Lab 5 - Giacomo Ruberti

income_products <- transactions %>%
  inner_join(products, by = "product_id") %>%
  inner_join(demographics, by = "household_id") %>%
  mutate(
    income_group = case_when(
      income %in% c("Under 15K", "15-24K", "25-34K") ~ "34k or less",
      income %in% c("35-49K", "50-74K") ~ "35-74K",
      income %in% c("75-99K", "100-124K", "125-149K") ~ "75-149K",
      income %in% c("150-174K", "175-199K", "200-249K", "250K+") ~ "150K or more",
    )) %>% 
  group_by(income_group, product_category) %>% 
  summarize(total_spent = sum(quantity, na.rm = TRUE), .groups = "drop") %>%
  arrange(income_group, desc(total_spent)) %>%  
  group_by(income_group) %>%
  filter(product_category != "COUPON/MISC ITEMS") %>%
  slice_max(total_spent, n = 5) 

ggplot(income_products, aes(x = reorder(product_category, total_spent), y = total_spent, fill = income_group)) +
  geom_col(show.legend = FALSE) +
  coord_flip() +
  facet_wrap(~income_group, scales = "free_y") +  # Facet per ogni fascia di reddito
  labs(
    title = "Top 5 most mold products by income level",
    x = "Product",
    y = "Total Quantity Sold",
    caption = "The product category 'COUPON/MISC ITEMS' has the highest selling in each income group,\nhence it has not been included in the comparison"
  ) +
  theme_minimal() + 
   theme(
    plot.title = element_text(size = 12, face = "bold", margin = margin(b = 20)),
    strip.text = element_text(size = 9, face = "bold"),
    plot.caption = element_text(size = 7, hjust = 0, margin = margin(t = 30)),
    axis.title.x = element_text(margin = margin(t = 15)),  
    axis.title.y = element_text(margin = margin(r = 10))  
  )

discount_effectiveness <- transactions %>%
  inner_join(products, by = "product_id") %>%
  mutate(discounted = ifelse(coupon_disc > 0 | retail_disc > 0, "Discounted", "Full Price")) %>%
  group_by(product_category, discounted) %>%
  summarize(count = n(), .groups = "drop") %>%
  mutate(pct = count / sum(count) * 100) %>%
  filter(discounted == "Discounted") %>%
  arrange(desc(pct)) %>%
  slice_max(pct, n = 10) 

# Plot
ggplot(discount_effectiveness, aes(x = reorder(product_category, pct), y = pct, fill = pct)) +
  geom_col() +
  coord_flip() +
  labs(
    title = "Discount usage by product category",
    x = "Product Category",
    y = "Percentage of Transactions Using a Discount (%)",
    caption = "The study has been limited to the first 10 categories with the highest %discount usage",
    fill = "Discount Usage (%)"
  ) +
  theme_minimal() + 
  theme(
    plot.title = element_text(size = 12, face = "bold", margin = margin(b = 20)),
    strip.text = element_text(size = 9, face = "bold"),
    plot.caption = element_text(size = 7, hjust = 0, margin = margin(t = 30)),
    axis.title.x = element_text(margin = margin(t = 15)),  
    axis.title.y = element_text(margin = margin(r = 10))  
  )

soft_drinks_trend <- transactions %>%
  left_join(products, by = "product_id") %>%
  filter(product_category == "SOFT DRINKS") %>%
  mutate(month = floor_date(transaction_timestamp, "month")) %>%
  group_by(month) %>%
  summarize(total_sales = sum(sales_value, na.rm = TRUE), .groups = "drop")

# Creazione del grafico a linee
ggplot(soft_drinks_trend, aes(x = month, y = total_sales)) +
  geom_line(color = "blue", linewidth = 1) +
  geom_point(color = "red", size = 2) + 
  labs(
    title = "Monthly sales of soft srinks",
    subtitle = "Aggregated sales per month to identify seasonality",
    x = "Month",
    y = "Total Sales ($)",
  ) +
  theme_minimal() +
  theme(
    plot.title = element_text(size = 12, hjust = 0.5, face = "bold"),
    plot.subtitle = element_text(size = 10, hjust = 0.5, margin = margin(b = 15)),
    axis.title.x = element_text(margin = margin(t = 10)),
    axis.title.y = element_text(margin = margin(r = 10)),
  )