Grammar of graphics II

1. Using the mtcars data, create a scatterplot of the fuel efficiency as a function of weight.

library(tidyverse)

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mtcars |> 
  ggplot(aes(x = wt, y = mpg)) +
  geom_point()

2. Repeat the scatterplot, but make the points violet open squares of size 5.

mtcars |> 
  ggplot(aes(x = wt, y = mpg)) +
  geom_point(color = "violet", shape = 0, size = 5)

3. Repeat the scatterplot but with separate colors for cylinder levels.

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

4. Why does the legend look like that? Fix it so there are separate colors for cylinder levels.

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

5. Overlay separate regression lines for each cylinder level.

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

`geom_smooth()` using formula = 'y ~ x'

6. Overlay a single firebrick regression line over the points with a firebrick1-colored confidence band.

mtcars |> 
  ggplot(aes(x = wt, y = mpg)) +
  geom_point(aes(color = as.factor(cyl))) +
  geom_smooth(method = "lm", color = "firebrick", fill = "firebrick1")

`geom_smooth()` using formula = 'y ~ x'

7. Plot the mean and standard error of the mean of fuel efficiency for each level of cylinder and color them sienna.

mtcars |> 
  ggplot(aes(x = as.factor(cyl), y = mpg)) +
  stat_summary(color = "sienna")

No summary function supplied, defaulting to `mean_se()`