Select (and optionally rename) variables in a data.frame, using a concise mini-language that makes it easy to refer to variables based on their name (e.g. a:f selects all columns from a on the left to f on the right). You can also use predicate functions like is.numeric() to select variables based on their properties.

select(.data, ...)

Arguments

.data

A data.frame.

...

<poor-select> One or more unquoted expressions separated by commas. Variable names can be used as if they were positions in the data frame, so expressions like x:y can be used to select a range of variables.

Value

An object of the same type as .data. The output has the following properties:

  • Rows are not affected.

  • Output columns are a subset of input columns, potentially with a different order. Columns will be renamed if new_name = old_name form is used.

  • Data frame attributes are preserved.

  • Groups are maintained; you can't select off grouping variables.

Details

Overview of selection features

poorman selections implement a dialect of R where operators make it easy to select variables:

  • : for selecting a range of consecutive variables.

  • ! for taking the complement of a set of variables.

  • & and | for selecting the intersection or the union of two sets of variables.

  • c() for combining selections.

In addition, you can use selection helpers. Some helpers select specific columns:

These helpers select variables by matching patterns in their names:

These helpers select variables from a character vector:

  • all_of(): Matches variable names in a character vector. All names must be present, otherwise an out-of-bounds error is thrown.

  • any_of(): Same as all_of(), except that no error is thrown for names that don't exist.

This helper selects variables with a function:

  • where(): Applies a function to all variables and selects those for which the function returns TRUE.

