Filters .data according to all records in the filter

If filters is NULL, no filtering is done. Otherwise, the .data object is filtered via an inner_join() using all columns of the filter: inner_join(.data, filter, by = colnames(filter))

by and na_by can overwrite the inner_join() columns used in the filtering.

Usage

filter_keys(.data, filters, by = NULL, na_by = NULL, ...)

Arguments

.data

(data.frame(1), tibble(1), data.table(1), or tbl_dbi(1))
Data object.

filters

(data.frame(1), tibble(1), data.table(1), or tbl_dbi(1))
A object subset data by. If filters is NULL, no filtering occurs. Otherwise, an inner_join() is performed using all columns of the filter object.

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().

na_by

(character())
Columns where NA should match with NA.

...

Further arguments passed to dplyr::inner_join().

Value

An object of same class as .data

Examples

  # Filtering with null means no filtering is done
  filter <- NULL
  identical(filter_keys(mtcars, filter), mtcars) # TRUE
#> [1] TRUE

  # Filtering by vs = 0
  filter <- data.frame(vs = 0)
  identical(filter_keys(mtcars, filter), dplyr::filter(mtcars, vs == 0)) # TRUE
#> [1] FALSE

  # Filtering by the specific combinations of vs = 0 and am = 1
  filter <- dplyr::distinct(mtcars, vs, am)
  filter_keys(mtcars, filter)
#>     mpg cyl  disp  hp drat    wt  qsec vs am gear carb
#> 1  21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4
#> 2  21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4
#> 3  22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1
#> 4  21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1
#> 5  18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2
#> 6  18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1
#> 7  14.3   8 360.0 245 3.21 3.570 15.84  0  0    3    4
#> 8  24.4   4 146.7  62 3.69 3.190 20.00  1  0    4    2
#> 9  22.8   4 140.8  95 3.92 3.150 22.90  1  0    4    2
#> 10 19.2   6 167.6 123 3.92 3.440 18.30  1  0    4    4
#> 11 17.8   6 167.6 123 3.92 3.440 18.90  1  0    4    4
#> 12 16.4   8 275.8 180 3.07 4.070 17.40  0  0    3    3
#> 13 17.3   8 275.8 180 3.07 3.730 17.60  0  0    3    3
#> 14 15.2   8 275.8 180 3.07 3.780 18.00  0  0    3    3
#> 15 10.4   8 472.0 205 2.93 5.250 17.98  0  0    3    4
#> 16 10.4   8 460.0 215 3.00 5.424 17.82  0  0    3    4
#> 17 14.7   8 440.0 230 3.23 5.345 17.42  0  0    3    4
#> 18 32.4   4  78.7  66 4.08 2.200 19.47  1  1    4    1
#> 19 30.4   4  75.7  52 4.93 1.615 18.52  1  1    4    2
#> 20 33.9   4  71.1  65 4.22 1.835 19.90  1  1    4    1
#> 21 21.5   4 120.1  97 3.70 2.465 20.01  1  0    3    1
#> 22 15.5   8 318.0 150 2.76 3.520 16.87  0  0    3    2
#> 23 15.2   8 304.0 150 3.15 3.435 17.30  0  0    3    2
#> 24 13.3   8 350.0 245 3.73 3.840 15.41  0  0    3    4
#> 25 19.2   8 400.0 175 3.08 3.845 17.05  0  0    3    2
#> 26 27.3   4  79.0  66 4.08 1.935 18.90  1  1    4    1
#> 27 26.0   4 120.3  91 4.43 2.140 16.70  0  1    5    2
#> 28 30.4   4  95.1 113 3.77 1.513 16.90  1  1    5    2
#> 29 15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4
#> 30 19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6
#> 31 15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8
#> 32 21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2