Quickly reading very large tables as dataframes?

Question

I have very large tables (30 million rows) that I would like to load as a dataframes in R. `read.table()` has a lot of convenient features, but it seems like there is a lot of logic in the implementation that would slow things down. In my case, I am assuming I know the types of the columns ahead of time, the table does not contain any column headers or row names, and does not have any pathological characters that I have to worry about.I know that reading in a table as a list using `scan()` can be quite fast, e.g.: datalist <- scan('myfile',sep='\ ',list(url='',popularity=0,mintime=0,maxtime=0)))But some of my attempts to convert this to a dataframe appear to decrease the performance of the above by a factor of 6: df <- as.data.frame(scan('myfile',sep='\ ',list(url='',popularity=0,mintime=0,maxtime=0))))Is there a better way of doing this? Or quite possibly completely different approach to the problem?

Answer 1

One alternative is to use the fread() function from the {data.table} package, e.g., data.table::fread(myfile).