Get a summary for all combinations of grouping like proc summary in SAS

Question

(I do understand that my question is equivalent to this one : R function equivalent to proc summary in SAS But being a new user, I can't comment on the solutions to ask details or explanations and I can't get any of them to work.)

I'm trying to convert a script from SAS to R. The objective is to get a wide summary of a database across multiple variables.

The starting base is like this :

Student ID	Flag1	Flag2	Flag3	other flags...	weight	score
code1	level1	A	first	smth~~	2	12
code23	level5	C	third	smth~else~	3	9

And in the end I want something like this :

Flag1	Flag2	Flag3	other flags...	nb of students	weighted mean	std dev	min	1st quartile	...	max	nb of students in fist decile	...	nb of students in last decile
level1	A	first	smth~~	5	10.96	1.5	1	...	...	...	...	...	...
level5	.All	third	smth~else~	1500	8.70	2.7	3	...	...	...	...	...	...

In SAS it was really easy because proc summary does the summary for each combination of grouping possible, but in R, you only get the lowest level of grouping. With 9 different levels of grouping that's 512 combinations and I think there should be a way to loop some of the work.

Here's how I think I should proceed :

1- List all the different combinations in a dataframe :

Flag1	Flag2	Flag3
.All	.All	.All
.All	.All	first
.All	.All	second
.All	A	.All
.All	B	.All
LV1	.All	.All
LV2	.All	.All
.All	A	first
.All	A	second
.All	B	first
.All	B	second
LV1	.All	first
LV1	.All	second
LV2	.All	first
LV2	.All	second
LV1	A	.All
LV1	B	.All
LV2	A	.All
LV2	B	.All
LV1	A	first
LV1	A	second
LV1	B	first
LV1	B	second
LV2	A	first
LV2	A	second
LV2	B	first
LV2	B	second

2- Make a 2^n length loop that will call the following function :

3- The function would take a line from the last dataframe and then output a dataframe that would contain the summary grouping by some variables + columns with .All for the variables not used for grouping

4- stack each iteration of the loop on each other using bind_rows

Answer 1

I encountered multiple hurdles solving this problem but I ended with a satisfying solution :

#import the data
testbase <- read_excel("testbase.xlsx")
#list all the grouping variables
variables = c(quo(Flag1), quo(Flag2),quo(Flag3))
#create the powerset of the list of variables
listevars=powerSet(variables,length(variables),rev=FALSE)

for (i in 1:length(listevars)){
  testbase=ungroup(testbase)
  if (length(listevars[[i]])!=0){
    testbase=group_by(testbase,!!!listevars[[i]])
  }
  resumepartiel=summarize(testbase,weighted.mean(score,weight))
  varexcl=variables[!(variables %in% listevars[[i]])]
  if (length(varexcl)!=0){
    for(j in 1:length(varexcl)){
      colonne=data.frame(c(rep(".All",times = nrow(resumepartiel))))
      colonne=setNames(colonne,as_name(varexcl[[j]]))
      resumepartiel=bind_cols(colonne,resumepartiel)
    }
  }
  if(i==1){
    resume=resumepartiel
  }
  else{
    resume=bind_rows(resume,resumepartiel)
  }
}

this code will output what I want for three variables and only the weighted mean but adding more variables or more summary functions is trivial.