Examples

# Here we show the usage for the basic selection operators. See the # specific help pages to learn about helpers like [starts_with()]. # Select variables by name: mtcars %>% select(mpg)
#> mpg #> Mazda RX4 21.0 #> Mazda RX4 Wag 21.0 #> Datsun 710 22.8 #> Hornet 4 Drive 21.4 #> Hornet Sportabout 18.7 #> Valiant 18.1 #> Duster 360 14.3 #> Merc 240D 24.4 #> Merc 230 22.8 #> Merc 280 19.2 #> Merc 280C 17.8 #> Merc 450SE 16.4 #> Merc 450SL 17.3 #> Merc 450SLC 15.2 #> Cadillac Fleetwood 10.4 #> Lincoln Continental 10.4 #> Chrysler Imperial 14.7 #> Fiat 128 32.4 #> Honda Civic 30.4 #> Toyota Corolla 33.9 #> Toyota Corona 21.5 #> Dodge Challenger 15.5 #> AMC Javelin 15.2 #> Camaro Z28 13.3 #> Pontiac Firebird 19.2 #> Fiat X1-9 27.3 #> Porsche 914-2 26.0 #> Lotus Europa 30.4 #> Ford Pantera L 15.8 #> Ferrari Dino 19.7 #> Maserati Bora 15.0 #> Volvo 142E 21.4
# Select multiple variables by separating them with commas. Note # how the order of columns is determined by the order of inputs: mtcars %>% select(disp, gear, am)
#> disp gear am #> Mazda RX4 160.0 4 1 #> Mazda RX4 Wag 160.0 4 1 #> Datsun 710 108.0 4 1 #> Hornet 4 Drive 258.0 3 0 #> Hornet Sportabout 360.0 3 0 #> Valiant 225.0 3 0 #> Duster 360 360.0 3 0 #> Merc 240D 146.7 4 0 #> Merc 230 140.8 4 0 #> Merc 280 167.6 4 0 #> Merc 280C 167.6 4 0 #> Merc 450SE 275.8 3 0 #> Merc 450SL 275.8 3 0 #> Merc 450SLC 275.8 3 0 #> Cadillac Fleetwood 472.0 3 0 #> Lincoln Continental 460.0 3 0 #> Chrysler Imperial 440.0 3 0 #> Fiat 128 78.7 4 1 #> Honda Civic 75.7 4 1 #> Toyota Corolla 71.1 4 1 #> Toyota Corona 120.1 3 0 #> Dodge Challenger 318.0 3 0 #> AMC Javelin 304.0 3 0 #> Camaro Z28 350.0 3 0 #> Pontiac Firebird 400.0 3 0 #> Fiat X1-9 79.0 4 1 #> Porsche 914-2 120.3 5 1 #> Lotus Europa 95.1 5 1 #> Ford Pantera L 351.0 5 1 #> Ferrari Dino 145.0 5 1 #> Maserati Bora 301.0 5 1 #> Volvo 142E 121.0 4 1
# Rename variables: mtcars %>% select(MilesPerGallon = mpg, everything())
#> MilesPerGallon cyl disp hp drat wt qsec vs am gear #> Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 #> Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 #> Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 #> Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 #> Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 #> Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 #> Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 #> Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 #> Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 #> Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 #> Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 #> Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 #> Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 #> Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 #> Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 #> Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 #> Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 #> Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 #> Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 #> Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 #> Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 #> Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 #> AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 #> Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 #> Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 #> Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 #> Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 #> Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 #> Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 #> Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 #> Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 #> Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 #> carb #> Mazda RX4 4 #> Mazda RX4 Wag 4 #> Datsun 710 1 #> Hornet 4 Drive 1 #> Hornet Sportabout 2 #> Valiant 1 #> Duster 360 4 #> Merc 240D 2 #> Merc 230 2 #> Merc 280 4 #> Merc 280C 4 #> Merc 450SE 3 #> Merc 450SL 3 #> Merc 450SLC 3 #> Cadillac Fleetwood 4 #> Lincoln Continental 4 #> Chrysler Imperial 4 #> Fiat 128 1 #> Honda Civic 2 #> Toyota Corolla 1 #> Toyota Corona 1 #> Dodge Challenger 2 #> AMC Javelin 2 #> Camaro Z28 4 #> Pontiac Firebird 2 #> Fiat X1-9 1 #> Porsche 914-2 2 #> Lotus Europa 2 #> Ford Pantera L 4 #> Ferrari Dino 6 #> Maserati Bora 8 #> Volvo 142E 2
# The `:` operator selects a range of consecutive variables: select(mtcars, mpg:cyl)
#> mpg cyl #> Mazda RX4 21.0 6 #> Mazda RX4 Wag 21.0 6 #> Datsun 710 22.8 4 #> Hornet 4 Drive 21.4 6 #> Hornet Sportabout 18.7 8 #> Valiant 18.1 6 #> Duster 360 14.3 8 #> Merc 240D 24.4 4 #> Merc 230 22.8 4 #> Merc 280 19.2 6 #> Merc 280C 17.8 6 #> Merc 450SE 16.4 8 #> Merc 450SL 17.3 8 #> Merc 450SLC 15.2 8 #> Cadillac Fleetwood 10.4 8 #> Lincoln Continental 10.4 8 #> Chrysler Imperial 14.7 8 #> Fiat 128 32.4 4 #> Honda Civic 30.4 4 #> Toyota Corolla 33.9 4 #> Toyota Corona 21.5 4 #> Dodge Challenger 15.5 8 #> AMC Javelin 15.2 8 #> Camaro Z28 13.3 8 #> Pontiac Firebird 19.2 8 #> Fiat X1-9 27.3 4 #> Porsche 914-2 26.0 4 #> Lotus Europa 30.4 4 #> Ford Pantera L 15.8 8 #> Ferrari Dino 19.7 6 #> Maserati Bora 15.0 8 #> Volvo 142E 21.4 4
