Confusion with R's `filter` and `mutate` when working with grouped data and new columns
I'm encountering unexpected behavior when trying to filter a grouped data frame after using `mutate` to create a new column. I have a data frame with sales data, and I want to calculate the average sales per group. Here's a simplified version of my code: ```r library(dplyr) # Create a sample data frame sales_data <- data.frame( region = c('North', 'North', 'South', 'South', 'East', 'East'), sales = c(100, 200, 150, 300, 400, 500) ) # Attempt to calculate average sales and filter regions with average sales above 250 result <- sales_data %>% group_by(region) %>% mutate(avg_sales = mean(sales)) %>% filter(avg_sales > 250) print(result) ``` However, I'm getting an empty data frame as a result. The intended output should include the 'East' region, but it seems like the filtering is not recognizing the newly created `avg_sales` column properly. I've double-checked my calculations, and I believe they are correct. I’ve also tried using `summarize` instead of `mutate`, but that leads to a completely different structure: ```r result <- sales_data %>% group_by(region) %>% summarize(avg_sales = mean(sales)) %>% filter(avg_sales > 250) ``` This returns the expected result, but I need to keep the original data as well. Is there a way to achieve this while keeping my original rows intact? Am I missing something in how `mutate` and `filter` work with grouped data? Any insights would be greatly appreciated!