[Superseded]

Scoped verbs (_if, _at, _all) have been superseded by the use of across() in an existing verb. See vignette("colwise") for details.

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, .preserve = FALSE)

filter_if(.tbl, .predicate, .vars_predicate, .preserve = FALSE)

filter_at(.tbl, .vars, .vars_predicate, .preserve = FALSE)

Arguments

.tbl

A tbl object.

.vars_predicate

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

Can also be a function or purrr-like formula. In this case, the intersection of the results is taken by default and there's currently no way to request the union.

.preserve

when FALSE (the default), the grouping structure is recalculated based on the resulting data, otherwise it is kept as is.

.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(), a character vector of column names, a numeric vector of column positions, or NULL.

Grouping variables

The grouping variables that are part of the selection are taken into account to determine filtered rows.

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> #> expr: ^is.na(.) #> env: 0x7fb54d149b68
#> <predicate union> #> <quosure> #> expr: ^is.na(.) #> env: 0x7fb54d149b68
# 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)
# -> filter(mtcars, if_all(everything(), ~ .x > 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 #> Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 #> Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 #> Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 #> Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 #> Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 #> Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 #> Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 #> Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 #> Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3 #> Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 #> Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3 #> Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4 #> Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4 #> Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4 #> Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2 #> AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2 #> Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4 #> Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2 #> Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4 #> Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6 #> Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
# -> filter(mtcars, if_any(everything(), ~ . > 150))
#> mpg cyl disp hp drat wt qsec vs am gear carb #> Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 #> Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 #> Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 #> Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 #> Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 #> Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 #> Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 #> Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 #> Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3 #> Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 #> Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3 #> Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4 #> Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4 #> Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4 #> Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2 #> AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2 #> Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4 #> Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2 #> Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4 #> Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6 #> Maserati Bora 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 #> Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4 #> Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4 #> Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1 #> Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1 #> Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2 #> Duster 360 14.3 8 360 245 3.21 3.570 15.84 0 0 3 4 #> Cadillac Fleetwood 10.4 8 472 205 2.93 5.250 17.98 0 0 3 4 #> Lincoln Continental 10.4 8 460 215 3.00 5.424 17.82 0 0 3 4 #> Chrysler Imperial 14.7 8 440 230 3.23 5.345 17.42 0 0 3 4 #> Dodge Challenger 15.5 8 318 150 2.76 3.520 16.87 0 0 3 2 #> AMC Javelin 15.2 8 304 150 3.15 3.435 17.30 0 0 3 2 #> Camaro Z28 13.3 8 350 245 3.73 3.840 15.41 0 0 3 4 #> Pontiac Firebird 19.2 8 400 175 3.08 3.845 17.05 0 0 3 2
# -> filter(mtcars, if_any(starts_with("d"), ~ (.x %% 2) == 0))
#> mpg cyl disp hp drat wt qsec vs am gear carb #> Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4 #> Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4 #> Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1 #> Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1 #> Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2 #> Duster 360 14.3 8 360 245 3.21 3.570 15.84 0 0 3 4 #> Cadillac Fleetwood 10.4 8 472 205 2.93 5.250 17.98 0 0 3 4 #> Lincoln Continental 10.4 8 460 215 3.00 5.424 17.82 0 0 3 4 #> Chrysler Imperial 14.7 8 440 230 3.23 5.345 17.42 0 0 3 4 #> Dodge Challenger 15.5 8 318 150 2.76 3.520 16.87 0 0 3 2 #> AMC Javelin 15.2 8 304 150 3.15 3.435 17.30 0 0 3 2 #> Camaro Z28 13.3 8 350 245 3.73 3.840 15.41 0 0 3 4 #> Pontiac Firebird 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 #> Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 #> Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1 #> Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 #> Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1 #> Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 #> Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 #> Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
# -> is_int <- function(x) all(floor(x) == x) filter(mtcars, if_all(where(is_int), ~ .x != 0))
#> mpg cyl disp hp drat wt qsec vs am gear carb #> Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 #> Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1 #> Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 #> Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1 #> Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 #> Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 #> Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2