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"), ...)an object of class ped, see as_ped.
see dplyr documentation
an object of class ped, see as_ped.
an object of class ped, see as_ped.
<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.
Sample with or without replacement?
<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.
DEPRECATED.
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.
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().
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.
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.
a modified ped object (except for do)