tidyr v0.6.1


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by Hadley Wickham

Easily Tidy Data with 'spread()' and 'gather()' Functions

An evolution of 'reshape2'. It's designed specifically for data tidying (not general reshaping or aggregating) and works well with 'dplyr' data pipelines.



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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 timing fighting with the tools and more time working on your analysis.


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

# Alternatively, install just tidyr:

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

Getting started


There are two fundamental verbs of data tidying:

  • gather() takes multiple columns, and gathers them into key-value pairs: it makes "wide" data longer.

  • spread(). takes two columns (key & value) and spreads in to multiple columns, it makes "long" data wider.

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 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 translation between the terminology used in different places:

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

Functions in tidyr

Name Description
expand Expand data frame to include all combinations of values
drop_na Drop rows containing missing values
extract_ Standard-evaluation version of extract.
fill_ Standard-evaluation version of fill.
complete_ Standard-evaluation version of complete.
complete Complete a data frame with missing combinations of data.
extract_numeric Extract numeric component of variable.
expand_ Expand (standard evaluation).
extract Extract one column into multiple columns.
drop_na_ Standard-evaluation version of drop_na.
gather_ Gather (standard-evaluation).
nest_ Standard-evaluation version of nest.
nest Nest repeated values in a list-variable.
gather Gather columns into key-value pairs.
%>% Pipe operator
fill Fill in missing values.
full_seq Create the full sequence of values in a vector.
replace_na Replace missing values
separate_ Standard-evaluation version of separate.
separate_rows_ Standard-evaluation version of separate_rows.
table1 Example tabular representations
spread Spread a key-value pair across multiple columns.
unnest Unnest a list column.
unnest_ Standard-evaluation version of unnest.
unite_ Standard-evaluation version of unite
spread_ Standard-evaluation version of spread.
smiths Some data about the Smith family.
unite Unite multiple columns into one.
separate_rows Separate a collapsed column into multiple rows.
separate Separate one column into multiple columns.
who World Health Organization TB data
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License MIT + file LICENSE
LazyData true
URL http://tidyr.tidyverse.org, https://github.com/tidyverse/tidyr
BugReports https://github.com/tidyverse/tidyr/issues
Remotes RcppCore/Rcpp
VignetteBuilder knitr
LinkingTo Rcpp
RoxygenNote 5.0.1
NeedsCompilation yes
Packaged 2017-01-09 23:43:16 UTC; hadley
Repository CRAN
Date/Publication 2017-01-10 10:17:53

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