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.
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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.
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")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)
#> Auto-refreshing stale OAuth token.
bq_table_download(tb, max_results = 10)
#> # A tibble: 10 x 4
#> year month day weight_pounds
#> <int> <int> <int> <dbl>
#> 1 1969 1 20 6.44
#> 2 1969 1 9 6.38
#> 3 1969 1 9 7.19
#> 4 1969 1 11 8.13
#> 5 1969 1 3 7.25
#> 6 1969 1 15 5.06
#> 7 1969 1 25 NA
#> 8 1969 1 4 7.06
#> 9 1969 1 6 7.19
#> 10 1969 1 26 3.53library(DBI)
con <- dbConnect(
bigrquery::bigquery(),
project = "publicdata",
dataset = "samples",
billing = billing
)
con
#> <BigQueryConnection>
#> Dataset: publicdata.samples
#> Billing: bigrquery-examples
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 1 20 6.44
#> 2 1969 1 9 6.38
#> 3 1969 1 9 7.19
#> 4 1969 1 11 8.13
#> 5 1969 1 3 7.25
#> 6 1969 1 15 5.06
#> 7 1969 1 25 NA
#> 8 1969 1 4 7.06
#> 9 1969 1 6 7.19
#> 10 1969 1 26 3.53library(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 11 29 6.00
#> 2 1969 2 6 8.94
#> 3 1969 5 16 6.88
#> 4 1970 9 4 7.13
#> 5 1970 1 24 7.63
#> 6 1970 6 6 9.00
#> 7 1970 10 30 6.50
#> 8 1971 3 18 5.75
#> 9 1971 8 11 6.19
#> 10 1971 1 23 5.75When using bigquery interactively, you’ll be prompted to authorize
bigrquery in the
browser. Your credentials will be cached across sessions in
.httr-oauth. For non-interactive usage, you’ll need to download a
service token JSON file and use set_service_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()).
If you just want to play around with the bigquery API, it’s easiest to start with the 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:
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Open https://console.cloud.google.com/ and create a project. Make a note of the “Project ID” in the “Project info” box.
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Click on “APIs & Services”, then “Dashboard” in the left the left menu.
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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.