Tidyverse

The tidyverse is a collection of open source packages for the R programming language introduced by Hadley Wickham^[3] and his team that "share an underlying design philosophy, grammar, and data structures" of tidy data.^[4] Characteristic features of tidyverse packages include extensive use of non-standard evaluation and encouraging piping.^[5][6][7]

Tidyverse

The tidyverse hex logo
Initial release	September 15, 2016; 8 years ago (2016-09-15)[1][2]
Repository	github.com/tidyverse/tidyverse
Written in	R
Type	Package collection
License	MIT
Website	www.tidyverse.org

• Free software portal

As of November 2018, the tidyverse package and some of its individual packages comprise 5 out of the top 10 most downloaded R packages.^[8] The tidyverse is the subject of multiple books and papers.^{[9][10][11][12]} In 2019, the ecosystem has been published in the Journal of Open Source Software.^[13]

Its syntax has been referred to as "supremely readable",^[14] and some^[15] 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.^[16][15] 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.^[17] 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),^[18] where varied real-world datasets are released each week for the community to participate, share, practice, and make learning to work with data easier.^[19] 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.^[20][21]

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.^[22] An example of such a tidyverse principled approach is the pharmaverse, which is a collection of R packages for clinical reporting usage in pharma.^[23]

Packages

The core tidyverse packages, which provide functionality to model, transform, and visualize data, include:^[24]

• 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.^[25] Other packages based on the tidy data principles are regularly developed, such as tidytext^[26] for text analysis, tidymodels^[27] for machine learning, or tidyquant^[28] for financial operations.