These scoped filtering verbs apply a predicate expression to a selection of variables. The predicate expression should be quoted with all_vars() or any_vars() and should mention the pronoun . to refer to variables.

filter_all(.tbl, .vars_predicate)

filter_if(.tbl, .predicate, .vars_predicate)

filter_at(.tbl, .vars, .vars_predicate)

Arguments

.tbl

A tbl object.

.vars_predicate

A quoted predicate expression as returned by all_vars() or any_vars().

.predicate

A predicate function to be applied to the columns or a logical vector. The variables for which .predicate is or returns TRUE are selected. This argument is passed to rlang::as_function() and thus supports quosure-style lambda functions and strings representing function names.

.vars

A list of columns generated by vars(), or a character vector of column names, or a numeric vector of column positions.

Examples

# While filter() accepts expressions with specific variables, the # scoped filter verbs take an expression with the pronoun `.` and # replicate it over all variables. This expression should be quoted # with all_vars() or any_vars(): all_vars(is.na(.))
#> <predicate intersection> #> <quosure: local> #> ~is.na(.)
any_vars(is.na(.))
#> <predicate union> #> <quosure: local> #> ~is.na(.)
# You can take the intersection of the replicated expressions: filter_all(mtcars, all_vars(. > 150))
#> [1] mpg cyl disp hp drat wt qsec vs am gear carb #> <0 rows> (or 0-length row.names)
# Or the union: filter_all(mtcars, any_vars(. > 150))
#> 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 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 #> 4 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 #> 5 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 #> 6 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 #> 7 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 #> 8 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 #> 9 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3 #> 10 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 #> 11 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3 #> 12 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4 #> 13 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4 #> 14 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4 #> 15 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2 #> 16 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2 #> 17 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4 #> 18 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2 #> 19 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4 #> 20 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6 #> 21 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
# You can vary the selection of columns on which to apply the # predicate. filter_at() takes a vars() specification: filter_at(mtcars, vars(starts_with("d")), any_vars((. %% 2) == 0))
#> mpg cyl disp hp drat wt qsec vs am gear carb #> 1 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4 #> 2 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4 #> 3 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1 #> 4 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1 #> 5 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2 #> 6 14.3 8 360 245 3.21 3.570 15.84 0 0 3 4 #> 7 10.4 8 472 205 2.93 5.250 17.98 0 0 3 4 #> 8 10.4 8 460 215 3.00 5.424 17.82 0 0 3 4 #> 9 14.7 8 440 230 3.23 5.345 17.42 0 0 3 4 #> 10 15.5 8 318 150 2.76 3.520 16.87 0 0 3 2 #> 11 15.2 8 304 150 3.15 3.435 17.30 0 0 3 2 #> 12 13.3 8 350 245 3.73 3.840 15.41 0 0 3 4 #> 13 19.2 8 400 175 3.08 3.845 17.05 0 0 3 2
# And filter_if() selects variables with a predicate function: filter_if(mtcars, ~ all(floor(.) == .), all_vars(. != 0))
#> mpg cyl disp hp drat wt qsec vs am gear carb #> 1 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 #> 2 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1 #> 3 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 #> 4 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1 #> 5 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 #> 6 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 #> 7 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2