5.1 Grouping

• Grouping involves gathering multiple variables together and plotting them on a single graph
• You can modify different attributes of data points to reflect values of other variables
• Ex. color, size, shape, etc (within aes() function for mapping argument in ggplot functions)

• NOTE: Be mindful of how busy your graph looks.
• You can also modify the size and transparency of points.

ggplot(Salaries, aes(x=yrs.since.phd,
                     y=salary,
                      size=yrs.service)) + 
  # alpha allows you to change transparency
  # of points
  geom_point(alpha=0.6) + 
  labs(title="Academic salary by years since degree")

• Here’s another example of grouping with some regression lines incorporated into the graph.
• The benefits of grouping is that it also affects other ggplot layers you apply on top of the base ggplot function.

# plot experience vs. salary with 
# fit lines (color represents sex)
ggplot(Salaries, 
       aes(x = yrs.since.phd, 
           y = salary, 
           color = sex)) +
  geom_point(alpha = .4, 
             size = 3) +
  geom_smooth(se=FALSE, 
              method = "lm", 
              formula = y~poly(x,2), 
              size = 1.5) +
  labs(x = "Years Since Ph.D.",
       title = "Academic Salary by Sex and Years Experience",
       subtitle = "9-month salary for 2008-2009",
       y = "",
       color = "Sex") +
  scale_y_continuous(label = scales::dollar) +
  scale_color_brewer(palette = "Set1") +
  theme_minimal()