Stata seems to have been designed to make sense to social scientists and if this makes it confusing to programmers, then so be it. A simple example of this is that Stata uses the word “variable” in the sense meant by social scientists. More broadly, Stata is pretty bold about defaults so as to make things easy for beginners. It presumes that anything you’re doing applies to the dataset (aka the master data) – which is always a flat-file database. Other things that might be held in memory have a secondary status and beginning users don’t even know that they’re there. Likewise, commands distinguish between the important arguments (usually variables) and the secondary arguments, which Stata calls “options”. There’s also the very sensible assumptions about what to report and what to put in ephemeral data objects that can be accessed immediately after the primary command (but need not be stored as part of the original command, as they would in most other languages).
Note, I’m not complaining about any of this. Very few of Stata’s quirks are pointlessly arbitrary. (The only arbitrary deviation I can think of is using “*” instead of “#” for commenting). Most of Stata’s quirks are necessary in order to make it so user-friendly to social scientists. In a lot of ways R is a more conventional language than Stata, but most social scientists find Stata much easier to learn. In part because Stata is willing to deviate from the conventions of general purpose programming languages, running and interpreting a regression in Stata looks like this “reg y x” instead of this “summary(lm(y~x))” and loading a dataset looks like this “use mydata, clear” instead of this “data <- read.table(mydata.txt)”. Stata has some pretty complicated syntax (e.g., the entire Mata language) but you can get a lot done with just a handful of simple commands like “use,” “gen,” and “reg”.