advanced patterns when using `data.table` for group operations in R with large datasets
I'm stuck trying to I've been struggling with this for a few days now and could really use some help. This might be a silly question, but I'm working with a puzzling scenario when using the `data.table` package to perform group operations on a large dataset in R. I'm trying to calculate the mean of a numeric column grouped by a categorical variable, but the results seem inconsistent with what I expect. Initially, I subsetted the data and then applied the mean function, but I noticed that some groups are returning NA values unexpectedly, even though there are no NA values in the original dataset. Here's a simplified version of my code: ```R library(data.table) # Sample data creation set.seed(123) dt <- data.table(id = 1:1000, category = sample(LETTERS[1:5], 1000, replace = TRUE), value = rnorm(1000)) # Attempt to calculate mean values by category result <- dt[, .(mean_value = mean(value)), by = category] print(result) ``` When I run this code, I sometimes get NA for `mean_value` in certain categories. I've checked for NA values in the `value` column, and `anyNA(dt$value)` returns FALSE, so I don't understand why `mean()` would return NA for those groups. To troubleshoot, I experimented with filtering the dataset before calculating the mean: ```R dt_filtered <- dt[!is.na(value)] result_filtered <- dt_filtered[, .(mean_value = mean(value)), by = category] print(result_filtered) ``` However, this didn't resolve the question. Furthermore, I looked into the `data.table` version I'm using, which is `1.14.0`. I've also validated that the `category` variable doesn't contain any unexpected factor levels. Could this be a question with how `data.table` handles grouping with large datasets? Or am I missing something in the way I've structured my code? Any insights or tips on how to resolve this scenario would be greatly appreciated! My development environment is Windows. How would you solve this? For context: I'm using R on CentOS. What's the correct way to implement this? Is there a simpler solution I'm overlooking? The stack includes R and several other technologies. Am I missing something obvious?