Module 5 Visualization Lab

First Plot

totsal <- promotions_sample %>%
  filter(display_location == 7) %>%
  inner_join(transactions, by = "product_id") %>%
  group_by(product_id) %>%
  summarize(total_sales = sum(sales_value)) 

totsal %>%
  slice_max(order_by = total_sales, n = 10) %>%
  ggplot(aes(product_id, total_sales)) +
  geom_col(fill = "darkorange", color = "black") +
  labs(title = "Top 10 Products vs. Sales",
       subtitle = "Total sales for the top 10 products displayed in-aisle",
       x = "Products",
       y = "Total Sales ($)")

Second Plot

tot_pick <- products %>%
  filter(str_detect(product_type, regex('pickles', ignore_case = TRUE))) %>%
  inner_join(transactions, by = "product_id") %>%
  group_by(Month = month(transaction_timestamp, label = TRUE, abbr = TRUE)) %>%
  mutate(month = month(transaction_timestamp)) %>%
  summarise(total_sales = sum(sales_value))

tot_pick %>%
  ggplot(aes(Month, total_sales)) +
  coord_cartesian(ylim = c(0, 60)) +
  geom_col(fill = "lightgreen", color = "darkgreen") +
  labs(title = "Total Pickle Sales by Month",
       x = "Month",
       y = "Total Sales ($)")

Third Plot

plot_three <- transactions_sample %>%
  inner_join(products) %>%
  inner_join(demographics)

ggplot(plot_three, aes(x = quantity, y = sales_value)) +
  geom_point() +
  facet_grid(~ age) +
  geom_point(color = "firebrick") +
  theme(plot.background = element_rect(fill = 'grey')) +
  theme(plot.margin = unit(c(1,1,1,1), "cm")) +
  theme(axis.text.x = element_text(angle = 50, size = 5, vjust = 0.5)) +
  labs(title = "Sales by Age Ranges", subtitle = "Total sales by quantity across all age ranges", x = "Quantity", y = "Sales Value")