Hello! I am Daran Johnson. Welcome to my blog!
Data has been a part of my life for a few decades now. I help people utilize and understand their data in a strategic way. I concentrate on data exploration, visualization and strategy. I have worked with data for marketing, sales, digital, ecommerce, finance, and operations. I have been a DBA, database developer, and a data analyst. But for most of my career, I have been a digital strategist & analyst, primarily working with companies to get the most out of their website, media, and digital presence (you can check out my consulting company here).
Creating analysis that is well-documented, easily reproducible, simple to share and robust is fundamental to leveraging data successfully. Understanding how to code in an analytical programming language is a large part of how to achieve this end. R is an excellent programming language for this purpose.
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. I got so much more than that. Here are some of the reasons I like R so much:
- Reproducibility – 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.
- RMarkdown/Quarto – Like markdown, but with R. This can take the place of blog pages (most of my blog is written in RMarkdown), PDFs, PowerPoint, and many other formats. The advantage of using RMarkdown or 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.
- People – 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).
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 favorite textbooks 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.
This website is about sharing my expertise of analytics & data, mainly focused on digital marketing, media and ecommerce. 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.
To reach out to me, you can message me on LinkedIn or email me – daran at fujoanalytics.com.
A Couple Points About This Website
- All opinions expressed are my own and do not reflect anyone else’s opinions.
- I do not collect personal information. However, I do use a few analytics tools, such as Google Analytics, to monitor the website. These tools may place cookies on your browser and retrieve information on general things such as city, technology & number of visits.