Clarifying question - When is manual data mask needed in `rlang::eval_tidy`?

[mara]

Since you've already read the material in Advanced R, I'm lifting some relevant sections from the Create a data mask sectiion of the rlang documentation.

@ildi.czeller

when and how are data masks created

Many R functions evaluate quoted expressions in a data mask so these expressions can refer to objects within the user data.

Obviously not a home run answer, since "many R functions" could mean a lot of things, but ¯\(°_o)/¯

Most of the time you can just call eval_tidy() with user data and the data mask will be constructed automatically.

^^ is in the preface describing that, largely, the manual construction of data masks is meant for developers of tidy eval interfaces, as opposed to users.

There are three main use cases for manual creation of data masks:

1. When eval_tidy() is called with the same data in a tight loop. Tidy eval data masks are a bit expensive to build so it is best to construct it once and reuse it the other times for optimal performance.
2. When several expressions should be evaluated in the same environment because a quoted expression might create new objects that can be referred in other quoted expressions evaluated at a later time.
3. When your data mask requires special features. For instance the data frame columns in dplyr data masks are implemented with active bindings.

@ildi.czeller

in which scenarios should I provide the data argument to rlang::eval_tidy by hand?

This thread contains some good info re. providing the .data pronoun manually (referencing Programming with dplyr | Programming recipes) :

Using rlang::.data$ with dplyr verbs to generate queries

Hi there, I am trying to migrate over to a more tidy eval of doing things. As a warm up I was trying to be more explicit and use the rlang .data pronoun as outlined here: We can fix that ambiguity by being more explicit and using the .data pronoun. This will throw an informative error if the variable doesn’t exist: mutate_y <- function(df) { mutate(df, y = .data$a + .data$x) } This was an attempt to remove all those ugly R CMD check notes like this: checking R code for possible problems ... NOTE function: no visible binding for global variable 'VARIABLE' However I've run into a problem when using a dplyr verb on a database connection. I was having trouble creating a reproducible …