nick
September 25, 2017, 7:03pm
2
Any specific recommendations will depend on what kind of files they are and what you want to do with them. Also, check out these recent threads in case they will help:
One of the limitations of R that I have read about is that it needs to load entire data into memory, so its not suitable for analyses of big data. Has anything been done to address this problem? Are there any workarounds?
Hello, I am a data analyst using mainly R and SAS.
While I generally prefer to use R and tidyverse tools for my data science and programming tasks, I miss SAS datasets whenever R data frames consume all the memory.
I could use variety of R packages to handle large data (bigmemory, ff, dplyr interface to databases, etc.), but unified binary data format on disk as in SAS has several advantages: ease of data management, no need to learn additional syntax, being able to create many intermediate datasets without caring about RAM (which makes debugging easy).
So, I want to ask R community:
What are your ideas, techniques, workflows, and best practices to handle out-of-memory data in R ? (I …