write.csv for large data.table

Solution 1:

UPDATE 2019.01.07:

fwrite has been on CRAN since 2016-11-25.

install.packages("data.table")

UPDATE 08.04.2016:

fwrite has been recently added to the data.table package's development version. It also runs in parallel (implicitly).

# Install development version of data.table
install.packages("data.table", 
                  repos = "https://Rdatatable.github.io/data.table", type = "source")

# Load package
library(data.table)

# Load data        
data(USArrests)

# Write CSV
fwrite(USArrests, "USArrests_fwrite.csv")

According to the detailed benchmark tests shown under speeding up the performance of write.table, fwrite is ~17x faster than write.csv there (YMMV).

UPDATE 15.12.2015:

In the future there might be a fwrite function in the data.table package, see: https://github.com/Rdatatable/data.table/issues/580. In this thread a GIST is linked, which provides a prototype for such a function speeding up the process by a factor of 2 (according to the author, https://gist.github.com/oseiskar/15c4a3fd9b6ec5856c89).

ORIGINAL ANSWER:

I had the same problems (trying to write even larger CSV files) and decided finally against using CSV files.

I would recommend you to use SQLite as it is much faster than dealing with CSV files:

require("RSQLite")
# Set up database    
drv <- dbDriver("SQLite")
con <- dbConnect(drv, dbname = "test.db")
# Load example data
data(USArrests)
# Write data "USArrests" in table "USArrests" in database "test.db"    
dbWriteTable(con, "arrests", USArrests)

# Test if the data was correctly stored in the database, i.e. 
# run an exemplary query on the newly created database 
dbGetQuery(con, "SELECT * FROM arrests WHERE Murder > 10")       
# row_names Murder Assault UrbanPop Rape
# 1         Alabama   13.2     236       58 21.2
# 2         Florida   15.4     335       80 31.9
# 3         Georgia   17.4     211       60 25.8
# 4        Illinois   10.4     249       83 24.0
# 5       Louisiana   15.4     249       66 22.2
# 6        Maryland   11.3     300       67 27.8
# 7        Michigan   12.1     255       74 35.1
# 8     Mississippi   16.1     259       44 17.1
# 9          Nevada   12.2     252       81 46.0
# 10     New Mexico   11.4     285       70 32.1
# 11       New York   11.1     254       86 26.1
# 12 North Carolina   13.0     337       45 16.1
# 13 South Carolina   14.4     279       48 22.5
# 14      Tennessee   13.2     188       59 26.9
# 15          Texas   12.7     201       80 25.5

# Close the connection to the database
dbDisconnect(con)

For further information, see http://cran.r-project.org/web/packages/RSQLite/RSQLite.pdf

You can also use a software like http://sqliteadmin.orbmu2k.de/ to access the database and export the database to CSV etc.

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