Tidyverse

Collection of R packages From Wikipedia, the free encyclopedia

Tidyverse

The tidyverse is a collection of open source packages 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]

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Tidyverse
Repositorygithub.com/tidyverse/tidyverse
Written inR
TypePackage collection
LicenseMIT
Websitewww.tidyverse.org
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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]

Packages

The core tidyverse packages, which provide functionality to model, transform, and visualize data, include:[22]

  • ggplot2 – for data visualization
  • dplyr – for wrangling and transforming data
  • tidyr help transform data specifically into tidy data, where each variable is a column, each observation is a row; each row is an observation, and each value is a cell.
  • readr help read in common delimited, text files with data
  • purrr a functional programming toolkit
  • tibble a modern implementation of the built-in data frame data structure
  • stringr helps to manipulate string data types
  • forcats helps to manipulate category data types

Additional packages assist the core collection.[23] Other packages based on the tidy data principles are regularly developed, such as tidytext[24] for text analysis, tidymodels[25] for machine learning, or tidyquant[26] for financial operations.

References

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