Creating a group by loop using either single or multiple variables from a list in R

In base R I'd use strsplit,

> grp_by_list <- list("phase", "phase,region", "region") > > lapply(grp_by_list, \(g) { + g <- strsplit(g, ',')[[1]] + aggregate(reformulate(g, 'total'), dummy_data, \(x) c(sum=sum(x), n=length(x))) + }) [[1]] phase total.sum total.n 1 first 164 4 2 second 116 3 3 third 100 3 [[2]] phase region total.sum total.n 1 first east 67 1 2 second east 33 1 3 first north 29 1 4 second north 63 1 5 first south 12 1 6 third south 55 2 7 first west 56 1 8 second west 20 1 9 third west 45 1 [[3]] region total.sum total.n 1 east 100 2 2 north 92 2 3 south 67 3 4 west 121 3 

or a proper list in the first place if possible:

> grp_by_list1 <- list("phase", c("phase", "region"),"region") > > lapply(grp_by_list1, \(g) { + aggregate(reformulate(g, 'total'), dummy_data, \(x) c(sum=sum(x), n=length(x))) + }) [[1]] phase total.sum total.n 1 first 164 4 2 second 116 3 3 third 100 3 [[2]] phase region total.sum total.n 1 first east 67 1 2 second east 33 1 3 first north 29 1 4 second north 63 1 5 first south 12 1 6 third south 55 2 7 first west 56 1 8 second west 20 1 9 third west 45 1 [[3]] region total.sum total.n 1 east 100 2 2 north 92 2 3 south 67 3 4 west 121 3 

Same logic should be applicable across packages including dplyr or the like.

The reformulate creates a formula, see:

> reformulate(c("phase", "region"), 'total') total ~ phase + region 

You can simply strsplit on "," and pass to syms():

lapply(grp_by_list, \(gb) { dummy_data |> group_by(!!!syms(strsplit(gb,",")[[1]])) |> summarise(total =sum(total), n=n()) }) 

Output:

[[1]] # A tibble: 3 × 3 phase total n <chr> <dbl> <int> 1 first 164 4 2 second 116 3 3 third 100 3 [[2]] # A tibble: 9 × 4 # Groups: phase [3] phase region total n <chr> <chr> <dbl> <int> 1 first east 67 1 2 first north 29 1 3 first south 12 1 4 first west 56 1 5 second east 33 1 6 second north 63 1 7 second west 20 1 8 third south 55 2 9 third west 45 1 [[3]] # A tibble: 4 × 3 region total n <chr> <dbl> <int> 1 east 100 2 2 north 92 2 3 south 67 3 4 west 121 3 

Here is a solution:

grp_by_list <- list("phase","phase,region","region") # turn list into a vector grp_by_list_unl <- c(unlist(grp_by_list)) # split the vector element by comma grp_by_list_unl_split <- c(unlist(strsplit(grp_by_list_unl,split = ","))) # loop through the split elements vector and group_by column name for (i in grp_by_list_unl_split) { if (i == "phase") { phase_tab <- dummy_data |> group_by(.data[[i]]) |> summarize(total = sum(total),n = n()) |> ungroup() } } phase_tab A tibble: 3 × 3 phase total n <chr> <dbl> <int> 1 first 164 4 2 second 116 3 3 third 100 3 # region table for (i in grp_by_list_unl_split) { if (i == "region") { region_tab <- dummy_data |> group_by(.data[[i]]) |> summarize(total = sum(total),n = n()) |> ungroup() } } region_tab A tibble: 4 × 3 region total n <chr> <dbl> <int> 1 east 100 2 2 north 92 2 3 south 67 3 4 west 121 3 

This way, you have two tables grouped by the column names in the list.

A minimal code fix in {dplyr} would use the .by argument in summarize directly with the strsplit groups. I also retained your code, that names each list based on the applied grouping.

library(dplyr) table <- tibble(phase=c("first","second","third","second","third","first","third","second","first","first"), region=c("south","east","west","north","south","east","south","west","north","west"), total=c(12,33,45,63,45,67,10,20,29,56)) grp_by_list <- list("phase","phase,region","region") grp_tabs <- list() for(i in 1:length(grp_by_list)) { grp_tabs[[paste(grp_by_list[[i]])]] <- table |> summarise(total = sum(total), n = n(), .by = strsplit(grp_by_list[[i]],",")[[1]]) } > grp_tabs $phase # A tibble: 3 × 3 phase total n <chr> <dbl> <int> 1 first 164 4 2 second 116 3 3 third 100 3 $`phase,region` # A tibble: 9 × 4 phase region total n <chr> <chr> <dbl> <int> 1 first south 12 1 2 second east 33 1 3 third west 45 1 4 second north 63 1 5 third south 55 2 6 first east 67 1 7 second west 20 1 8 first north 29 1 9 first west 56 1 $region # A tibble: 4 × 3 region total n <chr> <dbl> <int> 1 south 67 3 2 east 100 2 3 west 121 3 4 north 92 2 

Two more options if you want to keep the order introduced by grouping

for(i in 1:length(grp_by_list)) { grp_tabs[[paste(grp_by_list[[i]])]] <- table |> group_by(across(all_of(strsplit(grp_by_list[[i]],",")[[1]]))) |> summarise(total = sum(total), n = n()) } # or with lapply lapply(grp_by_list, \(x) {table |> group_by(across(all_of(strsplit(x,",")[[1]]))) |> summarise(total = sum(total), n = n())})