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Subsetting operators

Source: R/programming_helpers.R
subsetters.Rd

%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

# Default S3 method
x %not% y

# Default S3 method
x %not% y <- value

# S3 method for class 'data.frame'
x %not% y

# S3 method for class 'data.frame'
x %not% y <- value

# S3 method for class 'matrix'
x %not% y

# S3 method for class 'matrix'
x %not% y <- value

# S3 method for class 'list'
x %not% y

# S3 method for class 'list'
x %not% y <- value

# Default S3 method
x %just% y

# Default S3 method
x %just% y <- value

# S3 method for class 'data.frame'
x %just% y

# S3 method for class 'data.frame'
x %just% y <- value

# S3 method for class 'matrix'
x %just% y

# S3 method for class 'matrix'
x %just% y <- value

# S3 method for class 'list'
x %just% y

# S3 method for class 'list'
x %just% y <- value

Arguments

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

Value

All of x that are in y (%just%) or all of x that are not in y (%not%).

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"))