tidytable was originally built using dt_verb() syntax.
These functions have since been deprecated.
Please use the verb.() versions in your code,
as the dt_verb() functions will be removed by the end of 2020.
dt_arrange(.df, ...)dt_bind_rows(..., .id = NULL)
dt_bind_cols(...)
dt_case(..., default = NA)
dt_count(.df, ...)
dt_distinct(.df, ..., .keep_all = FALSE)
dt_drop_na(.df, ...)
dt_fill(.df, ..., .direction = c("down", "up", "downup", "updown"), .by = NULL)
dt_filter(.df, ..., .by = NULL)
dt_get_dummies(
.df,
cols = c(where(is.character), where(is.factor)),
prefix = TRUE,
prefix_sep = "_",
drop_first = FALSE,
dummify_na = TRUE
)
dt_group_split(.df, ..., .keep = TRUE)
dt_ifelse(conditions, true, false, na = NA)
dt_left_join(x, y, by = NULL)
dt_inner_join(x, y, by = NULL)
dt_right_join(x, y, by = NULL)
dt_full_join(x, y, by = NULL, suffix = c(".x", ".y"))
dt_anti_join(x, y, by = NULL)
dt_map(.x, .f, ...)
dt_map_lgl(.x, .f, ...)
dt_map_int(.x, .f, ...)
dt_map_dbl(.x, .f, ...)
dt_map_chr(.x, .f, ...)
dt_map_dfc(.x, .f, ...)
dt_map_dfr(.x, .f, ..., .id = NULL)
dt_map_df(.x, .f, ...)
dt_map2(.x, .y, .f, ...)
dt_map2_lgl(.x, .y, .f, ...)
dt_map2_int(.x, .y, .f, ...)
dt_map2_dbl(.x, .y, .f, ...)
dt_map2_chr(.x, .y, .f, ...)
dt_map2_dfc(.x, .y, .f, ...)
dt_map2_dfr(.x, .y, .f, ..., .id = NULL)
dt_map2_df(.x, .y, .f, ..., .id = NULL)
dt_mutate(.df, ..., .by = NULL)
dt_mutate_across(.df, .cols = everything(), .fns, ..., .by = NULL)
dt_n()
dt_group_nest(.df, ..., .key = "data", .keep = FALSE)
dt_pivot_longer(
.df,
cols = everything(),
names_to = "name",
values_to = "value",
values_drop_na = FALSE,
...
)
dt_pivot_wider(
.df,
names_from = name,
values_from = value,
id_cols = NULL,
names_sep = "_",
values_fn = NULL
)
dt_pull(.df, var = -1)
dt_relocate(.df, ..., .before = NULL, .after = NULL)
dt_rename(.df, ...)
dt_rename_with(.df, .fn, .cols = everything(), ...)
dt_replace_na(.x, replace = NA)
dt_row_number()
dt_select(.df, ...)
dt_separate(.df, col, into, sep = "[^[:alnum:]]+", remove = TRUE, ...)
dt_slice(.df, ..., .by = NULL)
dt_slice_head(.df, n = 5, .by = NULL)
dt_slice_tail(.df, n = 5, .by = NULL)
dt_slice_min(.df, order_by, n = 1, .by = NULL)
dt_slice_max(.df, order_by, n = 1, .by = NULL)
dt_summarise(.df, ..., .by = NULL)
dt_summarize(.df, ..., .by = NULL)
dt_transmute(.df, ..., .by = NULL)
dt_unite(.df, col = "new_col", ..., sep = "_", remove = TRUE, na.rm = FALSE)
dt_unnest_legacy(.df, ..., .keep_all = FALSE)
A data.frame or data.table
Variables to arrange by
If TRUE, an integer column is made as a group id
Default value. Set to NA by default.
Only relevant if columns are provided to ... arg. This keeps all columns, but only keeps the first row of each distinct values of columns provided to ... arg.
Direction in which to fill missing values. Currently "down" (the default), "up", "downup" (first down then up), or "updown" (first up and then down)
Columns to group by when filling should be done by group
A single column or a vector of unquoted columns to dummify.
Defaults to all character & factor columns using c(where(is.character), where(is.factor)).
tidyselect compatible.
TRUE/FALSE - If TRUE, a prefix will be added to new column names
Separator for new column names
TRUE/FALSE - If TRUE, the first dummy column will be dropped
TRUE/FALSE - If TRUE, NAs will also get dummy columns
Should the grouping columns be kept
Conditions to test on
Values to return if conditions evaluate to TRUE
Values to return if conditions evaluate to FALSE
Value to return if an element of test is NA.
A data.frame or data.table
A data.frame or data.table
Columns to group by when filling should be done by group
Append created for duplicated column names when using full_join.()
A data.frame or data.table
A function
A data.frame or data.table
A single column or a vector of unquoted columns to dummify.
Defaults to all character & factor columns using c(where(is.character), where(is.factor)).
tidyselect compatible.
Functions to pass. Can pass a list of functions.
Name of the new column created by nesting.
Name of the new "names" column. Must be a string.
Name of the new "values" column. Must be a string.
If TRUE, rows will be dropped that contain NAs.
A pair of arguments describing which column (or columns) to get the name of the output column (name_from),
and which column (or columns) to get the cell values from (values_from).
tidyselect compatible.
A pair of arguments describing which column (or columns) to get the name of the output column (name_from),
and which column (or columns) to get the cell values from (values_from).
tidyselect compatible.
A set of columns that uniquely identifies each observation.
Defaults to all columns in the data table except for the columns specified in names_from and values_from.
Typically used when you have additional variables that is directly related.
tidyselect compatible.
the separator between the names of the columns
Should the data be aggregated before casting? If the formula doesn't identify a single observation for each cell, then aggregation defaults to length with a message.
The column to pull from the data.table as:
a variable name
a positive integer giving the column position
a negative integer giving the column position counting from the right
Column to move selection before
Column to move selection after
Function to transform the names with.
If .x is a data frame, a list() of replacement values for specified columns.
If .x is a vector, a single replacement value.
The column to split into multiple columns
New column names to split into. A character vector.
Separator to split on. Can be specified or detected automatically
If TRUE, remove the input column from the output data.table
Number of rows to grab
Variable to arrange by
If TRUE, NA values will be not be part of the concatenation