Adding column names to vars() inside a dplyr function - r

Question

I have a function that can be used for summarizing a variable based on some user-defined groups, making use of dplyr:
library(tidyverse)
get_var_summary <- function(.data, .target_var, .group_vars = vars()) {
.target_var = enquo(.target_var)
return(
.data %>%
filter(!is.na(!! .target_var)) %>%
group_by_at(.vars = .group_vars) %>%
summarize(
mean = mean(!! .target_var),
sd = sd(!! .target_var),
ci = qnorm(0.975) * sd(!! .target_var) / sqrt(n()),
median = median(!! .target_var),
n = n()
) %>%
mutate(
sd = ifelse(is.na(sd), Inf, sd),
ci = ifelse(is.na(ci), Inf, ci)
) %>%
ungroup()
)
}
mtcars %>%
get_var_summary(wt, .group_vars = vars(cyl))
Returns:
# A tibble: 3 x 6
cyl mean sd ci median n
<dbl> <dbl> <dbl> <dbl> <dbl> <int>
1 4. 2.29 0.570 0.337 2.20 11
2 6. 3.12 0.356 0.264 3.22 7
3 8. 4.00 0.759 0.398 3.76 14
Now, in order to be able to easily repeat the .group_vars, but occasionally supply another grouping var in addition, I would like to define another function that calls get_var_summary, but with one additional column added to .group_vars:
get_var_summary_by_another <- function(.data, .extra_var, .target_var, .group_vars = vars()) {
# how do I add .extra_var to .group_vars?
}
How can I do that?

Answer 1

The idea is to first splice the .group_vars with !!!, and add the .extra_var to a new vars() call:
get_var_summary_by_another <- function(.data, .extra_var, .target_var, .group_vars = vars()) {
.extra_var = enquo(.extra_var)
.target_var = enquo(.target_var)
.group_vars = vars(!!! .group_vars, !! .extra_var)
return(
.data %>% get_var_summary(
!! .target_var,
.group_vars
)
)
}
mtcars %>%
get_var_summary_by_another(gear, .target_var = wt, .group_vars = vars(cyl))
Returns:
# A tibble: 8 x 7
cyl gear mean sd ci median n
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int>
1 4. 3. 2.46 Inf Inf 2.46 1
2 4. 4. 2.38 0.601 0.416 2.26 8
3 4. 5. 1.83 0.443 0.614 1.83 2
4 6. 3. 3.34 0.173 0.240 3.34 2
5 6. 4. 3.09 0.413 0.405 3.16 4
6 6. 5. 2.77 Inf Inf 2.77 1
7 8. 3. 4.10 0.768 0.435 3.81 12
8 8. 5. 3.37 0.283 0.392 3.37 2

Answer 2

You only need to create one function to accomplish your goal of using an arbitrary number of grouping variables to summarize on. You can rewrite the original function using a combination of dplyr::group_by(), dplyr::across(), and curly curly embracing {{. This works with dplyr version 1.0.0 and greater.
I've edited the original example and code for clarity.
library(tidyverse)
var_summary <- function(.data, target, group = NULL) {
.data %>%
filter(!is.na({{ target }})) %>%
group_by(across({{ group }})) %>%
summarize(
"mean_{{target}}" := mean({{ target }}),
sd := sd({{ target }}),
ci := qnorm(0.975) * sd({{ target }}) / sqrt(n()),
"median_{{target}}" := median({{ target }}),
"n_{{target}}" := n()
) %>%
mutate(
sd := if_else(is.na(sd), Inf, sd),
ci := if_else(is.na(ci), Inf, ci)
) %>%
rename("sd_{{target}}" := sd, "ci_{{target}}" := ci)
}
var_summary(mtcars, target = wt)
#> # A tibble: 1 x 5
#> mean_wt sd_wt ci_wt median_wt n_wt
#> <dbl> <dbl> <dbl> <dbl> <int>
#> 1 3.22 0.978 0.339 3.32 32
var_summary(mtcars, target = wt, group = cyl)
#> # A tibble: 3 x 6
#> cyl mean_wt sd_wt ci_wt median_wt n_wt
#> <dbl> <dbl> <dbl> <dbl> <dbl> <int>
#> 1 4 2.29 0.570 0.337 2.2 11
#> 2 6 3.12 0.356 0.264 3.22 7
#> 3 8 4.00 0.759 0.398 3.76 14
var_summary(mtcars, target = wt, group = c(cyl, gear))
#> `summarise()` has grouped output by 'cyl'. You can override using the `.groups` argument.
#> # A tibble: 8 x 7
#> # Groups: cyl [3]
#> cyl gear mean_wt sd_wt ci_wt median_wt n_wt
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int>
#> 1 4 3 2.46 Inf Inf 2.46 1
#> 2 4 4 2.38 0.601 0.416 2.26 8
#> 3 4 5 1.83 0.443 0.614 1.83 2
#> 4 6 3 3.34 0.173 0.240 3.34 2
#> 5 6 4 3.09 0.413 0.405 3.16 4
#> 6 6 5 2.77 Inf Inf 2.77 1
#> 7 8 3 4.10 0.768 0.435 3.81 12
#> 8 8 5 3.37 0.283 0.392 3.37 2
Created on 2021-09-06 by the reprex package (v2.0.0)