data.table provides a high-performance version of base R’s data.frame with syntax and feature enhancements for ease of use, convenience and programming speed.
The data.table project uses a custom governance agreement
and is fiscally sponsored by NumFOCUS. Consider making
a tax-deductible donation to help the project
pay for developer time, professional services, travel, workshops, and a variety of other needs.
Why data.table?
- concise syntax: fast to type, fast to read
- fast speed
- memory efficient
- careful API lifecycle management
- community
- feature rich
Features
- fast and friendly delimited file reader:
?fread, see also convenience features for small data
- fast and feature rich delimited file writer:
?fwrite
- low-level parallelism: many common operations are internally parallelized to use multiple CPU threads
- fast and scalable aggregations; e.g. 100GB in RAM (see benchmarks on up to two billion rows)
- fast and feature rich joins: ordered joins (e.g. rolling forwards, backwards, nearest and limited staleness), overlapping range joins (similar to
IRanges::findOverlaps), non-equi joins (i.e. joins using operators >, >=, <, <=), aggregate on join (by=.EACHI), update on join
- fast add/update/delete columns by reference by group using no copies at all
- fast and feature rich reshaping data:
?dcast (pivot/wider/spread) and ?melt (unpivot/longer/gather)
- any R function from any R package can be used in queries not just the subset of functions made available by a database backend, also columns of type
list are supported
- has no dependencies at all other than base R itself, for simpler production/maintenance
- the R dependency is as old as possible for as long as possible, currently R 3.5.0 (2018), and we continuously test against that version
Installation
install.packages("data.table")
# latest development version (only if newer available)
data.table::update_dev_pkg()
# latest development version (force install)
install.packages("data.table", repos="https://rdatatable.gitlab.io/data.table")
See the Installation wiki for more details.
Usage
Use data.table subset [ operator the same way you would use data.frame one, but…
- no need to prefix each column with
DT$ (like subset() and with() but built-in)
- any R expression using any package is allowed in
j argument, not just list of columns
- extra argument
by to compute j expression by group
library(data.table)
DT = as.data.table(iris)
# FROM[WHERE, SELECT, GROUP BY]
# DT [i, j, by]
DT[Petal.Width > 1.0, mean(Petal.Length), by = Species]
# Species V1
#1: versicolor 4.362791
#2: virginica 5.552000
Getting started
Cheatsheets
data.table is widely used by the R community. It is being directly used by hundreds of CRAN and Bioconductor packages, and indirectly by thousands. It is one of the top most starred R packages on GitHub, and was highly rated by the Depsy project. If you need help, the data.table community is active on StackOverflow.
A list of packages that significantly support, extend, or make use of data.table can be found in the Seal of Approval document.
Stay up-to-date
Contributing
Guidelines for filing issues / pull requests: Contribution Guidelines.
Links
Citation
Barrett T, Dowle M, Srinivasan A, Gorecki J, Chirico M, Hocking T,
Schwendinger B, Krylov I (2026). data.table: Extension of
data.frame. R package version 1.18.99, https://r-datatable.com.
@Manual{,
title = {data.table: Extension of `data.frame`},
author = {Tyson Barrett and Matt Dowle and Arun Srinivasan and Jan Gorecki and Michael Chirico and Toby Hocking and Benjamin Schwendinger and Ivan Krylov},
year = {2026},
note = {R package version 1.18.99},
url = {https://r-datatable.com},
}