Evaluate conditional expressions and if it evaluates to TRUE, execute the function call's logic. Note that these
functions are subtly different from the scoped variants of dplyr functions in that they can evaluate any condition.
If the condition evaluates to FALSE, these functions will return the original data.
arrange_when(.data, .cond, ...) distinct_when(.data, .cond, ...) filter_when(.data, .cond, ...) group_by_when(.data, .cond, ...) mutate_when(.data, .cond, ...) select_when(.data, .cond, ...) summarise_when(.data, .cond, ...) transmute_when(.data, .cond, ...)
| .data | A |
|---|---|
| .cond | A condition that will evaluate to a |
| ... | Additional parameters to pass to the dplyr function. |
A tbl_spark or a data.frame depending on the input, .data.
# Let's say we have another object and based on this object we # want to perform some conditional logic previous_result <- 42 # We can evaluate expressions in the same way as the dplyr function. # If the evaluation is FALSE, it will return the original data. mtcars %>% mutate_when(previous_result < 42, mpg * 2)#> 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 #> Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 #> 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 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 #> Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 #> 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 #> 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 #> Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1 #> 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 #> Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 #> Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2 #> Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 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 #> Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2# And if the condition if TRUE, it will evaluate mtcars %>% mutate_when(previous_result >= 42, mpg * 2)#> mpg cyl disp hp drat wt qsec vs am gear carb mpg * 2 #> Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 42.0 #> Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 42.0 #> Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 45.6 #> Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 42.8 #> Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 37.4 #> Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 36.2 #> Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 28.6 #> Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 48.8 #> Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 45.6 #> Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 38.4 #> Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 35.6 #> Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3 32.8 #> Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 34.6 #> Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3 30.4 #> Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4 20.8 #> Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4 20.8 #> Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4 29.4 #> Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1 64.8 #> Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 60.8 #> Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1 67.8 #> Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1 43.0 #> Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2 31.0 #> AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2 30.4 #> Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4 26.6 #> Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2 38.4 #> Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 54.6 #> Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2 52.0 #> Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 60.8 #> Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4 31.6 #> Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6 39.4 #> Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8 30.0 #> Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2 42.8# We can evaluate multiple expressions mtcars %>% mutate_when(previous_result >= 42, mpg2 = mpg * 2, mpg4 = mpg * 2)#> mpg cyl disp hp drat wt qsec vs am gear carb mpg2 #> Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 42.0 #> Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 42.0 #> Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 45.6 #> Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 42.8 #> Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 37.4 #> Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 36.2 #> Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 28.6 #> Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 48.8 #> Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 