Augmenting your data

From the results of glance(), you learned that using the available features the linear model fits well with an adjusted \(R^2\) of 0.99. The augment() function can help you explore this fit by appending the predictions to the original data.

Here you will leverage this to compare the predicted values of life_expectancy with the original ones based on the year feature.

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Machine Learning in the Tidyverse

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Instructions

Build the augmented data frame algeria_fitted using augment().
Visualize the fit of the model with respect to year by plotting both life_expectancy as points and .fitted as a line.

Exercice interactif pratique

Essayez cet exercice en complétant cet exemple de code.

# Build the augmented data frame
algeria_fitted <- ___

# Compare the predicted values with the actual values of life expectancy
algeria_fitted %>% 
  ggplot(aes(x = ___)) +
  geom_point(aes(y = ___)) + 
  geom_line(aes(y = ___), color = "red")

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