Package index • dplyr

Data frame verbs
Rows Verbs that principally operate on rows.
`arrange()`	Order rows using column values
`distinct()`	Keep distinct/unique rows
`filter()`	Keep rows that match a condition
`slice()` `slice_head()` `slice_tail()` `slice_min()` `slice_max()` `slice_sample()`	Subset rows using their positions
Columns Verbs that principally operate on columns.
`glimpse`	Get a glimpse of your data
`mutate()`	Create, modify, and delete columns
`pull()`	Extract a single column
`relocate()`	Change column order
`rename()` `rename_with()`	Rename columns
`select()`	Keep or drop columns using their names and types
Groups Verbs that principally operate on groups of rows.
`count()` `tally()` `add_count()` `add_tally()`	Count the observations in each group
`group_by()` `ungroup()`	Group by one or more variables
`dplyr_by`	Per-operation grouping with `.by`/`by`
`rowwise()`	Group input by rows
`summarise()` `summarize()`	Summarise each group down to one row
`reframe()`	Transform each group to an arbitrary number of rows
`n()` `cur_group()` `cur_group_id()` `cur_group_rows()` `cur_column()`	Information about the "current" group or variable
Data frames Verbs that principally operate on pairs of data frames.
`bind_cols()`	Bind multiple data frames by column
`bind_rows()`	Bind multiple data frames by row
`intersect()` `union()` `union_all()` `setdiff()` `setequal()` `symdiff()`	Set operations
`inner_join()` `left_join()` `right_join()` `full_join()`	Mutating joins
`nest_join()`	Nest join
`semi_join()` `anti_join()`	Filtering joins
`cross_join()`	Cross join
`join_by()`	Join specifications
`rows_insert()` `rows_append()` `rows_update()` `rows_patch()` `rows_upsert()` `rows_delete()`	Manipulate individual rows
Multiple columns Pair these functions with `mutate()`, `summarise()`, `filter()`, and `group_by()` to operate on multiple columns simultaneously.
`across()` `if_any()` `if_all()`	Apply a function (or functions) across multiple columns
`c_across()`	Combine values from multiple columns
`pick()`	Select a subset of columns
Vector functions Unlike other dplyr functions, these functions work on individual vectors, not data frames.
`between()`	Detect where values fall in a specified range
`case_match()`	A general vectorised `switch()`
`case_when()`	A general vectorised if-else
`coalesce()`	Find the first non-missing element
`consecutive_id()`	Generate a unique identifier for consecutive combinations
`cumall()` `cumany()` `cummean()`	Cumulativate versions of any, all, and mean
`desc()`	Descending order
`if_else()`	Vectorised if-else
`lag()` `lead()`	Compute lagged or leading values
`n_distinct()`	Count unique combinations
`na_if()`	Convert values to `NA`
`near()`	Compare two numeric vectors
`nth()` `first()` `last()`	Extract the first, last, or nth value from a vector
`ntile()`	Bucket a numeric vector into `n` groups
`order_by()`	A helper function for ordering window function output
`percent_rank()` `cume_dist()`	Proportional ranking functions
`recode()` `recode_factor()`	Recode values
`row_number()` `min_rank()` `dense_rank()`	Integer ranking functions
Built in datasets
`band_members` `band_instruments` `band_instruments2`	Band membership
`starwars`	Starwars characters
`storms`	Storm tracks data
Grouping helpers This (mostly) experimental family of functions are used to manipulate groups in various ways.
`group_cols()`	Select grouping variables
`group_map()` `group_modify()` `group_walk()`	Apply a function to each group
`group_trim()`	Trim grouping structure
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
`all_vars()` `any_vars()`	Apply predicate to all variables
`vars()`	Select variables
`with_groups()`	Perform an operation with temporary groups
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.

Reference

Data frame verbs

Rows

Columns

Groups

Data frames

Multiple columns

Vector functions

Built in datasets

Grouping helpers

Superseded

Remote tables