# The `!` operator negates a selection: mtcars %>% select(!(mpg:qsec))
#> vs am gear carb #> Mazda RX4 0 1 4 4 #> Mazda RX4 Wag 0 1 4 4 #> Datsun 710 1 1 4 1 #> Hornet 4 Drive 1 0 3 1 #> Hornet Sportabout 0 0 3 2 #> Valiant 1 0 3 1 #> Duster 360 0 0 3 4 #> Merc 240D 1 0 4 2 #> Merc 230 1 0 4 2 #> Merc 280 1 0 4 4 #> Merc 280C 1 0 4 4 #> Merc 450SE 0 0 3 3 #> Merc 450SL 0 0 3 3 #> Merc 450SLC 0 0 3 3 #> Cadillac Fleetwood 0 0 3 4 #> Lincoln Continental 0 0 3 4 #> Chrysler Imperial 0 0 3 4 #> Fiat 128 1 1 4 1 #> Honda Civic 1 1 4 2 #> Toyota Corolla 1 1 4 1 #> Toyota Corona 1 0 3 1 #> Dodge Challenger 0 0 3 2 #> AMC Javelin 0 0 3 2 #> Camaro Z28 0 0 3 4 #> Pontiac Firebird 0 0 3 2 #> Fiat X1-9 1 1 4 1 #> Porsche 914-2 0 1 5 2 #> Lotus Europa 1 1 5 2 #> Ford Pantera L 0 1 5 4 #> Ferrari Dino 0 1 5 6 #> Maserati Bora 0 1 5 8 #> Volvo 142E 1 1 4 2
mtcars %>% select(!ends_with("p"))
#> mpg cyl drat wt qsec vs am gear carb #> Mazda RX4 21.0 6 3.90 2.620 16.46 0 1 4 4 #> Mazda RX4 Wag 21.0 6 3.90 2.875 17.02 0 1 4 4 #> Datsun 710 22.8 4 3.85 2.320 18.61 1 1 4 1 #> Hornet 4 Drive 21.4 6 3.08 3.215 19.44 1 0 3 1 #> Hornet Sportabout 18.7 8 3.15 3.440 17.02 0 0 3 2 #> Valiant 18.1 6 2.76 3.460 20.22 1 0 3 1 #> Duster 360 14.3 8 3.21 3.570 15.84 0 0 3 4 #> Merc 240D 24.4 4 3.69 3.190 20.00 1 0 4 2 #> Merc 230 22.8 4 3.92 3.150 22.90 1 0 4 2 #> Merc 280 19.2 6 3.92 3.440 18.30 1 0 4 4 #> Merc 280C 17.8 6 3.92 3.440 18.90 1 0 4 4 #> Merc 450SE 16.4 8 3.07 4.070 17.40 0 0 3 3 #> Merc 450SL 17.3 8 3.07 3.730 17.60 0 0 3 3 #> Merc 450SLC 15.2 8 3.07 3.780 18.00 0 0 3 3 #> Cadillac Fleetwood 10.4 8 2.93 5.250 17.98 0 0 3 4 #> Lincoln Continental 10.4 8 3.00 5.424 17.82 0 0 3 4 #> Chrysler Imperial 14.7 8 3.23 5.345 17.42 0 0 3 4 #> Fiat 128 32.4 4 4.08 2.200 19.47 1 1 4 1 #> Honda Civic 30.4 4 4.93 1.615 18.52 1 1 4 2 #> Toyota Corolla 33.9 4 4.22 1.835 19.90 1 1 4 1 #> Toyota Corona 21.5 4 3.70 2.465 20.01 1 0 3 1 #> Dodge Challenger 15.5 8 2.76 3.520 16.87 0 0 3 2 #> AMC Javelin 15.2 8 3.15 3.435 17.30 0 0 3 2 #> Camaro Z28 13.3 8 3.73 3.840 15.41 0 0 3 4 #> Pontiac Firebird 19.2 8 3.08 3.845 17.05 0 0 3 2 #> Fiat X1-9 27.3 4 4.08 1.935 18.90 1 1 4 1 #> Porsche 914-2 26.0 4 4.43 2.140 16.70 0 1 5 2 #> Lotus Europa 30.4 4 3.77 1.513 16.90 1 1 5 2 #> Ford Pantera L 15.8 8 4.22 3.170 14.50 0 1 5 4 #> Ferrari Dino 19.7 6 3.62 2.770 15.50 0 1 5 6 #> Maserati Bora 15.0 8 3.54 3.570 14.60 0 1 5 8 #> Volvo 142E 21.4 4 4.11 2.780 18.60 1 1 4 2
