Function reference • tidytable

tidytable helpers
`case.()`	Improved data.table::fcase()
`dt()`	Pipeable data.table call
`get_dummies.()`	Convert character and factor columns to dummy variables
`%notin%`	notin operator
dplyr
Core dplyr verbs
`arrange.()`	Arrange/reorder rows
`filter.()`	Filter rows on one or more conditions
`mutate.()`	Add/modify/delete columns
`select.()`	Select or drop columns
`summarize.()` `summarise.()`	Aggregate data using summary statistics
Other dplyr functions
`arrange_across.()`	Arrange by a selection of variables
`bind_cols.()` `bind_rows.()`	Bind data.tables by row and column
`case_when.()`	Case when
`count.()`	Count observations by group
`distinct.()`	Select distinct/unique rows
`group_split.()`	Split data frame by groups
`ifelse.()`	Fast ifelse
`lags.()` `leads.()`	Get lagging or leading values
`left_join.()` `inner_join.()` `right_join.()` `full_join.()` `anti_join.()` `semi_join.()`	Join two data.tables together
`mutate_across.()`	Mutate multiple columns simultaneously
`pull.()`	Pull out a single variable
`relocate.()`	Relocate a column to a new position
`rename.()`	Rename variables by name
`rename_with.()`	Rename multiple columns
`slice.()` `slice_head.()` `slice_tail.()` `slice_max.()` `slice_min.()`	Choose rows in a data.table
`summarize_across.()` `summarise_across.()`	Summarize multiple columns
`transmute.()`	Add new variables and drop all others
tidyr
`complete.()`	Complete a data.table with missing combinations of data
`crossing.()`	Create a data.table from all unique combinations of inputs
`drop_na.()`	Drop rows containing missing values
`expand.()`	Expand a data.table to use all combinations of values
`expand_grid.()`	Create a data.table from all combinations of inputs
`fill.()`	Fill in missing values with previous or next value
`nest_by.()`	Nest data.tables
`pivot_longer.()`	Pivot data from wide to long
`pivot_wider.()`	Pivot data from long to wide
`replace_na.()`	Replace missing values
`separate.()`	Separate a character column into multiple columns
`separate_rows.()`	Separate a collapsed column into multiple rows
`uncount.()`	Uncount a data.table
`unite.()`	Unite multiple columns by pasting strings together
`unnest.()`	Unnest a nested data.table
purrr
`map.()` `map_lgl.()` `map_int.()` `map_dbl.()` `map_chr.()` `map_dfc.()` `map_dfr.()` `map_df.()` `walk.()` `map2.()` `map2_lgl.()` `map2_int.()` `map2_dbl.()` `map2_chr.()` `map2_dfc.()` `map2_dfr.()` `map2_df.()`	Apply a function to each element of a vector or list
reexports These functions are reexported from `data.table`, `rlang`, and `tidyselect` for easier access.
`reexports`	Objects exported from other packages
tidytable constructors A tidytable is simply a data.table with nice printing features. As such it can be used exactly like a data.table would be used. Note: All tidytable functions automatically convert data.frame/tibble inputs to a data.table/tidytable.
`as_tidytable()` `as_dt()`	Coerce an object to a data.table/tidytable
`is_tidytable()`	Test if the object is a tidytable
`tidytable()`	Build a data.table/tidytable

Reference

tidytable helpers

dplyr

Core dplyr verbs

Other dplyr functions

tidyr

purrr

reexports

tidytable constructors