googleAnalyticsR v1.0.0 - GA4 API, Automatic Shiny Dashboarding, Improved UI

The original intention for this blog was to announce updates to my R packages, but I tend to rely more on Twitter and other social media for that these days. A lot of documentation is also written for the package websites so I feel little need to put it on this blog as well. But for googleAnalyticsR v1.0.0 there are so many cool new things that I think it deserves a bit more limelight.

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Creating your own cookieless analytics tool with GTM Server Side, Cloud Run, BigQuery and Shiny

This is an example of how GTM server side can be used to create your own digital analytics tool. It is a proof of concept of what you can do given the power of GTM server side and its BigQuery integration. I customise the stream by adding cookieless tracking, displaying the data in Shiny and running it all on Cloud Run to keep costs down but performance good.

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Data Privacy Engineering with Google Tag Manager Server Side and Consent Mode

A new interest at the moment is engineering through various data privacy requirements with some of the new tools Google has available. With respect to my guiding principle of blogging about what I wish I could have read 6 months ago, I thought it worth writing about what is now possible. Data privacy engineering also requires clear thinking about the legal and technical details and so this will also help organise my communication around the subject.

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Using Google Tag Manager Server Side to Trigger Webhook Events

How to configure Google Tag Manager Server Side so it can send webhooks to services upon certain events

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Google Tag Manager Server Side on Cloud Run - Pros and Cons

Is Cloud Run viable for running Google Tag Manager server-side?

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Shiny on Google Cloud Run - Scale-to-Zero R Web Apps

How to create scale-to-zero Shiny apps on Google Cloud Run: pitfalls, how and why

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Online payments for data science apps (DSaaS) using R, Shiny, Firebase, Paddle and Google Cloud Functions

A bootstrap example on how to create a paid data science app (DSaaS)

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Introducing googleCloudRunner - serverless R on Google Cloud Platform

googleCloudRunner includes tools for R users to create APIs, cloud builds or scheduled scripts in the Google Cloud Platform

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gago: Blazingly fast Google Analytics API downloads with Go

Announcing gago: Go library for Google Analytics API v4 that can speed up API fetches by 85%

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R at scale on the Google Cloud Platform

Current thinking on what I consider the optimal way to work with R on Google Cloud Platform

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Auto Google Analytics Data Imports from Cloud Storage

Use Google Cloud Functions to auto-load your data imports into Google Analytics form Cloud Storage

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Turning GA360 BigQuery exports into partitioned tables using Cloud Functions

A guide to using Cloud Functions with GA360 exports, including a demo on creating a partitioned table out of the raw exports.

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Talking Google Analytics dashboards via R, Shiny and Text-to-Speech APIs

A video workshop tutorial on creating a talking Google Analytics dashboard in Shiny

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R on Kubernetes - serverless Shiny, R APIs and scheduled scripts

Some examples of running R applications on Google Kubernetes Engine

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Embedding Google Data Studio into RMarkdown

A quick how-to embed Data Studio in RMarkdown

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Comparing Google Search Console queries with Google's Cloud Natural Language API

With the launch of the Google Natural Language API (NLP API), and the emphasis of machine learning that is said to account for up to 30% of the SEO algorithmn for Google search, a natural question is whether you can use Google’s own macine learning APIs to help optimise your website for search. Whilst I don’t believe they will offer exactly the same results, I can see useful applications that include:

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Run RStudio Server on a Chromebook as a Cloud Native

I recently got an Asus Chromebook Flip with which I’m very happy, but it did make me realise that if a Chromebook was to replace my normal desktop as my primary workstation, my RStudio Server setup would need to be more cloud native than was available up until now. TL;DR - A how-to on making RStudio Server run on a Chromebook that automatically backs up data and configuration settings to Google Cloud Storage is on the googleComputeEngineR website here.

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Five Ways to Schedule R scripts on Google Cloud Platform

A common question I come across is how to automate scheduling of R scripts downloading data. This post goes through some options that I have played around with, which I’ve mostly used for downloading API data such as Google Analytics using the Google Cloud platform, but the same principles could apply for AWS or Azure.

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My R Packages

A full list of R packages I have published are on my Github, but some notable ones are below. Some are part of the cloudyR project, which has many packages useful for using R in the cloud. I concentrate on the Google cloud below, but be sure to check out the other packages if you’re looking to work with AWS or other cloud based services. CRAN Status URL Description googleAuthR The central workhorse for authentication on Google APIs googleAnalyticsR Works with Google Analytics Reporting V3/V4 and Management APIs googleComputeEngineR Launch Virtual Machines within the Google Cloud, via templates or your own Docker containers.

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New Blog Down

A new year, a new blogging platform! This time I’m moving from Jekyll to RStudio’s new blogdown format. This keeps the advantages of Jekyll (a static, high performance website; markdown for editing; free hosting on Github) but with the extra bonus of being able to render in RMarkdown plus adding some nice looking capabilities from the Hugo project.

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