How to undo ensym and specify the environment for a function within a function?-CodePudding

So, I'm asking this as a follow-up to another question, to the solution to which I thought would fix all of my problems. It seems like that's not the case. Take the following setup

library(tidyverse)

set.seed(1) 

mytib <- tibble(a = as.character(c(1:5, NA)), 
                b = as.character(c(6:8, NA, 9:10)), 
                c = as.character(sample(x = c(0,1), size = 6, replace = TRUE)))

vars <- c("a", "b")

Taking the function Ritchie Sacramento created in the other post

convert_tib <- function(tib, var) {
  tib %>% 
    transmute(across(c(var, c), as.integer)) %>%
    filter(!is.na(.data[[var]]))
}

I would like to add another function call within that function. The idea is that I first transform and filter my variables (code above) and then I feed it into this function experiment::ATEnocov(Y = a, Z = c, data = tib) to obtain the average treatment effect for the transformed and filtered data. I'd then like to run the whole function with purrr:map across a bunch of variables.

Unfortunately, adding this function call at the end of the convert_tib function produces the error message Error in eval(call$Y, envir = data) : object 'a' not found. Quite clearly this has to do with the environments in which ATEnocov is called but I just can't figure out how to feed the variable into the function inside the same function.

CodePudding user response：

You don't really need to play around with NSE to get this to work, you can simply do:

library(dplyr)
library(purrr)
library(experiment)

convert_tib <- function(tib, var) {
  d <- tib %>%
    transmute(across(c(all_of(var), c), as.integer)) %>%
    filter(!is.na(.data[[var]]))
  ATEnocov(d[[1]], d[[2]])
}

map(set_names(vars), convert_tib, tib = mytib)

Notice that the above maps over a named vector - this is to give a named list as output because the ATEnocov() function returns its call which when executed this way is rather uninformative. This gives:

$a
$call
ATEnocov(Y = d[[1]], Z = d[[2]])

$Y
[1] 1 2 3 4 5

$Z
[1] 0 1 0 0 1

$match
NULL

$ATE.est
[1] 0.8333333

$ATE.var
[1] 3.027778

attr(,"class")
[1] "ATEnocov"

$b
$call
ATEnocov(Y = d[[1]], Z = d[[2]])

$Y
[1]  6  7  8  9 10

$Z
[1] 0 1 0 1 0

$match
NULL

$ATE.est
[1] 0

$ATE.var
[1] 2.333333

attr(,"class")
[1] "ATEnocov"

If you want it to return the call in a nicer way you can instead use:

convert_tib <- function(tib, var) {
  tib %>% 
    transmute(across(c(all_of(var), c), as.integer)) %>%
    filter(!is.na(.data[[var]])) %>%
    {
      do.call("ATEnocov", list(as.name(var), as.name("c"), data = quote(.)))
    }
}

Which returns the call as:

$a
$call
ATEnocov(Y = a, Z = c, data = .)

...

Or similarly using rlang:

convert_tib <- function(tib, var) {
  tib %>% 
    transmute(across(c(all_of(var), c), as.integer)) %>%
    filter(!is.na(.data[[var]])) %>%
    {
      rlang::inject(ATEnocov(!!sym(var), c, data = .))
    }
}