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Resample a data frame, with optional hierarchical cluster resampling

Source: R/resample_data.R
resample_data.Rd

For simple bootstrap resampling, call resample_data(data) with no other arguments. For cluster resampling, supply N and ID_labels. For multi-level hierarchical resampling, supply vectors for both.

Usage

resample_data(data, N, ID_labels = NULL, unique_labels = FALSE)

Arguments

data

A data frame.

N

Resample size. May be:

  • Missing: resample nrow(data) units (standard bootstrap).

  • A single unnamed number, with no ID_labels: resample that many rows with replacement.

  • A named integer vector: names are cluster ID column names (from outermost to innermost), values are the number of units to resample at each level. Use the constant ALL to pass through all units at a level.

  • An unnamed integer vector: same as above but supply ID_labels separately.

ID_labels

Character vector of cluster ID column names (outermost to innermost). Required when N is unnamed.

unique_labels

Logical. When TRUE, adds a <ID>_unique column with globally unique labels after resampling (useful for multi-level bootstraps where the same cluster is drawn multiple times). Default FALSE.

Value

A tibble of the resampled rows: nrow(data) of them for the default bootstrap, N for an unnamed scalar N, and for a hierarchical call the sizes requested at each level multiplied together. ID columns are carried over from the rows that were drawn rather than regenerated, so a cluster drawn twice appears twice under one ID; pass unique_labels = TRUE to add an <ID>_unique column that tells the copies apart.

Examples

# Simple bootstrap
df <- fabricate(N = 50, Y = rnorm(N))
boot <- resample_data(df)
nrow(boot)
#> [1] 50

# Cluster bootstrap: resample 10 of 20 clusters
clustered <- fabricate(
  clusters = add_level(N = 20, gdp = rnorm(N)),
  units    = nest_level(N = 5, Y = gdp + rnorm(N))
)
resample_data(clustered, N = c(clusters = 10))
#> # A tibble: 50 × 4
#>    clusters    gdp units       Y
#>    <chr>     <dbl> <chr>   <dbl>
#>  1 06       -0.228 026   -0.910 
#>  2 06       -0.228 027    1.19  
#>  3 06       -0.228 028   -1.08  
#>  4 06       -0.228 029   -0.0445
#>  5 06       -0.228 030    1.37  
#>  6 09        0.923 041    0.826 
#>  7 09        0.923 042    1.50  
#>  8 09        0.923 043   -0.730 
#>  9 09        0.923 044    0.110 
#> 10 09        0.923 045    1.39  
#> # ℹ 40 more rows