dplyr Verbs for ped-Objects

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
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
)

Arguments

.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.
keep_attributes	conserve attributes? defaults to TRUE
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 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().
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

Value

a modified ped object (except for do)