Plotting x-y data: time series

1. Using the mpg data, calculate the mean highway fuel efficiency for each number of cylinders and plot a line graph of fuel efficiency by cylinder number.

library(tidyverse)

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mpg |> 
  group_by(cyl) |> 
  summarise(mean_hwy = mean(hwy)) |> 
  ggplot(aes(x = cyl, y = mean_hwy)) +
  geom_line()

2. Repeat the previous plot but also group by class and plot separately colored lines for different classes.

mpg |> 
  group_by(cyl, class) |> 
  summarise(mean_hwy = mean(hwy)) |> 
  ggplot(aes(x = cyl, y = mean_hwy, color = class)) +
  geom_line()

`summarise()` has grouped output by 'cyl'. You can override using the `.groups`
argument.

3. Create a new column called low_high that codes high fuel efficiency greater than or equal to 25 as 1 and less than 25 as 0. Plot low_high as a function of displacement with a bubble chart (no legend) and include a logistic regression curve and band.

mpg |> 
  mutate(low_high = ifelse(hwy > 25, 1, 0)) |> 
  ggplot(aes(x = displ, y = low_high)) +
  geom_count(show.legend = FALSE) +
  geom_smooth(method = "glm", formula = y ~ x, method.args = list(family = "binomial"))

4. Plot highway fuel efficiency for each class as points first, then add jitter, finding an appropriate amount of jitter to add.

mpg |> 
  ggplot(aes(x = class, y = hwy)) +
  geom_point()

mpg |> 
  ggplot(aes(x = class, y = hwy)) +
  geom_jitter(width = 0.1, height = 2)

5. Repeat plot #4 with a beeswarm plot.

library(ggbeeswarm)
mpg |> 
  ggplot(aes(x = class, y = hwy)) +
  geom_beeswarm()