Expand to blank with unnest - r

Question

I find purrr SUPER useful for making results tables for a bunch of different variables. I was wondering if there was a way for the unnest() function (or otherwise) to expand a high order variable into blanks, rather than just repeating.
For example, with this code:
library(tidyverse)
data <- data.frame(
group1 = sample(c('dog','cat', 'gecko'), 100, replace = T),
group2 = sample(c('hot dog', 'not hotdog', 'other'), 100, replace = T)
)
my_freq <- function(var){
result <- as.data.frame(table(data[[var]]))
colnames(result) <- c('level', 'n')
return(result)
}
the_table <- data.frame(var = c('group1', 'group2'))
the_table <- the_table %>%
mutate(
result = map(var, my_freq)
) %>%
unnest(result)
Instead of the resulting table looking like:
It would look like this:
I guess this would be a multi-level index in python, but not sure how to accomplish in r.

Answer 1

Extending rmagno's solution to other high order variables
...%>%
mutate_at(
.vars = vars(high_order_vars),
.funs = function(x) ifelse(duplicated(.[['var']]), NA, x)
)

Answer 2

Not sure what you mean by blank (in this example I am going with NA). The critical line is:
mutate(var = if_else(!duplicated(var), var, NA_integer_))
Minimal working example:
library(tidyverse)
data <- data.frame(
group1 = sample(c('dog','cat', 'gecko'), 100, replace = T),
group2 = sample(c('hot dog', 'not hotdog', 'other'), 100, replace = T)
)
my_freq <- function(var){
result <- as.data.frame(table(data[[var]]))
colnames(result) <- c('level', 'n')
return(result)
}
the_table <- data.frame(var = c('group1', 'group2'))
the_table <- the_table %>%
mutate(
result = map(var, my_freq)
) %>%
unnest(result) %>%
mutate(var = if_else(!duplicated(var), var, NA_integer_))
the_table
#> # A tibble: 6 x 3
#> var level n
#> <fct> <fct> <int>
#> 1 group1 cat 38
#> 2 <NA> dog 38
#> 3 <NA> gecko 24
#> 4 group2 hot dog 36
#> 5 <NA> not hotdog 34
#> 6 <NA> other 30
Created on 2020-02-29 by the reprex package (v0.3.0)

Answer 3

You could just use an lapply to get a list I call tb of the two tables. Then create a matrix with names(tb) in the first row and the rest blanks and convert it to a data frame. Finally Map assigns the desired names applying cbind on the columns of both data frames consecutively.
tb <- lapply(data, function(x) setNames(as.data.frame(table(x)), c("level", "n")))
res <- do.call(rbind,
Map(cbind,
var=data.frame(
matrix(c(names(tb), rep("", (el(lapply(tb, nrow)) - 1)*2)),
ncol=2, byrow=TRUE)),
tb))
res
# var level n
# X1.1 group1 cat 31
# X1.2 dog 26
# X1.3 gecko 43
# X2.1 group2 hot dog 35
# X2.2 not hotdog 37
# X2.3 other 28