data.table <a href="https://r-datatable.com"><img src="https://raw.githubusercontent.com/Rdatatable/data.table/master/.graphics/logo.png" align="right" height="140" /></a>

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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.

<div align="center"> <a href="https://numfocus.org/project/data-table"> <img width="25%" src="https://raw.githubusercontent.com/numfocus/templates/master/images/numfocus-logo.png" align="center"> </a> </div> <br>

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

• Introduction to data.table vignette
• Getting started wiki page
• Examples produced by example(data.table)

Cheatsheets

<a href="https://raw.githubusercontent.com/rstudio/cheatsheets/master/datatable.pdf"><img src="https://raw.githubusercontent.com/rstudio/cheatsheets/master/pngs/datatable.png" width="615" height="242"/></a>

Community

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

• click the Watch button at the top and right of GitHub project page
• read NEWS file
• follow #rdatatable and the r_data_table account on X/Twitter
• follow #rdatatable and the r_data_table account on fosstodon
• follow the data.table community page on LinkedIn
• watch recent Presentations
• read recent Articles
• read posts on The Raft

Contributing

Guidelines for filing issues / pull requests: Contribution Guidelines.