Why Gordon Shotwell uses R

This blog by Gordon Shotwell has passed my Twitter feed a couple of times now and I thought I’d share it here: blog.shotwell.ca/posts/why_i_use_r It in, Gordon present his reasons for using R, describing R’s four unique selling point, and outlining a discussion full of perfectly quotable thoughts and opinions. Do have a look at the…

Need to save R's lm() or glm() models? Trim the fat!

I was training a predictive model for work for use in a Shiny App. However, as the training set was quite large (700k+ obs.), the model object to save was also quite large in size (500mb). This slows down your operation significantly! Basically, all you really need are the coefficients (and a link function, in…

Learning Functional Programming & purrr

The R for Data Science (R4DS) book by Hadley Wickham is a definite must-read for every R programmer. Amongst others, the power of functional programming is explained in it very well in the chapter on Iteration. I wrote about functional programming before, but I recently re-read the R4DS book section after coming across some new valuable…

7 tips for writing cleaner JavaScript code, translated to 3.5 tips for R programming

I recently came across this lovely article where Ali Spittel provides 7 tips for writing cleaner JavaScript code. Enthusiastic about her guidelines, I wanted to translate them to the R programming environment. However, since R is not an object-oriented programming language, not all tips were equally relevant in my opinion. Here’s what really stood out for me. 1….

Generating Book Covers By Their Words — My Dissertation Cover

As some of you might know, I am defending my PhD dissertation later this year. It’s titled “Data-Driven Human Resource Management: The rise of people analytics and its application to expatriate management” and, over the past few months, I was tasked with designing its cover. Now, I didn’t want to buy some random stock photo…

Functional programming and why not to “grow” vectors in R

For fresh R programmers, vectorization can sound awfully complicated. Consider two math problems, one vectorized, and one not: Why on earth should R spend more time calculating one over the other? In both cases there are the same three addition operations to perform, so why the difference? This is what we will try to illustrate…