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 the

Answer 1

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'))