How to configure Google Tag Manager Server Side so it can send webhooks to services upon certain events
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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:
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.
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.
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.
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.
As analysts, we are often called upon to see how website metrics have improved or declined over time. This is easy enough when looking at trends, but if you are looking to break down over other dimensions, it can involve a lot of ETL to get to what you need. For instance, if you are looking at landing page performance of SEO traffic you can sort by the top performers, but not by the top most improved performers.