The data is in a library(gapmider)

gapminder

The questions are in the 3.8 Where to go next? in Healy, Kieran book. Data Visualization: A Practical Introduction (p. 71).

1) What happens when you put the geom_smooth() functionbefore geom_point() instead of after it? What does this tell youabout how the plot is drawn? Think about how this might beuseful when drawing plots.

2) Change the mappings in the aes() function so that you plotlife expectancy against population (pop) rather than per capitaGDP. What does that look like? What does it tell you about theunit of observation in the dataset?

3) 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 y instead of x. Experiment with them to see whateffect they have on the plot, and whether they make any senseto use.

4) What happens if you map color to year instead of continent?Is the result what you expected? Think about what class ofobject year is. Remember you can get a quick look at the top ofthe data, which includes some shorthand information on theclass of each variable, by typing gapminder. Instead of mapping color = year, what happens if you trycolor = factor(year)?

5) As you look at these different scatterplots, think aboutfigure 3.13 (in the book) a little more critically. We worked it up to the pointwhere it was reasonably polished, but is it really the best way todisplay this country-year data? What are we gaining and losing by ignoring the temporal and country-level structure of thedata? How could we do better? Sketch out what an alternativevisualization might look like.