R package

R packages are extensions to the R statistical programming language. R packages contain code, data, and documentation in a standardised collection format that can be installed by users of R, typically via a centralised software repository such as CRAN (the Comprehensive R Archive Network).[1][2] The large number of packages available for R, and the ease of installing and using them, has been cited as a major factor in driving the widespread adoption of the language in data science.[3][4][5][6]

Compared to libraries in other programming language, R packages must conform to a relatively strict specification.[3] The Writing R Extensions manual[7] specifies a standard directory structure for R source code, data, documentation, and package metadata, which enables them to be installed and loaded using R's in-built package management tools.[3] Packages distributed on CRAN must meet additional standards.[3][8] According to John Chambers, whilst these requirements "impose considerable demands" on package developers, they improve the usability and long-term stability of packages for end users.[3]

Repositories

Comprehensive R Archive Network (CRAN)

The Comprehensive R Archive Network (CRAN) is R's central software repository, supported by the R Foundation.[9] It contains an archive of the latest and previous versions of the R distribution, documentation, and contributed R packages.[10] It includes both source packages and pre-compiled binaries for Windows and macOS.[11] As of November 2020, more than 16,000 packages are available.[12] CRAN was created by Kurt Hornik and Friedrich Leisch in 1997[13][14] and is currently maintained by Hornik and a team of volunteers.[9] The master site is located at the Vienna University of Economics and Business and is mirrored on servers around the world.[10]

The number of CRAN packages has grown exponentially for many years,[15] and as of 2018 an average of 21 submissions of new or updated packages were made every day.[6] Since each submission is manually reviewed by a small team of CRAN maintainers, many of whom, according to R core developer Peter Dalgaard, are "approaching pensionable age", there is a concern that this system is not sustainable in the long term.[6] The growth of CRAN has exposed limitations of its dependency management infrastructure, particularly the fact that it assumes that dependencies always refer to the latest version of a package, meaning that new releases of CRAN packages must always be backwards compatible,[16] and that CRAN packages cannot have dependencies that are not on CRAN.[17] It has also led to concerns about declining quality of packages.[18]

R is distributed with fourteen "base packages": base, compiler, datasets, grDevices, graphics, grid, methods, parallel, splines, stats, stats4, tcltk, tools, and utils.[19]

In addition, there are fifteen "recommended packages" from CRAN which are included with binary distributions of R: KernSmooth, MASS, Matrix, boot, class, cluster, codetools, foreign, lattice, mgcv, nlme, nnet, rpart, spatial, and survival.[19]

datasets.load
datasets.load
datasets.load GUI searching and loading datasets
Original author(s)Bastiaan Quast
Initial release14 December 2016 (2016-12-14)
Stable release
1.4.0 / 27 April 2020 (2020-04-27)
Preview release
1.4.0.9000 / 1 May 2020 (2020-05-01)
Repositoryhttps://github.com/bquast/datasets.load
Written inR
Operating systemWindows, MacOS, Linux
Size520.6 kB (v. 1.2.0)
Available inR
LicenseGPL v3
Websitecran.r-project.org

datasets.load is an R package and RStudio plugin, that provides both a Graphical User Interface (GUI), as well as a Command Line Interface for loading datasets.[20] Normally, R only makes visible datasets of packages that are loaded, datasets.load shows the list of all installed datasets on the local library, including datasets included with packages that are not loaded. It is one of the top 10% of most downloaded R packages.

The basic functionality of datasets.load is to expose all installed datasets to the user, including datasets in packages that are not loaded. There is a Command Line Interface (CLI) which can be used from any R terminal.

In addition to the CLI, there is also a Graphical User Interface for RStudio using RStudio Addins.

The initial release on CRAN of version 0.1.0 took place in December 2016, and averaged a download rate of 1,000 times per month, from the RStudio servers alone. With the release of version 0.3.0 in 2018, the download rate increased to 2,000 times per month, putting the package in the 9th percentile of most popular R packages.[21] The package was reviewed in the 2017 article "R Packages worth a look" on Data Analytics & R,[22] which further increased usage.

