Argument type: data-masking — tidyr_data

This page describes the <data-masking> argument modifier which indicates that the argument uses data masking, a sub-type of tidy evaluation. If you've never heard of tidy evaluation before, start with the practical introduction in https://r4ds.hadley.nz/functions.html#data-frame-functions then then read more about the underlying theory in https://rlang.r-lib.org/reference/topic-data-mask.html.

Key techniques

To allow the user to supply the column name in a function argument, embrace the argument, e.g. filter(df, {{ var }}).

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

To work with a column name recorded as a string, use the .data pronoun, e.g. summarise(df, mean = mean(.data[[var]])).

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

lapply(names(mtcars), function(var) mtcars %>% count(.data[[var]]))

To suppress R CMD check NOTEs about unknown variables use .data$var instead of var:
```
# has NOTE
df %>% mutate(z = x + y)

# no NOTE
df %>% mutate(z = .data$x + .data$y)
```
You'll also need to import .data from rlang with (e.g.) @importFrom rlang .data.

Dot-dot-dot (...)

... automatically provides indirection, so you can use it as is (i.e. without embracing) inside a function:

grouped_mean <- function(df, var, ...) {
  df %>%
    group_by(...) %>%
    summarise(mean = mean({{ var }}))
}

You can also use := instead of = to enable a glue-like syntax for creating variables from user supplied data:

var_name <- "l100km"
mtcars %>% mutate("{var_name}" := 235 / mpg)

summarise_mean <- function(df, var) {
  df %>%
    summarise("mean_of_{{var}}" := mean({{ var }}))
}
mtcars %>% group_by(cyl) %>% summarise_mean(mpg)

Learn more in https://rlang.r-lib.org/reference/topic-data-mask-programming.html.