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

  1. Each variable is a column; each column is a variable.
  2. Each observation is a row; each row is an observation.
  3. Each value is a cell; each cell is a single value.

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. Learn more about tidy data in vignette("tidy-data").


# The easiest way to get tidyr is to install the whole tidyverse:

# Alternatively, install just tidyr:

# Or the development version from GitHub:
# install.packages("pak")


Getting started


tidyr functions fall into five main categories:

tidyr supersedes 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).

data.table provides high-performance implementations of melt() and dcast()

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 1.0.0 pivot longer pivot wider
tidyr < 1.0.0 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.