The tidyverse is a collection of open sourcepackages for the R programming language introduced by Hadley Wickham[1] and his team that "share an underlying design philosophy, grammar, and data structures" of tidy data.[2] Characteristic features of tidyverse packages include extensive use of non-standard evaluation and encouraging piping.[3][4][5]
As of November 2018, the tidyverse package and some of its individual packages comprise 5 out of the top 10 most downloaded R packages.[6] The tidyverse is the subject of multiple books and papers.[7][8][9][10] In 2019, the ecosystem has been published in the Journal of Open Source Software.[11]
Its syntax has been referred to as "supremely readable",[12] and some[13] have argued that tidyverse is an effective way to introduce complete beginners to programming, as pedagogically it allows students to quickly begin doing data processing tasks.[14][13] Moreover, some practitioners have pointed out that data processing tasks are intuitively easier to chain together with tidyverse compared to Python's equivalent data processing package, pandas.[15] There is also an active R community around the tidyverse. For example, there is the TidyTuesday social data project organised by the Data Science Learning Community (DSLC),[16] where varied real-world datasets are released each week for the community to participate, share, practice, and make learning to work with data easier.[17] Critics of the tidyverse have argued it promotes tools that are harder to teach and learn than their built-in, base R equivalents and are too dissimilar to some programming languages.[18][19]
The tidyverse principles more generally encourage and help ensure that a universe of streamlined packages, in principle, will help alleviate dependency issues and compatibility with current and future features.[20] An example of such a tidyverse principled approach is the pharmaverse, which is a collection of R packages for clinical reporting usage in pharma.[21]
^C., Boehmke, Bradley (2016-11-17). Data wrangling with R. Cham. ISBN9783319455990. OCLC964404346.{{cite book}}: CS1 maint: location missing publisher (link) CS1 maint: multiple names: authors list (link)
^Hadley, Wickham (2017). R for data science : import, tidy, transform, visualize, and model data. Grolemund, Garrett (First ed.). Sebastopol, CA. ISBN9781491910399. OCLC968213225.{{cite book}}: CS1 maint: location missing publisher (link)
^Wickham, Hadley; Averick, Mara; Bryan, Jennifer; Chang, Winston; McGowan, Lucy D'Agostino; François, Romain; Grolemund, Garrett; Hayes, Alex; Henry, Lionel; Hester, Jim; Kuhn, Max; Pedersen, Thomas Lin; Miller, Evan; Bache, Stephan Milton; Müller, Kirill; Ooms, Jeroen; Robinson, David; Seidel, Dana Paige; Spinu, Vitalie; Takahashi, Kohske; Vaughan, Davis; Wilke, Claus; Woo, Kara; Yutani, Hiroaki (21 November 2019). "Welcome to the Tidyverse". Journal of Open Source Software. 4 (43): 1686. Bibcode:2019JOSS....4.1686W. doi:10.21105/joss.01686. S2CID214002773.