dplyr Verbs for ped-ObjectsSee 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
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, ..., keep_attributes = TRUE)
# 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"),
...,
keep_attributes = TRUE
)
# S3 method for ped
right_join(
x,
y,
by = NULL,
copy = FALSE,
suffix = c(".x", ".y"),
...,
keep_attributes = TRUE
)
# S3 method for nested_fdf
arrange(.data, ...)
# S3 method for nested_fdf
group_by(.data, ..., .add = FALSE)
# S3 method for nested_fdf
ungroup(x, ...)
# S3 method for nested_fdf
filter(.data, ...)
# S3 method for nested_fdf
sample_n(tbl, size, replace = FALSE, weight = NULL, .env = NULL, ...)
# S3 method for nested_fdf
sample_frac(tbl, size = 1, replace = FALSE, weight = NULL, .env = NULL, ...)
# S3 method for nested_fdf
slice(.data, ...)
# S3 method for nested_fdf
select(.data, ...)
# S3 method for nested_fdf
mutate(.data, ..., keep_attributes = TRUE)
# S3 method for nested_fdf
rename(.data, ...)
# S3 method for nested_fdf
summarise(.data, ...)
# S3 method for nested_fdf
summarize(.data, ...)
# S3 method for nested_fdf
transmute(.data, ...)
# S3 method for nested_fdf
inner_join(x, y, by = NULL, copy = FALSE, suffix = c(".x", ".y"), ...)
# S3 method for nested_fdf
full_join(x, y, by = NULL, copy = FALSE, suffix = c(".x", ".y"), ...)
# S3 method for nested_fdf
left_join(
x,
y,
by = NULL,
copy = FALSE,
suffix = c(".x", ".y"),
...,
keep_attributes = TRUE
)
# S3 method for nested_fdf
right_join(
x,
y,
by = NULL,
copy = FALSE,
suffix = c(".x", ".y"),
...,
keep_attributes = TRUE
)
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.
conserve attributes? defaults to TRUE
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 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 by different variables on x and y, use a named vector.
For example, by = c("a" = "b") will match x$a to y$b.
To join by multiple variables, use a vector with length > 1.
For example, by = c("a", "b") will match x$a to y$a and x$b to
y$b. Use a named vector to match different variables in x and y.
For example, by = c("a" = "b", "c" = "d") will match x$a to y$b and
x$c to y$d.
To perform a cross-join, generating all combinations of x and y,
use by = character().
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.
see summarize_all
see dplyr documentation
a modified ped object (except for do)