How to efficiently filter a large data frame with multiple conditions in R without running into memory issues?
I'm having trouble with I'm sure I'm missing something obvious here, but After trying multiple solutions online, I still can't figure this out... Quick question that's been bugging me - I'm working with a substantial data frame in R, consisting of over 1 million rows and 20 columns... I need to filter this data frame based on multiple conditions, specifically to find rows where the `age` column is greater than 30, the `income` column is above 50000, and the `status` column is 'active'. I initially tried using the base R `subset()` function like this: ```r filtered_data <- subset(my_data, age > 30 & income > 50000 & status == 'active') ``` However, this approach seems to consume a lot of memory and takes a long time to execute, leading to R crashing occasionally. I also tried using `dplyr` for a cleaner syntax: ```r library(dplyr) filtered_data <- my_data %>% filter(age > 30, income > 50000, status == 'active') ``` While the `dplyr` approach is more readable, the performance is still not optimal, and I encounter similar memory issues. I looked into using `data.table` as an alternative, thinking it might handle larger datasets better. Here's what I tried: ```r library(data.table) setDT(my_data) filtered_data <- my_data[age > 30 & income > 50000 & status == 'active'] ``` While this did seem to improve performance slightly, I still received warnings about memory allocation, and it isn't as fast as I'd hoped. Is there a more efficient way to filter large data frames in R without running into these memory issues? Are there best practices I should be considering, or perhaps specific functions or packages designed for processing large datasets? I'm working on a web app that needs to handle this. Thanks in advance! This is part of a larger application I'm building. Is there a better approach? This is part of a larger application I'm building. The project is a CLI tool built with R. Any advice would be much appreciated. I'm open to any suggestions.