Are you interested in using R for your digital analytics projects? Do you need to perform prediction modelling and visualizations on your digital data and Excel can´t just do the job as you wanted?
Or, you simply have no idea how R could help you in your digital analytics problems and you would like to see some real working examples first?
Well, there are 2 good news for you.
The first one is that you are not alone. There is a quite vibrant community out there, sharing more and more examples on how to get real value from using R in digital analytics. They often post/tweet around the #rstats hashtag.
The second news is that I decided to write a post on this. I am going to list here the main blogs (and people) that might be useful to add to your “R Stats + Digital Analytics” reading list.
I came up with a list of 8 top contributors for now (please add up!). A few of them actually don’t have a blog, but it still made sense to include them here. What these people have in common is that:
- they are promoting R as a powerful tool for digital analytics and are encouraging analysts to move away from traditional tools like Excel spreadsheet.
- they are helping those wanting to learn R and apply it to digital analytics, to get started, sharing examples of real case analysis.
- most of them have been nominated by the Digital Analytics Associations this year for the “Most Influential “Vendor/Agency” award, because of effort thay are making to help digital analytics practitioners skill up and smarten up when it comes to R Stats.
For each blog/people I have included:
- a brief introductory information and links to their main works.
- Twitter account
- Github account (if they have one)
- Blog (if they publish on a blog)
Tatvic is a Google Analytics Certified Partner offering Web Analytics Consulting Services for Google Analytics, Omniture, etc.
First of all, one of his team members, Kushan Shah, is the mantainer of the RGoogleAnalytics popular package, which lets you connect to GA API through R (this package was initially built by a team at Google).
Secondly, they are actively promoting the use of R for analysing web analytics data and buillding predictive models based on it. They do run practical webinars and have a blog where they publish real case scenarios of mining Google Analytics data and generate insights through R.
A couple of very interesting applications they produced are:
Predicting product revenue with R
Web Analytics Visualization through ggplot
Predict Bounce Rate based on Page Load Time in Google Analytics
If you want to see real examples of using R to explore Google Analytics data, I recommend you follow Tatvic.
2. Online Behaviour
Online Behaviour is a blog that focuses on Web Analytics, Usability, Testing and Digital Marketing techniques. His founder, Daniel Waisberg, who works as Analytics Advocate at Google, is an R user and is actively promoting the use of R within the digital analytics community.
His blog is also a great place for other experts in the digital analytics to field to share their work. So, if you have built something interesting with R + Google Analytics, you might want to let him know!
Have a read at these great articles published on Online Behaviour:
Visualizing Google Analytics Data With R [Tutorial] (by Daniel Waisberg)
Big Data – What It Means For The Digital Analyst (by Daniel Waisberg):
Building A Google Analytics App With Shiny & R (guest post by Chris Beeley):
Testing Statistical Significance On Google Analytics Data (guest post by Mark Edmondson)
Github: check out each single post author
3. Mark Edmondson
Mark is working as a Digital Analyst at Wunderman and is being sharing some very interesting web applications using R and the Shiny package. A great feature he includes in his apps is the automation of the authentication process, which allows you authenticate with your Google Analytics account/profile and run the app using your own data. Amazing.
He is got his own blog and he is also guest editor at Online Behaviour blog. I do recommend you put him on your reading list, and check out these posts below. By the way, he also made an amazing presentation available at RPubs about how/why to use R in digital analytics. After reading it, you will be more than tempted to close your Excel spreasheet and download R studio!
R in a Digital Analytics Worklow (RPubs presentation)
My Google Analytics Time Series Shiny App (Alpha)
Finding the ROI of Title tag changes using Google’s CausalImpact R package
How I made GA Effect – creating an online statistics dashboard using R
Testing Statistical Significance On Google Analytics Data (guest post at Online Behaviour)
4. Randy Zwitch
Randy Zwitch is a Data Scientist, and he is the Lead developer for RSiteCatalyst, an R package for accessing the Adobe SiteCatalyst (Omniture) Reporting API. So if you are a SiteCatalyst user, you must try this package.
Randy was nominated for the 2015 DAA Practitioner of The Year because of his innovative work in the areas of data science, big data and his ability to create real products for the digital analytics community.
He shares his work via his personal blog, where you can find a specific section about digital analytics.
Here are a few posts you might like check from his blog:
Analysing the percentage of Google organic search terms that are listed as “(not provided)”
Visualizing Website Pathing With Sankey Charts
Clustering Search Keywords Using K-Means Clustering
5. Johann Deboer
Johann is the author of the ganalytics R package, another package that lets you query Google Analytics data through R.
He works at Loves Data (I recommend you put their blog on your Digital Analytics reading list) and he is actively encouraging the use of R for Web Analytics. Check out his presentation on doing Web Analytics with R , where, among other things, he explains why you would use R instead of traditional spreadsheets like Excel.
6. Bror Skardhamar
He is the author of the RGA package, another package designed to extract data from the Google Analytics API to R.
Check out his github account and follow him on Twitter for more info.
Lunametrics is a very well known name within the digital analytics community. They have in-depth knowledge and experience in Google Analytics thanks to their close relationship with Google, and you can soon realize it by reading some of the technical posts published on their blog.
They have also made use of R script in a few occasions, and I guess they will be publishing more R content in 2015. Have a read at Google Analytics Data Mining with Big Query and R post, where
the author, Noah Haibach, provides an R script for generating an E-commerce report with visualizations that are not currently possible inside Google Analytics platform.
To be sure not to miss anything, I would include them too into your “R stats + Digital Analytics” reading list.
Last but not least, I include in this list the site R-Bloggers. What can I say about R-Bloggers? If you decide to invest in R, then you must follow R-Bloggers since it´s currently the main blog aggregator of content collected from other R bloggers.
R-Bloggers is not a blog about digital analytics (most of posts published are not related to it). However, it´s very likely that a new post about digital analytics will be published there too. Finally, I strongly recommend you to follow R-Bloggers:
- to learn about R and get updated on new packages, developments in the R community
- to make sure you don´t miss any digital analytics related post
Github: check out each single R blogger
I hope that this list will be useful for you. Whether you are new to R or already have hands on experience and you now want to apply it to your digital analytics data, you should follow these people I mentioned above.
I’ve also created a list on Twitter with all of them. It’s called “R for Digital Analytics” and you can click on the link to subscribe if you like.
I also tweet myself quite often around the #rstat hashtag (follow me on Twitter at @mcpasin) and have recently wrote a post about creating Google Analytics Dashboards with R. You might want to have a read at it too.
And please please please, feel free to suggest other people to include in this list.