See `dplyr`

documentation of the respective functions for
description and examples.

```
# S3 method for ped
arrange(.data, ...)
# S3 method for ped
group_by(.data, ..., .add = FALSE)
# S3 method for ped
ungroup(x, ...)
# S3 method for ped
distinct(.data, ..., .keep_all = FALSE)
# S3 method for ped
filter(.data, ...)
# S3 method for ped
sample_n(tbl, size, replace = FALSE, weight = NULL, .env = NULL, ...)
# S3 method for ped
sample_frac(tbl, size = 1, replace = FALSE, weight = NULL, .env = NULL, ...)
# S3 method for ped
slice(.data, ...)
# S3 method for ped
select(.data, ...)
# S3 method for ped
mutate(.data, ...)
# S3 method for ped
rename(.data, ...)
# S3 method for ped
summarise(.data, ...)
# S3 method for ped
summarize(.data, ...)
# S3 method for ped
transmute(.data, ...)
# S3 method for ped
inner_join(x, y, by = NULL, copy = FALSE, suffix = c(".x", ".y"), ...)
# S3 method for ped
full_join(x, y, by = NULL, copy = FALSE, suffix = c(".x", ".y"), ...)
# S3 method for ped
left_join(x, y, by = NULL, copy = FALSE, suffix = c(".x", ".y"), ...)
# S3 method for ped
right_join(x, y, by = NULL, copy = FALSE, suffix = c(".x", ".y"), ...)
```

- .data
an object of class

`ped`

, see`as_ped`

.- ...
see

`dplyr`

documentation- x
an object of class

`ped`

, see`as_ped`

.- tbl
an object of class

`ped`

, see`as_ped`

.- size
<

`tidy-select`

> 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
<

`tidy-select`

> 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.- .env
DEPRECATED.

- y
A pair of data frames, data frame extensions (e.g. a tibble), or lazy data frames (e.g. from dbplyr or dtplyr). See

*Methods*, below, for more details.- by
A join specification created with

`join_by()`

, or a character vector of variables to join by.If

`NULL`

, the default,`*_join()`

will perform a natural join, using all variables in common across`x`

and`y`

. A message lists the variables so that you can check they're correct; suppress the message by supplying`by`

explicitly.To join on different variables between

`x`

and`y`

, use a`join_by()`

specification. For example,`join_by(a == b)`

will match`x$a`

to`y$b`

.To join by multiple variables, use a

`join_by()`

specification with multiple expressions. For example,`join_by(a == b, c == d)`

will match`x$a`

to`y$b`

and`x$c`

to`y$d`

. If the column names are the same between`x`

and`y`

, you can shorten this by listing only the variable names, like`join_by(a, c)`

.`join_by()`

can also be used to perform inequality, rolling, and overlap joins. See the documentation at ?join_by for details on these types of joins.For simple equality joins, you can alternatively specify a character vector of variable names to join by. For example,

`by = c("a", "b")`

joins`x$a`

to`y$a`

and`x$b`

to`y$b`

. If variable names differ between`x`

and`y`

, use a named character vector like`by = c("x_a" = "y_a", "x_b" = "y_b")`

.To perform a cross-join, generating all combinations of

`x`

and`y`

, see`cross_join()`

.- 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`

a modified `ped`

object (except for `do`

)