implementing merging two large data frames in R using dplyr: unexpected duplications
I'm performance testing and After trying multiple solutions online, I still can't figure this out. Quick question that's been bugging me - I'm working with unexpected duplications when merging two large data frames using the `dplyr` package in R... I'm working with R version 4.3.0 and dplyr version 1.1.0. My goal is to merge a sales dataset with a customer dataset based on a common `customer_id` column. Here's a simplified version of my code: ```r library(dplyr) # Sample sales data sales_data <- data.frame( transaction_id = 1:100, customer_id = sample(1:10, 100, replace = TRUE), amount = runif(100, 10, 100) ) # Sample customer data customer_data <- data.frame( customer_id = 1:10, customer_name = paste("Customer", 1:10) ) # Attempting to merge datasets merged_data <- sales_data %>% left_join(customer_data, by = "customer_id") ``` After running this code, I expected each transaction in `sales_data` to be paired with the appropriate `customer_name` from `customer_data`. However, upon inspecting `merged_data`, I noticed that some `customer_name` values are repeated multiple times, which leads to confusion about the actual sales per customer. I even tried using `distinct()` to remove duplicates, but it didn't resolve the scenario: ```r merged_data <- merged_data %>% distinct(customer_id, .keep_all = TRUE) ``` Could this duplication scenario stem from my data? I've validated that the `customer_id` in `customer_data` has unique values. Is there a better way to ensure that the merge behaves as expected? Any help would be greatly appreciated. For context: I'm using R on macOS. How would you solve this? This is my first time working with R LTS. Any advice would be much appreciated.