7.4 Fitting models

models <- mtcars %>% 
  split(.$cyl) %>% 
  map(function(df) lm(mpg ~ wt, data = df))

models %>%
    map(summary) %>%
    map_dbl(~ .$r.squared)
##         4         6         8 
## 0.5086326 0.4645102 0.4229655

models %>%
    map_df(broom::tidy)
## # A tibble: 6 x 5
##   term        estimate std.error statistic    p.value
##   <chr>          <dbl>     <dbl>     <dbl>      <dbl>
## 1 (Intercept)    39.6      4.35       9.10 0.00000777
## 2 wt             -5.65     1.85      -3.05 0.0137    
## 3 (Intercept)    28.4      4.18       6.79 0.00105   
## 4 wt             -2.78     1.33      -2.08 0.0918    
## 5 (Intercept)    23.9      3.01       7.94 0.00000405
## 6 wt             -2.19     0.739     -2.97 0.0118