1. Tidying models with broom
In our last section,
2. A model fit is a “messy” object
you learned to perform a linear regression and interpret the results, noticing in particular the estimate of the slope and the p-value in this coefficients table. However, while we were able to see these values in the printed output, we didn’t discuss how to extract them out within R.
This is particularly important when combining multiple models.
3. Models are difficult to combine
If we had a linear regression for Afghanistan, for the United States, and for Canada, we wouldn’t have an easy way to combine these models, compare them, or visualize them.
It’s possible to get these values out using built-in functions, but if you’re familiar with R you may recognize that there are some pitfalls that can make it unexpectedly difficult. There’s a tool that makes it particularly easy: my own broom package.
4. broom turns a model into a data frame
The broom package offers a function, tidy, that turns a linear model into a data frame of coefficients. In this case, the tidied coefficients have one row for the intercept and one for the slope, the ones we are interested in . Importantly, since this is a data frame, it is easy to extract values from it, and we’ll be able to use all our standard dplyr tools on it.
In particular, this makes it possible to combine multiple models.
5. Tidy models can be combined
If you have two linear models, one for Afghanistan and one for the US, you could tidy each of them, and since the tidied models are the same shape they can be combined with dplyr’s bind_rows function. In the following sections you’ll build one model for each country and combine all of them.
6. Let's practice!