My feeling is that people with a more mathematical background tend to like developping DSLs that look more like math than code, and is typically written once an

My feeling is that people with a more mathematical background tend to like developping DSLs that look more like math than code, and is typically written once and then thrown away; whereas people with a more software engineering background tend to prefer code that is more explicit about what it does, and have a better understanding about long term implications for maintenanability/extensibility. Which for me is the summary of the R versus Python debate in general. One can see that in the JVM world with java vs scala: people attracted to scala tend to like "cute" DSL, java people tend to be more careful with shiny new features. (This is an oversimplification, of course) Specifically for dplyr: it looks cute and tends to be easier to use in a REPL setting (you can build your pipeline step by step by running your command, looking at the output, get the command from history, add a step, run again; and at the end you get a single line to copy paste in your script). But if you want to wrap it in a function, it tends to create issues.