Color

1. Using the mtcars data, create a scatterplot of the fuel efficiency as a function of weight with color based on the number of cylinders.

library(tidyverse)

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mtcars |> 
  mutate(cyl = as.factor(cyl)) |> 
  ggplot(aes(x = wt, y = mpg, color = cyl)) +
  geom_point()

2. Repeat the scatterplot but use scale_color_brewer() to set the palette to Set1.

mtcars |> 
  mutate(cyl = as.factor(cyl)) |> 
  ggplot(aes(x = wt, y = mpg, color = cyl)) +
  geom_point() +
  scale_color_brewer(palette = "Set1")

3. Why did you use scale_color_brewer() not scale_fill_brewer() or scale_color_distiller()?

4. Repeat scatterplot #1 but use scale_color_manual() to set the three colors to red, green, and blue.

mtcars |> 
  mutate(cyl = as.factor(cyl)) |> 
  ggplot(aes(x = wt, y = mpg, color = cyl)) +
  geom_point() +
  scale_color_manual(values = c("red", "green", "blue"))

5. Find three colors (either names or hex codes), and repeat scatterplot #4 with your own colors.

mtcars |> 
  mutate(cyl = as.factor(cyl)) |> 
  ggplot(aes(x = wt, y = mpg, color = cyl)) +
  geom_point() +
  scale_color_manual(values = c("coral2", "palegreen4", "slateblue4"))

6. Using the mtcars data, create a scatterplot of the fuel efficiency as a function of weight with color based on horsepower using the BuPu palette and reverse the direction of the gradient.

mtcars |> 
  ggplot(aes(x = wt, y = mpg, color = hp)) +
  geom_point() +
  scale_color_distiller(palette = "BuPu", direction = 2)

7. Create a new column in mtcars that centers and scales displacement with the scale() function. Create a scatterplot of fuel efficiency as a function of weight with color based on the rescaled displacement, using a diverging scale of your choice.

mtcars |> 
  mutate(disp2 = scale(disp)) |> 
  ggplot(aes(x = wt, y = mpg, color = disp2)) +
  geom_point() +
  scale_color_distiller(palette = "RdBu")