# `&` and `|` take the intersection or the union of two selections: iris %>% select(starts_with("Petal") & ends_with("Width"))
#> Petal.Length Petal.Width Sepal.Width #> 1 1.4 0.2 3.5 #> 2 1.4 0.2 3.0 #> 3 1.3 0.2 3.2 #> 4 1.5 0.2 3.1 #> 5 1.4 0.2 3.6 #> 6 1.7 0.4 3.9 #> 7 1.4 0.3 3.4 #> 8 1.5 0.2 3.4 #> 9 1.4 0.2 2.9 #> 10 1.5 0.1 3.1 #> 11 1.5 0.2 3.7 #> 12 1.6 0.2 3.4 #> 13 1.4 0.1 3.0 #> 14 1.1 0.1 3.0 #> 15 1.2 0.2 4.0 #> 16 1.5 0.4 4.4 #> 17 1.3 0.4 3.9 #> 18 1.4 0.3 3.5 #> 19 1.7 0.3 3.8 #> 20 1.5 0.3 3.8 #> 21 1.7 0.2 3.4 #> 22 1.5 0.4 3.7 #> 23 1.0 0.2 3.6 #> 24 1.7 0.5 3.3 #> 25 1.9 0.2 3.4 #> 26 1.6 0.2 3.0 #> 27 1.6 0.4 3.4 #> 28 1.5 0.2 3.5 #> 29 1.4 0.2 3.4 #> 30 1.6 0.2 3.2 #> 31 1.6 0.2 3.1 #> 32 1.5 0.4 3.4 #> 33 1.5 0.1 4.1 #> 34 1.4 0.2 4.2 #> 35 1.5 0.2 3.1 #> 36 1.2 0.2 3.2 #> 37 1.3 0.2 3.5 #> 38 1.4 0.1 3.6 #> 39 1.3 0.2 3.0 #> 40 1.5 0.2 3.4 #> 41 1.3 0.3 3.5 #> 42 1.3 0.3 2.3 #> 43 1.3 0.2 3.2 #> 44 1.6 0.6 3.5 #> 45 1.9 0.4 3.8 #> 46 1.4 0.3 3.0 #> 47 1.6 0.2 3.8 #> 48 1.4 0.2 3.2 #> 49 1.5 0.2 3.7 #> 50 1.4 0.2 3.3 #> 51 4.7 1.4 3.2 #> 52 4.5 1.5 3.2 #> 53 4.9 1.5 3.1 #> 54 4.0 1.3 2.3 #> 55 4.6 1.5 2.8 #> 56 4.5 1.3 2.8 #> 57 4.7 1.6 3.3 #> 58 3.3 1.0 2.4 #> 59 4.6 1.3 2.9 #> 60 3.9 1.4 2.7 #> 61 3.5 1.0 2.0 #> 62 4.2 1.5 3.0 #> 63 4.0 1.0 2.2 #> 64 4.7 1.4 2.9 #> 65 3.6 1.3 2.9 #> 66 4.4 1.4 3.1 #> 67 4.5 1.5 3.0 #> 68 4.1 1.0 2.7 #> 69 4.5 1.5 2.2 #> 70 3.9 1.1 2.5 #> 71 4.8 1.8 3.2 #> 72 4.0 1.3 2.8 #> 73 4.9 1.5 2.5 #> 74 4.7 1.2 2.8 #> 75 4.3 1.3 2.9 #> 76 4.4 1.4 3.0 #> 77 4.8 1.4 2.8 #> 78 5.0 1.7 3.0 #> 79 4.5 1.5 2.9 #> 80 3.5 1.0 2.6 #> 81 3.8 1.1 2.4 #> 82 3.7 1.0 2.4 #> 83 3.9 1.2 2.7 #> 84 5.1 1.6 2.7 #> 85 4.5 1.5 3.0 #> 86 4.5 1.6 3.4 #> 87 4.7 1.5 3.1 #> 88 4.4 1.3 2.3 #> 89 4.1 1.3 3.0 #> 90 4.0 1.3 2.5 #> 91 4.4 1.2 2.6 #> 92 4.6 1.4 3.0 #> 93 4.0 1.2 2.6 #> 94 3.3 1.0 2.3 #> 95 4.2 1.3 2.7 #> 96 4.2 1.2 3.0 #> 97 4.2 1.3 2.9 #> 98 4.3 1.3 2.9 #> 99 3.0 1.1 2.5 #> 100 4.1 1.3 2.8 #> 101 6.0 2.5 3.3 #> 102 5.1 1.9 2.7 #> 103 5.9 2.1 3.0 #> 104 5.6 1.8 2.9 #> 105 5.8 2.2 3.0 #> 106 6.6 2.1 3.0 #> 107 4.5 1.7 2.5 #> 108 6.3 1.8 2.9 #> 109 5.8 1.8 