Tidy Messy Data
Check assumptions about a pivot spec
Chop and unchop
Complete a data frame with missing combinations of data
Deprecated SE versions of main verbs
Drop rows containing missing values
Expand data frame to include all possible combinations of values
Create a tibble from all combinations of inputs
Extract a character column into multiple columns using regular express...
Extract numeric component of variable.
Fill in missing values with previous or next value
Create the full sequence of values in a vector
Gather columns into key-value pairs
Hoist values out of list-columns
Nest rows into a list-column of data frames
Legacy versions of nest() and unnest()
Pack and unpack
Pipe operator
Pivot data from wide to long
Pivot data from wide to long using a spec
Pivot data from long to wide
Pivot data from long to wide using a spec
Objects exported from other packages
Replace NAs with specified values
Separate a character column into multiple columns with a regular expre...
Split a string into rows
Separate a collapsed column into multiple rows
Split a string into columns
Spread a key-value pair across multiple columns
tidyr: Tidy Messy Data
Argument type: data-masking
Legacy name repair
Argument type: tidy-select
"Uncount" a data frame
Unite multiple columns into one by pasting strings together
Unnest a list-column of data frames into rows and columns
Automatically call unnest_wider() or unnest_longer()
Unnest a list-column into rows
Unnest a list-column into columns
Tools to help to create tidy data, where each column is a variable, each row is an observation, and each cell contains a single value. 'tidyr' contains tools for changing the shape (pivoting) and hierarchy (nesting and 'unnesting') of a dataset, turning deeply nested lists into rectangular data frames ('rectangling'), and extracting values out of string columns. It also includes tools for working with missing values (both implicit and explicit).
Useful links