Tidy functions (mainly from dplyr and tidyr) to manipulate data frames

The Tidyverse defines a coherent set of tools to manipulate data frames that use a non-standard evaluation and sometimes require extra care. These functions, like mutate() or summarise() are defined in the {dplyr} and {tidyr} packages. When using variants, like {dtplyr} for data.frame objects, or {dbplyr} to work with external databases, successive commands in a pipeline are pooled together but not computed. One has to collect() the result to get its final form. Most of the tidy functions that have their "speedy" counterpart prefixed with "s" are listed withlist_tidy_functions(). Their main usages are (excluding less used arguments, or those that are not compatibles with the speedy "s" counterpart functions):

group_by(.data, ...)
ungroup(.data)
rename(.data, ...)
rename_with(.data, .fn, .cols = everything(), ...)
filter(.data, ...)
select(.data, ...)
mutate(.data, ..., .keep = "all")
transmute(.data, ...)
summarise(.data, ...)
full_join(x, y, by = NULL, suffix = c(".x", ".y"), copy = FALSE, ...)
left_join(x, y, by = NULL, suffix = c(".x", ".y"), copy = FALSE, ...)
right_join(x, y, by = NULL, suffix = c(".x", ".y"), copy = FALSE, ...)
inner_join(x, y, by = NULL, suffix = c(".x", ".y"), copy = FALSE, ...)
bind_rows(..., .id = NULL)
bind_cols(..., .name_repair = c("unique", "universal", "check_unique", "minimal"))
arrange(.data, ..., .by_group = FALSE)
count(x, ..., wt = NULL, sort = FALSE, name = NULL)
tally(x, wt = NULL, sort = FALSE, name = NULL)
add_count(x, ..., wt = NULL, sort = FALSE, name = NULL)
add_tally(x, wt = NULL, sort = FALSE, name = NULL)
pull(.data, var = -1, name = NULL)
distinct(.data, ..., .keep_all = FALSE)
drop_na(data, ...)
replace_na(data, replace)
pivot_longer(data, cols, names_to = "name", values_to = "value")
pivot_wider(data, names_from = name, values_from = value)
uncount(data, weights, .remove = TRUE, .id = NULL)
unite(data, col, ..., sep = "_", remove = TRUE, na.rm = FALSE)
separate(data, col, into, sep = "[^[:alnum:]]+", remove = TRUE, convert = FALSE)
separate_rows(data, ..., sep = "[^[:alnum:].]+", convert = FALSE)
fill(data, ..., .direction = c("down", "up", "downup", "updown"))
extract(data, col, into, regex = "([[:alnum:]]+)", remove = TRUE, convert = FALSE) plus the functions defined here under.

Usage

list_tidy_functions()

filter_ungroup(.data, ...)

mutate_ungroup(.data, ..., .keep = "all")

transmute_ungroup(.data, ...)

Arguments

.data: A data frame, data frame extension (e.g. a tibble), or a lazy data frame (e.g. from dbplyr or dtplyr). See mutate() for more details.
...: Arguments dependent to the context of the function and most of the time, not evaluated in a standard way (cf. the tidyverse approach).
.keep: Which columns to keep. The default is "all", possible values are "used", "unused", or "none" (see mutate()).

Value

See corresponding "non-t" function for the full help page with indication of the return values. list_tidy_functions() returns a list of all the tidy(verse) functions that have their speedy "s" counterpart, see speedy_functions.

Note

The help page here is very basic and it aims mainly to list all the tidy functions. For more complete help, see the {dplyr} or {tidyr} packages. From {dplyr}, the slice() and slice_xxx() functions are not added yet because they are not available for {dbplyr}. Also anti_join(), semi_join() and nest_join() are not implemented yet. From {dplyr}, the slice() and slice_xxx() functions are not added yet because they are not available for {dbplyr}. Also anti_join(), semi_join() and nest_join() are not implemented yet. From {tidyr} expand(), chop(), unchop(), nest(), unnest(), unnest_longer(), unnest_wider(), hoist(), pack() and unpack() are not implemented yet.

Examples

# TODO...