using argument function with stringr::str_extract [duplicate] - r

Question

I would like to write a function that uses dplyr inside and I supply variable names as strings. Unfortunately dplyr-s use of NSE makes it rather complicated. From Programming with dplyr I get the following example
my_summarise <- function(df, var) {
var <- enquo(var)
df %>%
group_by(!!var) %>%
summarise(a = mean(a))
}
my_summarise(df, g1)
However, I would like to write function where instead of g1 I could provide "g1" and I am not able to wrap my head around how to do that.

Answer 1

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

Answer 2

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

Answer 3

This is how to do it using only dplyr and the very useful as.name function from base R:
my_summarise <- function(df, var) {
varName <- as.name(var)
enquo_varName <- enquo(varName)
df %>%
group_by(!!enquo_varName) %>%
summarise(a = mean(a))
}
my_summarise(df, "g1")
Basically, with as.name() we generate a name object that matches var (here var is a string). Then, following Programming with dplyr, we use enquo() to look at that name and return the associated value as a quosure. This quosure can then be unquoted inside the group_by() call using !!.