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‎.Rbuildignore‎

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^\.httr-oauth$
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^\.travis\.yml$
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^CONTRIBUTING\.md$
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^revdep

‎DESCRIPTION‎

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Package: bigQueryR
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Title: Interface with Google BigQuery with Shiny Compatibility
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Version: 0.1.0.9000
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Version: 0.2.0
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Authors@R: c(person("Mark", "Edmondson",email = "[email protected]",
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role = c("aut", "cre")),
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person("Hadley", "Wickham", , "[email protected]", role = "ctb")
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)
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Description: Interface with Google BigQuery,
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Description: Interface with 'Google BigQuery',
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see <https://cloud.google.com/bigquery/> for more information.
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This package uses 'googleAuthR' so is compatible with similar packages,
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including Google Cloud Storage (<https://cloud.google.com/storage/>) for result extracts.
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including 'Google Cloud Storage' (<https://cloud.google.com/storage/>) for result extracts.
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URL: http://code.markedmondson.me/bigQueryR/
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BugReports: https://github.com/cloudyr/bigQueryR/issues
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License: MIT + file LICENSE
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R (>= 3.2.0)
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Imports:
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googleAuthR (>= 0.3.1),
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googleCloudStorageR (>= 0.1.0)
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googleCloudStorageR
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Suggests:
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shiny (>= 0.12.1),
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jsonlite (>= 1.0),

‎NEWS.md‎

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# bigQueryR 0.1.0.9000
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# bigQueryR 0.2.0 (CRAN)
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* Download asynch queries straight to disk via googleCloudStorageR
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# bigQueryR 0.1.0 (CRAN)
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# bigQueryR 0.1.0
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* Added a `NEWS.md` file to track changes to the package.
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* Initial release
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‎cran-comments.md‎

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## Test environments
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* local OS X install, R 3.3.0
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* ubuntu 12.04 (on travis-ci), R 3.3.0
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* ubuntu 12.04 (on travis-ci), R 3.3.1
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* win-builder (devel and release)
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## R CMD check results
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0 errors | 0 warnings | 2 notes
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0 errors | 0 warnings | 1 notes
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* This is a new release.
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* Possibly mis-spelled words in DESCRIPTION: BigQuery - this is spelt correctly.
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* Possibly mis-spelled words in DESCRIPTION:
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BigQuery (2:30)
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This is spelt correctly
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## Reverse dependencies
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This is a new release, so there are no reverse dependencies.
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googleAnalyticsR is a dependency, that when checked had 0 errors.
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‎vignettes/bigQueryR.html‎

