[babahoyo]

Check out DataFramesMeta, which unfortunately isn't working on 1.0 yet. They have basically a 1-1 matching of `dplyr` verbs to julia versions.

I don't think a standardized and idiomatic data-cleaning process has been established yet, which is for the best right now. There is `JuliaDBMeta` for metaprogramming with JuliaDB tables, and the `Queryverse` for working with a wide array of objects.

One way that Julia's metaprogramming shines is with the ability to go into the AST and replace symbols, enabling local scopes that are more readable than other scopes. One workflow I'm excited to experiment with is something like this

    @as my_long_dataset d begin # make d = my_long_dataset in this scope
    @with d begin 
    t = :x1 + :x2 + x3 # these symbols are arrays inside this @with scope
    d.new_var = t # assign the variable
    end
    end

Of course, with the `@as` macro you probably don't save that many keystrokes if you are just doing `d.x` or `d[:x1, :x2]`... The ecosystem is still evolving but the point is that I like how you can replicate something like `attach` scoping in R without all the headaches. I think it makes a cleaning script feel more like you are only working with the data you care about.