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.

This exercise is part of the course

Machine Learning in the Tidyverse

View Course

Exercise instructions

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().

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# 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(___)

Edit and Run Code