Function reference • dplyr

One table verbs
`arrange()`	Arrange rows by column values
`count()` `tally()` `add_count()` `add_tally()`	Count observations by group
`distinct()`	Subset distinct/unique rows
`filter()`	Subset rows using column values
`mutate()` `transmute()`	Create, modify, and delete columns
`pull()`	Extract a single column
`relocate()`	Change column order
`rename()` `rename_with()`	Rename columns
`select()`	Subset columns using their names and types
`summarise()` `summarize()`	Summarise each group to fewer rows
`slice()` `slice_head()` `slice_tail()` `slice_min()` `slice_max()` `slice_sample()`	Subset rows using their positions
Two table verbs
`bind_rows()` `bind_cols()`	Efficiently bind multiple data frames by row and column
`reexports`	Objects exported from other packages
`union_all()`	Set operations
`inner_join()` `left_join()` `right_join()` `full_join()`	Mutating joins
`nest_join()`	Nest join
`semi_join()` `anti_join()`	Filtering joins
Grouping
`group_by()` `ungroup()`	Group by one or more variables
`group_cols()`	Select grouping variables
`rowwise()`	Group input by rows
Vector functions
`across()` `if_any()` `if_all()`	Apply a function (or functions) across multiple columns
`c_across()`	Combine values from multiple columns
`between()`	Do values in a numeric vector fall in specified range?
`case_when()`	A general vectorised if
`coalesce()`	Find first non-missing element
`cumall()` `cumany()` `cummean()`	Cumulativate versions of any, all, and mean
`desc()`	Descending order
`if_else()`	Vectorised if
`lag()` `lead()`	Compute lagged or leading values
`order_by()`	A helper function for ordering window function output
`n()` `cur_data()` `cur_data_all()` `cur_group()` `cur_group_id()` `cur_group_rows()` `cur_column()`	Context dependent expressions
`n_distinct()`	Efficiently count the number of unique values in a set of vectors
`na_if()`	Convert values to NA
`near()`	Compare two numeric vectors
`nth()` `first()` `last()`	Extract the first, last or nth value from a vector
`row_number()` `ntile()` `min_rank()` `dense_rank()` `percent_rank()` `cume_dist()`	Windowed rank functions.
`recode()` `recode_factor()`	Recode values
Data
`band_members` `band_instruments` `band_instruments2`	Band membership
`starwars`	Starwars characters
`storms`	Storm tracks data
Remote tables
`auto_copy()`	Copy tables to same source, if necessary
`compute()` `collect()` `collapse()`	Force computation of a database query
`copy_to()`	Copy a local data frame to a remote src
`ident()`	Flag a character vector as SQL identifiers
`explain()` `show_query()`	Explain details of a tbl
`tbl()` `is.tbl()`	Create a table from a data source
`sql()`	SQL escaping.
Experimental Experimental functions are a testing ground for new approaches that we believe to be worthy of greater exposure. There is no guarantee that these functions will stay around in the future, so please reach out if you find them useful.
`group_map()` `group_modify()` `group_walk()`	Apply a function to each group
`group_trim()`	Trim grouping structure
`group_split()`	Split data frame by groups
`with_groups()`	Perform an operation with temporary groups
`rows_insert()` `rows_update()` `rows_patch()` `rows_upsert()` `rows_delete()`	Manipulate individual rows
Questioning We have our doubts about questioning functions. We’re not certain that they’re inadequate, or we don’t have a good replacement in mind, but these functions are at risk of removal in the future.
`all_equal()`	Flexible equality comparison for data frames
Superseded Superseded functions have been replaced by new approaches that we believe to be superior, but we don’t want to force you to change until you’re ready, so the existing functions will stay around for several years.
`sample_n()` `sample_frac()`	Sample n rows from a table
`top_n()` `top_frac()`	Select top (or bottom) n rows (by value)
`scoped`	Operate on a selection of variables
`arrange_all()` `arrange_at()` `arrange_if()`	Arrange rows by a selection of variables
`distinct_all()` `distinct_at()` `distinct_if()`	Select distinct rows by a selection of variables
`filter_all()` `filter_if()` `filter_at()`	Filter within a selection of variables
`group_by_all()` `group_by_at()` `group_by_if()`	Group by a selection of variables
`mutate_all()` `mutate_if()` `mutate_at()` `transmute_all()` `transmute_if()` `transmute_at()`	Mutate multiple columns
`summarise_all()` `summarise_if()` `summarise_at()` `summarize_all()` `summarize_if()` `summarize_at()`	Summarise multiple columns
`all_vars()` `any_vars()`	Apply predicate to all variables
`vars()`	Select variables

Reference

One table verbs

Two table verbs

Grouping

Vector functions

Data

Remote tables

Experimental

Questioning

Superseded