Replace multiple `summarize`statements by function - r

Question

I'm currently repeating a lot code, since I need to summarize always the same columns for different groups. How can I do this effectively by writing the summarize function (which is always the same) only once, but define the output name and group_by arguments case by case?
A minimum example:
col1 <- c("UK", "US", "UK", "US")
col2 <- c("Tech", "Social", "Social", "Tech")
col3 <- c("0-5years", "6-10years", "0-5years", "0-5years")
col4 <- 1:4
col5 <- 5:8
df <- data.frame(col1, col2, col3, col4, col5)
result1 <- df %>%
group_by(col1, col2) %>%
summarize(sum1 = sum(col4, col5))
result2 <- df %>%
group_by(col2, col3) %>%
summarize(sum1 = sum(col4, col5))
result3 <- df %>%
group_by(col1, col3) %>%
summarize(sum1 = sum(col4, col5))

Answer 1

Using combn:
combn(colnames(df)[1:3], 2, FUN = function(x){
df %>%
group_by(.dots = x) %>%
summarize(sum1 = sum(col4, col5))
}, simplify = FALSE)

Answer 2

To use dplyr in own functions, you can use tidy evaluation. The reason for this is the way dplyr evaluates dplyr code, something called non standard evaluation, which wraps everything what does not behave like normal R Code. I recommend to read this:
https://tidyeval.tidyverse.org/modifying-inputs.html#modifying-quoted-expressions
summarizefunction <- function(data, ..., sumvar1, sumvar2) {
groups <- enquos(...)
sumvar1 <- enquo(sumvar1)
sumvar2 <- enquo(sumvar2)
result <- data %>%
group_by(!!!groups) %>%
summarise(sum1 = sum(!!sumvar1, !!sumvar2))
return(result)
}
summarizefunction(df, col1, col2, sumvar1 = col4, sumvar2 = col5)
You can use the enquo keyword to wrap quote parameters which prevents them from being evaluated immediately. This you can use the !! (called bang bang) operator to unquote the parameter. I think this is the most flexible and reuseable solution, even when you have to write some more initial code.

Answer 3

You can also use purrr::partial in these situations :
library(purrr)
summarize45 <- partial(summarize, sum1 = sum(col4, col5))
result1b <- df %>%
group_by(col1, col2) %>%
summarize45()
identical(result1, result1b)
# [1] TRUE
Or pushing it further :
gb_df <- partial(group_by, df)
result1c <- gb_df(col1, col2) %>% summarize45()
identical(result1, result1c)
# [1] TRUE

Answer 4

Firstly you'll need to evaluate the variables with a function as such:
library(tidyverse)
res_func <- function(x, y){
df %>%
group_by(!!as.symbol(x), !!as.symbol(y)) %>%
summarize(sum1 = sum(col4, col5))
}
works a charm:
res_func("col1", "col2")
# A tibble: 4 x 3
# Groups: col1 [2]
col1 col2 sum1
<fct> <fct> <int>
1 UK Social 10
2 UK Tech 6
3 US Social 8
4 US Tech 12
We can use assign to create a function that names your frame against the parameters you've passed in through the function:
res_func2 <- function(x, y){
assign(paste0("result_", x, y),
df %>%
group_by(!!as.symbol(x), !!as.symbol(y)) %>%
summarize(sum1 = sum(col4, col5)),
envir = parent.frame())
}
This creates a new df called result_col1col2 by simply running res_func2("col1", "col2")
> result_col1col2
# A tibble: 4 x 3
# Groups: col1 [2]
col1 col2 sum1
<fct> <fct> <int>
1 UK Social 10
2 UK Tech 6
3 US Social 8
4 US Tech 12