p_nest_join {dtrackr}

Nest join

Description

Mutating joins behave as dplyr joins, except the history graph of the two sides of the joins is merged resulting in a tracked dataframe with the history of both input dataframes. See dplyr::nest_join() for more details on the underlying functions.

Usage

p_nest_join(
  x,
  y,
  ...,
  .messages = c("{.count.lhs} on LHS", "{.count.rhs} on RHS", "{.count.out} matched"),
  .headline = "Nest join by {.keys}"
)

Arguments

x, 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.

...

Arguments passed on to dplyr::nest_join

x,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.

keep

Should the new list-column contain join keys? The default will preserve the join keys for inequality joins.

name

The name of the list-column created by the join. If NULL, the default, the name of y is used.

.messages

a set of glue specs. The glue code can use any global variable, {.keys} for the joining columns, {.count.lhs}, {.count.rhs}, {.count.out} for the input and output dataframes sizes respectively

.headline

a glue spec. The glue code can use any global variable, {.keys} for the joining columns, {.count.lhs}, {.count.rhs}, {.count.out} for the input and output dataframes sizes respectively

Value

the join of the two dataframes with the history graph updated.

See Also

dplyr::nest_join()

Examples

library(dplyr)
library(dtrackr)
# Joins across data sets

# example data uses the dplyr starways data
people = starwars %>% select(-films, -vehicles, -starships)
films = starwars %>% select(name,films) %>% tidyr::unnest(cols = c(films))

lhs = people %>% track() %>% comment("People df {.total}")
rhs = films %>% track() %>% comment("Films df {.total}") %>%
  comment("a test comment")

# Nest join
join = lhs %>% nest_join(rhs, by="name") %>% comment("joined {.total}")
# See what the history of the graph is:
join %>% history() %>% print()
nrow(join)
# Display the tracked graph (not run in examples)
# join %>% flowchart()