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

1. Every column is variable.
2. Every row is an observation.
3. Every 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:
install.packages("tidyverse")

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

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

library(tidyr)

tidyr functions fall into five main categories:

• “Pivoting” which converts between long and wide forms. tidyr 1.0.0 introduces pivot_longer() and pivot_wider(), replacing the older spread() and gather() functions. See vignette("pivot") for more details.

• “Rectangling”, which turns deeply nested lists (as from JSON) into tidy tibbles. See unnest_longer(), unnest_wider(), hoist(), and vignette("rectangle") for more details.

• Nesting converts grouped data to a form where each group becomes a single row containing a nested data frame, and unnesting does the opposite. See nest(), unnest(), and vignette("nest") for more details.

• Splitting and combining character columns. Use separate() and extract() to pull a single character column into multiple columns; use unite() to combine multiple columns into a single character column.

• Make implicit missing values explicit with complete(); make explicit missing values implicit with drop_na(); replace missing values with next/previous value with fill(), or a known value with replace_na().

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:

• Wrangler: Interactive visual specification of data transformation scripts

• An interactive framework for data cleaning (Potter’s wheel)

• On efficiently implementing SchemaSQL on a SQL database system

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

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.