Skip to content

Latest commit

 

History

History
202 lines (163 loc) · 6.9 KB

README.md

File metadata and controls

202 lines (163 loc) · 6.9 KB
Raw

bigrquery

CRAN Status R-CMD-check Codecov test coverage

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 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("pak")
pak::pak("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, n_max = 10)
#> # A tibble: 10 × 4
#>     year month   day weight_pounds
#>    <int> <int> <int>         <dbl>
#>  1  1969    12    14          8.88
#>  2  1969     1    22          7.44
#>  3  1969     4    11          6.12
#>  4  1969     3    15          9.06
#>  5  1969    11    18          7.44
#>  6  1969     5     5          7.00
#>  7  1969     9    20          8.13
#>  8  1969     3    20          7.37
#>  9  1969     3    20          6.81
#> 10  1969    12     1          8.50

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 × 4
#>     year month   day weight_pounds
#>    <int> <int> <int>         <dbl>
#>  1  1969    12    14          8.88
#>  2  1969     1    22          7.44
#>  3  1969     4    11          6.12
#>  4  1969     3    15          9.06
#>  5  1969    11    18          7.44
#>  6  1969     5     5          7.00
#>  7  1969     9    20          8.13
#>  8  1969     3    20          7.37
#>  9  1969     3    20          6.81
#> 10  1969    12     1          8.50

dplyr

library(dplyr)

natality <- tbl(con, "natality")

natality %>%
  select(year, month, day, weight_pounds) %>% 
  head(10) %>%
  collect()
#> # A tibble: 10 × 4
#>     year month   day weight_pounds
#>    <int> <int> <int>         <dbl>
#>  1  2005     5    NA          7.56
#>  2  2005     6    NA          4.75
#>  3  2005    11    NA          7.37
#>  4  2005     6    NA          7.81
#>  5  2005     5    NA          3.69
#>  6  2005    10    NA          6.95
#>  7  2005    12    NA          8.44
#>  8  2005    10    NA          8.69
#>  9  2005    10    NA          7.63
#> 10  2005     7    NA          8.27

Important details

BigQuery account

To use bigrquery, you’ll need a BigQuery project. Fortunately, if you just want to play around with the BigQuery API, it’s easy to start with Google’s 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 “Project ID” as you’ll use this as the billing project whenever you work with free sample data; and as the project when you work with your own data.

Authentication and authorization

When using bigrquery interactively, you’ll be prompted to authorize bigrquery in the browser. You’ll 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:

  • 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.
  • 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 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.

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.

Privacy policy