In this tutorial, we’ll cover everything you need to set up and use Google BigQuery. If you know R and/or Python, there’s some bonus content for you, but no programming is necessary to follow this guide. Specifically, we’ll cover Setting up a Google Cloud Project Setting up a BigQuery dataset and table Transferring data from Google Cloud Storage to BigQuery Transferring data from AWS S3 to BigQuery Querying your data Gotchas, Tips, and Best Practices BigQuery for R and Python users Before we get into the details…
Adobe Analytics’ clickstream data is the raw hit data that adobe tracks on your website. Used properly, it’s a powerful source of data as it tells you exactly what someone did when they visited your site - what they clicked on, what their IP address is, the exact time of every hit, etc. A consequence of the granularity of the data is that this dataset is big, especially if your site gets a lot of traffic.
Connecting AWS S3 to Python is easy thanks to the boto3 package. In this tutorial, we’ll see how to Set up credentials to connect Python to S3 Authenticate with boto3 Read and write data from/to S3 1. Set Up Credentials To Connect Python To S3 If you haven’t done so already, you’ll need to create an AWS account. Sign in to the management console. Search for and pull up the S3 homepage.
Connecting AWS S3 to R is easy thanks to the aws.s3 package. In this tutorial, we’ll see how to Set up credentials to connect R to S3 Authenticate with aws.s3 Read and write data from/to S3 1. Set Up Credentials To Connect R To S3 If you haven’t done so already, you’ll need to create an AWS account. Sign in to the management console. Search for and pull up the S3 homepage.
In this tutorial, we’ll anlayze the performance of my website using Google Analytics, R, and googleAnalyticsR. Setup Before we get started with R, I’m assuming you have some basic familiarity with Google Analytics. For example, if you want to use Google Analytics with R, you’ll obviously need to set up a Google Analytics account and property (i.e. website), and you’ll need to insert your google analytics tracking code into your website.
In case you missed it, here’s part 1 in a nutshell. library(googleAnalyticsR) # Authenticate googleAuthR::gar_auth_service( json_file = "/Users/bgorman/Documents/Projects/R/googleAnalyticsR/gormanalysis-7b0c90a25f87.json", scope = "https://www.googleapis.com/auth/analytics.readonly" ) googleAuthR::gar_set_client( json = "/Users/bgorman/Documents/Projects/R/googleAnalyticsR/client_secret.apps.googleusercontent.com.json", scopes = c("https://www.googleapis.com/auth/analytics.readonly") ) ## 2020-06-24 08:50:08> Setting client.id from /Users/bgorman/Documents/Projects/R/googleAnalyticsR/client_secret.apps.googleusercontent.com.json ##  "gormanalysis" # Query list of accounts (to get viewId) accounts <- ga_account_list() accounts[, c("accountName", "webPropertyName", "viewId", "viewName")] ## # A tibble: 1 x 4 ## accountName webPropertyName viewId viewName ## <chr> <chr> <chr> <chr> ## 1 Ben519 GormAnalysis 79581596 All Web Site Data Analyses I think the best way to familiarize yourself with the google analytics API is just to walk through various analyses.