Tidy up the coefficients of your models

In this exercise you will leverage the list column workflow along with the tidy() function from broom to extract and explore the coefficients for the 77 models you built.

Remember the gap_models data frame contains a model predicting life expectancy by year for 77 countries.

Cet exercice fait partie du cours

Machine Learning in the Tidyverse

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Instructions

Use tidy() to append a column (coef) containing coefficient statistics for each model to the gap_models data frame and save it as model_coef_nested.
Simplify this data frame using unnest() to extract these coefficients in your data frame.
Explore the coefficient estimates for the year feature across your 77 models by plotting a histogram of their values.

Exercice interactif pratique

Essayez cet exercice en complétant cet exemple de code.

# Extract the coefficient statistics of each model into nested data frames
model_coef_nested <- gap_models %>% 
    mutate(coef = map(model, ~___(.x)))
    
# Simplify the coef data frames for each model    
model_coef <- model_coef_nested %>%
    unnest(___)

# Plot a histogram of the coefficient estimates for year         
model_coef %>% 
  filter(term == "___") %>% 
  ggplot(aes(x = ___)) +
  geom_histogram()

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