R package data.table extends data.frame.

Fast aggregation of large data (e.g. 100GB in RAM), fast ordered joins, fast add/modify/delete of columns by reference by group using no copies at all, cells can contain vectors, chained queries and a fast file reader (fread). Offers a natural and flexible syntax, for faster development.

The main benefit of data.table is its syntax - ability to combine where, select|update and by into one query without having to string together a sequence of isolated function calls. Infact, speed is only secondary.

data.table builds on base-R functionality to reduce two types of time:

1. Programming time (easier to write, read, debug and maintain).
2. Compute time.

* DT[X]                          # fast join for large data, DT and X are data.tables.
* DT[, sum(b*c), by=a]           # fast aggregation, a, b and c are column names
* DT[i, b := 3.14]               # add new column (or modify existing column) by reference
* DT[, p := x/sum(x), by=grp]    # fast sub-assignment (to column b) by reference.
* fread('big.csv')               # is 3+ times faster than read.csv(, colClasses, nrow, etc).

All even numbered releases (ex: 1.9.0, 1.9.2 etc.) are stable versions available on CRAN. Similarly all odd numbered releases are development versions.

The current stable release is v1.9.2 on CRAN, released 27^th Feb 2014. To install, open an R session and type:

install.packages("data.table")

The current development version is 1.9.3. If you're interested in staying up-to-date, you can do so by installing the latest commit using devtools as follows:

devtools:::install_github("datatable", "Rdatatable")

To be updated...

Stackoverflow's data.table tag is an excellent place to get started. You can search if your question has already been answered, and if not, you can post a question with a nice reproducible example there. At the time of writing, 93.7% of the questions under the data.table tag have answers.

Another place to ask questions is the data.table mailing list. It requires a subscription, which is fairly straightforward. Once you've subscribed to the mailing list, you can start posting by sending an email to datatable-help @ lists.r-forge.r-project.org.

You can browse the questions asked previously on the mailing list on Nabble, Gmane, HTML archive or RSS feed.