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.