Nesting by topic and country

In the last chapter, you constructed a linear model for each country by nesting the data in each country, fitting a model to each dataset, then tidying each model with broom and unnesting the coefficients. The code looked something like this:

country_coefficients <- by_year_country %>%
  nest(-country) %>%
  mutate(model = map(data, ~ lm(percent_yes ~ year, data = .)),
         tidied = map(model, tidy)) %>%
  unnest(tidied)

Now, you'll again be modeling change in "percentage" yes over time, but instead of fitting one model for each country, you'll fit one for each combination of country and topic.

Load the purrr, tidyr, and broom packages.
Print the by_country_year_topic dataset to the console.
Fit a linear model within each country and topic in this dataset, saving the result as country_topic_coefficients. You can use the above code as a starting point.
Print the country_topic_coefficients dataset to the console.

Exercise

Nesting by topic and country

Instructions