Classwork 4

The Layered Grammar of Graphics

Question 1. Geometric Objects

Q1a.

Run this code in your head and predict what the output will look like. Then, run the code in R and check your predictions.

ggplot(data = mpg, 
       mapping = aes(x = displ, y = hwy, 
                     color = drv)) +
  geom_point(alpha = .4) +
  geom_smooth(se = FALSE)

Q1b.

What does show.legend = FALSE do? What happens if you remove it?

ggplot(data = mpg) +
  geom_point(mapping = aes(x = displ, y = hwy, 
                           color = drv),
             alpha = .5,
             show.legend = FALSE)

Answer:

Q1c.

Recreate the R code necessary to generate the following graphs.

(a)

(b)

(c)

(d)

(e)

(f)

Q1d.

• Use the data.frame, organdata_simple to visualize the yearly trend of the variable donors for each country.

organdata_simple <- read_csv('https://bcdanl.github.io/data/organdata_simple.csv')

Question 2. Statistical Transformation and Position Adjustment

Q2a.

What does geom_col() do? How is it different to geom_bar()?

Answer:

Q2b.

• Install the R package, nycflights13, which provides the flights data.frame.

flights <- nycflights13::flights
airlines <- nycflights13::airlines

flights <- flights |> 
  left_join(airlines)

• Visualize the distribution of carrier.

• Visualize how the distribution of carrier varies by origin.

Answer:

Question 3. Work a little more with gapminder data

library(gapminder)
p <- ggplot(data = gapminder,
            mapping = aes(x = gdpPercap,
                          y = lifeExp))
p + 
  geom_point() +
  geom_smooth()

Q3a.

• 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.

Q3b.

• 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?

Q3c.

• 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)?

Discussion

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