45.6 #> Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 38.4 #> Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 35.6 #> Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3 32.8 #> Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 34.6 #> Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3 30.4 #> Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4 20.8 #> Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4 20.8 #> Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4 29.4 #> Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1 64.8 #> Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 60.8 #> Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1 67.8 #> Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1 43.0 #> Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2 31.0 #> AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2 30.4 #> Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4 26.6 #> Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2 38.4 #> Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 54.6 #> Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2 52.0 #> Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 60.8 #> Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4 31.6 #> Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6 39.4 #> Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8 30.0 #> Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2 42.8 #> mpg4 #> Mazda RX4 42.0 #> Mazda RX4 Wag 42.0 #> Datsun 710 45.6 #> Hornet 4 Drive 42.8 #> Hornet Sportabout 37.4 #> Valiant 36.2 #> Duster 360 28.6 #> Merc 240D 48.8 #> Merc 230 45.6 #> Merc 280 38.4 #> Merc 280C 35.6 #> Merc 450SE 32.8 #> Merc 450SL 34.6 #> Merc 450SLC 30.4 #> Cadillac Fleetwood 20.8 #> Lincoln Continental 20.8 #> Chrysler Imperial 29.4 #> Fiat 128 64.8 #> Honda Civic 60.8 #> Toyota Corolla 67.8 #> Toyota Corona 43.0 #> Dodge Challenger 31.0 #> AMC Javelin 30.4 #> Camaro Z28 26.6 #> Pontiac Firebird 38.4 #> Fiat X1-9 54.6 #> Porsche 914-2 52.0 #> Lotus Europa 60.8 #> Ford Pantera L 31.6 #> Ferrari Dino 39.4 #> Maserati Bora 30.0 #> Volvo 142E 42.8# We can still use functionality such as tidy-select mtcars %>% select_when(previous_result >= 42, Cylinders = cyl, everything())#> Cylinders mpg disp hp drat wt qsec vs am gear carb #> Mazda RX4 6 21.0 160.0 110 3.90 2.620 16.46 0 1 4 4 #> Mazda RX4 Wag 6 21.0 160.0 110 3.90 2.875 17.02 0 1 4 4 #> Datsun 710 4 22.8 108.0 93 3.85 2.320 18.61 1 1 4 1 #> Hornet 4 Drive 6 21.4 258.0 110 3.08 3.215 19.44 1 0 3 1 #> Hornet Sportabout 8 18.7 360.0 175 3.15 3.440 17.02 0 0 3 2 #> Valiant 6 18.1 225.0 105 2.76 3.460 20.22 1 0 3 1 #> Duster 360 8 14.3 360.0 245 3.21 3.570 15.84 0 0 3 4 #> Merc 240D 4 24.4 146.7 62 3.69 3.190 20.00 1 0 4 2 #> Merc 230 4 22.8 140.8 95 3.92 3.150 22.90 1 0 4 2 #> Merc 280 6 19.2 167.6 123 3.92 3.440 18.30 1 0 4 4 #> Merc 280C 6 17.8 167.6 123 3.92 3.440 18.90 1 0 4 4 #> Merc 450SE 8 16.4 275.8 180 3.07 4.070 17.40 0 0 3 3 #> Merc 450SL 8 17.3 275.8 180 3.07 3.730 17.60 0 0 3 3 #> Merc 450SLC 8 15.2 275.8 180 3.07 3.780 18.00 0 0 3 3 #> Cadillac Fleetwood 8 10.4 472.0 205 2.93 5.250 17.98 0 0 3 4 #> Lincoln Continental 8 10.4 460.0 215 3.00 5.424 17.82 0 0 3 4 #> Chrysler Imperial 8 14.7 440.0 230 3.23 5.345 17.42 0 0 3 4 #> Fiat 128 4 32.4 78.7 66 4.08 2.200 19.47 1 1 4 1 #> Honda Civic 4 30.4 75.7 52 4.93 1.615 18.52 1 1 4 2 #> Toyota Corolla 4 33.9 71.1 65 4.22 1.835 19.90 1 1 4 1 #> Toyota Corona 4 21.5 120.1 97 3.70 2.465 20.01 1 0 3 1 #> Dodge Challenger 8 15.5 318.0 150 2.76 3.520 16.87 0 0 3 2 #> AMC Javelin 8 15.2 304.0 150 3.15 3.435 17.30 0 0 3 2 #> Camaro Z28 8 13.3 350.0 245 3.73 3.840 15.41 0 0 3 4 #> Pontiac Firebird 8 19.2 400.0 175 3.08 3.845 17.05 0 0 3 2 #> Fiat X1-9 4 27.3 79.0 66 4.08 1.935 18.90 1 1 4 1 #> Porsche 914-2 4 26.0 120.3 91 4.43 2.140 16.70 0 1 5 2 #> Lotus Europa 4 30.4 95.1 113 3.77 1.513 16.90 1 1 5 2 #> Ford Pantera L 8 15.8 351.0 264 4.22 3.170 14.50 0 1 5 4 #> Ferrari Dino 6 19.7 145.0 175 3.62 2.770 15.50 0 1 5 6 #> Maserati Bora 8 15.0 301.0 335 3.54 3.570 14.60 0 1 5 8 #> Volvo 142E 4 21.4 121.0 109 4.11 2.780 18.60 1 1 4 2# We can also evaluate logic that is conditional on .data mtcars %>% filter_when("mpg" %in% colnames(.), cyl == 4)#> 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 #> Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 #> Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 #> 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 #> Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1 #> Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 #> Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2 #> 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