I have a R program that combines 10 files each file is of size 296MB and I have increased the memory size to 8GB (Size of RAM)
--max-mem-size=8192M
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I suggest incorporating the suggestions in ?read.csv2:
Memory usage:
These functions can use a surprising amount of memory when reading large files. There is extensive discussion in the ‘R Data Import/Export’ manual, supplementing the notes here. Less memory will be used if ‘colClasses’ is specified as one of the six atomic vector classes. This can be particularly so when reading a column that takes many distinct numeric values, as storing each distinct value as a character string can take up to 14 times as much memory as storing it as an integer. Using ‘nrows’, even as a mild over-estimate, will help memory usage. Using ‘comment.char = ""’ will be appreciably faster than the ‘read.table’ default. ‘read.table’ is not the right tool for reading large matrices, especially those with many columns: it is designed to read _data frames_ which may have columns of very different classes. Use ‘scan’ instead for matrices.