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.
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.
- ...
Other parameters passed onto methods. Named arguments passed on to
dplyr::nest_join
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 acrossx
andy
. A message lists the variables so that you can check they're correct; suppress the message by supplyingby
explicitly.To join on different variables between
x
andy
, use ajoin_by()
specification. For example,join_by(a == b)
will matchx$a
toy$b
.To join by multiple variables, use a
join_by()
specification with multiple expressions. For example,join_by(a == b, c == d)
will matchx$a
toy$b
andx$c
toy$d
. If the column names are the same betweenx
andy
, you can shorten this by listing only the variable names, likejoin_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")
joinsx$a
toy$a
andx$b
toy$b
. If variable names differ betweenx
andy
, use a named character vector likeby = c("x_a" = "y_a", "x_b" = "y_b")
.To perform a cross-join, generating all combinations of
x
andy
, seecross_join()
.copy
If
x
andy
are not from the same data source, andcopy
isTRUE
, theny
will be copied into the same src asx
. 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 ofy
is used.na_matches
Should two
NA
or twoNaN
values match?unmatched
How 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
andy
. In this case,unmatched
is also allowed to be a character vector of length 2 to specify the behavior forx
andy
independently.
- .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
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()
#> dtrackr history:
#> number of flowchart steps: 5 (approx)
#> tags defined: <none>
#> items excluded so far: <not capturing exclusions>
#> last entry / entries:
#> └ "joined 87"
nrow(join)
#> [1] 87
# Display the tracked graph (not run in examples)
# join %>% flowchart()