Semi join

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::semi_join() for more details on the underlying functions.

Usage

p_semi_join(
  x,
  y,
  ...,
  .messages = c("{.count.lhs} on LHS", "{.count.rhs} on RHS",
    "{.count.out} in intersection"),
  .headline = "Semi 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.

...

Other parameters passed onto methods. Named arguments passed on to dplyr::semi_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 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.

na_matches

Should two NA or two NaN values match?

"na", the default, treats two NA or two NaN values as equal, like %in%, match(), and merge().
"never" treats two NA or two NaN values as different, and will never match them together or to any other values. This is similar to joins for database sources and to base::merge(incomparables = NA).

.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.

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")

# Semi join
join = lhs %>% semi_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()

Usage

Arguments

Value

See also

Examples