library(tidyverse)

# take the mpg data set and pipe it to the mutate function
# then add a new column to the data set and save to mpg_metric
mpg_metric <- mpg %>%
  mutate(city_metric = 0.425144 * cty)

# View the metric data set
View(mpg_metric)

# group the mpg dataset by classes
# then summarize the data by mean and median cty
mpg %>%
  group_by(class) %>%
  summarise(mean(cty),
            median(cty))

# Data viz with ggplot2
ggplot(mpg, aes(x = cty)) + 
  geom_histogram() +
  labs(x = "City mileage")

# Scatter plot between city and highway mileage
# this shows a linear relationship
# then add a regression line on top
ggplot(mpg, aes(x = cty,
                y = hwy)) +
  geom_point() +
  geom_smooth(method = "lm")