How to implement guide with using `data.table` for memory-efficient operations on large datasets in r 4.2.1
I'm trying to debug I need some guidance on I've searched everywhere and can't find a clear answer. I'm working with a scenario with memory usage while performing operations on a large dataset using the `data.table` package in R version 4.2.1. My dataset is about 10 million rows with around 15 columns, and I want to calculate the mean of a numeric column grouped by a categorical column. However, when I execute my code, it seems to run out of memory and crashes R. I've tried using the `setDT()` function to convert my data frame to a data.table, and I attempted to leverage the `by` argument for grouping, but the memory usage is still excessively high. Here’s the code snippet I'm using: ```r library(data.table) df <- as.data.table(your_large_dataframe) result <- df[, .(mean_value = mean(numeric_column, na.rm = TRUE)), by = categorical_column] ``` I also tried using `gc()` to clear memory, but the question continues. The behavior message I get is: `behavior: want to allocate vector of size X Gb`. I suspect that the way I’m handling NAs might be contributing to this scenario. Are there any best practices or optimizations in `data.table` I can implement to reduce memory consumption during this operation? Any advice on efficient ways to handle large datasets in R would be greatly appreciated. How would you solve this? This is part of a larger application I'm building. How would you solve this? Is there a better approach?