bigrquery
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 formbq_noun_verb()
. This level of abstraction is most appropriate if you’re 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 don’t want to write SQL, but instead want dbplyr to write it for you.
Installation
The current bigrquery release can be installed from CRAN:
install.packages("bigrquery")
The newest development release can be installed from GitHub:
# install.packages('devtools')
devtools::install_github("r-dbi/bigrquery")
Usage
Low-level API
library(bigrquery)
billing <- bq_test_project() # replace this with your project ID
sql <- "SELECT year, month, day, weight_pounds FROM `publicdata.samples.natality`"
tb <- bq_project_query(billing, sql)
bq_table_download(tb, max_results = 10)
#> # A tibble: 10 x 4
#> year month day weight_pounds
#> <int> <int> <int> <dbl>
#> 1 1969 2 4 6.12
#> 2 1969 4 15 6.44
#> 3 1969 4 8 8.88
#> 4 1969 8 15 6.44
#> 5 1969 1 21 7.50
#> 6 1969 4 14 7.06
#> 7 1969 11 3 6.56
#> 8 1969 2 3 8.13
#> 9 1969 11 20 8.19
#> 10 1969 9 1 6.25
DBI
library(DBI)
con <- dbConnect(
bigrquery::bigquery(),
project = "publicdata",
dataset = "samples",
billing = billing
)
con
#> <BigQueryConnection>
#> Dataset: publicdata.samples
#> Billing: gargle-169921
dbListTables(con)
#> [1] "github_nested" "github_timeline" "gsod" "natality"
#> [5] "shakespeare" "trigrams" "wikipedia"
dbGetQuery(con, sql, n = 10)
#> # A tibble: 10 x 4
#> year month day weight_pounds
#> <int> <int> <int> <dbl>
#> 1 1969 2 4 6.12
#> 2 1969 4 15 6.44
#> 3 1969 4 8 8.88
#> 4 1969 8 15 6.44
#> 5 1969 1 21 7.50
#> 6 1969 4 14 7.06
#> 7 1969 11 3 6.56
#> 8 1969 2 3 8.13
#> 9 1969 11 20 8.19
#> 10 1969 9 1 6.25
dplyr
library(dplyr)
natality <- tbl(con, "natality")
natality %>%
select(year, month, day, weight_pounds) %>%
head(10) %>%
collect()
#> # A tibble: 10 x 4
#> year month day weight_pounds
#> <int> <int> <int> <dbl>
#> 1 1969 3 12 5.81
#> 2 1969 2 18 7.23
#> 3 1969 8 22 7.06
#> 4 1970 4 1 8.56
#> 5 1970 2 20 7.87
#> 6 1970 6 22 6.69
#> 7 1970 4 27 7.50
#> 8 1970 6 21 4.81
#> 9 1969 7 9 6.62
#> 10 1969 8 16 8.44
Important details
Authentication and authorization
When using bigrquery interactively, you’ll be prompted to authorize
bigrquery in the
browser. Your token will be cached across sessions inside the folder
~/.R/gargle/gargle-oauth/
, by default. For non-interactive usage, it
is preferred to use a service account token and put it into force via
bq_auth(path = "/path/to/your/service-account.json")
. More places to
learn about auth:
- Help for
bigrquery::bq_auth()
. - How gargle gets
tokens.
- bigrquery obtains a token with
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.
- bigrquery obtains a token with
- Non-interactive auth. Explains how to set up a project when code must run without any user interaction.
- How to get your own API credentials. Instructions for getting your own OAuth client (or “app”) or service account token.
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.
Billing project
If you just want to play around with the BigQuery API, it’s easiest to start with Google’s free sample data. You’ll still need to create a project, but if you’re just playing around, it’s unlikely that you’ll go over the free limit (1 TB of queries / 10 GB of storage).
To create a project:
-
Open https://console.cloud.google.com/ and create a project. Make a note of the “Project ID” in the “Project info” box.
-
Click on “APIs & Services”, then “Dashboard” in the left the left menu.
-
Click on “Enable Apis and Services” at the top of the page, then search for “BigQuery API” and “Cloud storage”.
Use your project ID as the billing
project whenever you work with free
sample data; and as the project
when you work with your own data.
Useful links
Policies
Please note that the ‘bigrquery’ project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.