Hello! I am Daran J. Johnson and I have worked with data for over 25 years. I have been a database developer, DBA, programmer, and data/digital analyst/scientist (often at the same time!).
For most of my career I have been a consultant & business owner as well as working inside organizations. This has allowed me to work with a wide diversity of data tech as well as working deeply within organizations with their selected data tech.
My main love is analysis – bringing data analytics & data science together that provide actionable insights. I think that creating analysis that is well-documented, easily reproducible, simple to share and robust is fundamental to leveraging data to impact organizational success (which is a long-winded way of saying solid data analysis helps companies do better). Understanding how to code in an analytical programming language is a large part of how to achieve this end.
Using R For Data Analysis
I really love the R programming language, and it is my go to when it comes to data analysis. I was looking for a better data visualization tool some years ago and stumbled upon R, but I got so much more than that. Below are some of the reasons I like R so much.
R is code, so any analysis that you do in R can be run any where. Watch this simple analysis by Hadley Wickham and imagine doing this in Excel. It’s not that you can’t, but it’s a lot more work.
This can take the place of blog pages (some of my blog posts are written entirely in Quarto), PDFs, PowerPoint, and many other formats. The advantage of using Quarto is reproducibility, customizability, and the ability to directly pull data into the document and prep that data and display it in very customized charts and tables. I have found it to be perfect for A/B test result papers.
The community of people that use R are awesome! Since R is open-source, it is accessible to anyone that has a computer. And since R is a statistical programming language, it is used mainly by people focused on some kind of data analysis – from students and Professors to Data Scientists and Data Analysts. Scientists use R, so do business people. They tend to to share a lot – from code snippets to package development (GitHub has been great for this).
Google’s Marketing & Cloud Platforms
I have been involved with Google’s Marketing & Cloud Platforms since near the beginning of their inception. Today, for a reasonable cost, you can capture web & app data in GA4, flow it into BigQuery and create reports & dashboards in Looker Studio. You can also create analysis from data stored in BigQuery from GA4, Google Ads and other sources as well.
I have found that R & Google’s Marketing & Cloud Platforms work very well together. I have created many analytics projects (I’ve blogged about some) that bridge the divide between digital analytics & data science. These projects have allowed stakeholders to have better insights into the performance of their online assets and activities. I am constantly learning new ways that these platforms compliment each other to further our understanding of digital performance.
Learning Never Stops
I think it’s important to never stop learning. I am always studying one book or another and I love a good textbook. A couple of my timeless favorites are:
- Chris Date’s An Introduction to Database Systems – Few textbooks are as in-depth on the subject of relational database systems as this one. This book taught me to see data structures in a whole new way.
- Andy Field’s Discovering Statistics Using R – I think this book is revolutionary in how it is structured. First of all, it teaches you R and statistics at the same time. As you study each chapter, the data is interesting and/or funny and Andy Field inserts his own life in the the chapters, so the book doesn’t have that stoic academic anonymity to it. Of all textbooks I have ever studied, this is my favorite.
- Andy Field’s An Adventure In Statistics – This book teaches statistics as the story of Zack, who wakes up to find his soulmate, Alice, has vanished. He must use statistics to locate her. The illustrations are better than any textbook I’ve studied and the approach to teaching the material is on a whole different level.
When I started my data journey in 1998, there were only a handful of databases and not too much else. Today, it’s impossible to keep up on all the new and evolving data tech. However, I do think that it is important to learn and grow as things change. In addition to the above, I have also been working with PowerBI, Tableau, MongoDB & Python.
This website is about sharing my knowledge of data analytics & data science (as well as the data engineering that brings all the data together – what can I say, I like the end-to-end process). Any post focused on coding a functional app will have the full code available on GitHub. I also strive to have a workable example available, if possible.
I’m also an avid runner and Strava has a great running group for R programmers called The RStats Running Club which I am active on. So if you run and you like R, please join.
A Couple Points About This Website
- All opinions expressed are my own and do not reflect anyone else’s opinions.