scenarios in `data.table` aggregation with NA values causing unexpected results in R 4.3.1
I'm performance testing and I'm sure I'm missing something obvious here, but I've been banging my head against this for hours....... I'm working with an scenario while trying to aggregate data using the `data.table` package in R 4.3.1. Specifically, when I perform a sum operation on a column that contains NA values, it seems to be returning unexpected results. I want to calculate the total sales by region, but instead of treating NA values as zeros, it appears they are being ignored entirely, leading to incorrect totals. Here's a simplified version of my code: ```r library(data.table) dt <- data.table( region = c('North', 'South', 'East', 'West', 'North'), sales = c(100, 200, NA, 300, 400) ) result <- dt[, .(total_sales = sum(sales)), by = region] print(result) ``` When I run this code, I get the following output: ``` region total_sales 1: East NA 2: North 500 3: South 200 4: West 300 ``` The 'East' region shows NA instead of 0, and I expected it to count as zero in the total. I've tried using `na.rm = TRUE` within the sum function, but it seems to still lead to incorrect results when grouping. Is there a way to properly handle NA values in this scenario so that they count as zero in my sums? I also checked the documentation for `data.table`, but I didn’t find anything explicitly addressing NA handling in grouped summaries. Any suggestions would be greatly appreciated! Has anyone else encountered this? This is part of a larger application I'm building. What am I doing wrong? I've been using R for about a year now. Thanks, I really appreciate it! For context: I'm using R on Ubuntu 22.04. Any help would be greatly appreciated! This is part of a larger microservice I'm building. I'd really appreciate any guidance on this. I'm working with R in a Docker container on Ubuntu 20.04. I'd be grateful for any help.