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:

1. Using vroom from the tidyverse package vroom for importing data from csv/tab-delimited files directly into an R tibble. See Hector's answer.

2. Using fread in data.table for importing data from csv/tab-delimited files directly into R. See mnel's answer.

3. 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).

4. read.csv.raw from iotools provides a third option for quickly reading CSV files.

5. 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.

6. 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.

1. Set nrows=the number of records in your data (nmax in scan).

2. Make sure that comment.char="" to turn off interpretation of comments.

3. Explicitly define the classes of each column using colClasses in read.table.

4. 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.

Here is an example that utilizes fread from data.table 1.8.7

The examples come from the help page to fread, with the timings on my windows XP Core 2 duo E8400.

library(data.table)# Demo speedupn=1e6DT = data.table( a=sample(1:1000,n,replace=TRUE),                 b=sample(1:1000,n,replace=TRUE),                 c=rnorm(n),                 d=sample(c("foo","bar","baz","qux","quux"),n,replace=TRUE),                 e=rnorm(n),                 f=sample(1:1000,n,replace=TRUE) )DT[2,b:=NA_integer_]DT[4,c:=NA_real_]DT[3,d:=NA_character_]DT[5,d:=""]DT[2,e:=+Inf]DT[3,e:=-Inf]

standard read.table

write.table(DT,"test.csv",sep=",",row.names=FALSE,quote=FALSE)cat("File size (MB):",round(file.info("test.csv")$size/1024^2),"\n")    ## File size (MB): 51 system.time(DF1 <- read.csv("test.csv",stringsAsFactors=FALSE))        ##    user  system elapsed ##   24.71    0.15   25.42# second run will be fastersystem.time(DF1 <- read.csv("test.csv",stringsAsFactors=FALSE))        ##    user  system elapsed ##   17.85    0.07   17.98

optimized read.table

system.time(DF2 <- read.table("test.csv",header=TRUE,sep=",",quote="",                            stringsAsFactors=FALSE,comment.char="",nrows=n,                                             colClasses=c("integer","integer","numeric",                                                               "character","numeric","integer")))##    user  system elapsed ##   10.20    0.03   10.32

fread

require(data.table)system.time(DT <- fread("test.csv"))                                   ##    user  system elapsed ##    3.12    0.01    3.22

sqldf

require(sqldf)system.time(SQLDF <- read.csv.sql("test.csv",dbname=NULL))             ##    user  system elapsed ##   12.49    0.09   12.69# sqldf as on SOf <- file("test.csv")system.time(SQLf <- sqldf("select * from f", dbname = tempfile(), file.format = list(header = T, row.names = F)))##    user  system elapsed ##   10.21    0.47   10.73

ff / ffdf

 require(ff) system.time(FFDF <- read.csv.ffdf(file="test.csv",nrows=n))    ##    user  system elapsed  ##   10.85    0.10   10.99

In summary:

##    user  system elapsed  Method##   24.71    0.15   25.42  read.csv (first time)##   17.85    0.07   17.98  read.csv (second time)##   10.20    0.03   10.32  Optimized read.table##    3.12    0.01    3.22  fread##   12.49    0.09   12.69  sqldf##   10.21    0.47   10.73  sqldf on SO##   10.85    0.10   10.99  ffdf

I didn't see this question initially and asked a similar question a few days later. I am going to take my previous question down, but I thought I'd add an answer here to explain how I used sqldf() to do this.

There's been little bit of discussion as to the best way to import 2GB or more of text data into an R data frame. Yesterday I wrote a blog post about using sqldf() to import the data into SQLite as a staging area, and then sucking it from SQLite into R. This works really well for me. I was able to pull in 2GB (3 columns, 40mm rows) of data in < 5 minutes. By contrast, the read.csv command ran all night and never completed.

Here's my test code:

Set up the test data:

bigdf <- data.frame(dim=sample(letters, replace=T, 4e7), fact1=rnorm(4e7), fact2=rnorm(4e7, 20, 50))write.csv(bigdf, 'bigdf.csv', quote = F)

I restarted R before running the following import routine:

library(sqldf)f <- file("bigdf.csv")system.time(bigdf <- sqldf("select * from f", dbname = tempfile(), file.format = list(header = T, row.names = F)))

I let the following line run all night but it never completed:

system.time(big.df <- read.csv('bigdf.csv'))