2.5 #> 110 6.1 2.5 3.6 #> 111 5.1 2.0 3.2 #> 112 5.3 1.9 2.7 #> 113 5.5 2.1 3.0 #> 114 5.0 2.0 2.5 #> 115 5.1 2.4 2.8 #> 116 5.3 2.3 3.2 #> 117 5.5 1.8 3.0 #> 118 6.7 2.2 3.8 #> 119 6.9 2.3 2.6 #> 120 5.0 1.5 2.2 #> 121 5.7 2.3 3.2 #> 122 4.9 2.0 2.8 #> 123 6.7 2.0 2.8 #> 124 4.9 1.8 2.7 #> 125 5.7 2.1 3.3 #> 126 6.0 1.8 3.2 #> 127 4.8 1.8 2.8 #> 128 4.9 1.8 3.0 #> 129 5.6 2.1 2.8 #> 130 5.8 1.6 3.0 #> 131 6.1 1.9 2.8 #> 132 6.4 2.0 3.8 #> 133 5.6 2.2 2.8 #> 134 5.1 1.5 2.8 #> 135 5.6 1.4 2.6 #> 136 6.1 2.3 3.0 #> 137 5.6 2.4 3.4 #> 138 5.5 1.8 3.1 #> 139 4.8 1.8 3.0 #> 140 5.4 2.1 3.1 #> 141 5.6 2.4 3.1 #> 142 5.1 2.3 3.1 #> 143 5.1 1.9 2.7 #> 144 5.9 2.3 3.2 #> 145 5.7 2.5 3.3 #> 146 5.2 2.3 3.0 #> 147 5.0 1.9 2.5 #> 148 5.2 2.0 3.0 #> 149 5.4 2.3 3.4 #> 150 5.1 1.8 3.0
iris %>% select(starts_with("Petal") | ends_with("Width"))
#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species #> 1 5.1 3.5 1.4 0.2 setosa #> 2 4.9 3.0 1.4 0.2 setosa #> 3 4.7 3.2 1.3 0.2 setosa #> 4 4.6 3.1 1.5 0.2 setosa #> 5 5.0 3.6 1.4 0.2 setosa #> 6 5.4 3.9 1.7 0.4 setosa #> 7 4.6 3.4 1.4 0.3 setosa #> 8 5.0 3.4 1.5 0.2 setosa #> 9 4.4 2.9 1.4 0.2 setosa #> 10 4.9 3.1 1.5 0.1 setosa #> 11 5.4 3.7 1.5 0.2 setosa #> 12 4.8 3.4 1.6 0.2 setosa #> 13 4.8 3.0 1.4 0.1 setosa #> 14 4.3 3.0 1.1 0.1 setosa #> 15 5.8 4.0 1.2 0.2 setosa #> 16 5.7 4.4 1.5 0.4 setosa #> 17 5.4 3.9 1.3 0.4 setosa #> 18 5.1 3.5 1.4 0.3 setosa #> 19 5.7 3.8 1.7 0.3 setosa #> 20 5.1 3.8 1.5 0.3 setosa #> 21 5.4 3.4 1.7 0.2 setosa #> 22 5.1 3.7 1.5 0.4 setosa #> 23 4.6 3.6 1.0 0.2 setosa #> 24 5.1 3.3 1.7 0.5 setosa #> 25 4.8 3.4 1.9 0.2 setosa #> 26 5.0 3.0 1.6 0.2 setosa #> 27 5.0 3.4 1.6 0.4 setosa #> 28 5.2 3.5 1.5 0.2 setosa #> 29 5.2 3.4 1.4 0.2 setosa #> 30 4.7 3.2 1.6 0.2 setosa #> 31 4.8 3.1 1.6 0.2 setosa #> 32 5.4 3.4 1.5 0.4 setosa #> 33 5.2 4.1 1.5 0.1 setosa #> 34 5.5 4.2 1.4 0.2 setosa #> 35 4.9 3.1 1.5 0.2 setosa #> 36 5.0 3.2 1.2 0.2 setosa #> 37 5.5 3.5 1.3 0.2 setosa #> 38 4.9 3.6 1.4 0.1 setosa #> 39 4.4 3.0 1.3 0.2 setosa #> 40 5.1 3.4 1.5 0.2 setosa #> 41 5.0 3.5 1.3 0.3 setosa #> 42 4.5 2.3 1.3 0.3 setosa #> 43 4.4 3.2 1.3 0.2 setosa #> 44 5.0 3.5 1.6 0.6 setosa #> 45 5.1 3.8 1.9 0.4 setosa #> 46 4.8 3.0 1.4 0.3 setosa #> 47 5.1 3.8 1.6 0.2 setosa #> 48 4.6 3.2 1.4 0.2 setosa #> 49 5.3 3.7 1.5 0.2 setosa #> 50 5.0 3.3 1.4 0.2 setosa #> 51 7.0 3.2 4.7 1.4 versicolor #> 52 6.4 3.2 4.5 1.5 versicolor #> 53 6.9 3.1 4.9 1.5 versicolor #> 54 