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.
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.
... 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.