Notice that I used geom_path(). This is a geom I did not cover earlier in the series. It’s not a common geom, though it does show up in charts like this where we want to display data for three variables. The geom_line() geom follows the basic rules for a line: that the variable on the y axis is a function of the variable on the x axis, which means that for each element of the domain, there is one and only one corresponding element of the range (and I have a middle school algebra teacher who would be very happy right now that I still remember the definition she drilled into our heads all those years ago).
But when you have two variables which change over time, there’s no guarantee that this will be the case, and that’s where geom_path() comes in. The geom_path() geom does not plot y based on sequential x values, but instead plots values according to a third variable. The trick is, though, that we don’t define this third variable—it’s implicit in the data set order. In our case, our data frame comes in ordered by year, but we could decide to order by, for example, life expectancy by setting
data = arrange(global_avg, m_lifeExp). Note that in a scenario like these global numbers, geom_line() and geom_path() produce the same output because we’ve seen consistent improvements in both GDP per capita and life expectancy over the 55-year data set. So let’s look at a place where that’s not true.
The cowplot library gives you an easier way of linking together different plots of different sizes in a couple lines of code, which is much easier than using ggplot2 by itself.