R中{{}}是什么意思？

var_summary <- function(data, var) {
  data %>%
    summarise(n = n(), min = min({{ var }}), max = max({{ var }}))
}
mtcars %>% 
  group_by(cyl) %>% 
  var_summary(mpg)

When you have the data-variable in a function argument (i.e. an env-variable that holds a promise²), you need to embrace the argument by surrounding it in doubled braces, like filter(df, {{ var }}).

for (var in names(mtcars)) {
  mtcars %>% count(.data[[var]]) %>% print()
}

Note that .data is not a data frame; it’s a special construct, a pronoun, that allows you to access the current variables either directly, with .data$x or indirectly with .data[[var]]. Don’t expect other functions to work with it.

If you want to use the names of variables in the output, you can use glue syntax in conjunction with :=:

my_summarise4 <- function(data, expr) {
  data %>% summarise(
    "mean_{{expr}}" := mean({{ expr }}),
    "sum_{{expr}}" := sum({{ expr }}),
    "n_{{expr}}" := n()
  )
}
my_summarise5 <- function(data, mean_var, sd_var) {
  data %>% 
    summarise(
      "mean_{{mean_var}}" := mean({{ mean_var }}), 
      "sd_{{sd_var}}" := sd({{ sd_var }})
    )
}

If you want to take an arbitrary number of user supplied expressions, use .... This is most often useful when you want to give the user full control over a single part of the pipeline, like a [group_by()](https://dplyr.tidyverse.org/reference/group_by.html) or a [mutate()](https://dplyr.tidyverse.org/reference/mutate.html).

my_summarise <- function(.data, ...) {
  .data %>%
    group_by(...) %>%
    summarise(mass = mean(mass, na.rm = TRUE), height = mean(height, na.rm = TRUE))
}

starwars %>% my_summarise(homeworld)
#> # A tibble: 49 x 3
#>   homeworld    mass height
#>   <chr>       <dbl>  <dbl>
#> 1 Alderaan       64   176.
#> 2 Aleen Minor    15    79 
#> 3 Bespin         79   175 
#> 4 Bestine IV    110   180 
#> # … with 45 more rows
starwars %>% my_summarise(sex, gender)
#> `summarise()` has grouped output by 'sex'. You can override using the `.groups` argument.
#> # A tibble: 6 x 4
#> # Groups:   sex [5]
#>   sex            gender      mass height
#>   <chr>          <chr>      <dbl>  <dbl>
#> 1 female         feminine    54.7   169.
#> 2 hermaphroditic masculine 1358     175 
#> 3 male           masculine   81.0   179.
#> 4 none           feminine   NaN      96 
#> # … with 2 more rows

https://dplyr.tidyverse.org/articles/programming.html
https://bookdown.org/wangminjie/R4DS/tidyverse-beauty-of-across1.html