Tidyverse
Collection of R packages
From Wikipedia, the free encyclopedia
The tidyverse is a collection of open source packages for the R programming language introduced by Hadley Wickham[4] and his team that "share an underlying design philosophy, grammar, and data structures" of tidy data.[5] Characteristic features of tidyverse packages include extensive use of non-standard evaluation and encouraging piping.[6][7][8]
| Tidyverse | |
|---|---|
The tidyverse hex logo | |
| Initial release | September 15, 2016[1][2] |
| Stable release | |
| Written in | R |
| Type | Package collection |
| License | MIT |
| Website | www |
| Repository | github |
As of November 2018, the tidyverse package and some of its individual packages comprise 5 out of the top 10 most downloaded R packages.[9] The tidyverse is the subject of multiple books and papers.[10][11][12][13] In 2019, the ecosystem has been published in the Journal of Open Source Software.[14]
Its syntax has been referred to as "supremely readable",[15] and some[16] 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.[17][16] 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.[18] 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),[19] where varied real-world datasets are released each week for the community to participate, share, practice, and make learning to work with data easier.[20] 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.[21][22]
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.[23] An example of such a tidyverse principled approach is the pharmaverse, which is a collection of R packages for clinical reporting usage in pharma.[24]
Packages
The core tidyverse packages, which provide functionality to model, transform, and visualize data, include:[25]
- tidyr – help transform data specifically into tidy data, a table where each row is a single observation. The value of each observation is given in a column of the table. Variables describing the observation are included as additional columns.
- ggplot2 – for data visualization
- dplyr – for wrangling and transforming data
- 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.[26] Other packages based on the tidy data principles are regularly developed, such as tidytext[27] for text analysis, tidymodels[28] for machine learning, or tidyquant[29] for financial operations.