%just% and %not% are subsetting convenience functions
for situations when you would do x[x %in% y] or x[x %nin% y]. See
details for behavior when x is a data frame or matrix.
Usage
x %not% y
x %not% y <- value
x %just% y
x %just% y <- value
# S3 method for default
%not%(x, y)
# S3 method for default
%not%(x, y) <- value
# S3 method for data.frame
%not%(x, y)
# S3 method for data.frame
%not%(x, y) <- value
# S3 method for matrix
%not%(x, y)
# S3 method for matrix
%not%(x, y) <- value
# S3 method for list
%not%(x, y)
# S3 method for list
%not%(x, y) <- value
# S3 method for default
%just%(x, y)
# S3 method for default
%just%(x, y) <- value
# S3 method for data.frame
%just%(x, y)
# S3 method for data.frame
%just%(x, y) <- value
# S3 method for matrix
%just%(x, y)
# S3 method for matrix
%just%(x, y) <- value
# S3 method for list
%just%(x, y)
# S3 method for list
%just%(x, y) <- valueArguments
- x
Object to subset
- y
List of items to include if they are/aren't in
x- value
The object(s) to assign to the subsetted
x
Details
The behavior of %not% and %just% are different when you're subsetting
data frames or matrices. The subset y in this case is interpreted as
column names or indices.
You can also make assignments to the subset in the same way you could if subsetting with brackets.
See also
Other subsetters:
%nin%()
Other subsetters:
%nin%()
Other subsetters:
%nin%()
Other subsetters:
%nin%()
Other subsetters:
%nin%()
Other subsetters:
%nin%()
Other subsetters:
%nin%()
Other subsetters:
%nin%()
Other subsetters:
%nin%()
Other subsetters:
%nin%()
Other subsetters:
%nin%()
Other subsetters:
%nin%()
Other subsetters:
%nin%()
Other subsetters:
%nin%()
Other subsetters:
%nin%()
Other subsetters:
%nin%()
Other subsetters:
%nin%()
Other subsetters:
%nin%()
Other subsetters:
%nin%()
Other subsetters:
%nin%()
Examples
x <- 1:5
y <- 3:8
x %just% y # 3 4 5
#> [1] 3 4 5
x %not% y # 1 2
#> [1] 1 2
# Assignment works too
x %just% y <- NA # 1 2 NA NA NA
x %not% y <- NA # NA NA 3 4 5
mtcars %just% c("mpg", "qsec", "cyl") # keeps only columns with those names
#> mpg cyl qsec
#> Mazda RX4 21.0 6 16.46
#> Mazda RX4 Wag 21.0 6 17.02
#> Datsun 710 22.8 4 18.61
#> Hornet 4 Drive 21.4 6 19.44
#> Hornet Sportabout 18.7 8 17.02
#> Valiant 18.1 6 20.22
#> Duster 360 14.3 8 15.84
#> Merc 240D 24.4 4 20.00
#> Merc 230 22.8 4 22.90
#> Merc 280 19.2 6 18.30
#> Merc 280C 17.8 6 18.90
#> Merc 450SE 16.4 8 17.40
#> Merc 450SL 17.3 8 17.60
#> Merc 450SLC 15.2 8 18.00
#> Cadillac Fleetwood 10.4 8 17.98
#> Lincoln Continental 10.4 8 17.82
#> Chrysler Imperial 14.7 8 17.42
#> Fiat 128 32.4 4 19.47
#> Honda Civic 30.4 4 18.52
#> Toyota Corolla 33.9 4 19.90
#> Toyota Corona 21.5 4 20.01
#> Dodge Challenger 15.5 8 16.87
#> AMC Javelin 15.2 8 17.30
#> Camaro Z28 13.3 8 15.41
#> Pontiac Firebird 19.2 8 17.05
#> Fiat X1-9 27.3 4 18.90
#> Porsche 914-2 26.0 4 16.70
#> Lotus Europa 30.4 4 16.90
#> Ford Pantera L 15.8 8 14.50
#> Ferrari Dino 19.7 6 15.50
#> Maserati Bora 15.0 8 14.60
#> Volvo 142E 21.4 4 18.60
mtcars %not% 1:5 # drops columns 1 through 5
#> wt qsec vs am gear carb
#> Mazda RX4 2.620 16.46 0 1 4 4
#> Mazda RX4 Wag 2.875 17.02 0 1 4 4
#> Datsun 710 2.320 18.61 1 1 4 1
#> Hornet 4 Drive 3.215 19.44 1 0 3 1
#> Hornet Sportabout 3.440 17.02 0 0 3 2
#> Valiant 3.460 20.22 1 0 3 1
#> Duster 360 3.570 15.84 0 0 3 4
#> Merc 240D 3.190 20.00 1 0 4 2
#> Merc 230 3.150 22.90 1 0 4 2
#> Merc 280 3.440 18.30 1 0 4 4
#> Merc 280C 3.440 18.90 1 0 4 4
#> Merc 450SE 4.070 17.40 0 0 3 3
#> Merc 450SL 3.730 17.60 0 0 3 3
#> Merc 450SLC 3.780 18.00 0 0 3 3
#> Cadillac Fleetwood 5.250 17.98 0 0 3 4
#> Lincoln Continental 5.424 17.82 0 0 3 4
#> Chrysler Imperial 5.345 17.42 0 0 3 4
#> Fiat 128 2.200 19.47 1 1 4 1
#> Honda Civic 1.615 18.52 1 1 4 2
#> Toyota Corolla 1.835 19.90 1 1 4 1
#> Toyota Corona 2.465 20.01 1 0 3 1
#> Dodge Challenger 3.520 16.87 0 0 3 2
#> AMC Javelin 3.435 17.30 0 0 3 2
#> Camaro Z28 3.840 15.41 0 0 3 4
#> Pontiac Firebird 3.845 17.05 0 0 3 2
#> Fiat X1-9 1.935 18.90 1 1 4 1
#> Porsche 914-2 2.140 16.70 0 1 5 2
#> Lotus Europa 1.513 16.90 1 1 5 2
#> Ford Pantera L 3.170 14.50 0 1 5 4
#> Ferrari Dino 2.770 15.50 0 1 5 6
#> Maserati Bora 3.570 14.60 0 1 5 8
#> Volvo 142E 2.780 18.60 1 1 4 2
# Assignment works for data frames as well
mtcars %just% c("mpg", "qsec") <- gscale(mtcars, c("mpg", "qsec"))
#> Warning: provided 11 variables to replace 2 variables
mtcars %not% c("mpg", "qsec") <- gscale(mtcars %not% c("mpg", "qsec"))