Mixing
Efficient random sampling in R
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)
r data.table tidyverse
asked Nov 19 '18 at 14:18
Philippe Massicotte
1979
add a comment |
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)
r data.table tidyverse
asked Nov 19 '18 at 14:18
Philippe Massicotte
1979
1
Just sample row-indices and use a subset of your data each time rather than copying the data.
– Gregor
Nov 19 '18 at 14:36
add a comment |
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)
r data.table tidyverse
asked Nov 19 '18 at 14:18
Philippe Massicotte
1979
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)
r data.table tidyverse
r data.table tidyverse
asked Nov 19 '18 at 14:18
Philippe Massicotte
1979
asked Nov 19 '18 at 14:18
Philippe Massicotte
1979
asked Nov 19 '18 at 14:18
Philippe Massicotte
1979
asked Nov 19 '18 at 14:18
Philippe Massicotte
1979
asked Nov 19 '18 at 14:18
Philippe Massicotte
1979
1979
1
Just sample row-indices and use a subset of your data each time rather than copying the data.
– Gregor
Nov 19 '18 at 14:36
add a comment |
1
Just sample row-indices and use a subset of your data each time rather than copying the data.
– Gregor
Nov 19 '18 at 14:36
1
1
Just sample row-indices and use a subset of your data each time rather than copying the data.
– Gregor
Nov 19 '18 at 14:36
Just sample row-indices and use a subset of your data each time rather than copying the data.
– Gregor
Nov 19 '18 at 14:36
add a comment |
2 Answers
2
active
oldest
votes
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
answered Nov 19 '18 at 14:51
AntoniosK
12.5k1822
Works as intended, thank you.
– Philippe Massicotte
Nov 19 '18 at 15:47
add a comment |
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
answered Nov 19 '18 at 14:39
iod
3,5592722
add a comment |
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2 Answers
2
active
oldest
votes
2 Answers
2
active
oldest
votes
active
oldest
votes
active
oldest
votes
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
answered Nov 19 '18 at 14:51
AntoniosK
12.5k1822
Works as intended, thank you.
– Philippe Massicotte
Nov 19 '18 at 15:47
add a comment |
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
answered Nov 19 '18 at 14:51
AntoniosK
12.5k1822
Works as intended, thank you.
– Philippe Massicotte
Nov 19 '18 at 15:47
add a comment |
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
answered Nov 19 '18 at 14:51
AntoniosK
12.5k1822
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
answered Nov 19 '18 at 14:51
AntoniosK
12.5k1822
answered Nov 19 '18 at 14:51
AntoniosK
12.5k1822
answered Nov 19 '18 at 14:51
AntoniosK
12.5k1822
answered Nov 19 '18 at 14:51
AntoniosK
12.5k1822
12.5k1822
Works as intended, thank you.
– Philippe Massicotte
Nov 19 '18 at 15:47
add a comment |
Works as intended, thank you.
– Philippe Massicotte
Nov 19 '18 at 15:47
Works as intended, thank you.
– Philippe Massicotte
Nov 19 '18 at 15:47
Works as intended, thank you.
– Philippe Massicotte
Nov 19 '18 at 15:47
add a comment |
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
answered Nov 19 '18 at 14:39
iod
3,5592722
add a comment |
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
answered Nov 19 '18 at 14:39
iod
3,5592722
add a comment |
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
answered Nov 19 '18 at 14:39
iod
3,5592722
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
answered Nov 19 '18 at 14:39
iod
3,5592722
answered Nov 19 '18 at 14:39
iod
3,5592722
answered Nov 19 '18 at 14:39
iod
3,5592722
answered Nov 19 '18 at 14:39
iod
3,5592722
3,5592722
add a comment |
add a comment |
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Just sample row-indices and use a subset of your data each time rather than copying the data.
– Gregor
Nov 19 '18 at 14:36