1 Multiple Choice Question

Carefully look at gapminder dataset. We are going to build the world class predictive model? Which variable can be target variable?

1. country
2. continent
   
3. year
4. lifeExp
   
5. pop gdpPercap

Hint: “What Is the Most Important Thing in Life?”

library(gapminder)

gapminder

# A tibble: 1,704 x 6
   country     continent  year lifeExp      pop gdpPercap
   <fct>       <fct>     <int>   <dbl>    <int>     <dbl>
 1 Afghanistan Asia       1952    28.8  8425333      779.
 2 Afghanistan Asia       1957    30.3  9240934      821.
 3 Afghanistan Asia       1962    32.0 10267083      853.
 4 Afghanistan Asia       1967    34.0 11537966      836.
 5 Afghanistan Asia       1972    36.1 13079460      740.
 6 Afghanistan Asia       1977    38.4 14880372      786.
 7 Afghanistan Asia       1982    39.9 12881816      978.
 8 Afghanistan Asia       1987    40.8 13867957      852.
 9 Afghanistan Asia       1992    41.7 16317921      649.
10 Afghanistan Asia       1997    41.8 22227415      635.
# ... with 1,694 more rows

2 Parsons Problems

• Use gapminder dataframe
• Group by continent and country, and then nest()
• Fit simple linear regression by each country through mutate(), map() pattern
• Extract \(R^2\) values from list-columns
• Arrange country by \(R^2\) values

gapminder %>% 
  group_by(continent, ____) %>% 
  ____ %>% 
  mutate(model = map(____, ~lm(lifeExp ~ year, data=.))) %>% 
  mutate(model_glance = map(model, ____),
         rsquare = map_dbl(model_glance, ~.$r.squared)) %>% 
  arrange(_____))