(Note: If your dataset isn't showing, inspect the Job History to see the status of your upload job. Navigate to the BigQuery web interface and select your dataset and table by clicking the link that appears in the left navigation of the console as shown below. Observing the data using the BigQuery web interface Insert_upload_job("your-project-id", "test_dataset", "stash", stash)Ĭongratulations, you've now saved your work to the Cloud! Let's verify it's there. # devtools::install_github("rstats-db/bigrquery") # Install BigRQuery if you haven't already. The following code creates a dummy frame from a couple of vectors: Then, you’ll see how to access that data frame later from the BigQuery web interface and RStudio.Īlthough you probably already have a data frame that you can use, let's mock up some data that will later be stored in BigQuery. In this blog post, you’ll learn (using bigrquery, an open source library for R created by Hadley Wickham) how to create an R data frame and stash it in BigQuery. The natural next question you may have is, "How do I take data that I've processed in R and store it in BigQuery?" By doing that, you can query your data frame from BigQuery or stash analyzed data so your colleagues can import the data into their R projects or create data frames in other data science platforms, such as Python pandas. This is a great option when you have a dataset available on BigQuery (GCP’s fully-managed data warehouse service). In a previous blog post, we talked about how to query public datasets for analysis and visualization in R.
0 Comments
Leave a Reply. |