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pammtools (version 0.2.3)

dplyr_verbs: dplyr Verbs for ped-Objects

Description

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

Usage

# 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

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

Value

a modified ped object (except for do)