Filtering Customers Over 50 with Purchases Above $500 (Past Year)

Assuming purchase_date is in Date format

filtered_data <- customer_data %>%
  filter(age > 50,
         purchase_amount > 500,
         purchase_date >= (Sys.Date() - years(1))) %>%
  arrange(desc(purchase_amount))

print(filtered_data)
##   customer_id age region purchase_amount purchase_date
## 1     CUST026  69   East          978.33    2024-06-19
## 2     CUST006  59  South          959.58    2024-06-29
## 3     CUST024  56  North          916.77    2024-07-23
## 4     CUST005  59  North          892.83    2025-01-17
Explanation of Each Function:
filter() – selects rows where:

age > 50

purchase_amount > 500

purchase_date is within the past year (using lubridate::years()).

arrange(desc(purchase_amount)) – sorts the results in descending order by purchase amount.

%>% – the pipe operator connects each function, creating a readable data processing pipeline.


library(ggplot2)

print(
  ggplot(customer_data, aes(x = region, y = purchase_amount, fill = region)) +
    geom_boxplot() +
    labs(title = "Distribution of Purchase Amounts by Region",
         x = "Region",
         y = "Purchase Amount ($)") +
    theme_minimal() +
    theme(legend.position = "none",
          axis.text.x = element_text(angle = 45, hjust = 1))
)

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