library(tidyverse)
library(tidyselect)
library(janitor)
library(lubridate)

Randomly select a proportion with replacement

Code

iris %>% 
  slice_sample(prop = 0.8, replace = T)

Output

# A tibble: 120 × 5
   Sepal.Length Sepal.Width Petal.Length Petal.Width Species   
          <dbl>       <dbl>        <dbl>       <dbl> <fct>     
 1          5.3         3.7          1.5         0.2 setosa    
 2          5.7         2.6          3.5         1   versicolor
 3          5.5         2.5          4           1.3 versicolor
 4          5.5         2.3          4           1.3 versicolor
 5          6.2         2.2          4.5         1.5 versicolor
 6          4.4         2.9          1.4         0.2 setosa    
 7          5.4         3.9          1.7         0.4 setosa    
 8          5           3.6          1.4         0.2 setosa    
 9          4.5         2.3          1.3         0.3 setosa    
10          6.9         3.1          5.4         2.1 virginica 
# … with 110 more rows

Randomly select a number of rows

Code

iris %>% 
  slice_sample(n = 5)

Output

# A tibble: 5 × 5
  Sepal.Length Sepal.Width Petal.Length Petal.Width Species  
         <dbl>       <dbl>        <dbl>       <dbl> <fct>    
1          5           3.5          1.3         0.3 setosa   
2          6.9         3.1          5.1         2.3 virginica
3          6.4         3.1          5.5         1.8 virginica
4          4.4         3.2          1.3         0.2 setosa   
5          5.8         2.8          5.1         2.4 virginica

Create bootstrapped datasets (and store in list-column format)

Code

enframe(
  map(1:10, # Number of bootstrapped datasets to create
  ~ iris %>% # Original dataset
    slice_sample(prop = 1, replace = TRUE)), 
  name = "bootstrap_id",
  value = "bootstrap_resamples")

Output

# A tibble: 10 × 2
   bootstrap_id bootstrap_resamples
          <int> <list>             
 1            1 <df [150 × 5]>     
 2            2 <df [150 × 5]>     
 3            3 <df [150 × 5]>     
 4            4 <df [150 × 5]>     
 5            5 <df [150 × 5]>     
 6            6 <df [150 × 5]>     
 7            7 <df [150 × 5]>     
 8            8 <df [150 × 5]>     
 9            9 <df [150 × 5]>     
10           10 <df [150 × 5]>