dplyr_verbs: 'dplyr' Verbs for 'ped'-Objects
In adibender/ped: What the Package Does (One Line, Title Case)

See dplyr documentation of the respective functions for description and examples.

## S3 method for class 'ped'
arrange(.data, ...)

## S3 method for class 'ped'
group_by(.data, ..., add = FALSE)

## S3 method for class 'ped'
ungroup(x, ...)

## S3 method for class 'ped'
filter(.data, ...)

## S3 method for class 'ped'
sample_n(tbl, size, replace = FALSE, weight = NULL,
  .env = NULL, ...)

## S3 method for class 'ped'
sample_frac(tbl, size = 1, replace = FALSE,
  weight = NULL, .env = NULL, ...)

## S3 method for class 'ped'
slice(.data, ...)

## S3 method for class 'ped'
select(.data, ...)

## S3 method for class 'ped'
mutate(.data, ..., keep_attributes = TRUE)

## S3 method for class 'ped'
rename(.data, ...)

## S3 method for class 'ped'
summarise(.data, ...)

## S3 method for class 'ped'
summarize(.data, ...)

## S3 method for class 'ped'
transmute(.data, ...)

## S3 method for class 'ped'
inner_join(x, y, by = NULL, copy = FALSE,
  suffix = c(".x", ".y"), ...)

## S3 method for class 'ped'
full_join(x, y, by = NULL, copy = FALSE,
  suffix = c(".x", ".y"), ...)

## S3 method for class 'ped'
left_join(x, y, by = NULL, copy = FALSE,
  suffix = c(".x", ".y"), ..., keep_attributes = TRUE)

## S3 method for class 'ped'
right_join(x, y, by = NULL, copy = FALSE,
  suffix = c(".x", ".y"), ..., keep_attributes = TRUE)

## S3 method for class 'nested_fdf'
arrange(.data, ...)

## S3 method for class 'nested_fdf'
group_by(.data, ..., add = FALSE)

## S3 method for class 'nested_fdf'
ungroup(x, ...)

## S3 method for class 'nested_fdf'
filter(.data, ...)

## S3 method for class 'nested_fdf'
sample_n(tbl, size, replace = FALSE,
  weight = NULL, .env = NULL, ...)

## S3 method for class 'nested_fdf'
sample_frac(tbl, size = 1, replace = FALSE,
  weight = NULL, .env = NULL, ...)

## S3 method for class 'nested_fdf'
slice(.data, ...)

## S3 method for class 'nested_fdf'
select(.data, ...)

## S3 method for class 'nested_fdf'
mutate(.data, ..., keep_attributes = TRUE)

## S3 method for class 'nested_fdf'
rename(.data, ...)

## S3 method for class 'nested_fdf'
summarise(.data, ...)

## S3 method for class 'nested_fdf'
summarize(.data, ...)

## S3 method for class 'nested_fdf'
transmute(.data, ...)

## S3 method for class 'nested_fdf'
inner_join(x, y, by = NULL, copy = FALSE,
  suffix = c(".x", ".y"), ...)

## S3 method for class 'nested_fdf'
full_join(x, y, by = NULL, copy = FALSE,
  suffix = c(".x", ".y"), ...)

## S3 method for class 'nested_fdf'
left_join(x, y, by = NULL, copy = FALSE,
  suffix = c(".x", ".y"), ..., keep_attributes = TRUE)

## S3 method for class 'nested_fdf'
right_join(x, y, by = NULL, copy = FALSE,
  suffix = c(".x", ".y"), ..., keep_attributes = TRUE)

`.data`	an object of class `ped`, see `as_ped`.
`...`	see `dplyr` documentation
`add`	When `add = FALSE`, the default, `group_by()` will override existing groups. To add to the existing groups, use `add = TRUE`.
`x`	an object of class `ped`, see `as_ped`.
`tbl`	an object of class `ped`, see `as_ped`.
`size`	For `sample_n()`, the number of rows to select. For `sample_frac()`, the fraction of rows to select. If `tbl` is grouped, `size` applies to each group.
`replace`	Sample with or without replacement?
`weight`	Sampling weights. This must evaluate to a vector of non-negative numbers the same length as the input. Weights are automatically standardised to sum to 1. This argument is automatically quoted and later evaluated in the context of the data frame. It supports unquoting. See `vignette("programming")` for an introduction to these concepts.
`.env`	This variable is deprecated and no longer has any effect. To evaluate `weight` in a particular context, you can now unquote a quosure.
`keep_attributes`	conserve attributes? defaults to `TRUE`
`y`	tbls to join
`by`	a character vector of variables to join by. If `NULL`, the default, `*_join()` will do a natural join, using all variables with common names across the two tables. A message lists the variables so that you can check they're right (to suppress the message, simply explicitly list the variables that you want to join). To join by different variables on x and y use a named vector. For example, `by = c("a" = "b")` will match `x.a` to `y.b`.
`copy`	If `x` and `y` are not from the same data source, and `copy` is `TRUE`, then `y` will be copied into the same src as `x`. This allows you to join tables across srcs, but it is a potentially expensive operation so you must opt into it.
`suffix`	If there are non-joined duplicate variables in `x` and `y`, these suffixes will be added to the output to disambiguate them. Should be a character vector of length 2.
`funs`	see `summarize_all`
`.dots`	see `dplyr` documentation
`keep_attributes`	conserve attributes? defaults to `TRUE`