interesting question: does it still make sense to have different or specialized languages for scientific computing vs generalized software engineering?
It makes a lot of sense in fields where there are benefits to using code (instead of GUIs) to generate statistics and figures, as long as learning the basic syntax of the language to write this code can be done in a week or two.
Learning just enough R (or Julia or Python) for the purposes of making replicable, easily updated, easily styled, easily adjusted tables and figures for research is something that a freshman in social sciences and humanities can handle. And for many academics that level of knowledge is enough for grad school and eventually even tenure.
That level of knowledge will not necessarily include even the most rudimentary software engineering skills: the code will (most likely) live in a single long file. That file will contain both working chunks of code, old/broken/redundant chunks of code as well as comments and notes both related and unrelated to the task at hand. It will never be executable in a linear fashion (only chunks at a time in a REPL). And some data importing or package/library management will still have to be done through a GUI.
you make an important point -- there's the choice and mechanics of a computer language (which is where i was thinking), and then the overall hci of the data analysis activity. should it look like software development or use of an (incredibly powerful) interactive calculator with dsls that are carefully designed to match the lingua franca of the fields they serve. (or, assuming possible, should the lingua franca move to a dsl and hci paradigm that is easier for humans and machines to reason about with minimized ambiguity?)
