Importing `data.table`

This document is focused on using data.table as a dependency in other R packages. If you are interested in using data.table C code from a non-R application, or in calling its C functions directly, jump to the last section of this vignette.

Importing data.table is no different from importing other R packages. This vignette is meant to answer the most common questions arising around that subject; the lessons presented here can be applied to other R packages.

Why to import data.table

One of the biggest features of data.table is its concise syntax which makes exploratory analysis faster and easier to write and perceive; this convenience can drive packages authors to use data.table in their own packages. Another maybe even more important reason is high performance. When outsourcing heavy computing tasks from your package to data.table, you usually get top performance without needing to re-invent any high of these numerical optimization tricks on your own.

Importing data.table is easy

It is very easy to use data.table as a dependency due to the fact that data.table does not have any of its own dependencies. This statement is valid for both operating system dependencies and R dependencies. It means that if you have R installed on your machine, it already has everything needed to install data.table. This also means that adding data.table as a dependency of your package will not result in a chain of other recursive dependencies to install, making it very convenient for offline installation.


The first place to define a dependency in a package is the DESCRIPTION file. Most commonly, you will need to add data.table under the Imports: field. Doing so will necessitate an installation of data.table before your package can compile/install. As mentioned above, no other packages will be installed because data.table does not have any dependencies of its own. You can also specify the minimal required version of a dependency; for example, if your package is using the fwrite function, which was introduced in data.table in version 1.9.8, you should incorporate this as Imports: data.table (>= 1.9.8). This way you can ensure that the version of data.table installed is 1.9.8 or later before your users will be able to install your package. Besides the Imports: field, you can also use Depends: data.table but we strongly discourage this approach (and may disallow it in future) because this loads data.table into your user's workspace; i.e. it enables data.table functionality in your user's scripts without them requesting that. Imports: is the proper way to use data.table within your package without inflicting data.table on your user. In fact, we hope the Depends: field is eventually deprecated in R since this is true for all packages.


The next thing is to define what content of data.table your package is using. This needs to be done in the NAMESPACE file. Most commonly, package authors will want to use import(data.table) which will import all (exported, i.e., listed in data.table's own NAMESPACE file) functions from data.table.

You may also want to use just a subset of data.table functions; for example, some packages may simply make use of data.table's high-performance CSV reader and writer, for which you can add importFrom(data.table, fread, fwrite) in your NAMESPACE file. It is also possible to import all functions from a package excluding particular ones using import(data.table, except=c(fread, fwrite)).


As an example we will define two functions in a.pkg package that uses data.table. One function, gen, will generate a simple data.table; another, aggr, will do a simple aggregation of it.

gen = function (n = 100L) { dt = = seq_len(n))) dt[, grp := ((id - 1) %% 26) + 1 ][, grp := letters[grp] ][] } aggr = function (x) { stopifnot(, "grp" %in% names(x) ) x[, .N, by = grp] }


Be sure to include tests in your package. Before each major release of data.table, we check reverse dependencies. This means that if any changes in data.table would break your code, we will be able to spot breaking changes and inform you before releasing the new version. This of course assumes you will publish your package to CRAN or Bioconductor. The most basic test can be a plaintext R script in your package directory tests/test.R:

library(a.pkg) dt = gen() stopifnot(nrow(dt) == 100) dt2 = aggr(dt) stopifnot(nrow(dt2) < 100)

When testing your package, you may want to use R CMD check --no-stop-on-test-error, which will continue after an error and run all your tests (as opposed to stopping on the first line of script that failed) NB this requires R 3.4.0 or greater.

Testing using testthat

It is very common to use the testthat package for purpose of tests. Testing a package that imports data.table is no different from testing other packages. An example test script tests/testthat/test-pkg.R:

context("pkg tests") test_that("generate dt", { expect_true(nrow(gen()) == 100) }) test_that("aggregate dt", { expect_true(nrow(aggr(gen())) < 100) })

Dealing with "undefined global functions or variables"

data.table's use of R's deferred evaluation (especially on the left-hand side of :=) is not well-recognised by R CMD check. This results in NOTEs like the following during package check:

* checking R code for possible problems ... NOTE
aggr: no visible binding for global variable 'grp'
gen: no visible binding for global variable 'grp'
gen: no visible binding for global variable 'id'
Undefined global functions or variables:
grp id

