Nesting your data

In this course, you will work with a collection of economic and social indicators for 77 countries over a period of 52 years. This data is stored in the gapminder data frame.

In this exercise, you will transform your gapminder data into a nested data frame by using the first tool needed to build the foundation of tidy machine learning skills: nest().

Note: This is a more granular version than the dataset available from the gapminder package. This version is available in the dslabs package.

Cet exercice fait partie du cours

<cours>Machine Learning in the Tidyverse</cours>

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Instructions de l’exercice

Take a look at the first six rows of gapminder.
Now leverage group_by() and nest() to nest your data frames by country, save this as gap_nested.
Explore the first six rows of the newly created data frame gap_nested, note the new complex column data containing tibbles.

Exercice interactif pratique

Essayez cet exercice en complétant ce code d’exemple.

# Explore gapminder
head(___)

# Prepare the nested data frame gap_nested
library(tidyverse)
gap_nested <- gapminder %>% 
  group_by(___) %>% 
  ___()

# Explore gap_nested
head(___)

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