Version 1.0.0 was released on 12 December 2019, with a version 1.4.0 which was built against R 4.0.0 being released on 27 April 2020.[23]

The RStudio CRAN mirror download logs [24] show that the package is downloaded more than 2,000 times per month from those servers ,[25] with a total of over 65,000 downloads since the first release ,[26] according to RDocumentation.org, this puts the package in the 9th percentile of most popular R packages.[27] On Rdocumentation.org it is listed as the second most downloaded package under the keyword "datasets"[28] (after the base R package datasets), with "datasets" being the most popular keyword on Rdocumentation.org.[29]

See also

References
  1. Hornik, Kurt (2020-02-20). "Frequently Asked Questions on R". The Comprehensive R Archive Network. 7.29: What is the difference between package and library?. Retrieved 2 November 2020.CS1 maint: location (link)
  2. Wickham, Hadley; Bryan, Jennifer. "Introduction". R Packages (2nd ed.).
  3. Chambers, John M. (2020). "S, R, and Data Science". The R Journal. 12 (1): 462–476. ISSN 2073-4859.
  4. Vance, Ashlee (2009-01-06). "Data Analysts Captivated by R's Power". New York Times.
  5. Tippmann, Sylvia (2014-12-29). "Programming tools: Adventures with R". Nature News. 517 (7532): 109. doi:10.1038/517109a.
  6. Thieme, Nick (2018). "R generation". Significance. 15 (4): 14–19. doi:10.1111/j.1740-9713.2018.01169.x. ISSN 1740-9713.
  7. R Core Team. "Writing R Extensions". The Comprehensive R Archive Network. Retrieved 2020-11-02.CS1 maint: uses authors parameter (link)
  8. CRAN Repository Maintainers. "CRAN Repository Policy". The Comprehensive R Archive Network. Retrieved 2020-11-02.CS1 maint: uses authors parameter (link)
  9. CRAN Repository Maintainers. "CRAN Repository Policy". The Comprehensive R Archive Network. R Project. Retrieved 20 November 2020.
  10. Hornik, Kurt (2020-02-20). "Frequently Asked Questions on R". The Comprehensive R Archive Network. 2.1: What is CRAN?: R Project. Retrieved 20 November 2020.CS1 maint: location (link)
  11. CRAN Repository Maintainers. "The Comprehensive R Archive Network". R Project. Retrieved 20 November 2020.
  12. CRAN Repository Maintainers. "CRAN - Contributed Packages". The Comprehensive R Archive Network. CRAN. Retrieved 20 November 2020.
  13. Hornik, Kurt (1997-04-23). "ANNOUNCE: CRAN". r-announce (Mailing list). Retrieved 20 November 2020.
  14. Thieme, Nick (2018). "R generation". Significance. 15 (4): 14–19. doi:10.1111/j.1740-9713.2018.01169.x. ISSN 1740-9713.
  15. April 21, Matt Asay in Open Source on; 2016; Pst, 12:32 Pm. "Exponential growth of R's open source community threatens commercial competitors". TechRepublic. Retrieved 2020-11-02.CS1 maint: numeric names: authors list (link)
  16. Ooms, Jeroen (2013). "Possible Directions for Improving Dependency Versioning in R". The R Journal. 5 (1): 197–206. ISSN 2073-4859.
  17. Decan, A.; Mens, T.; Claes, M.; Grosjean, P. (2016). "When GitHub Meets CRAN: An Analysis of Inter-Repository Package Dependency Problems". 2016 IEEE 23rd International Conference on Software Analysis, Evolution, and Reengineering (SANER). 1: 493–504. doi:10.1109/SANER.2016.12.
  18. Hornik, Kurt (2012). "Are There Too Many R Packages?". Austrian Journal of Statistics. 41 (1): 59–66–59–66. doi:10.17713/ajs.v41i1.188. ISSN 1026-597X.
  19. Hornik, Kurt (2020-02-20). "Frequently Asked Questions on R". The Comprehensive R Archive Network. 5.1: Which add-on packages exist for R?. Retrieved 2 November 2020.CS1 maint: location (link)
  20. Quast, Bastiaan (2019-12-12), datasets.load: Interfaces for Loading Datasets, archived from the original on 2020-04-30, retrieved 2020-04-30
  21. Quast, Bastiaan (2020-04-30), Visual interface for loading datasets in RStudio, archived from the original on 2020-05-01, retrieved 2020-05-01
  22. Laux, Michael (2017-01-05). "R Packages worth a look". Data Analytics & R. Archived from the original on 2020-01-03. Retrieved 2020-05-01.
  23. "R packages installed - ITS - Carlpedia - Carleton College Wiki". wiki.carleton.edu. Archived from the original on 2020-01-03. Retrieved 2020-01-03.
  24. "RStudio CRAN logs".
  25. "CRANlogs datasets.load package".
  26. "CRANlogs datasets.load package".
  27. "datasets.load package R Documentation". www.rdocumentation.org. Archived from the original on 2020-01-03. Retrieved 2020-04-30.
  28. "RDocumentation: Results for "datasets"". www.rdocumentation.org. Archived from the original on 2020-01-03. Retrieved 2020-04-30.
  29. "RDocumentation Trends". www.rdocumentation.org. Archived from the original on 2020-01-03. Retrieved 2020-04-30.

Further reading
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