Summary Tables using Nested Tibbles

I am trying to generate a table of summary statistics using purrr/tibble methods. I am able to calculate group-wise mean (sd) and counts using the following:

library(dplyr)
library(tidyr)
library(purrr)
library(tibble)

mtcars %>%
  gather(variable, value, -vs, -am) %>%
  group_by(vs, am, variable) %>% 
  nest() %>% 
  filter(variable %in% c("mpg", "hp")) %>% 
  mutate(
    mean = map_dbl(data, ~mean(.$value, na.rm = TRUE)),
    sd = map_dbl(data, ~sd(.$value, na.rm = TRUE)),
    n = map_dbl(data, ~sum(!is.na(.$value)))
  )  %>% 
  select(vs:variable, mean:n) %>% 
  mutate_at(vars(mean, sd), round, 3) %>% 
  mutate(mean_sd = paste0(mean, " (", sd, ")"),
         var_group = paste(vs, am, variable, sep = "_")) %>% 
  select(n:var_group) %>%
  nest(n, mean_sd, .key = "summary") %>% 
  spread(key = var_group, value = summary) %>% 
  unnest()

My immediate question is, how do I retain the column names as seen in spread(key = var_group, value = summary) in the unnest()-ed output?

edit: Thanks to all for the responses. https://stackoverflow.com/a/55912326/5745045 has the advantages of being easier to read and not storing a temporary variable. A disadvantage is the change of numeric to character in the n columns.

The final goal is to replace the column names with formatted text within the context of a grouped kable table.

Solution

Here's another method that doesn't require creating a temporary variable. Instead of nesting the data at the end, I used gather() and unite() to restructure the data so that it ends up as one key and value pair.

library(tidyverse)
#> Registered S3 methods overwritten by 'ggplot2':
#>   method         from 
#>   [.quosures     rlang
#>   c.quosures     rlang
#>   print.quosures rlang
#> Registered S3 method overwritten by 'rvest':
#>   method            from
#>   read_xml.response xml2
mtcars %>%
  gather(variable, value, -vs, -am) %>%
  group_by(vs, am, variable) %>% 
  nest() %>% 
  filter(variable %in% c("mpg", "hp")) %>% 
  mutate(
    mean = map_dbl(data, ~mean(.$value, na.rm = TRUE)),
    sd = map_dbl(data, ~sd(.$value, na.rm = TRUE)),
    n = map_dbl(data, ~sum(!is.na(.$value)))
  )  %>% 
  select(vs:variable, mean:n) %>% 
  mutate_at(vars(mean, sd), round, 3) %>% 
  mutate(mean_sd = paste0(mean, " (", sd, ")"),
         var_group = paste(vs, am, variable, sep = "_")) %>% 
  select(n:var_group) %>% 
  gather(key, value, -var_group) %>% 
  unite(var_group_key, var_group, key) %>% 
  spread(var_group_key, value)
#> # A tibble: 1 x 16
#>   `0_0_hp_mean_sd` `0_0_hp_n` `0_0_mpg_mean_s… `0_0_mpg_n` `0_1_hp_mean_sd`
#>   <chr>            <chr>      <chr>            <chr>       <chr>           
#> 1 194.167 (33.36)  12         15.05 (2.774)    12          180.833 (98.816)
#> # … with 11 more variables: `0_1_hp_n` <chr>, `0_1_mpg_mean_sd` <chr>,
#> #   `0_1_mpg_n` <chr>, `1_0_hp_mean_sd` <chr>, `1_0_hp_n` <chr>,
#> #   `1_0_mpg_mean_sd` <chr>, `1_0_mpg_n` <chr>, `1_1_hp_mean_sd` <chr>,
#> #   `1_1_hp_n` <chr>, `1_1_mpg_mean_sd` <chr>, `1_1_mpg_n` <chr>

^{Created on 2019-04-29 by the reprex package (v0.2.1)}