How to write a function with tidy eval when using the "arrow" R package (arrow::open_dataset) and dplyr verbs?

Question

What I'm trying to do

I'm attempting to write a function that uses dplyr verbs and that takes an "arrow open dataset" as the first argument, and a column in that dataset as the second argument. Since I would like to pass the column as a string (necessary for the context of my actual task I'm working on, i.e. Shiny), I'm using the syntax .data[[.column]]. Below is an image of the error I'm getting and some code to reproduce said error. Any help or insight is appreciated.

Image of error message

Code to reproduce error

# install.packages(c("dplyr", "ggplot2", "arrow"))
library(dplyr)

arrow::write_parquet(x = ggplot2::mpg, sink = "sample_data.parquet")

dat <- arrow::open_dataset("sample_data.parquet")

glimpse(dat)

get_metric <- function(.data, .metric) {
  
  .data %>%
    group_by(manufacturer, cyl) %>% 
    summarize(
      new_col = sum(.data[[.metric]], na.rm = T)
    ) %>% 
    ungroup() 
}

get_metric(dat, "cty") %>% collect()

Additional code that works but doesn't use arrow as much so not ideal for speed

In this code I collect before the tidy eval stuff so its just essentially regular dplyr code. It runs, but is a slower than code that I've successfully gotten to run before extracting stuff into said function.

get_metric2 <- function(.data, .metric) {
  
  .data %>%
    collect() %>% 
    group_by(manufacturer, cyl) %>% 
    summarize(
      new_col = sum(.data[[.metric]], na.rm = T)
    ) %>% 
    ungroup() 
}

get_metric2(dat, "cty")

Answer 1

Use the !! nomenclature.

arrow::write_parquet(x = ggplot2::mpg, sink = "sample_data.parquet")
dat <- arrow::open_dataset("sample_data.parquet")

get_metric <- function(.data, .metric) {
  .metric <- rlang::sym(.metric)
  .data %>%
    group_by(manufacturer, cyl) %>% 
    summarize(
      new_col = sum(!!.metric, na.rm = T)
    ) %>% 
    ungroup() 
}

get_metric(dat, "cty") %>%
  collect()
# # A tibble: 32 × 3
#    manufacturer   cyl new_col
#    <chr>        <int>   <int>
#  1 audi             4     153
#  2 audi             6     148
#  3 audi             8      16
#  4 chevrolet        8     191
#  5 chevrolet        4      41
#  6 chevrolet        6      53
#  7 dodge            4      18
#  8 dodge            6     225
#  9 dodge            8     243
# 10 ford             8     197
# # ℹ 22 more rows
# # ℹ Use `print(n = ...)` to see more rows