[krumbie]

That's true. However, I believe that many R programmers don't know when non-standard evaluation happens or what it is exactly. Functions with or without it cannot be told apart just by looking at the syntax.

While NSE enables the dplyr syntax that many people enjoy, for me it's too magic and I have trouble reasoning about variable names in other people's code.

[coop_solution]

What does dplyr syntax look like?

[pdeffebach]

Let's say you have a data frame

    df = tibble(a = c(1, 2))

and you want to use a dplyr verb to modify it

    mutate(df, b = a + 1)

the `a` in the above expression refers to the column in `df`, but this means it's hard to reference a variable in the outer scope named `a`. Furthermore, if you have a string referring to the column name `"a"`, you can't simply write

    mutate(df, b = a_var + 1)

Contrast this with DataFramesMeta.jl, which is a dply-like library for Julia, written with macros.

    df = DataFrame(a = [1, 2])
    @transform df :b = :a .+ 1 

Because of the use of Symbols, there is no ambiguity about scopes. To work with a variable referring to column `a` you can write

    a_str = "a"
    @transform df :b = $a_str .+ 1

I won't pretend this isn't more complicated or harder to learn. Some of the complexity is due to Julia's high performance limiting non-standard evaluation in subtle ways. But a core strength of Julia's macros is that it's easy to inspect these expressions and understand exactly what's going on, with `@macroexpand` as shown in the blog post.

DataFramesMeta.jl repo: https://github.com/JuliaData/DataFramesMeta.jl