dplyr used to offer twin versions of each verb suffixed with an underscore. These versions had standard evaluation (SE) semantics: rather than taking arguments by code, like NSE verbs, they took arguments by value. Their purpose was to make it possible to program with dplyr. However, dplyr now uses tidy evaluation semantics. NSE verbs still capture their arguments, but you can now unquote parts of these arguments. This offers full programmability with NSE verbs. Thus, the underscored versions are now superfluous.

Unquoting triggers immediate evaluation of its operand and inlines the result within the captured expression. This result can be a value or an expression to be evaluated later with the rest of the argument. See vignette("programming") for more information.

add_count_(x, vars, wt = NULL, sort = FALSE)

arrange_(.data, ..., .dots = list())

count_(x, vars, wt = NULL, sort = FALSE, .drop = group_by_drop_default(x))

distinct_(.data, ..., .dots, .keep_all = FALSE)

do_(.data, ..., .dots = list())

filter_(.data, ..., .dots = list())

funs_(dots, args = list(), env = base_env())

group_by_(.data, ..., .dots = list(), add = FALSE)

group_indices_(.data, ..., .dots = list())

mutate_(.data, ..., .dots = list())

tally_(x, wt, sort = FALSE)

transmute_(.data, ..., .dots = list())

rename_(.data, ..., .dots = list())

rename_vars_(vars, args)

select_(.data, ..., .dots = list())

select_vars_(vars, args, include = chr(), exclude = chr())

slice_(.data, ..., .dots = list())

summarise_(.data, ..., .dots = list())

summarize_(.data, ..., .dots = list())

## Arguments

x A tbl() Various meanings depending on the verb. <data-masking> Frequency weights. Can be NULL or a variable: If NULL (the default), counts the number of rows in each group. If a variable, computes sum(wt) for each group. If TRUE, will show the largest groups at the top. A data frame. Drop groups formed by factor levels that don't appear in the data? The default is TRUE except when .data has been previously grouped with .drop = FALSE. See group_by_drop_default() for details. If TRUE, keep all variables in .data. If a combination of ... is not distinct, this keeps the first row of values. Pair/values of expressions coercible to lazy objects. Various meanings depending on the verb. The environment in which functions should be evaluated. When FALSE, the default, group_by() will override existing groups. To add to the existing groups, use .add = TRUE. This argument was previously called add, but that prevented creating a new grouping variable called add, and conflicts with our naming conventions. Character vector of column names to always include/exclude.