Access the estimate inside a model object in R

I am running a simulation and would like to know if there was a way to access the "x" estimate inside of my model using broom, dplyr, modelr, or purrr.

This gives me exactly what I want, but I don't want to use [[1]] in the last chunk of code.

library(tidyverse)
library(purrr)
library(broom)
mod <- function(df) {
  lm(y ~ x, data = df)
}
sim <- tibble(
model = "model1",
mu = 5,             #this is unknown in practice                         
beta = 2.7,         #this is unknown in practice
sigma = 0.15,       #this is unknown in practice
mu_e = 0,
sigma_e = 1
)
sim_dat <- sim %>% 
crossing(replication = 1:10000) %>%
mutate(e = rnorm(mu_e, mu_e),
       x = sample(c(0,1),size=n(),replace = TRUE,prob=c(0.5, 0.5)),
       y = mu+x*beta+e) %>% 
  group_by(model) %>% 
  nest() %>% 
  mutate(model_fit = map(data, mod)) 

broom::tidy(sim_dat$model_fit[[1]]) %>% 
  filter(term=="x") %>% 
  select(estimate)

you could use purrr::map_df():

map_df(sim_dat$model_fit, broom::tidy) %>% 
    filter(term=="x") %>% 
    select(estimate)

you could also drop it in mutate(model_fit = ...) like this:

sim_dat <- sim %>% 
    crossing(replication = 1:10000) %>%
    mutate(e = rnorm(mu_e, mu_e),
           x = sample(c(0,1),size=n(),replace = TRUE,prob=c(0.5, 0.5)),
           y = mu+x*beta+e) %>% 
    group_by(model) %>% 
    nest() %>% 
    mutate(model_fit = map(data, mod),

           # you can pipe inside of mutate()

           x_coef = map_dbl(model_fit, ~broom::tidy(.) %>%
               filter(term =="x") %>% 
               select(estimate) %>%
               unlist() ) ) 

Depending on what class of object you want to return for x_coef, you could monkey around with the map_suffix() and possibly drop the unlist() I just thought dbl made sense.