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?