3

I have an issue understanding how to use the dplyr bootstrap function properly.

What I want is to generate a bootstrap distribution from two randomly assigned groups and compute the difference in means, like this for example : 

library(dplyr) 
library(broom) 
data(mtcars) 

mtcars %>% 
  mutate(treat = sample(c(0, 1), 32, replace = T)) %>% 
  group_by(treat) %>%
  summarise(m = mean(disp)) %>% 
  summarise(m = m[treat == 1] - m[treat == 0])

The issue is that I need to repeat this operation 100, 1000, or more times.

Using replicate, I can do 

frep = function(mtcars) mtcars %>% 
  mutate(treat = sample(c(0, 1), 32, replace = T)) %>% 
  group_by(treat) %>%
  summarise(m = mean(disp)) %>% 
  summarise(m = m[treat == 1] - m[treat == 0])

replicate(1000, frep(mtcars = mtcars), simplify = T) %>% unlist()

and get the distribution

enter image description here

I don't really get how to use bootstraphere. How should I start ? 

mtcars %>% 
  bootstrap(10) %>% 
  mutate(treat = sample(c(0, 1), 32, replace = T)) 

mtcars %>% 
  bootstrap(10) %>% 
  do(tidy(treat = sample(c(0, 1), 32, replace = T)))

It's not really working. Where should I put the bootstrap pip ?

Thanks.

Michael Petch
  • 46,082
  • 8
  • 107
  • 198
giac
  • 4,261
  • 5
  • 30
  • 59
  • yes ! thank you. This is perfect. So can you explain me, I have to create a `data.frame` inside the `do` ? can you post your answer ? thanks again! – giac Sep 17 '16 at 16:21
  • 1
    As we are creating a column 'treat', we wrap it with `data.frame` to get the output as data.frame. The `.` signifies the contents of the dataset on the `lhs` of `%>%` – akrun Sep 17 '16 at 16:29

1 Answers1

2

In the do step, we wrap with data.frame and create the 'treat' column, then we can group by 'replicate' and 'treat' to get the summarised output column

mtcars %>% 
    bootstrap(10) %>% 
    do(data.frame(., treat = sample(c(0,1), 32, replace=TRUE))) %>% 
    group_by(replicate, treat) %>% 
    summarise(m = mean(disp)) %>%
    summarise(m = m[treat == 1] - m[treat == 0])
    #or as 1 occurs second and 0 second, we can also use
    #summarise(m = last(m) - first(m))
akrun
  • 874,273
  • 37
  • 540
  • 662