5.5 2.3 4.0 1.3 versicolor #> 55 6.5 2.8 4.6 1.5 versicolor #> 56 5.7 2.8 4.5 1.3 versicolor #> 57 6.3 3.3 4.7 1.6 versicolor #> 58 4.9 2.4 3.3 1.0 versicolor #> 59 6.6 2.9 4.6 1.3 versicolor #> 60 5.2 2.7 3.9 1.4 versicolor #> 61 5.0 2.0 3.5 1.0 versicolor #> 62 5.9 3.0 4.2 1.5 versicolor #> 63 6.0 2.2 4.0 1.0 versicolor #> 64 6.1 2.9 4.7 1.4 versicolor #> 65 5.6 2.9 3.6 1.3 versicolor #> 66 6.7 3.1 4.4 1.4 versicolor #> 67 5.6 3.0 4.5 1.5 versicolor #> 68 5.8 2.7 4.1 1.0 versicolor #> 69 6.2 2.2 4.5 1.5 versicolor #> 70 5.6 2.5 3.9 1.1 versicolor #> 71 5.9 3.2 4.8 1.8 versicolor #> 72 6.1 2.8 4.0 1.3 versicolor #> 73 6.3 2.5 4.9 1.5 versicolor #> 74 6.1 2.8 4.7 1.2 versicolor #> 75 6.4 2.9 4.3 1.3 versicolor #> 76 6.6 3.0 4.4 1.4 versicolor #> 77 6.8 2.8 4.8 1.4 versicolor #> 78 6.7 3.0 5.0 1.7 versicolor #> 79 6.0 2.9 4.5 1.5 versicolor #> 80 5.7 2.6 3.5 1.0 versicolor #> 81 5.5 2.4 3.8 1.1 versicolor #> 82 5.5 2.4 3.7 1.0 versicolor #> 83 5.8 2.7 3.9 1.2 versicolor #> 84 6.0 2.7 5.1 1.6 versicolor #> 85 5.4 3.0 4.5 1.5 versicolor #> 86 6.0 3.4 4.5 1.6 versicolor #> 87 6.7 3.1 4.7 1.5 versicolor #> 88 6.3 2.3 4.4 1.3 versicolor #> 89 5.6 3.0 4.1 1.3 versicolor #> 90 5.5 2.5 4.0 1.3 versicolor #> 91 5.5 2.6 4.4 1.2 versicolor #> 92 6.1 3.0 4.6 1.4 versicolor #> 93 5.8 2.6 4.0 1.2 versicolor #> 94 5.0 2.3 3.3 1.0 versicolor #> 95 5.6 2.7 4.2 1.3 versicolor #> 96 5.7 3.0 4.2 1.2 versicolor #> 97 5.7 2.9 4.2 1.3 versicolor #> 98 6.2 2.9 4.3 1.3 versicolor #> 99 5.1 2.5 3.0 1.1 versicolor #> 100 5.7 2.8 4.1 1.3 versicolor #> 101 6.3 3.3 6.0 2.5 virginica #> 102 5.8 2.7 5.1 1.9 virginica #> 103 7.1 3.0 5.9 2.1 virginica #> 104 6.3 2.9 5.6 1.8 virginica #> 105 6.5 3.0 5.8 2.2 virginica #> 106 7.6 3.0 6.6 2.1 virginica #> 107 4.9 2.5 4.5 1.7 virginica #> 108 7.3 2.9 6.3 1.8 virginica #> 109 6.7 2.5 5.8 1.8 virginica #> 110 7.2 3.6 6.1 2.5 virginica #> 111 6.5 3.2 5.1 2.0 virginica #> 112 6.4 2.7 5.3 1.9 virginica #> 113 6.8 3.0 5.5 2.1 virginica #> 114 5.7 2.5 5.0 2.0 virginica #> 115 5.8 2.8 5.1 2.4 virginica #> 116 6.4 3.2 5.3 2.3 virginica #> 117 6.5 3.0 5.5 1.8 virginica #> 118 7.7 3.8 6.7 2.2 virginica #> 119 7.7 2.6 6.9 2.3 virginica #> 120 6.0 2.2 5.0 1.5 virginica #> 121 6.9 3.2 5.7 2.3 virginica #> 122 5.6 2.8 4.9 2.0 virginica #> 123 7.7 2.8 6.7 2.0 virginica #> 124 6.3 2.7 4.9 1.8 virginica #> 125 6.7 3.3 5.7 2.1 virginica #> 126 7.2 3.2 6.0 1.8 virginica #> 127 6.2 2.8 4.8 1.8 virginica #> 128 6.1 