Performing linear regression on each nested dataset

Now that you've divided the data for each country into a separate dataset in the data column, you need to fit a linear model to each of these datasets.

The map() function from purrr works by applying a formula to each item in a list, where . represents the individual item. For example, you could add one to each of a list of numbers:

map(numbers, ~ 1 + .)

This means that to fit a model to each dataset, you can do:

map(data, ~ lm(percent_yes ~ year, data = .))

where . represents each individual item from the data column in by_year_country. Recall that each item in the data column is a dataset that pertains to a specific country.

This exercise is part of the course

Case Study: Exploratory Data Analysis in R

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Exercise instructions

Load the tidyr and purrr packages.
After nesting, use the map() function within a mutate() to perform a linear regression on each dataset (i.e. each item in the data column in by_year_country) modeling percent_yes as a function of year. Save the results to the model column.

Hands-on interactive exercise

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

# Load tidyr and purrr



# Perform a linear regression on each item in the data column
by_year_country %>%
  nest(-country)

Edit and Run Code