Bootsrapping a statistic in a nested data column and retrieve results in tidy format-CodePudding

I'm trying to do some bootstrapping in a more tidy way (I know ho to do it in base R and get the results, but am wondering how to put all of it into a more tidy pipeline).

First I define two functions. One for the statistic to bootstrap and one for the boostrap itself:

library(boot)
library(tidyverse)

share <- function(data, i)
{
  share_boot <- data[i, ] %>%
    summarize(across(everything(), mean)) %>%
    pivot_longer(everything()) %>%
    summarize(value/sum(value)) %>%
    pull()
  
  return(share_boot)
}

boot_results <- function(data, statistic, R)
{
  boot_results_function <- boot(data = data,
                                statistic = statistic,
                                R = R)
  
  return(boot_results_function)
}

I then want to bootstrap my statistic on some nested data where I basically want to do the bootstrap for each row:

# Creating toy data
set.seed(1)
df <- tibble(country = rep(1:3, each = 20),
             group = rep(rep(1:2, each = 10), 3),
             value1 = runif(60),
             value2 = runif(60),
             value3 = runif(60))

# Doing the boostrap and retreiving results
df2 <- df %>%
  group_by(country, group) %>%
  nest(data = -c(country, group)) %>%
  rowwise() %>%
  mutate(results = list(boot_results(data, share, 5))) %>%
  ungroup() %>%
  hoist(., results, "t0", "t") %>%
  select(-results)

This gives me nested list columns for t, t0 and data.

# A tibble: 6 x 5
  country group data              t0        t            
    <int> <int> <list>            <list>    <list>       
1       1     1 <tibble [10 x 3]> <dbl [3]> <dbl [5 x 3]>
2       1     2 <tibble [10 x 3]> <dbl [3]> <dbl [5 x 3]>
3       2     1 <tibble [10 x 3]> <dbl [3]> <dbl [5 x 3]>
4       2     2 <tibble [10 x 3]> <dbl [3]> <dbl [5 x 3]>
5       3     1 <tibble [10 x 3]> <dbl [3]> <dbl [5 x 3]>
6       3     2 <tibble [10 x 3]> <dbl [3]> <dbl [5 x 3]>

I now want to do several things:

kind of unnest_longer these three columns, so that I end up with three rows per country/group.
retrieve the column names from the data column.

I'm probably heavily overcomplicating things, but I'm stuck at this last step, where I don't know how to pivot/unnest the list columns.

Expected result:

# A tibble: 18 x 5
   country group data_names    t0 t            
     <int> <int> <chr>      <dbl> <list>       
 1       1     1 value1     0.355 <dbl [5 x 1]>
 2       1     1 value2     0.329 <dbl [5 x 1]>
 3       1     1 value3     0.315 <dbl [5 x 1]>
 4       1     2 value1     0.324 <dbl [5 x 1]>
 5       1     2 value2     0.361 <dbl [5 x 1]>
 6       1     2 value3     0.315 <dbl [5 x 1]>
 7       2     1 value1     0.320 <dbl [5 x 1]>
 8       2     1 value2     0.310 <dbl [5 x 1]>
 9       2     1 value3     0.371 <dbl [5 x 1]>
10       2     2 value1     0.360 <dbl [5 x 1]>
11       2     2 value2     0.386 <dbl [5 x 1]>
12       2     2 value3     0.254 <dbl [5 x 1]>
13       3     1 value1     0.368 <dbl [5 x 1]>
14       3     1 value2     0.319 <dbl [5 x 1]>
15       3     1 value3     0.314 <dbl [5 x 1]>
16       3     2 value1     0.263 <dbl [5 x 1]>
17       3     2 value2     0.293 <dbl [5 x 1]>
18       3     2 value3     0.443 <dbl [5 x 1]>

CodePudding user response：

Extract the column names using map, split the matrix column wise to create 1-column matrix and unnest them together.

library(tidyverse)

df2 %>%
  mutate(data = map(data, names), 
         t = map(t, ~map(asplit(.x, 2), matrix, ncol = 1))) %>%
  unnest(c(data, t0, t)) 

#   country group data      t0 t            
#     <int> <int> <chr>  <dbl> <list>       
# 1       1     1 value1 0.355 <dbl [5 × 1]>
# 2       1     1 value2 0.329 <dbl [5 × 1]>
# 3       1     1 value3 0.315 <dbl [5 × 1]>
# 4       1     2 value1 0.324 <dbl [5 × 1]>
# 5       1     2 value2 0.361 <dbl [5 × 1]>
# 6       1     2 value3 0.315 <dbl [5 × 1]>
# 7       2     1 value1 0.320 <dbl [5 × 1]>
# 8       2     1 value2 0.310 <dbl [5 × 1]>
# 9       2     1 value3 0.371 <dbl [5 × 1]>
#10       2     2 value1 0.360 <dbl [5 × 1]>
#11       2     2 value2 0.386 <dbl [5 × 1]>
#12       2     2 value3 0.254 <dbl [5 × 1]>
#13       3     1 value1 0.368 <dbl [5 × 1]>
#14       3     1 value2 0.319 <dbl [5 × 1]>
#15       3     1 value3 0.314 <dbl [5 × 1]>
#16       3     2 value1 0.263 <dbl [5 × 1]>
#17       3     2 value2 0.293 <dbl [5 × 1]>
#18       3     2 value3 0.443 <dbl [5 × 1]>