3.0 4.9 1.8 virginica #> 129 6.4 2.8 5.6 2.1 virginica #> 130 7.2 3.0 5.8 1.6 virginica #> 131 7.4 2.8 6.1 1.9 virginica #> 132 7.9 3.8 6.4 2.0 virginica #> 133 6.4 2.8 5.6 2.2 virginica #> 134 6.3 2.8 5.1 1.5 virginica #> 135 6.1 2.6 5.6 1.4 virginica #> 136 7.7 3.0 6.1 2.3 virginica #> 137 6.3 3.4 5.6 2.4 virginica #> 138 6.4 3.1 5.5 1.8 virginica #> 139 6.0 3.0 4.8 1.8 virginica #> 140 6.9 3.1 5.4 2.1 virginica #> 141 6.7 3.1 5.6 2.4 virginica #> 142 6.9 3.1 5.1 2.3 virginica #> 143 5.8 2.7 5.1 1.9 virginica #> 144 6.8 3.2 5.9 2.3 virginica #> 145 6.7 3.3 5.7 2.5 virginica #> 146 6.7 3.0 5.2 2.3 virginica #> 147 6.3 2.5 5.0 1.9 virginica #> 148 6.5 3.0 5.2 2.0 virginica #> 149 6.2 3.4 5.4 2.3 virginica #> 150 5.9 3.0 5.1 1.8 virginica
# To take the difference between two selections, combine the `&` and # `!` operators: iris %>% select(starts_with("Petal") & !ends_with("Width"))
#> Petal.Length #> 1 1.4 #> 2 1.4 #> 3 1.3 #> 4 1.5 #> 5 1.4 #> 6 1.7 #> 7 1.4 #> 8 1.5 #> 9 1.4 #> 10 1.5 #> 11 1.5 #> 12 1.6 #> 13 1.4 #> 14 1.1 #> 15 1.2 #> 16 1.5 #> 17 1.3 #> 18 1.4 #> 19 1.7 #> 20 1.5 #> 21 1.7 #> 22 1.5 #> 23 1.0 #> 24 1.7 #> 25 1.9 #> 26 1.6 #> 27 1.6 #> 28 1.5 #> 29 1.4 #> 30 1.6 #> 31 1.6 #> 32 1.5 #> 33 1.5 #> 34 1.4 #> 35 1.5 #> 36 1.2 #> 37 1.3 #> 38 1.4 #> 39 1.3 #> 40 1.5 #> 41 1.3 #> 42 1.3 #> 43 1.3 #> 44 1.6 #> 45 1.9 #> 46 1.4 #> 47 1.6 #> 48 1.4 #> 49 1.5 #> 50 1.4 #> 51 4.7 #> 52 4.5 #> 53 4.9 #> 54 4.0 #> 55 4.6 #> 56 4.5 #> 57 4.7 #> 58 3.3 #> 59 4.6 #> 60 3.9 #> 61 3.5 #> 62 4.2 #> 63 4.0 #> 64 4.7 #> 65 3.6 #> 66 4.4 #> 67 4.5 #> 68 4.1 #> 69 4.5 #> 70 3.9 #> 71 4.8 #> 72 4.0 #> 73 4.9 #> 74 4.7 #> 75 4.3 #> 76 4.4 #> 77 4.8 #> 78 5.0 #> 79 4.5 #> 80 3.5 #> 81 3.8 #> 82 3.7 #> 83 3.9 #> 84 5.1 #> 85 4.5 #> 86 4.5 #> 87 4.7 #> 88 4.4 #> 89 4.1 #> 90 4.0 #> 91 4.4 #> 92 4.6 #> 93 4.0 #> 94 3.3 #> 95 4.2 #> 96 4.2 #> 97 4.2 #> 98 4.3 #> 99 3.0 #> 100 4.1 #> 101 6.0 #> 102 5.1 #> 103 5.9 #> 104 5.6 #> 105 5.8 #> 106 6.6 #> 107 4.5 #> 108 6.3 #> 109 5.8 #> 110 6.1 #> 111 5.1 #> 112 5.3 #> 113 5.5 #> 114 5.0 #> 115 5.1 #> 116 5.3 #> 117 5.5 #> 118 6.7 #> 119 6.9 #> 120 5.0 #> 121 5.7 #> 122 4.9 #> 123 6.7 #> 124 4.9 #> 125 5.7 #> 126 6.0 #> 127 4.8 #> 128 4.9 #> 129 5.6 #> 130 5.8 #> 131 6.1 #> 132 6.4 #> 133 5.6 #> 134 5.1 #> 135 5.6 #> 136 6.1 #> 137 5.6 #> 138 5.5 #> 139 4.8 #> 140 5.4 #> 141 5.6 #> 142 5.1 #> 143 5.1 #> 144 5.9 #> 145 5.7 #> 146 5.2 #> 147 5.0 #> 148 5.2 #> 149 5.4 #> 150 5.1