👀 Views: 1 💬 Answers: 1 📅 Created: 2025-06-11

r dplyr dataframe R

I'm following best practices but I'm working with an scenario when trying to apply a custom function to a grouped data frame using dplyr in R 4.3. I have a data frame containing sales data for different products across various regions, and I want to calculate the average sales per region while also including the total count of sales for each product in that region. However, I'm working with an behavior when trying to do this with the `summarise()` function. Here's the code I wrote: ```r library(dplyr) # Sample data frame sales_data <- data.frame( region = c('North', 'South', 'North', 'South', 'East'), product = c('A', 'B', 'A', 'B', 'A'), sales = c(200, 150, 220, 180, 300) ) # Custom function to calculate average and count custom_summary <- function(x) { return(c(avg_sales = mean(x), total_count = length(x))) } result <- sales_data %>% group_by(region, product) %>% summarise(custom_summary(sales)) ``` When I run this code, I get the following behavior message: `behavior: Must be a data frame, not a numeric vector`. I tried to troubleshoot by ensuring that I'm passing the correct data type to the custom function, but the function seems to be returning a named vector, which I believe is causing the scenario. I also attempted to use `summarise(across(sales, custom_summary))`, but that doesn't seem to be working either. What is the correct way to apply a custom function to grouped data in this scenario? Is there a specific method or best practice I should follow to avoid this behavior? Any insights would be greatly appreciated! Any examples would be super helpful.