Package index • tidyr

Pivoting

Pivoting changes the representation of a rectangular dataset, without changing the data inside of it. See vignette("pivot") for more details and examples.

pivot_longer(): Pivot data from wide to long

pivot_wider(): Pivot data from long to wide

Rectangling

Rectangling turns deeply nested lists into tidy tibbles. See vignette("rectangle") for more details and examples.

unnest_longer(): Unnest a list-column into rows

unnest_wider(): Unnest a list-column into columns

unnest(): Unnest a list-column of data frames into rows and columns

hoist(): Hoist values out of list-columns

Character vectors

Multiple variables are sometimes pasted together into a single column, and these tools help you separate back out into individual columns.

separate_longer_delim() separate_longer_position() experimental: Split a string into rows

separate_wider_delim() separate_wider_position() separate_wider_regex() experimental: Split a string into columns

unite(): Unite multiple columns into one by pasting strings together

Missing values

Tools for converting between implicit (absent rows) and explicit (NA) missing values, and for handling explicit NAs.

complete(): Complete a data frame with missing combinations of data

drop_na(): Drop rows containing missing values

expand() crossing() nesting(): Expand data frame to include all possible combinations of values

expand_grid(): Create a tibble from all combinations of inputs

fill(): Fill in missing values with previous or next value

full_seq(): Create the full sequence of values in a vector

replace_na(): Replace NAs with specified values

Miscellanea

chop() unchop(): Chop and unchop

pack() unpack(): Pack and unpack

uncount(): "Uncount" a data frame

nest(): Nest rows into a list-column of data frames

Data

billboard: Song rankings for Billboard top 100 in the year 2000

cms_patient_experience cms_patient_care: Data from the Centers for Medicare & Medicaid Services

construction: Completed construction in the US in 2018

fish_encounters: Fish encounters

household: Household data

relig_income: Pew religion and income survey

smiths: Some data about the Smith family

table1 table2 table3 table4a table4b table5: Example tabular representations

us_rent_income: US rent and income data

who who2 population: World Health Organization TB data

world_bank_pop: Population data from the World Bank

Superseded

Superseded functions have been replaced by superior solutions, but due to their widespread use will not go away. However, they will not get any new features and will only receive critical bug fixes.

extract() superseded: Extract a character column into multiple columns using regular expression groups

separate() superseded: Separate a character column into multiple columns with a regular expression or numeric locations

separate_rows() superseded: Separate a collapsed column into multiple rows

spread() superseded: Spread a key-value pair across multiple columns

gather() superseded: Gather columns into key-value pairs

nest_legacy() unnest_legacy() superseded: Legacy versions of nest() and unnest()