Ytukyg

From a data frame, I am trying randomly sample 1:20 observations where for
each number of observation I would like to replicate the process 4 times. I
came up with this working solution, but it is very slow since it is
involving coping many times a large data frame because of the crossing()
function. Anyone can point me toward a more efficient solution?

library(tidyverse)



mtcars %>% 

  group_by(cyl) %>% 

  nest() %>% 

  crossing(n_random_sample = 1:20, n_replicate = 1:4) %>% 

  mutate(res = map2_dbl(data, n_random_sample, function(data, n) {



    data %>%

      sample_n(n, replace = TRUE) %>%

      summarise(mean_mpg = mean(mpg)) %>%

      pull(mean_mpg)



  }))

#> # A tibble: 240 x 5

#>      cyl data              n_random_sample n_replicate   res

#>    <dbl> <list>                      <int>       <int> <dbl>

#>  1     6 <tibble [7 × 10]>               1           1  17.8

#>  2     6 <tibble [7 × 10]>               1           2  21  

#>  3     6 <tibble [7 × 10]>               1           3  19.2

#>  4     6 <tibble [7 × 10]>               1           4  18.1

#>  5     6 <tibble [7 × 10]>               2           1  19.6

#>  6     6 <tibble [7 × 10]>               2           2  19.4

#>  7     6 <tibble [7 × 10]>               2           3  19.6

#>  8     6 <tibble [7 × 10]>               2           4  20.4

#>  9     6 <tibble [7 × 10]>               3           1  20.1

#> 10     6 <tibble [7 × 10]>               3           2  18.9

#> # ... with 230 more rows

^{Created on 2018-11-19 by the reprex package (v0.2.1)}

This is an alternative solution, which subsets your original dataset and picks a sample of rows using a function, instead of using nest to create the sub-datasets and store them as a list variable and then pick a sample using map:

library(tidyverse)



# create function to sample rows

f = function(c, n) {

  mtcars %>%

    filter(cyl == c) %>%

    sample_n(n, replace = TRUE) %>%

    summarise(mean_mpg = mean(mpg)) %>%

    pull(mean_mpg)

}



# vectorise function

f = Vectorize(f)



# set seed for reproducibility

set.seed(11)



tbl_df(mtcars) %>%

  distinct(cyl) %>%

  crossing(n_random_sample = 1:20, n_replicate = 1:4) %>%

  mutate(res = f(cyl, n_random_sample))



# # A tibble: 240 x 4

#     cyl n_random_sample n_replicate   res

#   <dbl>           <int>       <int> <dbl>

# 1     6               1           1  21  

# 2     6               1           2  21  

# 3     6               1           3  18.1

# 4     6               1           4  21  

# 5     6               2           1  20.4

# 6     6               2           2  21.2

# 7     6               2           3  20.4

# 8     6               2           4  19.6

# 9     6               3           1  18.4

#10     6               3           2  19.6

# # ... with 230 more rows

mm<-lapply(rep(1:20, each=4), sample_n, tbl=mtcars)

This will give you a list of tables of nrows=1:20, each 4 times.

You can follow up with this to name the elements of the list:

names(mm)<-paste0("sample.",apply(expand.grid(1:4,1:20),1,paste,collapse="-"))

Result:

head(mm,5)

$`sample.1-1`

              mpg cyl disp  hp drat    wt qsec vs am gear carb

Lotus Europa 30.4   4 95.1 113 3.77 1.513 16.9  1  1    5    2



$`sample.2-1`

              mpg cyl disp  hp drat   wt qsec vs am gear carb

Ferrari Dino 19.7   6  145 175 3.62 2.77 15.5  0  1    5    6



$`sample.3-1`

             mpg cyl disp hp drat    wt  qsec vs am gear carb

Honda Civic 30.4   4 75.7 52 4.93 1.615 18.52  1  1    4    2



$`sample.4-1`

               mpg cyl  disp hp drat    wt  qsec vs am gear carb

Toyota Corona 21.5   4 120.1 97  3.7 2.465 20.01  1  0    3    1



$`sample.1-2`

              mpg cyl disp  hp drat   wt qsec vs am gear carb

Ferrari Dino 19.7   6  145 175 3.62 2.77 15.5  0  1    5    6

Volvo 142E   21.4   4  121 109 4.11 2.78 18.6  1  1    4    2