pass a varying list of functions to dplyr summarize - r

Question

Is it possible to pass a list of functions to dplyr::summarize in a way to allow the list of functions to vary? I'd like to create an overall function to create a summary table but allow different for different groups of functions in the output - [edit: when the functions are not all being applied to the same column].
I was thinking this could be done by creating an overall function with which group of summary functions to be included with T/F arguments (where funA=T/F, funB=T/F are lists of functions and the user could include all functions from funA, funB or both), but am not how to write the initial list functions (funA, funB)- when the functions are not all being applied to the same column. Below is an idea of how it would be structured. Is this possible, or is there a better way to do this?
#Essentially - how would I write a function to selectively include a group of functions (for example either funA = c(n, min, max) or funB=c(n_na, n_neg), or both).
extract_all <- function(x){
x %>% summarize(n=n(),
min = min(disp, na.rm=TRUE),
max = max(disp, na.rm=TRUE),
n_na = sum(is.na(wt)),
n_neg = sum(vs < 0, na.rm=TRUE))
}
test <- mtcars %>% group_by(cyl) %>% extract_all()
#Does this structure work?
extract_summaries <- function(x, funA=TRUE, funB=FALSE){
funAls <- list() #but how do you write n, min, max in here?
funBls <- list() #and n_na, n_neg in here
funls <- append(funAls[funA], funBls[funB])
summarize(x, funls)
}
#which could be run with:
test <- mtcars %>% group_by(cyl) %>% extract_summaries(funA=TRUE, funB=TRUE)
}

Answer 1

Here is one option
extract_summaries <- function(x, colnm, funA=TRUE, funB=FALSE){
funAls <- list(n = length, min= min, max = max)
funBls <- list(n_na = function(y) sum(is.na(y)),
n_neg = function(y) sum(y < 0, na.rm=TRUE))
funls <- append(funAls[funA], funBls[funB])
x %>%
summarise_at(vars({{colnm}}), funls)
}
test <- mtcars %>%
group_by(cyl) %>%
extract_summaries(mpg, funA=TRUE, funB=TRUE)
test
# A tibble: 3 x 6
# cyl n min max n_na n_neg
# <dbl> <int> <dbl> <dbl> <int> <int>
#1 4 11 21.4 33.9 0 0
#2 6 7 17.8 21.4 0 0
#3 8 14 10.4 19.2 0 0
test <- mtcars %>%
group_by(cyl) %>%
extract_summaries(mpg, funA = FALSE, funB = TRUE)
test
# A tibble: 3 x 3
# cyl n_na n_neg
# <dbl> <int> <int>
#1 4 0 0
#2 6 0 0
#3 8 0 0