learn the ropes.
To get started, open https://console.cloud.google.com/bigquery and create a project. Make a note of the âProject IDâ as youâll use this as the billing project whenever you work with free sample data; and as the project when you work with your own data.
When using bigrquery interactively, youâll be prompted to authorize bigrquery in the browser. Youâll be asked if you want to cache tokens for reuse in future sessions. For non-interactive usage, it is preferred to use a service account token, if possible. More places to learn about auth:
bigrquery::bq_auth().gargle::token_fetch(), which supports a variety of token flows. This article provides full details, such as how to take advantage of Application Default Credentials or service accounts on GCE VMs.Note that bigrquery requests permission to modify your data; but it will never do so unless you explicitly request it (e.g. by calling bq_table_delete() or bq_table_upload()). Our Privacy policy provides more info.
Please note that the âbigrqueryâ project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.
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