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rel="stylesheet" type="text/css" />
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<body>
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<h1 class="title toc-ignore">bigQueryR</h1>
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<h4 class="author"><em>Mark Edmondson</em></h4>
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<h4 class="date"><em>2016-08-11</em></h4>
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<div id="introduction" class="section level2">
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<h2>Introduction</h2>
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<p>This is a package for interating with <a href="https://cloud.google.com/bigquery/">BigQuery</a> from within R.</p>
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<p>You may want instead to use <a href="https://github.com/hadley/bigrquery">bigrquery</a> which is more developed with integration with <code>dplyr</code> etc. Some functions from <code>bigrquery</code> are used in this package.</p>
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<div id="why-this-package-then" class="section level3">
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<h3>Why this package then?</h3>
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<p>This package is here as it uses <a href="https://github.com/MarkEdmondson1234/googleAuthR">googleAuthR</a> as backend, so has Shiny support, and compatibility with other googleAuthR dependent packages.</p>
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<p>It also has support for data extracts to Google Cloud Storage, meaning you can download data and make the download URL available to a user via their Google email. If you do a query normally with over 100000 results it hangs and errors.</p>
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<p>An example of a BigQuery Shiny app running OAuth2 is here, the <a href="https://mark.shinyapps.io/bigquery-viz/">BigQuery Visualiser</a></p>
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</div>
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</div>
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<div id="authentication" class="section level2">
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<h2>Authentication</h2>
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<p>Authentication is as used in other <code>googleAuthR</code> libraries:</p>
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<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">library</span>(bigQueryR)
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## this will open your browser
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## Authenticate with an email that has access to the BigQuery project you need
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<span class="kw">bqr_auth</span>()
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## verify under a new user
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<span class="kw">bqr_auth</span>(<span class="dt">new_user=</span><span class="ot">TRUE</span>)</code></pre></div>
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<p>If you are authenticating under several APIs via <code>googleAuthR</code>then use <code>gar_auth()</code> instead with the appropriate scopes set.</p>
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<p>You can also use service-to-service JSON files and multi-user authentication under Shiny, see the <code>googleAuthR</code> readme for details.</p>
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</div>
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<div id="listing-bigquery-meta-data" class="section level2">
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<h2>Listing BigQuery meta data</h2>
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<p>Various functions for listing what is in your BigQuery account.</p>
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<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">library</span>(bigQueryR)
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## this will open your browser
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## Authenticate with an email that has access to the BigQuery project you need
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<span class="kw">bqr_auth</span>()
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## verify under a new user
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<span class="kw">bqr_auth</span>(<span class="dt">new_user=</span><span class="ot">TRUE</span>)
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## get projects
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projects &lt;-<span class="st"> </span><span class="kw">bqr_list_projects</span>()
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my_project &lt;-<span class="st"> </span>projects[<span class="dv">1</span>]
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## for first project, get datasets
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datasets &lt;-<span class="st"> </span>bqr_list_datasets[my_project]
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my_dataset &lt;-<span class="st"> </span>datasets[<span class="dv">1</span>]
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## list tables
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my_table &lt;-<span class="st"> </span><span class="kw">bqr_list_tables</span>(my_project, my_dataset)
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## get metadata for table
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meta_table &lt;-<span class="st"> </span><span class="kw">bqr_table_meta</span>(my_project, my_dataset, my_table)</code></pre></div>
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</div>
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<div id="simple-queries" class="section level2">
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<h2>Simple Queries</h2>
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<p>You can pass in queries that have results under ~ 100000 rows using this command:</p>
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<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">bqr_query</span>(<span class="st">&quot;big-query-r&quot;</span>,<span class="st">&quot;samples&quot;</span>,
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<span class="st">&quot;SELECT COUNT(repository.url) FROM [publicdata:samples.github_nested]&quot;</span>)</code></pre></div>
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<p>More than that, and the API starts to hang and you are limited by your download bandwidth.</p>
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</div>
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<div id="asynchronous-queries" class="section level2">
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<h2>Asynchronous Queries</h2>
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<p>For bigger queries, asynchronous queries save the results to another BigQuery table. You can check the progress of the job via <code>bqr_get_job</code></p>
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<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">library</span>(bigQueryR)
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## Auth with a project that has at least BigQuery and Google Cloud Storage scope
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<span class="kw">bqr_auth</span>()
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## make a big query
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job &lt;-<span class="st"> </span><span class="kw">bqr_query_asynch</span>(<span class="st">&quot;your_project&quot;</span>,
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<span class="st">&quot;your_dataset&quot;</span>,
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<span class="st">&quot;SELECT * FROM blah LIMIT 9999999&quot;</span>,
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<span class="dt">destinationTableId =</span> <span class="st">&quot;bigResultTable&quot;</span>)
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## poll the job to check its status
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## its done when job$status$state == &quot;DONE&quot;
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<span class="kw">bqr_get_job</span>(<span class="st">&quot;your_project&quot;</span>, job$jobReference$jobId)
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##once done, the query results are in &quot;bigResultTable&quot;</code></pre></div>
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<p>You may now want to download this data. For large datasets, this is best done via extracting the BigQuery result to Google Cloud Storage, then downloading the data from there.</p>
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<p>You can create a bucket at Google Cloud Storage see <a href="https://cloud.google.com/storage/docs/cloud-console" class="uri">https://cloud.google.com/storage/docs/cloud-console</a>, or you can use <a href="https://github.com/cloudyr/googleCloudStorageR">library(googleCloudStorageR)</a></p>
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<p>Once created, you can extract your data via the below:</p>
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<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">## Create the data extract from BigQuery to Cloud Storage
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job_extract &lt;-<span class="st"> </span><span class="kw">bqr_extract_data</span>(<span class="st">&quot;your_project&quot;</span>,
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<span class="st">&quot;your_dataset&quot;</span>,
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<span class="st">&quot;bigResultTable&quot;</span>,
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<span class="st">&quot;your_cloud_storage_bucket_name&quot;</span>)
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## poll the extract job to check its status
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## its done when job$status$state == &quot;DONE&quot;
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<span class="kw">bqr_get_job</span>(<span class="st">&quot;your_project&quot;</span>, job_extract$jobReference$jobId)
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## to download via a URL and not logging in via Google Cloud Storage interface:
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## Use an email that is Google account enabled
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## Requires scopes:
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## https://www.googleapis.com/auth/devstorage.full_control
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## https://www.googleapis.com/auth/cloud-platform
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## set via options(&quot;bigQueryR.scopes&quot;) and reauthenticate if needed
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download_url &lt;-<span class="st"> </span><span class="kw">bqr_grant_extract_access</span>(job_extract, <span class="st">&quot;[email protected]&quot;</span>)
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## download_url may be multiple if the data is &gt; 1GB
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&gt;<span class="st"> </span>[<span class="dv">1</span>] <span class="st">&quot;https://storage.cloud.google.com/big-query-r-extracts/extract-20160311112410-000000000000.csv&quot;</span>
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&gt;<span class="st"> </span>[<span class="dv">2</span>] <span class="st">&quot;https://storage.cloud.google.com/big-query-r-extracts/extract-20160311112410-000000000001.csv&quot;</span>
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&gt;<span class="st"> </span>[<span class="dv">3</span>] <span class="st">&quot;https://storage.cloud.google.com/big-query-r-extracts/extract-20160311112410-000000000002.csv&quot;</span></code></pre></div>
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