tidyr

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Overview

The goal of tidyr is to help you create tidy data. Tidy data is data where:

  1. Each variable is in a column.
  2. Each observation is a row.
  3. Each value is a cell.

Tidy data describes a standard way of storing data that is used wherever possible throughout the tidyverse. If you ensure that your data is tidy, you’ll spend less time fighting with the tools and more time working on your analysis.

Installation

# The easiest way to get tidyr is to install the whole tidyverse:
install.packages("tidyverse")

# Alternatively, install just tidyr:
install.packages("tidyr")

# Or the development version from GitHub:
# install.packages("devtools")
devtools::install_github("tidyverse/tidyr")

Cheatsheet

Getting started

library(tidyr)

There are two fundamental verbs of data tidying:

tidyr also provides separate() and extract() functions which makes it easier to pull apart a column that represents multiple variables. The complement to separate() is unite().

To get started, read the tidy data vignette (vignette("tidy-data")) and check out the demos (demo(package = "tidyr")).

tidyr replaces reshape2 (2010-2014) and reshape (2005-2010). Somewhat counterintuitively each iteration of the package has done less. tidyr is designed specifically for tidying data, not general reshaping (reshape2), or the general aggregation (reshape).

If you’d like to read more about data reshaping from a CS perspective, I’d recommend the following three papers:

To guide your reading, here’s a translation between the terminology used in different places:

tidyr gather spread
reshape(2) melt cast
spreadsheets unpivot pivot
databases fold unfold

Getting help

If you encounter a clear bug, please file a minimal reproducible example on github. For questions and other discussion, please use community.rstudio.com.


Please note that the tidyr project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.