The easiest way to deal with this is to pre-define those variables within your package and set them to NULL, optionally adding a comment (as is done in the refined version of gen below). When possible, you could also use a character vector instead of symbols (as in aggr below):

gen = function (n = 100L) { id = grp = NULL # due to NSE notes in R CMD check dt = = seq_len(n))) dt[, grp := ((id - 1) %% 26) + 1 ][, grp := letters[grp] ][] } aggr = function (x) { stopifnot(, "grp" %in% names(x) ) x[, .N, by = "grp"] }

The case for := is slightly different, because := is interpreted as a function in the above code; so instead of registering := as NULL, you must register it as a function, e.g.:

`:=` = function(...) NULL

If you don't mind having id and grp registered as variables globally in your package namespace you can use ?globalVariables. Be aware that these notes do not have any impact on the code or its functionality; if you are not going to publish your package, you may simply choose to ignore them.


If you face any problems in this process, before trying to ask questions or reporting issues, please confirm that the problem is reproducible in a clean R session using the R console: R CMD check

Some of the most common issues developers are facing are usually related to helper tools that are meant to automate some package development tasks, for example, using roxygen to generate your NAMESPACE file from metadata in the R code files. Others are related to helpers that build and check the package. Unfortunately, these helpers sometimes have unintended/hidden side effects which can obscure the source of your troubles. As such, be sure to double check using R console (run R on the command line) and ensure the import is defined in the DESCRIPTION and NAMESPACE files following the instructions above.

If you are not able to reproduce problems you have using the plain R console build and check, you may try to get some support based on past issues we've encountered with data.table interacting with helper tools: devtools#192 or devtools#1472.


Since version 1.10.5 data.table is licensed as Mozilla Public License (MPL). The reasons for the change from GPL should be read in full here and you can read more about MPL on Wikipedia here and here.

Optionally import data.table: Suggests

If you want to use data.table conditionally, i.e., only when it is installed, you should use Suggests: data.table in your DESCRIPTION file instead of using Imports: data.table. By default this definition will not force installation of data.table when installing your package. This also requires you to conditionally use data.table in your package code which should be done using the ?requireNamespace function. The below example demonstrates conditional use of data.table's fast CSV writer ?fwrite. If the data.table package is not installed, the much-slower base R ?write.table function is used instead.

my.write = function (x) { if(requireNamespace("data.table", quietly=TRUE)) { data.table::fwrite(x, "data.csv") } else { write.table(x, "data.csv") } }

A slightly more extended version of this would also ensure that the installed version of data.table is recent enough to have the fwrite function available:

my.write = function (x) { if(requireNamespace("data.table", quietly=TRUE) && utils::packageVersion("data.table") >= "1.9.8") { data.table::fwrite(x, "data.csv") } else { write.table(x, "data.csv") } }

When using a package as a suggested dependency, you should not import it in the NAMESPACE file. Just mention it in the DESCRIPTION file. You also must use the data.table:: prefix when calling data.table functions because none of them are imported.

data.table in Imports but nothing imported

Some users (e.g.) may prefer to eschew using importFrom or import in their NAMESPACE file and instead use data.table:: qualification on all internal code (of course keeping data.table under their Imports: in DESCRIPTION).

In this case, the un-exported function [.data.table will revert to calling [.data.frame as a safeguard since data.table has no way of knowing that the parent package is aware it's attempting to make calls against the syntax of data.table's query API (which could lead to unexpected behavior as the structure of calls to [.data.frame and [.data.table fundamentally differ, e.g. the latter has many more arguments).

If this is anyway your preferred approach to package development, please define .datatable.aware = TRUE anywhere in your R source code (no need to export). This tells data.table that you as a package developer have designed your code to intentionally rely on data.table functionality even though it may not be obvious from inspecting your NAMESPACE file.

data.table determines on the fly whether the calling function is aware it's tapping into data.table with the internal cedta function (Calling Environment is Data Table Aware), which, beyond checking the?getNamespaceImports` for your package, also checks the existence of this variable (among other things).

Further information on dependencies

For more canonical documentation of defining packages dependency check the official manual: Writing R Extensions

Importing from non-R Applications {non-r-API}

Some tiny parts of data.table C code were isolated from the R C API and can now be used from non-R applications by linking to .so / .dll files. More concrete details about this will be provided later; for now you can study the C code that was isolated from the R C API in src/fread.c and src/fwrite.c.