Programming with dplyr using string as input

dplyr >= 1.0

Use combination of double braces and the across function:

my_summarise2 <- function(df, group_var) {
  df %>% group_by(across({{ group_var }})) %>% 
    summarise(mpg = mean(mpg))
}

my_summarise2(mtcars, "cyl")

# A tibble: 3 x 2
#    cyl   mpg
#  <dbl> <dbl>
# 1     4  26.7
# 2     6  19.7
# 3     8  15.1

# same result as above, passing cyl without quotes
my_summarise(mtcars, cyl)

dplyr < 1.0

As far as I know, you could use as.name or sym (from the rlang package - I don't know if dplyr will import it eventually):

library(dplyr)
my_summarise <- function(df, var) {
  var <- rlang::sym(var)
  df %>%
    group_by(!!var) %>%
    summarise(mpg = mean(mpg))
}

or

my_summarise <- function(df, var) {
  var <- as.name(var)
  df %>%
    group_by(!!var) %>%
    summarise(mpg = mean(mpg))
}

my_summarise(mtcars, "cyl")
# # A tibble: 3 × 2
#     cyl      mpg
#   <dbl>    <dbl>
# 1     4 26.66364
# 2     6 19.74286
# 3     8 15.10000

Using the .data pronoun from rlang is another option that works directly with column names stored as strings.

The function with .data would look like

my_summarise <- function(df, var) {
     df %>%
          group_by(.data[[var]]) %>%
          summarise(mpg = mean(mpg))
}

my_summarise(mtcars, "cyl")
# A tibble: 3 x 2
    cyl   mpg
  <dbl> <dbl>
1     4  26.7
2     6  19.7
3     8  15.1