How to handle memory allocation issues when merging large data frames in R using data.table?
After trying multiple solutions online, I still can't figure this out. I'm stuck trying to I'm working on a project and hit a roadblock. I'm working with memory allocation issues when trying to merge two large data frames (around 10 million rows each) using the `data.table` package in R. Specifically, I receive the behavior message: `behavior: want to allocate vector of size X GB`. I am running R version 4.1.0 on a machine with 16GB RAM, and I have already increased the memory limit using `memory.limit(size = 16000)`. Here's a simplified version of what I'm trying to do: ```R library(data.table) df1 <- data.table(id = 1:10000000, value = rnorm(10000000)) df2 <- data.table(id = 1:10000000, another_value = rnorm(10000000)) # Attempting to merge the two data tables result <- merge(df1, df2, by = 'id') ``` The behavior occurs during the merge operation, and I suspect it might be related to how `data.table` handles memory under the hood. I've tried using the `setkey()` function to index the data tables before merging, but it doesn't seem to help. Here’s what I did: ```R setkey(df1, id) setkey(df2, id) result <- merge(df1, df2, by = 'id') ``` This still results in the same memory allocation behavior. I've also looked into using the `dplyr` package for merging, but I am concerned about performance with such large datasets. Are there any best practices for merging large data frames in `data.table` that I might be missing? Also, is there a way to check if my data is larger than the available memory before attempting the merge? Any suggestions would be greatly appreciated. This is part of a larger application I'm building. I'm using R stable in this project. Could someone point me to the right documentation? My development environment is Ubuntu 20.04.