From b750cfed1d466480ddf02bb253f56b5915d474ad Mon Sep 17 00:00:00 2001
From: Caiwen Li <130128348+LisaLi525@users.noreply.github.com>
Date: Wed, 13 Dec 2023 22:07:47 +0200
Subject: [PATCH] Create Seasonality Analysis.R

---
 Seasonality Analysis.R | 21 +++++++++++++++++++++
 1 file changed, 21 insertions(+)
 create mode 100644 Seasonality Analysis.R

diff --git a/Seasonality Analysis.R b/Seasonality Analysis.R
new file mode 100644
index 0000000..6d6aa8e
--- /dev/null
+++ b/Seasonality Analysis.R	
@@ -0,0 +1,21 @@
+analyze_seasonality <- function(total_transaction_data, active_customers_data, special_dates) {
+  # Extracting week numbers from special dates for comparison
+  special_weeks <- lapply(special_dates, function(date) {
+    lubridate::week(as.Date(date))
+  })
+  
+  # Analyzing transactions during special weeks
+  seasonality_analysis <- total_transaction_data %>%
+    mutate(tran_week = lubridate::week(trandate)) %>%
+    filter(tran_week %in% unlist(special_weeks)) %>%
+    inner_join(active_customers_data, by = "customerid") %>%
+    group_by(buckets, tran_week) %>%
+    summarise(total_spend = sum(totitemsaleprice),
+              total_transactions = n_distinct(tranid),
+              total_customers = n_distinct(customerid))
+
+  # Optionally, you can extend this function to perform more detailed analysis,
+  # such as comparing spend across different seasonal events, customer segments, etc.
+  
+  return(seasonality_analysis)
+}