Summarize using different grouping variables in dplyr - r

Question

I would like summarize a dataframe using different grouping variables for each summary I wish to be carried out. As an example I have three variables (x1, x2, x3). I want to group the dataframe by x1 and get the number of observations in that group, but I want to do the same for x2 and x3.
I would like to accomplish this with the same block of piping but so far the only solution I have come up with is to save multiple outputs for each individual grouping I would like.
To reproduce my dataframe:
x1 <- c(0,1,1,2,2,3,3,3,4,4,5,6,6,7,8,9,9,10)
x2 <- c(0,0,1,1,0,1,2,0,0,2,1,0,3,4,2,3,0,3)
x3 <- c(0,1,0,1,2,2,1,3,4,2,4,6,3,3,6,6,9,7)
df <- data.frame(x1,x2,x3)
My expected output would look something like this, where x is the min and max number across the variables and n_x1-3 are the number of observations at a specific number and using that variable as a grouping variable:
x n_x1 n_x2 n_x3
1 0 1 7 2
2 1 2 4 3
3 2 2 3 3
4 3 3 3 3
5 4 2 1 2
6 5 1 NA NA
7 6 2 NA 3
8 7 1 NA 1
9 8 1 NA NA
10 9 2 NA 1
11 10 1 NA NA
So far I have come up with summarizing and grouping by each variable individually and then joining them all together as a last step.
x1_count <- df %>%
group_by(x1) %>%
summarise(n_x1=n())
x2_count <- df %>%
group_by(x2) %>%
summarise(n_x2=n())
x3_count <- df %>%
group_by(x3) %>%
summarise(n_x3=n())
all_count <- full_join(x1_count, x2_count,
by=c("x1"="x2")) %>%
full_join(., x3_count,
by=c("x1"="x3")) %>%
rename("x"="x1")
Is there some type of work around where I wouldn't have to output multiple dataframes and later join them together. I would prefer a cleaner more elegant solution.

Answer 1

a simple tidyr solution
library(tidyr)
df %>%
pivot_longer(everything(),names_to="variables",values_to="values") %>%
group_by(variables,values) %>%
summarize(n_x=n()) %>%
ungroup() %>%
pivot_wider(names_from = variables,values_from=n_x)
# A tibble: 11 x 4
values x1 x2 x3
<dbl> <int> <int> <int>
1 0 1 7 2
2 1 2 4 3
3 2 2 3 3
4 3 3 3 3
5 4 2 1 2
6 5 1 NA NA
7 6 2 NA 3
8 7 1 NA 1
9 8 1 NA NA
10 9 2 NA 1
11 10 1 NA NA

Answer 2

We can use a simple map with full_join
library(dplyr)
library(purrr)
map(names(df), ~ df %>%
count(!!rlang::sym(.x)) %>%
rename_at(1, ~ 'x')) %>%
reduce(full_join, by = 'x') %>%
rename_at(-1, ~ str_c('n_x', seq_along(.)))
# x n_x1 n_x2 n_x3
#1 0 1 7 2
#2 1 2 4 3
#3 2 2 3 3
#4 3 3 3 3
#5 4 2 1 2
#6 5 1 NA NA
#7 6 2 NA 3
#8 7 1 NA 1
#9 8 1 NA NA
#10 9 2 NA 1
#11 10 1 NA NA
Or using a simple base R option
t(table(c(col(df)), unlist(df)))