A Journey into Data Science
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Setting up R for Minimal Code Compilation and Maximum Speed
UPDATE: This article has been superseded by Setting up R on macOS 10.15 Catalina (Complete Guide)
If you’re not careful how you install R on macOS, when you try to install R packages, R may try to build those packages from source. We like our package managers. They usually make finding and installing software a cinch. On macOS, the two most popular ones are MacPorts and Homebrew (a.k.a.
brew). In this post, I’ll be usingbrew– partially. If you’re already abrewfan and have had to install R, you most like did so withbrew install r. This is probably the wrong choice for most people as this could prevent R from using pre-built binaries when installing packages. -
The Basics of R Markdown to Presentation Output
If you’re new to the world of R Studio, wrapping your head around R Markdown notebooks and documents can be a little tricky. Notebooks are meant to be interactive and are great for mixing markdown with R code blocks as you work your way through a report. Then you try to print and… not what you expected. So, what’s going on under the hood and how do you fix it?
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Putting the Brakes on Data Science
It’s almost a distant memory. When did all the Data Science hype begin? For me, it started with “Big Data.” This term was coined before we stared talking about Data Science as a discipline. The world was starting to produce massive amounts of data and the prospect of being able to do something useful with all this data was interesting. I was curious but just an observer. Where was all this going? Would it grow legs? Then we started talking about Machine Learning and Data Science. And I grew more and more curious. After all, how can you not be curious at the future that will be at the core of everything we do and affect so many aspects of our lives from AI-driven personal assistants to self-driving cars.
