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.
p_nest_join(
x,
y,
...,
.messages = c("{.count.lhs} on LHS", "{.count.rhs} on RHS", "{.count.out} matched"),
.headline = "Nest join by {.keys}"
)the join of the two dataframes with the history graph updated.
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.
Other parameters passed onto methods.
Named arguments passed on to dplyr::nest_join
byA 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().
copyIf 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.
keepShould the new list-column contain join keys? The default will preserve the join keys for inequality joins.
nameThe name of the list-column created by the join. If NULL,
the default, the name of y is used.
na_matchesShould two NA or two NaN values match?
unmatchedHow should unmatched keys that would result in dropped rows be handled?
"drop" drops unmatched keys from the result.
"error" throws an error if unmatched keys are detected.
unmatched is intended to protect you from accidentally dropping rows
during a join. It only checks for unmatched keys in the input that could
potentially drop rows.
For left joins, it checks y.
For right joins, it checks x.
For inner joins, it checks both x and y. In this case, unmatched is
also allowed to be a character vector of length 2 to specify the behavior
for x and y independently.
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
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
dplyr::nest_join()
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()
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