Machine learning models get stuck at the deployment stage all the time. This stuff is hard.

Drake is my new favourite R package.

I’m creating an R API wrapper around my state’s public transport service. To make life easier for the users, the responses from the API calls are parsed and returned as tibbles/data frames. To make life easier for me, I need to keep track of the API call behind each tibble. I do this by using the tibble::new_tibble() function to attach metadata to the tibble as attributes, and creating a custom print method to make the metadata visible.

The last few weeks have been all about R package development for me. First I was exploring GitHub actions with the lovely people at the rOpenSci OzUnconf, and then I was off to San Francisco to learn about Building Tidy Tools with the Wickham siblings. I’ve picked up a lot about package development, so I’m documenting some of trickier things that I’ve learnt.

There’s a concept in R of an analysis as a package, in which everything you need for your data analysis is contained within a custom package. When you install the package and build the vignettes, the data analysis is performed and results saved as a pretty HTML or PDF file, generated with R Markdown. I wanted to extend this concept to a machine learning model as a package.