library(tidyverse)
library(rsample)

#the data
ttSample <- data.frame(grad =  c(0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1))
#the function bootstrapping it n number of times
bootstrapper <- function(n) {bootstraps(data = ttSample, times = n)}
#the function calculating p_hat for each cell
meanTaker <- function(columnVal)
{
  for(i in 1:length(columnVal))
  {
    meanHolder <- vector("double", nrow(columnVal[[i]]))
    for(j in 1:nrow(columnVal[[i]]))
    {
      meanHolder[[j]] <- mean(as.data.frame(columnVal$splits[[j]])$grad)
      print(meanHolder[[j]])
    }
    print(meanHolder)
    mean(meanHolder)
  }
}
#the data frame- everything will run smoothly before the last pipe
bootFrame <-  data.frame(n = rep(c(250, 1000, 5000, 10000), 3), confLev = rep(c(0.9, 0.95, 0.99))) %>%
  arrange(n, confLev) %>%
  mutate(alpha = 1 - confLev,
    upperCI = confLev + (alpha / 2),
    lowerCI = confLev - (alpha / 2),
    samples = map(ttSample, list),
    boots = map(.x = .$n, .f = bootstrapper)) %>%
#this is where I get the problem.
#I was trying to get p-hat from finding the mean of the means of each sample
  mutate(p_hat = map(.x = .$boots, .f = meanTaker))