It’s no secret that I love R and begrudgingly use Python. But there’s a another option for data science, and it promises the speed of C with the ease of use of R/Python. That language is Julia, and it’s a delight to use. I took some time to learn the basics, and I’m sharing my impressions here.

drake is a package for orchestrating R workflows. Suppose I have some data in S3 that I want to pull into R through a drake plan. In this post I’ll use the S3 object’s ETag to make drake only re-download the data if it’s changed.

After I posted my efforts to use MLflow to serve a model with R, I was worried that people may think I don’t like MLflow. I want to declare this: MLflow is awesome. I’ll showcase its model tracking features, and how to integrate them into a tidymodels model.

There’s always a need for more tidymodels examples on the Internet. Here’s a simple machine learning model using the recent coffee Tidy Tuesday data set. The plot above gives the approach: I’ll define some preprocessing and a model, optimise some hyperparameters, and fit and evaluate the result. And I’ll piece all of the components together using targets, an experimental alternative to the drake package that I love so much.

I’m obsessed with how to structure a data science project. The time I spend worrying about project structure would be better spent on actually writing code. Here’s my preferred R workflow, and a few notes on Python as well.