I mean I get yout point. Julia has a bit of a Lisp's Curse http://winestockwebdesign.com/Essays/Lisp_Curse.html Writing a performant and easy to use data wrangling library for R is a bunch of work and means dealing with C/C++ etc. So few people are willing to do so, and just contribute to a small number of libraries like dplyr. (I feel like there are at least 2 other major compeditors to that in R?) Where as in julia it's really easy to write a new data wrangling library. Its just not that much work. So people: A) do it for just fun / student projects (None of those ones are though). B) do it because they have a nontrivially resolvable opinion (e.g. Queryverse has a marginally more performant but marginally harder to use system for missing data)

Nice thing about julia, especially for tabular data (thanks to Tables.jl), is everything works together. It's actually completely possible to mix and match all of those libraries in a single data processing pipeline. Which while is generally a weird thing to do, it does mean if you have a external package uses any of them it works into a pipeline of another. (One common case is that queryverse has CSVFiles.jl, but CSV.jl actually is generally faster, and you can just swap one for ther other, inside a Query.jl pipeline)

I absolutely argee this makes learning harder.

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Also that particular example:

> "I need pipes to help me wrangle data more efficiently do I use Base Julia, Chain.jl, Pipe.jl, or Lazy.jl?"

It's piping. Something would have to massively be screwed up if any of those options were more or less efficient than the others. The only question is what semantics do you want. Each is pretty opinionated about how piping should look.