Start by playing around with the gapminder data a little more. You can try each of these explorations with geom_point() and then with geom_smooth(), or both together. What happens when you put the geom_smooth() function before geom_point() instead of after it? What does this tell you about how the plot is drawn? Think about how this might be useful when drawing plots. Change the mappings in the aes() function so that you plot Life Expectancy against population (pop) rather than per capita GDP. What does that look like? What does it tell you about the unit of observation in the dataset? Try some alternative scale mappings. Besides scale_x_log10() you can try scale_x_sqrt() and scale_x_reverse(). There are corresponding functions for y-axis transformations. Just write yinstead of x. Experiment with them to see what sort of effect they have on the plot, and whether they make any sense to use. What happens if you map color to year instead of continent? Is the result what you expected? Think about what class of object year is. Remember you can get a quick look at the top of the data, which includes some shorthand information on the class of each variable, by typing gapminder. Instead of mapping color = year, what happens if you try color = factor(year)?