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The Ultimate Guide to the data.table Package in R
Explore valuable documentation and insights to make the most of the data.table package in R. Get ready to unlock the full potential of the data.table package!
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What is the data.table package?
In this section, we’ll delve into the fundamental aspects and key features of the package.
Data.table is a package that provides an enhanced version of data frames in R. It supports fast reading and writing of large datasets, efficient manipulation of data, and a variety of operations such as aggregation, merging, and reshaping.
- Title: Extension of `data.frame`
- Description: Fast aggregation of large data (e.g. 100GB in RAM), fast ordered joins, fast add/modify/delete of columns by group using no copies at all, list columns, friendly and fast character-separated-value read/write. Offers a natural and flexible syntax, for faster development.
- Author: Matt Dowle [aut, cre], Arun Srinivasan [aut], Jan Gorecki [ctb], Michael Chirico [ctb], Pasha Stetsenko [ctb], Tom Short [ctb], Steve Lianoglou [ctb], Eduard Antonyan [ctb], Markus Bonsch [ctb], Hugh Parsonage [ctb], Scott Ritchie [ctb], Kun Ren [ctb], Xianying Tan [ctb], Rick Saporta [ctb], Otto Seiskari [ctb], Xianghui Dong [ctb], Michel Lang [ctb], Watal Iwasaki [ctb], Seth Wenchel [ctb], Karl Broman [ctb], Tobias Schmidt [ctb], David Arenburg [ctb], Ethan Smith [ctb], Francois Cocquemas [ctb], Matthieu Gomez [ctb], Philippe Chataignon [ctb], Nello Blaser [ctb], Dmitry Selivanov [ctb], Andrey Riabushenko [ctb], Cheng Lee [ctb], Declan Groves [ctb], Daniel Possenriede [ctb], Felipe Parages [ctb], Denes Toth [ctb], Mus Yaramaz-David [ctb], Ayappan Perumal [ctb], James Sams [ctb], Martin Morgan [ctb], Michael Quinn [ctb], @javrucebo [ctb], @marc-outins [ctb], Roy Storey [ctb], Manish Saraswat [ctb], Morgan Jacob [ctb], Michael Schubmehl [ctb], Davis Vaughan [ctb], Toby Hocking [ctb], Leonardo Silvestri [ctb], Tyson Barrett [ctb], Jim Hester [ctb], Anthony Damico [ctb], Sebastian Freundt [ctb], David Simons [ctb], Elliott Sales de Andrade [ctb], Cole Miller [ctb], Jens Peder Meldgaard [ctb], Vaclav Tlapak [ctb], Kevin Ushey [ctb], Dirk Eddelbuettel [ctb], Ben Schwen [ctb]
- Maintainer: Matt Dowle
How to install the data.table package?
In this section, we’ll walk you through the process of installing and loading the data.table package. By following these steps, you can seamlessly add new functions, datasets, and other resources to your R environment for a more robust workflow.
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What package information should you know?
In this section, we’ll go over the technical aspects of the data.table package.
Key features
- Datasets: Many R packages include built-in datasets that you can use to familiarize yourself with their functionalities. To identify built-in datasets. To identify the datasets for the data.table package, visit our database of R datasets.
- Vignettes: R vignettes are documents that include examples for using a package. To view the list of available vignettes for the data.table package, you can visit our visit our database of R vignettes.
- Citation: Citing R packages in your publications is important as it recognizes the contributions of the developers. To find citation information for the data.table package, visit our database of R package citations.
Technical details
- License type: MPL-2.0 | file LICENSE. For license details, visit the Open Source Initiative website.
- Compilation requirements: Some R packages include internal code that must be compiled for them to function correctly. The data.table package has compilation requirements.
- Required dependencies: A required dependency refers to another package that is essential for the functioning of the main package. The data.table package has the following required dependencies: R (>= 3.1.0).
- Suggested dependencies: A suggested dependency adds extra features to the main package, but the main package can work without it. The data.table package has the following suggested dependencies: bit64 (>= 4.0.0), bit (>= 4.0.4), curl, R.utils, xts, nanotime, zoo (>= 1.8-1), yaml, knitr, rmarkdown.
- External dependencies: External dependencies are other packages that the main package depends on for linking at compile time. The data.table package does not use any external sources.
- Imported packages: Importing packages allows developers to leverage existing code and functionalities without having to reinvent the wheel. The data.table package has the following imported packages: methods.
- Enhancements: Enhancements help developers expand the capabilities of their packages without starting from scratch. The data.table package has no enhancements.
How to get help with the data.table package?
In this section, we’ll discuss a variety of available resources for getting help with the data.table package.
Key resources
- The help() function: R’s built-in help system is a handy tool to find documentation. You can use the help("data.table") function to retrieve detailed information, examples, and usage instructions. Alternatively, you can use the ? operator as a shortcut.
-
Package website: The data.table package has a dedicated website. You can visit: https://r-datatable.com, https://Rdatatable.gitlab.io/data.table, https://github.com/Rdatatable/data.table.
- Developer support: You can email Matt Dowle . For contact information, visit our R community directory.
Additional resources
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- Online courses: Try our handpicked collection of R programming courses designed to boost your proficiency in R programming.
- Books: Explore our curated selection of R programming books tailored to help you master R programming.
- Discussion forums: Online forums are excellent platforms to ask questions, share knowledge, and troubleshoot issues. The most popular forums for R programmers are StackOverflow and Posit Community.
Other package guides
Explore our comprehensive guides for other R packages. These guides are valuable resources for accessing a wide range of information, making it easier to navigate R documentation in one place.
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