Function reference • tidytable

tidytable helpers
`dt()`	Pipeable data.table call
`%in%` `%notin%`	Fast `%in%` and `%notin%` operators
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
`across()`	Apply a function across a selection of columns
`add_count()` `add_tally()`	Add a count column to the data frame
`between()`	Do the values from x fall between the left and right bounds?
`bind_cols()` `bind_rows()`	Bind data.tables by row and column
`c_across()`	Combine values from multiple columns
`case_match()`	Vectorized `switch()`
`case_when()`	Case when
`coalesce()`	Coalesce missing values
`consecutive_id()`	Generate a unique id for consecutive values
`count()` `tally()`	Count observations by group
`cur_column()` `cur_data()` `cur_group_id()` `cur_group_rows()`	Context functions
`cross_join()`	Cross join
`desc()`	Descending order
`distinct()`	Select distinct/unique rows
`enframe()`	Convert a vector to a data.table/tidytable
`group_by()` `ungroup()`	Grouping
`group_cols()`	Selection helper for grouping columns
`group_vars()`	Get the grouping variables
`group_split()`	Split data frame by groups
`first()` `last()` `nth()`	Extract the first, last, or nth value from a vector
`if_all()` `if_any()`	Create conditions on a selection of columns
`if_else()`	Fast if_else
`is_grouped_df()`	Check if the tidytable is grouped
`lag()` `lead()`	Get lagging or leading values
`left_join()` `right_join()` `inner_join()` `full_join()` `anti_join()` `semi_join()`	Join two data.tables together
`mutate_rowwise()`	Add/modify columns by row
`n()`	Number of observations in each group
`n_distinct()`	Count the number of unique values in a vector
`na_if()`	Convert values to `NA`
`nest_by()`	Nest data.tables
`nest_join()`	Nest join
`pick()`	Selection version of `across()`
`pull()`	Pull out a single variable
`relocate()`	Relocate a column to a new position
`rename()`	Rename variables by name
`rename_with()`	Rename multiple columns
`reframe()`	Reframe a data frame
`row_number()` `min_rank()` `dense_rank()` `percent_rank()` `cume_dist()`	Ranking functions
`rowwise()`	Convert to a rowwise tidytable
`slice_head()` `slice_tail()` `slice_max()` `slice_min()` `slice()` `slice_sample()`	Choose rows in a data.table
`transmute()`	Add new variables and drop all others
`tribble()`	Rowwise tidytable creation
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()` `nesting()`	Expand a data.table to use all combinations of values
`expand_grid()`	Create a data.table from all combinations of inputs
`extract()`	Extract a character column into multiple columns using regex
`fill()`	Fill in missing values with previous or next value
`nest()`	Nest columns into a list-column
`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
`separate_longer_delim()`	Split a string into rows
`separate_wider_delim()`	Separate a character column into multiple columns
`separate_wider_regex()`	Separate a character column into multiple columns using regex patterns
`uncount()`	Uncount a data.table
`unite()`	Unite multiple columns by pasting strings together
`unnest()`	Unnest list-columns
`unnest_longer()`	Unnest a list-column of vectors into regular columns
`unnest_wider()`	Unnest a list-column of vectors into a wide data frame
purrr
`map()` `map_lgl()` `map_int()` `map_dbl()` `map_chr()` `map_dfc()` `map_dfr()` `map_df()` `walk()` `map_vec()` `map2()` `map2_lgl()` `map2_int()` `map2_dbl()` `map2_chr()` `map2_dfc()` `map2_dfr()` `map2_df()` `map2_vec()` `pmap()` `pmap_lgl()` `pmap_int()` `pmap_dbl()` `pmap_chr()` `pmap_dfc()` `pmap_dfr()` `pmap_df()` `pmap_vec()`	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` `starts_with` `contains` `ends_with` `everything` `any_of` `all_of` `matches` `num_range` `last_col` `where` `data.table` `fwrite` `getDTthreads` `setDTthreads` `%between%` `%like%` `%chin%` `enexpr` `enexprs` `enquo` `enquos` `expr` `exprs` `quo` `quos` `sym` `syms` `glimpse`	Objects exported from other packages
tidytable constructors A tidytable is simply a data.table with nice printing features. Note: All tidytable functions automatically convert data.frame/tibble inputs to a data.table/tidytable.
`as_tidytable()`	Coerce an object to a data.table/tidytable
`is_tidytable()`	Test if the object is a tidytable
`new_tidytable()`	Create a tidytable from a list
`tidytable()`	Build a data.table/tidytable
Read/write files `fread()` is a simple wrapper around `data.table::fread()` that returns a tidytable. `fwrite()` is reexported for use by the user.
`fread()`	Read/write files

Reference

tidytable helpers

dplyr

Core dplyr verbs

Other dplyr functions

tidyr

purrr

reexports

tidytable constructors

Read/write files