Week5_Lab_CBishop

Plot 1

transactions %>%
  inner_join(demographics, by = 'household_id') %>%
  mutate(month = month(transaction_timestamp, label = TRUE)) %>%
  filter(month %in% c("Jun", "Jul", "Aug")) %>%
  group_by(month, income) %>%
  summarize(total_sales = sum(sales_value, na.rm = TRUE), .groups = 'drop') %>%
  ggplot(aes(x = income, y = total_sales, color = month)) +
  geom_point(size = 2, shape = 18) +
  facet_wrap(~month, nrow = 3) +
  labs(title = "Total Sales by Income During the Summer Months",
       x = "Income",
       y = "Total Sales") 

Plot 2

products %>% 
  inner_join(transactions, by = 'product_id') %>%
  inner_join(demographics, by = 'household_id') %>%
  mutate(marital_status = replace_na(marital_status, "Unknown")) %>%
  filter(product_category == "VITAMINS") %>%
  group_by(marital_status) %>%
  ggplot(aes(x = marital_status)) +
  geom_bar() +
  labs(title = "Vitamins Purchased Based on Marital Status",
       x = "Marital Status",
       y = "Number of Vitamins")

Plot 3

coupon_redemptions %>%
  inner_join(demographics, by = "household_id") %>%
  inner_join(transactions, by = "household_id", relationship = "many-to-many") %>%
  group_by(household_size) %>%
  summarize(total_redemption_value = sum(coupon_disc, na.rm = TRUE)) %>%
  ggplot(aes(x = household_size, y = total_redemption_value, color = household_size)) +
  geom_point(size = 3) + 
  labs(title = "Coupon Redemption Value By Household Size",
       x = "Household Size",
       y = "Total Value of Coupon Redemptions") +
  scale_y_continuous(label = scales::dollar, breaks = seq(from = 0, to = 2000, by = 100))