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855

answers:

5

I have some very big delimited data files and I want to process only certain columns in R without taking the time and memory to create a data.frame for the whole file.

The only options I know of are read.table which is very wasteful when I only want a couple of columns or scan which seems too low level for what I want.

Is there a better option, either with pure R or perhaps calling out to some other shell script to do the column extraction and then using scan or read.table on it's output? (Which begs the question how to call a shell script and capture its output in R?).

+3  A: 

One possibility is to use pipe() in lieu of the filename and have awk or similar filters extract only the columns you want.

See help(connection) for more on pipe and friends.

Edit: read.table() can also do this for you if you are very explicit about colClasses -- a value of NULL for a given column skips the column alltogether. See help(read.table). So there we have a solution in base R without additional packages or tools.

Dirk Eddelbuettel
+2  A: 

I think Dirk's approach is straight forward as well as fast. An alternative that I've used is to load the data into sqlite which loads MUCH faster than read.table() and then pull out only what you want. the package sqldf() makes this all quite easy. Here's a link to a previous stack overflow answer that gives code examples for sqldf().

JD Long
+1  A: 

This is probably more than you need, but if you're operating on very large data sets then you might also have a look at the HadoopStreaming package which provides a map-reduce routine using Hadoop.

Shane
+2  A: 

There is a package, colbycol, designed to do exactly what you are looking for:

http://cran.r-project.org/web/packages/colbycol/index.html

Robert
Actually this looks like it still processes all the columns but without the in memory restrictions. Might be very useful in a slightly different context.
Alex Stoddard
+4  A: 

Sometimes I do something like this when I have the data in a tab-delimited file:

df <- read.table(pipe("cut -f1,5,28 myFile.txt"))

That lets cut do the data selection, which it can do without using much memory at all.

The pure-R equivalent (but using a lot more memory) would be:

df <- read.table("myFile.txt")[c(1,5,28)]
Ken Williams
Your first example is pretty much exactly what I have ended up using. (awk instead of cut in my case because of an irregular delimited file format).Your second example isn't truly equivalent as I understand it. Isn't it going to create the whole `data.frame` only to throw it away again? When I want 2 of 10 columns from a million row file that is a big different in performance.
Alex Stoddard
No, the pure R equivalant would be something like (assuming 28 colums) `mycols <- rep(NULL, 28); mycols[c(1,5,28)] <- NA; df <- read.table(file, colClasses=mycols)`
Dirk Eddelbuettel