Unnest Tidy Bootstrap Tibble — bootstrap_unnest_tbl • TidyDensity
Description: Unnest the data output from tidy_bootstrap().

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# Unnest Tidy Bootstrap Tibble

Source: `R/bootstrap-unnest-tbl.R`
`bootstrap_unnest_tbl.Rd`

Unnest the data output from `tidy_bootstrap()`.

## Usage

    bootstrap_unnest_tbl(.data)

## Arguments

.data

  The data that is passed from the `tidy_bootstrap()` function.

## Value

A tibble

## Details

This function takes as input the output of the `tidy_bootstrap()` function and returns a two column tibble. The columns are `sim_number` and `y`

It looks for an attribute that comes from using `tidy_bootstrap()` so it will not work unless the data comes from that function.

## See also

Other Bootstrap: `bootstrap_density_augment()`, `bootstrap_p_augment()`, `bootstrap_p_vec()`, `bootstrap_q_augment()`, `bootstrap_q_vec()`, `bootstrap_stat_plot()`, `tidy_bootstrap()`

## Author

Steven P. Sanderson II, MPH

## Examples

    tb <- tidy_bootstrap(.x = mtcars$mpg)
    bootstrap_unnest_tbl(tb)
    #> # A tibble: 50,000 × 2
    #>    sim_number     y
    #>    <fct>      <dbl>
    #>  1 1           17.8
    #>  2 1           21
    #>  3 1           21.4
    #>  4 1           18.1
    #>  5 1           15.8
    #>  6 1           10.4
    #>  7 1           18.7
    #>  8 1           14.7
    #>  9 1           17.3
    #> 10 1           15.2
    #> # ℹ 49,990 more rows

    bootstrap_unnest_tbl(tb) %>%
      tidy_distribution_summary_tbl(sim_number)
    #> # A tibble: 2,000 × 13
    #>    sim_number mean_val median_val std_val min_val max_val skewness kurtosis
    #>    <fct>         <dbl>      <dbl>   <dbl>   <dbl>   <dbl>    <dbl>    <dbl>
    #>  1 1              19.4       17.3    6.18    10.4    33.9    1.18      3.36
    #>  2 2              21.1       21      5.32    13.3    30.4    0.378     2.05
    #>  3 3              19.5       18.1    6.19    10.4    33.9    0.606     2.99
    #>  4 4              19.0       17.8    6.41    10.4    33.9    0.854     3.12
    #>  5 5              21.6       21      7.05    10.4    33.9    0.459     2.07
    #>  6 6              22.0       21.4    5.11    10.4    32.4   -0.106     2.90
    #>  7 7              19.8       21      5.62    10.4    32.4    0.656     2.94
    #>  8 8              17.9       16.4    6.25    10.4    33.9    0.985     3.35
    #>  9 9              21.4       21.4    6.11    10.4    32.4    0.102     2.48
    #> 10 10             20.7       19.7    5.94    13.3    33.9    0.581     2.28
    #> # ℹ 1,990 more rows
    #> # ℹ 5 more variables: range <dbl>, iqr <dbl>, variance <dbl>, ci_low <dbl>,
    #> #   ci_high <dbl>