R reading a huge csv

Question

I have a huge csv file. Its size is around 9 gb. I have 16 gb of ram. I followed the advises from the page and implemented them below.

If you get the error that R cannot allocate a vector of length x, close out of R and add the following line to the ``Target'' field: 
--max-vsize=500M 

Still I am getting the error and warnings below. How should I read the file of 9 gb into my R? I have R 64 bit 3.3.1 and I am running below command in the rstudio 0.99.903. I have windows server 2012 r2 standard, 64 bit os.

> memory.limit()
[1] 16383
> answer=read.csv("C:/Users/a-vs/results_20160291.csv")
Error: cannot allocate vector of size 500.0 Mb
In addition: There were 12 warnings (use warnings() to see them)
> warnings()
Warning messages:
1: In scan(file = file, what = what, sep = sep, quote = quote,  ... :
  Reached total allocation of 16383Mb: see help(memory.size)
2: In scan(file = file, what = what, sep = sep, quote = quote,  ... :
  Reached total allocation of 16383Mb: see help(memory.size)
3: In scan(file = file, what = what, sep = sep, quote = quote,  ... :
  Reached total allocation of 16383Mb: see help(memory.size)
4: In scan(file = file, what = what, sep = sep, quote = quote,  ... :
  Reached total allocation of 16383Mb: see help(memory.size)
5: In scan(file = file, what = what, sep = sep, quote = quote,  ... :
  Reached total allocation of 16383Mb: see help(memory.size)
6: In scan(file = file, what = what, sep = sep, quote = quote,  ... :
  Reached total allocation of 16383Mb: see help(memory.size)
7: In scan(file = file, what = what, sep = sep, quote = quote,  ... :
  Reached total allocation of 16383Mb: see help(memory.size)
8: In scan(file = file, what = what, sep = sep, quote = quote,  ... :
  Reached total allocation of 16383Mb: see help(memory.size)
9: In scan(file = file, what = what, sep = sep, quote = quote,  ... :
  Reached total allocation of 16383Mb: see help(memory.size)
10: In scan(file = file, what = what, sep = sep, quote = quote,  ... :
  Reached total allocation of 16383Mb: see help(memory.size)
11: In scan(file = file, what = what, sep = sep, quote = quote,  ... :
  Reached total allocation of 16383Mb: see help(memory.size)
12: In scan(file = file, what = what, sep = sep, quote = quote,  ... :
  Reached total allocation of 16383Mb: see help(memory.size)

------------------- Update1

My 1st try based upon suggested answer

> thefile=fread("C:/Users/a-vs/results_20160291.csv", header = T)
Read 44099243 rows and 36 (of 36) columns from 9.399 GB file in 00:13:34
Warning messages:
1: In fread("C:/Users/a-vsingh/results_tendo_20160201_20160215.csv",  :
  Reached total allocation of 16383Mb: see help(memory.size)
2: In fread("C:/Users/a-vsingh/results_tendo_20160201_20160215.csv",  :
  Reached total allocation of 16383Mb: see help(memory.size)

------------------- Update2

my 2nd try based upon suggested answer is as below

thefile2 <- read.csv.ffdf(file="C:/Users/a-vs/results_20160291.csv", header=TRUE, VERBOSE=TRUE, 
+                    first.rows=-1, next.rows=50000, colClasses=NA)
read.table.ffdf 1..
Error: cannot allocate vector of size 125.0 Mb
In addition: There were 14 warnings (use warnings() to see them)

How could I read this file into a single object so that I can analyze the entire data in one go

------------------update 3

We bought an expensive machine. It has 10 cores and 256 gb ram. That is not the most efficient solution but it works at least in near future. I looked at below answers and I dont think they solve my problem :( I appreciate these answers. I want to perform the market basket analysis and I dont think there is no other way around rather than keeping my data in RAM

Answer 1

Make sure you're using 64-bit R, not just 64-bit Windows, so that you can increase your RAM allocation to all 16 GB.

In addition, you can read in the file in chunks:

file_in    <- file("in.csv","r")
chunk_size <- 100000 # choose the best size for you
x          <- readLines(file_in, n=chunk_size)

You can use data.table to handle reading and manipulating large files more efficiently:

require(data.table)
fread("in.csv", header = T)

If needed, you can leverage storage memory with ff:

library("ff")
x <- read.csv.ffdf(file="file.csv", header=TRUE, VERBOSE=TRUE, 
                   first.rows=10000, next.rows=50000, colClasses=NA)