Filtering joins filter rows from x based on the presence or absence
of matches in y:
semi_join() return all rows from x with a match in y.
anti_join() return all rows from x without a match in y.
# S3 method for class 'SingleCellExperiment'
anti_join(x, y, by = NULL, copy = FALSE, ...)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.
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().
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.
Other parameters passed onto methods.
An object of the same type as x. The output has the following properties:
Rows are a subset of the input, but appear in the same order.
Columns are not modified.
Data frame attributes are preserved.
Groups are taken from x. The number of groups may be reduced.
These function are generics, which means that packages can provide implementations (methods) for other classes. See the documentation of individual methods for extra arguments and differences in behaviour.
Methods available in currently loaded packages:
semi_join(): dbplyr (tbl_lazy), dplyr (data.frame)
.
anti_join(): (SingleCellExperiment), dbplyr (tbl_lazy), dplyr (data.frame)
.
Other joins:
cross_join(),
mutate-joins,
nest_join()
data(pbmc_small)
tt <- pbmc_small
tt |> anti_join(tt |>
distinct(groups) |>
mutate(new_column=1:2) |>
slice(1))
#> tidySingleCellExperiment says: A data frame is returned for independent data analysis.
#> Joining with `by = join_by(groups)`
#> # A SingleCellExperiment-tibble abstraction: 44 × 17
#> # Features=230 | Cells=44 | Assays=counts, logcounts
#> .cell orig.ident nCount_RNA nFeature_RNA RNA_snn_res.0.8 letter.idents groups
#> <chr> <fct> <dbl> <int> <fct> <fct> <chr>
#> 1 CATG… SeuratPro… 85 52 0 A g1
#> 2 TCTG… SeuratPro… 70 48 0 A g1
#> 3 TGGT… SeuratPro… 64 36 0 A g1
#> 4 GCAG… SeuratPro… 72 45 0 A g1
#> 5 GATA… SeuratPro… 52 36 0 A g1
#> 6 AATG… SeuratPro… 100 41 0 A g1
#> 7 AGAG… SeuratPro… 191 61 0 A g1
#> 8 CTAA… SeuratPro… 168 44 0 A g1
#> 9 TTGG… SeuratPro… 135 45 0 A g1
#> 10 CATC… SeuratPro… 79 43 0 A g1
#> # ℹ 34 more rows
#> # ℹ 10 more variables: RNA_snn_res.1 <fct>, file <chr>, ident <fct>,
#> # PC_1 <dbl>, PC_2 <dbl>, PC_3 <dbl>, PC_4 <dbl>, PC_5 <dbl>, tSNE_1 <dbl>,
#> # tSNE_2 <dbl>