Mapping your data

In combination with mutate(), you can use map() to append the results of your calculation to a data frame. Since the map() function always returns a vector of lists you must use unnest() to extract this information into a numeric vector.

Here you will explore this functionality by calculating the mean population of each country in the gapminder dataset.

Cet exercice fait partie du cours

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

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Instructions de l’exercice

Use map() to apply the mean() function to calculate the population mean for each country and append this new list column called mean_pop using mutate().
Explore the first 6 rows of pop_nested.
Use unnest() to convert the mean_pop list into a numeric column and save this as the pop_mean data frame.
Explore pop_mean using head().

Exercice interactif pratique

Essayez cet exercice en complétant ce code d’exemple.

# Calculate the mean population for each country
pop_nested <- gap_nested %>%
  mutate(mean_pop = map(___, ~mean(.x$___)))

# Take a look at pop_nested
head(___)

# Extract the mean_pop value by using unnest
pop_mean <- pop_nested %>% 
  unnest(___)

# Take a look at pop_mean
head(___)

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