Quickly reading very large tables as dataframes
dataframe, import, r, r-faq
Solution
An update, several years later
This answer is old, and R has moved on. Tweaking `read.table` to run a bit faster has precious little benefit. Your options are:
Using `vroom` from the tidyverse package `vroom` for importing data from csv/tab-delimited files directly into an R tibble. See Hector's answer.
Using `fread` in `data.table` for importing data from csv/tab-delimited files directly into R. See mnel's answer.
Using `read_table` in `readr` (on CRAN from April 2015). This works much like `fread` above. The readme in the link explains the difference between the two functions (`readr` currently claims to be "1.5-2x slower" than `data.table::fread`).
`read.csv.raw` from `iotools` provides a third option for quickly reading CSV files.
Trying to store as much data as you can in databases rather than flat files. (As well as being a better permanent storage medium, data is passed to and from R in a binary format, which is faster.) `read.csv.sql` in the `sqldf` package, as described in JD Long's answer, imports data into a temporary SQLite database and then reads it into R. See also: the `RODBC` package, and the reverse depends section of the `DBI` package page. `MonetDB.R` gives you a data type that pretends to be a data frame but is really a MonetDB underneath, increasing performance. Import data with its `monetdb.read.csv` function. `dplyr` allows you to work directly with data stored in several types of database.
Storing data in binary formats can also be useful for improving performance. Use `saveRDS`/`readRDS` (see below), the `h5` or `rhdf5` packages for HDF5 format, or `write_fst`/`read_fst` from the `fst` package.
The original answer
There are a couple of simple things to try, whether you use read.table or scan.
Set `nrows`=the number of records in your data (`nmax` in `scan`).
Make sure that `comment.char=""` to turn off interpretation of comments.
Explicitly define the classes of each column using `colClasses` in `read.table`.
Setting `multi.line=FALSE` may also improve performance in scan.
If none of these thing work, then use one of the profiling packages to determine which lines are slowing things down. Perhaps you can write a cut down version of `read.table` based on the results.
The other alternative is filtering your data before you read it into R.
Or, if the problem is that you have to read it in regularly, then use these methods to read the data in once, then save the data frame as a binary blob with `save` `saveRDS`, then next time you can retrieve it faster with `load` `readRDS`.
Problem
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?