How can R reading large datasets with insufficient memory be solved, and is there an efficient way to do it if utilizing Linux?

One relatively simple and violent way is the nrows parameter setting in fread.

R all read data function has nrows and skip these two parameters, you can write a loop, a file read in segments, skip skip the previous read, with nrows control the maximum number of lines read, to prevent memory shortage.

Write the file locally after segmentation, use rm to eliminate larger variables, and use gc() to free this memory.

- the advantage is simple and rough multi-platform available, the disadvantage is that, really slow ......

If the conditions are still recommended to apply for a server to run, for example, students can ask the mentor faculty whether there are servers for large-scale computations

It is also recommended to apply for a server to run it.

Also Unix systems do have better memory limitations than Windows, but unfortunately I don't know the right way to open it either QAQ

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