Profiling R code performance while aggregating large datasets using data.table
I'm experimenting with Does anyone know how to Recently started working with an R application that processes extensive datasets for real-time analytics. Given the size of the data, performance is critical. I decided to use the `data.table` package for its speed and efficiency. However, I ran into performance bottlenecks while aggregating data. My current code looks something like this: ```r library(data.table) # Sample dataset set.seed(42) dt <- data.table(id = rep(1:1000, each = 100), value = rnorm(100000)) # Attempting aggregation result <- dt[, .(mean_value = mean(value)), by = id] ``` While this works, the execution time isn’t satisfactory when scaled to larger datasets (over 10 million rows). I've tried using the `setkey()` function prior to aggregation, hoping to improve the performance: ```r setkey(dt, id) result <- dt[, .(mean_value = mean(value)), by = id] ``` This provided some gains, but the performance still lags, especially as the data size grows. I read about using `dplyr` for its clarity, but I’m worried about potential slowdowns with larger datasets. I also explored parallel processing with the `future` and `furrr` packages: ```r library(furrr) plan(multisession) result <- future_map_dfr(unique(dt$id), function(i) { dt[id == i, .(mean_value = mean(value))] }) ``` This approach seems promising, but I'm uncertain if it genuinely outperforms the `data.table` approach in this context. Any insights on best practices for optimizing data aggregation in R, particularly for massive datasets? Recommendations on profiling tools to analyze where the bottlenecks might be would be greatly appreciated. For context: I'm using R on macOS. How would you solve this? I'm working on a service that needs to handle this. Any advice would be much appreciated. I recently upgraded to R stable. Any feedback is welcome!