Quickly reading very large tables as dataframes
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='\t',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='\t',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?