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