Jonathan Carroll has my attention:
I’ve been learning Haskell for a few years now and I am really liking a lot of the features, not least the strong typing and functional approach. I thought it was lacking some of the things I missed from R until I found the dataHaskell (www.datahaskell.org) project.
There have been several attempts recently to enhance R with some strong types, e.g. vapour (vapour.run), typr (github.com), using {rlang}’s checks (josiahparry.com), and even discussions about implementations at the core level e.g. in September 2025 (stat.ethz.ch) continued in November 2025 (stat.ethz.ch). While these try to bend R towards types, perhaps an all-in solution makes more sense.
In this post I’ll demonstrate some of the features and explain why I think it makes for a good (great?) data science language.
I’ve been a big fan of F# for data science work as well for similar reasons, so it was interesting to read this article on Haskell. H/T R-Bloggers.