After attending months of Twitter discussion about what could be the best software–R or Python–for data science several months ago, this is now the time of the R vs. Stata debate, here and there. Arguably, Stata is a paid software and does not offer the same scripting facilities than R for some tasks, mainly non-statistical tasks. However, what’s the point? Did anyone ever mentioned the fact that Stata has a GUI which completely mimics the command-line operations, so that people afraid of typing commands or just interested in running a logistic regression on a well-formed dataset can just do it in under a minute? It is slow with some estimators or optimization approaches (e.g., gglamm), and we had to wait a bit long to get full support for unicode and XLS, better graphical rendering, etc. But the versioning system allows to repoduce any result prior to the current version of Stata. And it does interact very well with Stan and R, too. The question is not which software is better, the real question is who’s the end user? #rstats #stata

Fun fact: I saved a database from Stata 15 in old format (i.e., compatible with Stata 13). I cannot view unicode characters in Stata GUI, but it works perfectly fine when run through Emacs/ESS! #stata

Back to a fully functional Spacemacs, after a complete reinstall. Some minor annoyances with MELPA actually, but nothing serious; fixed a weird bug with the ocaml layer, since I learned that the syntax-version layer should come before ocaml, but otherwise everything is fine. Also, I’m trying to go all Helm instead of Ivy. #emacs

Death Cab for Cutie, Thank You for Today. Just streaming the Apple alt’ radio:

I am not very lucky with Spacemacs these days. Now, SPC-/ to search project for text (aka, spacemacs/search-project-auto) is no longer working. Not funny, trust me. #emacs

  PJ Harvey, Stories from the City, Stories from the Sea.

A recent tweet reminded me of gtools, a Stata package that aims to speed up built-in command for data wrangling. I should give it a go. #stata

Hacker Tools: A user-friendly introduction to various command line utilities, editors and VCS. (via @newsycombinator)

Apache Arrow and Feather are two interesting projects that I think should be available in data science-related PLs. Recently, Rust joined the list, at least regarding Arrow: DataFusion: A Rust-native Query Engine for Apache Arrow. #rust

📖 Zoé Valdés, Une habanera à Paris (Gallimard, 2005)