Limitations of R

[nick]

There is a pretty good discussion of this topic here:

Best practice to handle out-of-memory data

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 …

As @Dripdrop12 alluded to, part of the trick is that R can provide an excellent interface to the "standard" big data tools. Beyond that, recommendations on specific solutions generally depend on the specific problem.